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TiKick

TiKick-main/setup.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """""" import os from setuptools import setup, find_packages import setuptools def get_version() -> str: # https://packaging.python.org/guides/single-sourcing-package-version/ init = open(os.path.join("tmarl", "__init__.py"), "r").read().split() return init[init.index("__version__") + 2][1:-1] setup( name="tmarl", # Replace with your own username version=get_version(), description="marl algorithms", long_description=open("README.md", encoding="utf8").read(), long_description_content_type="text/markdown", author="tmarl", author_email="tmarl_contact@tartrl.cn", packages=setuptools.find_packages(), classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", "Programming Language :: Python :: 3", "License :: OSI Approved :: Apache License", "Operating System :: OS Independent", ], keywords="multi-agent reinforcement learning algorithms pytorch", python_requires='>=3.6', )

py

TiKick

TiKick-main/tmarl/__init__.py

__version__ = "0.0.3"

py

TiKick

TiKick-main/tmarl/networks/policy_network.py

import torch import torch.nn as nn from tmarl.networks.utils.util import init, check from tmarl.networks.utils.mlp import MLPBase, MLPLayer from tmarl.networks.utils.rnn import RNNLayer from tmarl.networks.utils.act import ACTLayer from tmarl.networks.utils.popart import PopArt from tmarl.utils.util import get_shape_from_obs_space # networks are defined here class PolicyNetwork(nn.Module): def __init__(self, args, obs_space, action_space, device=torch.device("cpu")): super(PolicyNetwork, self).__init__() self.hidden_size = args.hidden_size self._gain = args.gain self._use_orthogonal = args.use_orthogonal self._activation_id = args.activation_id self._use_policy_active_masks = args.use_policy_active_masks self._use_naive_recurrent_policy = args.use_naive_recurrent_policy self._use_recurrent_policy = args.use_recurrent_policy self._use_influence_policy = args.use_influence_policy self._influence_layer_N = args.influence_layer_N self._use_policy_vhead = args.use_policy_vhead self._recurrent_N = args.recurrent_N self.tpdv = dict(dtype=torch.float32, device=device) obs_shape = get_shape_from_obs_space(obs_space) self._mixed_obs = False self.base = MLPBase(args, obs_shape, use_attn_internal=False, use_cat_self=True) input_size = self.base.output_size if self._use_naive_recurrent_policy or self._use_recurrent_policy: self.rnn = RNNLayer(input_size, self.hidden_size, self._recurrent_N, self._use_orthogonal) input_size = self.hidden_size if self._use_influence_policy: self.mlp = MLPLayer(obs_shape[0], self.hidden_size, self._influence_layer_N, self._use_orthogonal, self._activation_id) input_size += self.hidden_size self.act = ACTLayer(action_space, input_size, self._use_orthogonal, self._gain) if self._use_policy_vhead: init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][self._use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0)) if self._use_popart: self.v_out = init_(PopArt(input_size, 1, device=device)) else: self.v_out = init_(nn.Linear(input_size, 1)) self.to(device) def forward(self, obs, rnn_states, masks, available_actions=None, deterministic=False): if self._mixed_obs: for key in obs.keys(): obs[key] = check(obs[key]).to(**self.tpdv) else: obs = check(obs).to(**self.tpdv) rnn_states = check(rnn_states).to(**self.tpdv) masks = check(masks).to(**self.tpdv) if available_actions is not None: available_actions = check(available_actions).to(**self.tpdv) actor_features = self.base(obs) if self._use_naive_recurrent_policy or self._use_recurrent_policy: actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks) if self._use_influence_policy: mlp_obs = self.mlp(obs) actor_features = torch.cat([actor_features, mlp_obs], dim=1) actions, action_log_probs = self.act(actor_features, available_actions, deterministic) return actions, action_log_probs, rnn_states def evaluate_actions(self, obs, rnn_states, action, masks, available_actions=None, active_masks=None): if self._mixed_obs: for key in obs.keys(): obs[key] = check(obs[key]).to(**self.tpdv) else: obs = check(obs).to(**self.tpdv) rnn_states = check(rnn_states).to(**self.tpdv) action = check(action).to(**self.tpdv) masks = check(masks).to(**self.tpdv) if available_actions is not None: available_actions = check(available_actions).to(**self.tpdv) if active_masks is not None: active_masks = check(active_masks).to(**self.tpdv) actor_features = self.base(obs) if self._use_naive_recurrent_policy or self._use_recurrent_policy: actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks) if self._use_influence_policy: mlp_obs = self.mlp(obs) actor_features = torch.cat([actor_features, mlp_obs], dim=1) action_log_probs, dist_entropy = self.act.evaluate_actions(actor_features, action, available_actions, active_masks = active_masks if self._use_policy_active_masks else None) values = self.v_out(actor_features) if self._use_policy_vhead else None return action_log_probs, dist_entropy, values def get_policy_values(self, obs, rnn_states, masks): if self._mixed_obs: for key in obs.keys(): obs[key] = check(obs[key]).to(**self.tpdv) else: obs = check(obs).to(**self.tpdv) rnn_states = check(rnn_states).to(**self.tpdv) masks = check(masks).to(**self.tpdv) actor_features = self.base(obs) if self._use_naive_recurrent_policy or self._use_recurrent_policy: actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks) if self._use_influence_policy: mlp_obs = self.mlp(obs) actor_features = torch.cat([actor_features, mlp_obs], dim=1) values = self.v_out(actor_features) return values

py

TiKick

TiKick-main/tmarl/networks/__init__.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """"""

py

TiKick

TiKick-main/tmarl/networks/utils/distributions.py

import torch import torch.nn as nn from .util import init """ Modify standard PyTorch distributions so they are compatible with this code. """ # # Standardize distribution interfaces # # Categorical class FixedCategorical(torch.distributions.Categorical): def sample(self): return super().sample().unsqueeze(-1) def log_probs(self, actions): return ( super() .log_prob(actions.squeeze(-1)) .view(actions.size(0), -1) .sum(-1) .unsqueeze(-1) ) def mode(self): return self.probs.argmax(dim=-1, keepdim=True) # Normal class FixedNormal(torch.distributions.Normal): def log_probs(self, actions): return super().log_prob(actions).sum(-1, keepdim=True) def entrop(self): return super.entropy().sum(-1) def mode(self): return self.mean # Bernoulli class FixedBernoulli(torch.distributions.Bernoulli): def log_probs(self, actions): return super.log_prob(actions).view(actions.size(0), -1).sum(-1).unsqueeze(-1) def entropy(self): return super().entropy().sum(-1) def mode(self): return torch.gt(self.probs, 0.5).float() class Categorical(nn.Module): def __init__(self, num_inputs, num_outputs, use_orthogonal=True, gain=0.01): super(Categorical, self).__init__() init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain) self.linear = init_(nn.Linear(num_inputs, num_outputs)) def forward(self, x, available_actions=None): x = self.linear(x) if available_actions is not None: x[available_actions == 0] = -1e10 return FixedCategorical(logits=x) class DiagGaussian(nn.Module): def __init__(self, num_inputs, num_outputs, use_orthogonal=True, gain=0.01): super(DiagGaussian, self).__init__() init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain) self.fc_mean = init_(nn.Linear(num_inputs, num_outputs)) self.logstd = AddBias(torch.zeros(num_outputs)) def forward(self, x): action_mean = self.fc_mean(x) # An ugly hack for my KFAC implementation. zeros = torch.zeros(action_mean.size()) if x.is_cuda: zeros = zeros.cuda() action_logstd = self.logstd(zeros) return FixedNormal(action_mean, action_logstd.exp()) class Bernoulli(nn.Module): def __init__(self, num_inputs, num_outputs, use_orthogonal=True, gain=0.01): super(Bernoulli, self).__init__() init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain) self.linear = init_(nn.Linear(num_inputs, num_outputs)) def forward(self, x): x = self.linear(x) return FixedBernoulli(logits=x) class AddBias(nn.Module): def __init__(self, bias): super(AddBias, self).__init__() self._bias = nn.Parameter(bias.unsqueeze(1)) def forward(self, x): if x.dim() == 2: bias = self._bias.t().view(1, -1) else: bias = self._bias.t().view(1, -1, 1, 1) return x + bias

py

TiKick

TiKick-main/tmarl/networks/utils/mlp.py

import torch.nn as nn from .util import init, get_clones class MLPLayer(nn.Module): def __init__(self, input_dim, hidden_size, layer_N, use_orthogonal, activation_id): super(MLPLayer, self).__init__() self._layer_N = layer_N active_func = [nn.Tanh(), nn.ReLU(), nn.LeakyReLU(), nn.ELU()][activation_id] init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] gain = nn.init.calculate_gain(['tanh', 'relu', 'leaky_relu', 'leaky_relu'][activation_id]) def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain=gain) self.fc1 = nn.Sequential( init_(nn.Linear(input_dim, hidden_size)), active_func, nn.LayerNorm(hidden_size)) self.fc_h = nn.Sequential(init_( nn.Linear(hidden_size, hidden_size)), active_func, nn.LayerNorm(hidden_size)) self.fc2 = get_clones(self.fc_h, self._layer_N) def forward(self, x): x = self.fc1(x) for i in range(self._layer_N): x = self.fc2[i](x) return x class MLPBase(nn.Module): def __init__(self, args, obs_shape, use_attn_internal=False, use_cat_self=True): super(MLPBase, self).__init__() self._use_feature_normalization = args.use_feature_normalization self._use_orthogonal = args.use_orthogonal self._activation_id = args.activation_id self._use_conv1d = args.use_conv1d self._stacked_frames = args.stacked_frames self._layer_N = args.layer_N self.hidden_size = args.hidden_size obs_dim = obs_shape[0] inputs_dim = obs_dim if self._use_feature_normalization: self.feature_norm = nn.LayerNorm(obs_dim) self.mlp = MLPLayer(inputs_dim, self.hidden_size, self._layer_N, self._use_orthogonal, self._activation_id) def forward(self, x): if self._use_feature_normalization: x = self.feature_norm(x) x = self.mlp(x) return x @property def output_size(self): return self.hidden_size

py

TiKick

TiKick-main/tmarl/networks/utils/popart.py

import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F class PopArt(torch.nn.Module): def __init__(self, input_shape, output_shape, norm_axes=1, beta=0.99999, epsilon=1e-5, device=torch.device("cpu")): super(PopArt, self).__init__() self.beta = beta self.epsilon = epsilon self.norm_axes = norm_axes self.tpdv = dict(dtype=torch.float32, device=device) self.input_shape = input_shape self.output_shape = output_shape self.weight = nn.Parameter(torch.Tensor(output_shape, input_shape)).to(**self.tpdv) self.bias = nn.Parameter(torch.Tensor(output_shape)).to(**self.tpdv) self.stddev = nn.Parameter(torch.ones(output_shape), requires_grad=False).to(**self.tpdv) self.mean = nn.Parameter(torch.zeros(output_shape), requires_grad=False).to(**self.tpdv) self.mean_sq = nn.Parameter(torch.zeros(output_shape), requires_grad=False).to(**self.tpdv) self.debiasing_term = nn.Parameter(torch.tensor(0.0), requires_grad=False).to(**self.tpdv) self.reset_parameters() def reset_parameters(self): torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) torch.nn.init.uniform_(self.bias, -bound, bound) self.mean.zero_() self.mean_sq.zero_() self.debiasing_term.zero_() def forward(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) return F.linear(input_vector, self.weight, self.bias) @torch.no_grad() def update(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) old_mean, old_stddev = self.mean, self.stddev batch_mean = input_vector.mean(dim=tuple(range(self.norm_axes))) batch_sq_mean = (input_vector ** 2).mean(dim=tuple(range(self.norm_axes))) self.mean.mul_(self.beta).add_(batch_mean * (1.0 - self.beta)) self.mean_sq.mul_(self.beta).add_(batch_sq_mean * (1.0 - self.beta)) self.debiasing_term.mul_(self.beta).add_(1.0 * (1.0 - self.beta)) self.stddev = (self.mean_sq - self.mean ** 2).sqrt().clamp(min=1e-4) self.weight = self.weight * old_stddev / self.stddev self.bias = (old_stddev * self.bias + old_mean - self.mean) / self.stddev def debiased_mean_var(self): debiased_mean = self.mean / self.debiasing_term.clamp(min=self.epsilon) debiased_mean_sq = self.mean_sq / self.debiasing_term.clamp(min=self.epsilon) debiased_var = (debiased_mean_sq - debiased_mean ** 2).clamp(min=1e-2) return debiased_mean, debiased_var def normalize(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.debiased_mean_var() out = (input_vector - mean[(None,) * self.norm_axes]) / torch.sqrt(var)[(None,) * self.norm_axes] return out def denormalize(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.debiased_mean_var() out = input_vector * torch.sqrt(var)[(None,) * self.norm_axes] + mean[(None,) * self.norm_axes] out = out.cpu().numpy() return out

py

TiKick

TiKick-main/tmarl/networks/utils/util.py

import copy import numpy as np import torch import torch.nn as nn def init(module, weight_init, bias_init, gain=1): weight_init(module.weight.data, gain=gain) bias_init(module.bias.data) return module def get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) def check(input): output = torch.from_numpy(input) if type(input) == np.ndarray else input return output

py

TiKick

TiKick-main/tmarl/networks/utils/act.py

from .distributions import Bernoulli, Categorical, DiagGaussian import torch import torch.nn as nn class ACTLayer(nn.Module): def __init__(self, action_space, inputs_dim, use_orthogonal, gain): super(ACTLayer, self).__init__() self.multidiscrete_action = False self.continuous_action = False self.mixed_action = False if action_space.__class__.__name__ == "Discrete": action_dim = action_space.n self.action_out = Categorical(inputs_dim, action_dim, use_orthogonal, gain) elif action_space.__class__.__name__ == "Box": self.continuous_action = True action_dim = action_space.shape[0] self.action_out = DiagGaussian(inputs_dim, action_dim, use_orthogonal, gain) elif action_space.__class__.__name__ == "MultiBinary": action_dim = action_space.shape[0] self.action_out = Bernoulli(inputs_dim, action_dim, use_orthogonal, gain) elif action_space.__class__.__name__ == "MultiDiscrete": self.multidiscrete_action = True action_dims = action_space.high - action_space.low + 1 self.action_outs = [] for action_dim in action_dims: self.action_outs.append(Categorical(inputs_dim, action_dim, use_orthogonal, gain)) self.action_outs = nn.ModuleList(self.action_outs) else: # discrete + continous self.mixed_action = True continous_dim = action_space[0].shape[0] discrete_dim = action_space[1].n self.action_outs = nn.ModuleList([DiagGaussian(inputs_dim, continous_dim, use_orthogonal, gain), Categorical( inputs_dim, discrete_dim, use_orthogonal, gain)]) def forward(self, x, available_actions=None, deterministic=False): if self.mixed_action : actions = [] action_log_probs = [] for action_out in self.action_outs: action_logit = action_out(x) action = action_logit.mode() if deterministic else action_logit.sample() action_log_prob = action_logit.log_probs(action) actions.append(action.float()) action_log_probs.append(action_log_prob) actions = torch.cat(actions, -1) action_log_probs = torch.sum(torch.cat(action_log_probs, -1), -1, keepdim=True) elif self.multidiscrete_action: actions = [] action_log_probs = [] for action_out in self.action_outs: action_logit = action_out(x) action = action_logit.mode() if deterministic else action_logit.sample() action_log_prob = action_logit.log_probs(action) actions.append(action) action_log_probs.append(action_log_prob) actions = torch.cat(actions, -1) action_log_probs = torch.cat(action_log_probs, -1) elif self.continuous_action: action_logits = self.action_out(x) actions = action_logits.mode() if deterministic else action_logits.sample() action_log_probs = action_logits.log_probs(actions) else: action_logits = self.action_out(x, available_actions) actions = action_logits.mode() if deterministic else action_logits.sample() action_log_probs = action_logits.log_probs(actions) return actions, action_log_probs def get_probs(self, x, available_actions=None): if self.mixed_action or self.multidiscrete_action: action_probs = [] for action_out in self.action_outs: action_logit = action_out(x) action_prob = action_logit.probs action_probs.append(action_prob) action_probs = torch.cat(action_probs, -1) elif self.continuous_action: action_logits = self.action_out(x) action_probs = action_logits.probs else: action_logits = self.action_out(x, available_actions) action_probs = action_logits.probs return action_probs def get_log_1mp(self, x, action, available_actions=None, active_masks=None): action_logits = self.action_out(x, available_actions) action_prob = torch.gather(action_logits.probs, 1, action.long()) action_prob = torch.clamp(action_prob, 0, 1-1e-6) action_log_1mp = torch.log(1 - action_prob) return action_log_1mp def evaluate_actions(self, x, action, available_actions=None, active_masks=None): if self.mixed_action: a, b = action.split((2, 1), -1) b = b.long() action = [a, b] action_log_probs = [] dist_entropy = [] for action_out, act in zip(self.action_outs, action): action_logit = action_out(x) action_log_probs.append(action_logit.log_probs(act)) if active_masks is not None: if len(action_logit.entropy().shape) == len(active_masks.shape): dist_entropy.append((action_logit.entropy() * active_masks).sum()/active_masks.sum()) else: dist_entropy.append((action_logit.entropy() * active_masks.squeeze(-1)).sum()/active_masks.sum()) else: dist_entropy.append(action_logit.entropy().mean()) action_log_probs = torch.sum(torch.cat(action_log_probs, -1), -1, keepdim=True) dist_entropy = dist_entropy[0] * 0.0025 + dist_entropy[1] * 0.01 elif self.multidiscrete_action: action = torch.transpose(action, 0, 1) action_log_probs = [] dist_entropy = [] for action_out, act in zip(self.action_outs, action): action_logit = action_out(x) action_log_probs.append(action_logit.log_probs(act)) if active_masks is not None: dist_entropy.append((action_logit.entropy()*active_masks.squeeze(-1)).sum()/active_masks.sum()) else: dist_entropy.append(action_logit.entropy().mean()) action_log_probs = torch.cat(action_log_probs, -1) # ! could be wrong dist_entropy = torch.tensor(dist_entropy).mean() elif self.continuous_action: action_logits = self.action_out(x) action_log_probs = action_logits.log_probs(action) if active_masks is not None: dist_entropy = (action_logits.entropy()*active_masks).sum()/active_masks.sum() else: dist_entropy = action_logits.entropy().mean() else: action_logits = self.action_out(x, available_actions) action_log_probs = action_logits.log_probs(action) if active_masks is not None: dist_entropy = (action_logits.entropy()*active_masks.squeeze(-1)).sum()/active_masks.sum() else: dist_entropy = action_logits.entropy().mean() return action_log_probs, dist_entropy

py

TiKick

TiKick-main/tmarl/networks/utils/rnn.py

import torch import torch.nn as nn class RNNLayer(nn.Module): def __init__(self, inputs_dim, outputs_dim, recurrent_N, use_orthogonal): super(RNNLayer, self).__init__() self._recurrent_N = recurrent_N self._use_orthogonal = use_orthogonal self.rnn = nn.GRU(inputs_dim, outputs_dim, num_layers=self._recurrent_N) for name, param in self.rnn.named_parameters(): if 'bias' in name: nn.init.constant_(param, 0) elif 'weight' in name: if self._use_orthogonal: nn.init.orthogonal_(param) else: nn.init.xavier_uniform_(param) self.norm = nn.LayerNorm(outputs_dim) def forward(self, x, hxs, masks): if x.size(0) == hxs.size(0): x, hxs = self.rnn(x.unsqueeze(0), (hxs * masks.repeat(1, self._recurrent_N).unsqueeze(-1)).transpose(0, 1).contiguous()) x = x.squeeze(0) hxs = hxs.transpose(0, 1) else: # x is a (T, N, -1) tensor that has been flatten to (T * N, -1) N = hxs.size(0) T = int(x.size(0) / N) # unflatten x = x.view(T, N, x.size(1)) # Same deal with masks masks = masks.view(T, N) # Let's figure out which steps in the sequence have a zero for any agent # We will always assume t=0 has a zero in it as that makes the logic cleaner has_zeros = ((masks[1:] == 0.0) .any(dim=-1) .nonzero() .squeeze() .cpu()) # +1 to correct the masks[1:] if has_zeros.dim() == 0: # Deal with scalar has_zeros = [has_zeros.item() + 1] else: has_zeros = (has_zeros + 1).numpy().tolist() # add t=0 and t=T to the list has_zeros = [0] + has_zeros + [T] hxs = hxs.transpose(0, 1) outputs = [] for i in range(len(has_zeros) - 1): # We can now process steps that don't have any zeros in masks together! # This is much faster start_idx = has_zeros[i] end_idx = has_zeros[i + 1] temp = (hxs * masks[start_idx].view(1, -1, 1).repeat(self._recurrent_N, 1, 1)).contiguous() rnn_scores, hxs = self.rnn(x[start_idx:end_idx], temp) outputs.append(rnn_scores) # assert len(outputs) == T # x is a (T, N, -1) tensor x = torch.cat(outputs, dim=0) # flatten x = x.reshape(T * N, -1) hxs = hxs.transpose(0, 1) x = self.norm(x) return x, hxs

py

TiKick

TiKick-main/tmarl/drivers/__init__.py

py

TiKick

TiKick-main/tmarl/drivers/shared_distributed/base_driver.py

import numpy as np import torch def _t2n(x): return x.detach().cpu().numpy() class Driver(object): def __init__(self, config, client=None): self.all_args = config['all_args'] self.envs = config['envs'] self.eval_envs = config['eval_envs'] self.device = config['device'] self.num_agents = config['num_agents'] if 'signal' in config: self.actor_id = config['signal'].actor_id self.weight_ids = config['signal'].weight_ids else: self.actor_id = 0 self.weight_ids = [0] # parameters self.env_name = self.all_args.env_name self.algorithm_name = self.all_args.algorithm_name self.experiment_name = self.all_args.experiment_name self.use_centralized_V = self.all_args.use_centralized_V self.use_obs_instead_of_state = self.all_args.use_obs_instead_of_state self.num_env_steps = self.all_args.num_env_steps if hasattr(self.all_args,'num_env_steps') else self.all_args.eval_num self.episode_length = self.all_args.episode_length self.n_rollout_threads = self.all_args.n_rollout_threads self.learner_n_rollout_threads = self.all_args.n_rollout_threads self.n_eval_rollout_threads = self.all_args.n_eval_rollout_threads self.hidden_size = self.all_args.hidden_size self.recurrent_N = self.all_args.recurrent_N # interval self.save_interval = self.all_args.save_interval self.use_eval = self.all_args.use_eval self.eval_interval = self.all_args.eval_interval self.log_interval = self.all_args.log_interval # dir self.model_dir = self.all_args.model_dir if self.algorithm_name == "rmappo": from tmarl.algorithms.r_mappo_distributed.mappo_algorithm import MAPPOAlgorithm as TrainAlgo from tmarl.algorithms.r_mappo_distributed.mappo_module import MAPPOModule as AlgoModule else: raise NotImplementedError if self.envs: share_observation_space = self.envs.share_observation_space[0] \ if self.use_centralized_V else self.envs.observation_space[0] # policy network self.algo_module = AlgoModule(self.all_args, self.envs.observation_space[0], share_observation_space, self.envs.action_space[0], device=self.device) else: share_observation_space = self.eval_envs.share_observation_space[0] \ if self.use_centralized_V else self.eval_envs.observation_space[0] # policy network self.algo_module = AlgoModule(self.all_args, self.eval_envs.observation_space[0], share_observation_space, self.eval_envs.action_space[0], device=self.device) if self.model_dir is not None: self.restore() # algorithm self.trainer = TrainAlgo(self.all_args, self.algo_module, device=self.device) # buffer from tmarl.replay_buffers.normal.shared_buffer import SharedReplayBuffer self.buffer = SharedReplayBuffer(self.all_args, self.num_agents, self.envs.observation_space[0] if self.envs else self.eval_envs.observation_space[0], share_observation_space, self.envs.action_space[0] if self.envs else self.eval_envs.action_space[0]) def run(self): raise NotImplementedError def warmup(self): raise NotImplementedError def collect(self, step): raise NotImplementedError def insert(self, data): raise NotImplementedError def restore(self): policy_actor_state_dict = torch.load(str(self.model_dir) + '/actor.pt', map_location=self.device) self.algo_module.actor.load_state_dict(policy_actor_state_dict)

py

TiKick

TiKick-main/tmarl/drivers/shared_distributed/football_driver.py

from tqdm import tqdm import numpy as np from tmarl.drivers.shared_distributed.base_driver import Driver def _t2n(x): return x.detach().cpu().numpy() class FootballDriver(Driver): def __init__(self, config): super(FootballDriver, self).__init__(config) def run(self): self.trainer.prep_rollout() episodes = int(self.num_env_steps) total_num_steps = 0 for episode in range(episodes): print('Episode {}:'.format(episode)) self.eval(total_num_steps) def eval(self, total_num_steps): eval_episode_rewards = [] eval_obs, eval_share_obs, eval_available_actions = self.eval_envs.reset() agent_num = eval_obs.shape[1] used_buffer = self.buffer rnn_shape = [self.n_eval_rollout_threads, agent_num, *used_buffer.rnn_states_critic.shape[3:]] eval_rnn_states = np.zeros(rnn_shape, dtype=np.float32) eval_rnn_states_critic = np.zeros(rnn_shape, dtype=np.float32) eval_masks = np.ones((self.n_eval_rollout_threads, agent_num, 1), dtype=np.float32) finished = None for eval_step in tqdm(range(3001)): self.trainer.prep_rollout() _, eval_action, eval_action_log_prob, eval_rnn_states, _ = \ self.trainer.algo_module.get_actions(np.concatenate(eval_share_obs), np.concatenate(eval_obs), np.concatenate(eval_rnn_states), None, np.concatenate(eval_masks), np.concatenate(eval_available_actions), deterministic=True) eval_actions = np.array( np.split(_t2n(eval_action), self.n_eval_rollout_threads)) eval_rnn_states = np.array( np.split(_t2n(eval_rnn_states), self.n_eval_rollout_threads)) if self.eval_envs.action_space[0].__class__.__name__ == 'Discrete': eval_actions_env = np.squeeze( np.eye(self.eval_envs.action_space[0].n)[eval_actions], 2) else: raise NotImplementedError # Obser reward and next obs eval_obs, eval_share_obs, eval_rewards, eval_dones, eval_infos, eval_available_actions = \ self.eval_envs.step(eval_actions_env) eval_rewards = eval_rewards.reshape([-1, agent_num]) # [roll_out, num_agents] if finished is None: eval_r = eval_rewards[:,:self.num_agents] eval_episode_rewards.append(eval_r) finished = eval_dones.copy() else: eval_r = (eval_rewards * ~finished)[:,:self.num_agents] eval_episode_rewards.append(eval_r) finished = eval_dones.copy() | finished eval_masks = np.ones( (self.n_eval_rollout_threads, agent_num, 1), dtype=np.float32) eval_masks[eval_dones == True] = np.zeros( ((eval_dones == True).sum(), 1), dtype=np.float32) eval_rnn_states[eval_dones == True] = np.zeros( ((eval_dones == True).sum(), self.recurrent_N, self.hidden_size), dtype=np.float32) if finished.all() == True: break eval_episode_rewards = np.array(eval_episode_rewards) # [step,rollout,num_agents] ally_goal = np.sum((eval_episode_rewards == 1), axis=0) enemy_goal = np.sum((eval_episode_rewards == -1), axis=0) net_goal = np.sum(eval_episode_rewards, axis=0) winning_rate = np.mean(net_goal, axis=-1) eval_env_infos = {} eval_env_infos['eval_average_winning_rate'] = winning_rate>0 eval_env_infos['eval_average_losing_rate'] = winning_rate<0 eval_env_infos['eval_average_draw_rate'] = winning_rate==0 eval_env_infos['eval_average_ally_score'] = ally_goal eval_env_infos['eval_average_enemy_score'] = enemy_goal eval_env_infos['eval_average_net_score'] = net_goal print("\tSuccess Rate: " + str(np.mean(winning_rate>0)) )

py

TiKick

TiKick-main/tmarl/envs/env_wrappers.py

""" Modified from OpenAI Baselines code to work with multi-agent envs """ import numpy as np from multiprocessing import Process, Pipe from abc import ABC, abstractmethod from tmarl.utils.util import tile_images class CloudpickleWrapper(object): """ Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) """ def __init__(self, x): self.x = x def __getstate__(self): import cloudpickle return cloudpickle.dumps(self.x) def __setstate__(self, ob): import pickle self.x = pickle.loads(ob) class ShareVecEnv(ABC): """ An abstract asynchronous, vectorized environment. Used to batch data from multiple copies of an environment, so that each observation becomes an batch of observations, and expected action is a batch of actions to be applied per-environment. """ closed = False viewer = None metadata = { 'render.modes': ['human', 'rgb_array'] } def __init__(self, num_envs, observation_space, share_observation_space, action_space): self.num_envs = num_envs self.observation_space = observation_space self.share_observation_space = share_observation_space self.action_space = action_space @abstractmethod def reset(self): """ Reset all the environments and return an array of observations, or a dict of observation arrays. If step_async is still doing work, that work will be cancelled and step_wait() should not be called until step_async() is invoked again. """ pass @abstractmethod def step_async(self, actions): """ Tell all the environments to start taking a step with the given actions. Call step_wait() to get the results of the step. You should not call this if a step_async run is already pending. """ pass @abstractmethod def step_wait(self): """ Wait for the step taken with step_async(). Returns (obs, rews, dones, infos): - obs: an array of observations, or a dict of arrays of observations. - rews: an array of rewards - dones: an array of "episode done" booleans - infos: a sequence of info objects """ pass def close_extras(self): """ Clean up the extra resources, beyond what's in this base class. Only runs when not self.closed. """ pass def close(self): if self.closed: return if self.viewer is not None: self.viewer.close() self.close_extras() self.closed = True def step(self, actions): """ Step the environments synchronously. This is available for backwards compatibility. """ self.step_async(actions) return self.step_wait() def render(self, mode='human'): imgs = self.get_images() bigimg = tile_images(imgs) if mode == 'human': self.get_viewer().imshow(bigimg) return self.get_viewer().isopen elif mode == 'rgb_array': return bigimg else: raise NotImplementedError def get_images(self): """ Return RGB images from each environment """ raise NotImplementedError @property def unwrapped(self): if isinstance(self, VecEnvWrapper): return self.venv.unwrapped else: return self def get_viewer(self): if self.viewer is None: from gym.envs.classic_control import rendering self.viewer = rendering.SimpleImageViewer() return self.viewer def worker(remote, parent_remote, env_fn_wrapper): parent_remote.close() env = env_fn_wrapper.x() while True: cmd, data = remote.recv() if cmd == 'step': ob, reward, done, info = env.step(data) if 'bool' in done.__class__.__name__: if done: ob = env.reset() else: if np.all(done): ob = env.reset() remote.send((ob, reward, done, info)) elif cmd == 'reset': ob = env.reset() remote.send((ob)) elif cmd == 'render': if data == "rgb_array": fr = env.render(mode=data) remote.send(fr) elif data == "human": env.render(mode=data) elif cmd == 'reset_task': ob = env.reset_task() remote.send(ob) elif cmd == 'close': env.close() remote.close() break elif cmd == 'get_spaces': remote.send((env.observation_space, env.share_observation_space, env.action_space)) elif cmd == 'get_max_step': remote.send((env.max_steps)) elif cmd == 'get_action': # for behavior cloning action = env.get_action() remote.send((action)) else: raise NotImplementedError class SubprocVecEnv(ShareVecEnv): def __init__(self, env_fns, spaces=None): """ envs: list of gym environments to run in subprocesses """ self.waiting = False self.closed = False nenvs = len(env_fns) self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)]) self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)] for p in self.ps: p.daemon = True # if the main process crashes, we should not cause things to hang p.start() for remote in self.work_remotes: remote.close() self.remotes[0].send(('get_spaces', None)) observation_space, share_observation_space, action_space = self.remotes[0].recv() ShareVecEnv.__init__(self, len(env_fns), observation_space, share_observation_space, action_space) def step_async(self, actions): for remote, action in zip(self.remotes, actions): remote.send(('step', action)) self.waiting = True def step_wait(self): results = [remote.recv() for remote in self.remotes] self.waiting = False obs, rews, dones, infos = zip(*results) return np.stack(obs), np.stack(rews), np.stack(dones), infos def reset(self): for remote in self.remotes: remote.send(('reset', None)) obs = [remote.recv() for remote in self.remotes] return np.stack(obs) def get_max_step(self): for remote in self.remotes: remote.send(('get_max_step', None)) return np.stack([remote.recv() for remote in self.remotes]) def reset_task(self): for remote in self.remotes: remote.send(('reset_task', None)) return np.stack([remote.recv() for remote in self.remotes]) def close(self): if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(('close', None)) for p in self.ps: p.join() self.closed = True def render(self, mode="rgb_array"): for remote in self.remotes: remote.send(('render', mode)) if mode == "rgb_array": frame = [remote.recv() for remote in self.remotes] return np.stack(frame) def shareworker(remote, parent_remote, env_fn_wrapper): parent_remote.close() env = env_fn_wrapper.x() while True: cmd, data = remote.recv() if cmd == 'step': ob, s_ob, reward, done, info, available_actions = env.step(data) if 'bool' in done.__class__.__name__: if done: ob, s_ob, available_actions = env.reset() else: if np.all(done): ob, s_ob, available_actions = env.reset() remote.send((ob, s_ob, reward, done, info, available_actions)) elif cmd == 'reset': ob, s_ob, available_actions = env.reset() remote.send((ob, s_ob, available_actions)) elif cmd == 'reset_task': ob = env.reset_task() remote.send(ob) elif cmd == 'render': if data == "rgb_array": fr = env.render(mode=data) remote.send(fr) elif data == "human": env.render(mode=data) elif cmd == 'close': env.close() remote.close() break elif cmd == 'get_spaces': remote.send( (env.observation_space, env.share_observation_space, env.action_space)) elif cmd == 'render_vulnerability': fr = env.render_vulnerability(data) remote.send((fr)) elif cmd == 'get_action': # for behavior cloning action = env.get_action() remote.send((action)) else: raise NotImplementedError class ShareSubprocVecEnv(ShareVecEnv): def __init__(self, env_fns, spaces=None): """ envs: list of gym environments to run in subprocesses """ self.waiting = False self.closed = False nenvs = len(env_fns) self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)]) self.ps = [Process(target=shareworker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)] for p in self.ps: p.daemon = True # if the main process crashes, we should not cause things to hang p.start() for remote in self.work_remotes: remote.close() self.remotes[0].send(('get_spaces', None)) observation_space, share_observation_space, action_space = self.remotes[0].recv( ) ShareVecEnv.__init__(self, len(env_fns), observation_space, share_observation_space, action_space) def step_async(self, actions): for remote, action in zip(self.remotes, actions): remote.send(('step', action)) self.waiting = True def step_wait(self): results = [remote.recv() for remote in self.remotes] self.waiting = False obs, share_obs, rews, dones, infos, available_actions = zip(*results) return np.stack(obs), np.stack(share_obs), np.stack(rews), np.stack(dones), infos, np.stack(available_actions) def reset(self): for remote in self.remotes: remote.send(('reset', None)) results = [remote.recv() for remote in self.remotes] obs, share_obs, available_actions = zip(*results) return np.stack(obs), np.stack(share_obs), np.stack(available_actions) def reset_task(self): for remote in self.remotes: remote.send(('reset_task', None)) return np.stack([remote.recv() for remote in self.remotes]) def close(self): if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(('close', None)) for p in self.ps: p.join() self.closed = True def get_action(self): # for behavior clonging for remote in self.remotes: remote.send(('get_action', None)) results = [remote.recv() for remote in self.remotes] return np.concatenate(results) # single env class DummyVecEnv(ShareVecEnv): def __init__(self, env_fns): self.envs = [fn() for fn in env_fns] env = self.envs[0] ShareVecEnv.__init__(self, len( env_fns), env.observation_space, env.share_observation_space, env.action_space) self.actions = None def step_async(self, actions): self.actions = actions def step_wait(self): results = [env.step(a) for (a, env) in zip(self.actions, self.envs)] obs, rews, dones, infos = map(np.array, zip(*results)) for (i, done) in enumerate(dones): if 'bool' in done.__class__.__name__: if done: obs[i] = self.envs[i].reset() else: if np.all(done): obs[i] = self.envs[i].reset() self.actions = None return obs, rews, dones, infos def reset(self): obs = [env.reset() for env in self.envs] return np.array(obs) def get_max_step(self): return [env.max_steps for env in self.envs] def close(self): for env in self.envs: env.close() def render(self, mode="human", playeridx=None): if mode == "rgb_array": if playeridx == None: return np.array([env.render(mode=mode) for env in self.envs]) else: return np.array([env.render(mode=mode,playeridx=playeridx) for env in self.envs]) elif mode == "human": for env in self.envs: if playeridx == None: env.render(mode=mode) else: env.render(mode=mode, playeridx=playeridx) else: raise NotImplementedError class ShareDummyVecEnv(ShareVecEnv): def __init__(self, env_fns): self.envs = [fn() for fn in env_fns] env = self.envs[0] ShareVecEnv.__init__(self, len( env_fns), env.observation_space, env.share_observation_space, env.action_space) self.actions = None def step_async(self, actions): self.actions = actions def step_wait(self): results = [env.step(a) for (a, env) in zip(self.actions, self.envs)] obs, share_obs, rews, dones, infos, available_actions = map( np.array, zip(*results)) for (i, done) in enumerate(dones): if 'bool' in done.__class__.__name__: if done: obs[i], share_obs[i], available_actions[i] = self.envs[i].reset() else: if np.all(done): obs[i], share_obs[i], available_actions[i] = self.envs[i].reset() self.actions = None return obs, share_obs, rews, dones, infos, available_actions def reset(self): results = [env.reset() for env in self.envs] obs, share_obs, available_actions = map(np.array, zip(*results)) return obs, share_obs, available_actions def close(self): for env in self.envs: env.close() def render(self, mode="human"): if mode == "rgb_array": return np.array([env.render(mode=mode) for env in self.envs]) elif mode == "human": for env in self.envs: env.render(mode=mode) else: raise NotImplementedError def save_replay(self): for env in self.envs: env.save_replay() def get_action(self): # for behavior cloning results = [env.reset() for env in self.envs] return results

py

TiKick

TiKick-main/tmarl/envs/__init__.py

py

TiKick

TiKick-main/tmarl/envs/football/__init__.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """"""

py

TiKick

TiKick-main/tmarl/envs/football/football.py

import numpy as np import gym from ray.rllib.env.multi_agent_env import MultiAgentEnv import tmarl.envs.football.env as football_env class RllibGFootball(MultiAgentEnv): """An example of a wrapper for GFootball to make it compatible with rllib.""" def __init__(self, all_args, rank, log_dir=None, isEval=False): self.num_agents = all_args.num_agents self.num_rollout = all_args.n_rollout_threads self.isEval = isEval self.rank = rank # create env # need_render = (rank == 0) and isEval need_render = (rank == 0) # and (not isEval or self.use_behavior_cloning) self.env = football_env.create_environment( env_name=all_args.scenario_name, stacked=False, logdir=log_dir, representation=all_args.representation, rewards='scoring' if isEval else all_args.rewards, write_goal_dumps=False, write_full_episode_dumps=need_render, render=need_render, dump_frequency=1 if need_render else 0, number_of_left_players_agent_controls=self.num_agents, number_of_right_players_agent_controls=0, other_config_options={'action_set':'full'}) # state self.last_loffside = np.zeros(11) self.last_roffside = np.zeros(11) # dimension self.action_size = 33 if all_args.scenario_name == "11_vs_11_kaggle": self.avail_size = 20 else: self.avail_size = 19 if all_args.representation == 'raw': obs_space_dim = 268 obs_space_low = np.zeros(obs_space_dim) - 1e6 obs_space_high = np.zeros(obs_space_dim) + 1e6 obs_space_type = 'float64' else: raise NotImplementedError self.action_space = [gym.spaces.Discrete( self.action_size) for _ in range(self.num_agents)] self.observation_space = [gym.spaces.Box( low=obs_space_low, high=obs_space_high, dtype=obs_space_type) for _ in range(self.num_agents)] self.share_observation_space = [gym.spaces.Box( low=obs_space_low, high=obs_space_high, dtype=obs_space_type) for _ in range(self.num_agents)] def reset(self): # available actions avail_actions = np.ones([self.num_agents, self.action_size]) avail_actions[:, self.avail_size:] = 0 # state self.last_loffside = np.zeros(11) self.last_roffside = np.zeros(11) # obs raw_obs = self.env.reset() raw_obs = self._notFullGame(raw_obs) obs = self.raw2vec(raw_obs) share_obs = obs.copy() return obs, share_obs, avail_actions def step(self, actions): # step actions = np.argmax(actions, axis=-1) raw_o, r, d, info = self.env.step(actions.astype('int32')) raw_o = self._notFullGame(raw_o) obs = self.raw2vec(raw_o) share_obs = obs.copy() # available actions avail_actions = np.ones([self.num_agents, self.action_size]) avail_actions[:, self.avail_size:] = 0 # translate to specific form rewards = [] infos, dones = [], [] for i in range(self.num_agents): infos.append(info) dones.append(d) reward = r[i] if self.num_agents > 1 else r reward = -0.01 if d and reward < 1 and not self.isEval else reward rewards.append(reward) rewards = np.expand_dims(np.array(rewards), axis=1) return obs, share_obs, rewards, dones, infos, avail_actions def seed(self, seed=None): if seed is None: np.random.seed(1) else: np.random.seed(seed) def close(self): self.env.close() def raw2vec(self, raw_obs): obs = [] ally = np.array(raw_obs[0]['left_team']) ally_d = np.array(raw_obs[0]['left_team_direction']) enemy = np.array(raw_obs[0]['right_team']) enemy_d = np.array(raw_obs[0]['right_team_direction']) lo, ro = self.get_offside(raw_obs[0]) for a in range(self.num_agents): # prepocess me = ally[int(raw_obs[a]['active'])] ball = raw_obs[a]['ball'][:2] ball_dist = np.linalg.norm(me - ball) enemy_dist = np.linalg.norm(me - enemy, axis=-1) to_enemy = enemy - me to_ally = ally - me to_ball = ball - me o = [] # shape = 0 o.extend(ally.flatten()) o.extend(ally_d.flatten()) o.extend(enemy.flatten()) o.extend(enemy_d.flatten()) # shape = 88 o.extend(raw_obs[a]['ball']) o.extend(raw_obs[a]['ball_direction']) # shape = 94 if raw_obs[a]['ball_owned_team'] == -1: o.extend([1, 0, 0]) if raw_obs[a]['ball_owned_team'] == 0: o.extend([0, 1, 0]) if raw_obs[a]['ball_owned_team'] == 1: o.extend([0, 0, 1]) # shape = 97 active = [0] * 11 active[raw_obs[a]['active']] = 1 o.extend(active) # shape = 108 game_mode = [0] * 7 game_mode[raw_obs[a]['game_mode']] = 1 o.extend(game_mode) # shape = 115 o.extend(raw_obs[a]['sticky_actions'][:10]) # shape = 125) ball_dist = 1 if ball_dist > 1 else ball_dist o.extend([ball_dist]) # shape = 126) o.extend(raw_obs[a]['left_team_tired_factor']) # shape = 137) o.extend(raw_obs[a]['left_team_yellow_card']) # shape = 148) o.extend(raw_obs[a]['left_team_active']) # red cards # shape = 159) o.extend(lo) # ! # shape = 170) o.extend(ro) # ! # shape = 181) o.extend(enemy_dist) # shape = 192) to_ally[:, 0] /= 2 o.extend(to_ally.flatten()) # shape = 214) to_enemy[:, 0] /= 2 o.extend(to_enemy.flatten()) # shape = 236) to_ball[0] /= 2 o.extend(to_ball.flatten()) # shape = 238) steps_left = raw_obs[a]['steps_left'] o.extend([1.0 * steps_left / 3001]) # steps left till end if steps_left > 1500: steps_left -= 1501 # steps left till halfend steps_left = 1.0 * min(steps_left, 300.0) # clip steps_left /= 300.0 o.extend([steps_left]) score_ratio = 1.0 * \ (raw_obs[a]['score'][0] - raw_obs[a]['score'][1]) score_ratio /= 5.0 score_ratio = min(score_ratio, 1.0) score_ratio = max(-1.0, score_ratio) o.extend([score_ratio]) # shape = 241 o.extend([0.0] * 27) # shape = 268 obs.append(o) return np.array(obs) def get_offside(self, obs): ball = np.array(obs['ball'][:2]) ally = np.array(obs['left_team']) enemy = np.array(obs['right_team']) if obs['game_mode'] != 0: self.last_loffside = np.zeros(11, np.float32) self.last_roffside = np.zeros(11, np.float32) return np.zeros(11, np.float32), np.zeros(11, np.float32) need_recalc = False effective_ownball_team = -1 effective_ownball_player = -1 if obs['ball_owned_team'] > -1: effective_ownball_team = obs['ball_owned_team'] effective_ownball_player = obs['ball_owned_player'] need_recalc = True else: ally_dist = np.linalg.norm(ball - ally, axis=-1) enemy_dist = np.linalg.norm(ball - enemy, axis=-1) if np.min(ally_dist) < np.min(enemy_dist): if np.min(ally_dist) < 0.017: need_recalc = True effective_ownball_team = 0 effective_ownball_player = np.argmin(ally_dist) elif np.min(enemy_dist) < np.min(ally_dist): if np.min(enemy_dist) < 0.017: need_recalc = True effective_ownball_team = 1 effective_ownball_player = np.argmin(enemy_dist) if not need_recalc: return self.last_loffside, self.last_roffside left_offside = np.zeros(11, np.float32) right_offside = np.zeros(11, np.float32) if effective_ownball_team == 0: right_xs = [obs['right_team'][k][0] for k in range(1, 11)] right_xs = np.array(right_xs) right_xs.sort() for k in range(1, 11): if obs['left_team'][k][0] > right_xs[-1] and k != effective_ownball_player \ and obs['left_team'][k][0] > 0.0: left_offside[k] = 1.0 else: left_xs = [obs['left_team'][k][0] for k in range(1, 11)] left_xs = np.array(left_xs) left_xs.sort() for k in range(1, 11): if obs['right_team'][k][0] < left_xs[0] and k != effective_ownball_player \ and obs['right_team'][k][0] < 0.0: right_offside[k] = 1.0 self.last_loffside = left_offside self.last_roffside = right_offside return left_offside, right_offside def _notFullGame(self, raw_obs): # use this function when there are less than 11 players in the scenario left_ok = len(raw_obs[0]['left_team']) == 11 right_ok = len(raw_obs[0]['right_team']) == 11 if left_ok and right_ok: return raw_obs # set player's coordinate at (-1,0), set player's velocity as (0,0) for obs in raw_obs: obs['left_team'] = np.array(obs['left_team']) obs['right_team'] = np.array(obs['right_team']) obs['left_team_direction'] = np.array(obs['left_team_direction']) obs['right_team_direction'] = np.array(obs['right_team_direction']) while len(obs['left_team']) < 11: obs['left_team'] = np.concatenate([obs['left_team'], np.array([[-1,0]])], axis=0) obs['left_team_direction'] = np.concatenate([obs['left_team_direction'], np.zeros([1,2])], axis=0) obs['left_team_tired_factor'] = np.concatenate([obs['left_team_tired_factor'], np.zeros(1)], axis=0) obs['left_team_yellow_card'] = np.concatenate([obs['left_team_yellow_card'], np.zeros(1)], axis=0) obs['left_team_active'] = np.concatenate([obs['left_team_active'], np.ones(1)], axis=0) while len(obs['right_team']) < 11: obs['right_team'] = np.concatenate([obs['right_team'], np.array([[-1,0]])], axis=0) obs['right_team_direction'] = np.concatenate([obs['right_team_direction'], np.zeros([1,2])], axis=0) return raw_obs

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/11_vs_11_kaggle.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 3000 builder.config().second_half = 1500 builder.config().right_team_difficulty = 1.0 builder.config().left_team_difficulty = 1.0 builder.config().deterministic = False if builder.EpisodeNumber() % 2 == 0: first_team = Team.e_Left second_team = Team.e_Right else: first_team = Team.e_Right second_team = Team.e_Left builder.SetTeam(first_team) builder.AddPlayer(-1.000000, 0.000000, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.000000, 0.020000, e_PlayerRole_RM) builder.AddPlayer(0.000000, -0.020000, e_PlayerRole_CF) builder.AddPlayer(-0.422000, -0.19576, e_PlayerRole_LB) builder.AddPlayer(-0.500000, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.500000, 0.063559, e_PlayerRole_CB) builder.AddPlayer(-0.422000, 0.195760, e_PlayerRole_RB) builder.AddPlayer(-0.184212, -0.10568, e_PlayerRole_CM) builder.AddPlayer(-0.267574, 0.000000, e_PlayerRole_CM) builder.AddPlayer(-0.184212, 0.105680, e_PlayerRole_CM) builder.AddPlayer(-0.010000, -0.21610, e_PlayerRole_LM) builder.SetTeam(second_team) builder.AddPlayer(-1.000000, 0.000000, e_PlayerRole_GK, controllable=False) builder.AddPlayer(-0.050000, 0.000000, e_PlayerRole_RM) builder.AddPlayer(-0.010000, 0.216102, e_PlayerRole_CF) builder.AddPlayer(-0.422000, -0.19576, e_PlayerRole_LB) builder.AddPlayer(-0.500000, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.500000, 0.063559, e_PlayerRole_CB) builder.AddPlayer(-0.422000, 0.195760, e_PlayerRole_RB) builder.AddPlayer(-0.184212, -0.10568, e_PlayerRole_CM) builder.AddPlayer(-0.267574, 0.000000, e_PlayerRole_CM) builder.AddPlayer(-0.184212, 0.105680, e_PlayerRole_CM) builder.AddPlayer(-0.010000, -0.21610, e_PlayerRole_LM)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/11_vs_11_lazy.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 3000 builder.config().second_half = 1500 builder.config().right_team_difficulty = 1.0 builder.config().left_team_difficulty = 1.0 builder.config().deterministic = False if builder.EpisodeNumber() % 2 == 0: first_team = Team.e_Left second_team = Team.e_Right else: first_team = Team.e_Right second_team = Team.e_Left builder.SetTeam(first_team) builder.AddPlayer(-1.000000, 0.000000, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.000000, 0.020000, e_PlayerRole_RM) builder.AddPlayer(0.000000, -0.020000, e_PlayerRole_CF) builder.AddPlayer(-0.422000, -0.19576, e_PlayerRole_LB) builder.AddPlayer(-0.500000, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.500000, 0.063559, e_PlayerRole_CB) builder.AddPlayer(-0.422000, 0.195760, e_PlayerRole_RB) builder.AddPlayer(-0.184212, -0.10568, e_PlayerRole_CM) builder.AddPlayer(-0.267574, 0.000000, e_PlayerRole_CM) builder.AddPlayer(-0.184212, 0.105680, e_PlayerRole_CM) builder.AddPlayer(-0.010000, -0.21610, e_PlayerRole_LM) builder.SetTeam(second_team) builder.AddPlayer(-1.000000, 0.000000, e_PlayerRole_GK, controllable=False) builder.AddPlayer(-0.050000, 0.000000, e_PlayerRole_RM, lazy=True) builder.AddPlayer(-0.010000, 0.216102, e_PlayerRole_CF, lazy=True) builder.AddPlayer(-0.422000, -0.19576, e_PlayerRole_LB, lazy=True) builder.AddPlayer(-0.500000, -0.06356, e_PlayerRole_CB, lazy=True) builder.AddPlayer(-0.500000, 0.063559, e_PlayerRole_CB, lazy=True) builder.AddPlayer(-0.422000, 0.195760, e_PlayerRole_RB, lazy=True) builder.AddPlayer(-0.184212, -0.10568, e_PlayerRole_CM, lazy=True) builder.AddPlayer(-0.267574, 0.000000, e_PlayerRole_CM, lazy=True) builder.AddPlayer(-0.184212, 0.105680, e_PlayerRole_CM, lazy=True) builder.AddPlayer(-0.010000, -0.21610, e_PlayerRole_LM, lazy=True)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_3_vs_1_with_keeper.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.62, 0.0) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.6, 0.0, e_PlayerRole_CM) builder.AddPlayer(0.7, 0.2, e_PlayerRole_CM) builder.AddPlayer(0.7, -0.2, e_PlayerRole_CM) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(-0.75, 0.0, e_PlayerRole_CB)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_empty_goal.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.02, 0.0) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.0, 0.0, e_PlayerRole_CB) builder.SetTeam(Team.e_Right) builder.AddPlayer(1.0, 0.0, e_PlayerRole_GK)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_run_to_score_with_keeper.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.02, 0.0) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.0, 0.0, e_PlayerRole_CB) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(0.12, 0.2, e_PlayerRole_LB) builder.AddPlayer(0.12, 0.1, e_PlayerRole_CB) builder.AddPlayer(0.12, 0.0, e_PlayerRole_CM) builder.AddPlayer(0.12, -0.1, e_PlayerRole_CB) builder.AddPlayer(0.12, -0.2, e_PlayerRole_RB)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_counterattack_hard.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.26, -0.11) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(-0.672, -0.19576, e_PlayerRole_LB) builder.AddPlayer(-0.75, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.75, 0.063559, e_PlayerRole_CB) builder.AddPlayer(-0.672, 0.19576, e_PlayerRole_RB) builder.AddPlayer(-0.434, -0.10568, e_PlayerRole_CM) builder.AddPlayer(-0.434, 0.10568, e_PlayerRole_CM) builder.AddPlayer(0.5, -0.3161, e_PlayerRole_CM) builder.AddPlayer(0.25, -0.1, e_PlayerRole_LM) builder.AddPlayer(0.25, 0.1, e_PlayerRole_RM) builder.AddPlayer(0.35, 0.316102, e_PlayerRole_CF) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(0.128, -0.19576, e_PlayerRole_LB) builder.AddPlayer(-0.4, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.4, 0.063559, e_PlayerRole_CB) builder.AddPlayer(0.128, -0.19576, e_PlayerRole_RB) builder.AddPlayer(0.365, -0.10568, e_PlayerRole_CM) builder.AddPlayer(0.282, 0.0, e_PlayerRole_CM) builder.AddPlayer(0.365, 0.10568, e_PlayerRole_CM) builder.AddPlayer(0.54, -0.3161, e_PlayerRole_LM) builder.AddPlayer(0.51, 0.0, e_PlayerRole_RM) builder.AddPlayer(0.54, 0.316102, e_PlayerRole_CF)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_run_to_score.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.02, 0.0) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.0, 0.0, e_PlayerRole_CB) builder.SetTeam(Team.e_Right) builder.AddPlayer(1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(0.12, 0.2, e_PlayerRole_LB) builder.AddPlayer(0.12, 0.1, e_PlayerRole_CB) builder.AddPlayer(0.12, 0.0, e_PlayerRole_CM) builder.AddPlayer(0.12, -0.1, e_PlayerRole_CB) builder.AddPlayer(0.12, -0.2, e_PlayerRole_RB)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_empty_goal_close.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.77, 0.0) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.75, 0.0, e_PlayerRole_CB) builder.SetTeam(Team.e_Right) builder.AddPlayer(1.0, 0.0, e_PlayerRole_GK)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_corner.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = False builder.SetBallPosition(0.99, 0.41) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(1.0, 0.42, e_PlayerRole_LB) builder.AddPlayer(0.7, 0.15, e_PlayerRole_CB) builder.AddPlayer(0.7, 0.05, e_PlayerRole_CB) builder.AddPlayer(0.7, -0.05, e_PlayerRole_RB) builder.AddPlayer(0.0, 0.0, e_PlayerRole_CM) builder.AddPlayer(0.6, 0.35, e_PlayerRole_CM) builder.AddPlayer(0.8, 0.07, e_PlayerRole_CM) builder.AddPlayer(0.8, -0.03, e_PlayerRole_LM) builder.AddPlayer(0.8, -0.13, e_PlayerRole_RM) builder.AddPlayer(0.7, -0.3, e_PlayerRole_CF) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(-0.75, -0.18, e_PlayerRole_LB) builder.AddPlayer(-0.75, -0.08, e_PlayerRole_CB) builder.AddPlayer(-0.75, 0.02, e_PlayerRole_CB) builder.AddPlayer(-1.0, -0.1, e_PlayerRole_RB) builder.AddPlayer(-0.8, -0.25, e_PlayerRole_CM) builder.AddPlayer(-0.88, -0.07, e_PlayerRole_CM) builder.AddPlayer(-0.88, 0.03, e_PlayerRole_CM) builder.AddPlayer(-0.88, 0.13, e_PlayerRole_LM) builder.AddPlayer(-0.75, 0.25, e_PlayerRole_RM) builder.AddPlayer(-0.2, 0.0, e_PlayerRole_CF)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/__init__.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gfootball_engine as libgame e_PlayerRole_GK = libgame.e_PlayerRole.e_PlayerRole_GK e_PlayerRole_CB = libgame.e_PlayerRole.e_PlayerRole_CB e_PlayerRole_LB = libgame.e_PlayerRole.e_PlayerRole_LB e_PlayerRole_RB = libgame.e_PlayerRole.e_PlayerRole_RB e_PlayerRole_DM = libgame.e_PlayerRole.e_PlayerRole_DM e_PlayerRole_CM = libgame.e_PlayerRole.e_PlayerRole_CM e_PlayerRole_LM = libgame.e_PlayerRole.e_PlayerRole_LM e_PlayerRole_RM = libgame.e_PlayerRole.e_PlayerRole_RM e_PlayerRole_AM = libgame.e_PlayerRole.e_PlayerRole_AM e_PlayerRole_CF = libgame.e_PlayerRole.e_PlayerRole_CF Team = libgame.e_Team

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_pass_and_shoot_with_keeper.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.7, -0.28) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.7, 0.0, e_PlayerRole_CB) builder.AddPlayer(0.7, -0.3, e_PlayerRole_CB) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(-0.75, 0.3, e_PlayerRole_CB)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_run_pass_and_shoot_with_keeper.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.7, -0.28) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(0.7, 0.0, e_PlayerRole_CB) builder.AddPlayer(0.7, -0.3, e_PlayerRole_CB) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(-0.75, 0.1, e_PlayerRole_CB)

py

TiKick

TiKick-main/tmarl/envs/football/scenarios/academy_counterattack_easy.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import * def build_scenario(builder): builder.config().game_duration = 400 builder.config().deterministic = False builder.config().offsides = False builder.config().end_episode_on_score = True builder.config().end_episode_on_out_of_play = True builder.config().end_episode_on_possession_change = True builder.SetBallPosition(0.26, -0.11) builder.SetTeam(Team.e_Left) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK, controllable=False) builder.AddPlayer(-0.672, -0.19576, e_PlayerRole_LB) builder.AddPlayer(-0.75, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.75, 0.063559, e_PlayerRole_CB) builder.AddPlayer(-0.672, 0.19576, e_PlayerRole_RB) builder.AddPlayer(-0.434, -0.10568, e_PlayerRole_CM) builder.AddPlayer(-0.434, 0.10568, e_PlayerRole_CM) builder.AddPlayer(0.5, -0.3161, e_PlayerRole_CM) builder.AddPlayer(0.25, -0.1, e_PlayerRole_LM) builder.AddPlayer(0.25, 0.1, e_PlayerRole_RM) builder.AddPlayer(0.35, 0.316102, e_PlayerRole_CF) builder.SetTeam(Team.e_Right) builder.AddPlayer(-1.0, 0.0, e_PlayerRole_GK) builder.AddPlayer(0.128, -0.19576, e_PlayerRole_LB) builder.AddPlayer(0.4, -0.06356, e_PlayerRole_CB) builder.AddPlayer(-0.4, 0.063559, e_PlayerRole_CB) builder.AddPlayer(0.128, -0.19576, e_PlayerRole_RB) builder.AddPlayer(0.365, -0.10568, e_PlayerRole_CM) builder.AddPlayer(0.282, 0.0, e_PlayerRole_CM) builder.AddPlayer(0.365, 0.10568, e_PlayerRole_CM) builder.AddPlayer(0.54, -0.3161, e_PlayerRole_LM) builder.AddPlayer(0.51, 0.0, e_PlayerRole_RM) builder.AddPlayer(0.54, 0.316102, e_PlayerRole_CF)

py

TiKick

TiKick-main/tmarl/envs/football/env/football_env_core.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Football environment as close as possible to a GYM environment.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import logging import copy try: import gfootball_engine as libgame from gfootball_engine import GameState except ImportError: print('Cannot import gfootball_engine. Package was not installed properly.') from tmarl.envs.football.env import config as cfg from gfootball.env import constants from gfootball.env import football_action_set from gfootball.env import observation_processor import numpy as np import six.moves.cPickle from six.moves import range import timeit _unused_engines = [] _unused_rendering_engine = None _active_rendering = False try: import cv2 except ImportError: import cv2 class EnvState(object): def __init__(self): self.previous_score_diff = 0 self.previous_game_mode = -1 self.prev_ball_owned_team = -1 class FootballEnvCore(object): def __init__(self, config): global _unused_engines self._config = config self._sticky_actions = football_action_set.get_sticky_actions(config) self._use_rendering_engine = False if _unused_engines: self._env = _unused_engines.pop() else: self._env = self._get_new_env() # Reset is needed here to make sure render() API call before reset() API # call works fine (get/setState makes sure env. config is the same). self.reset(inc=0) def _get_new_env(self): env = libgame.GameEnv() env.game_config.physics_steps_per_frame = self._config[ 'physics_steps_per_frame'] env.game_config.render_resolution_x = self._config['render_resolution_x'] env.game_config.render_resolution_y = self._config['render_resolution_y'] return env def _reset(self, animations, inc): global _unused_engines global _unused_rendering_engine assert (self._env.state == GameState.game_created or self._env.state == GameState.game_running or self._env.state == GameState.game_done) # Variables that are part of the set_state/get_state snapshot. self._state = EnvState() # Variables being re-computed upon set_state call, no need to snapshot. self._observation = None # Not snapshoted variables. self._steps_time = 0 self._step = 0 self._config.NewScenario(inc=inc) if self._env.state == GameState.game_created: self._env.start_game() self._env.state = GameState.game_running scenario_config = self._config.ScenarioConfig() assert ( not scenario_config.dynamic_player_selection or not scenario_config.control_all_players ), ('For this scenario you need to control either 0 or all players on the ' 'team ({} for left team, {} for right team).').format( scenario_config.controllable_left_players, scenario_config.controllable_right_players) self._env.reset(scenario_config, animations) def reset(self, inc=1): """Reset environment for a new episode using a given config.""" self._episode_start = timeit.default_timer() self._action_set = football_action_set.get_action_set(self._config) trace = observation_processor.ObservationProcessor(self._config) self._cumulative_reward = 0 self._step_count = 0 self._trace = trace self._reset(self._env.game_config.render, inc=inc) while not self._retrieve_observation(): self._env.step() return True def _rendering_in_use(self): global _active_rendering if not self._use_rendering_engine: assert not _active_rendering, ('Environment does not support multiple ' 'rendering instances in the same process.') _active_rendering = True self._use_rendering_engine = True self._env.game_config.render = True def _release_engine(self): global _unused_engines global _unused_rendering_engine global _active_rendering if self._env: if self._use_rendering_engine: assert not _unused_rendering_engine _unused_rendering_engine = self._env _active_rendering = False else: _unused_engines.append(self._env) self._env = None def close(self): self._release_engine() if self._trace: del self._trace self._trace = None def __del__(self): self.close() def step(self, action, extra_data={}): assert self._env.state != GameState.game_done, ( 'Cant call step() once episode finished (call reset() instead)') assert self._env.state == GameState.game_running, ( 'reset() must be called before step()') action = [ football_action_set.named_action_from_action_set(self._action_set, a) for a in action ] self._step_count += 1 assert len(action) == ( self._env.config.left_agents + self._env.config.right_agents) debug = {} debug['action'] = action action_index = 0 for left_team in [True, False]: agents = self._env.config.left_agents if left_team else self._env.config.right_agents for i in range(agents): player_action = action[action_index] # If agent 'holds' the game for too long, just start it. if self._env.waiting_for_game_count == 20: player_action = football_action_set.action_short_pass elif self._env.waiting_for_game_count > 20: player_action = football_action_set.action_idle controlled_players = self._observation[ 'left_agent_controlled_player'] if left_team else self._observation[ 'right_agent_controlled_player'] if self._observation['ball_owned_team'] != -1 and self._observation[ 'ball_owned_team'] ^ left_team and controlled_players[ i] == self._observation['ball_owned_player']: if self._env.waiting_for_game_count < 30: player_action = football_action_set.action_left else: player_action = football_action_set.action_right action_index += 1 assert isinstance(player_action, football_action_set.CoreAction) self._env.perform_action(player_action._backend_action, left_team, i) while True: enter_time = timeit.default_timer() self._env.step() self._steps_time += timeit.default_timer() - enter_time if self._retrieve_observation(): break if 'frame' in self._observation: self._trace.add_frame(self._observation['frame']) debug['frame_cnt'] = self._step # Finish the episode on score. if self._env.config.end_episode_on_score: if self._observation['score'][0] > 0 or self._observation['score'][1] > 0: self._env.state = GameState.game_done # Finish the episode if the game is out of play (e.g. foul, corner etc) if (self._env.config.end_episode_on_out_of_play and self._observation['game_mode'] != int( libgame.e_GameMode.e_GameMode_Normal) and self._state.previous_game_mode == int( libgame.e_GameMode.e_GameMode_Normal)): self._env.state = GameState.game_done self._state.previous_game_mode = self._observation['game_mode'] # End episode when team possessing the ball changes. if (self._env.config.end_episode_on_possession_change and self._observation['ball_owned_team'] != -1 and self._state.prev_ball_owned_team != -1 and self._observation['ball_owned_team'] != self._state.prev_ball_owned_team): self._env.state = GameState.game_done if self._observation['ball_owned_team'] != -1: self._state.prev_ball_owned_team = self._observation['ball_owned_team'] # Compute reward. score_diff = self._observation['score'][0] - self._observation['score'][1] reward = score_diff - self._state.previous_score_diff self._state.previous_score_diff = score_diff if reward == 1: self._trace.write_dump('score') elif reward == -1: self._trace.write_dump('lost_score') debug['reward'] = reward if self._observation['game_mode'] != int( libgame.e_GameMode.e_GameMode_Normal): self._env.waiting_for_game_count += 1 else: self._env.waiting_for_game_count = 0 if self._step >= self._env.config.game_duration: self._env.state = GameState.game_done episode_done = self._env.state == GameState.game_done debug['time'] = timeit.default_timer() debug.update(extra_data) self._cumulative_reward += reward single_observation = copy.deepcopy(self._observation) trace = { 'debug': debug, 'observation': single_observation, 'reward': reward, 'cumulative_reward': self._cumulative_reward } info = {} self._trace.update(trace) dumps = self._trace.process_pending_dumps(episode_done) if dumps: info['dumps'] = dumps if episode_done: del self._trace self._trace = None fps = self._step_count / (debug['time'] - self._episode_start) game_fps = self._step_count / self._steps_time logging.info( 'Episode reward: %.2f score: [%d, %d], steps: %d, ' 'FPS: %.1f, gameFPS: %.1f', self._cumulative_reward, single_observation['score'][0], single_observation['score'][1], self._step_count, fps, game_fps) if self._step_count == 1: # Start writing episode_done self.write_dump('episode_done') return self._observation, reward, episode_done, info def _retrieve_observation(self): """Constructs observations exposed by the environment. Returns whether game is on or not. """ info = self._env.get_info() result = {} if self._env.game_config.render: frame = self._env.get_frame() frame = np.frombuffer(frame, dtype=np.uint8) frame = np.reshape(frame, [ self._config['render_resolution_x'], self._config['render_resolution_y'], 3 ]) frame = np.reshape( np.concatenate([frame[:, :, 0], frame[:, :, 1], frame[:, :, 2]]), [ 3, self._config['render_resolution_y'], self._config['render_resolution_x'] ]) frame = np.transpose(frame, [1, 2, 0]) frame = np.flip(frame, 0) result['frame'] = frame result['ball'] = np.array( [info.ball_position[0], info.ball_position[1], info.ball_position[2]]) # Ball's movement direction represented as [x, y] distance per step. result['ball_direction'] = np.array([ info.ball_direction[0], info.ball_direction[1], info.ball_direction[2] ]) # Ball's rotation represented as [x, y, z] rotation angle per step. result['ball_rotation'] = np.array( [info.ball_rotation[0], info.ball_rotation[1], info.ball_rotation[2]]) self._convert_players_observation(info.left_team, 'left_team', result) self._convert_players_observation(info.right_team, 'right_team', result) result['left_agent_sticky_actions'] = [] result['left_agent_controlled_player'] = [] result['right_agent_sticky_actions'] = [] result['right_agent_controlled_player'] = [] for i in range(self._env.config.left_agents): result['left_agent_controlled_player'].append( info.left_controllers[i].controlled_player) result['left_agent_sticky_actions'].append( np.array(self.sticky_actions_state(True, i), dtype=np.uint8)) for i in range(self._env.config.right_agents): result['right_agent_controlled_player'].append( info.right_controllers[i].controlled_player) result['right_agent_sticky_actions'].append( np.array(self.sticky_actions_state(False, i), dtype=np.uint8)) result['game_mode'] = int(info.game_mode) result['score'] = [info.left_goals, info.right_goals] result['ball_owned_team'] = info.ball_owned_team result['ball_owned_player'] = info.ball_owned_player result['steps_left'] = self._env.config.game_duration - info.step self._observation = result self._step = info.step return info.is_in_play def _convert_players_observation(self, players, name, result): """Converts internal players representation to the public one. Internal representation comes directly from gameplayfootball engine. Public representation is part of environment observations. Args: players: collection of team players to convert. name: name of the team being converted (left_team or right_team). result: collection where conversion result is added. """ positions = [] directions = [] tired_factors = [] active = [] yellow_cards = [] roles = [] designated_player = -1 for id, player in enumerate(players): positions.append(player.position[0]) positions.append(player.position[1]) directions.append(player.direction[0]) directions.append(player.direction[1]) tired_factors.append(player.tired_factor) active.append(player.is_active) yellow_cards.append(player.has_card) roles.append(player.role) if player.designated_player: designated_player = id result[name] = np.reshape(np.array(positions), [-1, 2]) # Players' movement direction represented as [x, y] distance per step. result['{}_direction'.format(name)] = np.reshape( np.array(directions), [-1, 2]) # Players' tired factor in the range [0, 1] (0 means not tired). result['{}_tired_factor'.format(name)] = np.array(tired_factors) result['{}_active'.format(name)] = np.array(active) result['{}_yellow_card'.format(name)] = np.array(yellow_cards) result['{}_roles'.format(name)] = np.array(roles) result['{}_designated_player'.format(name)] = designated_player def observation(self): """Returns the current observation of the game.""" assert (self._env.state == GameState.game_running or self._env.state == GameState.game_done), ( 'reset() must be called before observation()') return copy.deepcopy(self._observation) def sticky_actions_state(self, left_team, player_id): result = [] for a in self._sticky_actions: result.append( self._env.sticky_action_state(a._backend_action, left_team, player_id)) return np.uint8(result) def get_state(self, to_pickle): assert (self._env.state == GameState.game_running or self._env.state == GameState.game_done), ( 'reset() must be called before get_state()') to_pickle['FootballEnvCore'] = self._state pickle = six.moves.cPickle.dumps(to_pickle) return self._env.get_state(pickle) def set_state(self, state): assert (self._env.state == GameState.game_running or self._env.state == GameState.game_done), ( 'reset() must be called before set_state()') res = self._env.set_state(state) assert self._retrieve_observation() from_picle = six.moves.cPickle.loads(res) self._state = from_picle['FootballEnvCore'] if self._trace is None: self._trace = observation_processor.ObservationProcessor(self._config) return from_picle def tracker_setup(self, start, end): self._env.tracker_setup(start, end) def write_dump(self, name): return self._trace.write_dump(name) def render(self, mode): global _unused_rendering_engine if self._env.state == GameState.game_created: self._rendering_in_use() return False if not self._env.game_config.render: if not self._use_rendering_engine: if self._env.state != GameState.game_created: state = self.get_state({}) self._release_engine() if _unused_rendering_engine: self._env = _unused_rendering_engine _unused_rendering_engine = None else: self._env = self._get_new_env() self._rendering_in_use() self._reset(animations=False, inc=0) self.set_state(state) # We call render twice, as the first call has bad camera position. self._env.render(False) else: self._env.game_config.render = True self._env.render(True) self._retrieve_observation() if mode == 'rgb_array': frame = self._observation['frame'] b, g, r = cv2.split(frame) return cv2.merge((r, g, b)) elif mode == 'human': return True return False def disable_render(self): self._env.game_config.render = False

py

TiKick

TiKick-main/tmarl/envs/football/env/script_helpers.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Set of functions used by command line scripts.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tmarl.envs.football.env import config from gfootball.env import football_action_set from tmarl.envs.football.env import football_env from gfootball.env import observation_processor import copy import six.moves.cPickle import os import tempfile class ScriptHelpers(object): """Set of methods used by command line scripts.""" def __init__(self): pass def __modify_trace(self, replay, fps): """Adopt replay to the new framerate and add additional steps at the end.""" trace = [] min_fps = replay[0]['debug']['config']['physics_steps_per_frame'] assert fps % min_fps == 0, ( 'Trace has to be rendered in framerate being multiple of {}'.format( min_fps)) assert fps <= 100, ('Framerate of up to 100 is supported') empty_steps = int(fps / min_fps) - 1 for f in replay: trace.append(f) idle_step = copy.deepcopy(f) idle_step['debug']['action'] = [football_action_set.action_idle ] * len(f['debug']['action']) for _ in range(empty_steps): trace.append(idle_step) # Add some empty steps at the end, so that we can record videos. for _ in range(10): trace.append(idle_step) return trace def __build_players(self, dump_file, spec): players = [] for player in spec: players.extend(['replay:path={},left_players=1'.format( dump_file)] * config.count_left_players(player)) players.extend(['replay:path={},right_players=1'.format( dump_file)] * config.count_right_players(player)) return players def load_dump(self, dump_file): dump = [] with open(dump_file, 'rb') as in_fd: while True: try: step = six.moves.cPickle.load(in_fd) except EOFError: return dump dump.append(step) def dump_to_txt(self, dump_file, output, include_debug): with open(output, 'w') as out_fd: dump = self.load_dump(dump_file) if not include_debug: for s in dump: if 'debug' in s: del s['debug'] with open(output, 'w') as f: f.write(str(dump)) def dump_to_video(self, dump_file): dump = self.load_dump(dump_file) cfg = config.Config(dump[0]['debug']['config']) cfg['dump_full_episodes'] = True cfg['write_video'] = True cfg['display_game_stats'] = True processor = observation_processor.ObservationProcessor(cfg) processor.write_dump('episode_done') for frame in dump: processor.update(frame) def replay(self, dump, fps=10, config_update={}, directory=None, render=True): replay = self.load_dump(dump) trace = self.__modify_trace(replay, fps) fd, temp_path = tempfile.mkstemp(suffix='.dump') with open(temp_path, 'wb') as f: for step in trace: six.moves.cPickle.dump(step, f) assert replay[0]['debug']['frame_cnt'] == 0, ( 'Trace does not start from the beginning of the episode, can not replay') cfg = config.Config(replay[0]['debug']['config']) cfg['players'] = self.__build_players(temp_path, cfg['players']) config_update['physics_steps_per_frame'] = int(100 / fps) config_update['real_time'] = False if directory: config_update['tracesdir'] = directory config_update['write_video'] = True # my edition # config_update['display_game_stats'] = False # config_update['video_quality_level'] = 2 cfg.update(config_update) env = football_env.FootballEnv(cfg) if render: env.render() env.reset() done = False try: while not done: _, _, done, _ = env.step([]) except KeyboardInterrupt: env.write_dump('shutdown') exit(1) os.close(fd)

py

TiKick

TiKick-main/tmarl/envs/football/env/scenario_builder.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Class responsible for generating scenarios.""" import importlib import os import pkgutil import random import sys from absl import flags from absl import logging import gfootball_engine as libgame Player = libgame.FormationEntry Role = libgame.e_PlayerRole Team = libgame.e_Team FLAGS = flags.FLAGS def all_scenarios(): path = os.path.abspath(__file__) path = os.path.join(os.path.dirname(os.path.dirname(path)), 'scenarios') scenarios = [] for m in pkgutil.iter_modules([path]): # There was API change in pkgutil between Python 3.5 and 3.6... if m.__class__ == tuple: scenarios.append(m[1]) else: scenarios.append(m.name) return scenarios class Scenario(object): def __init__(self, config): # Game config controls C++ engine and is derived from the main config. self._scenario_cfg = libgame.ScenarioConfig.make() self._config = config self._active_team = Team.e_Left scenario = None try: scenario = importlib.import_module('tmarl.envs.football.scenarios.{}'.format(config['level'])) except ImportError as e: logging.error('Loading scenario "%s" failed' % config['level']) logging.error(e) sys.exit(1) scenario.build_scenario(self) self.SetTeam(libgame.e_Team.e_Left) self._FakePlayersForEmptyTeam(self._scenario_cfg.left_team) self.SetTeam(libgame.e_Team.e_Right) self._FakePlayersForEmptyTeam(self._scenario_cfg.right_team) self._BuildScenarioConfig() def _FakePlayersForEmptyTeam(self, team): if len(team) == 0: self.AddPlayer(-1.000000, 0.420000, libgame.e_PlayerRole.e_PlayerRole_GK, True) def _BuildScenarioConfig(self): """Builds scenario config from gfootball.environment config.""" self._scenario_cfg.real_time = self._config['real_time'] self._scenario_cfg.left_agents = self._config.number_of_left_players() self._scenario_cfg.right_agents = self._config.number_of_right_players() # This is needed to record 'game_engine_random_seed' in the dump. if 'game_engine_random_seed' not in self._config._values: self._config.set_scenario_value('game_engine_random_seed', random.randint(0, 2000000000)) if not self._scenario_cfg.deterministic: self._scenario_cfg.game_engine_random_seed = ( self._config['game_engine_random_seed']) if 'reverse_team_processing' not in self._config: self._config['reverse_team_processing'] = ( bool(self._config['game_engine_random_seed'] % 2)) if 'reverse_team_processing' in self._config: self._scenario_cfg.reverse_team_processing = ( self._config['reverse_team_processing']) def config(self): return self._scenario_cfg def SetTeam(self, team): self._active_team = team def AddPlayer(self, x, y, role, lazy=False, controllable=True): """Build player for the current scenario. Args: x: x coordinate of the player in the range [-1, 1]. y: y coordinate of the player in the range [-0.42, 0.42]. role: Player's role in the game (goal keeper etc.). lazy: Computer doesn't perform any automatic actions for lazy player. controllable: Whether player can be controlled. """ player = Player(x, y, role, lazy, controllable) if self._active_team == Team.e_Left: self._scenario_cfg.left_team.append(player) else: self._scenario_cfg.right_team.append(player) def SetBallPosition(self, ball_x, ball_y): self._scenario_cfg.ball_position[0] = ball_x self._scenario_cfg.ball_position[1] = ball_y def EpisodeNumber(self): return self._config['episode_number'] def ScenarioConfig(self): return self._scenario_cfg

py

TiKick

TiKick-main/tmarl/envs/football/env/config.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Config loader.""" from __future__ import print_function import copy from absl import flags import gfootball_engine as libgame FLAGS = flags.FLAGS def parse_player_definition(definition): """Parses player definition. An example of player definition is: "agent:players=4" or "replay:path=...". Args: definition: a string defining a player Returns: A tuple (name, dict). """ name = definition d = {'left_players': 0, 'right_players': 0} if ':' in definition: (name, params) = definition.split(':') for param in params.split(','): (key, value) = param.split('=') d[key] = value if d['left_players'] == 0 and d['right_players'] == 0: d['left_players'] = 1 return name, d def count_players(definition): """Returns a number of players given a definition.""" _, player_definition = parse_player_definition(definition) return (int(player_definition['left_players']) + int(player_definition['right_players'])) def count_left_players(definition): """Returns a number of left players given a definition.""" return int(parse_player_definition(definition)[1]['left_players']) def count_right_players(definition): """Returns a number of players given a definition.""" return int(parse_player_definition(definition)[1]['right_players']) def get_agent_number_of_players(players): """Returns a total number of players controlled by an agent.""" return sum([count_players(player) for player in players if player.startswith('agent')]) class Config(object): def __init__(self, values=None): self._values = { 'action_set': 'default', 'custom_display_stats': None, 'display_game_stats': True, 'dump_full_episodes': False, 'dump_scores': False, 'players': ['agent:left_players=1'], 'level': '11_vs_11_stochastic', 'physics_steps_per_frame': 10, 'render_resolution_x': 1280, 'real_time': False, 'tracesdir': '/tmp/dumps', 'video_format': 'avi', 'video_quality_level': 0, # 0 - low, 1 - medium, 2 - high 'write_video': False } self._values['render_resolution_y'] = int( 0.5625 * self._values['render_resolution_x']) if values: self._values.update(values) self.NewScenario() def number_of_left_players(self): return sum([count_left_players(player) for player in self._values['players']]) def number_of_right_players(self): return sum([count_right_players(player) for player in self._values['players']]) def number_of_players_agent_controls(self): return get_agent_number_of_players(self._values['players']) def __eq__(self, other): assert isinstance(other, self.__class__) return self._values == other._values and self._scenario_values == other._scenario_values def __ne__(self, other): return not self.__eq__(other) def __getitem__(self, key): if key in self._scenario_values: return self._scenario_values[key] return self._values[key] def __setitem__(self, key, value): self._values[key] = value def __contains__(self, key): return key in self._scenario_values or key in self._values def get_dictionary(self): cfg = copy.deepcopy(self._values) cfg.update(self._scenario_values) return cfg def set_scenario_value(self, key, value): """Override value of specific config key for a single episode.""" self._scenario_values[key] = value def serialize(self): return self._values def update(self, config): self._values.update(config) def ScenarioConfig(self): return self._scenario_cfg def NewScenario(self, inc = 1): if 'episode_number' not in self._values: self._values['episode_number'] = 0 self._values['episode_number'] += inc self._scenario_values = {} from tmarl.envs.football.env import scenario_builder self._scenario_cfg = scenario_builder.Scenario(self).ScenarioConfig()

py

TiKick

TiKick-main/tmarl/envs/football/env/__init__.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """GFootball Environment.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tmarl.envs.football.env import config from gfootball.env import football_env from gfootball.env import observation_preprocessing from gfootball.env import wrappers def _process_reward_wrappers(env, rewards): assert 'scoring' in rewards.split(',') if 'checkpoints' in rewards.split(','): env = wrappers.CheckpointRewardWrapper(env) return env def _process_representation_wrappers(env, representation, channel_dimensions): """Wraps with necessary representation wrappers. Args: env: A GFootball gym environment. representation: See create_environment.representation comment. channel_dimensions: (width, height) tuple that represents the dimensions of SMM or pixels representation. Returns: Google Research Football environment. """ if representation.startswith('pixels'): env = wrappers.PixelsStateWrapper(env, 'gray' in representation, channel_dimensions) elif representation == 'simple115': env = wrappers.Simple115StateWrapper(env) elif representation == 'simple115v2': env = wrappers.Simple115StateWrapper(env, True) elif representation == 'extracted': env = wrappers.SMMWrapper(env, channel_dimensions) elif representation == 'raw': pass else: raise ValueError('Unsupported representation: {}'.format(representation)) return env def _apply_output_wrappers(env, rewards, representation, channel_dimensions, apply_single_agent_wrappers, stacked): """Wraps with necessary wrappers modifying the output of the environment. Args: env: A GFootball gym environment. rewards: What rewards to apply. representation: See create_environment.representation comment. channel_dimensions: (width, height) tuple that represents the dimensions of SMM or pixels representation. apply_single_agent_wrappers: Whether to reduce output to single agent case. stacked: Should observations be stacked. Returns: Google Research Football environment. """ env = _process_reward_wrappers(env, rewards) env = _process_representation_wrappers(env, representation, channel_dimensions) if apply_single_agent_wrappers: if representation != 'raw': env = wrappers.SingleAgentObservationWrapper(env) env = wrappers.SingleAgentRewardWrapper(env) if stacked: env = wrappers.FrameStack(env, 4) env = wrappers.GetStateWrapper(env) return env def create_environment(env_name='', stacked=False, representation='extracted', rewards='scoring', write_goal_dumps=False, write_full_episode_dumps=False, render=False, write_video=False, dump_frequency=1, logdir='', extra_players=None, number_of_left_players_agent_controls=1, number_of_right_players_agent_controls=0, channel_dimensions=( observation_preprocessing.SMM_WIDTH, observation_preprocessing.SMM_HEIGHT), other_config_options={}): """Creates a Google Research Football environment. Args: env_name: a name of a scenario to run, e.g. "11_vs_11_stochastic". The list of scenarios can be found in directory "scenarios". stacked: If True, stack 4 observations, otherwise, only the last observation is returned by the environment. Stacking is only possible when representation is one of the following: "pixels", "pixels_gray" or "extracted". In that case, the stacking is done along the last (i.e. channel) dimension. representation: String to define the representation used to build the observation. It can be one of the following: 'pixels': the observation is the rendered view of the football field downsampled to 'channel_dimensions'. The observation size is: 'channel_dimensions'x3 (or 'channel_dimensions'x12 when "stacked" is True). 'pixels_gray': the observation is the rendered view of the football field in gray scale and downsampled to 'channel_dimensions'. The observation size is 'channel_dimensions'x1 (or 'channel_dimensions'x4 when stacked is True). 'extracted': also referred to as super minimap. The observation is composed of 4 planes of size 'channel_dimensions'. Its size is then 'channel_dimensions'x4 (or 'channel_dimensions'x16 when stacked is True). The first plane P holds the position of players on the left team, P[y,x] is 255 if there is a player at position (x,y), otherwise, its value is 0. The second plane holds in the same way the position of players on the right team. The third plane holds the position of the ball. The last plane holds the active player. 'simple115'/'simple115v2': the observation is a vector of size 115. It holds: - the ball_position and the ball_direction as (x,y,z) - one hot encoding of who controls the ball. [1, 0, 0]: nobody, [0, 1, 0]: left team, [0, 0, 1]: right team. - one hot encoding of size 11 to indicate who is the active player in the left team. - 11 (x,y) positions for each player of the left team. - 11 (x,y) motion vectors for each player of the left team. - 11 (x,y) positions for each player of the right team. - 11 (x,y) motion vectors for each player of the right team. - one hot encoding of the game mode. Vector of size 7 with the following meaning: {NormalMode, KickOffMode, GoalKickMode, FreeKickMode, CornerMode, ThrowInMode, PenaltyMode}. Can only be used when the scenario is a flavor of normal game (i.e. 11 versus 11 players). rewards: Comma separated list of rewards to be added. Currently supported rewards are 'scoring' and 'checkpoints'. write_goal_dumps: whether to dump traces up to 200 frames before goals. write_full_episode_dumps: whether to dump traces for every episode. render: whether to render game frames. Must be enable when rendering videos or when using pixels representation. write_video: whether to dump videos when a trace is dumped. dump_frequency: how often to write dumps/videos (in terms of # of episodes) Sub-sample the episodes for which we dump videos to save some disk space. logdir: directory holding the logs. extra_players: A list of extra players to use in the environment. Each player is defined by a string like: "$player_name:left_players=?,right_players=?,$param1=?,$param2=?...." number_of_left_players_agent_controls: Number of left players an agent controls. number_of_right_players_agent_controls: Number of right players an agent controls. channel_dimensions: (width, height) tuple that represents the dimensions of SMM or pixels representation. other_config_options: dict that allows directly setting other options in the Config Returns: Google Research Football environment. """ assert env_name scenario_config = config.Config({'level': env_name}).ScenarioConfig() players = [('agent:left_players=%d,right_players=%d' % ( number_of_left_players_agent_controls, number_of_right_players_agent_controls))] # Enable MultiAgentToSingleAgent wrapper? multiagent_to_singleagent = False if scenario_config.control_all_players: if (number_of_left_players_agent_controls in [0, 1] and number_of_right_players_agent_controls in [0, 1]): multiagent_to_singleagent = True players = [('agent:left_players=%d,right_players=%d' % (scenario_config.controllable_left_players if number_of_left_players_agent_controls else 0, scenario_config.controllable_right_players if number_of_right_players_agent_controls else 0))] if extra_players is not None: players.extend(extra_players) config_values = { 'dump_full_episodes': write_full_episode_dumps, 'dump_scores': write_goal_dumps, 'players': players, 'level': env_name, 'tracesdir': logdir, 'write_video': write_video, } config_values.update(other_config_options) c = config.Config(config_values) env = football_env.FootballEnv(c) if multiagent_to_singleagent: env = wrappers.MultiAgentToSingleAgent( env, number_of_left_players_agent_controls, number_of_right_players_agent_controls) if dump_frequency > 1: env = wrappers.PeriodicDumpWriter(env, dump_frequency, render) elif render: env.render() env = _apply_output_wrappers( env, rewards, representation, channel_dimensions, (number_of_left_players_agent_controls + number_of_right_players_agent_controls == 1), stacked) return env def create_remote_environment( username, token, model_name='', track='', stacked=False, representation='raw', rewards='scoring', channel_dimensions=( observation_preprocessing.SMM_WIDTH, observation_preprocessing.SMM_HEIGHT), include_rendering=False): """Creates a remote Google Research Football environment. Args: username: User name. token: User token. model_name: A model identifier to be displayed on the leaderboard. track: which competition track to connect to. stacked: If True, stack 4 observations, otherwise, only the last observation is returned by the environment. Stacking is only possible when representation is one of the following: "pixels", "pixels_gray" or "extracted". In that case, the stacking is done along the last (i.e. channel) dimension. representation: See create_environment.representation comment. rewards: Comma separated list of rewards to be added. Currently supported rewards are 'scoring' and 'checkpoints'. channel_dimensions: (width, height) tuple that represents the dimensions of SMM or pixels representation. include_rendering: Whether to return frame as part of the output. Returns: Google Research Football environment. """ from gfootball.env import remote_football_env env = remote_football_env.RemoteFootballEnv( username, token, model_name=model_name, track=track, include_rendering=include_rendering) env = _apply_output_wrappers( env, rewards, representation, channel_dimensions, env._config.number_of_players_agent_controls() == 1, stacked) return env

py

TiKick

TiKick-main/tmarl/envs/football/env/football_env.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Allows different types of players to play against each other.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import importlib from absl import logging from tmarl.envs.football.env import config as cfg from gfootball.env import constants from gfootball.env import football_action_set from tmarl.envs.football.env import football_env_core from gfootball.env import observation_rotation import gym import numpy as np class FootballEnv(gym.Env): """Allows multiple players to play in the same environment.""" def __init__(self, config): self._config = config player_config = {'index': 0} # There can be at most one agent at a time. We need to remember its # team and the index on the team to generate observations appropriately. self._agent = None self._agent_index = -1 self._agent_left_position = -1 self._agent_right_position = -1 self._players = self._construct_players(config['players'], player_config) self._env = football_env_core.FootballEnvCore(self._config) self._num_actions = len(football_action_set.get_action_set(self._config)) self._cached_observation = None @property def action_space(self): if self._config.number_of_players_agent_controls() > 1: return gym.spaces.MultiDiscrete( [self._num_actions] * self._config.number_of_players_agent_controls()) return gym.spaces.Discrete(self._num_actions) def _construct_players(self, definitions, config): result = [] left_position = 0 right_position = 0 for definition in definitions: (name, d) = cfg.parse_player_definition(definition) config_name = 'player_{}'.format(name) if config_name in config: config[config_name] += 1 else: config[config_name] = 0 try: player_factory = importlib.import_module( 'gfootball.env.players.{}'.format(name)) except ImportError as e: logging.error('Failed loading player "%s"', name) logging.error(e) exit(1) player_config = copy.deepcopy(config) player_config.update(d) player = player_factory.Player(player_config, self._config) if name == 'agent': assert not self._agent, 'Only one \'agent\' player allowed' self._agent = player self._agent_index = len(result) self._agent_left_position = left_position self._agent_right_position = right_position result.append(player) left_position += player.num_controlled_left_players() right_position += player.num_controlled_right_players() config['index'] += 1 return result def _convert_observations(self, original, player, left_player_position, right_player_position): """Converts generic observations returned by the environment to the player specific observations. Args: original: original observations from the environment. player: player for which to generate observations. left_player_position: index into observation corresponding to the left player. right_player_position: index into observation corresponding to the right player. """ observations = [] for is_left in [True, False]: adopted = original if is_left or player.can_play_right( ) else observation_rotation.flip_observation(original, self._config) prefix = 'left' if is_left or not player.can_play_right() else 'right' position = left_player_position if is_left else right_player_position for x in range(player.num_controlled_left_players() if is_left else player.num_controlled_right_players()): o = {} for v in constants.EXPOSED_OBSERVATIONS: o[v] = copy.deepcopy(adopted[v]) assert (len(adopted[prefix + '_agent_controlled_player']) == len( adopted[prefix + '_agent_sticky_actions'])) o['designated'] = adopted[prefix + '_team_designated_player'] if position + x >= len(adopted[prefix + '_agent_controlled_player']): o['active'] = -1 o['sticky_actions'] = [] else: o['active'] = ( adopted[prefix + '_agent_controlled_player'][position + x]) o['sticky_actions'] = np.array(copy.deepcopy( adopted[prefix + '_agent_sticky_actions'][position + x])) # There is no frame for players on the right ATM. if is_left and 'frame' in original: o['frame'] = original['frame'] observations.append(o) return observations def _action_to_list(self, a): if isinstance(a, np.ndarray): return a.tolist() if not isinstance(a, list): return [a] return a def _get_actions(self): obs = self._env.observation() left_actions = [] right_actions = [] left_player_position = 0 right_player_position = 0 for player in self._players: adopted_obs = self._convert_observations(obs, player, left_player_position, right_player_position) left_player_position += player.num_controlled_left_players() right_player_position += player.num_controlled_right_players() a = self._action_to_list(player.take_action(adopted_obs)) assert len(adopted_obs) == len( a), 'Player provided {} actions instead of {}.'.format( len(a), len(adopted_obs)) if not player.can_play_right(): for x in range(player.num_controlled_right_players()): index = x + player.num_controlled_left_players() a[index] = observation_rotation.flip_single_action( a[index], self._config) left_actions.extend(a[:player.num_controlled_left_players()]) right_actions.extend(a[player.num_controlled_left_players():]) actions = left_actions + right_actions return actions def step(self, action): action = self._action_to_list(action) if self._agent: self._agent.set_action(action) else: assert len( action ) == 0, 'step() received {} actions, but no agent is playing.'.format( len(action)) _, reward, done, info = self._env.step(self._get_actions()) score_reward = reward if self._agent: reward = ([reward] * self._agent.num_controlled_left_players() + [-reward] * self._agent.num_controlled_right_players()) self._cached_observation = None info['score_reward'] = score_reward return (self.observation(), np.array(reward, dtype=np.float32), done, info) def reset(self): self._env.reset() for player in self._players: player.reset() self._cached_observation = None return self.observation() def observation(self): if not self._cached_observation: self._cached_observation = self._env.observation() if self._agent: self._cached_observation = self._convert_observations( self._cached_observation, self._agent, self._agent_left_position, self._agent_right_position) return self._cached_observation def write_dump(self, name): return self._env.write_dump(name) def close(self): self._env.close() def get_state(self, to_pickle={}): return self._env.get_state(to_pickle) def set_state(self, state): self._cached_observation = None return self._env.set_state(state) def tracker_setup(self, start, end): self._env.tracker_setup(start, end) def render(self, mode='human'): self._cached_observation = None return self._env.render(mode=mode) def disable_render(self): self._cached_observation = None return self._env.disable_render()

py

TiKick

TiKick-main/tmarl/algorithms/__init__.py

py

TiKick

TiKick-main/tmarl/algorithms/r_mappo_distributed/mappo_algorithm.py

import torch from tmarl.utils.valuenorm import ValueNorm # implement the loss of the MAPPO here class MAPPOAlgorithm(): def __init__(self, args, init_module, device=torch.device("cpu")): self.device = device self.tpdv = dict(dtype=torch.float32, device=device) self.algo_module = init_module self.clip_param = args.clip_param self.ppo_epoch = args.ppo_epoch self.num_mini_batch = args.num_mini_batch self.data_chunk_length = args.data_chunk_length self.policy_value_loss_coef = args.policy_value_loss_coef self.value_loss_coef = args.value_loss_coef self.entropy_coef = args.entropy_coef self.max_grad_norm = args.max_grad_norm self.huber_delta = args.huber_delta self._use_recurrent_policy = args.use_recurrent_policy self._use_naive_recurrent = args.use_naive_recurrent_policy self._use_max_grad_norm = args.use_max_grad_norm self._use_clipped_value_loss = args.use_clipped_value_loss self._use_huber_loss = args.use_huber_loss self._use_popart = args.use_popart self._use_valuenorm = args.use_valuenorm self._use_value_active_masks = args.use_value_active_masks self._use_policy_active_masks = args.use_policy_active_masks self._use_policy_vhead = args.use_policy_vhead assert (self._use_popart and self._use_valuenorm) == False, ("self._use_popart and self._use_valuenorm can not be set True simultaneously") if self._use_popart: self.value_normalizer = self.algo_module.critic.v_out if self._use_policy_vhead: self.policy_value_normalizer = self.algo_module.actor.v_out elif self._use_valuenorm: self.value_normalizer = ValueNorm(1, device = self.device) if self._use_policy_vhead: self.policy_value_normalizer = ValueNorm(1, device = self.device) else: self.value_normalizer = None if self._use_policy_vhead: self.policy_value_normalizer = None def prep_rollout(self): self.algo_module.actor.eval()

py

TiKick

TiKick-main/tmarl/algorithms/r_mappo_distributed/mappo_module.py

import torch from tmarl.networks.policy_network import PolicyNetwork class MAPPOModule: def __init__(self, args, obs_space, share_obs_space, act_space, device=torch.device("cpu")): self.device = device self.lr = args.lr self.critic_lr = args.critic_lr self.opti_eps = args.opti_eps self.weight_decay = args.weight_decay self.obs_space = obs_space self.share_obs_space = share_obs_space self.act_space = act_space self.actor = PolicyNetwork(args, self.obs_space, self.act_space, self.device) self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=self.lr, eps=self.opti_eps, weight_decay=self.weight_decay) def get_actions(self, share_obs, obs, rnn_states_actor, rnn_states_critic, masks, available_actions=None, deterministic=False): actions, action_log_probs, rnn_states_actor = self.actor(obs, rnn_states_actor, masks, available_actions, deterministic) return None, actions, action_log_probs, rnn_states_actor, None

py

TiKick

TiKick-main/tmarl/algorithms/r_mappo_distributed/__init__.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """"""

py

TiKick

TiKick-main/tmarl/loggers/utils.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """""" import time def timer(function): """ 装饰器函数timer :param function:想要计时的函数 :return: """ def wrapper(*args, **kwargs): time_start = time.time() res = function(*args, **kwargs) cost_time = time.time() - time_start print("{} running time: {}s".format(function.__name__, cost_time)) return res return wrapper

py

TiKick

TiKick-main/tmarl/loggers/__init__.py

py

TiKick

TiKick-main/tmarl/loggers/TSee/__init__.py

py

TiKick

TiKick-main/tmarl/replay_buffers/__init__.py

py

TiKick

TiKick-main/tmarl/replay_buffers/normal/shared_buffer.py

import torch import numpy as np from collections import defaultdict from tmarl.utils.util import check,get_shape_from_obs_space, get_shape_from_act_space def _flatten(T, N, x): return x.reshape(T * N, *x.shape[2:]) def _cast(x): return x.transpose(1, 2, 0, 3).reshape(-1, *x.shape[3:]) class SharedReplayBuffer(object): def __init__(self, args, num_agents, obs_space, share_obs_space, act_space): self.episode_length = args.episode_length self.n_rollout_threads = args.n_rollout_threads self.hidden_size = args.hidden_size self.recurrent_N = args.recurrent_N self.gamma = args.gamma self.gae_lambda = args.gae_lambda self._use_gae = args.use_gae self._use_popart = args.use_popart self._use_valuenorm = args.use_valuenorm self._use_proper_time_limits = args.use_proper_time_limits self._mixed_obs = False # for mixed observation obs_shape = get_shape_from_obs_space(obs_space) share_obs_shape = get_shape_from_obs_space(share_obs_space) # for mixed observation if 'Dict' in obs_shape.__class__.__name__: self._mixed_obs = True self.obs = {} self.share_obs = {} for key in obs_shape: self.obs[key] = np.zeros((self.episode_length + 1, self.n_rollout_threads, num_agents, *obs_shape[key].shape), dtype=np.float32) for key in share_obs_shape: self.share_obs[key] = np.zeros((self.episode_length + 1, self.n_rollout_threads, num_agents, *share_obs_shape[key].shape), dtype=np.float32) else: # deal with special attn format if type(obs_shape[-1]) == list: obs_shape = obs_shape[:1] if type(share_obs_shape[-1]) == list: share_obs_shape = share_obs_shape[:1] self.share_obs = np.zeros((self.episode_length + 1, self.n_rollout_threads, num_agents, *share_obs_shape), dtype=np.float32) self.obs = np.zeros((self.episode_length + 1, self.n_rollout_threads, num_agents, *obs_shape), dtype=np.float32) self.rnn_states = np.zeros((self.episode_length + 1, self.n_rollout_threads, num_agents, self.recurrent_N, self.hidden_size), dtype=np.float32) self.rnn_states_critic = np.zeros_like(self.rnn_states) self.value_preds = np.zeros( (self.episode_length + 1, self.n_rollout_threads, num_agents, 1), dtype=np.float32) self.returns = np.zeros_like(self.value_preds) if act_space.__class__.__name__ == 'Discrete': self.available_actions = np.ones((self.episode_length + 1, self.n_rollout_threads, num_agents, act_space.n), dtype=np.float32) else: self.available_actions = None act_shape = get_shape_from_act_space(act_space) self.actions = np.zeros( (self.episode_length, self.n_rollout_threads, num_agents, act_shape), dtype=np.float32) self.action_log_probs = np.zeros( (self.episode_length, self.n_rollout_threads, num_agents, act_shape), dtype=np.float32) self.rewards = np.zeros( (self.episode_length, self.n_rollout_threads, num_agents, 1), dtype=np.float32) self.masks = np.ones((self.episode_length + 1, self.n_rollout_threads, num_agents, 1), dtype=np.float32) self.bad_masks = np.ones_like(self.masks) self.active_masks = np.ones_like(self.masks) self.step = 0 def insert(self, share_obs, obs, rnn_states, rnn_states_critic, actions, action_log_probs, value_preds, rewards, masks, bad_masks=None, active_masks=None, available_actions=None): if self._mixed_obs: for key in self.share_obs.keys(): self.share_obs[key][self.step + 1] = share_obs[key].copy() for key in self.obs.keys(): self.obs[key][self.step + 1] = obs[key].copy() else: self.share_obs[self.step + 1] = share_obs.copy() self.obs[self.step + 1] = obs.copy() self.rnn_states[self.step + 1] = rnn_states.copy() self.rnn_states_critic[self.step + 1] = rnn_states_critic.copy() self.actions[self.step] = actions.copy() self.action_log_probs[self.step] = action_log_probs.copy() self.value_preds[self.step] = value_preds.copy() self.rewards[self.step] = rewards.copy() self.masks[self.step + 1] = masks.copy() if bad_masks is not None: self.bad_masks[self.step + 1] = bad_masks.copy() if active_masks is not None: self.active_masks[self.step + 1] = active_masks.copy() if available_actions is not None: self.available_actions[self.step + 1] = available_actions.copy() self.step = (self.step + 1) % self.episode_length def init_buffer(self,share_obs,obs): self.share_obs[0] = share_obs self.obs[0] = obs def chooseinsert(self, share_obs, obs, rnn_states, rnn_states_critic, actions, action_log_probs, value_preds, rewards, masks, bad_masks=None, active_masks=None, available_actions=None): self.share_obs[self.step] = share_obs.copy() self.obs[self.step] = obs.copy() self.rnn_states[self.step + 1] = rnn_states.copy() self.rnn_states_critic[self.step + 1] = rnn_states_critic.copy() self.actions[self.step] = actions.copy() self.action_log_probs[self.step] = action_log_probs.copy() self.value_preds[self.step] = value_preds.copy() self.rewards[self.step] = rewards.copy() self.masks[self.step + 1] = masks.copy() if bad_masks is not None: self.bad_masks[self.step + 1] = bad_masks.copy() if active_masks is not None: self.active_masks[self.step] = active_masks.copy() if available_actions is not None: self.available_actions[self.step] = available_actions.copy() self.step = (self.step + 1) % self.episode_length def after_update(self): if self._mixed_obs: for key in self.share_obs.keys(): self.share_obs[key][0] = self.share_obs[key][-1].copy() for key in self.obs.keys(): self.obs[key][0] = self.obs[key][-1].copy() else: self.share_obs[0] = self.share_obs[-1].copy() self.obs[0] = self.obs[-1].copy() self.rnn_states[0] = self.rnn_states[-1].copy() self.rnn_states_critic[0] = self.rnn_states_critic[-1].copy() self.masks[0] = self.masks[-1].copy() self.bad_masks[0] = self.bad_masks[-1].copy() self.active_masks[0] = self.active_masks[-1].copy() if self.available_actions is not None: self.available_actions[0] = self.available_actions[-1].copy() def chooseafter_update(self): self.rnn_states[0] = self.rnn_states[-1].copy() self.rnn_states_critic[0] = self.rnn_states_critic[-1].copy() self.masks[0] = self.masks[-1].copy() self.bad_masks[0] = self.bad_masks[-1].copy() def compute_returns(self, next_value, value_normalizer=None): if self._use_proper_time_limits: if self._use_gae: self.value_preds[-1] = next_value gae = 0 for step in reversed(range(self.rewards.shape[0])): if self._use_popart or self._use_valuenorm: # step + 1 delta = self.rewards[step] + self.gamma * value_normalizer.denormalize(self.value_preds[step + 1]) * self.masks[step + 1] \ - value_normalizer.denormalize(self.value_preds[step]) gae = delta + self.gamma * self.gae_lambda * gae * self.masks[step + 1] gae = gae * self.bad_masks[step + 1] self.returns[step] = gae + value_normalizer.denormalize(self.value_preds[step]) else: delta = self.rewards[step] + self.gamma * self.value_preds[step + 1] * self.masks[step + 1] - self.value_preds[step] gae = delta + self.gamma * self.gae_lambda * self.masks[step + 1] * gae gae = gae * self.bad_masks[step + 1] self.returns[step] = gae + self.value_preds[step] else: self.returns[-1] = next_value for step in reversed(range(self.rewards.shape[0])): if self._use_popart or self._use_valuenorm: self.returns[step] = (self.returns[step + 1] * self.gamma * self.masks[step + 1] + self.rewards[step]) * self.bad_masks[step + 1] \ + (1 - self.bad_masks[step + 1]) * value_normalizer.denormalize(self.value_preds[step]) else: self.returns[step] = (self.returns[step + 1] * self.gamma * self.masks[step + 1] + self.rewards[step]) * self.bad_masks[step + 1] \ + (1 - self.bad_masks[step + 1]) * self.value_preds[step] else: if self._use_gae: self.value_preds[-1] = next_value gae = 0 for step in reversed(range(self.rewards.shape[0])): if self._use_popart or self._use_valuenorm: delta = self.rewards[step] + self.gamma * value_normalizer.denormalize(self.value_preds[step + 1]) * self.masks[step + 1] \ - value_normalizer.denormalize(self.value_preds[step]) gae = delta + self.gamma * self.gae_lambda * self.masks[step + 1] * gae self.returns[step] = gae + value_normalizer.denormalize(self.value_preds[step]) else: delta = self.rewards[step] + self.gamma * self.value_preds[step + 1] * self.masks[step + 1] - self.value_preds[step] gae = delta + self.gamma * self.gae_lambda * self.masks[step + 1] * gae self.returns[step] = gae + self.value_preds[step] else: self.returns[-1] = next_value for step in reversed(range(self.rewards.shape[0])): self.returns[step] = self.returns[step + 1] * self.gamma * self.masks[step + 1] + self.rewards[step] def feed_forward_generator(self, advantages, num_mini_batch=None, mini_batch_size=None): episode_length, n_rollout_threads, num_agents = self.rewards.shape[0:3] batch_size = n_rollout_threads * episode_length * num_agents if mini_batch_size is None: assert batch_size >= num_mini_batch, ( "PPO requires the number of processes ({}) " "* number of steps ({}) * number of agents ({}) = {} " "to be greater than or equal to the number of PPO mini batches ({})." "".format(n_rollout_threads, episode_length, num_agents, n_rollout_threads * episode_length * num_agents, num_mini_batch)) mini_batch_size = batch_size // num_mini_batch rand = torch.randperm(batch_size).numpy() sampler = [rand[i*mini_batch_size:(i+1)*mini_batch_size] for i in range(num_mini_batch)] if self._mixed_obs: share_obs = {} obs = {} for key in self.share_obs.keys(): share_obs[key] = self.share_obs[key][:-1].reshape(-1, *self.share_obs[key].shape[3:]) for key in self.obs.keys(): obs[key] = self.obs[key][:-1].reshape(-1, *self.obs[key].shape[3:]) else: share_obs = self.share_obs[:-1].reshape(-1, *self.share_obs.shape[3:]) obs = self.obs[:-1].reshape(-1, *self.obs.shape[3:]) rnn_states = self.rnn_states[:-1].reshape(-1, *self.rnn_states.shape[3:]) rnn_states_critic = self.rnn_states_critic[:-1].reshape(-1, *self.rnn_states_critic.shape[3:]) actions = self.actions.reshape(-1, self.actions.shape[-1]) if self.available_actions is not None: available_actions = self.available_actions[:-1].reshape(-1, self.available_actions.shape[-1]) value_preds = self.value_preds[:-1].reshape(-1, 1) returns = self.returns[:-1].reshape(-1, 1) masks = self.masks[:-1].reshape(-1, 1) active_masks = self.active_masks[:-1].reshape(-1, 1) action_log_probs = self.action_log_probs.reshape(-1, self.action_log_probs.shape[-1]) advantages = advantages.reshape(-1, 1) for indices in sampler: # obs size [T+1 N M Dim]-->[T N M Dim]-->[T*N*M,Dim]-->[index,Dim] if self._mixed_obs: share_obs_batch = {} obs_batch = {} for key in share_obs.keys(): share_obs_batch[key] = share_obs[key][indices] for key in obs.keys(): obs_batch[key] = obs[key][indices] else: share_obs_batch = share_obs[indices] obs_batch = obs[indices] rnn_states_batch = rnn_states[indices] rnn_states_critic_batch = rnn_states_critic[indices] actions_batch = actions[indices] if self.available_actions is not None: available_actions_batch = available_actions[indices] else: available_actions_batch = None value_preds_batch = value_preds[indices] return_batch = returns[indices] masks_batch = masks[indices] active_masks_batch = active_masks[indices] old_action_log_probs_batch = action_log_probs[indices] if advantages is None: adv_targ = None else: adv_targ = advantages[indices] yield share_obs_batch, obs_batch, rnn_states_batch, rnn_states_critic_batch, actions_batch, value_preds_batch, return_batch, masks_batch, active_masks_batch, old_action_log_probs_batch, adv_targ, available_actions_batch def naive_recurrent_generator(self, advantages, num_mini_batch): episode_length, n_rollout_threads, num_agents = self.rewards.shape[0:3] batch_size = n_rollout_threads*num_agents assert n_rollout_threads*num_agents >= num_mini_batch, ( "PPO requires the number of processes ({})* number of agents ({}) " "to be greater than or equal to the number of " "PPO mini batches ({}).".format(n_rollout_threads, num_agents, num_mini_batch)) num_envs_per_batch = batch_size // num_mini_batch perm = torch.randperm(batch_size).numpy() if self._mixed_obs: share_obs = {} obs = {} for key in self.share_obs.keys(): share_obs[key] = self.share_obs[key].reshape(-1, batch_size, *self.share_obs[key].shape[3:]) for key in self.obs.keys(): obs[key] = self.obs[key].reshape(-1, batch_size, *self.obs[key].shape[3:]) else: share_obs = self.share_obs.reshape(-1, batch_size, *self.share_obs.shape[3:]) obs = self.obs.reshape(-1, batch_size, *self.obs.shape[3:]) rnn_states = self.rnn_states.reshape(-1, batch_size, *self.rnn_states.shape[3:]) rnn_states_critic = self.rnn_states_critic.reshape(-1, batch_size, *self.rnn_states_critic.shape[3:]) actions = self.actions.reshape(-1, batch_size, self.actions.shape[-1]) if self.available_actions is not None: available_actions = self.available_actions.reshape(-1, batch_size, self.available_actions.shape[-1]) value_preds = self.value_preds.reshape(-1, batch_size, 1) returns = self.returns.reshape(-1, batch_size, 1) masks = self.masks.reshape(-1, batch_size, 1) active_masks = self.active_masks.reshape(-1, batch_size, 1) action_log_probs = self.action_log_probs.reshape(-1, batch_size, self.action_log_probs.shape[-1]) advantages = advantages.reshape(-1, batch_size, 1) for start_ind in range(0, batch_size, num_envs_per_batch): if self._mixed_obs: share_obs_batch = defaultdict(list) obs_batch = defaultdict(list) else: share_obs_batch = [] obs_batch = [] rnn_states_batch = [] rnn_states_critic_batch = [] actions_batch = [] available_actions_batch = [] value_preds_batch = [] return_batch = [] masks_batch = [] active_masks_batch = [] old_action_log_probs_batch = [] adv_targ = [] for offset in range(num_envs_per_batch): ind = perm[start_ind + offset] if self._mixed_obs: for key in share_obs.keys(): share_obs_batch[key].append(share_obs[key][:-1, ind]) for key in obs.keys(): obs_batch[key].append(obs[key][:-1, ind]) else: share_obs_batch.append(share_obs[:-1, ind]) obs_batch.append(obs[:-1, ind]) rnn_states_batch.append(rnn_states[0:1, ind]) rnn_states_critic_batch.append(rnn_states_critic[0:1, ind]) actions_batch.append(actions[:, ind]) if self.available_actions is not None: available_actions_batch.append(available_actions[:-1, ind]) value_preds_batch.append(value_preds[:-1, ind]) return_batch.append(returns[:-1, ind]) masks_batch.append(masks[:-1, ind]) active_masks_batch.append(active_masks[:-1, ind]) old_action_log_probs_batch.append(action_log_probs[:, ind]) adv_targ.append(advantages[:, ind]) # [N[T, dim]] T, N = self.episode_length, num_envs_per_batch # These are all from_numpys of size (T, N, -1) if self._mixed_obs: for key in share_obs_batch.keys(): share_obs_batch[key] = np.stack(share_obs_batch[key], 1) for key in obs_batch.keys(): obs_batch[key] = np.stack(obs_batch[key], 1) else: share_obs_batch = np.stack(share_obs_batch, 1) obs_batch = np.stack(obs_batch, 1) actions_batch = np.stack(actions_batch, 1) if self.available_actions is not None: available_actions_batch = np.stack(available_actions_batch, 1) value_preds_batch = np.stack(value_preds_batch, 1) return_batch = np.stack(return_batch, 1) masks_batch = np.stack(masks_batch, 1) active_masks_batch = np.stack(active_masks_batch, 1) old_action_log_probs_batch = np.stack(old_action_log_probs_batch, 1) adv_targ = np.stack(adv_targ, 1) # States is just a (N, dim) from_numpy [N[1,dim]] rnn_states_batch = np.stack(rnn_states_batch).reshape(N, *self.rnn_states.shape[3:]) rnn_states_critic_batch = np.stack(rnn_states_critic_batch).reshape(N, *self.rnn_states_critic.shape[3:]) # Flatten the (T, N, ...) from_numpys to (T * N, ...) if self._mixed_obs: for key in share_obs_batch.keys(): share_obs_batch[key] = _flatten(T, N, share_obs_batch[key]) for key in obs_batch.keys(): obs_batch[key] = _flatten(T, N, obs_batch[key]) else: share_obs_batch = _flatten(T, N, share_obs_batch) obs_batch = _flatten(T, N, obs_batch) actions_batch = _flatten(T, N, actions_batch) if self.available_actions is not None: available_actions_batch = _flatten(T, N, available_actions_batch) else: available_actions_batch = None value_preds_batch = _flatten(T, N, value_preds_batch) return_batch = _flatten(T, N, return_batch) masks_batch = _flatten(T, N, masks_batch) active_masks_batch = _flatten(T, N, active_masks_batch) old_action_log_probs_batch = _flatten(T, N, old_action_log_probs_batch) adv_targ = _flatten(T, N, adv_targ) yield share_obs_batch, obs_batch, rnn_states_batch, rnn_states_critic_batch, actions_batch, value_preds_batch, return_batch, masks_batch, active_masks_batch, old_action_log_probs_batch, adv_targ, available_actions_batch def recurrent_generator(self, advantages, num_mini_batch, data_chunk_length): episode_length, n_rollout_threads, num_agents = self.rewards.shape[0:3] batch_size = n_rollout_threads * episode_length * num_agents data_chunks = batch_size // data_chunk_length # [C=r*T*M/L] mini_batch_size = data_chunks // num_mini_batch assert n_rollout_threads * episode_length * num_agents >= data_chunk_length, ( "PPO requires the number of processes ({})* number of agents ({}) * episode length ({}) " "to be greater than or equal to the number of " "data chunk length ({}).".format(n_rollout_threads, num_agents, episode_length ,data_chunk_length)) rand = torch.randperm(data_chunks).numpy() sampler = [rand[i*mini_batch_size:(i+1)*mini_batch_size] for i in range(num_mini_batch)] if self._mixed_obs: share_obs = {} obs = {} for key in self.share_obs.keys(): if len(self.share_obs[key].shape) == 6: share_obs[key] = self.share_obs[key][:-1].transpose(1, 2, 0, 3, 4, 5).reshape(-1, *self.share_obs[key].shape[3:]) elif len(self.share_obs[key].shape) == 5: share_obs[key] = self.share_obs[key][:-1].transpose(1, 2, 0, 3, 4).reshape(-1, *self.share_obs[key].shape[3:]) else: share_obs[key] = _cast(self.share_obs[key][:-1]) for key in self.obs.keys(): if len(self.obs[key].shape) == 6: obs[key] = self.obs[key][:-1].transpose(1, 2, 0, 3, 4, 5).reshape(-1, *self.obs[key].shape[3:]) elif len(self.obs[key].shape) == 5: obs[key] = self.obs[key][:-1].transpose(1, 2, 0, 3, 4).reshape(-1, *self.obs[key].shape[3:]) else: obs[key] = _cast(self.obs[key][:-1]) else: if len(self.share_obs.shape) > 4: share_obs = self.share_obs[:-1].transpose(1, 2, 0, 3, 4, 5).reshape(-1, *self.share_obs.shape[3:]) obs = self.obs[:-1].transpose(1, 2, 0, 3, 4, 5).reshape(-1, *self.obs.shape[3:]) else: share_obs = _cast(self.share_obs[:-1]) obs = _cast(self.obs[:-1]) actions = _cast(self.actions) action_log_probs = _cast(self.action_log_probs) advantages = _cast(advantages) value_preds = _cast(self.value_preds[:-1]) returns = _cast(self.returns[:-1]) masks = _cast(self.masks[:-1]) active_masks = _cast(self.active_masks[:-1]) # rnn_states = _cast(self.rnn_states[:-1]) # rnn_states_critic = _cast(self.rnn_states_critic[:-1]) rnn_states = self.rnn_states[:-1].transpose(1, 2, 0, 3, 4).reshape(-1, *self.rnn_states.shape[3:]) rnn_states_critic = self.rnn_states_critic[:-1].transpose(1, 2, 0, 3, 4).reshape(-1, *self.rnn_states_critic.shape[3:]) if self.available_actions is not None: available_actions = _cast(self.available_actions[:-1]) for indices in sampler: if self._mixed_obs: share_obs_batch = defaultdict(list) obs_batch = defaultdict(list) else: share_obs_batch = [] obs_batch = [] rnn_states_batch = [] rnn_states_critic_batch = [] actions_batch = [] available_actions_batch = [] value_preds_batch = [] return_batch = [] masks_batch = [] active_masks_batch = [] old_action_log_probs_batch = [] adv_targ = [] for index in indices: ind = index * data_chunk_length # size [T+1 N M Dim]-->[T N M Dim]-->[N,M,T,Dim]-->[N*M*T,Dim]-->[L,Dim] if self._mixed_obs: for key in share_obs.keys(): share_obs_batch[key].append(share_obs[key][ind:ind+data_chunk_length]) for key in obs.keys(): obs_batch[key].append(obs[key][ind:ind+data_chunk_length]) else: share_obs_batch.append(share_obs[ind:ind+data_chunk_length]) obs_batch.append(obs[ind:ind+data_chunk_length]) actions_batch.append(actions[ind:ind+data_chunk_length]) if self.available_actions is not None: available_actions_batch.append(available_actions[ind:ind+data_chunk_length]) value_preds_batch.append(value_preds[ind:ind+data_chunk_length]) return_batch.append(returns[ind:ind+data_chunk_length]) masks_batch.append(masks[ind:ind+data_chunk_length]) active_masks_batch.append(active_masks[ind:ind+data_chunk_length]) old_action_log_probs_batch.append(action_log_probs[ind:ind+data_chunk_length]) adv_targ.append(advantages[ind:ind+data_chunk_length]) # size [T+1 N M Dim]-->[T N M Dim]-->[N M T Dim]-->[N*M*T,Dim]-->[1,Dim] rnn_states_batch.append(rnn_states[ind]) rnn_states_critic_batch.append(rnn_states_critic[ind]) L, N = data_chunk_length, mini_batch_size # These are all from_numpys of size (L, N, Dim) if self._mixed_obs: for key in share_obs_batch.keys(): share_obs_batch[key] = np.stack(share_obs_batch[key], axis=1) for key in obs_batch.keys(): obs_batch[key] = np.stack(obs_batch[key], axis=1) else: share_obs_batch = np.stack(share_obs_batch, axis=1) obs_batch = np.stack(obs_batch, axis=1) actions_batch = np.stack(actions_batch, axis=1) if self.available_actions is not None: available_actions_batch = np.stack(available_actions_batch, axis=1) value_preds_batch = np.stack(value_preds_batch, axis=1) return_batch = np.stack(return_batch, axis=1) masks_batch = np.stack(masks_batch, axis=1) active_masks_batch = np.stack(active_masks_batch, axis=1) old_action_log_probs_batch = np.stack(old_action_log_probs_batch, axis=1) adv_targ = np.stack(adv_targ, axis=1) # States is just a (N, -1) from_numpy rnn_states_batch = np.stack(rnn_states_batch).reshape(N, *self.rnn_states.shape[3:]) rnn_states_critic_batch = np.stack(rnn_states_critic_batch).reshape(N, *self.rnn_states_critic.shape[3:]) # Flatten the (L, N, ...) from_numpys to (L * N, ...) if self._mixed_obs: for key in share_obs_batch.keys(): share_obs_batch[key] = _flatten(L, N, share_obs_batch[key]) for key in obs_batch.keys(): obs_batch[key] = _flatten(L, N, obs_batch[key]) else: share_obs_batch = _flatten(L, N, share_obs_batch) obs_batch = _flatten(L, N, obs_batch) actions_batch = _flatten(L, N, actions_batch) if self.available_actions is not None: available_actions_batch = _flatten(L, N, available_actions_batch) else: available_actions_batch = None value_preds_batch = _flatten(L, N, value_preds_batch) return_batch = _flatten(L, N, return_batch) masks_batch = _flatten(L, N, masks_batch) active_masks_batch = _flatten(L, N, active_masks_batch) old_action_log_probs_batch = _flatten(L, N, old_action_log_probs_batch) adv_targ = _flatten(L, N, adv_targ) yield share_obs_batch, obs_batch, rnn_states_batch, rnn_states_critic_batch, actions_batch, value_preds_batch, return_batch, masks_batch, active_masks_batch, old_action_log_probs_batch, adv_targ, available_actions_batch

py

TiKick

TiKick-main/tmarl/replay_buffers/normal/__init__.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """"""

py

TiKick

TiKick-main/tmarl/configs/config.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """""" import argparse def get_config(): parser = argparse.ArgumentParser( description='TiKick', formatter_class=argparse.RawDescriptionHelpFormatter) # prepare parameters parser.add_argument("--algorithm_name", type=str, default='rmappo', choices=["rmappo"]) parser.add_argument("--experiment_name", type=str, default="check", help="an identifier to distinguish different experiment.") parser.add_argument("--seed", type=int, default=1, help="Random seed for numpy/torch") parser.add_argument("--disable_cuda", action='store_true', default=False, help="by default False, will use GPU to train; or else will use CPU;") parser.add_argument("--cuda_deterministic", action='store_false', default=True, help="by default, make sure random seed effective. if set, bypass such function.") parser.add_argument("--n_rollout_threads", type=int, default=2, help="Number of parallel envs for training rollout") parser.add_argument("--n_eval_rollout_threads", type=int, default=1, help="Number of parallel envs for evaluating rollout") parser.add_argument("--n_render_rollout_threads", type=int, default=1, help="Number of parallel envs for rendering rollout") parser.add_argument("--eval_num", type=int, default=1, help='Number of environment steps to evaluate (default: 1)') # env parameters parser.add_argument("--env_name", type=str, default='StarCraft2', help="specify the name of environment") parser.add_argument("--use_obs_instead_of_state", action='store_true', default=False, help="Whether to use global state or concatenated obs") # replay buffer parameters parser.add_argument("--episode_length", type=int, default=200, help="Max length for any episode") # network parameters parser.add_argument("--separate_policy", action='store_true', default=False, help='Whether agent seperate the policy') parser.add_argument("--use_centralized_V", action='store_false', default=True, help="Whether to use centralized V function") parser.add_argument("--use_conv1d", action='store_true', default=False, help="Whether to use conv1d") parser.add_argument("--stacked_frames", type=int, default=1, help="Dimension of hidden layers for actor/critic networks") parser.add_argument("--use_stacked_frames", action='store_true', default=False, help="Whether to use stacked_frames") parser.add_argument("--hidden_size", type=int, default=256, help="Dimension of hidden layers for actor/critic networks") # TODO @zoeyuchao. The same comment might in need of change. parser.add_argument("--layer_N", type=int, default=3, help="Number of layers for actor/critic networks") parser.add_argument("--activation_id", type=int, default=1, help="choose 0 to use tanh, 1 to use relu, 2 to use leaky relu, 3 to use elu") parser.add_argument("--use_popart", action='store_true', default=False, help="by default False, use PopArt to normalize rewards.") parser.add_argument("--use_valuenorm", action='store_false', default=True, help="by default True, use running mean and std to normalize rewards.") parser.add_argument("--use_feature_normalization", action='store_false', default=True, help="Whether to apply layernorm to the inputs") parser.add_argument("--use_orthogonal", action='store_false', default=True, help="Whether to use Orthogonal initialization for weights and 0 initialization for biases") parser.add_argument("--gain", type=float, default=0.01, help="The gain # of last action layer") parser.add_argument("--cnn_layers_params", type=str, default=None, help="The parameters of cnn layer") parser.add_argument("--use_maxpool2d", action='store_true', default=False, help="Whether to apply layernorm to the inputs") # recurrent parameters parser.add_argument("--use_naive_recurrent_policy", action='store_true', default=False, help='Whether to use a naive recurrent policy') parser.add_argument("--use_recurrent_policy", action='store_false', default=True, help='use a recurrent policy') parser.add_argument("--recurrent_N", type=int, default=1, help="The number of recurrent layers.") parser.add_argument("--data_chunk_length", type=int, default=25, help="Time length of chunks used to train a recurrent_policy") parser.add_argument("--use_influence_policy", action='store_true', default=False, help='use a recurrent policy') parser.add_argument("--influence_layer_N", type=int, default=1, help="Number of layers for actor/critic networks") # optimizer parameters parser.add_argument("--lr", type=float, default=5e-4, help='learning rate (default: 5e-4)') parser.add_argument("--tau", type=float, default=0.995, help='soft update polyak (default: 0.995)') parser.add_argument("--critic_lr", type=float, default=5e-4, help='critic learning rate (default: 5e-4)') parser.add_argument("--opti_eps", type=float, default=1e-5, help='RMSprop optimizer epsilon (default: 1e-5)') parser.add_argument("--weight_decay", type=float, default=0) # ppo parameters parser.add_argument("--ppo_epoch", type=int, default=15, help='number of ppo epochs (default: 15)') parser.add_argument("--use_policy_vhead", action='store_true', default=False, help="by default, do not use policy vhead. if set, use policy vhead.") parser.add_argument("--use_clipped_value_loss", action='store_false', default=True, help="by default, clip loss value. If set, do not clip loss value.") parser.add_argument("--clip_param", type=float, default=0.2, help='ppo clip parameter (default: 0.2)') parser.add_argument("--num_mini_batch", type=int, default=1, help='number of batches for ppo (default: 1)') parser.add_argument("--policy_value_loss_coef", type=float, default=1, help='policy value loss coefficient (default: 0.5)') parser.add_argument("--entropy_coef", type=float, default=0.01, help='entropy term coefficient (default: 0.01)') parser.add_argument("--value_loss_coef", type=float, default=1, help='value loss coefficient (default: 0.5)') parser.add_argument("--use_max_grad_norm", action='store_false', default=True, help="by default, use max norm of gradients. If set, do not use.") parser.add_argument("--max_grad_norm", type=float, default=10.0, help='max norm of gradients (default: 0.5)') parser.add_argument("--use_gae", action='store_false', default=True, help='use generalized advantage estimation') parser.add_argument("--gamma", type=float, default=0.99, help='discount factor for rewards (default: 0.99)') parser.add_argument("--gae_lambda", type=float, default=0.95, help='gae lambda parameter (default: 0.95)') parser.add_argument("--use_proper_time_limits", action='store_true', default=False, help='compute returns taking into account time limits') parser.add_argument("--use_huber_loss", action='store_false', default=True, help="by default, use huber loss. If set, do not use huber loss.") parser.add_argument("--use_value_active_masks", action='store_false', default=True, help="by default True, whether to mask useless data in value loss.") parser.add_argument("--use_policy_active_masks", action='store_false', default=True, help="by default True, whether to mask useless data in policy loss.") parser.add_argument("--huber_delta", type=float, default=10.0, help=" coefficience of huber loss.") # save parameters parser.add_argument("--save_interval", type=int, default=1, help="time duration between contiunous twice models saving.") # log parameters parser.add_argument("--log_interval", type=int, default=5, help="time duration between contiunous twice log printing.") # eval parameters parser.add_argument("--use_eval", action='store_true', default=False, help="by default, do not start evaluation. If set`, start evaluation alongside with training.") parser.add_argument("--eval_interval", type=int, default=25, help="time duration between contiunous twice evaluation progress.") parser.add_argument("--eval_episodes", type=int, default=64, help="number of episodes of a single evaluation.") # pretrained parameters parser.add_argument("--model_dir", type=str, default=None, help="by default None. set the path to pretrained model.") parser.add_argument("--replay_save_dir", type=str, default=None, help="replay file save dir") # replay buffer parameters return parser

py

TiKick

TiKick-main/tmarl/configs/__init__.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """"""

py

TiKick

TiKick-main/tmarl/wrappers/__init__.py

py

TiKick

TiKick-main/tmarl/wrappers/TWrapper/__init__.py

py

TiKick

TiKick-main/tmarl/runners/base_evaluator.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """""" import random import numpy as np import torch from tmarl.configs.config import get_config from tmarl.runners.base_runner import Runner def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) class Evaluator(Runner): def __init__(self, argv,program_type=None, client=None): super().__init__(argv) parser = get_config() all_args = self.extra_args_func(argv, parser) all_args.cuda = not all_args.disable_cuda self.algorithm_name = all_args.algorithm_name # cuda if not all_args.disable_cuda and torch.cuda.is_available(): device = torch.device("cuda:0") if all_args.cuda_deterministic: torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True else: print("choose to use cpu...") device = torch.device("cpu") # run dir run_dir = self.setup_run_dir(all_args) # env init Env_Class, SubprocVecEnv, DummyVecEnv = self.get_env() eval_envs = self.env_init( all_args, Env_Class, SubprocVecEnv, DummyVecEnv) num_agents = all_args.num_agents config = { "all_args": all_args, "envs": None, "eval_envs": eval_envs, "num_agents": num_agents, "device": device, "run_dir": run_dir, } self.all_args, self.envs, self.eval_envs, self.config \ = all_args, None, eval_envs, config self.driver = self.init_driver() def run(self): # run experiments self.driver.run() self.stop() def stop(self): pass def extra_args_func(self, argv, parser): raise NotImplementedError def get_env(self): raise NotImplementedError def init_driver(self): raise NotImplementedError def make_eval_env(self, all_args, Env_Class, SubprocVecEnv, DummyVecEnv): def get_env_fn(rank): def init_env(): env = Env_Class(all_args) env.seed(all_args.seed * 50000 + rank * 10000) return env return init_env if all_args.n_eval_rollout_threads == 1: return DummyVecEnv([get_env_fn(0)]) else: return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_eval_rollout_threads)]) def env_init(self, all_args, Env_Class, SubprocVecEnv, DummyVecEnv): eval_envs = self.make_eval_env( all_args, Env_Class, SubprocVecEnv, DummyVecEnv) if all_args.use_eval else None return eval_envs def setup_run_dir(self, all_args): return None

py

TiKick

TiKick-main/tmarl/runners/__init__.py

py

TiKick

TiKick-main/tmarl/runners/base_runner.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """""" import os import random import socket import setproctitle import numpy as np from pathlib import Path import torch from tmarl.configs.config import get_config def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) class Runner: def __init__(self, argv): self.argv = argv def run(self): # main run raise NotImplementedError

py

TiKick

TiKick-main/tmarl/runners/football/football_evaluator.py

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 The TARTRL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """""" import sys import os from pathlib import Path from tmarl.runners.base_evaluator import Evaluator from tmarl.envs.football.football import RllibGFootball from tmarl.envs.env_wrappers import ShareSubprocVecEnv, ShareDummyVecEnv class FootballEvaluator(Evaluator): def __init__(self, argv): super(FootballEvaluator, self).__init__(argv) def setup_run_dir(self, all_args): dump_dir = Path(all_args.replay_save_dir) if not dump_dir.exists(): os.makedirs(str(dump_dir)) self.dump_dir = dump_dir return super(FootballEvaluator, self).setup_run_dir(all_args) def make_eval_env(self, all_args, Env_Class, SubprocVecEnv, DummyVecEnv): def get_env_fn(rank): def init_env(): env = Env_Class(all_args, rank, log_dir=str(self.dump_dir), isEval=True) env.seed(all_args.seed * 50000 + rank * 10000) return env return init_env if all_args.n_eval_rollout_threads == 1: return DummyVecEnv([get_env_fn(0)]) else: return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_eval_rollout_threads)]) def extra_args_func(self, args, parser): parser.add_argument('--scenario_name', type=str, default='simple_spread', help="Which scenario to run on") parser.add_argument('--num_agents', type=int, default=0, help="number of players") # football config parser.add_argument('--representation', type=str, default='raw', help="format of the observation in gfootball env") parser.add_argument('--rewards', type=str, default='scoring', help="format of the reward in gfootball env") parser.add_argument("--render_only", action='store_true', default=False, help="if ture, render without training") all_args = parser.parse_known_args(args)[0] return all_args def get_env(self): return RllibGFootball, ShareSubprocVecEnv, ShareDummyVecEnv def init_driver(self): if not self.all_args.separate_policy: from tmarl.drivers.shared_distributed.football_driver import FootballDriver as Driver else: raise NotImplementedError driver = Driver(self.config) return driver def main(argv): evaluator = FootballEvaluator(argv) evaluator.run() if __name__ == "__main__": main(sys.argv[1:])

py

TiKick

TiKick-main/tmarl/utils/multi_discrete.py

import gym import numpy as np # An old version of OpenAI Gym's multi_discrete.py. (Was getting affected by Gym updates) # (https://github.com/openai/gym/blob/1fb81d4e3fb780ccf77fec731287ba07da35eb84/gym/spaces/multi_discrete.py) class MultiDiscrete(gym.Space): """ - The multi-discrete action space consists of a series of discrete action spaces with different parameters - It can be adapted to both a Discrete action space or a continuous (Box) action space - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space - It is parametrized by passing an array of arrays containing [min, max] for each discrete action space where the discrete action space can take any integers from `min` to `max` (both inclusive) Note: A value of 0 always need to represent the NOOP action. e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces: 1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4 2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 - Can be initialized as MultiDiscrete([ [0,4], [0,1], [0,1] ]) """ def __init__(self, array_of_param_array): self.low = np.array([x[0] for x in array_of_param_array]) self.high = np.array([x[1] for x in array_of_param_array]) self.num_discrete_space = self.low.shape[0] self.n = np.sum(self.high) + 2 def sample(self): """ Returns a array with one sample from each discrete action space """ # For each row: round(random .* (max - min) + min, 0) random_array = np.random.rand(self.num_discrete_space) return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)] def contains(self, x): return len(x) == self.num_discrete_space and (np.array(x) >= self.low).all() and (np.array(x) <= self.high).all() @property def shape(self): return self.num_discrete_space def __repr__(self): return "MultiDiscrete" + str(self.num_discrete_space) def __eq__(self, other): return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high)

py

TiKick

TiKick-main/tmarl/utils/valuenorm.py

import numpy as np import torch import torch.nn as nn class ValueNorm(nn.Module): """ Normalize a vector of observations - across the first norm_axes dimensions""" def __init__(self, input_shape, norm_axes=1, beta=0.99999, per_element_update=False, epsilon=1e-5, device=torch.device("cpu")): super(ValueNorm, self).__init__() self.input_shape = input_shape self.norm_axes = norm_axes self.epsilon = epsilon self.beta = beta self.per_element_update = per_element_update self.tpdv = dict(dtype=torch.float32, device=device) self.running_mean = nn.Parameter(torch.zeros(input_shape), requires_grad=False).to(**self.tpdv) self.running_mean_sq = nn.Parameter(torch.zeros(input_shape), requires_grad=False).to(**self.tpdv) self.debiasing_term = nn.Parameter(torch.tensor(0.0), requires_grad=False).to(**self.tpdv) self.reset_parameters() def reset_parameters(self): self.running_mean.zero_() self.running_mean_sq.zero_() self.debiasing_term.zero_() def running_mean_var(self): debiased_mean = self.running_mean / self.debiasing_term.clamp(min=self.epsilon) debiased_mean_sq = self.running_mean_sq / self.debiasing_term.clamp(min=self.epsilon) debiased_var = (debiased_mean_sq - debiased_mean ** 2).clamp(min=1e-2) return debiased_mean, debiased_var @torch.no_grad() def update(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) batch_mean = input_vector.mean(dim=tuple(range(self.norm_axes))) batch_sq_mean = (input_vector ** 2).mean(dim=tuple(range(self.norm_axes))) if self.per_element_update: batch_size = np.prod(input_vector.size()[:self.norm_axes]) weight = self.beta ** batch_size else: weight = self.beta self.running_mean.mul_(weight).add_(batch_mean * (1.0 - weight)) self.running_mean_sq.mul_(weight).add_(batch_sq_mean * (1.0 - weight)) self.debiasing_term.mul_(weight).add_(1.0 * (1.0 - weight)) def normalize(self, input_vector): # Make sure input is float32 if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.running_mean_var() out = (input_vector - mean[(None,) * self.norm_axes]) / torch.sqrt(var)[(None,) * self.norm_axes] return out def denormalize(self, input_vector): """ Transform normalized data back into original distribution """ if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.running_mean_var() out = input_vector * torch.sqrt(var)[(None,) * self.norm_axes] + mean[(None,) * self.norm_axes] out = out.cpu().numpy() return out

py

TiKick

TiKick-main/tmarl/utils/util.py

import copy import numpy as np import math import gym import torch import torch.nn as nn import torch.nn.functional as F import torch.distributed as dist from torch.autograd import Variable from gym.spaces import Box, Discrete, Tuple def check(input): if type(input) == np.ndarray: return torch.from_numpy(input) def get_gard_norm(it): sum_grad = 0 for x in it: if x.grad is None: continue sum_grad += x.grad.norm() ** 2 return math.sqrt(sum_grad) def update_linear_schedule(optimizer, epoch, total_num_epochs, initial_lr): """Decreases the learning rate linearly""" lr = initial_lr - (initial_lr * (epoch / float(total_num_epochs))) for param_group in optimizer.param_groups: param_group['lr'] = lr def huber_loss(e, d): a = (abs(e) <= d).float() b = (e > d).float() return a*e**2/2 + b*d*(abs(e)-d/2) def mse_loss(e): return e**2/2 def get_shape_from_obs_space(obs_space): if obs_space.__class__.__name__ == 'Box': obs_shape = obs_space.shape elif obs_space.__class__.__name__ == 'list': obs_shape = obs_space elif obs_space.__class__.__name__ == 'Dict': obs_shape = obs_space.spaces else: raise NotImplementedError return obs_shape def get_shape_from_act_space(act_space): if act_space.__class__.__name__ == 'Discrete': act_shape = 1 elif act_space.__class__.__name__ == "MultiDiscrete": act_shape = act_space.shape elif act_space.__class__.__name__ == "Box": act_shape = act_space.shape[0] elif act_space.__class__.__name__ == "MultiBinary": act_shape = act_space.shape[0] else: # agar act_shape = act_space[0].shape[0] + 1 return act_shape def tile_images(img_nhwc): """ Tile N images into one big PxQ image (P,Q) are chosen to be as close as possible, and if N is square, then P=Q. input: img_nhwc, list or array of images, ndim=4 once turned into array n = batch index, h = height, w = width, c = channel returns: bigim_HWc, ndarray with ndim=3 """ img_nhwc = np.asarray(img_nhwc) N, h, w, c = img_nhwc.shape H = int(np.ceil(np.sqrt(N))) W = int(np.ceil(float(N)/H)) img_nhwc = np.array( list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)]) img_HWhwc = img_nhwc.reshape(H, W, h, w, c) img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4) img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c) return img_Hh_Ww_c def to_torch(input): return torch.from_numpy(input) if type(input) == np.ndarray else input def to_numpy(x): return x.detach().cpu().numpy() class FixedCategorical(torch.distributions.Categorical): def sample(self): return super().sample() def log_probs(self, actions): return ( super() .log_prob(actions.squeeze(-1)) .view(actions.size(0), -1) .sum(-1) .unsqueeze(-1) ) def mode(self): return self.probs.argmax(dim=-1, keepdim=True) class MultiDiscrete(gym.Space): """ - The multi-discrete action space consists of a series of discrete action spaces with different parameters - It can be adapted to both a Discrete action space or a continuous (Box) action space - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space - It is parametrized by passing an array of arrays containing [min, max] for each discrete action space where the discrete action space can take any integers from `min` to `max` (both inclusive) Note: A value of 0 always need to represent the NOOP action. e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces: 1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4 2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 - Can be initialized as MultiDiscrete([ [0,4], [0,1], [0,1] ]) """ def __init__(self, array_of_param_array): self.low = np.array([x[0] for x in array_of_param_array]) self.high = np.array([x[1] for x in array_of_param_array]) self.num_discrete_space = self.low.shape[0] self.n = np.sum(self.high) + 2 def sample(self): """ Returns a array with one sample from each discrete action space """ # For each row: round(random .* (max - min) + min, 0) random_array = np.random.rand(self.num_discrete_space) return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)] def contains(self, x): return len(x) == self.num_discrete_space and (np.array(x) >= self.low).all() and (np.array(x) <= self.high).all() @property def shape(self): return self.num_discrete_space def __repr__(self): return "MultiDiscrete" + str(self.num_discrete_space) def __eq__(self, other): return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high) class DecayThenFlatSchedule(): def __init__(self, start, finish, time_length, decay="exp"): self.start = start self.finish = finish self.time_length = time_length self.delta = (self.start - self.finish) / self.time_length self.decay = decay if self.decay in ["exp"]: self.exp_scaling = (-1) * self.time_length / \ np.log(self.finish) if self.finish > 0 else 1 def eval(self, T): if self.decay in ["linear"]: return max(self.finish, self.start - self.delta * T) elif self.decay in ["exp"]: return min(self.start, max(self.finish, np.exp(- T / self.exp_scaling))) pass def huber_loss(e, d): a = (abs(e) <= d).float() b = (e > d).float() return a*e**2/2 + b*d*(abs(e)-d/2) def mse_loss(e): return e**2 def init(module, weight_init, bias_init, gain=1): weight_init(module.weight.data, gain=gain) bias_init(module.bias.data) return module def get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) # https://github.com/ikostrikov/pytorch-ddpg-naf/blob/master/ddpg.py#L11 def soft_update(target, source, tau): """ Perform DDPG soft update (move target params toward source based on weight factor tau) Inputs: target (torch.nn.Module): Net to copy parameters to source (torch.nn.Module): Net whose parameters to copy tau (float, 0 < x < 1): Weight factor for update """ for target_param, param in zip(target.parameters(), source.parameters()): target_param.data.copy_( target_param.data * (1.0 - tau) + param.data * tau) # https://github.com/ikostrikov/pytorch-ddpg-naf/blob/master/ddpg.py#L15 def hard_update(target, source): """ Copy network parameters from source to target Inputs: target (torch.nn.Module): Net to copy parameters to source (torch.nn.Module): Net whose parameters to copy """ for target_param, param in zip(target.parameters(), source.parameters()): target_param.data.copy_(param.data) # https://github.com/seba-1511/dist_tuto.pth/blob/gh-pages/train_dist.py def average_gradients(model): """ Gradient averaging. """ size = float(dist.get_world_size()) for param in model.parameters(): dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM, group=0) param.grad.data /= size def onehot_from_logits(logits, avail_logits=None, eps=0.0): """ Given batch of logits, return one-hot sample using epsilon greedy strategy (based on given epsilon) """ # get best (according to current policy) actions in one-hot form logits = to_torch(logits) dim = len(logits.shape) - 1 if avail_logits is not None: avail_logits = to_torch(avail_logits) logits[avail_logits == 0] = -1e10 argmax_acs = (logits == logits.max(dim, keepdim=True)[0]).float() if eps == 0.0: return argmax_acs # get random actions in one-hot form rand_acs = Variable(torch.eye(logits.shape[1])[[np.random.choice( range(logits.shape[1]), size=logits.shape[0])]], requires_grad=False) # chooses between best and random actions using epsilon greedy return torch.stack([argmax_acs[i] if r > eps else rand_acs[i] for i, r in enumerate(torch.rand(logits.shape[0]))]) # modified for PyTorch from https://github.com/ericjang/gumbel-softmax/blob/master/Categorical%20VAE.ipynb def sample_gumbel(shape, eps=1e-20, tens_type=torch.FloatTensor): """Sample from Gumbel(0, 1)""" U = Variable(tens_type(*shape).uniform_(), requires_grad=False) return -torch.log(-torch.log(U + eps) + eps) # modified for PyTorch from https://github.com/ericjang/gumbel-softmax/blob/master/Categorical%20VAE.ipynb def gumbel_softmax_sample(logits, avail_logits, temperature, device=torch.device('cpu')): """ Draw a sample from the Gumbel-Softmax distribution""" if str(device) == 'cpu': y = logits + sample_gumbel(logits.shape, tens_type=type(logits.data)) else: y = (logits.cpu() + sample_gumbel(logits.shape, tens_type=type(logits.data))).cuda() dim = len(logits.shape) - 1 if avail_logits is not None: avail_logits = to_torch(avail_logits).to(device) y[avail_logits == 0] = -1e10 return F.softmax(y / temperature, dim=dim) # modified for PyTorch from https://github.com/ericjang/gumbel-softmax/blob/master/Categorical%20VAE.ipynb def gumbel_softmax(logits, avail_logits=None, temperature=1.0, hard=False, device=torch.device('cpu')): """Sample from the Gumbel-Softmax distribution and optionally discretize. Args: logits: [batch_size, n_class] unnormalized log-probs temperature: non-negative scalar hard: if True, take argmax, but differentiate w.r.t. soft sample y Returns: [batch_size, n_class] sample from the Gumbel-Softmax distribution. If hard=True, then the returned sample will be one-hot, otherwise it will be a probabilitiy distribution that sums to 1 across classes """ y = gumbel_softmax_sample(logits, avail_logits, temperature, device) if hard: y_hard = onehot_from_logits(y) y = (y_hard - y).detach() + y return y def gaussian_noise(shape, std): return torch.empty(shape).normal_(mean=0, std=std) def get_obs_shape(obs_space): if obs_space.__class__.__name__ == "Box": obs_shape = obs_space.shape elif obs_space.__class__.__name__ == "list": obs_shape = obs_space else: raise NotImplementedError return obs_shape def get_dim_from_space(space): if isinstance(space, Box): dim = space.shape[0] elif isinstance(space, Discrete): dim = space.n elif isinstance(space, Tuple): dim = sum([get_dim_from_space(sp) for sp in space]) elif "MultiDiscrete" in space.__class__.__name__: return (space.high - space.low) + 1 elif isinstance(space, list): dim = space[0] else: raise Exception("Unrecognized space: ", type(space)) return dim def get_state_dim(observation_dict, action_dict): combined_obs_dim = sum([get_dim_from_space(space) for space in observation_dict.values()]) combined_act_dim = 0 for space in action_dict.values(): dim = get_dim_from_space(space) if isinstance(dim, np.ndarray): combined_act_dim += int(sum(dim)) else: combined_act_dim += dim return combined_obs_dim, combined_act_dim, combined_obs_dim+combined_act_dim def get_cent_act_dim(action_space): cent_act_dim = 0 for space in action_space: dim = get_dim_from_space(space) if isinstance(dim, np.ndarray): cent_act_dim += int(sum(dim)) else: cent_act_dim += dim return cent_act_dim def is_discrete(space): if isinstance(space, Discrete) or "MultiDiscrete" in space.__class__.__name__: return True else: return False def is_multidiscrete(space): if "MultiDiscrete" in space.__class__.__name__: return True else: return False def make_onehot(int_action, action_dim, seq_len=None): if type(int_action) == torch.Tensor: int_action = int_action.cpu().numpy() if not seq_len: return np.eye(action_dim)[int_action] if seq_len: onehot_actions = [] for i in range(seq_len): onehot_action = np.eye(action_dim)[int_action[i]] onehot_actions.append(onehot_action) return np.stack(onehot_actions) def avail_choose(x, avail_x=None): x = to_torch(x) if avail_x is not None: avail_x = to_torch(avail_x) x[avail_x == 0] = -1e10 return x # FixedCategorical(logits=x) def tile_images(img_nhwc): """ Tile N images into one big PxQ image (P,Q) are chosen to be as close as possible, and if N is square, then P=Q. input: img_nhwc, list or array of images, ndim=4 once turned into array n = batch index, h = height, w = width, c = channel returns: bigim_HWc, ndarray with ndim=3 """ img_nhwc = np.asarray(img_nhwc) N, h, w, c = img_nhwc.shape H = int(np.ceil(np.sqrt(N))) W = int(np.ceil(float(N)/H)) img_nhwc = np.array( list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)]) img_HWhwc = img_nhwc.reshape(H, W, h, w, c) img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4) img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c) return img_Hh_Ww_c

py

TiKick

TiKick-main/tmarl/utils/segment_tree.py

import numpy as np def unique(sorted_array): """ More efficient implementation of np.unique for sorted arrays :param sorted_array: (np.ndarray) :return:(np.ndarray) sorted_array without duplicate elements """ if len(sorted_array) == 1: return sorted_array left = sorted_array[:-1] right = sorted_array[1:] uniques = np.append(right != left, True) return sorted_array[uniques] class SegmentTree(object): def __init__(self, capacity, operation, neutral_element): """ Build a Segment Tree data structure. https://en.wikipedia.org/wiki/Segment_tree Can be used as regular array that supports Index arrays, but with two important differences: a) setting item's value is slightly slower. It is O(lg capacity) instead of O(1). b) user has access to an efficient ( O(log segment size) ) `reduce` operation which reduces `operation` over a contiguous subsequence of items in the array. :param capacity: (int) Total size of the array - must be a power of two. :param operation: (lambda (Any, Any): Any) operation for combining elements (eg. sum, max) must form a mathematical group together with the set of possible values for array elements (i.e. be associative) :param neutral_element: (Any) neutral element for the operation above. eg. float('-inf') for max and 0 for sum. """ assert capacity > 0 and capacity & ( capacity - 1) == 0, "capacity must be positive and a power of 2." self._capacity = capacity self._value = [neutral_element for _ in range(2 * capacity)] self._operation = operation self.neutral_element = neutral_element def _reduce_helper(self, start, end, node, node_start, node_end): if start == node_start and end == node_end: return self._value[node] mid = (node_start + node_end) // 2 if end <= mid: return self._reduce_helper(start, end, 2 * node, node_start, mid) else: if mid + 1 <= start: return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) else: return self._operation( self._reduce_helper(start, mid, 2 * node, node_start, mid), self._reduce_helper( mid + 1, end, 2 * node + 1, mid + 1, node_end) ) def reduce(self, start=0, end=None): """ Returns result of applying `self.operation` to a contiguous subsequence of the array. self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) :param start: (int) beginning of the subsequence :param end: (int) end of the subsequences :return: (Any) result of reducing self.operation over the specified range of array elements. """ if end is None: end = self._capacity if end < 0: end += self._capacity end -= 1 return self._reduce_helper(start, end, 1, 0, self._capacity - 1) def __setitem__(self, idx, val): # indexes of the leaf idxs = idx + self._capacity self._value[idxs] = val if isinstance(idxs, int): idxs = np.array([idxs]) # go up one level in the tree and remove duplicate indexes idxs = unique(idxs // 2) while len(idxs) > 1 or idxs[0] > 0: # as long as there are non-zero indexes, update the corresponding values self._value[idxs] = self._operation( self._value[2 * idxs], self._value[2 * idxs + 1] ) # go up one level in the tree and remove duplicate indexes idxs = unique(idxs // 2) def __getitem__(self, idx): assert np.max(idx) < self._capacity assert 0 <= np.min(idx) return self._value[self._capacity + idx] class SumSegmentTree(SegmentTree): def __init__(self, capacity): super(SumSegmentTree, self).__init__( capacity=capacity, operation=np.add, neutral_element=0.0 ) self._value = np.array(self._value) def sum(self, start=0, end=None): """ Returns arr[start] + ... + arr[end] :param start: (int) start position of the reduction (must be >= 0) :param end: (int) end position of the reduction (must be < len(arr), can be None for len(arr) - 1) :return: (Any) reduction of SumSegmentTree """ return super(SumSegmentTree, self).reduce(start, end) def find_prefixsum_idx(self, prefixsum): """ Find the highest index `i` in the array such that sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum for each entry in prefixsum if array values are probabilities, this function allows to sample indexes according to the discrete probability efficiently. :param prefixsum: (np.ndarray) float upper bounds on the sum of array prefix :return: (np.ndarray) highest indexes satisfying the prefixsum constraint """ if isinstance(prefixsum, float): prefixsum = np.array([prefixsum]) assert 0 <= np.min(prefixsum) assert np.max(prefixsum) <= self.sum() + 1e-5 assert isinstance(prefixsum[0], float) idx = np.ones(len(prefixsum), dtype=int) cont = np.ones(len(prefixsum), dtype=bool) while np.any(cont): # while not all nodes are leafs idx[cont] = 2 * idx[cont] prefixsum_new = np.where( self._value[idx] <= prefixsum, prefixsum - self._value[idx], prefixsum) # prepare update of prefixsum for all right children idx = np.where(np.logical_or( self._value[idx] > prefixsum, np.logical_not(cont)), idx, idx + 1) # Select child node for non-leaf nodes prefixsum = prefixsum_new # update prefixsum cont = idx < self._capacity # collect leafs return idx - self._capacity class MinSegmentTree(SegmentTree): def __init__(self, capacity): super(MinSegmentTree, self).__init__( capacity=capacity, operation=np.minimum, neutral_element=float('inf') ) self._value = np.array(self._value) def min(self, start=0, end=None): """ Returns min(arr[start], ..., arr[end]) :param start: (int) start position of the reduction (must be >= 0) :param end: (int) end position of the reduction (must be < len(arr), can be None for len(arr) - 1) :return: (Any) reduction of MinSegmentTree """ return super(MinSegmentTree, self).reduce(start, end)

py

TiKick

TiKick-main/tmarl/utils/__init__.py

py

TiKick

TiKick-main/tmarl/utils/gpu_mem_track.py

# code from https://github.com/Oldpan/Pytorch-Memory-Utils import gc import datetime import inspect import torch import numpy as np dtype_memory_size_dict = { torch.float64: 64/8, torch.double: 64/8, torch.float32: 32/8, torch.float: 32/8, torch.float16: 16/8, torch.half: 16/8, torch.int64: 64/8, torch.long: 64/8, torch.int32: 32/8, torch.int: 32/8, torch.int16: 16/8, torch.short: 16/6, torch.uint8: 8/8, torch.int8: 8/8, } # compatibility of torch1.0 if getattr(torch, "bfloat16", None) is not None: dtype_memory_size_dict[torch.bfloat16] = 16/8 if getattr(torch, "bool", None) is not None: dtype_memory_size_dict[torch.bool] = 8/8 # pytorch use 1 byte for a bool, see https://github.com/pytorch/pytorch/issues/41571 def get_mem_space(x): try: ret = dtype_memory_size_dict[x] except KeyError: print(f"dtype {x} is not supported!") return ret class MemTracker(object): """ Class used to track pytorch memory usage Arguments: detail(bool, default True): whether the function shows the detail gpu memory usage path(str): where to save log file verbose(bool, default False): whether show the trivial exception device(int): GPU number, default is 0 """ def __init__(self, detail=True, path='', verbose=False, device=0): self.print_detail = detail self.last_tensor_sizes = set() self.gpu_profile_fn = path + f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_track.txt' self.verbose = verbose self.begin = True self.device = device def get_tensors(self): for obj in gc.get_objects(): try: if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): tensor = obj else: continue if tensor.is_cuda: yield tensor except Exception as e: if self.verbose: print('A trivial exception occured: {}'.format(e)) def get_tensor_usage(self): sizes = [np.prod(np.array(tensor.size())) * get_mem_space(tensor.dtype) for tensor in self.get_tensors()] return np.sum(sizes) / 1024**2 def get_allocate_usage(self): return torch.cuda.memory_allocated() / 1024**2 def clear_cache(self): gc.collect() torch.cuda.empty_cache() def print_all_gpu_tensor(self, file=None): for x in self.get_tensors(): print(x.size(), x.dtype, np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, file=file) def track(self): """ Track the GPU memory usage """ frameinfo = inspect.stack()[1] where_str = frameinfo.filename + ' line ' + str(frameinfo.lineno) + ': ' + frameinfo.function with open(self.gpu_profile_fn, 'a+') as f: if self.begin: f.write(f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |" f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb" f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n") self.begin = False if self.print_detail is True: ts_list = [(tensor.size(), tensor.dtype) for tensor in self.get_tensors()] new_tensor_sizes = {(type(x), tuple(x.size()), ts_list.count((x.size(), x.dtype)), np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, x.dtype) for x in self.get_tensors()} for t, s, n, m, data_type in new_tensor_sizes - self.last_tensor_sizes: f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n') for t, s, n, m, data_type in self.last_tensor_sizes - new_tensor_sizes: f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n') self.last_tensor_sizes = new_tensor_sizes f.write(f"\nAt {where_str:<50}" f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb" f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n")

py

TiKick

TiKick-main/tmarl/utils/modelsize_estimate.py

# code from https://github.com/Oldpan/Pytorch-Memory-Utils import torch.nn as nn import numpy as np def modelsize(model, input, type_size=4): para = sum([np.prod(list(p.size())) for p in model.parameters()]) # print('Model {} : Number of params: {}'.format(model._get_name(), para)) print('Model {} : params: {:4f}M'.format(model._get_name(), para * type_size / 1000 / 1000)) input_ = input.clone() input_.requires_grad_(requires_grad=False) mods = list(model.modules()) out_sizes = [] for i in range(1, len(mods)): m = mods[i] if isinstance(m, nn.ReLU): if m.inplace: continue out = m(input_) out_sizes.append(np.array(out.size())) input_ = out total_nums = 0 for i in range(len(out_sizes)): s = out_sizes[i] nums = np.prod(np.array(s)) total_nums += nums # print('Model {} : Number of intermedite variables without backward: {}'.format(model._get_name(), total_nums)) # print('Model {} : Number of intermedite variables with backward: {}'.format(model._get_name(), total_nums*2)) print('Model {} : intermedite variables: {:3f} M (without backward)' .format(model._get_name(), total_nums * type_size / 1000 / 1000)) print('Model {} : intermedite variables: {:3f} M (with backward)' .format(model._get_name(), total_nums * type_size*2 / 1000 / 1000))

py

TiKick

TiKick-main/scripts/football/replay2video.py

# coding=utf-8 # Copyright 2019 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Script allowing to replay a given trace file. Example usage: python replay.py --trace_file=/tmp/dumps/shutdown_20190521-165136974075.dump """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tmarl.envs.football.env import script_helpers from absl import app from absl import flags FLAGS = flags.FLAGS flags.DEFINE_string('replay_file', None, 'replay file path') flags.DEFINE_string('video_save_dir', '../../results/videos', 'video save dir') flags.DEFINE_integer('fps', 10, 'How many frames per second to render') flags.mark_flag_as_required('replay_file') def main(_): script_helpers.ScriptHelpers().replay(FLAGS.replay_file, FLAGS.fps,directory=FLAGS.video_save_dir) if __name__ == '__main__': app.run(main)

py

criterion.rs

criterion.rs-master/benches/benchmarks/external_process.py

import time import sys def fibonacci(n): if n == 0 or n == 1: return 1 return fibonacci(n - 1) + fibonacci(n - 2) MILLIS = 1000 MICROS = MILLIS * 1000 NANOS = MICROS * 1000 def benchmark(): depth = int(sys.argv[1]) for line in sys.stdin: iters = int(line.strip()) # Setup start = time.perf_counter() for x in range(iters): fibonacci(depth) end = time.perf_counter() # Teardown delta = end - start nanos = int(delta * NANOS) print("%d" % nanos) sys.stdout.flush() benchmark()

py

RobDanns

RobDanns-main/deep_learning/setup.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Setup pycls.""" from setuptools import setup setup( name='pycls', packages=['pycls'] )

py

RobDanns

RobDanns-main/deep_learning/yaml_gen.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Generate yaml files for experiment configurations.""" import yaml # import math import os import re import argparse import numpy as np import shutil def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser() parser.add_argument( '--task', dest='task', help='Generate configs for the given tasks: e.g., mlp_cifar, cnn_cifar, cnn_imagenet, resnet18_tinyimagenet, resenet18_imagenet', default='mlp_cifar10', type=str ) return parser.parse_args() def makedirs_rm_exist(dir): if os.path.isdir(dir): shutil.rmtree(dir) os.makedirs(dir, exist_ok=True) def purge(dir, pattern): for f in os.listdir(dir): if re.search(pattern, f): os.remove(os.path.join(dir, f)) def gen(dir_in, dir_out, fname_base, vars_label, vars_alias, vars_value): '''Generate yaml files''' with open(dir_in + fname_base + '.yaml') as f: data_base = yaml.unsafe_load(f) for vars in vars_value: data = data_base.copy() fname_new = fname_base for id, var in enumerate(vars): if vars_label[id][0] in data: # if key1 exist data[vars_label[id][0]][vars_label[id][1]] = var else: data[vars_label[id][0]] = {vars_label[id][1]: var} if vars_label[id][1] == 'TRANS_FUN': var = var.split('_')[0] fname_new += '_{}{}'.format(vars_alias[id], var) with open(dir_out + fname_new + '.yaml', "w") as f: yaml.dump(data, f, default_flow_style=False) def gen_single(dir_in, dir_out, fname_base, vars_label, vars_alias, vars, comment='best'): '''Generate yaml files for a single experiment''' with open(dir_in + fname_base + '.yaml') as f: data_base = yaml.unsafe_load(f) data = data_base.copy() fname_new = '{}_{}'.format(fname_base, comment) for id, var in enumerate(vars): if vars_label[id][0] in data: # if key1 exist data[vars_label[id][0]][vars_label[id][1]] = var else: data[vars_label[id][0]] = {vars_label[id][1]: var} with open(dir_out + fname_new + '.yaml', "w") as f: yaml.dump(data, f, default_flow_style=False) def grid2list(grid): '''grid search to list''' list_in = [[i] for i in grid[0]] grid.pop(0) for grid_temp in grid: list_out = [] for val in grid_temp: for list_temp in list_in: list_out.append(list_temp + [val]) list_in = list_out return list_in args = parse_args() # Format for all experiments # Note: many arguments are deprecated, they are kept to be consistent with existing experimental results vars_value = [] vars_label = [['RESNET', 'TRANS_FUN'], ['RGRAPH', 'TALK_MODE'], ['RGRAPH', 'GROUP_NUM'], ['RGRAPH', 'MESSAGE_TYPE'], ['RGRAPH', 'SPARSITY'], ['RGRAPH', 'P'], ['RGRAPH', 'AGG_FUNC'], ['RGRAPH', 'SEED_GRAPH'], ['RGRAPH', 'SEED_TRAIN_START'], ['RGRAPH', 'SEED_TRAIN_END'], ['RGRAPH', 'KEEP_GRAPH'], ['RGRAPH', 'ADD_1x1'], ['RGRAPH', 'UPPER'], ['TRAIN', 'AUTO_MATCH'], ['OPTIM', 'MAX_EPOCH'], ['TRAIN', 'CHECKPOINT_PERIOD']] vars_alias = ['trans', 'talkmode', 'num', 'message', 'sparsity', 'p', 'agg', 'graphseed', 'starttrainseed', 'endtrainseed', 'keep', 'add1x1', 'upper', 'match', 'epoch', 'chkpt' ] ## Note: (1) how many relational graphs used to run: graphs_n64_54, graphs_n64_441, graphs_n64_3854 ## (2): "best_id" is to be discovered based on experimental results. Given best_id is for graph2nn experiments ## (3): Each ImageNet experiment provides with 1 seed. One can change SEED_TRAIN_START and SEED_TRAIN_END ## to get results for multiple seeds ### 5 layer 64 dim MLP, CIFAR-10 # usage: python yaml_gen.py --task mlp_cifar10 if args.task == 'mlp_cifar10': # best_id = 3552 # best_id is for graph2nn experiments. fname_bases = ['mlp_bs128_1gpu_layer3'] # graphs = np.load('analysis/graphs_n64_53.npy') # To load the .npy file np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['talklinear_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 6, True, 0, True, True, 200, 200]] vars_value += [['linear_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 6, True, 0, True, True, 200, 200]] ### CNN, CIFAR-10 # usage : python yaml_gen.py --task cnn_cifar10 if args.task == 'cnn_cifar10': # best_id = 3552 # best_id is for graph2nn experiments. fname_bases = ['cnn6_bs1024_8gpu_64d'] # graphs = np.load('analysis/graphs_n64_53.npy') # To load the .npy file np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['convtalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 6, True, 0, True, True, 100, 100]] vars_value += [['convbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 6, True, 0, True, True, 100, 100]] ### CNN, CIFAR-100 # uage python yaml_gen.py --task cnn_cifar100 elif args.task == 'cnn_cifar100': # best_id = 3552 # best_id is for graph2nn experiments. fname_bases = ['cnn6_bs640_1gpu_64d'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['convtalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 6, True, 0, True, True, 100, 100]] vars_value += [['convbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 6, True, 0, True, True, 100, 100]] ### Res18, tinyimagenet # usage: python yaml_gen.py --task resnet18_tinyimagenet elif args.task == 'resnet18_tinyimagenet': fname_bases = ['R-18_tiny_bs256_1gpu'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupbasictalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 75, 25]] vars_value += [['channelbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 75, 25]] ### CNN, imagenet elif args.task == 'cnn_imagenet': # best_id = 27 # best_id is for graph2nn experiments. fname_bases = ['cnn6_bs32_1gpu_64d', 'cnn6_bs256_8gpu_64d'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['convtalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 100]] vars_value += [['convbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 100]] ### Res18, ImageNet # usage : python yaml_gen.py --task resnet18_imagenet elif args.task == 'resnet18_imagenet': # best_id = 37 # best_id is for graph2nn experiments. fname_bases = ['R-18_bs450_1gpu'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) graphs = np.load('analysis/graphs_n64_53.npy') np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupbasictalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 75, 25]] vars_value += [['channelbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 75, 25]] ### Res34, ImageNet # usage: python yaml_gen.py --task resnet34_imagenet elif args.task == 'resnet34_imagenet': # best_id = 37 # best_id is for graph2nn experiments. fname_bases = ['R-34_bs32_1gpu', 'R-34_bs256_8gpu'] # graphs = np.load('analysis/graphs_n64_52.npy') np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) graphs = np.load('analysis/graphs_n64_53.npy') np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupbasictalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['channelbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] ### Res34-sep, ImageNet elif args.task == 'resnet34sep_imagenet': # best_id = 36 # best_id is for graph2nn experiments. fname_bases = ['R-34_bs32_1gpu', 'R-34_bs256_8gpu'] graphs = np.load('analysis/graphs_n64_53.npy') for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupseptalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['channelsep_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] ### Res50, ImageNet elif args.task == 'resnet50_imagenet': # best_id = 22 # best_id is for graph2nn experiments. fname_bases = ['R-50_bs32_1gpu', 'R-50_bs256_8gpu'] graphs = np.load('analysis/graphs_n64_53.npy') for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['talkbottleneck_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['bottleneck_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] ### Efficient net, ImageNet # uage : python yaml_gen.py --task efficient_imagenet elif args.task == 'efficient_imagenet': # best_id = 42 # best_id is for graph2nn experiments. fname_bases = ['EN-B0_bs64_1gpu_nms', 'EN-B0_bs512_8gpu_nms'] # graphs = np.load('analysis/graphs_n64_53.npy')) np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) graphs = np.load('analysis/graphs_n64_53.npy') np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['mbtalkconv_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['mbconv_transform', 'dense', 16, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] # ### MLP, cifar10, bio # elif args.task == 'mlp_cifar10_bio': # fname_bases = ['mlp_bs128_1gpu_layer3'] # for graph_type in ['mcwholeraw']: # vars_value += [['talklinear_transform', 'dense', 71, # graph_type, 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] # for graph_type in ['mcvisualraw']: # vars_value += [['talklinear_transform', 'dense', 30, # graph_type, 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] # for graph_type in ['catraw']: # vars_value += [['talklinear_transform', 'dense', 52, # graph_type, 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] # vars_value += [['linear_transform', 'dense', 64, # 'ws', 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] if args.task == 'cifar0': dir_name = 'cifar10' elif 'cifar100' in args.task: dir_name = 'cifar100' elif 'tinyimagenet' in args.task: dir_name = 'tinyimagenet200' else: dir_name = 'imagenet' dir_in = 'configs/baselines/{}/'.format(dir_name) dir_out = 'configs/baselines/{}/{}/'.format(dir_name, args.task) dir_out_all = 'configs/baselines/{}/{}/all/'.format(dir_name, args.task) dir_out_best = 'configs/baselines/{}/{}/best/'.format(dir_name, args.task) # makedirs_rm_exist(dir_out) # makedirs_rm_exist(dir_out_all) # makedirs_rm_exist(dir_out_best) # print(vars_value) for fname_base in fname_bases: if 'bio' not in args.task: gen(dir_in, dir_out_all, fname_base, vars_label, vars_alias, vars_value) # gen_single(dir_in, dir_out_best, fname_base, vars_label, vars_alias, vars_value[best_id], comment='best') gen_single(dir_in, dir_out_best, fname_base, vars_label, vars_alias, vars_value[-1], comment='baseline') else: gen(dir_in, dir_out_best, fname_base, vars_label, vars_alias, vars_value)

py

RobDanns

RobDanns-main/deep_learning/tools/corruptions-inference-tinyimagenet.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Train a classification model.""" from __future__ import print_function import argparse import numpy as np import os import sys import torch import multiprocessing as mp import math import pdb import torch.utils.data import torchvision.datasets as datasets import torchvision.transforms as transforms from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter from PIL import Image import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.paths as dp import time from datetime import datetime from tensorboardX import SummaryWriter from torchvision.utils import save_image from skimage.util import random_noise print("Let's use GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() # TEST(VAL) DATA_LOADER FOR TINY_IMAGENET200 def parseClasses(file): classes = [] filenames = [] with open(file) as f: lines = f.readlines() lines = [x.strip() for x in lines] for x in range(0, len(lines)): tokens = lines[x].split() classes.append(tokens[1]) filenames.append(tokens[0]) return filenames, classes def load_allimages(dir): images = [] if not os.path.isdir(dir): sys.exit(-1) for root, _, fnames in sorted(os.walk(dir)): for fname in sorted(fnames): #if datasets.folder.is_image_file(fname): if datasets.folder.has_file_allowed_extension(fname,['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']): path = os.path.join(root, fname) item = path images.append(item) return images class TinyImageNet(torch.utils.data.Dataset): """ TinyImageNet200 validation dataloader.""" def __init__(self, img_path, gt_path, class_to_idx=None, transform=None): self.img_path = img_path self.transform = transform self.gt_path = gt_path self.class_to_idx = class_to_idx self.classidx = [] self.imgs, self.classnames = parseClasses(gt_path) for classname in self.classnames: self.classidx.append(self.class_to_idx[classname]) def __getitem__(self, index): """inputs: Index, retrns: tuple(im, label)""" img = None with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f: img = Image.open(f) img = img.convert('RGB') if self.transform is not None: img = self.transform(img) label = self.classidx[index] return img, label def __len__(self): return len(self.imgs) def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) eval_stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': eval_stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) # return eval_stats def save_noisy_image(img, name): if img.size(2) == 32: img = img.view(img.size(0), 3, 32, 32) save_image(img, name) if img.size(2) == 64: img = img.view(img.size(0), 3, 64, 64) save_image(img, name) else: img = img.view(img.size(0), 3, 224, 224) save_image(img, name) ## Functions to save noisy images. # def gaussian_noise(test_loader): # print("Adding gaussian_noise") # for data in test_loader: # img, _ = data[0], data[1] # gaussian_img_05 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.05, clip=True)) # gaussian_img_2 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.2, clip=True)) # gaussian_img_4 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.4, clip=True)) # gaussian_img_6 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.6, clip=True)) # save_noisy_image(gaussian_img_05, r"noisy-images/gaussian_05.png") # save_noisy_image(gaussian_img_2, r"noisy-images/gaussian_2.png") # save_noisy_image(gaussian_img_4, r"noisy-images/gaussian_4.png") # save_noisy_image(gaussian_img_6, r"noisy-images/gaussian_6.png") # break # def salt_pepper_noise(test_loader): # print("Adding salt_pepper_noise") # for data in test_loader: # img, _ = data[0], data[1] # s_vs_p_5 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.5, clip=True)) # s_vs_p_6 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.6, clip=True)) # s_vs_p_7 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.7, clip=True)) # save_noisy_image(s_vs_p_5, r"noisy-images/s&p_5.png") # break # def speckle_noise(test_loader): # print("Adding speckle_noise") # for data in test_loader: # img, _ = data[0], data[1] # speckle_img_05 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.05, clip=True)) # speckle_img_2 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.2, clip=True)) # speckle_img_4 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.4, clip=True)) # speckle_img_6 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.6, clip=True)) # save_noisy_image(speckle_img_05, r"noisy-images/speckle_05.png") # save_noisy_image(speckle_img_2, r"noisy-images/speckle_2.png") # save_noisy_image(speckle_img_4, r"noisy-images/speckle_4.png") # save_noisy_image(speckle_img_6, r"noisy-images/speckle_6.png") # break def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders data_path = dp.get_data_path(cfg.TRAIN.DATASET) # Retrieve the data path for the dataset traindir = os.path.join(data_path, cfg.TRAIN.SPLIT) valdir = os.path.join(data_path, cfg.TEST.SPLIT, 'images') valgtfile = os.path.join(data_path, cfg.TEST.SPLIT, 'val_annotations.txt') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # create training dataset and loader train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=True) # create validation dataset test_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor(), normalize])) # create validation loader test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # Create meters test_meter = TestMeter(len(test_loader)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) noise_mode = ['gaussian', 'speckle', 's&p'] noise_std = [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6] # change the variance values as desired. model.eval() accuracies_gaussian = [] accuracies_saltpepper = [] accuracies_speckle = [] for mode in noise_mode: for level in noise_std: print("Adding noise={} at level={} to images".format(mode, level)) ctr = 0 correct = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader): if not 's&p' in mode: noisy_img = torch.tensor(random_noise(inputs, mode=mode, mean=0, var=level, clip=True)) else: noisy_img = torch.tensor(random_noise(inputs, mode=mode, salt_vs_pepper=0.5, clip=True)) noisy_img, labels = noisy_img.cuda(), labels.cuda(non_blocking=True) outputs = model(noisy_img.float()) _, predicted = torch.max(outputs.data, 1) ctr += 1 total += labels.size(0) correct += (predicted == labels).sum() if total > X: # replace X with the number of images to be generated for adversarial attacks. break acc = 100 * float(correct) / total print("acc =", round(acc, 2), "correct =", float(correct), "total =", total) if 'gaussian' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_gaussian.append(round(acc, 2)) print("Guassian Accuracies after append :", accuracies_gaussian) elif 'speckle' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_speckle.append(round(acc, 2)) print("Speckle Accuracies after append :", accuracies_speckle) elif 's&p' in mode: print('Robust Accuracy = {:.3f} for S&P noise'.format(acc)) accuracies_saltpepper.append(round(acc, 2)) print("Salt&Pepper Accuracies after append :", accuracies_saltpepper) break else: print("noise mode not supported") # gaussian_noise(test_loader) # salt_pepper_noise(test_loader) # speckle_noise(test_loader) # Change the number of variable as desired number of outputs. gaus_001, gaus_01, gaus_05, gaus_1, gaus_2, gaus_3, gaus_4, gaus_5, gaus_6 = (items for items in accuracies_gaussian) speck_001, speck_01, speck_05, speck_1, speck_2, speck_3, speck_4, speck_5, speck_6 = (items for items in accuracies_speckle) saltpepper = accuracies_saltpepper[0] # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_gaussian = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(gaus_001), str(gaus_01), str(gaus_05), str(gaus_1), str(gaus_2), str(gaus_3), str(gaus_4), str(gaus_5), str(gaus_6)]) result_speck = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(speck_001), str(speck_01), str(speck_05), str(speck_1), str(speck_2), str(speck_3), str(speck_4), str(speck_5), str(speck_6)]) result_sp = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(saltpepper)]) with open("{}/gaus_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Gaussian:{} ".format(accuracies_gaussian)) text_file.write(result_gaussian + '\n') with open("{}/saltpepper_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Salt & Pepper:{} ".format(accuracies_saltpepper)) text_file.write(result_sp + '\n') with open("{}/speckle_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Speckle:{} ".format(accuracies_speckle)) text_file.write(result_speck + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Inference seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/train_resnet18_on_tinyimagenet200.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Train a classification model.""" from __future__ import print_function import argparse import numpy as np import os import sys import torch import multiprocessing as mp import math import pdb import torch.utils.data import torchvision.datasets as datasets import torchvision.transforms as transforms from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter from PIL import Image import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.paths as dp import time from datetime import datetime from tensorboardX import SummaryWriter logger = lu.get_logger(__name__) print("Let's use GPU :", torch.cuda.current_device()) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() # TEST/VAL DATA_LOADER FOR TINY_IMAGENET200 def parseClasses(file): classes = [] filenames = [] with open(file) as f: lines = f.readlines() lines = [x.strip() for x in lines] for x in range(0, len(lines)): tokens = lines[x].split() classes.append(tokens[1]) filenames.append(tokens[0]) return filenames, classes def load_allimages(dir): images = [] if not os.path.isdir(dir): sys.exit(-1) for root, _, fnames in sorted(os.walk(dir)): for fname in sorted(fnames): #if datasets.folder.is_image_file(fname): if datasets.folder.has_file_allowed_extension(fname,['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']): path = os.path.join(root, fname) item = path images.append(item) return images class TinyImageNet(torch.utils.data.Dataset): """ TinyImageNet200 validation dataloader.""" def __init__(self, img_path, gt_path, class_to_idx=None, transform=None): self.img_path = img_path self.transform = transform self.gt_path = gt_path self.class_to_idx = class_to_idx self.classidx = [] self.imgs, self.classnames = parseClasses(gt_path) # logger.info('Number of images: {}'.format(len(self.imgs))) # logger.info('Number of classes: {}'.format(len(self.classnames))) for classname in self.classnames: self.classidx.append(self.class_to_idx[classname]) def __getitem__(self, index): """inputs: Index, retrns: tuple(im, label)""" img = None with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f: img = Image.open(f) img = img.convert('RGB') if self.transform is not None: img = self.transform(img) label = self.classidx[index] return img, label def __len__(self): return len(self.imgs) def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops def train_epoch( train_loader, model, loss_fun, optimizer, train_meter, cur_epoch, writer_train=None, params=0, flops=0, is_master=False): """Performs one epoch of training.""" # Shuffle the data loader.shuffle(train_loader, cur_epoch) # Update the learning rate lr = optim.get_epoch_lr(cur_epoch) optim.set_lr(optimizer, lr) # Enable training mode model.train() train_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(train_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Perform the forward pass preds = model(inputs) # Compute the loss loss = loss_fun(preds, labels) # Perform the backward pass optimizer.zero_grad() loss.backward() # Update the parameters optimizer.step() # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the stats across the GPUs if cfg.NUM_GPUS > 1: loss, top1_err, top5_err = du.scaled_all_reduce( [loss, top1_err, top5_err] ) # Copy the stats from GPU to CPU (sync point) loss, top1_err, top5_err = loss.item(), top1_err.item(), top5_err.item() train_meter.iter_toc() # Update and log stats train_meter.update_stats( top1_err, top5_err, loss, lr, inputs.size(0) * cfg.NUM_GPUS ) train_meter.log_iter_stats(cur_epoch, cur_iter) train_meter.iter_tic() # Log epoch stats train_meter.log_epoch_stats(cur_epoch, writer_train, params, flops, is_master=is_master) trg_stats = train_meter.get_epoch_stats(cur_epoch) train_meter.reset() return trg_stats @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) eval_stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': eval_stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) return eval_stats def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(): last_checkpoint = cu.get_checkpoint_last() checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 # Create data loaders # Retrieve the data path for the dataset data_path = dp.get_data_path(cfg.TRAIN.DATASET) traindir = os.path.join(data_path, cfg.TRAIN.SPLIT) valdir = os.path.join(data_path, cfg.TEST.SPLIT, 'images') valgtfile = os.path.join(data_path, cfg.TEST.SPLIT, 'val_annotations.txt') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # create training dataset and loader train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=True) # create validation dataset test_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor(), normalize])) # create validation loader test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # Create meters train_meter = TrainMeter(len(train_loader)) test_meter = TestMeter(len(test_loader)) # Create meters for fgsm test_meter_fgsm = TestMeter(len(test_loader_adv)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) # do eval at initialization initial_eval_stats = eval_epoch(test_loader, model, test_meter, -1, writer_eval, params, flops, is_master=is_master) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 last_epoch_eval_stats = eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) else: for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH): print('Epoch {} Started'.format(cur_epoch)) # Train for one epoch trg_stats = train_epoch( train_loader, model, loss_fun, optimizer, train_meter, cur_epoch, writer_train, is_master=is_master ) # Compute precise BN stats if cfg.BN.USE_PRECISE_STATS: nu.compute_precise_bn_stats(model, train_loader) # Save a checkpoint if cu.is_checkpoint_epoch(cur_epoch): checkpoint_file = cu.save_checkpoint(model, optimizer, cur_epoch) logger.info('Wrote checkpoint to: {}'.format(checkpoint_file)) # Evaluate the model if is_eval_epoch(cur_epoch): eval_stats = eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Train the model train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): if cfg.NUM_GPUS > 1: mpu.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=single_proc_train) else: single_proc_train() else: print('Seed {} exists, skip!'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/adversarial-inference-tinyimagenet200.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Train a classification model.""" from __future__ import print_function import argparse import numpy as np import os import sys import torch import multiprocessing as mp import math import pdb import torch.utils.data import torchvision.datasets as datasets import torchvision.transforms as transforms from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter from PIL import Image import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.paths as dp import time from datetime import datetime from tensorboardX import SummaryWriter print("Let's use GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() # TEST/VAL DATA_LOADER FOR TINY_IMAGENET200 def parseClasses(file): classes = [] filenames = [] with open(file) as f: lines = f.readlines() lines = [x.strip() for x in lines] for x in range(0, len(lines)): tokens = lines[x].split() classes.append(tokens[1]) filenames.append(tokens[0]) return filenames, classes def load_allimages(dir): images = [] if not os.path.isdir(dir): sys.exit(-1) for root, _, fnames in sorted(os.walk(dir)): for fname in sorted(fnames): # if datasets.folder.is_image_file(fname): if datasets.folder.has_file_allowed_extension(fname,['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']): path = os.path.join(root, fname) item = path images.append(item) return images class TinyImageNet(torch.utils.data.Dataset): """ TinyImageNet200 validation dataloader.""" def __init__(self, img_path, gt_path, class_to_idx=None, transform=None): self.img_path = img_path self.transform = transform self.gt_path = gt_path self.class_to_idx = class_to_idx self.classidx = [] self.imgs, self.classnames = parseClasses(gt_path) for classname in self.classnames: self.classidx.append(self.class_to_idx[classname]) def __getitem__(self, index): """inputs: Index, retrns: tuple(im, label)""" img = None with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f: img = Image.open(f) img = img.convert('RGB') if self.transform is not None: img = self.transform(img) label = self.classidx[index] return img, label def __len__(self): return len(self.imgs) def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) eval_stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': eval_stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) # return eval_stats class Normalize(torch.nn.Module): def __init__(self, mean, std): super(Normalize, self).__init__() self.register_buffer('mean', torch.Tensor(mean)) self.register_buffer('std', torch.Tensor(std)) def forward(self, input): # Broadcasting mean = self.mean.reshape(1,3,1,1) std = self.std.reshape(1,3,1,1) norm_img = (input - mean) / std return norm_img # Helper class for printing model layers class PrintLayer(torch.nn.Module): def __init__(self): super(PrintLayer, self).__init__() def forward(self, x): # Do your print / debug stuff here print(x) return x def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # for name, param in model.named_parameters(): # print(name, param.shape) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders data_path = dp.get_data_path(cfg.TRAIN.DATASET) # Retrieve the data path for the dataset traindir = os.path.join(data_path, cfg.TRAIN.SPLIT) valdir = os.path.join(data_path, cfg.TEST.SPLIT, 'images') valgtfile = os.path.join(data_path, cfg.TEST.SPLIT, 'val_annotations.txt') # normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # create training dataset and loader train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=True) # create validation dataset test_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor(), normalize])) # create validation loader test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # create adversarial dataset adv_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor()])) # create adversarial loader test_loader_adv = torch.utils.data.DataLoader( adv_dataset, batch_size=1, shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # Create meters test_meter = TestMeter(len(test_loader)) test_meter_adv = TestMeter(len(test_loader_adv)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) # when epsilon=0 --> PGD, epsilon=1 --> CW, otherwise FGSM-->replace eps1, eps2, ... with required epsilon of attack versions epsilons = [0, eps1, eps2, ... epsN, 1] # Per-channel mean and SD values in BGR order for TinyImageNet dataset tinyimagenet_MEAN = [0.485, 0.456, 0.406] tinyimagenet_SD = [0.229, 0.224, 0.225] accuracies = [] # add normalization layer to the model norm_layer = Normalize(mean=tinyimagenet_MEAN, std=tinyimagenet_SD) net = torch.nn.Sequential(norm_layer, model).cuda() net = net.eval() for epsilon in epsilons: if epsilon == 0: print("Running PGD Attack") atk = torchattacks.PGD(net, eps=1/510, alpha=2/225, steps=7) # for relevant dataset, use parameters from torchattacks official notebook elif epsilon == 1: print("Running CW Attack") atk = torchattacks.CW(net, c=0.1, kappa=0, steps=100, lr=0.01) # choose suitable values for c, kappa, steps, and lr. else: print("Running FGSM Attacks on epsilon :", epsilon) atk = torchattacks.FGSM(net, eps=epsilon) ctr = 0 correct = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader_adv): inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) adv_images = atk(inputs, labels) outputs = net(adv_images) _, predicted = torch.max(outputs.data, 1) ctr += 1 total += 1 correct += (predicted == labels).sum() if ctr > X: # replace X with the number of images to be generated for adversarial attacks. print(ctr, " images done for epsilon:", epsilon) break acc = 100 * float(correct) / total print("acc =", round(acc, 2), "correct =", float(correct), "total =", total) accuracies.append(round(acc, 2)) print('Attack Accuracy = {:.3f} with epsilon = {:.4f}'.format(acc, epsilon)) print("accuracies after apend :", accuracies) # save items inside accuracies list to separate float objects, update the # of variables according to requirement. accPGD, accFGSM1, accFGSM2, accFGSM3, accFGSM4, accFGSM5, accFGSM6, accFGSM7, accCW = (items for items in accuracies) # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_info = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(accPGD), str(accFGSM1), str(accFGSM2), str(accFGSM3), str(accFGSM4), str(accFGSM5), str(accFGSM6), str(accFGSM7), str(accCW)]) with open("{}/stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies {} ".format(accuracies)) text_file.write(result_info + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Inference seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/adversarial-inference.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Train a classification model.""" import argparse import pickle import numpy as np import os import sys import torch import math import torchvision import torchattacks from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.transforms as transforms from datetime import datetime from tensorboardX import SummaryWriter import foolbox as fb import art import art.attacks.evasion as evasion from art.estimators.classification import PyTorchClassifier print("Using GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() # val_input_imgs, for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) class Normalize(torch.nn.Module): def __init__(self, mean, std): super(Normalize, self).__init__() self.register_buffer('mean', torch.Tensor(mean)) self.register_buffer('std', torch.Tensor(std)) def forward(self, input): # Broadcasting mean = self.mean.reshape(1,3,1,1) std = self.std.reshape(1,3,1,1) norm_img = (input - mean) / std return norm_img # Helper class for printing model layers class PrintLayer(torch.nn.Module): def __init__(self): super(PrintLayer, self).__init__() def forward(self, x): # Do your print / debug stuff here print(x) return x def train_model(writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': if cfg.MODEL.DEPTH == 20: stats_baseline = 40813184 # ResNet20 elif cfg.MODEL.DEPTH == 26: stats_baseline = 56140000 # ResNet26 elif cfg.MODEL.DEPTH == 34: stats_baseline = 71480000 # ResNet34 elif cfg.MODEL.DEPTH == 38: stats_baseline = 86819000 # ResNet38 elif cfg.MODEL.DEPTH == 50: stats_baseline = 130000000 # ResNet50 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 elif cfg.TRAIN.DATASET == 'imagenet': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) if cfg.IS_INFERENCE and cfg.IS_DDP: model = torch.nn.parallel.DataParallel(model) # for name, param in model.named_parameters(): # print(name, param.shape) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders test_loader = loader.construct_test_loader() test_loader_adv = loader.construct_test_loader_adv() # Create meters test_meter = TestMeter(len(test_loader)) test_meter_adv = TestMeter(len(test_loader_adv)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) # when epsilon=0, 1 --> PGD, epsilon=2, 3 --> CW, otherwise FGSM-->replace eps1, eps2, ... with required epsilon of attack versions epsilons = [0, 1, eps1, eps2, ... epsN, 2, 3] # Per-channel mean and SD values in BGR order for ImageNet dataset cifar10_MEAN = [0.491, 0.482, 0.4465] cifar10_SD = [0.247, 0.243, 0.262] cifar100_MEAN = [0.507, 0.487, 0.441] cifar100_SD = [0.267, 0.256, 0.276] imagenet_MEAN = [0.406, 0.456, 0.485] imagenet_SD = [0.225, 0.224, 0.229] accuracies = [] # replace the MEAN and SD variable in the following line for the relevant dataset. norm_layer = Normalize(mean=cifar10_MEAN, std=cifar10_SD) net = torch.nn.Sequential(norm_layer, model).cuda() # net = torch.nn.Sequential(norm_layer, PrintLayer(), model).cuda() net = net.eval() print("Adversarial Loader Batch Size =", test_loader_adv.batch_size) for epsilon in epsilons: if epsilon == 0: print("Running PGD Attack") atk_ta = torchattacks.PGD(net, eps=6/255, alpha=2/255, steps=7) # for relevant dataset, use parameters from torchattacks official notebook elif epsilon == 1: print("Running PGD Attack") atk_ta = torchattacks.PGD(net, eps=9/255, alpha=2/255, steps=7) # for relevant dataset, use parameters from torchattacks official notebook elif epsilon == 2: print("Running Torchattacks.CW") atk_ta = torchattacks.CW(net, c=0.15, kappa=0, steps=100, lr=0.01) # replace the values of c and steps according to hyperparameters reported in the paper. elif epsilon == 3: print("Running Torchattacks.CW") atk_ta = torchattacks.CW(net, c=0.25, kappa=0, steps=100, lr=0.01) # replace the values of c and steps according to hyperparameters reported in the paper. # For Foolbox or ART attacks, uncomment the following lines. # print("-> FoolBox.CW") # fmodel = fb.PyTorchModel(net, bounds=(0, 1)) # atk_fb = fb.attacks.L2CarliniWagnerAttack(binary_search_steps=1, initial_const=0.05, # confidence=0, steps=100, stepsize=0.01) # print("-> Adversarial Robustness Toolbox.CW") # classifier = PyTorchClassifier(model=net, clip_values=(0, 1), # loss=loss_fun, # optimizer=optimizer, # input_shape=(3, 32, 32), nb_classes=10) # atk_art = evasion.CarliniL2Method(batch_size=1, classifier=classifier, # binary_search_steps=1, initial_const=0.05, # confidence=0, max_iter=100, # learning_rate=0.01) else: print("Running FGSM Attacks on epsilon :", epsilon) atk_ta = torchattacks.FGSM(net, eps=epsilon) ctr = 0 correct_ta = 0 # correct_fb = 0 # correct_art = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader_adv): inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) inputs = inputs.float().div(255) adv_images_ta = atk_ta(inputs, labels) # _, adv_images_fb, _ = atk_fb(fmodel, inputs, labels, epsilons=1) # adv_images_art = torch.tensor(atk_art.generate(inputsnp, labelsnp)).cuda() adv_inputs_ta = adv_images_ta.float() # adv_inputs_fb = adv_images_fb.float() # adv_inputs_art = adv_images_art.float() outputs_ta = net(adv_inputs_ta) # outputs_fb = net(adv_inputs_fb) # outputs_art = net(adv_inputs_art) _, predicted_ta = torch.max(outputs_ta.data, 1) # _, predicted_fb = torch.max(outputs_fb.data, 1) # _, predicted_art = torch.max(outputs_art.data, 1) ctr += 1 total += 1 correct_ta += (predicted_ta == labels).sum() # correct_fb += (predicted_fb == labels).sum() # correct_art += (predicted_art == labels).sum() if ctr > X: # replace X with the number of images to be generated for adversarial attacks. print(ctr, " images done for epsilon:", epsilon) break acc_ta = 100 * float(correct_ta) / total # acc_fb = 100 * float(correct_fb) / total # acc_art = 100 * float(correct_art) / total print("ta acc =", round(acc_ta, 2), ", ta correct =", float(correct_ta), ", total =", total) # print("fb acc =", round(acc_fb, 2), ", fb correct =", float(correct_fb), ", total =", total) # print("art acc =", round(acc_art, 2), ", art correct =", float(correct_art), ", total =", total) accuracies.append(round(acc_ta, 2)) print('Attack Accuracy = {:.3f} with epsilon = {:.2f}'.format(acc_ta, epsilon)) print("accuracies after apend :", accuracies) # save items inside accuracies list to separate float objects, update the # of variables according to requirement. accPGD_6by255, accPGD_9by255, accFGSM1, accFGSM2, accFGSM3, accFGSM4, accFGSM5, accCW_15, accCW_25 = (items for items in accuracies) # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_info = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(accPGD_6by255), str(accPGD_9by255), str(accFGSM1), str(accFGSM2), str(accFGSM3), str(accFGSM4), str(accFGSM5), str(accCW_15), str(accCW_25)]) # with open("{}/stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies {} ".format(accuracies)) text_file.write(result_info + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None # If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_eval, is_master=du.is_master_proc()) if writer_eval is not None: # writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Trained seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/corruptions-inference.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Train a classification model.""" import argparse import pickle import numpy as np import os import sys import torch import math import torchvision import torchattacks from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.transforms as transforms from datetime import datetime from tensorboardX import SummaryWriter from torchvision.utils import save_image from skimage.util import random_noise print("Using GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() # val_input_imgs, for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) def save_noisy_image(img, name): if img.size(2) == 32: img = img.view(img.size(0), 3, 32, 32) save_image(img, name) else: img = img.view(img.size(0), 3, 224, 224) save_image(img, name) ## Functions to save noisy images. # def gaussian_noise(test_loader): # print("Adding gaussian_noise") # for data in test_loader: # img, _ = data[0], data[1] # gaussian_img_05 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.05, clip=True)) # gaussian_img_2 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.2, clip=True)) # gaussian_img_4 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.4, clip=True)) # gaussian_img_6 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.6, clip=True)) # save_noisy_image(gaussian_img_05, r"noisy-images/gaussian_05.png") # save_noisy_image(gaussian_img_2, r"noisy-images/gaussian_2.png") # save_noisy_image(gaussian_img_4, r"noisy-images/gaussian_4.png") # save_noisy_image(gaussian_img_6, r"noisy-images/gaussian_6.png") # break # def salt_pepper_noise(test_loader): # print("Adding salt_pepper_noise") # for data in test_loader: # img, _ = data[0], data[1] # s_vs_p_5 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.5, clip=True)) # s_vs_p_6 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.6, clip=True)) # s_vs_p_7 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.7, clip=True)) # save_noisy_image(s_vs_p_5, r"noisy-images/s&p_5.png") # save_noisy_image(s_vs_p_6, r"noisy-images/s&p_6.png") # save_noisy_image(s_vs_p_7, r"noisy-images/s&p_7.png") # break # def speckle_noise(test_loader): # print("Adding speckle_noise") # for data in test_loader: # img, _ = data[0], data[1] # speckle_img_05 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.05, clip=True)) # speckle_img_2 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.2, clip=True)) # speckle_img_4 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.4, clip=True)) # speckle_img_6 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.6, clip=True)) # save_noisy_image(speckle_img_05, r"noisy-images/speckle_05.png") # save_noisy_image(speckle_img_2, r"noisy-images/speckle_2.png") # save_noisy_image(speckle_img_4, r"noisy-images/speckle_4.png") # save_noisy_image(speckle_img_6, r"noisy-images/speckle_6.png") # break def train_model(writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 if cfg.MODEL.DEPTH == 20: stats_baseline = 40813184 # ResNet20 elif cfg.MODEL.DEPTH == 26: stats_baseline = 56140000 # ResNet26 elif cfg.MODEL.DEPTH == 34: stats_baseline = 71480000 # ResNet34 elif cfg.MODEL.DEPTH == 38: stats_baseline = 86819000 # ResNet38 elif cfg.MODEL.DEPTH == 50: stats_baseline = 130000000 # ResNet50 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 elif cfg.TRAIN.DATASET == 'imagenet': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) if cfg.IS_INFERENCE and cfg.IS_DDP: model = torch.nn.parallel.DataParallel(model) # for name, param in model.named_parameters(): # print(name, param.shape) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders test_loader = loader.construct_test_loader() # Create meters test_meter = TestMeter(len(test_loader)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) noise_mode = ['gaussian', 'speckle', 's&p'] noise_var = [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6] # change the variance values as desired. model.eval() accuracies_gaussian = [] accuracies_saltpepper = [] accuracies_speckle = [] for mode in noise_mode: for level in noise_var: print("Adding noise={} at level={} to images".format(mode, level)) ctr = 0 correct = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader): if not 's&p' in mode: noisy_img = torch.tensor(random_noise(inputs, mode=mode, mean=0, var=level, clip=True)) else: noisy_img = torch.tensor(random_noise(inputs, mode=mode, salt_vs_pepper=0.5, clip=True)) noisy_img, labels = noisy_img.cuda(), labels.cuda(non_blocking=True) outputs = model(noisy_img.float()) _, predicted = torch.max(outputs.data, 1) ctr += 1 total += labels.size(0) correct += (predicted == labels).sum() if total > X: # replace X with the number of images to be generated for adversarial attacks. break acc = 100 * float(correct) / total print("acc =", round(acc, 2), "correct =", float(correct), "total =", total) if 'gaussian' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_gaussian.append(round(acc, 2)) print("Guassian Accuracies after append :", accuracies_gaussian) elif 'speckle' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_speckle.append(round(acc, 2)) print("Speckle Accuracies after append :", accuracies_speckle) elif 's&p' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_saltpepper.append(round(acc, 2)) print("Salt&Pepper Accuracies after append :", accuracies_saltpepper) break else: print("noise mode not supported") # gaussian_noise(test_loader) # salt_pepper_noise(test_loader) # speckle_noise(test_loader) # Change the number of variable as desired number of outputs. gaus_001, gaus_01, gaus_05, gaus_1, gaus_2, gaus_3, gaus_4, gaus_5, gaus_6 = (items for items in accuracies_gaussian) speck_001, speck_01, speck_05, speck_1, speck_2, speck_3, speck_4, speck_5, speck_6 = (items for items in accuracies_speckle) saltpepper = accuracies_saltpepper[0] # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_gaussian = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(gaus_001), str(gaus_01), str(gaus_05), str(gaus_1), str(gaus_2), str(gaus_3), str(gaus_4), str(gaus_5), str(gaus_6)]) result_speck = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(speck_001), str(speck_01), str(speck_05), str(speck_1), str(speck_2), str(speck_3), str(speck_4), str(speck_5), str(speck_6)]) result_sp = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(saltpepper)]) with open("{}/gaus_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Gaussian:{} ".format(accuracies_gaussian)) text_file.write(result_gaussian + '\n') with open("{}/saltpepper_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Salt & Pepper:{} ".format(accuracies_saltpepper)) text_file.write(result_sp + '\n') with open("{}/speckle_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Speckle:{} ".format(accuracies_speckle)) text_file.write(result_speck + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None # If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_eval, is_master=du.is_master_proc()) if writer_eval is not None: # writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Inference seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/train_net.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Train a classification model.""" import argparse import pickle import numpy as np import os import sys import torch import math # import torchvision # import time from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu from datetime import datetime from tensorboardX import SummaryWriter # import wandb logger = lu.get_logger(__name__) print("Let's use GPU :", torch.cuda.current_device()) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops def train_epoch( train_loader, model, loss_fun, optimizer, train_meter, cur_epoch, writer_train=None, params=0, flops=0, is_master=False): """Performs one epoch of training.""" # Shuffle the data loader.shuffle(train_loader, cur_epoch) # Update the learning rate lr = optim.get_epoch_lr(cur_epoch) optim.set_lr(optimizer, lr) # Enable training mode model.train() train_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(train_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Perform the forward pass preds = model(inputs) # Compute the loss loss = loss_fun(preds, labels) # Perform the backward pass optimizer.zero_grad() loss.backward() # Update the parameters optimizer.step() # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the stats across the GPUs if cfg.NUM_GPUS > 1: loss, top1_err, top5_err = du.scaled_all_reduce( [loss, top1_err, top5_err] ) # Copy the stats from GPU to CPU (sync point) loss, top1_err, top5_err = loss.item(), top1_err.item(), top5_err.item() train_meter.iter_toc() # Update and log stats train_meter.update_stats( top1_err, top5_err, loss, lr, inputs.size(0) * cfg.NUM_GPUS ) train_meter.log_iter_stats(cur_epoch, cur_iter) train_meter.iter_tic() # Log epoch stats train_meter.log_epoch_stats(cur_epoch, writer_train, params, flops, is_master=is_master) trg_stats = train_meter.get_epoch_stats(cur_epoch) train_meter.reset() return trg_stats @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) return stats def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': stats_baseline = 40813184 # ResNet20 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': if cfg.MODEL.DEPTH == 20: stats_baseline = 40813184 # ResNet20 elif cfg.MODEL.DEPTH == 26: stats_baseline = 56140000 # ResNet26 elif cfg.MODEL.DEPTH == 34: stats_baseline = 71480000 # ResNet34 elif cfg.MODEL.DEPTH == 38: stats_baseline = 86819000 # ResNet38 elif cfg.MODEL.DEPTH == 50: stats_baseline = 130000000 # ResNet50 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'imagenet': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # wandb.watch(model) # Load a checkpoint if applicable start_epoch = 0 if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(): last_checkpoint = cu.get_checkpoint_last1() checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 # Create data loaders train_loader = loader.construct_train_loader() test_loader = loader.construct_test_loader() # Create meters train_meter = TrainMeter(len(train_loader)) test_meter = TestMeter(len(test_loader)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) # do eval at initialization initial_eval_stats = eval_epoch(test_loader, model, test_meter, -1, writer_eval, params, flops, is_master=is_master) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 last_epoch_eval_stats = eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) else: for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH): print('Epoch {} Started'.format(cur_epoch)) # Train for one epoch trg_stats = train_epoch( train_loader, model, loss_fun, optimizer, train_meter, cur_epoch, writer_train, is_master=is_master ) # Compute precise BN stats if cfg.BN.USE_PRECISE_STATS: nu.compute_precise_bn_stats(model, train_loader) # Save a checkpoint if cu.is_checkpoint_epoch(cur_epoch): checkpoint_file = cu.save_checkpoint(model, optimizer, cur_epoch) logger.info('Wrote checkpoint to: {}'.format(checkpoint_file)) # Evaluate the model if is_eval_epoch(cur_epoch): eval_stats = eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) # wandb.log({'Epoch': cur_epoch, 'Train top1_err': trg_stats['top1_err'], 'Test top1_err': eval_stats['top1_err']}) def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Train the model train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # wandb.init(project = 'Rob_G2NN', entity='rowanai-graph-robustness') # Parse cmd line args args = parse_args() # wandb.config.update(args) # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): if cfg.NUM_GPUS > 1: mpu.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=single_proc_train) else: single_proc_train() else: print('Seed {} exists, skip!'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/pycls/config.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Configuration file.""" import os from yacs.config import CfgNode as CN # Global config object _C = CN() # Example usage: # from core.config import cfg cfg = _C # ---------------------------------------------------------------------------- # # Model options # ---------------------------------------------------------------------------- # _C.MODEL = CN() # Model type to use _C.MODEL.TYPE = '' # Number of weight layers _C.MODEL.DEPTH = 0 # Number of classes _C.MODEL.NUM_CLASSES = 10 # Loss function (see pycls/models/loss.py for options) _C.MODEL.LOSS_FUN = 'cross_entropy' # Num layers, excluding the stem and head layers. Total layers used should +2 _C.MODEL.LAYERS = 3 # ---------------------------------------------------------------------------- # # ResNet options # ---------------------------------------------------------------------------- # _C.RESNET = CN() # Transformation function (see pycls/models/resnet.py for options) _C.RESNET.TRANS_FUN = 'basic_transform' # Number of groups to use (1 -> ResNet; > 1 -> ResNeXt) _C.RESNET.NUM_GROUPS = 1 # Width of each group (64 -> ResNet; 4 -> ResNeXt) _C.RESNET.WIDTH_PER_GROUP = 64 # Apply stride to 1x1 conv (True -> MSRA; False -> fb.torch) _C.RESNET.STRIDE_1X1 = False # Whether append 1x1 resblock _C.RESNET.APPEND1x1 = 0 # For group conv only _C.RESNET.GROUP_SIZE = 2 # ---------------------------------------------------------------------------- # # EfficientNet options # ---------------------------------------------------------------------------- # _C.EFFICIENT_NET = CN() # Stem width _C.EFFICIENT_NET.STEM_W = 32 # Depth for each stage (number of blocks in the stage) _C.EFFICIENT_NET.DEPTHS = [] # Width for each stage (width of each block in the stage) _C.EFFICIENT_NET.WIDTHS = [] # Expansion ratios for MBConv blocks in each stage _C.EFFICIENT_NET.EXP_RATIOS = [] # Squeeze-and-Excitation (SE) operation _C.EFFICIENT_NET.SE_ENABLED = True # Squeeze-and-Excitation (SE) ratio _C.EFFICIENT_NET.SE_RATIO = 0.25 # Linear projection _C.EFFICIENT_NET.LIN_PROJ = True # Strides for each stage (applies to the first block of each stage) _C.EFFICIENT_NET.STRIDES = [] # Kernel sizes for each stage _C.EFFICIENT_NET.KERNELS = [] # Head type ('conv_head' or 'simple_head') _C.EFFICIENT_NET.HEAD_TYPE = 'conv_head' # Head width (applies to 'conv_head') _C.EFFICIENT_NET.HEAD_W = 1280 # Ativation function _C.EFFICIENT_NET.ACT_FUN = 'swish' # Drop connect ratio _C.EFFICIENT_NET.DC_RATIO = 0.0 # Drop connect implementation _C.EFFICIENT_NET.DC_IMP = 'tf' # Dropout ratio _C.EFFICIENT_NET.DROPOUT_RATIO = 0.0 # ---------------------------------------------------------------------------- # # Relational graph options # ---------------------------------------------------------------------------- # _C.RGRAPH = CN() # dim for first layer. NOTE: this is fixed when matching FLOPs _C.RGRAPH.DIM_FIRST = 16 # dim for each stage _C.RGRAPH.DIM_LIST = [] # wide stem module _C.RGRAPH.STEM_MODE = 'default' # How to message exchange: dense, hier (deprecated) _C.RGRAPH.TALK_MODE = 'dense' # Num of nodes _C.RGRAPH.GROUP_NUM = 32 # Size of nodes in Stage 1 _C.RGRAPH.GROUP_SIZE = 1 # The type of message passing used _C.RGRAPH.MESSAGE_TYPE = 'ws' # Whether use directed graph _C.RGRAPH.DIRECTED = False # Graph sparsity _C.RGRAPH.SPARSITY = 0.5 # Graph Randomness _C.RGRAPH.P = 0.0 # Graph seed _C.RGRAPH.SEED_GRAPH = 1 # training seed used _C.RGRAPH.SEED_TRAIN = 1 # training seed, start, end _C.RGRAPH.SEED_TRAIN_START = 1 _C.RGRAPH.SEED_TRAIN_END = 2 # Keep graph across the network _C.RGRAPH.KEEP_GRAPH = True # Append additaion 1x1 layers for additional talks _C.RGRAPH.ADD_1x1 = 0 # Match upper computational bound _C.RGRAPH.UPPER = True # Auto match computational budget _C.RGRAPH.AUTO_MATCH = True # AGG func. Only sum is supported in current mask-based implementation _C.RGRAPH.AGG_FUNC = 'sum' # Save weight matrices as graphs. Warning: the saved matrices can be huge _C.RGRAPH.SAVE_GRAPH = False # ---------------------------------------------------------------------------- # # Batch norm options # ---------------------------------------------------------------------------- # _C.BN = CN() # BN epsilon _C.BN.EPS = 1e-5 # BN momentum (BN momentum in PyTorch = 1 - BN momentum in Caffe2) _C.BN.MOM = 0.1 # Precise BN stats _C.BN.USE_PRECISE_STATS = True _C.BN.NUM_SAMPLES_PRECISE = 1024 # Initialize the gamma of the final BN of each block to zero _C.BN.ZERO_INIT_FINAL_GAMMA = False # ---------------------------------------------------------------------------- # # Optimizer options # ---------------------------------------------------------------------------- # _C.OPTIM = CN() # Base learning rate _C.OPTIM.BASE_LR = 0.1 # Learning rate policy select from {'cos', 'exp', 'steps'} _C.OPTIM.LR_POLICY = 'cos' # Exponential decay factor _C.OPTIM.GAMMA = 0.1 # Step size for 'exp' and 'cos' policies (in epochs) _C.OPTIM.STEP_SIZE = 1 # Steps for 'steps' policy (in epochs) _C.OPTIM.STEPS = [] # Learning rate multiplier for 'steps' policy _C.OPTIM.LR_MULT = 0.1 # Maximal number of epochs _C.OPTIM.MAX_EPOCH = 200 # Momentum _C.OPTIM.MOMENTUM = 0.9 # Momentum dampening _C.OPTIM.DAMPENING = 0.0 # Nesterov momentum _C.OPTIM.NESTEROV = True # L2 regularization _C.OPTIM.WEIGHT_DECAY = 5e-4 # Start the warm up from OPTIM.BASE_LR * OPTIM.WARMUP_FACTOR _C.OPTIM.WARMUP_FACTOR = 0.1 # Gradually warm up the OPTIM.BASE_LR over this number of epochs _C.OPTIM.WARMUP_EPOCHS = 0 # ---------------------------------------------------------------------------- # # Training options # ---------------------------------------------------------------------------- # _C.TRAIN = CN() # Dataset and split _C.TRAIN.DATASET = '' _C.TRAIN.SPLIT = 'train' # Total mini-batch size _C.TRAIN.BATCH_SIZE = 128 # Evaluate model on test data every eval period epochs _C.TRAIN.EVAL_PERIOD = 1 # Save model checkpoint every checkpoint period epochs _C.TRAIN.CHECKPOINT_PERIOD = 50 # Resume training from the latest checkpoint in the output directory _C.TRAIN.AUTO_RESUME = True # Checkpoint to start training from (if no automatic checkpoint saved) _C.TRAIN.START_CHECKPOINT = '' _C.TRAIN.AUTO_MATCH = False # ---------------------------------------------------------------------------- # # Testing options # ---------------------------------------------------------------------------- # _C.TEST = CN() # Dataset and split _C.TEST.DATASET = '' _C.TEST.SPLIT = 'val' # Total mini-batch size _C.TEST.BATCH_SIZE = 200 # ---------------------------------------------------------------------------- # # Common train/test data loader options # ---------------------------------------------------------------------------- # _C.DATA_LOADER = CN() # Number of data loader workers per training process _C.DATA_LOADER.NUM_WORKERS = 4 # Load data to pinned host memory _C.DATA_LOADER.PIN_MEMORY = True # ---------------------------------------------------------------------------- # # Memory options # ---------------------------------------------------------------------------- # _C.MEM = CN() # Perform ReLU inplace _C.MEM.RELU_INPLACE = True # ---------------------------------------------------------------------------- # # CUDNN options # ---------------------------------------------------------------------------- # _C.CUDNN = CN() # Perform benchmarking to select the fastest CUDNN algorithms to use # Note that this may increase the memory usage and will likely not result # in overall speedups when variable size inputs are used (e.g. COCO training) _C.CUDNN.BENCHMARK = False # ---------------------------------------------------------------------------- # # Misc options # ---------------------------------------------------------------------------- # # Number of GPUs to use (applies to both training and testing) _C.NUM_GPUS = 1 # Output directory _C.OUT_DIR = '/tmp' # Checkpoint directory for inference _C.CHECKPT_DIR = '/tmp' _C.IS_INFERENCE = False _C.IS_DDP = False # Config destination (in OUT_DIR) _C.CFG_DEST = 'config.yaml' # Note that non-determinism may still be present due to non-deterministic # operator implementations in GPU operator libraries _C.RNG_SEED = 1 # Log destination ('stdout' or 'file') _C.LOG_DEST = 'file' # Log period in iters _C.LOG_PERIOD = 10 # Distributed backend _C.DIST_BACKEND = 'nccl' # Hostname and port for initializing multi-process groups _C.HOST = 'localhost' _C.PORT = 12002 # Computing flops by online foward pass _C.ONLINE_FLOPS = False # Whether use Tensorboard _C.TENSORBOARD = False def assert_cfg(): """Checks config values invariants.""" assert not _C.OPTIM.STEPS or _C.OPTIM.STEPS[0] == 0, \ 'The first lr step must start at 0' assert _C.TRAIN.SPLIT in ['train', 'val', 'test'], \ 'Train split \'{}\' not supported'.format(_C.TRAIN.SPLIT) assert _C.TRAIN.BATCH_SIZE % _C.NUM_GPUS == 0, \ 'Train mini-batch size should be a multiple of NUM_GPUS.' assert _C.TEST.SPLIT in ['train', 'val', 'test'], \ 'Test split \'{}\' not supported'.format(_C.TEST.SPLIT) assert _C.TEST.BATCH_SIZE % _C.NUM_GPUS == 0, \ 'Test mini-batch size should be a multiple of NUM_GPUS.' # assert not _C.BN.USE_PRECISE_STATS or _C.NUM_GPUS == 1, \ # 'Precise BN stats computation not verified for > 1 GPU' assert _C.LOG_DEST in ['stdout', 'file'], \ 'Log destination \'{}\' not supported'.format(_C.LOG_DEST) def dump_cfg(): """Dumps the config to the output directory.""" cfg_file = os.path.join(_C.OUT_DIR, _C.CFG_DEST) with open(cfg_file, 'w') as f: _C.dump(stream=f) def load_cfg(out_dir, cfg_dest='config.yaml'): """Loads config from specified output directory.""" cfg_file = os.path.join(out_dir, cfg_dest) _C.merge_from_file(cfg_file)

py

RobDanns

RobDanns-main/deep_learning/pycls/models/losses.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Loss functions.""" import torch.nn as nn from pycls.config import cfg # Supported losses _LOSS_FUNS = { 'cross_entropy': nn.CrossEntropyLoss, } def get_loss_fun(): """Retrieves the loss function.""" assert cfg.MODEL.LOSS_FUN in _LOSS_FUNS.keys(), \ 'Loss function \'{}\' not supported'.format(cfg.TRAIN.LOSS) return _LOSS_FUNS[cfg.MODEL.LOSS_FUN]().cuda()

py

RobDanns

RobDanns-main/deep_learning/pycls/models/efficientnet.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """EfficientNet models.""" import math import torch import torch.nn as nn from pycls.config import cfg import pycls.utils.net as nu import pycls.utils.logging as logging from .relation_graph import * logger = logging.get_logger(__name__) def get_conv(name): """Retrieves the transformation function by name.""" trans_funs = { 'mbconv_transform': MBConv, 'mbtalkconv_transform': MBTalkConv, } assert name in trans_funs.keys(), \ 'Transformation function \'{}\' not supported'.format(name) return trans_funs[name] def drop_connect_tf(x, drop_ratio): """Drop connect (tensorflow port).""" keep_ratio = 1.0 - drop_ratio rt = torch.rand([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device) rt.add_(keep_ratio) bt = torch.floor(rt) x.div_(keep_ratio) x.mul_(bt) return x def drop_connect_pt(x, drop_ratio): """Drop connect (pytorch version).""" keep_ratio = 1.0 - drop_ratio mask = torch.empty([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device) mask.bernoulli_(keep_ratio) x.div_(keep_ratio) x.mul_(mask) return x def get_act_fun(act_type): """Retrieves the activations function.""" act_funs = { 'swish': Swish, 'relu': nn.ReLU, } assert act_type in act_funs.keys(), \ 'Activation function \'{}\' not supported'.format(act_type) return act_funs[act_type] class SimpleHead(nn.Module): """Simple head.""" def __init__(self, dim_in, num_classes): super(SimpleHead, self).__init__() # AvgPool self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # Dropout if cfg.EFFICIENT_NET.DROPOUT_RATIO > 0.0: self.dropout = nn.Dropout(p=cfg.EFFICIENT_NET.DROPOUT_RATIO) # FC self.fc = nn.Linear(dim_in, num_classes, bias=True) def forward(self, x): x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.dropout(x) if hasattr(self, 'dropout') else x x = self.fc(x) return x class ConvHead(nn.Module): """EfficientNet conv head.""" def __init__(self, in_w, out_w, num_classes, act_fun): super(ConvHead, self).__init__() self._construct_class(in_w, out_w, num_classes, act_fun) def _construct_class(self, in_w, out_w, num_classes, act_fun): # 1x1, BN, Swish self.conv = nn.Conv2d( in_w, out_w, kernel_size=1, stride=1, padding=0, bias=False ) self.conv_bn = nn.BatchNorm2d( out_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.conv_swish = act_fun() # AvgPool self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # Dropout if cfg.EFFICIENT_NET.DROPOUT_RATIO > 0.0: self.dropout = nn.Dropout(p=cfg.EFFICIENT_NET.DROPOUT_RATIO) # FC self.fc = nn.Linear(out_w, num_classes, bias=True) def forward(self, x): # 1x1, BN, Swish x = self.conv_swish(self.conv_bn(self.conv(x))) # AvgPool x = self.avg_pool(x) x = x.view(x.size(0), -1) # Dropout x = self.dropout(x) if hasattr(self, 'dropout') else x # FC x = self.fc(x) return x class LinearHead(nn.Module): """EfficientNet linear head.""" def __init__(self, in_w, out_w, num_classes, act_fun): super(LinearHead, self).__init__() self._construct_class(in_w, out_w, num_classes, act_fun) def _construct_class(self, in_w, out_w, num_classes, act_fun): # AvgPool self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # FC0 self.fc0 = nn.Linear(in_w, out_w, bias=False) self.fc0_bn = nn.BatchNorm1d( out_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.fc0_swish = act_fun() # FC self.fc = nn.Linear(out_w, num_classes, bias=True) def forward(self, x): # AvgPool x = self.avg_pool(x) x = x.view(x.size(0), -1) # Linear, BN, Swish x = self.fc0_swish(self.fc0_bn(self.fc0(x))) # FC x = self.fc(x) return x class MBConv(nn.Module): """Mobile inverted bottleneck block with SE (MBConv).""" def __init__(self, in_w, exp_r, kernel, stride, se_r, out_w, act_fun, seed=None, exp_w=None): super(MBConv, self).__init__() self._construct_class(in_w, exp_r, kernel, stride, se_r, out_w, act_fun) def _construct_class(self, in_w, exp_r, kernel, stride, se_r, out_w, act_fun): # Expansion: 1x1, BN, Swish self.expand = None exp_w = int(in_w * exp_r) # Include exp ops only if the exp ratio is different from 1 if exp_w != in_w: self.expand = nn.Conv2d( in_w, exp_w, kernel_size=1, stride=1, padding=0, bias=False ) self.expand_bn = nn.BatchNorm2d( exp_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.expand_swish = act_fun() # Depthwise: 3x3 dwise, BN, Swish self.dwise = nn.Conv2d( exp_w, exp_w, kernel_size=kernel, stride=stride, groups=exp_w, bias=False, # Hacky padding to preserve res (supports only 3x3 and 5x5) padding=(1 if kernel == 3 else 2) ) self.dwise_bn = nn.BatchNorm2d( exp_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.dwise_swish = act_fun() # SE: x * F_ex(x) if cfg.EFFICIENT_NET.SE_ENABLED: se_w = int(in_w * se_r) self.se = SE(exp_w, se_w, act_fun) # Linear projection: 1x1, BN self.lin_proj = nn.Conv2d( exp_w, out_w, kernel_size=1, stride=1, padding=0, bias=False ) self.lin_proj_bn = nn.BatchNorm2d( out_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) # Nonlinear projection if not cfg.EFFICIENT_NET.LIN_PROJ: self.lin_proj_swish = act_fun() # Skip connections on blocks w/ same in and out shapes (MN-V2, Fig. 4) self.has_skip = (stride == 1) and (in_w == out_w) def forward(self, x): f_x = x # Expansion if self.expand: f_x = self.expand_swish(self.expand_bn(self.expand(f_x))) # Depthwise f_x = self.dwise_swish(self.dwise_bn(self.dwise(f_x))) # SE if cfg.EFFICIENT_NET.SE_ENABLED: f_x = self.se(f_x) # Linear projection f_x = self.lin_proj_bn(self.lin_proj(f_x)) # Nonlinear projection if not cfg.EFFICIENT_NET.LIN_PROJ: f_x = self.lin_proj_swish(f_x) # Skip connection if self.has_skip: # Drop connect if self.training and cfg.EFFICIENT_NET.DC_RATIO > 0.0: if cfg.EFFICIENT_NET.DC_IMP == 'tf': f_x = drop_connect_tf(f_x, cfg.EFFICIENT_NET.DC_RATIO) else: f_x = drop_connect_pt(f_x, cfg.EFFICIENT_NET.DC_RATIO) f_x = x + f_x return f_x class MBTalkConv(nn.Module): """Mobile inverted bottleneck block with SE (MBConv).""" def __init__(self, in_w, exp_r, kernel, stride, se_r, out_w, act_fun, seed=None, exp_w=None): super(MBTalkConv, self).__init__() self.seed=seed self._construct_class(in_w, exp_r, kernel, stride, se_r, out_w, act_fun, exp_w) def _construct_class(self, in_w, exp_r, kernel, stride, se_r, out_w, act_fun, exp_w): # Expansion: 1x1, BN, Swish self.expand = None if int(exp_r)==1: exp_w = in_w else: self.expand = TalkConv2d( in_w, exp_w, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.expand_bn = nn.BatchNorm2d( exp_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.expand_swish = act_fun() # Depthwise: 3x3 dwise, BN, Swish self.dwise = nn.Conv2d( exp_w, exp_w, kernel_size=kernel, stride=stride, groups=exp_w, bias=False, # Hacky padding to preserve res (supports only 3x3 and 5x5) padding=(1 if kernel == 3 else 2) ) self.dwise_bn = nn.BatchNorm2d( exp_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.dwise_swish = act_fun() # SE: x * F_ex(x) if cfg.EFFICIENT_NET.SE_ENABLED: se_w = int(in_w * se_r) self.se = SE(exp_w, se_w, act_fun) # Linear projection: 1x1, BN self.lin_proj = TalkConv2d( exp_w, out_w, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.lin_proj_bn = nn.BatchNorm2d( out_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) # Nonlinear projection if not cfg.EFFICIENT_NET.LIN_PROJ: self.lin_proj_swish = act_fun() # Skip connections on blocks w/ same in and out shapes (MN-V2, Fig. 4) self.has_skip = (stride == 1) and (in_w == out_w) def forward(self, x): f_x = x # Expansion if self.expand: f_x = self.expand_swish(self.expand_bn(self.expand(f_x))) # Depthwise f_x = self.dwise_swish(self.dwise_bn(self.dwise(f_x))) # SE if cfg.EFFICIENT_NET.SE_ENABLED: f_x = self.se(f_x) # Linear projection f_x = self.lin_proj_bn(self.lin_proj(f_x)) # Nonlinear projection if not cfg.EFFICIENT_NET.LIN_PROJ: f_x = self.lin_proj_swish(f_x) # Skip connection if self.has_skip: # Drop connect if self.training and cfg.EFFICIENT_NET.DC_RATIO > 0.0: if cfg.EFFICIENT_NET.DC_IMP == 'tf': f_x = drop_connect_tf(f_x, cfg.EFFICIENT_NET.DC_RATIO) else: f_x = drop_connect_pt(f_x, cfg.EFFICIENT_NET.DC_RATIO) f_x = x + f_x return f_x class Stage(nn.Module): """EfficientNet stage.""" def __init__(self, in_w, exp_r, kernel, stride, se_r, out_w, d, act_fun, exp_w=None): super(Stage, self).__init__() self._construct_class(in_w, exp_r, kernel, stride, se_r, out_w, d, act_fun, exp_w) def _construct_class(self, in_w, exp_r, kernel, stride, se_r, out_w, d, act_fun, exp_w): if cfg.RGRAPH.KEEP_GRAPH: seed = cfg.RGRAPH.SEED_GRAPH else: seed = int(cfg.RGRAPH.SEED_GRAPH*100) # Construct a sequence of blocks for i in range(d): trans_fun = get_conv(cfg.RESNET.TRANS_FUN) # Stride and input width apply to the first block of the stage stride_b = stride if i == 0 else 1 in_w_b = in_w if i == 0 else out_w # Construct the block self.add_module( 'b{}'.format(i + 1), trans_fun(in_w_b, exp_r, kernel, stride_b, se_r, out_w, act_fun, seed=seed, exp_w=exp_w) ) if not cfg.RGRAPH.KEEP_GRAPH: seed += 1 def forward(self, x): for block in self.children(): x = block(x) return x class StemIN(nn.Module): """EfficientNet stem for ImageNet.""" def __init__(self, in_w, out_w, act_fun): super(StemIN, self).__init__() self._construct_class(in_w, out_w, act_fun) def _construct_class(self, in_w, out_w, act_fun): self.conv = nn.Conv2d( in_w, out_w, kernel_size=3, stride=2, padding=1, bias=False ) self.bn = nn.BatchNorm2d( out_w, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.swish = act_fun() def forward(self, x): for layer in self.children(): x = layer(x) return x class EfficientNet(nn.Module): """EfficientNet model.""" def __init__(self): assert cfg.TRAIN.DATASET in ['imagenet'], \ 'Training on {} is not supported'.format(cfg.TRAIN.DATASET) assert cfg.TEST.DATASET in ['imagenet'], \ 'Testing on {} is not supported'.format(cfg.TEST.DATASET) assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ 'Train and test dataset must be the same for now' assert cfg.EFFICIENT_NET.HEAD_TYPE in ['conv_head', 'simple_head', 'linear_head'], \ 'Unsupported head type: {}'.format(cfg.EFFICIENT_NET.HEAD_TYPE) super(EfficientNet, self).__init__() self._construct_class( stem_w=cfg.EFFICIENT_NET.STEM_W, ds=cfg.EFFICIENT_NET.DEPTHS, ws=cfg.EFFICIENT_NET.WIDTHS, exp_rs=cfg.EFFICIENT_NET.EXP_RATIOS, se_r=cfg.EFFICIENT_NET.SE_RATIO, ss=cfg.EFFICIENT_NET.STRIDES, ks=cfg.EFFICIENT_NET.KERNELS, head_type=cfg.EFFICIENT_NET.HEAD_TYPE, head_w=cfg.EFFICIENT_NET.HEAD_W, act_type=cfg.EFFICIENT_NET.ACT_FUN, nc=cfg.MODEL.NUM_CLASSES ) self.apply(nu.init_weights) def _construct_class( self, stem_w, ds, ws, exp_rs, se_r, ss, ks, head_type, head_w, act_type, nc ): """Constructs imagenet models.""" # Group params by stage stage_params = list(zip(ds, ws, exp_rs, ss, ks)) # Activation function act_fun = get_act_fun(act_type) # Set dim for each stage dim_list = cfg.RGRAPH.DIM_LIST expdim_list = [int(cfg.EFFICIENT_NET.WIDTHS[i]*cfg.EFFICIENT_NET.EXP_RATIOS[i]) for i in range(len(cfg.EFFICIENT_NET.WIDTHS))] # Construct the stems self.stem = StemIN(3, stem_w, act_fun) prev_w = stem_w # Construct the stages for i, (d, w, exp_r, stride, kernel) in enumerate(stage_params): if cfg.RESNET.TRANS_FUN != 'mbconv_transform': w = dim_list[i] exp_w = expdim_list[i] self.add_module( 's{}'.format(i + 1), Stage(prev_w, exp_r, kernel, stride, se_r, w, d, act_fun, exp_w=exp_w) ) prev_w = w # Construct the head if head_type == 'conv_head': self.head = ConvHead(prev_w, head_w, nc, act_fun) elif head_type == 'linear_head': self.head = LinearHead(prev_w, head_w, nc, act_fun) else: self.head = SimpleHead(prev_w, nc) def forward(self, x): for module in self.children(): x = module(x) return x

py

RobDanns

RobDanns-main/deep_learning/pycls/models/resnet.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """ResNet or ResNeXt model.""" import torch.nn as nn import torch from pycls.config import cfg import pycls.utils.logging as lu import pycls.utils.net as nu from .relation_graph import * import time import pdb logger = lu.get_logger(__name__) # Stage depths for an ImageNet model {model depth -> (d2, d3, d4, d5)} _IN_MODEL_STAGE_DS = { 18: (2, 2, 2, 2), 34: (3, 4, 6, 3), 50: (3, 4, 6, 3), 101: (3, 4, 23, 3), 152: (3, 8, 36, 3), } def get_trans_fun(name): """Retrieves the transformation function by name.""" trans_funs = { ############ Res-34 'channelbasic_transform': ChannelBasicTransform, 'groupbasictalk_transform': GroupBasicTalkTransform, ############ Res-34-sep 'channelsep_transform': ChannelSepTransform, 'groupseptalk_transform': GroupSepTalkTransform, ############ Res-50 'bottleneck_transform': BottleneckTransform, 'talkbottleneck_transform': TalkBottleneckTransform, } assert name in trans_funs.keys(), \ 'Transformation function \'{}\' not supported'.format(name) return trans_funs[name] ############ Res-34 class ChannelBasicTransform(nn.Module): """Basic transformation: 3x3, 3x3""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): super(ChannelBasicTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # 3x3, BN, ReLU self.a = nn.Conv2d( dim_in, dim_out, kernel_size=3, stride=stride, padding=1, bias=False ) self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # 3x3, BN self.b = nn.Conv2d( dim_out, dim_out, kernel_size=3, stride=1, padding=1, bias=False ) self.b_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.b_bn.final_bn = True def forward(self, x): for layer in self.children(): x = layer(x) return x class GroupBasicTalkTransform(nn.Module): """Basic transformation: 3x3, 3x3, relational graph""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): self.seed = seed super(GroupBasicTalkTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # 3x3, BN, ReLU self.a = TalkConv2d( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=3, stride=stride, padding=1, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # 3x3, BN self.b = TalkConv2d( dim_out, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=3, stride=1, padding=1, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.b_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.b_bn.final_bn = True def forward(self, x): for layer in self.children(): x = layer(x) return x ############ Res-34-sep class ChannelSepTransform(nn.Module): """Separable transformation: 3x3, 3x3""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): super(ChannelSepTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # ReLU, 3x3, BN, 1x1, BN self.a_3x3 = nn.Conv2d( dim_in, dim_in, kernel_size=3, stride=stride, padding=1, bias=False, groups=dim_in ) self.a_1x1 = nn.Conv2d( dim_in, dim_out, kernel_size=1, stride=1, padding=0, bias=False ) self.a_1x1_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # ReLU, 3x3, BN, 1x1, BN self.b_3x3 = nn.Conv2d( dim_out, dim_out, kernel_size=3, stride=1, padding=1, bias=False, groups=dim_out ) self.b_1x1 = nn.Conv2d( dim_out, dim_out, kernel_size=1, stride=1, padding=0, bias=False ) self.b_1x1_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.b_1x1_bn.final_bn = True def forward(self, x): for layer in self.children(): x = layer(x) return x class GroupSepTalkTransform(nn.Module): """Separable transformation: 3x3, 3x3, relational graph""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): self.seed = seed super(GroupSepTalkTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # ReLU, 3x3, BN, 1x1, BN self.a_3x3 = nn.Conv2d( dim_in, dim_in, kernel_size=3, stride=stride, padding=1, bias=False, groups=dim_in ) self.a_1x1 = TalkConv2d( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.a_1x1_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # ReLU, 3x3, BN, 1x1, BN self.b_3x3 = nn.Conv2d( dim_out, dim_out, kernel_size=3, stride=1, padding=1, bias=False, groups=dim_out ) self.b_1x1 = TalkConv2d( dim_out, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.b_1x1_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.b_1x1_bn.final_bn = True def forward(self, x): for layer in self.children(): x = layer(x) return x ############ Res-50 class BottleneckTransform(nn.Module): """Bottleneck transformation: 1x1, 3x3, 1x1""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): super(BottleneckTransform, self).__init__() dim_inner = int(round(dim_out / 4)) self._construct_class(dim_in, dim_out, stride, dim_inner, num_gs, seed) def _construct_class(self, dim_in, dim_out, stride, dim_inner, num_gs, seed): # MSRA -> stride=2 is on 1x1; TH/C2 -> stride=2 is on 3x3 # (str1x1, str3x3) = (stride, 1) if cfg.RESNET.STRIDE_1X1 else (1, stride) (str1x1, str3x3) = (1, stride) # 1x1, BN, ReLU self.a = nn.Conv2d( dim_in, dim_inner, kernel_size=1, stride=str1x1, padding=0, bias=False ) self.a_bn = nn.BatchNorm2d( dim_inner, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # 3x3, BN, ReLU self.b = nn.Conv2d( dim_inner, dim_inner, kernel_size=3, stride=str3x3, padding=1, groups=num_gs, bias=False ) self.b_bn = nn.BatchNorm2d( dim_inner, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.b_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # 1x1, BN self.c = nn.Conv2d( dim_inner, dim_out, kernel_size=1, stride=1, padding=0, bias=False ) self.c_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.c_bn.final_bn = True def forward(self, x): for layer in self.children(): x = layer(x) return x class TalkBottleneckTransform(nn.Module): """Bottleneck transformation: 1x1, 3x3, 1x1, relational graph""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): super(TalkBottleneckTransform, self).__init__() dim_inner = int(round(dim_out / 4)) self.seed = seed self._construct_class(dim_in, dim_out, stride, dim_inner, num_gs, seed) def _construct_class(self, dim_in, dim_out, stride, dim_inner, num_gs, seed): # MSRA -> stride=2 is on 1x1; TH/C2 -> stride=2 is on 3x3 # (str1x1, str3x3) = (stride, 1) if cfg.RESNET.STRIDE_1X1 else (1, stride) (str1x1, str3x3) = (1, stride) # 1x1, BN, ReLU self.a = TalkConv2d( dim_in, dim_inner, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=str1x1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.a_bn = nn.BatchNorm2d( dim_inner, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # 3x3, BN, ReLU self.b = TalkConv2d( dim_inner, dim_inner, cfg.RGRAPH.GROUP_NUM, kernel_size=3, stride=str3x3, padding=1, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.b_bn = nn.BatchNorm2d( dim_inner, eps=cfg.BN.EPS, momentum=cfg.BN.MOM ) self.b_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # 1x1, BN self.c = TalkConv2d( dim_inner, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.c_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.c_bn.final_bn = True def forward(self, x): for layer in self.children(): x = layer(x) return x ##### Remaining ResNet code class ResBlock(nn.Module): """Residual block: x + F(x)""" def __init__( self, dim_in, dim_out, stride, trans_fun, dim_inner=None, num_gs=1, seed=None): super(ResBlock, self).__init__() self.seed = seed self._construct_class(dim_in, dim_out, stride, trans_fun, dim_inner, num_gs, seed) def _add_skip_proj(self, dim_in, dim_out, stride): if 'group' in cfg.RESNET.TRANS_FUN and 'share' not in cfg.RESNET.TRANS_FUN: self.proj = TalkConv2d( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=stride, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) else: self.proj = nn.Conv2d( dim_in, dim_out, kernel_size=1, stride=stride, padding=0, bias=False ) self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) def _construct_class(self, dim_in, dim_out, stride, trans_fun, dim_inner, num_gs, seed): # Use skip connection with projection if dim or res change self.proj_block = (dim_in != dim_out) or (stride != 1) if self.proj_block: self._add_skip_proj(dim_in, dim_out, stride) self.f = trans_fun(dim_in, dim_out, stride, dim_inner, num_gs, seed) self.act = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) def forward(self, x): if self.proj_block: x = self.bn(self.proj(x)) + self.f(x) else: x = x + self.f(x) x = self.act(x) return x class ResStage(nn.Module): """Stage of ResNet.""" def __init__( self, dim_in, dim_out, stride, num_bs, dim_inner=None, num_gs=1): super(ResStage, self).__init__() self._construct_class(dim_in, dim_out, stride, num_bs, dim_inner, num_gs) def _construct_class(self, dim_in, dim_out, stride, num_bs, dim_inner, num_gs): if cfg.RGRAPH.KEEP_GRAPH: seed = cfg.RGRAPH.SEED_GRAPH else: seed = int(cfg.RGRAPH.SEED_GRAPH * 100) for i in range(num_bs): # Stride and dim_in apply to the first block of the stage b_stride = stride if i == 0 else 1 b_dim_in = dim_in if i == 0 else dim_out # Retrieve the transformation function trans_fun = get_trans_fun(cfg.RESNET.TRANS_FUN) # Construct the block res_block = ResBlock( b_dim_in, dim_out, b_stride, trans_fun, dim_inner, num_gs, seed=seed ) if not cfg.RGRAPH.KEEP_GRAPH: seed += 1 self.add_module('b{}'.format(i + 1), res_block) for j in range(cfg.RGRAPH.ADD_1x1): trans_fun = get_trans_fun(cfg.RESNET.TRANS_FUN + '1x1') # Construct the block res_block = ResBlock( dim_out, dim_out, 1, trans_fun, dim_inner, num_gs, seed=seed ) if not cfg.RGRAPH.KEEP_GRAPH: seed += 1 self.add_module('b{}_{}1x1'.format(i + 1, j + 1), res_block) def forward(self, x): for block in self.children(): x = block(x) return x class ResStem(nn.Module): """Stem of ResNet.""" def __init__(self, dim_in, dim_out): assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ 'Train and test dataset must be the same for now' super(ResStem, self).__init__() if cfg.TRAIN.DATASET == 'cifar10': self._construct_cifar(dim_in, dim_out) else: self._construct_imagenet(dim_in, dim_out) def _construct_cifar(self, dim_in, dim_out): # 3x3, BN, ReLU # self.conv = nn.Conv2d( # dim_in, dim_out, kernel_size=3, # stride=1, padding=1, bias=False # ) self.conv = nn.Conv2d( dim_in, dim_out, kernel_size=7, stride=1, padding=3, bias=False ) self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) def _construct_imagenet(self, dim_in, dim_out): # 7x7, BN, ReLU, pool self.conv = nn.Conv2d( dim_in, dim_out, kernel_size=7, stride=2, padding=3, bias=False ) self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def forward(self, x): for layer in self.children(): x = layer(x) return x class ResHead(nn.Module): """ResNet head.""" def __init__(self, dim_in, num_classes): super(ResHead, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(dim_in, num_classes, bias=True) def forward(self, x): x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x class ResNet(nn.Module): """ResNet model.""" def __init__(self): assert cfg.TRAIN.DATASET in ['cifar10', 'cifar100', 'tinyimagenet200', 'imagenet'], \ 'Training ResNet on {} is not supported'.format(cfg.TRAIN.DATASET) assert cfg.TEST.DATASET in ['cifar10', 'cifar100', 'tinyimagenet200', 'imagenet'], \ 'Testing ResNet on {} is not supported'.format(cfg.TEST.DATASET) assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ 'Train and test dataset must be the same for now' super(ResNet, self).__init__() if cfg.TRAIN.DATASET == 'cifar10': self._construct_cifar() elif cfg.TRAIN.DATASET == 'cifar100': self._construct_cifar() else: self._construct_imagenet() self.apply(nu.init_weights) # # ##### basic transform def _construct_cifar(self): assert (cfg.MODEL.DEPTH - 2) % 6 == 0, \ 'Model depth should be of the format 6n + 2 for cifar' logger.info('Constructing: ResNet-{}, cifar'.format(cfg.MODEL.DEPTH)) # Each stage has the same number of blocks for cifar num_blocks = int((cfg.MODEL.DEPTH - 2) / 6) # length = num of stages (excluding stem and head) dim_list = cfg.RGRAPH.DIM_LIST # Stage 1: (N, 3, 32, 32) -> (N, 16, 32, 32)*8 # self.s1 = ResStem(dim_in=3, dim_out=16) self.s1 = ResStem(dim_in=3, dim_out=64) # Stage 2: (N, 16, 32, 32) -> (N, 16, 32, 32) # self.s2 = ResStage(dim_in=16, dim_out=dim_list[0], stride=1, num_bs=num_blocks) self.s2 = ResStage(dim_in=64, dim_out=dim_list[0], stride=1, num_bs=num_blocks) # Stage 3: (N, 16, 32, 32) -> (N, 32, 16, 16) self.s3 = ResStage(dim_in=dim_list[0], dim_out=dim_list[1], stride=2, num_bs=num_blocks) # Stage 4: (N, 32, 16, 16) -> (N, 64, 8, 8) self.s4 = ResStage(dim_in=dim_list[1], dim_out=dim_list[2], stride=2, num_bs=num_blocks) # Head: (N, 64, 8, 8) -> (N, num_classes) self.head = ResHead(dim_in=dim_list[2], num_classes=cfg.MODEL.NUM_CLASSES) # smaller imagenet def _construct_imagenet(self): logger.info('Constructing: ResNet-{}, Imagenet'.format(cfg.MODEL.DEPTH)) # Retrieve the number of blocks per stage (excluding base) (d2, d3, d4, d5) = _IN_MODEL_STAGE_DS[cfg.MODEL.DEPTH] # Compute the initial inner block dim dim_list = cfg.RGRAPH.DIM_LIST print(dim_list) # Stage 1: (N, 3, 224, 224) -> (N, 64, 56, 56) self.s1 = ResStem(dim_in=3, dim_out=64) # Stage 2: (N, 64, 56, 56) -> (N, 256, 56, 56) self.s2 = ResStage( dim_in=64, dim_out=dim_list[0], stride=1, num_bs=d2 ) # Stage 3: (N, 256, 56, 56) -> (N, 512, 28, 28) self.s3 = ResStage( dim_in=dim_list[0], dim_out=dim_list[1], stride=2, num_bs=d3 ) # Stage 4: (N, 512, 56, 56) -> (N, 1024, 14, 14) self.s4 = ResStage( dim_in=dim_list[1], dim_out=dim_list[2], stride=2, num_bs=d4 ) # Stage 5: (N, 1024, 14, 14) -> (N, 2048, 7, 7) self.s5 = ResStage( dim_in=dim_list[2], dim_out=dim_list[3], stride=2, num_bs=d5 ) # Head: (N, 2048, 7, 7) -> (N, num_classes) self.head = ResHead(dim_in=dim_list[3], num_classes=cfg.MODEL.NUM_CLASSES) def forward(self, x): for module in self.children(): x = module(x) return x

py

RobDanns

RobDanns-main/deep_learning/pycls/models/cnn.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """CNN model.""" import torch.nn as nn import torch from pycls.config import cfg import pycls.utils.logging as lu import pycls.utils.net as nu from .relation_graph import * logger = lu.get_logger(__name__) def get_trans_fun(name): """Retrieves the transformation function by name.""" trans_funs = { ##### (1) Level 1: channel ### (1.1) Basic Conv 'convbasic_transform': ConvBasicTransform, 'symconvbasic_transform': SymConvBasicTransform, 'convtalk_transform': ConvTalkTransform, # relational graph } assert name in trans_funs.keys(), \ 'Transformation function \'{}\' not supported'.format(name) return trans_funs[name] ##### (1) Level 1: channel ### (1.1) Basic Conv class ConvBasicTransform(nn.Module): """Basic transformation: 3x3""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): super(ConvBasicTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # 3x3, BN, ReLU self.a = nn.Conv2d( dim_in, dim_out, kernel_size=3, stride=stride, padding=1, bias=False ) self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # self.a_bn.final_bn = True self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x class SymConvBasicTransform(nn.Module): """Basic transformation: 3x3 conv, symmetric""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): super(SymConvBasicTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # 3x3, BN, ReLU self.a = SymConv2d( dim_in, dim_out, kernel_size=3, stride=stride, padding=1, bias=False ) self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # self.a_bn.final_bn = True self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x class ConvTalkTransform(nn.Module): """Basic transformation: 3x3 conv, relational graph""" def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): self.seed = seed super(ConvTalkTransform, self).__init__() self._construct_class(dim_in, dim_out, stride) def _construct_class(self, dim_in, dim_out, stride): # 3x3, BN, ReLU self.a = TalkConv2d( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=3, stride=stride, padding=1, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # self.a_bn.final_bn = True self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x ##### Remaining CNN code class CNNStage(nn.Module): """Stage of CNN.""" def __init__( self, dim_in, dim_out, stride, num_bs, dim_inner=None, num_gs=1): super(CNNStage, self).__init__() self._construct_class(dim_in, dim_out, stride, num_bs, dim_inner, num_gs) def _construct_class(self, dim_in, dim_out, stride, num_bs, dim_inner, num_gs): if cfg.RGRAPH.KEEP_GRAPH: seed = cfg.RGRAPH.SEED_GRAPH else: seed = int(cfg.RGRAPH.SEED_GRAPH * 100) for i in range(num_bs): # Stride and dim_in apply to the first block of the stage b_stride = stride if i == 0 else 1 b_dim_in = dim_in if i == 0 else dim_out # Retrieve the transformation function trans_fun = get_trans_fun(cfg.RESNET.TRANS_FUN) # Construct the block res_block = trans_fun( b_dim_in, dim_out, b_stride, dim_inner, num_gs, seed=seed ) if not cfg.RGRAPH.KEEP_GRAPH: seed += 1 self.add_module('b{}'.format(i + 1), res_block) def forward(self, x): for block in self.children(): x = block(x) return x class CNNStem(nn.Module): """Stem of CNN.""" def __init__(self, dim_in, dim_out): assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ 'Train and test dataset must be the same for now' super(CNNStem, self).__init__() if cfg.TRAIN.DATASET == 'cifar10': self._construct_cifar(dim_in, dim_out) elif cfg.TRAIN.DATASET == 'cifar100': self._construct_cifar(dim_in, dim_out) else: self._construct_imagenet(dim_in, dim_out) def _construct_cifar(self, dim_in, dim_out): # 3x3, BN, ReLU if cfg.RGRAPH.STEM_MODE == 'default': self.conv = nn.Conv2d( dim_in, dim_out, kernel_size=3, stride=1, padding=1, bias=False ) self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) elif cfg.RGRAPH.STEM_MODE == 'downsample': self.conv = nn.Conv2d( dim_in, dim_out, kernel_size=3, stride=1, padding=1, bias=False ) self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _construct_imagenet(self, dim_in, dim_out): # 3x3, BN, ReLU, pool self.conv = nn.Conv2d( dim_in, dim_out, kernel_size=3, stride=2, padding=1, bias=False ) self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def forward(self, x): for layer in self.children(): x = layer(x) return x class CNNHead(nn.Module): """CNN head.""" def __init__(self, dim_in, num_classes): super(CNNHead, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.dropout = nn.Dropout(p=0.15) self.fc = nn.Linear(dim_in, num_classes, bias=True) def forward(self, x): x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.dropout(x) x = self.fc(x) return x class CNN(nn.Module): """CNN model.""" def __init__(self): assert cfg.TRAIN.DATASET in ['cifar10', 'cifar100', 'tinyimagenet200', 'imagenet'], \ 'Training CNN on {} is not supported'.format(cfg.TRAIN.DATASET) assert cfg.TEST.DATASET in ['cifar10', 'cifar100', 'tinyimagenet200', 'imagenet'], \ 'Testing CNN on {} is not supported'.format(cfg.TEST.DATASET) assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ 'Train and test dataset must be the same for now' super(CNN, self).__init__() self._construct() self.apply(nu.init_weights) # # ##### basic transform def _construct(self): # Each stage has the same number of blocks for cifar dim_list = cfg.RGRAPH.DIM_LIST num_bs = cfg.MODEL.LAYERS // 3 self.s1 = CNNStem(dim_in=3, dim_out=cfg.RGRAPH.DIM_FIRST) self.s2 = CNNStage(dim_in=cfg.RGRAPH.DIM_FIRST, dim_out=dim_list[0], stride=2, num_bs=num_bs) self.s3 = CNNStage(dim_in=dim_list[0], dim_out=dim_list[1], stride=2, num_bs=num_bs) self.s4 = CNNStage(dim_in=dim_list[1], dim_out=dim_list[2], stride=2, num_bs=num_bs) # self.s5 = CNNStage(dim_in=dim_list[2], dim_out=dim_list[3], stride=2, num_bs=num_bs) self.head = CNNHead(dim_in=dim_list[2], num_classes=cfg.MODEL.NUM_CLASSES) def forward(self, x): for module in self.children(): x = module(x) return x # #!/usr/bin/env python3 # # Copyright (c) Facebook, Inc. and its affiliates. # # # # This source code is licensed under the MIT license found in the # # LICENSE file in the root directory of this source tree. # """CNN model.""" # import torch.nn as nn # import torch # from pycls.config import cfg # import pycls.utils.logging as lu # import pycls.utils.net as nu # from .relation_graph import * # logger = lu.get_logger(__name__) # def get_trans_fun(name): # """Retrieves the transformation function by name.""" # trans_funs = { # ##### (1) Level 1: channel # ### (1.1) Basic Conv # 'convbasic_transform': ConvBasicTransform, # 'symconvbasic_transform': SymConvBasicTransform, # 'convtalk_transform': ConvTalkTransform, # relational graph # } # assert name in trans_funs.keys(), \ # 'Transformation function \'{}\' not supported'.format(name) # return trans_funs[name] # ##### (1) Level 1: channel # ### (1.1) Basic Conv # class ConvBasicTransform(nn.Module): # """Basic transformation: 3x3""" # def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): # super(ConvBasicTransform, self).__init__() # self._construct_class(dim_in, dim_out, stride) # def _construct_class(self, dim_in, dim_out, stride): # # 3x3, BN, ReLU # self.a = nn.Conv2d( # dim_in, dim_out, kernel_size=3, # stride=stride, padding=1, bias=False # ) # self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # # self.a_bn.final_bn = True # self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # def forward(self, x): # for layer in self.children(): # x = layer(x) # return x # class SymConvBasicTransform(nn.Module): # """Basic transformation: 3x3 conv, symmetric""" # def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): # super(SymConvBasicTransform, self).__init__() # self._construct_class(dim_in, dim_out, stride) # def _construct_class(self, dim_in, dim_out, stride): # # 3x3, BN, ReLU # self.a = SymConv2d( # dim_in, dim_out, kernel_size=3, # stride=stride, padding=1, bias=False # ) # self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # # self.a_bn.final_bn = True # self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # def forward(self, x): # for layer in self.children(): # x = layer(x) # return x # class ConvTalkTransform(nn.Module): # """Basic transformation: 3x3 conv, relational graph""" # def __init__(self, dim_in, dim_out, stride, dim_inner=None, num_gs=1, seed=None): # self.seed = seed # super(ConvTalkTransform, self).__init__() # self._construct_class(dim_in, dim_out, stride) # def _construct_class(self, dim_in, dim_out, stride): # # 3x3, BN, ReLU # self.a = TalkConv2d( # dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=3, # stride=stride, padding=1, bias=False, # message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, # sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed # ) # self.a_bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # # self.a_bn.final_bn = True # self.a_relu = nn.ReLU(inplace=cfg.MEM.RELU_INPLACE) # def forward(self, x): # for layer in self.children(): # x = layer(x) # return x # ##### Remaining CNN code # class CNNStage(nn.Module): # """Stage of CNN.""" # def __init__( # self, dim_in, dim_out, stride, num_bs, dim_inner=None, num_gs=1): # super(CNNStage, self).__init__() # self._construct_class(dim_in, dim_out, stride, num_bs, dim_inner, num_gs) # def _construct_class(self, dim_in, dim_out, stride, num_bs, dim_inner, num_gs): # if cfg.RGRAPH.KEEP_GRAPH: # seed = cfg.RGRAPH.SEED_GRAPH # else: # seed = int(cfg.RGRAPH.SEED_GRAPH * 100) # for i in range(num_bs): # # Stride and dim_in apply to the first block of the stage # b_stride = stride if i == 0 else 1 # b_dim_in = dim_in if i == 0 else dim_out # # Retrieve the transformation function # trans_fun = get_trans_fun(cfg.RESNET.TRANS_FUN) # # Construct the block # res_block = trans_fun( # b_dim_in, dim_out, b_stride, dim_inner, num_gs, seed=seed # ) # if not cfg.RGRAPH.KEEP_GRAPH: # seed += 1 # self.add_module('b{}'.format(i + 1), res_block) # def forward(self, x): # for block in self.children(): # x = block(x) # return x # class CNNStem(nn.Module): # """Stem of CNN.""" # def __init__(self, dim_in, dim_out): # assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ # 'Train and test dataset must be the same for now' # super(CNNStem, self).__init__() # if cfg.TRAIN.DATASET == 'cifar10': # self._construct_cifar(dim_in, dim_out) # else: # self._construct_imagenet(dim_in, dim_out) # def _construct_cifar(self, dim_in, dim_out): # # 3x3, BN, ReLU # if cfg.RGRAPH.STEM_MODE == 'default': # self.conv = nn.Conv2d( # dim_in, dim_out, kernel_size=3, # stride=1, padding=1, bias=False # ) # self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, # momentum=cfg.BN.MOM) # self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) # elif cfg.RGRAPH.STEM_MODE == 'downsample': # self.conv = nn.Conv2d( # dim_in, dim_out, kernel_size=3, # stride=1, padding=1, bias=False # ) # self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, # momentum=cfg.BN.MOM) # self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) # self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # def _construct_imagenet(self, dim_in, dim_out): # # 3x3, BN, ReLU, pool # self.conv = nn.Conv2d( # dim_in, dim_out, kernel_size=3, # stride=2, padding=1, bias=False # ) # self.bn = nn.BatchNorm2d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) # self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) # self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # def forward(self, x): # for layer in self.children(): # x = layer(x) # return x # class CNNHead(nn.Module): # """CNN head.""" # def __init__(self, dim_in, num_classes): # super(CNNHead, self).__init__() # self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # self.fc = nn.Linear(dim_in, num_classes, bias=True) # def forward(self, x): # x = self.avg_pool(x) # x = x.view(x.size(0), -1) # x = self.fc(x) # return x # class CNN(nn.Module): # """CNN model.""" # def __init__(self): # assert cfg.TRAIN.DATASET in ['cifar10', 'imagenet'], \ # 'Training ResNet on {} is not supported'.format(cfg.TRAIN.DATASET) # assert cfg.TEST.DATASET in ['cifar10', 'imagenet'], \ # 'Testing ResNet on {} is not supported'.format(cfg.TEST.DATASET) # assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ # 'Train and test dataset must be the same for now' # super(CNN, self).__init__() # self._construct() # self.apply(nu.init_weights) # # # ##### basic transform # def _construct(self): # # Each stage has the same number of blocks for cifar # dim_list = cfg.RGRAPH.DIM_LIST # num_bs = cfg.MODEL.LAYERS // 3 # self.s1 = CNNStem(dim_in=3, dim_out=cfg.RGRAPH.DIM_FIRST) # self.s2 = CNNStage(dim_in=cfg.RGRAPH.DIM_FIRST, dim_out=dim_list[0], stride=2, num_bs=num_bs) # self.s3 = CNNStage(dim_in=dim_list[0], dim_out=dim_list[1], stride=2, num_bs=num_bs) # self.s4 = CNNStage(dim_in=dim_list[1], dim_out=dim_list[2], stride=2, num_bs=num_bs) # self.head = CNNHead(dim_in=dim_list[2], num_classes=cfg.MODEL.NUM_CLASSES) # def forward(self, x): # for module in self.children(): # x = module(x) # return x

py

RobDanns

RobDanns-main/deep_learning/pycls/models/vgg.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """VGG example""" import torch.nn as nn import torch.nn.functional as F from pycls.config import cfg import pycls.utils.net as nu from .relation_graph import * class VGG(nn.Module): def __init__(self, num_classes=1024): super(VGG, self).__init__() self.seed = cfg.RGRAPH.SEED_GRAPH def conv_bn(dim_in, dim_out, stride, stem=False): if stem: conv = get_conv('convbasic_transform', dim_in, dim_out, stride) else: conv = get_conv(cfg.RESNET.TRANS_FUN, dim_in, dim_out, stride) return nn.Sequential( conv, nn.BatchNorm2d(dim_out), nn.ReLU(inplace=True) ) def get_conv(name, dim_in, dim_out, stride=1): if not cfg.RGRAPH.KEEP_GRAPH: self.seed += 1 if name == 'convbasic_transform': return nn.Conv2d(dim_in, dim_out, kernel_size=3, stride=stride, padding=1, bias=False) elif name == 'convtalk_transform': return TalkConv2d( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=3, stride=stride, padding=1, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) self.dim_list = cfg.RGRAPH.DIM_LIST # print(self.dim_list) self.model = nn.Sequential( conv_bn(3, 64, 1, stem=True), conv_bn(64, self.dim_list[0], 1), nn.MaxPool2d(kernel_size=2, stride=2), conv_bn(self.dim_list[0], self.dim_list[1], 1), conv_bn(self.dim_list[1], self.dim_list[1], 1), nn.MaxPool2d(kernel_size=2, stride=2), conv_bn(self.dim_list[1], self.dim_list[2], 1), conv_bn(self.dim_list[2], self.dim_list[2], 1), nn.MaxPool2d(kernel_size=2, stride=2), conv_bn(self.dim_list[2], self.dim_list[3], 1), conv_bn(self.dim_list[3], self.dim_list[3], 1), nn.MaxPool2d(kernel_size=2, stride=2), conv_bn(self.dim_list[3], self.dim_list[3], 1), conv_bn(self.dim_list[3], self.dim_list[3], 1), ) self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(self.dim_list[3], num_classes) self.apply(nu.init_weights) def forward(self, x): x = self.model(x) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x

py

RobDanns

RobDanns-main/deep_learning/pycls/models/mlp.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """MLP model.""" import torch.nn as nn import torch from pycls.config import cfg import pycls.utils.logging as lu import pycls.utils.net as nu from .relation_graph import * import time import pdb logger = lu.get_logger(__name__) def get_trans_fun(name): """Retrieves the transformation function by name.""" trans_funs = { ##### (1) Level 1: channel 'linear_transform': LinearTransform, 'symlinear_transform': SymLinearTransform, 'grouplinear_transform': GroupLinearTransform, 'groupshufflelinear_transform': GroupShuffleLinearTransform, 'talklinear_transform': TalkLinearTransform, # relational graph } assert name in trans_funs.keys(), \ 'Transformation function \'{}\' not supported'.format(name) return trans_funs[name] ##### (0) Basic class LinearTransform(nn.Module): """Basic transformation: linear""" def __init__(self, dim_in, dim_out, seed=None): super(LinearTransform, self).__init__() self._construct_class(dim_in, dim_out) def _construct_class(self, dim_in, dim_out): # 3x3, BN, ReLU self.a = nn.Linear( dim_in, dim_out, bias=False ) self.a_bn = nn.BatchNorm1d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_bn.final_bn = True self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x class SymLinearTransform(nn.Module): """Basic transformation: linear, symmetric""" def __init__(self, dim_in, dim_out, seed=None): super(SymLinearTransform, self).__init__() self._construct_class(dim_in, dim_out) def _construct_class(self, dim_in, dim_out): # 3x3, BN, ReLU self.a = SymLinear( dim_in, dim_out, bias=False ) self.a_bn = nn.BatchNorm1d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_bn.final_bn = True self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x class GroupLinearTransform(nn.Module): """Basic transformation: linear, group""" def __init__(self, dim_in, dim_out, seed=None): super(GroupLinearTransform, self).__init__() self._construct_class(dim_in, dim_out) def _construct_class(self, dim_in, dim_out): # 3x3, BN, ReLU self.a = GroupLinear( dim_in, dim_out, bias=False, group_size=cfg.RGRAPH.GROUP_SIZE ) self.a_bn = nn.BatchNorm1d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_bn.final_bn = True self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x class GroupShuffleLinearTransform(nn.Module): """Basic transformation: linear, shuffle""" def __init__(self, dim_in, dim_out, seed=None): super(GroupShuffleLinearTransform, self).__init__() self._construct_class(dim_in, dim_out) def _construct_class(self, dim_in, dim_out): # 3x3, BN, ReLU self.a = GroupLinear( dim_in, dim_out, bias=False, group_size=cfg.RGRAPH.GROUP_SIZE ) self.a_bn = nn.BatchNorm1d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_bn.final_bn = True self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) self.shuffle_shape = (dim_out // cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.GROUP_NUM) def forward(self, x): x = self.a(x) x = x.view(x.shape[0], self.shuffle_shape[0], self.shuffle_shape[1]).permute(0, 2, 1).contiguous() x = x.view(x.shape[0], x.shape[1] * x.shape[2]) x = self.a_bn(x) x = self.relu(x) return x class TalkLinearTransform(nn.Module): """Basic transformation: linear, relational graph""" def __init__(self, dim_in, dim_out, seed=None): self.seed = seed super(TalkLinearTransform, self).__init__() self._construct_class(dim_in, dim_out) def _construct_class(self, dim_in, dim_out): self.a = TalkLinear( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed) self.a_bn = nn.BatchNorm1d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.a_bn.final_bn = True self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) def forward(self, x): for layer in self.children(): x = layer(x) return x class MLPStage(nn.Module): """Stage of MLPNet.""" def __init__( self, dim_in, dim_out, num_bs): super(MLPStage, self).__init__() self._construct_class(dim_in, dim_out, num_bs) def _construct_class(self, dim_in, dim_out, num_bs): if cfg.RGRAPH.KEEP_GRAPH: seed = cfg.RGRAPH.SEED_GRAPH else: seed = int(dim_out * 100 * cfg.RGRAPH.SPARSITY) for i in range(num_bs): b_dim_in = dim_in if i == 0 else dim_out trans_fun = get_trans_fun(cfg.RESNET.TRANS_FUN) res_block = trans_fun( b_dim_in, dim_out, seed=seed ) if not cfg.RGRAPH.KEEP_GRAPH: seed += 1 self.add_module('b{}'.format(i + 1), res_block) def forward(self, x): for block in self.children(): x = block(x) return x class MLPStem(nn.Module): """Stem of MLPNet.""" def __init__(self, dim_in, dim_out): super(MLPStem, self).__init__() if cfg.TRAIN.DATASET == 'cifar10': self._construct_cifar(dim_in, dim_out) else: raise NotImplementedError def _construct_cifar(self, dim_in, dim_out): self.linear = nn.Linear( dim_in, dim_out, bias=False ) self.bn = nn.BatchNorm1d(dim_out, eps=cfg.BN.EPS, momentum=cfg.BN.MOM) self.relu = nn.ReLU(cfg.MEM.RELU_INPLACE) def forward(self, x): x = x.view(x.size(0), -1) for layer in self.children(): x = layer(x) return x class MLPHead(nn.Module): """MLPNet head.""" def __init__(self, dim_in, num_classes): super(MLPHead, self).__init__() self.fc = nn.Linear(dim_in, num_classes, bias=True) def forward(self, x): x = self.fc(x) return x class MLPNet(nn.Module): """MLPNet model.""" def __init__(self): assert cfg.TRAIN.DATASET in ['cifar10'], \ 'Training MLPNet on {} is not supported'.format(cfg.TRAIN.DATASET) assert cfg.TEST.DATASET in ['cifar10'], \ 'Testing MLPNet on {} is not supported'.format(cfg.TEST.DATASET) assert cfg.TRAIN.DATASET == cfg.TEST.DATASET, \ 'Train and test dataset must be the same for now' super(MLPNet, self).__init__() if cfg.TRAIN.DATASET == 'cifar10': self._construct_cifar() else: raise NotImplementedError self.apply(nu.init_weights) # ##### basic transform def _construct_cifar(self): num_layers = cfg.MODEL.LAYERS dim_inner = cfg.RGRAPH.DIM_LIST[0] dim_first = cfg.RGRAPH.DIM_FIRST self.s1 = MLPStem(dim_in=3072, dim_out=dim_first) self.s2 = MLPStage(dim_in=dim_first, dim_out=dim_inner, num_bs=num_layers) self.head = MLPHead(dim_in=dim_inner, num_classes=cfg.MODEL.NUM_CLASSES) def forward(self, x): for module in self.children(): x = module(x) return x

py

RobDanns

RobDanns-main/deep_learning/pycls/models/model_builder.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Model construction functions.""" import torch from pycls.config import cfg from pycls.models.resnet import ResNet from pycls.models.mlp import MLPNet from pycls.models.cnn import CNN from pycls.models.mobilenet import MobileNetV1 from pycls.models.efficientnet import EfficientNet from pycls.models.vgg import VGG import pycls.utils.logging as lu import pycls.utils.metrics as mu logger = lu.get_logger(__name__) # Supported model types _MODEL_TYPES = { 'resnet': ResNet, 'mlpnet': MLPNet, 'cnn': CNN, 'mobilenet': MobileNetV1, 'efficientnet': EfficientNet, 'vgg': VGG, } def build_model(): """Builds the model.""" assert cfg.MODEL.TYPE in _MODEL_TYPES.keys(), \ 'Model type \'{}\' not supported'.format(cfg.MODEL.TYPE) assert cfg.NUM_GPUS <= torch.cuda.device_count(), \ 'Cannot use more GPU devices than available' # Construct the model model = _MODEL_TYPES[cfg.MODEL.TYPE]() # Determine the GPU used by the current process cur_device = torch.cuda.current_device() # Transfer the model to the current GPU device model = model.cuda(device=cur_device) # Use multi-process data parallel model in the multi-gpu setting if cfg.NUM_GPUS > 1: # Make model replica operate on the current device model = torch.nn.parallel.DistributedDataParallel( module=model, device_ids=[cur_device], output_device=cur_device ) return model ## auto match flop def build_model_stats(mode='flops'): """Builds the model.""" assert cfg.MODEL.TYPE in _MODEL_TYPES.keys(), \ 'Model type \'{}\' not supported'.format(cfg.MODEL.TYPE) assert cfg.NUM_GPUS <= torch.cuda.device_count(), \ 'Cannot use more GPU devices than available' # Construct the model model = _MODEL_TYPES[cfg.MODEL.TYPE]() if mode == 'flops': flops = mu.flops_count(model) return flops else: params = mu.params_count(model) return params

py

RobDanns

RobDanns-main/deep_learning/pycls/models/mobilenet.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """MobileNet example""" import torch.nn as nn import torch.nn.functional as F from pycls.config import cfg import pycls.utils.net as nu from .relation_graph import * class MobileNetV1(nn.Module): def __init__(self, num_classes=1024): super(MobileNetV1, self).__init__() if cfg.RGRAPH.KEEP_GRAPH: self.seed = cfg.RGRAPH.SEED_GRAPH else: self.seed = int(cfg.RGRAPH.SEED_GRAPH * 100) def conv_bn(dim_in, dim_out, stride): return nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, stride, 1, bias=False), nn.BatchNorm2d(dim_out), nn.ReLU(inplace=True) ) def get_conv(name, dim_in, dim_out): if not cfg.RGRAPH.KEEP_GRAPH: self.seed += 1 if name == 'channelbasic_transform': return nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False) elif name == 'groupbasictalk_transform': return TalkConv2d( dim_in, dim_out, cfg.RGRAPH.GROUP_NUM, kernel_size=1, stride=1, padding=0, bias=False, message_type=cfg.RGRAPH.MESSAGE_TYPE, directed=cfg.RGRAPH.DIRECTED, agg=cfg.RGRAPH.AGG_FUNC, sparsity=cfg.RGRAPH.SPARSITY, p=cfg.RGRAPH.P, talk_mode=cfg.RGRAPH.TALK_MODE, seed=self.seed ) def conv_dw(dim_in, dim_out, stride): conv1x1 = get_conv(cfg.RESNET.TRANS_FUN, dim_in, dim_out) return nn.Sequential( nn.Conv2d(dim_in, dim_in, 3, stride, 1, groups=dim_in, bias=False), nn.BatchNorm2d(dim_in), nn.ReLU(inplace=True), conv1x1, nn.BatchNorm2d(dim_out), nn.ReLU(inplace=True), ) self.dim_list = cfg.RGRAPH.DIM_LIST # print(self.dim_list) self.model = nn.Sequential( conv_bn(3, 32, 2), conv_dw(32, self.dim_list[1], 1), conv_dw(self.dim_list[1], self.dim_list[2], 2), conv_dw(self.dim_list[2], self.dim_list[2], 1), conv_dw(self.dim_list[2], self.dim_list[3], 2), conv_dw(self.dim_list[3], self.dim_list[3], 1), conv_dw(self.dim_list[3], self.dim_list[4], 2), conv_dw(self.dim_list[4], self.dim_list[4], 1), conv_dw(self.dim_list[4], self.dim_list[4], 1), conv_dw(self.dim_list[4], self.dim_list[4], 1), conv_dw(self.dim_list[4], self.dim_list[4], 1), conv_dw(self.dim_list[4], self.dim_list[4], 1), conv_dw(self.dim_list[4], self.dim_list[5], 2), conv_dw(self.dim_list[5], self.dim_list[5], 1), ) self.fc = nn.Linear(self.dim_list[5], num_classes) self.apply(nu.init_weights) def forward(self, x): x = self.model(x) x = F.avg_pool2d(x, 7) x = x.view(-1, self.dim_list[5]) x = self.fc(x) return x

py

RobDanns

RobDanns-main/deep_learning/pycls/models/optimizer.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Optimizer.""" import torch from pycls.config import cfg import pycls.utils.lr_policy as lr_policy def construct_optimizer(model): """Constructs the optimizer. Note that the momentum update in PyTorch differs from the one in Caffe2. In particular, Caffe2: V := mu * V + lr * g p := p - V PyTorch: V := mu * V + g p := p - lr * V where V is the velocity, mu is the momentum factor, lr is the learning rate, g is the gradient and p are the parameters. Since V is defined independently of the learning rate in PyTorch, when the learning rate is changed there is no need to perform the momentum correction by scaling V (unlike in the Caffe2 case). """ return torch.optim.SGD( model.parameters(), lr=cfg.OPTIM.BASE_LR, momentum=cfg.OPTIM.MOMENTUM, weight_decay=cfg.OPTIM.WEIGHT_DECAY, dampening=cfg.OPTIM.DAMPENING, nesterov=cfg.OPTIM.NESTEROV ) def get_epoch_lr(cur_epoch): """Retrieves the lr for the given epoch (as specified by the lr policy).""" return lr_policy.get_epoch_lr(cur_epoch) def set_lr(optimizer, new_lr): """Sets the optimizer lr to the specified value.""" for param_group in optimizer.param_groups: param_group['lr'] = new_lr

py

RobDanns

RobDanns-main/deep_learning/pycls/models/relation_graph.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Relational graph modules""" import math import torch import torch.nn as nn from torch.nn.parameter import Parameter import torch.nn.functional as F import torch.nn.init as init import networkx as nx import numpy as np from torch.nn.modules.utils import _pair from torch.nn.modules.conv import _ConvNd from torch.autograd import Function from itertools import repeat from networkx.utils import py_random_state from pycls.datasets.load_graph import load_graph import pdb import time import random def compute_count(channel, group): divide = channel // group remain = channel % group out = np.zeros(group, dtype=int) out[:remain] = divide + 1 out[remain:] = divide return out @py_random_state(3) def ws_graph(n, k, p, seed=1): """Returns a ws-flex graph, k can be real number in [2,n] """ assert k >= 2 and k <= n # compute number of edges: edge_num = int(round(k * n / 2)) count = compute_count(edge_num, n) # print(count) G = nx.Graph() for i in range(n): source = [i] * count[i] target = range(i + 1, i + count[i] + 1) target = [node % n for node in target] # print(source, target) G.add_edges_from(zip(source, target)) # rewire edges from each node nodes = list(G.nodes()) for i in range(n): u = i target = range(i + 1, i + count[i] + 1) target = [node % n for node in target] for v in target: if seed.random() < p: w = seed.choice(nodes) # Enforce no self-loops or multiple edges while w == u or G.has_edge(u, w): w = seed.choice(nodes) if G.degree(u) >= n - 1: break # skip this rewiring else: G.remove_edge(u, v) G.add_edge(u, w) return G @py_random_state(4) def connected_ws_graph(n, k, p, tries=100, seed=1): """Returns a connected ws-flex graph. """ for i in range(tries): # seed is an RNG so should change sequence each call G = ws_graph(n, k, p, seed) if nx.is_connected(G): return G raise nx.NetworkXError('Maximum number of tries exceeded') def nx_to_edge(graph, directed=False, add_self_loops=True, shuffle_id=False, seed=1): '''nx graph to edge index''' graph.remove_edges_from(graph.selfloop_edges()) # relabel graphs keys = list(graph.nodes) vals = list(range(graph.number_of_nodes())) # shuffle node id assignment if shuffle_id: random.seed(seed) random.shuffle(vals) mapping = dict(zip(keys, vals)) graph = nx.relabel_nodes(graph, mapping, copy=True) # get edges edge_index = np.array(list(graph.edges)) if not directed: edge_index = np.concatenate((edge_index, edge_index[:, ::-1]), axis=0) if add_self_loops: edge_self = np.arange(graph.number_of_nodes())[:, np.newaxis] edge_self = np.tile(edge_self, (1, 2)) edge_index = np.concatenate((edge_index, edge_self), axis=0) # sort edges idx = np.argsort(edge_index[:, 0]) edge_index = edge_index[idx, :] return edge_index # edge index generator def generate_index(message_type='ba', n=16, sparsity=0.5, p=0.2, directed=False, seed=123): degree = n * sparsity known_names = ['mcwhole', 'mcwholeraw', 'mcvisual', 'mcvisualraw', 'cat', 'catraw'] if message_type == 'er': graph = nx.gnm_random_graph(n=n, m=n * degree // 2, seed=seed) elif message_type == 'random': edge_num = int(n * n * sparsity) edge_id = np.random.choice(n * n, edge_num, replace=False) edge_index = np.zeros((edge_num, 2), dtype=int) for i in range(edge_num): edge_index[i, 0] = edge_id[i] // n edge_index[i, 1] = edge_id[i] % n elif message_type == 'ws': graph = connected_ws_graph(n=n, k=degree, p=p, seed=seed) elif message_type == 'ba': graph = nx.barabasi_albert_graph(n=n, m=degree // 2, seed=seed) elif message_type == 'hypercube': graph = nx.hypercube_graph(n=int(np.log2(n))) elif message_type == 'grid': m = degree n = n // degree graph = nx.grid_2d_graph(m=m, n=n) elif message_type == 'cycle': graph = nx.cycle_graph(n=n) elif message_type == 'tree': graph = nx.random_tree(n=n, seed=seed) elif message_type == 'regular': graph = nx.connected_watts_strogatz_graph(n=n, k=degree, p=0, seed=seed) elif message_type in known_names: graph = load_graph(message_type) edge_index = nx_to_edge(graph, directed=True, seed=seed) else: raise NotImplementedError if message_type != 'random' and message_type not in known_names: edge_index = nx_to_edge(graph, directed=directed, seed=seed) return edge_index def compute_size(channel, group, seed=1): np.random.seed(seed) divide = channel // group remain = channel % group out = np.zeros(group, dtype=int) out[:remain] = divide + 1 out[remain:] = divide out = np.random.permutation(out) return out def compute_densemask(in_channels, out_channels, group_num, edge_index): repeat_in = compute_size(in_channels, group_num) repeat_out = compute_size(out_channels, group_num) mask = np.zeros((group_num, group_num)) mask[edge_index[:, 0], edge_index[:, 1]] = 1 mask = np.repeat(mask, repeat_out, axis=0) mask = np.repeat(mask, repeat_in, axis=1) return mask def get_mask(in_channels, out_channels, group_num, message_type='ba', directed=False, sparsity=0.5, p=0.2, talk_mode='dense', seed=123): assert group_num <= in_channels and group_num <= out_channels # high-level graph edge index edge_index_high = generate_index(message_type=message_type, n=group_num, sparsity=sparsity, p=p, directed=directed, seed=seed) # get in/out size for each high-level node in_sizes = compute_size(in_channels, group_num) out_sizes = compute_size(out_channels, group_num) # decide low-level node num group_num_low = int(min(np.min(in_sizes), np.min(out_sizes))) # decide how to fill each node mask_high = compute_densemask(in_channels, out_channels, group_num, edge_index_high) return mask_high ############## Linear model class TalkLinear(nn.Linear): '''Relational graph version of Linear. Neurons "talk" according to the graph structure''' def __init__(self, in_channels, out_channels, group_num, bias=False, message_type='ba', directed=False, sparsity=0.5, p=0.2, talk_mode='dense', seed=None): group_num_max = min(in_channels, out_channels) if group_num > group_num_max: group_num = group_num_max # print(group_num, in_channels, out_channels, kernel_size, stride) super(TalkLinear, self).__init__( in_channels, out_channels, bias) self.mask = get_mask(in_channels, out_channels, group_num, message_type, directed, sparsity, p, talk_mode, seed) nonzero = np.sum(self.mask) self.mask = torch.from_numpy(self.mask).float().cuda() self.flops_scale = nonzero / (in_channels * out_channels) self.params_scale = self.flops_scale self.init_scale = torch.sqrt(out_channels / torch.sum(self.mask.cpu(), dim=0, keepdim=True)) def forward(self, x): weight = self.weight * self.mask # pdb.set_trace() return F.linear(x, weight, self.bias) class SymLinear(nn.Module): '''Linear with symmetric weight matrices''' def __init__(self, in_features, out_features, bias=True): super(SymLinear, self).__init__() self.in_features = in_features self.out_features = out_features self.weight = Parameter(torch.Tensor(out_features, in_features)) if bias: self.bias = Parameter(torch.Tensor(out_features)) else: self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self): init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound) def forward(self, input): weight = self.weight + self.weight.permute(1, 0) return F.linear(input, weight, self.bias) def extra_repr(self): return 'in_features={}, out_features={}, bias={}'.format( self.in_features, self.out_features, self.bias is not None ) ############## Conv model class TalkConv2d(_ConvNd): '''Relational graph version of Conv2d. Neurons "talk" according to the graph structure''' def __init__(self, in_channels, out_channels, group_num, kernel_size, stride=1, padding=0, dilation=1, bias=False, message_type='ba', directed=False, agg='sum', sparsity=0.5, p=0.2, talk_mode='dense', seed=None): group_num_max = min(in_channels, out_channels) if group_num > group_num_max: group_num = group_num_max kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) super(TalkConv2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), 1, bias, 'zeros') self.mask = get_mask(in_channels, out_channels, group_num, message_type, directed, sparsity, p, talk_mode, seed) nonzero = np.sum(self.mask) self.mask = torch.from_numpy(self.mask[:, :, np.newaxis, np.newaxis]).float().cuda() self.init_scale = torch.sqrt(out_channels / torch.sum(self.mask.cpu(), dim=0, keepdim=True)) self.flops_scale = nonzero / (in_channels * out_channels) self.params_scale = self.flops_scale def forward(self, input): weight = self.weight * self.mask return F.conv2d(input, weight, self.bias, self.stride, self.padding, self.dilation, 1) class SymConv2d(_ConvNd): '''Conv2d with symmetric weight matrices''' def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros'): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) super(SymConv2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias, padding_mode) def forward(self, input): weight = self.weight + self.weight.permute(1, 0, 2, 3) if self.padding_mode == 'circular': expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2, (self.padding[0] + 1) // 2, self.padding[0] // 2) return F.conv2d(F.pad(input, expanded_padding, mode='circular'), weight, self.bias, self.stride, _pair(0), self.dilation, self.groups) return F.conv2d(input, weight, self.bias, self.stride, self.padding, self.dilation, self.groups) ########### Other OPs class Swish(nn.Module): """Swish activation function: x * sigmoid(x)""" def __init__(self): super(Swish, self).__init__() def forward(self, x): return x * torch.sigmoid(x) class SE(nn.Module): """Squeeze-and-Excitation (SE) block w/ Swish activation fun.""" def __init__(self, in_w, se_w, act_fun): super(SE, self).__init__() self._construct_class(in_w, se_w, act_fun) def _construct_class(self, in_w, se_w, act_fun): # AvgPool self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # FC, Swish, FC, Sigmoid self.f_ex = nn.Sequential( nn.Conv2d(in_w, se_w, kernel_size=1, bias=True), act_fun(), nn.Conv2d(se_w, in_w, kernel_size=1, bias=True), nn.Sigmoid() ) def forward(self, x): return x * self.f_ex(self.avg_pool(x)) class SparseLinear(nn.Linear): '''Sparse Linear layer''' def __init__(self, group_num, in_scale, out_scale, bias=False, edge_index=None, flops_scale=0.5, params_scale=0.5): # mask is used for reset to zero mask_one = np.ones((out_scale, in_scale), dtype=bool) mask_zero = np.zeros((out_scale, in_scale), dtype=bool) mask_list = [[mask_one for i in range(group_num)] for j in range(group_num)] for i in range(edge_index.shape[0]): mask_list[edge_index[i, 0]][edge_index[i, 1]] = mask_zero self.mask = np.block(mask_list) self.edge_index = edge_index # todo: update to pytorch 1.2.0, then use bool() dtype self.mask = torch.from_numpy(self.mask).byte().cuda() self.flops_scale = flops_scale self.params_scale = params_scale super(SparseLinear, self).__init__( group_num * in_scale, group_num * out_scale, bias) def forward(self, x): weight = self.weight.clone().masked_fill_(self.mask, 0) # pdb.set_trace() return F.linear(x, weight, self.bias) class GroupLinear(nn.Module): '''Group conv style linear layer''' def __init__(self, in_channels, out_channels, bias=False, group_size=1): super(GroupLinear, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.group_size = group_size self.group_num = in_channels // group_size self.in_scale = in_channels // self.group_num self.out_scale = out_channels // self.group_num assert in_channels % self.group_num == 0 assert out_channels % self.group_num == 0 assert in_channels % self.group_size == 0 # Note: agg_fun is always sum self.edge_index = np.arange(self.group_num)[:, np.newaxis].repeat(2, axis=1) self.edge_num = self.edge_index.shape[0] flops_scale = self.edge_num / (self.group_num * self.group_num) params_scale = self.edge_num / (self.group_num * self.group_num) self.linear = SparseLinear(self.group_num, self.in_scale, self.out_scale, bias, edge_index=self.edge_index, flops_scale=flops_scale, params_scale=params_scale) def forward(self, x): x = self.linear(x) return x

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/cifar100.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """CIFAR100 dataset.""" import numpy as np import os import pickle import torch import torch.utils.data import pycls.datasets.transforms as transforms from torchvision import datasets import pycls.utils.logging as lu logger = lu.get_logger(__name__) # Per-channel mean and SD values in BGR order _MEAN = [129.3, 124.1, 112.4] _SD = [68.2, 65.4, 70.4] class Cifar100(torch.utils.data.Dataset): """CIFAR-100 dataset.""" def __init__(self, data_path, split, batch_size): assert os.path.exists(data_path), \ 'Data path \'{}\' not found'.format(data_path) assert split in ['train', 'test'], \ 'Split \'{}\' not supported for cifar'.format(split) logger.info('Constructing CIFAR-100 {}...'.format(split)) self._data_path = data_path self._split = split self._batch_size = batch_size # Data format: # self._inputs - (split_size, 3, 32, 32) ndarray # self._labels - split_size list self._inputs, self._labels = self._load_data() def _load_batch(self, batch_path): with open(batch_path, 'rb') as f: d = pickle.load(f, encoding='bytes') return d[b'data'], d[b'fine_labels'] # return d[b'data'], d[b'labels'] def _load_data(self): """Loads data in memory.""" logger.info('{} data path: {}'.format(self._split, self._data_path)) # Compute data batch names if self._split == 'train': batch_names = ['train'] # datasets.CIFAR100(self._data_path, train=True) # batch_names = ['data_batch_{}'.format(i) for i in range(1, 6)] else: batch_names = ['test'] # Load data batches inputs, labels = [], [] for batch_name in batch_names: batch_path = os.path.join(self._data_path, batch_name) inputs_batch, labels_batch = self._load_batch(batch_path) inputs.append(inputs_batch) labels += labels_batch # Combine and reshape the inputs inputs = np.vstack(inputs).astype(np.float32) inputs = inputs.reshape((-1, 3, 32, 32)) return inputs, labels def _transform_image(self, image): """Transforms the image for network input.""" if self._batch_size != 1: image = transforms.color_normalization(image, _MEAN, _SD) if self._split == 'train': image = transforms.horizontal_flip(image=image, prob=0.5) image = transforms.random_crop(image=image, size=32, pad_size=4) return image def __getitem__(self, index): image, label = self._inputs[index, ...], self._labels[index] image = self._transform_image(image) return image, label def __len__(self): return self._inputs.shape[0]

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/cifar10.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """CIFAR10 dataset.""" import numpy as np import os import pickle import torch import torch.utils.data import pycls.datasets.transforms as transforms import pycls.utils.logging as lu from pycls.config import cfg logger = lu.get_logger(__name__) # Per-channel mean and SD values in BGR order _MEAN = [125.3, 123.0, 113.9] _SD = [63.0, 62.1, 66.7] class Cifar10(torch.utils.data.Dataset): """CIFAR-10 dataset.""" def __init__(self, data_path, split, batch_size): assert os.path.exists(data_path), \ 'Data path \'{}\' not found'.format(data_path) assert split in ['train', 'test'], \ 'Split \'{}\' not supported for cifar'.format(split) logger.info('Constructing CIFAR-10 {}...'.format(split)) self._data_path = data_path self._split = split self._batch_size = batch_size # Data format: # self._inputs - (split_size, 3, 32, 32) ndarray # self._labels - split_size list self._inputs, self._labels = self._load_data() def _load_batch(self, batch_path): with open(batch_path, 'rb') as f: d = pickle.load(f, encoding='bytes') return d[b'data'], d[b'labels'] def _load_data(self): """Loads data in memory.""" logger.info('{} data path: {}'.format(self._split, self._data_path)) # Compute data batch names if self._split == 'train': batch_names = ['data_batch_{}'.format(i) for i in range(1, 6)] else: batch_names = ['test_batch'] # Load data batches inputs, labels = [], [] for batch_name in batch_names: batch_path = os.path.join(self._data_path, batch_name) inputs_batch, labels_batch = self._load_batch(batch_path) inputs.append(inputs_batch) labels += labels_batch # Combine and reshape the inputs inputs = np.vstack(inputs).astype(np.float32) inputs = inputs.reshape((-1, 3, 32, 32)) return inputs, labels def _transform_image(self, image): """Transforms the image for network input.""" if self._batch_size != 1: # Normalizing input images image = transforms.color_normalization(image, _MEAN, _SD) if self._split == 'train': image = transforms.horizontal_flip(image=image, prob=0.5) image = transforms.random_crop(image=image, size=32, pad_size=4) return image def __getitem__(self, index): image, label = self._inputs[index, ...], self._labels[index] image = self._transform_image(image) return image, label def __len__(self): return self._inputs.shape[0]

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/paths.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Dataset paths.""" import os # Default data directory (/path/pycls/pycls/datasets/data) _DEF_DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') # Data paths _paths = { 'cifar10': _DEF_DATA_DIR + '/cifar10', 'cifar100': _DEF_DATA_DIR + '/cifar100', 'tinyimagenet200': _DEF_DATA_DIR + '/tinyimagenet200', 'imagenet': _DEF_DATA_DIR + '/imagenet' } def has_data_path(dataset_name): """Determines if the dataset has a data path.""" return dataset_name in _paths.keys() def get_data_path(dataset_name): """Retrieves data path for the dataset.""" return _paths[dataset_name] def set_data_path(dataset_name, data_path): """Sets data path for the dataset.""" _paths[dataset_name] = data_path

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/loader.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Data loader.""" from torch.utils.data.distributed import DistributedSampler from torch.utils.data.sampler import RandomSampler import torch from pycls.config import cfg from pycls.datasets.cifar10 import Cifar10 from pycls.datasets.cifar100 import Cifar100 from pycls.datasets.tinyimagenet200 import TinyImageNet200 from pycls.datasets.imagenet import ImageNet import pycls.datasets.paths as dp # Supported datasets _DATASET_CATALOG = { 'cifar10': Cifar10, 'cifar100': Cifar100, 'tinyimagenet200': TinyImageNet200, 'imagenet': ImageNet } def _construct_loader(dataset_name, split, batch_size, shuffle, drop_last): """Constructs the data loader for the given dataset.""" assert dataset_name in _DATASET_CATALOG.keys(), \ 'Dataset \'{}\' not supported'.format(dataset_name) assert dp.has_data_path(dataset_name), \ 'Dataset \'{}\' has no data path'.format(dataset_name) # Retrieve the data path for the dataset data_path = dp.get_data_path(dataset_name) # Construct the dataset dataset = _DATASET_CATALOG[dataset_name](data_path, split, batch_size) # Create a sampler for multi-process training sampler = DistributedSampler(dataset) if cfg.NUM_GPUS > 1 else None # Create a loader loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=(False if sampler else shuffle), sampler=sampler, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=drop_last ) return loader def construct_train_loader(): """Train loader wrapper.""" return _construct_loader( dataset_name=cfg.TRAIN.DATASET, split=cfg.TRAIN.SPLIT, batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, drop_last=True ) def construct_test_loader(): """Test loader wrapper.""" return _construct_loader( dataset_name=cfg.TEST.DATASET, split=cfg.TEST.SPLIT, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, drop_last=False ) def construct_test_loader_adv(): """Test loader wrapper.""" return _construct_loader( dataset_name=cfg.TEST.DATASET, split=cfg.TEST.SPLIT, batch_size=1, shuffle=False, drop_last=False ) def shuffle(loader, cur_epoch): """"Shuffles the data.""" assert isinstance(loader.sampler, (RandomSampler, DistributedSampler)), \ 'Sampler type \'{}\' not supported'.format(type(loader.sampler)) # RandomSampler handles shuffling automatically if isinstance(loader.sampler, DistributedSampler): # DistributedSampler shuffles data based on epoch loader.sampler.set_epoch(cur_epoch)

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/imagenet.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """ImageNet dataset.""" import cv2 import numpy as np import os import torch import torch.utils.data import pycls.datasets.transforms as transforms import pycls.utils.logging as lu logger = lu.get_logger(__name__) # Per-channel mean and SD values in BGR order _MEAN = [0.406, 0.456, 0.485] _SD = [0.225, 0.224, 0.229] # Eig vals and vecs of the cov mat _EIG_VALS = [0.2175, 0.0188, 0.0045] _EIG_VECS = np.array([ [-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203] ]) class ImageNet(torch.utils.data.Dataset): """ImageNet dataset.""" def __init__(self, data_path, split, batch_size): assert os.path.exists(data_path), \ 'Data path \'{}\' not found'.format(data_path) assert split in ['train', 'val'], \ 'Split \'{}\' not supported for ImageNet'.format(split) logger.info('Constructing ImageNet {}...'.format(split)) self._data_path = data_path self._split = split self._batch_size = batch_size self._construct_imdb() def _construct_imdb(self): """Constructs the imdb.""" # Compile the split data path split_path = os.path.join(self._data_path, self._split) logger.info('{} data path: {}'.format(self._split, split_path)) # Map ImageNet class ids to contiguous ids self._class_ids = os.listdir(split_path) self._class_id_cont_id = {v: i for i, v in enumerate(self._class_ids)} # Construct the image db self._imdb = [] for class_id in self._class_ids: cont_id = self._class_id_cont_id[class_id] im_dir = os.path.join(split_path, class_id) for im_name in os.listdir(im_dir): self._imdb.append({ 'im_path': os.path.join(im_dir, im_name), 'class': cont_id, }) logger.info('Number of images: {}'.format(len(self._imdb))) logger.info('Number of classes: {}'.format(len(self._class_ids))) def _prepare_im(self, im): """Prepares the image for network input.""" # Train and test setups differ if self._split == 'train': # Scale and aspect ratio im = transforms.random_sized_crop( image=im, size=224, area_frac=0.08 ) # Horizontal flip im = transforms.horizontal_flip(image=im, prob=0.5, order='HWC') else: # Scale and center crop im = transforms.scale(256, im) im = transforms.center_crop(224, im) # HWC -> CHW im = transforms.HWC2CHW(im) # [0, 255] -> [0, 1] im = im / 255.0 # PCA jitter if self._split == 'train': im = transforms.lighting(im, 0.1, _EIG_VALS, _EIG_VECS) # Color normalization if self._batch_size != 1: im = transforms.color_normalization(im, _MEAN, _SD) return im def __getitem__(self, index): # Load the image im = cv2.imread(self._imdb[index]['im_path']) im = im.astype(np.float32, copy=False) # Prepare the image for training / testing im = self._prepare_im(im) # Retrieve the label label = self._imdb[index]['class'] return im, label def __len__(self): return len(self._imdb) # class ImageNet(torch.utils.data.Dataset): # """ImageNet dataset.""" # def __init__(self, data_path, split): # assert os.path.exists(data_path), \ # 'Data path \'{}\' not found'.format(data_path) # assert split in ['train', 'val'], \ # 'Split \'{}\' not supported for ImageNet'.format(split) # logger.info('Constructing ImageNet {}...'.format(split)) # self._data_path = data_path # self._split = split # self._construct_imdb() # def _construct_imdb(self): # """Constructs the imdb.""" # # Compile the split data path # split_path = os.path.join(self._data_path, self._split) # logger.info('{} data path: {}'.format(self._split, split_path)) # # Map ImageNet class ids to contiguous ids # self._class_ids = os.listdir(split_path) # self._class_id_cont_id = {v: i for i, v in enumerate(self._class_ids)} # # Construct the image db # self._imdb = [] # counter = 1 # for class_id in self._class_ids: # print('progress: {}/{}'.format(counter,len(self._class_ids))) # counter += 1 # cont_id = self._class_id_cont_id[class_id] # im_dir = os.path.join(split_path, class_id) # for im_name in os.listdir(im_dir): # self._imdb.append({ # 'im_path': os.path.join(im_dir, im_name), # 'class': cont_id, # 'img': cv2.imread(os.path.join(im_dir, im_name)).astype(np.float32, copy=False) # }) # logger.info('Number of images: {}'.format(len(self._imdb))) # logger.info('Number of classes: {}'.format(len(self._class_ids))) # def _prepare_im(self, im): # """Prepares the image for network input.""" # # Train and test setups differ # if self._split == 'train': # # Scale and aspect ratio # im = transforms.random_sized_crop( # image=im, size=224, area_frac=0.08 # ) # # Horizontal flip # im = transforms.horizontal_flip(image=im, prob=0.5, order='HWC') # else: # # Scale and center crop # im = transforms.scale(256, im) # im = transforms.center_crop(224, im) # # HWC -> CHW # im = transforms.HWC2CHW(im) # # [0, 255] -> [0, 1] # im = im / 255.0 # # PCA jitter # if self._split == 'train': # im = transforms.lighting(im, 0.1, _EIG_VALS, _EIG_VECS) # # Color normalization # im = transforms.color_normalization(im, _MEAN, _SD) # return im # def __getitem__(self, index): # # Load the image # im = self._imdb[index]['img'] # # Prepare the image for training / testing # im = self._prepare_im(im) # # Retrieve the label # label = self._imdb[index]['class'] # return im, label # def __len__(self): # return len(self._imdb)

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/transforms.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Image transformations.""" import cv2 import math import numpy as np def CHW2HWC(image): return image.transpose([1, 2, 0]) def HWC2CHW(image): return image.transpose([2, 0, 1]) def color_normalization(image, mean, std): """Expects image in CHW format.""" assert len(mean) == image.shape[0] assert len(std) == image.shape[0] for i in range(image.shape[0]): image[i] = image[i] - mean[i] image[i] = image[i] / std[i] return image def zero_pad(image, pad_size, order='CHW'): assert order in ['CHW', 'HWC'] if order == 'CHW': pad_width = ((0, 0), (pad_size, pad_size), (pad_size, pad_size)) else: pad_width = ((pad_size, pad_size), (pad_size, pad_size), (0, 0)) return np.pad(image, pad_width, mode='constant') def horizontal_flip(image, prob, order='CHW'): assert order in ['CHW', 'HWC'] if np.random.uniform() < prob: if order == 'CHW': image = image[:, :, ::-1] else: image = image[:, ::-1, :] return image def random_crop(image, size, pad_size=0, order='CHW'): assert order in ['CHW', 'HWC'] if pad_size > 0: image = zero_pad(image=image, pad_size=pad_size, order=order) if order == 'CHW': if image.shape[1] == size and image.shape[2] == size: return image height = image.shape[1] width = image.shape[2] y_offset = 0 if height > size: y_offset = int(np.random.randint(0, height - size)) x_offset = 0 if width > size: x_offset = int(np.random.randint(0, width - size)) cropped = image[:, y_offset:y_offset + size, x_offset:x_offset + size] assert cropped.shape[1] == size, "Image not cropped properly" assert cropped.shape[2] == size, "Image not cropped properly" else: if image.shape[0] == size and image.shape[1] == size: return image height = image.shape[0] width = image.shape[1] y_offset = 0 if height > size: y_offset = int(np.random.randint(0, height - size)) x_offset = 0 if width > size: x_offset = int(np.random.randint(0, width - size)) cropped = image[y_offset:y_offset + size, x_offset:x_offset + size, :] assert cropped.shape[0] == size, "Image not cropped properly" assert cropped.shape[1] == size, "Image not cropped properly" return cropped def scale(size, image): height = image.shape[0] width = image.shape[1] if ((width <= height and width == size) or (height <= width and height == size)): return image new_width = size new_height = size if width < height: new_height = int(math.floor((float(height) / width) * size)) else: new_width = int(math.floor((float(width) / height) * size)) img = cv2.resize( image, (new_width, new_height), interpolation=cv2.INTER_LINEAR ) return img.astype(np.float32) def center_crop(size, image): height = image.shape[0] width = image.shape[1] y_offset = int(math.ceil((height - size) / 2)) x_offset = int(math.ceil((width - size) / 2)) cropped = image[y_offset:y_offset + size, x_offset:x_offset + size, :] assert cropped.shape[0] == size, "Image height not cropped properly" assert cropped.shape[1] == size, "Image width not cropped properly" return cropped def random_sized_crop(image, size, area_frac=0.08): for _ in range(0, 10): height = image.shape[0] width = image.shape[1] area = height * width target_area = np.random.uniform(area_frac, 1.0) * area aspect_ratio = np.random.uniform(3.0 / 4.0, 4.0 / 3.0) w = int(round(math.sqrt(float(target_area) * aspect_ratio))) h = int(round(math.sqrt(float(target_area) / aspect_ratio))) if np.random.uniform() < 0.5: w, h = h, w if h <= height and w <= width: if height == h: y_offset = 0 else: y_offset = np.random.randint(0, height - h) if width == w: x_offset = 0 else: x_offset = np.random.randint(0, width - w) y_offset = int(y_offset) x_offset = int(x_offset) cropped = image[y_offset:y_offset + h, x_offset:x_offset + w, :] assert cropped.shape[0] == h and cropped.shape[1] == w, \ "Wrong crop size" cropped = cv2.resize( cropped, (size, size), interpolation=cv2.INTER_LINEAR ) return cropped.astype(np.float32) return center_crop(size, scale(size, image)) def lighting(img, alphastd, eigval, eigvec): if alphastd == 0: return img # generate alpha1, alpha2, alpha3 alpha = np.random.normal(0, alphastd, size=(1, 3)) eig_vec = np.array(eigvec) eig_val = np.reshape(eigval, (1, 3)) rgb = np.sum( eig_vec * np.repeat(alpha, 3, axis=0) * np.repeat(eig_val, 3, axis=0), axis=1 ) for idx in range(img.shape[0]): img[idx] = img[idx] + rgb[2 - idx] return img

py

RobDanns

RobDanns-main/deep_learning/pycls/datasets/load_graph.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """load bio neural networks""" import numpy as np import networkx as nx import matplotlib.pyplot as plt from networkx.utils import py_random_state from matplotlib.colors import ListedColormap import pdb def compute_stats(G): G_cluster = sorted(list(nx.clustering(G).values())) cluster = sum(G_cluster) / len(G_cluster) path = nx.average_shortest_path_length(G) # path return cluster, path def plot_graph(graph, name, dpi=200, width=0.5, layout='spring'): plt.figure(figsize=(10, 10)) pos = nx.spiral_layout(graph) if layout == 'spring': pos = nx.spring_layout(graph) elif layout == 'circular': pos = nx.circular_layout(graph) nx.draw(graph, pos=pos, node_size=100, width=width) plt.savefig('figs/graph_view_{}.png'.format(name), dpi=dpi, transparent=True) def load_graph(name, verbose=False, seed=1): if 'raw' in name: name = name[:-3] directed = True else: directed = False filename = '{}.txt'.format(name) # filename = 'pycls/datasets/{}.txt'.format(name) with open(filename) as f: content = f.readlines() content = [list(x.strip()) for x in content] adj = np.array(content).astype(int) if not directed: adj = np.logical_or(adj.transpose(), adj).astype(int) graph = nx.from_numpy_array(adj, create_using=nx.DiGraph) if verbose: print(type(graph)) print(graph.number_of_nodes(), graph.number_of_edges()) print(compute_stats(graph)) print(len(graph.edges)) # plot_graph(graph, 'mc_whole', dpi=60, width=1, layout='circular') cmap = ListedColormap(['w', 'k']) plt.matshow(nx.to_numpy_matrix(graph), cmap=cmap) plt.show() return graph def compute_count(channel, group): divide = channel // group remain = channel % group out = np.zeros(group, dtype=int) out[:remain] = divide + 1 out[remain:] = divide return out @py_random_state(3) def ws_graph(n, k, p, seed=1): """Returns a ws-flex graph, k can be real number in [2,n] """ assert k >= 2 and k <= n # compute number of edges: edge_num = int(round(k * n / 2)) count = compute_count(edge_num, n) # print(count) G = nx.Graph() for i in range(n): source = [i] * count[i] target = range(i + 1, i + count[i] + 1) target = [node % n for node in target] # print(source, target) G.add_edges_from(zip(source, target)) # rewire edges from each node nodes = list(G.nodes()) for i in range(n): u = i target = range(i + 1, i + count[i] + 1) target = [node % n for node in target] for v in target: if seed.random() < p: w = seed.choice(nodes) # Enforce no self-loops or multiple edges while w == u or G.has_edge(u, w): w = seed.choice(nodes) if G.degree(u) >= n - 1: break # skip this rewiring else: G.remove_edge(u, v) G.add_edge(u, w) return G @py_random_state(4) def connected_ws_graph(n, k, p, tries=100, seed=1): """Returns a connected ws-flex graph. """ for i in range(tries): # seed is an RNG so should change sequence each call G = ws_graph(n, k, p, seed) if nx.is_connected(G): return G raise nx.NetworkXError('Maximum number of tries exceeded') def generate_graph(message_type='ws', n=16, sparsity=0.5, p=0.2, directed=False, seed=123): ### for relaxed ws degree = n * sparsity if message_type == 'ws': graph = connected_ws_graph(n=n, k=degree, p=p, seed=seed) return graph # graph = load_graph('mcwhole', True) # graph = load_graph('mcwholeraw', True) # graph = load_graph('mcvisual', True) # graph = load_graph('mcvisualraw', True) # graph = load_graph('cat', True) # graph = load_graph('catraw', True)

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/checkpoint.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Functions that handle saving and loading of checkpoints.""" import os import torch from collections import OrderedDict from pycls.config import cfg import pycls.utils.distributed as du # Common prefix for checkpoint file names _NAME_PREFIX = 'model_epoch_' # Checkpoints directory name _DIR_NAME = 'checkpoints' def get_checkpoint_dir(): """Get location for storing checkpoints.""" return os.path.join(cfg.OUT_DIR, _DIR_NAME) def got_checkpoint_dir(): """Get location for storing checkpoints for inference time.""" return os.path.join(cfg.CHECKPT_DIR, _DIR_NAME) def get_checkpoint(epoch): """Get the full path to a checkpoint file.""" name = '{}{:04d}.pyth'.format(_NAME_PREFIX, epoch) return os.path.join(get_checkpoint_dir(), name) def got_checkpoint(epoch): """Get the full path to a checkpoint file for inference time.""" name = '{}{:04d}.pyth'.format(_NAME_PREFIX, epoch) return os.path.join(got_checkpoint_dir(), name) def get_checkpoint_last(): d = get_checkpoint_dir() names = os.listdir(d) if os.path.exists(d) else [] names = [f for f in names if _NAME_PREFIX in f] assert len(names), 'No checkpoints found in \'{}\'.'.format(d) name = sorted(names)[-1] return os.path.join(d, name) def got_checkpoint_last(): d = got_checkpoint_dir() names = os.listdir(d) if os.path.exists(d) else [] names = [f for f in names if _NAME_PREFIX in f] assert len(names), 'No checkpoints found in \'{}\'.'.format(d) name = sorted(names)[-1] return os.path.join(d, name) def has_checkpoint(): """Determines if the given directory contains a checkpoint.""" d = get_checkpoint_dir() print("checkpoint directory =", d) files = os.listdir(d) if os.path.exists(d) else [] return any(_NAME_PREFIX in f for f in files) def had_checkpoint(): """Determines if the given directory contains a checkpoint for inference time.""" d = got_checkpoint_dir() print("checkpoint directory =", d) files = os.listdir(d) if os.path.exists(d) else [] return any(_NAME_PREFIX in f for f in files) def is_checkpoint_epoch(cur_epoch): """Determines if a checkpoint should be saved on current epoch.""" return (cur_epoch + 1) % cfg.TRAIN.CHECKPOINT_PERIOD == 0 def save_checkpoint(model, optimizer, epoch): """Saves a checkpoint.""" # Save checkpoints only from the master process if not du.is_master_proc(): return os.makedirs(get_checkpoint_dir(), exist_ok=True) checkpoint = { 'epoch': epoch, 'model_state': model.state_dict(), 'optimizer_state': optimizer.state_dict(), 'cfg': cfg.dump() } checkpoint_file = get_checkpoint(epoch + 1) torch.save(checkpoint, checkpoint_file) return checkpoint_file def load_checkpoint(checkpoint_file, model, optimizer=None): """Loads the checkpoint from the given file.""" assert os.path.exists(checkpoint_file), \ 'Checkpoint \'{}\' not found'.format(checkpoint_file) # if cfg.IS_INFERENCE and cfg.IS_DDP: # state_dict = torch.load(checkpoint_file, map_location='cpu') # new_state_dict = OrderedDict() # print("state_dict.items() :", state_dict) # for k, v in state_dict.items(): # name = k[7:] # remove `module.` # new_state_dict[name] = v # # load params # epoch = state_dict['epoch'] # model.load_state_dict(new_state_dict['model_state']) # if optimizer: # optimizer.load_state_dict(new_state_dict['optimizer_state']) if cfg.IS_INFERENCE: print("Mapping model to CPU") checkpoint = torch.load(checkpoint_file, map_location='cpu') # print(checkpoint) else: checkpoint = torch.load(checkpoint_file) epoch = checkpoint['epoch'] print("Epochs from checkpoint = ", epoch) model.load_state_dict(checkpoint['model_state'], strict=False) if optimizer: optimizer.load_state_dict(checkpoint['optimizer_state']) return epoch

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/timer.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Timer.""" import time class Timer(object): """A simple timer (adapted from Detectron).""" def __init__(self): self.reset() def tic(self): # using time.time instead of time.clock because time time.clock # does not normalize for multithreading self.start_time = time.time() def toc(self): self.diff = time.time() - self.start_time self.total_time += self.diff self.calls += 1 self.average_time = self.total_time / self.calls def reset(self): self.total_time = 0. self.calls = 0 self.start_time = 0. self.diff = 0. self.average_time = 0.

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/error_handler.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Multiprocessing error handler.""" import os import signal import threading class ChildException(Exception): """Wraps an exception from a child process.""" def __init__(self, child_trace): super(ChildException, self).__init__(child_trace) class ErrorHandler(object): """Multiprocessing error handler (based on fairseq's). Listens for errors in child processes and propagates the tracebacks to the parent process. """ def __init__(self, error_queue): # Shared error queue self.error_queue = error_queue # Children processes sharing the error queue self.children_pids = [] # Start a thread listening to errors self.error_listener = threading.Thread(target=self.listen, daemon=True) self.error_listener.start() # Register the signal handler signal.signal(signal.SIGUSR1, self.signal_handler) def add_child(self, pid): """Registers a child process.""" self.children_pids.append(pid) def listen(self): """Listens for errors in the error queue.""" # Wait until there is an error in the queue child_trace = self.error_queue.get() # Put the error back for the signal handler self.error_queue.put(child_trace) # Invoke the signal handler os.kill(os.getpid(), signal.SIGUSR1) def signal_handler(self, _sig_num, _stack_frame): """Signal handler.""" # Kill children processes for pid in self.children_pids: os.kill(pid, signal.SIGINT) # Propagate the error from the child process raise ChildException(self.error_queue.get())

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/plotting.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Plotting functions.""" import colorlover as cl import matplotlib.pyplot as plt import plotly.graph_objs as go import plotly.offline as offline import pycls.utils.logging as lu def get_plot_colors(max_colors, color_format='pyplot'): """Generate colors for plotting.""" colors = cl.scales['11']['qual']['Paired'] if max_colors > len(colors): colors = cl.to_rgb(cl.interp(colors, max_colors)) if color_format == 'pyplot': return [[j / 255.0 for j in c] for c in cl.to_numeric(colors)] return colors def prepare_plot_data(log_files, names, key='top1_err'): """Load logs and extract data for plotting error curves.""" plot_data = [] for file, name in zip(log_files, names): d, log = {}, lu.load_json_stats(file) for phase in ['train', 'test']: x = lu.parse_json_stats(log, phase + '_epoch', 'epoch') y = lu.parse_json_stats(log, phase + '_epoch', key) d['x_' + phase], d['y_' + phase] = x, y d[phase + '_label'] = '[{:5.2f}] '.format(min(y) if y else 0) + name plot_data.append(d) assert len(plot_data) > 0, 'No data to plot' return plot_data def plot_error_curves_plotly(log_files, names, filename, key='top1_err'): """Plot error curves using plotly and save to file.""" plot_data = prepare_plot_data(log_files, names, key) colors = get_plot_colors(len(plot_data), 'plotly') # Prepare data for plots (3 sets, train duplicated w and w/o legend) data = [] for i, d in enumerate(plot_data): s = str(i) line_train = {'color': colors[i], 'dash': 'dashdot', 'width': 1.5} line_test = {'color': colors[i], 'dash': 'solid', 'width': 1.5} data.append(go.Scatter( x=d['x_train'], y=d['y_train'], mode='lines', name=d['train_label'], line=line_train, legendgroup=s, visible=True, showlegend=False )) data.append(go.Scatter( x=d['x_test'], y=d['y_test'], mode='lines', name=d['test_label'], line=line_test, legendgroup=s, visible=True, showlegend=True )) data.append(go.Scatter( x=d['x_train'], y=d['y_train'], mode='lines', name=d['train_label'], line=line_train, legendgroup=s, visible=False, showlegend=True )) # Prepare layout w ability to toggle 'all', 'train', 'test' titlefont = {'size': 18, 'color': '#7f7f7f'} vis = [[True, True, False], [False, False, True], [False, True, False]] buttons = zip(['all', 'train', 'test'], [[{'visible': v}] for v in vis]) buttons = [{'label': l, 'args': v, 'method': 'update'} for l, v in buttons] layout = go.Layout( title=key + ' vs. epoch<br>[dash=train, solid=test]', xaxis={'title': 'epoch', 'titlefont': titlefont}, yaxis={'title': key, 'titlefont': titlefont}, showlegend=True, hoverlabel={'namelength': -1}, updatemenus=[{ 'buttons': buttons, 'direction': 'down', 'showactive': True, 'x': 1.02, 'xanchor': 'left', 'y': 1.08, 'yanchor': 'top' }] ) # Create plotly plot offline.plot({'data': data, 'layout': layout}, filename=filename) def plot_error_curves_pyplot(log_files, names, filename=None, key='top1_err'): """Plot error curves using matplotlib.pyplot and save to file.""" plot_data = prepare_plot_data(log_files, names, key) colors = get_plot_colors(len(names)) for ind, d in enumerate(plot_data): c, lbl = colors[ind], d['test_label'] plt.plot(d['x_train'], d['y_train'], '--', c=c, alpha=0.8) plt.plot(d['x_test'], d['y_test'], '-', c=c, alpha=0.8, label=lbl) plt.title(key + ' vs. epoch\n[dash=train, solid=test]', fontsize=14) plt.xlabel('epoch', fontsize=14) plt.ylabel(key, fontsize=14) plt.grid(alpha=0.4) plt.legend() if filename: plt.savefig(filename) plt.clf() else: plt.show()

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/logging.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Logging.""" import builtins import decimal import logging import os import simplejson import sys from pycls.config import cfg import pycls.utils.distributed as du import pycls.utils.metrics as mu import pdb # Show filename and line number in logs _FORMAT = '[%(filename)s: %(lineno)3d]: %(message)s' # Log file name (for cfg.LOG_DEST = 'file') _LOG_FILE = 'stdout.log' # Printed json stats lines will be tagged w/ this _TAG = 'json_stats: ' def _suppress_print(): """Suppresses printing from the current process.""" def ignore(*_objects, _sep=' ', _end='\n', _file=sys.stdout, _flush=False): pass builtins.print = ignore def setup_logging(): """Sets up the logging.""" # Enable logging only for the master process if du.is_master_proc(): # Clear the root logger to prevent any existing logging config # (e.g. set by another module) from messing with our setup logging.root.handlers = [] # Construct logging configuration logging_config = { 'level': logging.INFO, 'format': _FORMAT } # Log either to stdout or to a file if cfg.LOG_DEST == 'stdout': logging_config['stream'] = sys.stdout else: logging_config['filename'] = os.path.join(cfg.OUT_DIR, _LOG_FILE) # Configure logging logging.basicConfig(**logging_config) else: pass # _suppress_print() def get_logger(name): """Retrieves the logger.""" return logging.getLogger(name) def log_json_stats(stats, cur_epoch=None, writer=None, is_epoch=False, params=0, flops=0, model=None, is_master=False): """Logs json stats.""" if writer is not None: for k, v in stats.items(): if isinstance(v, float) or isinstance(v, int): writer.add_scalar(k, v, cur_epoch + 1) # if model is not None: # for name, param in model.named_parameters(): # writer.add_histogram(name, param.clone().cpu().data.numpy(), cur_epoch) # Decimal + string workaround for having fixed len float vals in logs stats = { k: decimal.Decimal('{:.6f}'.format(v)) if isinstance(v, float) else v for k, v in stats.items() } json_stats = simplejson.dumps(stats, sort_keys=True, use_decimal=True) logger = get_logger(__name__) logger.info('{:s}{:s}'.format(_TAG, json_stats)) if is_epoch and cur_epoch is not None and is_master: epoch_id = cur_epoch + 1 result_info = ', '.join( [str(round(params / 1000000, 3)), str(round(flops / 1000000000, 3)), '{:.3f}'.format(stats['time_avg']), '{:.3f}'.format(stats['top1_err']), '{:.3f}'.format(stats['top5_err']), str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.DIM_LIST[0]), str(cfg.RGRAPH.SEED_TRAIN)]) with open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, epoch_id), "a") as text_file: text_file.write(result_info + '\n') def load_json_stats(log_file): """Loads json_stats from a single log file.""" with open(log_file, 'r') as f: lines = f.readlines() json_lines = [l[l.find(_TAG) + len(_TAG):] for l in lines if _TAG in l] json_stats = [simplejson.loads(l) for l in json_lines] return json_stats def parse_json_stats(log, row_type, key): """Extract values corresponding to row_type/key out of log.""" vals = [row[key] for row in log if row['_type'] == row_type and key in row] if key == 'iter' or key == 'epoch': vals = [int(val.split('/')[0]) for val in vals] return vals def get_log_files(log_dir, name_filter=''): """Get all log files in directory containing subdirs of trained models.""" names = [n for n in sorted(os.listdir(log_dir)) if name_filter in n] files = [os.path.join(log_dir, n, _LOG_FILE) for n in names] f_n_ps = [(f, n) for (f, n) in zip(files, names) if os.path.exists(f)] files, names = zip(*f_n_ps) return files, names

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/net.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Functions for manipulating networks.""" import itertools import math import torch import torch.nn as nn from pycls.config import cfg from ..models.relation_graph import * def init_weights(m): """Performs ResNet style weight initialization.""" if isinstance(m, nn.Conv2d) or isinstance(m, SymConv2d): # Note that there is no bias due to BN fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(mean=0.0, std=math.sqrt(2.0 / fan_out)) elif isinstance(m, TalkConv2d): # Note that there is no bias due to BN ### uniform init fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels * m.params_scale ### node specific init # fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(mean=0.0, std=math.sqrt(2.0 / fan_out)) # m.weight.data = m.weight.data*m.init_scale elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): zero_init_gamma = ( hasattr(m, 'final_bn') and m.final_bn and cfg.BN.ZERO_INIT_FINAL_GAMMA ) m.weight.data.fill_(0.0 if zero_init_gamma else 1.0) m.bias.data.zero_() elif isinstance(m, nn.Linear) or isinstance(m, TalkLinear) or isinstance(m, SymLinear): m.weight.data.normal_(mean=0.0, std=0.01) if m.bias is not None: m.bias.data.zero_() @torch.no_grad() def compute_precise_bn_stats(model, loader): """Computes precise BN stats on training data.""" # Compute the number of minibatches to use num_iter = min(cfg.BN.NUM_SAMPLES_PRECISE // loader.batch_size, len(loader)) # Retrieve the BN layers bns = [m for m in model.modules() if isinstance(m, torch.nn.BatchNorm2d)] # Initialize stats storage mus = [torch.zeros_like(bn.running_mean) for bn in bns] sqs = [torch.zeros_like(bn.running_var) for bn in bns] # Remember momentum values moms = [bn.momentum for bn in bns] # Disable momentum for bn in bns: bn.momentum = 1.0 # Accumulate the stats across the data samples for inputs, _labels in itertools.islice(loader, num_iter): model(inputs.cuda()) # Accumulate the stats for each BN layer for i, bn in enumerate(bns): m, v = bn.running_mean, bn.running_var sqs[i] += (v + m * m) / num_iter mus[i] += m / num_iter # Set the stats and restore momentum values for i, bn in enumerate(bns): bn.running_var = sqs[i] - mus[i] * mus[i] bn.running_mean = mus[i] bn.momentum = moms[i] def get_flat_weights(model): """Gets all model weights as a single flat vector.""" return torch.cat([p.data.view(-1, 1) for p in model.parameters()], 0) def set_flat_weights(model, flat_weights): """Sets all model weights from a single flat vector.""" k = 0 for p in model.parameters(): n = p.data.numel() p.data.copy_(flat_weights[k:(k + n)].view_as(p.data)) k += n assert k == flat_weights.numel() def model2adj(model): adj_dict = {} i = 0 for n, m in model.named_modules(): if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d): adj_dict['weight_{}'.format(i)] = m.weight.data.squeeze().cpu().numpy() i += 1 elif isinstance(m, SymLinear): weight = m.weight.data + m.weight.data.permute(1, 0) adj_dict['weight_{}'.format(i)] = weight.squeeze().cpu().numpy() i += 1 elif isinstance(m, SymConv2d): weight = m.weight.data + m.weight.data.permute(1, 0, 2, 3) adj_dict['weight_{}'.format(i)] = weight.squeeze().cpu().numpy() i += 1 elif isinstance(m, TalkLinear) or isinstance(m, TalkConv2d): adj_dict['weight_{}'.format(i)] = m.weight.data.squeeze().cpu().numpy() adj_dict['mask_{}'.format(i)] = m.mask.data.squeeze().cpu().numpy() i += 1 return adj_dict

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/distributed.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Distributed helpers.""" import torch from pycls.config import cfg def is_master_proc(): """Determines if the current process is the master process. Master process is responsible for logging, writing and loading checkpoints. In the multi GPU setting, we assign the master role to the rank 0 process. When training using a single GPU, there is only one training processes which is considered the master processes. """ return cfg.NUM_GPUS == 1 or torch.distributed.get_rank() == 0 def init_process_group(proc_rank, world_size): """Initializes the default process group.""" # Set the GPU to use torch.cuda.set_device(proc_rank) # Initialize the process group # print('--rank{},world{}--'.format(proc_rank, world_size)) # torch.distributed.init_process_group( # backend=cfg.DIST_BACKEND, # init_method="tcp://{}:{}".format(cfg.HOST, cfg.PORT), # world_size=world_size, # rank=proc_rank # ) torch.distributed.init_process_group( backend=cfg.DIST_BACKEND, init_method='env://', world_size=world_size, rank=proc_rank ) def destroy_process_group(): """Destroys the default process group.""" torch.distributed.destroy_process_group() def scaled_all_reduce(tensors): """Performs the scaled all_reduce operation on the provided tensors. The input tensors are modified in-place. Currently supports only the sum reduction operator. The reduced values are scaled by the inverse size of the process group (equivalent to cfg.NUM_GPUS). """ # Queue the reductions reductions = [] for tensor in tensors: reduction = torch.distributed.all_reduce(tensor, async_op=True) reductions.append(reduction) # Wait for reductions to finish for reduction in reductions: reduction.wait() # Scale the results for tensor in tensors: tensor.mul_(1.0 / cfg.NUM_GPUS) return tensors

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/metrics.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Functions for computing metrics.""" import numpy as np import torch import torch.nn as nn import pdb from pycls.config import cfg from functools import reduce import operator from ..models.relation_graph import * # Number of bytes in a megabyte _B_IN_MB = 1024 * 1024 def topks_correct(preds, labels, ks): """Computes the number of top-k correct predictions for each k.""" assert preds.size(0) == labels.size(0), \ 'Batch dim of predictions and labels must match' # Find the top max_k predictions for each sample _top_max_k_vals, top_max_k_inds = torch.topk( preds, max(ks), dim=1, largest=True, sorted=True ) # (batch_size, max_k) -> (max_k, batch_size) top_max_k_inds = top_max_k_inds.t() # (batch_size, ) -> (max_k, batch_size) rep_max_k_labels = labels.view(1, -1).expand_as(top_max_k_inds) # (i, j) = 1 if top i-th prediction for the j-th sample is correct top_max_k_correct = top_max_k_inds.eq(rep_max_k_labels) # Compute the number of topk correct predictions for each k topks_correct = [ top_max_k_correct[:k, :].view(-1).float().sum() for k in ks ] return topks_correct def topk_errors(preds, labels, ks): """Computes the top-k error for each k.""" num_topks_correct = topks_correct(preds, labels, ks) return [(1.0 - x / preds.size(0)) * 100.0 for x in num_topks_correct] def topk_accuracies(preds, labels, ks): """Computes the top-k accuracy for each k.""" num_topks_correct = topks_correct(preds, labels, ks) return [(x / preds.size(0)) * 100.0 for x in num_topks_correct] def params_count(model): """Computes the number of parameters.""" count = 0 for n,m in model.named_modules(): if isinstance(m, TalkConv2d) or isinstance(m, TalkLinear): count += np.sum([p.numel()*m.params_scale for p in m.parameters(recurse=False)]).item() else: count += np.sum([p.numel() for p in m.parameters(recurse=False)]).item() return int(count) def flops_count(model): """Computes the number of flops.""" assert cfg.TRAIN.DATASET in ['cifar10', 'cifar100', 'tinyimagenet200', 'imagenet'], \ 'Computing flops for {} is not supported'.format(cfg.TRAIN.DATASET) # im_size = 32 if cfg.TRAIN.DATASET == 'cifar10' else 224 if cfg.TRAIN.DATASET == 'cifar10': im_size = 32 elif cfg.TRAIN.DATASET == 'cifar100': im_size = 32 elif cfg.TRAIN.DATASET == 'tinyimagenet200': im_size = 64 else: im_size = 224 h, w = im_size, im_size count = 0 for n, m in model.named_modules(): if isinstance(m, nn.Conv2d): if '.se' in n: count += m.in_channels * m.out_channels + m.bias.numel() continue h_out = (h + 2 * m.padding[0] - m.kernel_size[0]) // m.stride[0] + 1 w_out = (w + 2 * m.padding[1] - m.kernel_size[1]) // m.stride[1] + 1 count += np.prod([ m.weight.numel(), h_out, w_out ]) if 'proj' not in n: h, w = h_out, w_out elif isinstance(m, TalkConv2d): h_out = (h + 2 * m.padding[0] - m.kernel_size[0]) // m.stride[0] + 1 w_out = (w + 2 * m.padding[1] - m.kernel_size[1]) // m.stride[1] + 1 count += int(np.prod([ m.weight.numel()*m.flops_scale, h_out, w_out ])) if 'proj' not in n and 'pool' not in n: h, w = h_out, w_out elif isinstance(m, nn.MaxPool2d): h = (h + 2 * m.padding - m.kernel_size) // m.stride + 1 w = (w + 2 * m.padding - m.kernel_size) // m.stride + 1 elif isinstance(m, TalkLinear): count += int(m.in_features * m.out_features * m.flops_scale) elif isinstance(m, nn.Linear): count += m.in_features * m.out_features return count def gpu_mem_usage(): """Computes the GPU memory usage for the current device (MB).""" mem_usage_bytes = torch.cuda.max_memory_allocated() return mem_usage_bytes / _B_IN_MB # Online FLOPs/Params calculation from CondenseNet codebase count_ops = 0 count_params = 0 def get_num_gen(gen): return sum(1 for x in gen) def is_pruned(layer): try: layer.mask return True except AttributeError: return False def is_leaf(model): return get_num_gen(model.children()) == 0 def get_layer_info(layer): layer_str = str(layer) type_name = layer_str[:layer_str.find('(')].strip() return type_name def get_layer_param(model): return sum([reduce(operator.mul, i.size(), 1) for i in model.parameters()]) ### The input batch size should be 1 to call this function def measure_layer(layer, x): global count_ops, count_params delta_ops = 0 delta_params = 0 multi_add = 1 type_name = get_layer_info(layer) ### ops_conv if type_name in ['Conv2d']: out_h = int((x.size()[2] + 2 * layer.padding[0] - layer.kernel_size[0]) / layer.stride[0] + 1) out_w = int((x.size()[3] + 2 * layer.padding[1] - layer.kernel_size[1]) / layer.stride[1] + 1) delta_ops = layer.in_channels * layer.out_channels * layer.kernel_size[0] * \ layer.kernel_size[1] * out_h * out_w / layer.groups * multi_add print(layer) print('out_h: ', out_h, 'out_w:', out_w) delta_params = get_layer_param(layer) ### ops_nonlinearity elif type_name in ['ReLU']: delta_ops = x.numel() delta_params = get_layer_param(layer) ### ops_pooling elif type_name in ['AvgPool2d', 'MaxPool2d']: in_w = x.size()[2] kernel_ops = layer.kernel_size * layer.kernel_size out_w = int((in_w + 2 * layer.padding - layer.kernel_size) / layer.stride + 1) out_h = int((in_w + 2 * layer.padding - layer.kernel_size) / layer.stride + 1) delta_ops = x.size()[0] * x.size()[1] * out_w * out_h * kernel_ops delta_params = get_layer_param(layer) elif type_name in ['AdaptiveAvgPool2d']: delta_ops = x.size()[0] * x.size()[1] * x.size()[2] * x.size()[3] delta_params = get_layer_param(layer) ### ops_linear elif type_name in ['Linear']: weight_ops = layer.weight.numel() * multi_add bias_ops = layer.bias.numel() delta_ops = x.size()[0] * (weight_ops + bias_ops) delta_params = get_layer_param(layer) elif type_name in ['WeightedSumTransform']: weight_ops = layer.weight.numel() * multi_add delta_ops = x.size()[0] * (weight_ops) delta_params = get_layer_param(layer) ### ops_nothing elif type_name in ['BatchNorm2d', 'Dropout2d', 'DropChannel', 'Dropout', 'Sigmoid', 'DirichletWeightedSumTransform', 'Softmax', 'Identity', 'Sequential']: delta_params = get_layer_param(layer) ### unknown layer type else: raise TypeError('unknown layer type: %s' % type_name) count_ops += delta_ops count_params += delta_params return def measure_model(model, H, W): global count_ops, count_params count_ops = 0 count_params = 0 data = torch.zeros(1, 3, H, W).cuda() def should_measure(x): return is_leaf(x) or is_pruned(x) def modify_forward(model): for child in model.children(): if should_measure(child): def new_forward(m): def lambda_forward(x): measure_layer(m, x) return m.old_forward(x) return lambda_forward child.old_forward = child.forward child.forward = new_forward(child) else: modify_forward(child) def restore_forward(model): for child in model.children(): # leaf node if is_leaf(child) and hasattr(child, 'old_forward'): child.forward = child.old_forward child.old_forward = None else: restore_forward(child) modify_forward(model) model.forward(data) restore_forward(model) return count_ops, count_params

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/multiprocessing.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Multiprocessing helpers.""" import multiprocessing as mp import traceback import subprocess import numpy as np import os from pycls.utils.error_handler import ErrorHandler import pycls.utils.distributed as du def run(proc_rank, world_size, error_queue, fun, fun_args, fun_kwargs): os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '12112' # print("--proc_rank{}, world_size{}--".format(proc_rank, world_size)) """Runs a function from a child process.""" try: # Initialize the process group du.init_process_group(proc_rank, world_size) # Run the function fun(*fun_args, **fun_kwargs) except KeyboardInterrupt: # Killed by the parent process pass except Exception: # Propagate exception to the parent process error_queue.put(traceback.format_exc()) finally: # Destroy the process group du.destroy_process_group() def multi_proc_run(num_proc, fun, fun_args=(), fun_kwargs={}): """Runs a function in a multi-proc setting.""" # Handle errors from training subprocesses error_queue = mp.SimpleQueue() error_handler = ErrorHandler(error_queue) # Run each training subprocess ps = [] for i in range(num_proc): p_i = mp.Process( target=run, args=(i, num_proc, error_queue, fun, fun_args, fun_kwargs) ) ps.append(p_i) p_i.start() error_handler.add_child(p_i.pid) # Wait for each subprocess to finish for p in ps: p.join() # get gpu usage def get_gpu_memory_map(): """Get the current gpu usage. Returns ------- usage: dict Keys are device ids as integers. Values are memory usage as integers in MB. """ result = subprocess.check_output( [ 'nvidia-smi', '--query-gpu=memory.used', '--format=csv,nounits,noheader' ], encoding='utf-8') # Convert lines into a dictionary gpu_memory = np.array([int(x) for x in result.strip().split('\n')]) return gpu_memory def auto_select_gpu(memory_threshold=7000, smooth_ratio=200): gpu_memory_raw = get_gpu_memory_map() + 10 gpu_memory = gpu_memory_raw / smooth_ratio gpu_memory = gpu_memory.sum() / (gpu_memory + 10) gpu_memory[gpu_memory_raw > memory_threshold] = 0 gpu_prob = gpu_memory / gpu_memory.sum() cuda = str(np.random.choice(len(gpu_prob), p=gpu_prob)) print('GPU select prob: {}, Select GPU {}'.format(gpu_prob, cuda)) return cuda

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/lr_policy.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Learning rate policies.""" import numpy as np from pycls.config import cfg def lr_fun_steps(cur_epoch): """Steps schedule (cfg.OPTIM.LR_POLICY = 'steps').""" ind = [i for i, s in enumerate(cfg.OPTIM.STEPS) if cur_epoch >= s][-1] return cfg.OPTIM.BASE_LR * (cfg.OPTIM.LR_MULT ** ind) def lr_fun_exp(cur_epoch): """Exponential schedule (cfg.OPTIM.LR_POLICY = 'exp').""" return cfg.OPTIM.BASE_LR * (cfg.OPTIM.GAMMA ** cur_epoch) def lr_fun_cos(cur_epoch): """Cosine schedule (cfg.OPTIM.LR_POLICY = 'cos').""" base_lr, max_epoch = cfg.OPTIM.BASE_LR, cfg.OPTIM.MAX_EPOCH return 0.5 * base_lr * (1.0 + np.cos(np.pi * cur_epoch / max_epoch)) def get_lr_fun(): """Retrieves the specified lr policy function""" lr_fun = 'lr_fun_' + cfg.OPTIM.LR_POLICY if lr_fun not in globals(): raise NotImplementedError('Unknown LR policy:' + cfg.OPTIM.LR_POLICY) return globals()[lr_fun] def get_epoch_lr(cur_epoch): """Retrieves the lr for the given epoch according to the policy.""" lr = get_lr_fun()(cur_epoch) # Linear warmup if cur_epoch < cfg.OPTIM.WARMUP_EPOCHS: alpha = cur_epoch / cfg.OPTIM.WARMUP_EPOCHS warmup_factor = cfg.OPTIM.WARMUP_FACTOR * (1.0 - alpha) + alpha lr *= warmup_factor return lr

py

RobDanns

RobDanns-main/deep_learning/pycls/utils/meters.py

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the original graph2nn github repo. # File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal # LICENSE file in the root directory of this source tree. """Meters.""" from collections import deque import datetime import numpy as np from pycls.config import cfg from pycls.utils.timer import Timer import pycls.utils.logging as lu import pycls.utils.metrics as metrics def eta_str(eta_td): """Converts an eta timedelta to a fixed-width string format.""" days = eta_td.days hrs, rem = divmod(eta_td.seconds, 3600) mins, secs = divmod(rem, 60) return '{0:02},{1:02}:{2:02}:{3:02}'.format(days, hrs, mins, secs) class ScalarMeter(object): """Measures a scalar value (adapted from Detectron).""" def __init__(self, window_size): self.deque = deque(maxlen=window_size) self.total = 0.0 self.count = 0 def reset(self): self.deque.clear() self.total = 0.0 self.count = 0 def add_value(self, value): self.deque.append(value) self.count += 1 self.total += value def get_win_median(self): return np.median(self.deque) def get_win_avg(self): return np.mean(self.deque) def get_global_avg(self): return self.total / self.count class TrainMeter(object): """Measures training stats.""" def __init__(self, epoch_iters): self.epoch_iters = epoch_iters self.max_iter = cfg.OPTIM.MAX_EPOCH * epoch_iters self.iter_timer = Timer() self.loss = ScalarMeter(cfg.LOG_PERIOD) self.loss_total = 0.0 self.lr = None # Current minibatch errors (smoothed over a window) self.mb_top1_err = ScalarMeter(cfg.LOG_PERIOD) self.mb_top5_err = ScalarMeter(cfg.LOG_PERIOD) # Number of misclassified examples self.num_top1_mis = 0 self.num_top5_mis = 0 self.num_samples = 0 def reset(self, timer=False): if timer: self.iter_timer.reset() self.loss.reset() self.loss_total = 0.0 self.lr = None self.mb_top1_err.reset() self.mb_top5_err.reset() self.num_top1_mis = 0 self.num_top5_mis = 0 self.num_samples = 0 def iter_tic(self): self.iter_timer.tic() def iter_toc(self): self.iter_timer.toc() def update_stats(self, top1_err, top5_err, loss, lr, mb_size): # Current minibatch stats self.mb_top1_err.add_value(top1_err) self.mb_top5_err.add_value(top5_err) self.loss.add_value(loss) self.lr = lr # Aggregate stats self.num_top1_mis += top1_err * mb_size self.num_top5_mis += top5_err * mb_size self.loss_total += loss * mb_size self.num_samples += mb_size def get_iter_stats(self, cur_epoch, cur_iter): eta_sec = self.iter_timer.average_time * ( self.max_iter - (cur_epoch * self.epoch_iters + cur_iter + 1) ) eta_td = datetime.timedelta(seconds=int(eta_sec)) mem_usage = metrics.gpu_mem_usage() stats = { '_type': 'train_iter', 'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH), 'iter': '{}/{}'.format(cur_iter + 1, self.epoch_iters), 'time_avg': self.iter_timer.average_time, 'time_diff': self.iter_timer.diff, 'eta': eta_str(eta_td), 'top1_err': self.mb_top1_err.get_win_median(), 'top5_err': self.mb_top5_err.get_win_median(), 'loss': self.loss.get_win_median(), 'lr': self.lr, 'mem': int(np.ceil(mem_usage)) } return stats def log_iter_stats(self, cur_epoch, cur_iter): if (cur_iter + 1) % cfg.LOG_PERIOD != 0: return stats = self.get_iter_stats(cur_epoch, cur_iter) lu.log_json_stats(stats) def get_epoch_stats(self, cur_epoch): eta_sec = self.iter_timer.average_time * ( self.max_iter - (cur_epoch + 1) * self.epoch_iters ) eta_td = datetime.timedelta(seconds=int(eta_sec)) mem_usage = metrics.gpu_mem_usage() top1_err = self.num_top1_mis / self.num_samples top5_err = self.num_top5_mis / self.num_samples avg_loss = self.loss_total / self.num_samples stats = { '_type': 'train_epoch', 'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH), 'time_avg': self.iter_timer.average_time, 'eta': eta_str(eta_td), 'top1_err': top1_err, 'top5_err': top5_err, 'loss': avg_loss, 'lr': self.lr, 'mem': int(np.ceil(mem_usage)) } return stats def log_epoch_stats(self, cur_epoch, writer, params=0, flops=0, is_master=False): stats = self.get_epoch_stats(cur_epoch) lu.log_json_stats(stats, cur_epoch, writer, is_epoch=False, params=params, flops=flops, is_master=is_master) class TestMeter(object): """Measures testing stats.""" def __init__(self, max_iter): self.max_iter = max_iter self.iter_timer = Timer() # Current minibatch errors (smoothed over a window) self.mb_top1_err = ScalarMeter(cfg.LOG_PERIOD) self.mb_top5_err = ScalarMeter(cfg.LOG_PERIOD) # Min errors (over the full test set) self.min_top1_err = 100.0 self.min_top5_err = 100.0 # Number of misclassified examples self.num_top1_mis = 0 self.num_top5_mis = 0 self.num_samples = 0 def reset(self, min_errs=False): if min_errs: self.min_top1_err = 100.0 self.min_top5_err = 100.0 self.iter_timer.reset() self.mb_top1_err.reset() self.mb_top5_err.reset() self.num_top1_mis = 0 self.num_top5_mis = 0 self.num_samples = 0 def iter_tic(self): self.iter_timer.tic() def iter_toc(self): self.iter_timer.toc() def update_stats(self, top1_err, top5_err, mb_size): self.mb_top1_err.add_value(top1_err) self.mb_top5_err.add_value(top5_err) self.num_top1_mis += top1_err * mb_size self.num_top5_mis += top5_err * mb_size self.num_samples += mb_size def get_iter_stats(self, cur_epoch, cur_iter): mem_usage = metrics.gpu_mem_usage() iter_stats = { '_type': 'test_iter', 'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH), 'iter': '{}/{}'.format(cur_iter + 1, self.max_iter), 'time_avg': self.iter_timer.average_time, 'time_diff': self.iter_timer.diff, 'top1_err': self.mb_top1_err.get_win_median(), 'top5_err': self.mb_top5_err.get_win_median(), 'mem': int(np.ceil(mem_usage)) } return iter_stats def log_iter_stats(self, cur_epoch, cur_iter): if (cur_iter + 1) % cfg.LOG_PERIOD != 0: return stats = self.get_iter_stats(cur_epoch, cur_iter) lu.log_json_stats(stats) def get_epoch_stats(self, cur_epoch): top1_err = self.num_top1_mis / self.num_samples top5_err = self.num_top5_mis / self.num_samples self.min_top1_err = min(self.min_top1_err, top1_err) self.min_top5_err = min(self.min_top5_err, top5_err) mem_usage = metrics.gpu_mem_usage() stats = { '_type': 'test_epoch', 'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH), 'time_avg': self.iter_timer.average_time, 'top1_err': top1_err, 'top5_err': top5_err, 'min_top1_err': self.min_top1_err, 'min_top5_err': self.min_top5_err, 'mem': int(np.ceil(mem_usage)) } return stats def log_epoch_stats(self, cur_epoch, writer, params=0, flops=0, model=None, is_master=False): stats = self.get_epoch_stats(cur_epoch) lu.log_json_stats(stats, cur_epoch, writer, is_epoch=True, params=params, flops=flops, model=model, is_master=is_master)

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