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import math
import torch
from torch import nn
from torch.optim.optimizer import Optimizer
###### Borrowed from https://github.com/heykeetae/Self-Attention-GAN/blob/master/sagan_models.py ######
class SelfAttn(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim):
super(SelfAttn, self).__init__()
self.chanel_in = in_dim
# self.activation = activation
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)
self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)
self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""
inputs :
x : input feature maps(B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize, C, width, height = x.size()
proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1) # B X CX(N)
proj_key = self.key_conv(x).view(m_batchsize, -1, width * height) # B X C x (*W*H)
energy = torch.bmm(proj_query, proj_key) # transpose check
attention = self.softmax(energy) # BX (N) X (N)
proj_value = self.value_conv(x).view(m_batchsize, -1, width * height) # B X C X N
out = torch.bmm(proj_value, attention.permute(0, 2, 1))
out = out.view(m_batchsize, C, width, height)
out = self.gamma * out + x
return out
# return out, attention
#######################################################################################################
class DenseNeck(nn.Module):
def __init__(self, n_channels, growth):
super().__init__()
self.main = nn.Sequential(
nn.BatchNorm2d(n_channels),
nn.Conv2d(n_channels, growth * 4, 1, bias=False),
nn.BatchNorm2d(growth * 4),
nn.Conv2d(growth * 4, growth, 3, padding=1, bias=False),
)
def forward(self, x):
return torch.cat((x, self.main(x)), -3)
class DenseTransition(nn.Module):
def __init__(self, channels, reduction=0.5):
super().__init__()
self.main = nn.Sequential(
nn.BatchNorm2d(channels),
nn.ReLU(inplace=True),
nn.Conv2d(channels, int(channels * reduction), kernel_size=1, bias=False),
nn.AvgPool2d(kernel_size=2, stride=2)
)
def forward(self, x):
return self.main(x)
class DenseBlock(nn.Module):
def __init__(self, n_channels, n=8, growth=16):
super().__init__()
layers = []
for i in range(n):
layers.append(DenseNeck(n_channels, growth))
n_channels += growth
pass
self.main = nn.Sequential(*layers)
def forward(self, x):
return self.main(x)
#######################################################################################################
class Lion(Optimizer):
r"""Implements Lion algorithm."""
def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
"""Initialize the hyperparameters.
Args:
params (iterable): iterable of parameters to optimize or dicts defining parameter groups
lr (float): learning rate (default: 1e-4)
betas (Tuple[float, float]): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.99))
weight_decay (float): weight decay (L2 penalty) (default: 0)
"""
defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
super(Lion, self).__init__(params, defaults)
def __setstate__(self, state):
super(Lion, self).__setstate__(state)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
if grad.is_sparse:
raise RuntimeError('Lion does not support sparse gradients')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p.data)
state['exp_avg_sq'] = torch.zeros_like(p.data)
# Get hyperparameters
lr = group['lr']
beta1, beta2 = group['betas']
weight_decay = group['weight_decay']
# Update biased first moment estimate
state['step'] += 1
exp_avg = state['exp_avg']
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
# Update biased second raw moment estimate
exp_avg_sq = state['exp_avg_sq']
exp_avg_sq.mul_(beta2).addcmul_(grad - exp_avg, grad - exp_avg, value=1 - beta2)
# Compute the bias-corrected first and second moment estimates
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
step_size = lr / bias_correction1
# Update parameters
p.addcdiv_(exp_avg, denom, value=-step_size)
# Weight decay
if weight_decay != 0:
p.data.add_(p.data, alpha=-weight_decay * lr)
return loss |