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import torch
import torch.nn.functional as F
import numpy
from torch_ac.utils import DictList
# dictionary that defines what head is required for each extra info used for auxiliary supervision
required_heads = {'seen_state': 'binary',
'see_door': 'binary',
'see_obj': 'binary',
'obj_in_instr': 'binary',
'in_front_of_what': 'multiclass9', # multi class classifier with 9 possible classes
'visit_proportion': 'continuous01', # continous regressor with outputs in [0, 1]
'bot_action': 'binary'
}
class ExtraInfoCollector:
'''
This class, used in rl.algos.base, allows connecting the extra information from the environment, and the
corresponding predictions using the specific heads in the model. It transforms them so that they are easy to use
to evaluate losses
'''
def __init__(self, aux_info, shape, device):
self.aux_info = aux_info
self.shape = shape
self.device = device
self.collected_info = dict()
self.extra_predictions = dict()
for info in self.aux_info:
self.collected_info[info] = torch.zeros(*shape, device=self.device)
if required_heads[info] == 'binary' or required_heads[info].startswith('continuous'):
# we predict one number only
self.extra_predictions[info] = torch.zeros(*shape, 1, device=self.device)
elif required_heads[info].startswith('multiclass'):
# means that this is a multi-class classification and we need to predict the whole proba distr
n_classes = int(required_heads[info].replace('multiclass', ''))
self.extra_predictions[info] = torch.zeros(*shape, n_classes, device=self.device)
else:
raise ValueError("{} not supported".format(required_heads[info]))
def process(self, env_info):
# env_info is now a tuple of dicts
env_info = [{k: v for k, v in dic.items() if k in self.aux_info} for dic in env_info]
env_info = {k: [env_info[_][k] for _ in range(len(env_info))] for k in env_info[0].keys()}
# env_info is now a dict of lists
return env_info
def fill_dictionaries(self, index, env_info, extra_predictions):
for info in self.aux_info:
dtype = torch.long if required_heads[info].startswith('multiclass') else torch.float
self.collected_info[info][index] = torch.tensor(env_info[info], dtype=dtype, device=self.device)
self.extra_predictions[info][index] = extra_predictions[info]
def end_collection(self, exps):
collected_info = dict()
extra_predictions = dict()
for info in self.aux_info:
# T x P -> P x T -> P * T
collected_info[info] = self.collected_info[info].transpose(0, 1).reshape(-1)
if required_heads[info] == 'binary' or required_heads[info].startswith('continuous'):
# T x P x 1 -> P x T x 1 -> P * T
extra_predictions[info] = self.extra_predictions[info].transpose(0, 1).reshape(-1)
elif type(required_heads[info]) == int:
# T x P x k -> P x T x k -> (P * T) x k
k = required_heads[info] # number of classes
extra_predictions[info] = self.extra_predictions[info].transpose(0, 1).reshape(-1, k)
# convert the dicts to DictLists, and add them to the exps DictList.
exps.collected_info = DictList(collected_info)
exps.extra_predictions = DictList(extra_predictions)
return exps
class SupervisedLossUpdater:
'''
This class, used by PPO, allows the evaluation of the supervised loss when using extra information from the
environment. It also handles logging accuracies/L2 distances/etc...
'''
def __init__(self, aux_info, supervised_loss_coef, recurrence, device):
self.aux_info = aux_info
self.supervised_loss_coef = supervised_loss_coef
self.recurrence = recurrence
self.device = device
self.log_supervised_losses = []
self.log_supervised_accuracies = []
self.log_supervised_L2_losses = []
self.log_supervised_prevalences = []
self.batch_supervised_loss = 0
self.batch_supervised_accuracy = 0
self.batch_supervised_L2_loss = 0
self.batch_supervised_prevalence = 0
def init_epoch(self):
self.log_supervised_losses = []
self.log_supervised_accuracies = []
self.log_supervised_L2_losses = []
self.log_supervised_prevalences = []
def init_batch(self):
self.batch_supervised_loss = 0
self.batch_supervised_accuracy = 0
self.batch_supervised_L2_loss = 0
self.batch_supervised_prevalence = 0
def eval_subbatch(self, extra_predictions, sb):
supervised_loss = torch.tensor(0., device=self.device)
supervised_accuracy = torch.tensor(0., device=self.device)
supervised_L2_loss = torch.tensor(0., device=self.device)
supervised_prevalence = torch.tensor(0., device=self.device)
binary_classification_tasks = 0
classification_tasks = 0
regression_tasks = 0
for pos, info in enumerate(self.aux_info):
coef = self.supervised_loss_coef[pos]
pred = extra_predictions[info]
target = dict.__getitem__(sb.collected_info, info)
if required_heads[info] == 'binary':
binary_classification_tasks += 1
classification_tasks += 1
supervised_loss += coef * F.binary_cross_entropy_with_logits(pred.reshape(-1), target)
supervised_accuracy += ((pred.reshape(-1) > 0).float() == target).float().mean()
supervised_prevalence += target.mean()
elif required_heads[info].startswith('continuous'):
regression_tasks += 1
mse = F.mse_loss(pred.reshape(-1), target)
supervised_loss += coef * mse
supervised_L2_loss += mse
elif required_heads[info].startswith('multiclass'):
classification_tasks += 1
supervised_accuracy += (pred.argmax(1).float() == target).float().mean()
supervised_loss += coef * F.cross_entropy(pred, target.long())
else:
raise ValueError("{} not supported".format(required_heads[info]))
if binary_classification_tasks > 0:
supervised_prevalence /= binary_classification_tasks
else:
supervised_prevalence = torch.tensor(-1)
if classification_tasks > 0:
supervised_accuracy /= classification_tasks
else:
supervised_accuracy = torch.tensor(-1)
if regression_tasks > 0:
supervised_L2_loss /= regression_tasks
else:
supervised_L2_loss = torch.tensor(-1)
self.batch_supervised_loss += supervised_loss.item()
self.batch_supervised_accuracy += supervised_accuracy.item()
self.batch_supervised_L2_loss += supervised_L2_loss.item()
self.batch_supervised_prevalence += supervised_prevalence.item()
return supervised_loss
def update_batch_values(self):
self.batch_supervised_loss /= self.recurrence
self.batch_supervised_accuracy /= self.recurrence
self.batch_supervised_L2_loss /= self.recurrence
self.batch_supervised_prevalence /= self.recurrence
def update_epoch_logs(self):
self.log_supervised_losses.append(self.batch_supervised_loss)
self.log_supervised_accuracies.append(self.batch_supervised_accuracy)
self.log_supervised_L2_losses.append(self.batch_supervised_L2_loss)
self.log_supervised_prevalences.append(self.batch_supervised_prevalence)
def end_training(self, logs):
logs["supervised_loss"] = numpy.mean(self.log_supervised_losses)
logs["supervised_accuracy"] = numpy.mean(self.log_supervised_accuracies)
logs["supervised_L2_loss"] = numpy.mean(self.log_supervised_L2_losses)
logs["supervised_prevalence"] = numpy.mean(self.log_supervised_prevalences)
return logs
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