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game.py
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game.py
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# 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.
from __future__ import print_function
import sys
import argparse
import contextlib
import torch.utils.data
import torch.nn.functional as F
import egg.core as core
from egg.zoo.external_game.features import CSVDataset
from torch.utils.data import DataLoader
from egg.zoo.external_game.archs import Sender, Receiver, ReinforceReceiver
def get_params():
parser = argparse.ArgumentParser()
parser.add_argument('--train_data', type=str, default=None,
help='Path to the train data')
parser.add_argument('--validation_data', type=str, default=None,
help='Path to the validation data')
parser.add_argument('--dump_data', type=str, default=None,
help='Path to the data for which to produce output information')
parser.add_argument('--dump_output', type=str, default=None,
help='Path for dumping output information')
parser.add_argument('--batches_per_epoch', type=int, default=1000,
help='Number of batches per epoch (default: 1000)')
parser.add_argument('--sender_hidden', type=int, default=10,
help='Size of the hidden layer of Sender (default: 10)')
parser.add_argument('--receiver_hidden', type=int, default=10,
help='Size of the hidden layer of Receiver (default: 10)')
parser.add_argument('--sender_embedding', type=int, default=10,
help='Dimensionality of the embedding hidden layer for Sender (default: 10)')
parser.add_argument('--receiver_embedding', type=int, default=10,
help='Dimensionality of the embedding hidden layer for Receiver (default: 10)')
parser.add_argument('--sender_cell', type=str, default='rnn',
help='Type of the cell used for Sender {rnn, gru, lstm} (default: rnn)')
parser.add_argument('--receiver_cell', type=str, default='rnn',
help='Type of the cell used for Receiver {rnn, gru, lstm} (default: rnn)')
parser.add_argument('--sender_layers', type=int, default=1,
help="Number of layers in Sender's RNN (default: 1)")
parser.add_argument('--receiver_layers', type=int, default=1,
help="Number of layers in Receiver's RNN (default: 1)")
parser.add_argument('--sender_entropy_coeff', type=float, default=1e-2,
help='The entropy regularisation coefficient for Sender (default: 1e-2)')
parser.add_argument('--receiver_entropy_coeff', type=float, default=1e-2,
help='The entropy regularisation coefficient for Receiver (default: 1e-2)')
parser.add_argument('--sender_lr', type=float, default=1e-1,
help="Learning rate for Sender's parameters (default: 1e-1)")
parser.add_argument('--receiver_lr', type=float, default=1e-1,
help="Learning rate for Receiver's parameters (default: 1e-1)")
parser.add_argument('--temperature', type=float, default=1.0,
help="GS temperature for the sender (default: 1.0)")
parser.add_argument('--train_mode', type=str, default='gs',
help="Selects whether GumbelSoftmax or Reinforce is used"
"(default: gs)")
parser.add_argument('--n_classes', type=int, default=None,
help='Number of classes for Receiver to output. If not set, is automatically deduced from '
'the training set')
parser.add_argument('--force_eos', action='store_true', default=False,
help="When set, forces that the last symbol of the message is EOS (default: False)")
args = core.init(parser)
return args
def dump(game, dataset, device, is_gs):
sender_inputs, messages, _, receiver_outputs, labels = \
core.dump_sender_receiver(game, dataset, gs=is_gs, device=device, variable_length=True)
for sender_input, message, receiver_output, label \
in zip(sender_inputs, messages, receiver_outputs, labels):
sender_input = ' '.join(map(str, sender_input.tolist()))
message = ' '.join(map(str, message.tolist()))
if is_gs: receiver_output = receiver_output.argmax()
print(f'{sender_input};{message};{receiver_output};{label.item()}')
def differentiable_loss(_sender_input, _message, _receiver_input, receiver_output, labels):
labels = labels.squeeze(1)
acc = (receiver_output.argmax(dim=1) == labels).detach().float()
loss = F.cross_entropy(receiver_output, labels, reduction="none")
return loss, {'acc': acc}
def non_differentiable_loss(_sender_input, _message, _receiver_input, receiver_output, labels):
labels = labels.squeeze(1)
acc = (receiver_output == labels).detach().float()
return -acc, {'acc': acc}
def build_model(opts, train_loader, dump_loader):
n_features = train_loader.dataset.get_n_features() if train_loader else dump_loader.dataset.get_n_features()
if opts.n_classes is not None:
receiver_outputs = opts.n_classes
else:
receiver_outputs = train_loader.dataset.get_output_max() + 1 if train_loader else \
dump_loader.dataset.get_output_max() + 1
sender = Sender(n_hidden=opts.sender_hidden, n_features=n_features)
if opts.train_mode.lower() == 'gs':
loss = differentiable_loss
receiver = Receiver(output_size=receiver_outputs, n_hidden=opts.receiver_hidden)
else:
loss = non_differentiable_loss
receiver = ReinforceReceiver(output_size=receiver_outputs, n_hidden=opts.receiver_hidden)
return sender, receiver, loss
if __name__ == "__main__":
opts = get_params()
print(f'Launching game with parameters: {opts}')
device = torch.device("cuda" if opts.cuda else "cpu")
train_loader = None
if opts.train_data:
train_loader = DataLoader(CSVDataset(path=opts.train_data),
batch_size=opts.batch_size,
shuffle=True, num_workers=1)
validation_loader = None
if opts.validation_data:
validation_loader = DataLoader(CSVDataset(path=opts.validation_data),
batch_size=opts.batch_size,
shuffle=False, num_workers=1)
dump_loader = None
if opts.dump_data:
dump_loader = DataLoader(CSVDataset(path=opts.dump_data),
batch_size=opts.batch_size,
shuffle=False, num_workers=1)
assert train_loader or dump_loader, 'Either training or dump data must be specified'
sender, receiver, loss = build_model(opts, train_loader, dump_loader)
if opts.train_mode.lower() == 'rf':
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, force_eos=opts.force_eos,
num_layers=opts.sender_layers)
receiver = core.RnnReceiverReinforce(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_layers)
game = core.SenderReceiverRnnReinforce(sender, receiver, non_differentiable_loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
elif opts.train_mode.lower() == 'gs':
sender = core.RnnSenderGS(sender, opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, temperature=opts.temperature,
force_eos=opts.force_eos)
receiver = core.RnnReceiverGS(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell)
game = core.SenderReceiverRnnGS(sender, receiver, differentiable_loss)
else:
raise NotImplementedError(f'Unknown training mode, {opts.mode}')
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=validation_loader)
if dump_loader is not None:
if opts.dump_output:
with open(opts.dump_output, 'w') as f, contextlib.redirect_stdout(f):
dump(game, dump_loader, device, opts.train_mode.lower() == 'gs')
else:
dump(game, dump_loader, device, opts.train_mode.lower() == 'gs')
else:
trainer.train(n_epochs=opts.n_epochs)
if opts.checkpoint_dir:
trainer.save_checkpoint()
core.close()