def main(params):
opts = get_params(params)
print(json.dumps(vars(opts)))
device = opts.device
train_loader = OneHotLoader(n_bits=opts.n_bits,
bits_s=opts.bits_s,
bits_r=opts.bits_r,
batch_size=opts.batch_size,
batches_per_epoch=opts.n_examples_per_epoch/opts.batch_size)
test_loader = UniformLoader(n_bits=opts.n_bits, bits_s=opts.bits_s, bits_r=opts.bits_r)
test_loader.batch = [x.to(device) for x in test_loader.batch]
sender = Sender(n_bits=opts.n_bits, n_hidden=opts.sender_hidden,
vocab_size=opts.vocab_size)
if opts.mode == 'gs':
receiver = Receiver(n_bits=opts.n_bits, n_hidden=opts.receiver_hidden, vocab_size=opts.vocab_size)
sender = core.GumbelSoftmaxWrapper(agent=sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss)
elif opts.mode == 'rf':
sender = core.ReinforceWrapper(agent=sender)
receiver = Receiver(n_bits=opts.n_bits, n_hidden=opts.receiver_hidden, vocab_size=opts.vocab_size)
receiver = core.ReinforceDeterministicWrapper(agent=receiver)
game = core.SymbolGameReinforce(sender, receiver, diff_loss, sender_entropy_coeff=opts.sender_entropy_coeff)
elif opts.mode == 'non_diff':
sender = core.ReinforceWrapper(agent=sender)
receiver = ReinforcedReceiver(n_bits=opts.n_bits, n_hidden=opts.receiver_hidden, vocab_size=opts.vocab_size)
game = core.SymbolGameReinforce(sender, receiver, non_diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
else:
assert False, 'Unknown training mode'
optimizer = torch.optim.Adam(
[
dict(params=sender.parameters(), lr=opts.sender_lr),
dict(params=receiver.parameters(), lr=opts.receiver_lr)
])
loss = game.loss
intervention = CallbackEvaluator(test_loader, device=device, is_gs=opts.mode == 'gs', loss=loss, var_length=False,
input_intervention=True)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr)
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, epoch_callback=intervention, as_json=True,
early_stopping=early_stopper)
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
print(opts)
label_mapping = torch.LongTensor([x % opts.n_labels for x in range(100)])
print('# label mapping', label_mapping.tolist())
kwargs = {'num_workers': 1, 'pin_memory': True} if opts.cuda else {}
transform = transforms.ToTensor()
train_dataset = datasets.MNIST('./data',
train=True,
download=True,
transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=opts.batch_size,
shuffle=True,
**kwargs)
train_loader = DoubleMnist(train_loader, label_mapping)
test_dataset = datasets.MNIST('./data', train=False, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16 * 1024,
shuffle=False,
**kwargs)
test_loader = DoubleMnist(test_loader, label_mapping)
sender = Sender(vocab_size=opts.vocab_size)
receiver = Receiver(vocab_size=opts.vocab_size,
n_classes=opts.n_labels,
n_hidden=opts.n_hidden)
sender = core.GumbelSoftmaxWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
intervention = CallbackEvaluator(test_loader,
device=opts.device,
loss=game.loss,
is_gs=True,
var_length=False,
input_intervention=False)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
intervention
])
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
print(json.dumps(vars(opts)))
kwargs = {'num_workers': 1, 'pin_memory': True} if opts.cuda else {}
transform = transforms.ToTensor()
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=True, download=True,
transform=transform),
batch_size=opts.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=False, transform=transform),
batch_size=16 * 1024, shuffle=False, **kwargs)
n_classes = 10
binarize = False
test_loader = SplitImages(TakeFirstLoader(test_loader, n=1), rows_receiver=opts.receiver_rows,
rows_sender=opts.sender_rows, binarize=binarize, receiver_bottom=True)
train_loader = SplitImages(train_loader, rows_sender=opts.sender_rows, rows_receiver=opts.receiver_rows,
binarize=binarize, receiver_bottom=True)
sender = Sender(vocab_size=opts.vocab_size)
receiver = Receiver(vocab_size=opts.vocab_size, n_classes=n_classes)
sender = core.GumbelSoftmaxWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
intervention = CallbackEvaluator(test_loader, device=opts.device, loss=game.loss,
is_gs=True,
var_length=False,
input_intervention=True)
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader,
callbacks=[core.ConsoleLogger(as_json=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
intervention])
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
print(opts)
device = opts.device
train_loader = OneHotLoader(n_bits=opts.n_bits,
bits_s=opts.bits_s,
bits_r=opts.bits_r,
batch_size=opts.batch_size,
batches_per_epoch=opts.n_examples_per_epoch /
opts.batch_size)
test_loader = UniformLoader(n_bits=opts.n_bits,
bits_s=opts.bits_s,
bits_r=opts.bits_r)
test_loader.batch = [x.to(device) for x in test_loader.batch]
if not opts.variable_length:
sender = Sender(n_bits=opts.n_bits,
n_hidden=opts.sender_hidden,
vocab_size=opts.vocab_size)
if opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(agent=sender,
temperature=opts.temperature)
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.SymbolReceiverWrapper(
receiver,
vocab_size=opts.vocab_size,
agent_input_size=opts.receiver_hidden)
game = core.SymbolGameGS(sender, receiver, diff_loss)
elif opts.mode == 'rf':
sender = core.ReinforceWrapper(agent=sender)
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.SymbolReceiverWrapper(
receiver,
vocab_size=opts.vocab_size,
agent_input_size=opts.receiver_hidden)
receiver = core.ReinforceDeterministicWrapper(agent=receiver)
game = core.SymbolGameReinforce(
sender,
receiver,
diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff)
elif opts.mode == 'non_diff':
sender = core.ReinforceWrapper(agent=sender)
receiver = ReinforcedReceiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.SymbolReceiverWrapper(
receiver,
vocab_size=opts.vocab_size,
agent_input_size=opts.receiver_hidden)
game = core.SymbolGameReinforce(
sender,
receiver,
non_diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
else:
if opts.mode != 'rf':
print('Only mode=rf is supported atm')
opts.mode = 'rf'
if opts.sender_cell == 'transformer':
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
sender = Sender(
n_bits=opts.n_bits,
n_hidden=opts.sender_hidden,
vocab_size=opts.sender_hidden) # TODO: not really vocab
sender = core.TransformerSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
max_len=opts.max_len,
num_layers=1,
num_heads=1,
hidden_size=opts.sender_hidden)
else:
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
sender = Sender(
n_bits=opts.n_bits,
n_hidden=opts.sender_hidden,
vocab_size=opts.sender_hidden) # TODO: not really vocab
sender = core.RnnSenderReinforce(agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
cell=opts.sender_cell)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_bits=opts.n_bits, n_hidden=opts.receiver_emb)
receiver = core.TransformerReceiverDeterministic(
receiver,
opts.vocab_size,
opts.max_len,
opts.receiver_emb,
num_heads=1,
hidden_size=opts.receiver_hidden,
num_layers=1)
else:
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver,
opts.vocab_size,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell)
game = core.SenderReceiverRnnGS(sender, receiver, diff_loss)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
optimizer = torch.optim.Adam([
dict(params=sender.parameters(), lr=opts.sender_lr),
dict(params=receiver.parameters(), lr=opts.receiver_lr)
])
loss = game.loss
intervention = CallbackEvaluator(test_loader,
device=device,
is_gs=opts.mode == 'gs',
loss=loss,
var_length=opts.variable_length,
input_intervention=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
intervention
])
trainer.train(n_epochs=opts.n_epochs)
core.close()