def retrain_receiver(receiver_generator, sender):
receiver = receiver_generator()
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=0.0,
receiver_entropy_coeff=0.0,
)
optimizer = torch.optim.Adam(receiver.parameters(), lr=opts.lr)
early_stopper = EarlyStopperAccuracy(
opts.early_stopping_thr, validation=True
)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[early_stopper, Evaluator(loaders, opts.device, freq=0)],
)
trainer.train(n_epochs=opts.n_epochs // 2)
accs = [x[1]["acc"] for x in early_stopper.validation_stats]
return accs
def main(params):
opts = get_params(params)
print(opts)
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)
test_dataset = datasets.MNIST("./data",
train=False,
download=False,
transform=transform)
n_classes = 10
label_mapping = torch.LongTensor([x % n_classes for x in range(100)])
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=opts.batch_size,
shuffle=True,
**kwargs)
train_loader = DoubleMnist(train_loader, label_mapping)
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,
linear_channel=opts.linear_channel == 1,
softmax_channel=opts.softmax_non_linearity,
)
receiver = Receiver(vocab_size=opts.vocab_size, n_classes=n_classes)
if opts.softmax_non_linearity == 0 and opts.linear_channel == 0:
sender = AlwaysRelaxedWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True, print_train_loss=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
],
)
trainer.train(n_epochs=opts.n_epochs)
core.close()
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_dataset = datasets.MNIST('./data',
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST('./data',
train=False,
download=False,
transform=transform)
n_classes = 10
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=opts.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=opts.batch_size,
shuffle=False,
**kwargs)
sender = Sender(vocab_size=opts.vocab_size,
linear_channel=opts.linear_channel == 1,
softmax_channel=opts.softmax_non_linearity)
receiver = Receiver(vocab_size=opts.vocab_size, n_classes=n_classes)
if opts.softmax_non_linearity == 0 and opts.linear_channel == 0:
sender = AlwaysRelaxedWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
as_json=True,
early_stopping=early_stopper,
print_train_loss=True)
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(json.dumps(vars(opts)))
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)
test_dataset = datasets.MNIST('./data', train=False, download=False,
transform=transform)
n_classes = 10
corrupt_labels_(dataset=train_dataset, p_corrupt=opts.p_corrupt, seed=opts.random_seed+1)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=opts.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=opts.batch_size, shuffle=False, **kwargs)
deeper_alice = opts.deeper_alice == 1 and opts.deeper == 1
deeper_bob = opts.deeper_alice != 1 and opts.deeper == 1
sender = Sender(vocab_size=opts.vocab_size, deeper=deeper_alice, linear_channel=opts.linear_channel == 1,
softmax_channel=opts.softmax_non_linearity == 1)
receiver = Receiver(vocab_size=opts.vocab_size, n_classes=n_classes, deeper=deeper_bob)
if opts.softmax_non_linearity != 1 and opts.linear_channel != 1:
sender = AlwaysRelaxedWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader,
callbacks=[core.ConsoleLogger(as_json=True, print_train_loss=True),
EarlyStopperAccuracy(opts.early_stopping_thr)]
)
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
print(torch.cuda.is_available())
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
#elif opts.probs == "creneau":
# ones = np.ones(int(opts.n_features/2))
# tens = 10*np.ones(opts.n_features-int(opts.n_features/2))
# probs = np.concatenate((tens,ones),axis=0)
#elif opts.probs == "toy":
# fives = 5*np.ones(int(opts.n_features/10))
# ones = np.ones(opts.n_features-int(opts.n_features/10))
# probs = np.concatenate((fives,ones),axis=0)
#elif opts.probs == "escalier":
# ones = np.ones(int(opts.n_features/4))
# tens = 10*np.ones(int(opts.n_features/4))
# huns = 100*np.ones(int(opts.n_features/4))
# thous = 1000*np.ones(opts.n_features-3*int(opts.n_features/4))
# probs = np.concatenate((thous,huns,tens,ones),axis=0)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
else:
receiver = Receiver(n_features=opts.receiver_hidden, n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatient(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
# If impatient 2
#receiver = RnnReceiverImpatient2(receiver, opts.vocab_size, opts.receiver_embedding,
# opts.receiver_hidden, cell=opts.receiver_cell,
# num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
if not opts.impatient:
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
else:
game = SenderImpatientReceiverRnnReinforce(sender, receiver, loss_impatient, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
for epoch in range(int(opts.n_epochs)):
print("Epoch: "+str(epoch))
if epoch%100==0:
trainer.optimizer.defaults["lr"]/=2
trainer.train(n_epochs=1)
if opts.checkpoint_dir:
trainer.save_checkpoint(name=f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}')
if not opts.impatient:
acc_vec,messages=dump(trainer.game, opts.n_features, device, False,epoch)
else:
acc_vec,messages=dump_impatient(trainer.game, opts.n_features, device, False,epoch)
# ADDITION TO SAVE MESSAGES
all_messages=[]
for x in messages:
x = x.cpu().numpy()
all_messages.append(x)
all_messages = np.asarray(all_messages)
if epoch%50==0:
torch.save(sender.state_dict(), opts.dir_save+"/sender/sender_weights"+str(epoch)+".pth")
torch.save(receiver.state_dict(), opts.dir_save+"/receiver/receiver_weights"+str(epoch)+".pth")
#print(acc_vec)
np.save(opts.dir_save+'/messages/messages_'+str((epoch))+'.npy', all_messages)
np.save(opts.dir_save+'/accuracy/accuracy_'+str((epoch))+'.npy', acc_vec)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
else:
receiver = Receiver(n_features=opts.receiver_hidden, n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatient(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
# If impatient 2
#receiver = RnnReceiverImpatient2(receiver, opts.vocab_size, opts.receiver_embedding,
# opts.receiver_hidden, cell=opts.receiver_cell,
# num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
sender.load_state_dict(torch.load(opts.sender_weights,map_location=torch.device('cpu')))
receiver.load_state_dict(torch.load(opts.receiver_weights,map_location=torch.device('cpu')))
if not opts.impatient:
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen)
else:
game = SenderImpatientReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
# Test impose message
if not opts.impatient:
acc_vec,messages=dump(trainer.game, opts.n_features, device, False)
else:
acc_vec,messages=dump_impatient(trainer.game, opts.n_features, device, False,save_dir=opts.save_dir)
all_messages=[]
for x in messages:
x = x.cpu().numpy()
all_messages.append(x)
all_messages = np.asarray(all_messages)
messages=-1*np.ones((opts.n_features,opts.max_len))
for i in range(len(all_messages)):
for j in range(all_messages[i].shape[0]):
messages[i,j]=all_messages[i][j]
np.save(opts.save_dir+"messages_analysis.npy",messages)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
elif opts.probs == 'perso':
probs = opts.n_features+1 - np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader,
callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True)])
""" mode accuracy chope a chaque epoch
accs=[]
all_messages,acc=dump(trainer.game, opts.n_features, device, False)
np.save('messages_0.npy', all_messages)
accs.append(acc)
for i in range(int(opts.n_epochs)):
print(i)
trainer.train(n_epochs=1)
all_messages,acc=dump(trainer.game, opts.n_features, device, False)
np.save('messages_'+str((i+1))+'.npy', all_messages)
accs.append(acc)
np.save('accuracy.npy',accs)
"""
trainer.train(n_epochs=opts.n_epochs)
#if opts.checkpoint_dir:
#trainer.save_checkpoint(name=f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}')
#for i in range(30):
# for k in range(30):
# if i<k:
# all_messages=dump(trainer.game, opts.n_features, device, False,pos_m=i,pos_M=k)
all_messages=dump(trainer.game, opts.n_features, device, False)
print(all_messages)
#freq=np.zeros(30)
#for message in all_messages[0]:
# if i in range(message.shape[0]):
# freq[int(message[i])]+=1
#print(freq)
core.close()
def main(params):
import copy
opts = get_params(params)
device = opts.device
train_loader, validation_loader = get_dsprites_dataloader(
path_to_data='egg/zoo/data_loaders/data/dsprites.npz',
batch_size=opts.batch_size,
subsample=opts.subsample,
image=False)
n_dim = opts.n_attributes
if opts.receiver_cell in ['lstm', 'rnn', 'gru']:
receiver = Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim)
receiver = core.RnnReceiverDeterministic(receiver,
opts.vocab_size + 1,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell)
else:
raise ValueError(f'Unknown receiver cell, {opts.receiver_cell}')
if opts.sender_cell in ['lstm', 'rnn', 'gru']:
sender = Sender(n_inputs=n_dim, n_hidden=opts.sender_hidden)
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,
force_eos=False,
cell=opts.sender_cell)
else:
raise ValueError(f'Unknown sender cell, {opts.sender_cell}')
sender = PlusOneWrapper(sender)
loss = DiffLoss(opts.n_attributes, opts.n_values)
baseline = {
'no': core.baselines.NoBaseline,
'mean': core.baselines.MeanBaseline,
'builtin': core.baselines.BuiltInBaseline
}[opts.baseline]
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0.0,
length_cost=0.0,
baseline_type=baseline)
optimizer = torch.optim.Adam(game.parameters(), lr=opts.lr)
latent_values, _ = zip(*[batch for batch in validation_loader])
latent_values = latent_values[0]
metrics_evaluator = Metrics(latent_values,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq)
loaders = []
#loaders.append(("generalization hold out", generalization_holdout_loader, DiffLoss(
#opts.n_attributes, opts.n_values, generalization=True)))
loaders.append(("uniform holdout", validation_loader,
DiffLoss(opts.n_attributes, opts.n_values)))
holdout_evaluator = Evaluator(loaders, opts.device, freq=0)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
core.ConsoleLogger(as_json=True,
print_train_loss=False),
early_stopper, metrics_evaluator,
holdout_evaluator
])
trainer.train(n_epochs=opts.n_epochs)
last_epoch_interaction = early_stopper.validation_stats[-1][1]
validation_acc = last_epoch_interaction.aux['acc'].mean()
uniformtest_acc = holdout_evaluator.results['uniform holdout']['acc']
# Train new agents
if validation_acc > 0.99:
def _set_seed(seed):
import random
import numpy as np
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
core.get_opts().preemptable = False
core.get_opts().checkpoint_path = None
# freeze Sender and probe how fast a simple Receiver will learn the thing
def retrain_receiver(receiver_generator, sender):
receiver = receiver_generator()
game = core.SenderReceiverRnnReinforce(sender,
receiver,
loss,
sender_entropy_coeff=0.0,
receiver_entropy_coeff=0.0)
optimizer = torch.optim.Adam(receiver.parameters(), lr=opts.lr)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
early_stopper,
Evaluator(loaders, opts.device, freq=0)
])
trainer.train(n_epochs=opts.n_epochs // 2)
accs = [x[1]['acc'] for x in early_stopper.validation_stats]
return accs
frozen_sender = Freezer(copy.deepcopy(sender))
def gru_receiver_generator(): return \
core.RnnReceiverDeterministic(Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=opts.receiver_hidden, cell='gru')
def small_gru_receiver_generator(): return \
core.RnnReceiverDeterministic(
Receiver(n_hidden=100, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=100, cell='gru')
def tiny_gru_receiver_generator(): return \
core.RnnReceiverDeterministic(
Receiver(n_hidden=50, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=50, cell='gru')
def nonlinear_receiver_generator(): return \
NonLinearReceiver(n_outputs=n_dim, vocab_size=opts.vocab_size + 1,
max_length=opts.max_len, n_hidden=opts.receiver_hidden)
for name, receiver_generator in [
('gru', gru_receiver_generator),
('nonlinear', nonlinear_receiver_generator),
('tiny_gru', tiny_gru_receiver_generator),
('small_gru', small_gru_receiver_generator),
]:
for seed in range(17, 17 + 3):
_set_seed(seed)
accs = retrain_receiver(receiver_generator, frozen_sender)
accs += [1.0] * (opts.n_epochs // 2 - len(accs))
auc = sum(accs)
print(
json.dumps({
"mode": "reset",
"seed": seed,
"receiver_name": name,
"auc": auc
}))
print('---End--')
core.close()
def main(params):
import copy
opts = get_params(params)
device = opts.device
full_data = enumerate_attribute_value(opts.n_attributes, opts.n_values)
if opts.density_data > 0:
sampled_data = select_subset_V2(
full_data, opts.density_data, opts.n_attributes, opts.n_values
)
full_data = copy.deepcopy(sampled_data)
train, generalization_holdout = split_holdout(full_data)
train, uniform_holdout = split_train_test(train, 0.1)
generalization_holdout, train, uniform_holdout, full_data = [
one_hotify(x, opts.n_attributes, opts.n_values)
for x in [generalization_holdout, train, uniform_holdout, full_data]
]
train, validation = ScaledDataset(train, opts.data_scaler), ScaledDataset(train, 1)
generalization_holdout, uniform_holdout, full_data = (
ScaledDataset(generalization_holdout),
ScaledDataset(uniform_holdout),
ScaledDataset(full_data),
)
generalization_holdout_loader, uniform_holdout_loader, full_data_loader = [
DataLoader(x, batch_size=opts.batch_size)
for x in [generalization_holdout, uniform_holdout, full_data]
]
train_loader = DataLoader(train, batch_size=opts.batch_size)
validation_loader = DataLoader(validation, batch_size=len(validation))
n_dim = opts.n_attributes * opts.n_values
if opts.receiver_cell in ["lstm", "rnn", "gru"]:
receiver = Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size + 1,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell,
)
else:
raise ValueError(f"Unknown receiver cell, {opts.receiver_cell}")
if opts.sender_cell in ["lstm", "rnn", "gru"]:
sender = Sender(n_inputs=n_dim, n_hidden=opts.sender_hidden)
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,
)
else:
raise ValueError(f"Unknown sender cell, {opts.sender_cell}")
sender = PlusOneWrapper(sender)
loss = DiffLoss(opts.n_attributes, opts.n_values)
baseline = {
"no": core.baselines.NoBaseline,
"mean": core.baselines.MeanBaseline,
"builtin": core.baselines.BuiltInBaseline,
}[opts.baseline]
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0.0,
length_cost=0.0,
baseline_type=baseline,
)
optimizer = torch.optim.Adam(game.parameters(), lr=opts.lr)
metrics_evaluator = Metrics(
validation.examples,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq,
)
loaders = []
loaders.append(
(
"generalization hold out",
generalization_holdout_loader,
DiffLoss(opts.n_attributes, opts.n_values, generalization=True),
)
)
loaders.append(
(
"uniform holdout",
uniform_holdout_loader,
DiffLoss(opts.n_attributes, opts.n_values),
)
)
holdout_evaluator = Evaluator(loaders, opts.device, freq=0)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr, validation=True)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
core.ConsoleLogger(as_json=True, print_train_loss=False),
early_stopper,
metrics_evaluator,
holdout_evaluator,
],
)
trainer.train(n_epochs=opts.n_epochs)
last_epoch_interaction = early_stopper.validation_stats[-1][1]
validation_acc = last_epoch_interaction.aux["acc"].mean()
uniformtest_acc = holdout_evaluator.results["uniform holdout"]["acc"]
# Train new agents
if validation_acc > 0.99:
def _set_seed(seed):
import random
import numpy as np
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
core.get_opts().preemptable = False
core.get_opts().checkpoint_path = None
# freeze Sender and probe how fast a simple Receiver will learn the thing
def retrain_receiver(receiver_generator, sender):
receiver = receiver_generator()
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=0.0,
receiver_entropy_coeff=0.0,
)
optimizer = torch.optim.Adam(receiver.parameters(), lr=opts.lr)
early_stopper = EarlyStopperAccuracy(
opts.early_stopping_thr, validation=True
)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[early_stopper, Evaluator(loaders, opts.device, freq=0)],
)
trainer.train(n_epochs=opts.n_epochs // 2)
accs = [x[1]["acc"] for x in early_stopper.validation_stats]
return accs
frozen_sender = Freezer(copy.deepcopy(sender))
def gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=opts.receiver_hidden,
cell="gru",
)
def small_gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=100, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=100,
cell="gru",
)
def tiny_gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=50, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=50,
cell="gru",
)
def nonlinear_receiver_generator():
return NonLinearReceiver(
n_outputs=n_dim,
vocab_size=opts.vocab_size + 1,
max_length=opts.max_len,
n_hidden=opts.receiver_hidden,
)
for name, receiver_generator in [
("gru", gru_receiver_generator),
("nonlinear", nonlinear_receiver_generator),
("tiny_gru", tiny_gru_receiver_generator),
("small_gru", small_gru_receiver_generator),
]:
for seed in range(17, 17 + 3):
_set_seed(seed)
accs = retrain_receiver(receiver_generator, frozen_sender)
accs += [1.0] * (opts.n_epochs // 2 - len(accs))
auc = sum(accs)
print(
json.dumps(
{
"mode": "reset",
"seed": seed,
"receiver_name": name,
"auc": auc,
}
)
)
print("---End--")
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
# For compatibility, after https://github.com/facebookresearch/EGG/pull/130
# the meaning of `length` changed a bit. Before it included the EOS symbol; now
# it doesn't. To ensure that hyperparameters/CL arguments do not change,
# we subtract it here.
opts.max_len -= 1
device = opts.device
if opts.probs == "uniform":
probs = np.ones(opts.n_features)
elif opts.probs == "powerlaw":
probs = 1 / np.arange(1, opts.n_features + 1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(",")], dtype=np.float32)
probs /= probs.sum()
print("the probs are: ", probs, flush=True)
train_loader = OneHotLoader(
n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch,
probs=probs,
)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == "transformer":
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender,
)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(
sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
)
if opts.receiver_cell == "transformer":
receiver = Receiver(
n_features=opts.n_features, n_hidden=opts.receiver_embedding
)
receiver = core.TransformerReceiverDeterministic(
receiver,
opts.vocab_size,
opts.max_len,
opts.receiver_embedding,
opts.receiver_num_heads,
opts.receiver_hidden,
opts.receiver_num_layers,
causal=opts.causal_receiver,
)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers,
)
empty_logger = LoggingStrategy.minimal()
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
train_logging_strategy=empty_logger,
length_cost=opts.length_cost,
)
optimizer = core.build_optimizer(game.parameters())
callbacks = [
EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True),
]
if opts.checkpoint_dir:
checkpoint_name = f"{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}"
callbacks.append(
core.CheckpointSaver(
checkpoint_path=opts.checkpoint_dir, prefix=checkpoint_name
)
)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=callbacks,
)
trainer.train(n_epochs=opts.n_epochs)
game.logging_strategy = LoggingStrategy.maximal() # now log everything
dump(trainer.game, opts.n_features, device, False)
core.close()
def main(params):
import copy
opts = get_params(params)
device = opts.device
full_data = enumerate_attribute_value(opts.n_attributes, opts.n_values)
if opts.density_data > 0:
sampled_data = select_subset_V2(full_data, opts.density_data,
opts.n_attributes, opts.n_values)
full_data = copy.deepcopy(sampled_data)
train, generalization_holdout = split_holdout(full_data)
train, uniform_holdout = split_train_test(train, 0.1)
generalization_holdout, train, uniform_holdout, full_data = [
one_hotify(x, opts.n_attributes, opts.n_values)
for x in [generalization_holdout, train, uniform_holdout, full_data]
]
train, validation = ScaledDataset(train, opts.data_scaler), ScaledDataset(
train, 1)
generalization_holdout, uniform_holdout, full_data = ScaledDataset(
generalization_holdout), ScaledDataset(uniform_holdout), ScaledDataset(
full_data)
generalization_holdout_loader, uniform_holdout_loader, full_data_loader = [
DataLoader(x, batch_size=opts.batch_size)
for x in [generalization_holdout, uniform_holdout, full_data]
]
train_loader = DataLoader(train, batch_size=opts.batch_size)
validation_loader = DataLoader(validation, batch_size=len(validation))
n_dim = opts.n_attributes * opts.n_values
if opts.receiver_cell in ['lstm', 'rnn', 'gru']:
receiver = MMReceiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim)
receiver = core.MMRnnReceiverDeterministic(receiver,
opts.vocab_size + 1,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell)
else:
raise ValueError(f'Unknown receiver cell, {opts.receiver_cell}')
if opts.sender_cell in ['lstm', 'rnn', 'gru']:
sender = MMSender(n_inputs=n_dim, n_hidden=opts.sender_hidden)
s1 = SplitWrapper(sender, 0)
s2 = SplitWrapper(sender, 1)
sender1 = core.RnnSenderReinforce(agent=s1,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
force_eos=False,
cell=opts.sender_cell)
sender1 = PlusNWrapper(sender1, 1)
sender2 = core.RnnSenderReinforce(agent=s2,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
force_eos=False,
cell=opts.sender_cell)
# sender2 = PlusNWrapper(sender2, opts.vocab_size + 1)
sender2 = PlusNWrapper(sender2, 1)
sender = CombineMMRnnSenderReinforce(sender1, sender2)
else:
raise ValueError(f'Unknown sender cell, {opts.sender_cell}')
loss = DiffLoss(opts.n_attributes, opts.n_values)
baseline = {
'no': core.baselines.NoBaseline,
'mean': core.baselines.MeanBaseline,
'builtin': core.baselines.BuiltInBaseline
}[opts.baseline]
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0.0,
length_cost=0.0,
baseline_type=baseline)
optimizer = torch.optim.Adam(game.parameters(), lr=opts.lr)
metrics_evaluator = MMMetrics(validation.examples,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq)
loaders = []
loaders.append(("generalization hold out", generalization_holdout_loader,
DiffLoss(opts.n_attributes,
opts.n_values,
generalization=True)))
loaders.append(("uniform holdout", uniform_holdout_loader,
DiffLoss(opts.n_attributes, opts.n_values)))
holdout_evaluator = Evaluator(loaders, opts.device, freq=0)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
core.ConsoleLogger(as_json=True,
print_train_loss=False),
early_stopper, metrics_evaluator,
holdout_evaluator
])
trainer.train(n_epochs=opts.n_epochs)
validation_acc = early_stopper.validation_stats[-1][1]['acc']
uniformtest_acc = holdout_evaluator.results['uniform holdout']['acc']
# Train new agents
if validation_acc > 0.99:
def _set_seed(seed):
import random
import numpy as np
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
core.get_opts().preemptable = False
core.get_opts().checkpoint_path = None
# freeze Sender and probe how fast a simple Receiver will learn the thing
def retrain_receiver(receiver_generator, sender):
receiver = receiver_generator()
game = core.SenderReceiverRnnReinforce(sender,
receiver,
loss,
sender_entropy_coeff=0.0,
receiver_entropy_coeff=0.0)
optimizer = torch.optim.Adam(receiver.parameters(), lr=opts.lr)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
early_stopper,
Evaluator(loaders, opts.device, freq=0)
])
trainer.train(n_epochs=opts.n_epochs // 2)
accs = [x[1]['acc'] for x in early_stopper.validation_stats]
return accs
frozen_sender = Freezer(copy.deepcopy(sender))
def gru_receiver_generator(): return \
core.MMRnnReceiverDeterministic(MMReceiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=opts.receiver_hidden, cell='gru')
def small_gru_receiver_generator(): return \
core.MMRnnReceiverDeterministic(
MMReceiver(n_hidden=50, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=50, cell='gru')
def tiny_gru_receiver_generator(): return \
core.MMRnnReceiverDeterministic(
MMReceiver(n_hidden=25, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=25, cell='gru')
def nonlinear_receiver_generator(): return \
MMNonLinearReceiver(n_outputs=n_dim, vocab_size=opts.vocab_size + 1,
max_length=opts.max_len, n_hidden=opts.receiver_hidden)
for name, receiver_generator in [
('gru', gru_receiver_generator),
('nonlinear', nonlinear_receiver_generator),
('tiny_gru', tiny_gru_receiver_generator),
('small_gru', small_gru_receiver_generator),
]:
for seed in range(17, 17 + 3):
_set_seed(seed)
accs = retrain_receiver(receiver_generator, frozen_sender)
accs += [1.0] * (opts.n_epochs // 2 - len(accs))
auc = sum(accs)
print(
json.dumps({
"mode": "reset",
"seed": seed,
"receiver_name": name,
"auc": auc
}))
print('---End--')
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features + 1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')],
dtype=np.float32)
probs /= probs.sum()
train_loader = OneHotLoader(n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch,
probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features,
n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features,
n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(
receiver,
opts.vocab_size,
opts.max_len,
opts.receiver_embedding,
opts.receiver_num_heads,
opts.receiver_hidden,
opts.receiver_num_layers,
causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
else:
receiver = Receiver(n_features=opts.receiver_hidden,
n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatient(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers,
max_len=opts.max_len,
n_features=opts.n_features)
sender.load_state_dict(
torch.load(opts.sender_weights, map_location=torch.device('cpu')))
receiver.load_state_dict(
torch.load(opts.receiver_weights, map_location=torch.device('cpu')))
if not opts.impatient:
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,
unigram_penalty=opts.unigram_pen)
else:
game = SenderImpatientReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,
unigram_penalty=opts.unigram_pen)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
# Debut test position
position_sieve = np.zeros((opts.n_features, opts.max_len))
for position in range(opts.max_len):
dataset = [[torch.eye(opts.n_features).to(device), None]]
if opts.impatient:
sender_inputs, messages, receiver_inputs, receiver_outputs, _ = \
dump_test_position_impatient(trainer.game,
dataset,
position=position,
voc_size=opts.vocab_size,
gs=False,
device=device,
variable_length=True)
else:
sender_inputs, messages, receiver_inputs, receiver_outputs, _ = \
dump_test_position(trainer.game,
dataset,
position=position,
voc_size=opts.vocab_size,
gs=False,
device=device,
variable_length=True)
acc_pos = []
for sender_input, message, receiver_output in zip(
sender_inputs, messages, receiver_outputs):
input_symbol = sender_input.argmax()
output_symbol = receiver_output.argmax()
acc = (input_symbol == output_symbol).float().item()
acc_pos.append(acc)
acc_pos = np.array(acc_pos)
position_sieve[:, position] = acc_pos
# Put -1 for position after message_length
_, messages = dump(trainer.game, opts.n_features, device, False)
# Convert messages to numpy array
messages_np = []
for x in messages:
x = x.cpu().numpy()
messages_np.append(x)
for i in range(len(messages_np)):
# Message i
message_i = messages_np[i]
id_0 = np.where(message_i == 0)[0]
if id_0.shape[0] > 0:
for j in range(id_0[0] + 1, opts.max_len):
position_sieve[i, j] = -1
np.save("analysis/position_sieve.npy", position_sieve)
core.close()
def main(params):
print(torch.cuda.is_available())
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
# Distribution of the inputs
if opts.probs=="uniform":
probs=[]
probs_by_att = np.ones(opts.n_values)
probs_by_att /= probs_by_att.sum()
for i in range(opts.n_attributes):
probs.append(probs_by_att)
if opts.probs=="entropy_test":
probs=[]
for i in range(opts.n_attributes):
probs_by_att = np.ones(opts.n_values)
probs_by_att[0]=1+(1*i)
probs_by_att /= probs_by_att.sum()
probs.append(probs_by_att)
if opts.probs_attributes=="uniform":
probs_attributes=[1]*opts.n_attributes
if opts.probs_attributes=="uniform_indep":
probs_attributes=[]
probs_attributes=[0.2]*opts.n_attributes
if opts.probs_attributes=="echelon":
probs_attributes=[]
for i in range(opts.n_attributes):
#probs_attributes.append(1.-(0.2)*i)
#probs_attributes.append(0.7+0.3/(i+1))
probs_attributes=[1.,0.95,0.9,0.85]
print("Probability by attribute is:",probs_attributes)
train_loader = OneHotLoaderCompositionality(n_values=opts.n_values, n_attributes=opts.n_attributes, batch_size=opts.batch_size*opts.n_attributes,
batches_per_epoch=opts.batches_per_epoch, probs=probs, probs_attributes=probs_attributes)
# single batches with 1s on the diag
test_loader = TestLoaderCompositionality(n_values=opts.n_values,n_attributes=opts.n_attributes)
### SENDER ###
sender = Sender(n_features=opts.n_attributes*opts.n_values, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
### RECEIVER ###
receiver = Receiver(n_features=opts.n_values, n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = RnnReceiverCompositionality(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_attributes=opts.n_attributes, n_values=opts.n_values)
else:
receiver = Receiver(n_features=opts.receiver_hidden, n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatientCompositionality(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_attributes=opts.n_attributes, n_values=opts.n_values)
if not opts.impatient:
game = CompositionalitySenderReceiverRnnReinforce(sender, receiver, loss_compositionality, sender_entropy_coeff=opts.sender_entropy_coeff,
n_attributes=opts.n_attributes,n_values=opts.n_values,receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
else:
game = CompositionalitySenderImpatientReceiverRnnReinforce(sender, receiver, loss_impatient_compositionality, sender_entropy_coeff=opts.sender_entropy_coeff,
n_attributes=opts.n_attributes,n_values=opts.n_values,att_weights=opts.att_weights,receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
optimizer = core.build_optimizer(game.parameters())
trainer = CompoTrainer(n_attributes=opts.n_attributes,n_values=opts.n_values,game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
curr_accs=[0]*7
game.att_weights=[1]*(game.n_attributes)
for epoch in range(int(opts.n_epochs)):
print("Epoch: "+str(epoch))
#if epoch%100==0:
# trainer.optimizer.defaults["lr"]/=2
trainer.train(n_epochs=1)
if opts.checkpoint_dir:
trainer.save_checkpoint(name=f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}')
if not opts.impatient:
acc_vec,messages=dump_compositionality(trainer.game, opts.n_attributes, opts.n_values, device, False,epoch)
else:
acc_vec,messages=dump_impatient_compositionality(trainer.game, opts.n_attributes, opts.n_values, device, False,epoch)
print(acc_vec.mean(0))
#print(trainer.optimizer.defaults["lr"])
# ADDITION TO SAVE MESSAGES
all_messages=[]
for x in messages:
x = x.cpu().numpy()
all_messages.append(x)
all_messages = np.asarray(all_messages)
if epoch%50==0:
torch.save(sender.state_dict(), opts.dir_save+"/sender/sender_weights"+str(epoch)+".pth")
torch.save(receiver.state_dict(), opts.dir_save+"/receiver/receiver_weights"+str(epoch)+".pth")
np.save(opts.dir_save+'/messages/messages_'+str((epoch))+'.npy', all_messages)
np.save(opts.dir_save+'/accuracy/accuracy_'+str((epoch))+'.npy', acc_vec)
print(acc_vec.T)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
empty_logger = LoggingStrategy.minimal()
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
train_logging_strategy=empty_logger,
length_cost=opts.length_cost)
optimizer = core.build_optimizer(game.parameters())
callbacks = [EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True)]
if opts.checkpoint_dir:
checkpoint_name = f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}'
callbacks.append(core.CheckpointSaver(checkpoint_path=opts.checkpoint_dir, prefix=checkpoint_name))
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader, validation_data=test_loader, callbacks=callbacks)
trainer.train(n_epochs=opts.n_epochs)
game.logging_strategy = LoggingStrategy.maximal() # now log everything
dump(trainer.game, opts.n_features, device, False)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features + 1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')],
dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch,
probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
#################################
# define sender (speaker) agent #
#################################
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = RnnSenderReinforce(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
force_eos=force_eos,
noise_loc=opts.sender_noise_loc,
noise_scale=opts.sender_noise_scale)
####################################
# define receiver (listener) agent #
####################################
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_hidden)
receiver = RnnReceiverDeterministic(receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers,
noise_loc=opts.receiver_noise_loc,
noise_scale=opts.receiver_noise_scale)
###################
# define channel #
###################
channel = Channel(vocab_size=opts.vocab_size, p=opts.channel_repl_prob)
game = SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,
effective_max_len=opts.effective_max_len,
channel=channel,
sender_entropy_common_ratio=opts.sender_entropy_common_ratio)
optimizer = core.build_optimizer(game.parameters())
callbacks = [
EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True)
]
if opts.checkpoint_dir:
'''
info in checkpoint_name:
- n_features as f
- vocab_size as vocab
- random_seed as rs
- lr as lr
- sender_hidden as shid
- receiver_hidden as rhid
- sender_entropy_coeff as sentr
- length_cost as reg
- max_len as max_len
- sender_noise_scale as sscl
- receiver_noise_scale as rscl
- channel_repl_prob as crp
- sender_entropy_common_ratio as scr
'''
checkpoint_name = (
f'{opts.name}' + '_aer' +
('_uniform' if opts.probs == 'uniform' else '') +
f'_f{opts.n_features}' + f'_vocab{opts.vocab_size}' +
f'_rs{opts.random_seed}' + f'_lr{opts.lr}' +
f'_shid{opts.sender_hidden}' + f'_rhid{opts.receiver_hidden}' +
f'_sentr{opts.sender_entropy_coeff}' + f'_reg{opts.length_cost}' +
f'_max_len{opts.max_len}' + f'_sscl{opts.sender_noise_scale}' +
f'_rscl{opts.receiver_noise_scale}' +
f'_crp{opts.channel_repl_prob}' +
f'_scr{opts.sender_entropy_common_ratio}')
callbacks.append(
core.CheckpointSaver(checkpoint_path=opts.checkpoint_dir,
checkpoint_freq=opts.checkpoint_freq,
prefix=checkpoint_name))
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=callbacks)
trainer.train(n_epochs=opts.n_epochs)
print('<div id="prefix test without eos">')
prefix_test(trainer.game, opts.n_features, device, add_eos=False)
print('</div>')
print('<div id="prefix test with eos">')
prefix_test(trainer.game, opts.n_features, device, add_eos=True)
print('<div id="suffix test">')
suffix_test(trainer.game, opts.n_features, device)
print('</div>')
print('<div id="replacement test">')
replacement_test(trainer.game, opts.n_features, opts.vocab_size, device)
print('</div>')
print('<div id="dump">')
dump(trainer.game, opts.n_features, device, False)
print('</div>')
core.close()
def main(params):
opts = get_params(params)
print(opts)
full_data, train, uniform_holdout, generalization_holdout = get_data(opts)
generalization_holdout, train, uniform_holdout, full_data = [
one_hotify(x, opts.n_attributes, opts.n_values)
for x in [generalization_holdout, train, uniform_holdout, full_data]
]
train, validation = ScaledDataset(train, opts.data_scaler), ScaledDataset(
train, 1)
generalization_holdout, uniform_holdout, full_data = (
ScaledDataset(generalization_holdout),
ScaledDataset(uniform_holdout),
ScaledDataset(full_data),
)
generalization_holdout_loader, uniform_holdout_loader, full_data_loader = [
DataLoader(x, batch_size=opts.batch_size)
for x in [generalization_holdout, uniform_holdout, full_data]
]
train_loader = DataLoader(train, batch_size=opts.batch_size, shuffle=True)
validation_loader = DataLoader(validation, batch_size=len(validation))
loss = DiffLoss(opts.n_attributes, opts.n_values)
sender = getattr(egg.zoo.compo_vs_generalization_ood.archs,
opts.sender)(opts)
receiver = getattr(egg.zoo.compo_vs_generalization_ood.archs,
opts.receiver)(opts)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0.0,
length_cost=0.0,
baseline_type=core.baselines.MeanBaseline,
)
optimizer = torch.optim.Adam(game.parameters(), lr=opts.lr)
metrics_evaluator = Metrics(
validation.examples,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq,
)
metrics_evaluator_generalization_holdout = Metrics(
generalization_holdout.examples,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq,
)
loaders = []
loaders.append((
"generalization hold out",
generalization_holdout_loader,
# DiffLoss(opts.n_attributes, opts.n_values, generalization=True),
# we don't want to ignore zeros:
DiffLoss(opts.n_attributes, opts.n_values, generalization=False),
))
loaders.append((
"uniform holdout",
uniform_holdout_loader,
DiffLoss(opts.n_attributes, opts.n_values),
))
holdout_evaluator = Evaluator(loaders, opts.device, freq=1)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
# print validation (i.e. unscaled training data) loss:
core.ConsoleLogger(as_json=True, print_train_loss=False),
early_stopper,
# print compositionality metrics at the end of training
# (validation, i.e, unscaled training data):
metrics_evaluator,
# print compositionality metrics at the end of training (holdout data):
metrics_evaluator_generalization_holdout,
# print generalization and uniform holdout accuracies at each epoch:
holdout_evaluator,
],
)
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()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
train_loader = OneHotLoader(n_features=opts.n_values, batch_size=opts.batch_size*opts.n_attributes,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_values)
### SENDER ###
sender = Sender(n_features=opts.n_attributes*opts.n_values, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
### RECEIVER ###
receiver = Receiver(n_features=opts.n_values, n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = RnnReceiverCompositionality(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_attributes=opts.n_attributes, n_values=opts.n_values)
else:
receiver = Receiver(n_features=opts.receiver_hidden, n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatientCompositionality(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_attributes=opts.n_attributes, n_values=opts.n_values)
sender.load_state_dict(torch.load(opts.sender_weights,map_location=torch.device('cpu')))
receiver.load_state_dict(torch.load(opts.receiver_weights,map_location=torch.device('cpu')))
if not opts.impatient:
game = CompositionalitySenderReceiverRnnReinforce(sender, receiver, loss_compositionality, sender_entropy_coeff=opts.sender_entropy_coeff,
n_attributes=opts.n_attributes,n_values=opts.n_values,att_weights=[1],receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
else:
game = CompositionalitySenderImpatientReceiverRnnReinforce(sender, receiver, loss_impatient_compositionality, sender_entropy_coeff=opts.sender_entropy_coeff,
n_attributes=opts.n_attributes,n_values=opts.n_values,att_weights=[1],receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
optimizer = core.build_optimizer(game.parameters())
trainer = CompoTrainer(n_attributes=opts.n_attributes,n_values=opts.n_values,game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
# Debut test position
position_sieve=np.zeros((opts.n_attributes**opts.n_values,opts.max_len,opts.n_attributes))
for position in range(opts.max_len):
one_hots = torch.eye(opts.n_values)
val=np.arange(opts.n_values)
combination=list(itertools.product(val,repeat=opts.n_attributes))
dataset=[]
for i in range(len(combination)):
new_input=torch.zeros(0)
for j in combination[i]:
new_input=torch.cat((new_input,one_hots[j]))
dataset.append(new_input)
dataset=torch.stack(dataset)
dataset=[[dataset,None]]
if opts.impatient:
sender_inputs, messages, receiver_inputs, receiver_outputs, _ = \
dump_test_position_impatient_compositionality(trainer.game,
dataset,
position=position,
voc_size=opts.vocab_size,
gs=False,
device=device,
variable_length=True)
else:
sender_inputs, messages, receiver_inputs, receiver_outputs, _ = \
dump_test_position_compositionality(trainer.game,
dataset,
position=position,
voc_size=opts.vocab_size,
gs=False,
device=device,
variable_length=True)
for i in range(len(receiver_outputs)):
message=messages[i]
correct=True
for j in range(len(list(combination[i]))):
if receiver_outputs[i][j]==list(combination[i])[j]:
position_sieve[i,position,j]=1
# Put -1 for position after message_length
if not opts.impatient:
acc_vec,messages=dump_compositionality(trainer.game, opts.n_attributes, opts.n_values, device, False,0)
else:
acc_vec,messages=dump_impatient_compositionality(trainer.game, opts.n_attributes, opts.n_values, device, False,0)
# Convert messages to numpy array
messages_np=[]
for x in messages:
x = x.cpu().numpy()
messages_np.append(x)
for i in range(len(messages_np)):
# Message i
message_i=messages_np[i]
id_0=np.where(message_i==0)[0]
if id_0.shape[0]>0:
for j in range(id_0[0]+1,opts.max_len):
position_sieve[i,j]=-1
np.save("analysis/position_sieve.npy",position_sieve)
core.close()