def get_game(opt):
feat_size = 4096
sender = InformedSender(opt.game_size,
feat_size,
opt.embedding_size,
opt.hidden_size,
opt.vocab_size,
temp=opt.tau_s)
receiver = Receiver(opt.game_size,
feat_size,
opt.embedding_size,
opt.vocab_size,
reinforce=(opts.mode == 'rf'))
if opts.mode == 'rf':
sender = core.ReinforceWrapper(sender)
receiver = core.ReinforceWrapper(receiver)
game = core.SymbolGameReinforce(sender,
receiver,
loss,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
elif opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(sender, temperature=opt.gs_tau)
game = core.SymbolGameGS(sender, receiver, loss_nll)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
return game
def test_toy_agent_gs():
core.init()
agent = core.GumbelSoftmaxWrapper(ToyAgent())
agent.eval()
output = agent(BATCH_X)
assert output.size() == torch.Size((8, 2))
assert (output > 0).sum() == 8
agent.train()
agent.temperature = 10.0
output = agent(BATCH_X)
assert output.size() == torch.Size((8, 2))
assert (output > 0).sum() == 16
agent.temperature = 0.5
optimizer = torch.optim.Adam(agent.parameters())
for _ in range(1000):
optimizer.zero_grad()
out = agent(BATCH_X)
loss = F.cross_entropy(out, BATCH_Y)
loss.backward()
optimizer.step()
assert (agent.agent.fc1.weight.t().argmax(dim=1) == BATCH_Y).all()
def test_snapshoting():
CHECKPOINT_PATH = Path("./test_checkpoints")
core.init()
sender = core.GumbelSoftmaxWrapper(ToyAgent(), temperature=1)
receiver = Receiver()
loss = lambda sender_input, message, receiver_input, receiver_output, labels, aux_input: (
F.cross_entropy(receiver_output, labels),
{},
)
game = core.SymbolGameGS(sender, receiver, loss)
optimizer = torch.optim.Adam(game.parameters())
data = Dataset()
trainer = core.Trainer(
game,
optimizer,
train_data=data,
validation_data=None,
callbacks=[core.CheckpointSaver(checkpoint_path=CHECKPOINT_PATH)],
)
trainer.train(2)
assert (CHECKPOINT_PATH / Path("1.tar")).exists()
assert (CHECKPOINT_PATH / Path("2.tar")).exists()
assert (CHECKPOINT_PATH / Path("final.tar")).exists()
shutil.rmtree(CHECKPOINT_PATH) # Clean-up
"""
def test_snapshoting():
CHECKPOINT_PATH = Path('./test_checkpoints')
core.init()
sender = core.GumbelSoftmaxWrapper(ToyAgent(), temperature=1)
receiver = Receiver()
loss = lambda sender_input, message, receiver_input, receiver_output, labels: \
(F.cross_entropy(receiver_output, labels), {})
game = core.SymbolGameGS(sender, receiver, loss)
optimizer = torch.optim.Adam(game.parameters())
data = Dataset()
trainer = core.Trainer(
game,
optimizer,
train_data=data,
validation_data=None,
callbacks=[core.CheckpointSaver(checkpoint_path=CHECKPOINT_PATH)])
trainer.train(2)
assert (CHECKPOINT_PATH / Path('1.tar')).exists()
assert (CHECKPOINT_PATH / Path('2.tar')).exists()
assert (CHECKPOINT_PATH / Path('final.tar')).exists()
del trainer
trainer = core.Trainer(game, optimizer,
train_data=data) # Re-instantiate trainer
trainer.load_from_latest(CHECKPOINT_PATH)
assert trainer.start_epoch == 2
trainer.train(3)
shutil.rmtree(CHECKPOINT_PATH) # Clean-up
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):
# initialize the egg lib
opts = core.init(params=params)
# get pre-defined common line arguments (batch/vocab size, etc).
# See egg/core/util.py for a list
# prepare the dataset
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=opts.batch_size,
shuffle=True,
**kwargs)
# initialize the agents and the game
sender = Sender(opts.vocab_size) # the "data" transform part of an agent
sender = core.GumbelSoftmaxWrapper(
sender, temperature=1.0) # wrapping into a GS interface
receiver = Receiver()
receiver = core.SymbolReceiverWrapper(receiver,
vocab_size=opts.vocab_size,
agent_input_size=400)
# setting up as a standard Sender/Receiver game with 1 symbol communication
game = core.SymbolGameGS(sender, receiver, loss)
# This callback would be called at the end of each epoch by the Trainer; it reduces the sampling
# temperature used by the GS
temperature_updater = core.TemperatureUpdater(agent=sender,
decay=0.75,
minimum=0.01)
# get an optimizer that is set up by common command line parameters,
# defaults to Adam
optimizer = core.build_optimizer(game.parameters())
# initialize and launch the trainer
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
temperature_updater,
core.ConsoleLogger(as_json=True, print_train_loss=True),
],
)
trainer.train(n_epochs=opts.n_epochs)
core.close()
def test_game_gs():
core.init()
sender = core.GumbelSoftmaxWrapper(ToyAgent())
receiver = Receiver()
loss = lambda sender_input, message, receiver_input, receiver_output, labels: \
(F.cross_entropy(receiver_output, labels), {})
game = core.SymbolGameGS(sender, receiver, loss)
optimizer = torch.optim.Adam(game.parameters())
data = Dataset()
trainer = core.Trainer(game, optimizer, train_data=data, validation_data=None)
trainer.train(1000)
assert (sender.agent.fc1.weight.t().argmax(dim=1).cpu() == BATCH_Y).all()
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 test_temperature_updater_callback():
core.init()
sender = core.GumbelSoftmaxWrapper(ToyAgent(), temperature=1)
receiver = Receiver()
loss = lambda sender_input, message, receiver_input, receiver_output, labels: \
(F.cross_entropy(receiver_output, labels), {})
game = core.SymbolGameGS(sender, receiver, loss)
optimizer = torch.optim.Adam(game.parameters())
data = Dataset()
trainer = core.Trainer(
game,
optimizer,
train_data=data,
validation_data=None,
callbacks=[core.TemperatureUpdater(agent=sender, decay=0.9)])
trainer.train(1)
assert sender.temperature == 0.9
def get_my_game(opt):
feat_size = 4096
out_hidden_size = 20
emb_size = 10
sender = InformedSender(opt.game_size, feat_size,
opt.embedding_size, opt.hidden_size, out_hidden_size,
temp=opt.tau_s)
receiver = MyReceiver(opt.game_size, feat_size,
opt.embedding_size, out_hidden_size, reinforce=(opts.mode == 'rf'))
if opts.mode == 'rf':
sender = core.MyRnnSenderReinforce(sender, opt.vocab_size, emb_size, out_hidden_size,
cell="gru", max_len=opt.max_len)
receiver = core.RnnReceiverReinforce(receiver, opt.vocab_size, emb_size,
out_hidden_size, cell="gru")
game = core.SenderReceiverRnnReinforce(
sender, receiver, loss, sender_entropy_coeff=0.01, receiver_entropy_coeff=0.01)
elif opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(sender, temperature=opt.gs_tau)
game = core.SymbolGameGS(sender, receiver, loss_nll)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
return game
def test_max_snapshoting():
CHECKPOINT_PATH = Path("./test_checkpoints")
core.init()
sender = core.GumbelSoftmaxWrapper(ToyAgent(), temperature=1)
receiver = Receiver()
loss = lambda sender_input, message, receiver_input, receiver_output, labels: (
F.cross_entropy(receiver_output, labels),
{},
)
game = core.SymbolGameGS(sender, receiver, loss)
optimizer = torch.optim.Adam(game.parameters())
data = Dataset()
trainer = core.Trainer(
game,
optimizer,
train_data=data,
validation_data=None,
callbacks=[
core.CheckpointSaver(checkpoint_path=CHECKPOINT_PATH,
max_checkpoints=2)
],
)
trainer.train(n_epochs=6)
assert (CHECKPOINT_PATH / Path("5.tar")).exists()
assert (CHECKPOINT_PATH / Path("6.tar")).exists()
assert (CHECKPOINT_PATH / Path("final.tar")).exists()
assert len([x for x in CHECKPOINT_PATH.glob("**/*") if x.is_file()]) == 3
del trainer
trainer = core.Trainer(game, optimizer,
train_data=data) # Re-instantiate trainer
trainer.load_from_latest(CHECKPOINT_PATH)
assert trainer.start_epoch == 6
trainer.train(3)
shutil.rmtree(CHECKPOINT_PATH) # Clean-up
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
corrupt_labels_(dataset=train_dataset,
p_corrupt=opts.p_corrupt,
seed=opts.random_seed + 1)
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)
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 and opts.force_discrete != 1:
sender = AlwaysRelaxedWrapper(sender, temperature=opts.temperature)
elif opts.force_discrete == 1:
sender = core.GumbelSoftmaxWrapper(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 get_my_game(opt):
feat_size = 4096
out_hidden_size = 20
emb_size = 10
pop = opts.pop_size
sender_list = []
receiver_list = []
for i in range(pop):
if not opts.multi_head:
sender = InformedSender(opt.game_size,
feat_size,
opt.embedding_size,
opt.hidden_size,
out_hidden_size,
temp=opt.tau_s)
else:
sender = InformedSenderMultiHead(opt.game_size,
feat_size,
opt.embedding_size,
opt.hidden_size,
out_hidden_size,
temp=opt.tau_s)
receiver = MyReceiver(opt.game_size,
feat_size,
opt.embedding_size,
out_hidden_size,
reinforce=(opts.mode == 'rf'))
if opts.mode == 'rf':
sender = core.MyRnnSenderReinforce(sender,
opt.vocab_size,
emb_size,
out_hidden_size,
multi_head=opt.multi_head,
cell="gru",
max_len=opt.max_len)
receiver = core.RnnReceiverReinforce(receiver,
opt.vocab_size,
emb_size,
out_hidden_size,
cell="gru")
elif opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(sender, temperature=opt.gs_tau)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
sender_list.append(sender)
receiver_list.append(receiver)
if opts.mode == 'rf':
if opts.pop_mode == 0:
game = core.PopSenderReceiverRnnReinforce(
sender_list,
receiver_list,
pop,
loss,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
elif opts.pop_mode == 1:
game = core.PopUncSenderReceiverRnnReinforce(
sender_list,
receiver_list,
pop,
loss,
use_critic_baseline=False,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
else:
game = core.PopUncSenderReceiverRnnReinforce(
sender_list,
receiver_list,
pop,
loss,
use_critic_baseline=True,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
elif opts.mode == 'gs':
game = core.PopSymbolGameGS(sender_list, receiver_list, pop, loss_nll)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
return game
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()
class GumbelSoftmaxMultiAgentEnsemble(nn.Module):
def __init__(self, agents: List[core.ReinforceWrapper]):
super(GumbelSoftmaxMultiAgentEnsemble, self).__init__()
self.agents = nn.ModuleList(agents)
def forward(self, input, agent_indices, **kwargs):
samples = [agent(input) for agent in self.agents]
if samples[0].dim() > 2: # RNN
agent_indices = agent_indices.reshape(1, agent_indices.size(0), 1, 1).expand(1, agent_indices.size(0), samples[0].size(1), samples[0].size(2))
else:
agent_indices = agent_indices.reshape(1, agent_indices.size(0), 1).expand(1, agent_indices.size(0), samples[0].size(2))
samples = torch.stack(samples, dim=0).gather(dim=0, index=agent_indices)
return samples.squeeze(dim=0)
if __name__ == "__main__":
from teamwork.agents import Sender
BATCH_SIZE, INPUT_SIZE, OUTPUT_SIZE = 8, 10, 5
AGENT_INDICES = torch.LongTensor([0, 1, 0, 1, 0, 1, 0, 1])
multi_agent = ReinforceMultiAgentEnsemble(agents=[core.ReinforceWrapper(Sender(OUTPUT_SIZE, INPUT_SIZE)),
core.ReinforceWrapper(Sender(OUTPUT_SIZE, INPUT_SIZE))])
samples, log_probs, entropies = multi_agent(torch.Tensor(BATCH_SIZE, INPUT_SIZE),
agent_indices=AGENT_INDICES)
assert samples.shape == log_probs.shape == entropies.shape == torch.Size([BATCH_SIZE])
multi_agent = GumbelSoftmaxMultiAgentEnsemble(agents=[core.GumbelSoftmaxWrapper(Sender(OUTPUT_SIZE, INPUT_SIZE)),
core.GumbelSoftmaxWrapper(Sender(OUTPUT_SIZE, INPUT_SIZE))])
samples = multi_agent(torch.Tensor(BATCH_SIZE, INPUT_SIZE), agent_indices=AGENT_INDICES)
assert samples.shape == torch.Size([BATCH_SIZE, OUTPUT_SIZE])
