lr_end=1e-4,
nb_epochs=10,
batch_size=25,
+ logger=None,
device=torch.device("cpu"),
):
+ if logger is None:
+ logger = lambda s: print(s)
+
mu, std = train_input.float().mean(), train_input.float().std()
def encoder_core(depth, dim):
nb_parameters = sum(p.numel() for p in model.parameters())
- print(f"nb_parameters {nb_parameters}")
+ logger(f"nb_parameters {nb_parameters}")
model.to(device)
acc_train_loss = 0.0
for input in tqdm.tqdm(train_input.split(batch_size), desc="vqae-train"):
+ input = input.to(device)
z = encoder(input)
zq = z if k < 2 else quantizer(z)
output = decoder(zq)
acc_test_loss = 0.0
for input in tqdm.tqdm(test_input.split(batch_size), desc="vqae-test"):
+ input = input.to(device)
z = encoder(input)
zq = z if k < 1 else quantizer(z)
output = decoder(zq)
train_loss = acc_train_loss / train_input.size(0)
test_loss = acc_test_loss / test_input.size(0)
- print(f"train_ae {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
+ logger(f"train_ae {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
sys.stdout.flush()
return encoder, quantizer, decoder
for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
frames, actions = generate_episode(steps)
all_frames += frames
- all_actions += [actions]
+ all_actions += [actions[None, :]]
return torch.cat(all_frames, 0).contiguous(), torch.cat(all_actions, 0)
nb_steps,
nb_epochs=10,
device=torch.device("cpu"),
+ device_storage=torch.device("cpu"),
+ logger=None,
):
assert mode in ["first_last"]
steps = [True] + [False] * (nb_steps + 1) + [True]
train_input, train_actions = generate_episodes(nb_train_samples, steps)
- train_input, train_actions = train_input.to(device), train_actions.to(device)
+ train_input, train_actions = train_input.to(device_storage), train_actions.to(device_storage)
test_input, test_actions = generate_episodes(nb_test_samples, steps)
- test_input, test_actions = test_input.to(device), test_actions.to(device)
+ test_input, test_actions = test_input.to(device_storage), test_actions.to(device_storage)
encoder, quantizer, decoder = train_encoder(
- train_input, test_input, nb_epochs=nb_epochs, device=device
+ train_input, test_input, nb_epochs=nb_epochs, logger=logger, device=device
)
encoder.train(False)
quantizer.train(False)
decoder.train(False)
- z = encoder(train_input[:1])
- pow2 = (2 ** torch.arange(z.size(1), device=z.device))[None, None, :]
+ z = encoder(train_input[:1].to(device))
+ pow2 = (2 ** torch.arange(z.size(1), device=device))[None, None, :]
z_h, z_w = z.size(2), z.size(3)
def frame2seq(input, batch_size=25):
seq = []
-
+ p = pow2.to(device)
for x in input.split(batch_size):
+ x=x.to(device)
z = encoder(x)
ze_bool = (quantizer(z) >= 0).long()
output = (
ze_bool.permute(0, 2, 3, 1).reshape(
ze_bool.size(0), -1, ze_bool.size(1)
)
- * pow2
+ * p
).sum(-1)
seq.append(output)
def seq2frame(input, batch_size=25, T=1e-2):
frames = []
-
+ p = pow2.to(device)
for seq in input.split(batch_size):
- zd_bool = (seq[:, :, None] // pow2) % 2
+ seq = seq.to(device)
+ zd_bool = (seq[:, :, None] // p) % 2
zd_bool = zd_bool.reshape(zd_bool.size(0), z_h, z_w, -1).permute(0, 3, 1, 2)
logits = decoder(zd_bool * 2.0 - 1.0)
logits = logits.reshape(