else:
return s
+class DiscreteSampler2d(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ s = (x >= x.max(-3,keepdim=True).values).float()
+
+ if self.training:
+ u = x.softmax(dim=-3)
+ return s + u - u.detach()
+ else:
+ return s
+
def loss_H(binary_logits, h_threshold=1):
p = binary_logits.sigmoid().mean(0)
def train_encoder(
train_input,
test_input,
- depth=2,
+ depth,
+ nb_bits_per_token,
dim_hidden=48,
- nb_bits_per_token=8,
lambda_entropy=0.0,
lr_start=1e-3,
lr_end=1e-4,
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)
+ zq = quantizer(z)
output = decoder(zq)
output = output.reshape(
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)
+ zq = quantizer(z)
output = decoder(zq)
output = output.reshape(
nb_test_samples,
mode,
nb_steps,
+ depth=3,
+ nb_bits_per_token=8,
nb_epochs=10,
device=torch.device("cpu"),
device_storage=torch.device("cpu"),
encoder, quantizer, decoder = train_encoder(
train_input,
test_input,
+ depth=depth,
+ nb_bits_per_token=nb_bits_per_token,
lambda_entropy=1.0,
nb_epochs=nb_epochs,
logger=logger,
seq2frame,
) = create_data_and_processors(
25000, 1000,
- nb_epochs=10,
+ nb_epochs=5,
mode="first_last",
nb_steps=20,
)