return s
+def loss_H(binary_logits, h_threshold=1):
+ p = binary_logits.sigmoid().mean(0)
+ h = (-p.xlogy(p) - (1 - p).xlogy(1 - p)) / math.log(2)
+ h.clamp_(max=h_threshold)
+ return h_threshold - h.mean()
+
+
def train_encoder(
train_input,
test_input,
depth=2,
dim_hidden=48,
nb_bits_per_token=8,
+ lambda_entropy=0.0,
lr_start=1e-3,
lr_end=1e-4,
nb_epochs=10,
train_loss = F.cross_entropy(output, input)
+ if lambda_entropy > 0:
+ loss = loss + lambda_entropy * loss_H(z, h_threshold=0.5)
+
acc_train_loss += train_loss.item() * input.size(0)
optimizer.zero_grad()
)
-def random_scene():
+def random_scene(nb_insert_attempts=3):
scene = []
colors = [
((Box.nb_rgb_levels - 1), 0, 0),
),
]
- for k in range(10):
+ for k in range(nb_insert_attempts):
wh = torch.rand(2) * 0.2 + 0.2
xy = torch.rand(2) * (1 - wh)
c = colors[torch.randint(len(colors), (1,))]
xh, yh = tuple(x.item() for x in torch.rand(2))
actions = torch.randint(len(effects), (len(steps),))
- change = False
+ nb_changes = 0
for s, a in zip(steps, actions):
if s:
frames.append(scene2tensor(xh, yh, scene, size=size))
- g, dx, dy = effects[a]
- if g:
+ grasp, dx, dy = effects[a]
+
+ if grasp:
for b in scene:
if b.x <= xh and b.x + b.w >= xh and b.y <= yh and b.y + b.h >= yh:
x, y = b.x, b.y
else:
xh += dx
yh += dy
- change = True
+ nb_changes += 1
else:
x, y = xh, yh
xh += dx
if xh < 0 or xh > 1 or yh < 0 or yh > 1:
xh, yh = x, y
- if change:
+ if nb_changes > len(steps) // 3:
break
return frames, actions
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_storage), train_actions.to(device_storage)
+ 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_storage), test_actions.to(device_storage)
+ 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, logger=logger, device=device
+ train_input,
+ test_input,
+ lambda_entropy=1.0,
+ nb_epochs=nb_epochs,
+ logger=logger,
+ device=device,
)
encoder.train(False)
quantizer.train(False)
seq = []
p = pow2.to(device)
for x in input.split(batch_size):
- x=x.to(device)
+ x = x.to(device)
z = encoder(x)
ze_bool = (quantizer(z) >= 0).long()
output = (