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Update.
[culture.git]
/
world.py
diff --git
a/world.py
b/world.py
index
c3eb101
..
da7de75
100755
(executable)
--- a/
world.py
+++ b/
world.py
@@
-62,18
+62,30
@@
class SignSTE(nn.Module):
return s
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,
def train_encoder(
train_input,
test_input,
- depth=
3
,
+ depth=
2
,
dim_hidden=48,
nb_bits_per_token=8,
dim_hidden=48,
nb_bits_per_token=8,
+ lambda_entropy=0.0,
lr_start=1e-3,
lr_end=1e-4,
nb_epochs=10,
batch_size=25,
lr_start=1e-3,
lr_end=1e-4,
nb_epochs=10,
batch_size=25,
+ logger=None,
device=torch.device("cpu"),
):
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):
mu, std = train_input.float().mean(), train_input.float().std()
def encoder_core(depth, dim):
@@
-132,7
+144,7
@@
def train_encoder(
nb_parameters = sum(p.numel() for p in model.parameters())
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)
model.to(device)
@@
-145,6
+157,7
@@
def train_encoder(
acc_train_loss = 0.0
for input in tqdm.tqdm(train_input.split(batch_size), desc="vqae-train"):
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)
z = encoder(input)
zq = z if k < 2 else quantizer(z)
output = decoder(zq)
@@
-155,6
+168,9
@@
def train_encoder(
train_loss = F.cross_entropy(output, input)
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()
acc_train_loss += train_loss.item() * input.size(0)
optimizer.zero_grad()
@@
-164,6
+180,7
@@
def train_encoder(
acc_test_loss = 0.0
for input in tqdm.tqdm(test_input.split(batch_size), desc="vqae-test"):
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)
z = encoder(input)
zq = z if k < 1 else quantizer(z)
output = decoder(zq)
@@
-179,7
+196,7
@@
def train_encoder(
train_loss = acc_train_loss / train_input.size(0)
test_loss = acc_test_loss / test_input.size(0)
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
sys.stdout.flush()
return encoder, quantizer, decoder
@@
-232,7
+249,7
@@
def scene2tensor(xh, yh, scene, size):
)
)
-def random_scene():
+def random_scene(
nb_insert_attempts=3
):
scene = []
colors = [
((Box.nb_rgb_levels - 1), 0, 0),
scene = []
colors = [
((Box.nb_rgb_levels - 1), 0, 0),
@@
-246,7
+263,7
@@
def random_scene():
),
]
),
]
- 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,))]
wh = torch.rand(2) * 0.2 + 0.2
xy = torch.rand(2) * (1 - wh)
c = colors[torch.randint(len(colors), (1,))]
@@
-280,14
+297,15
@@
def generate_episode(steps, size=64):
xh, yh = tuple(x.item() for x in torch.rand(2))
actions = torch.randint(len(effects), (len(steps),))
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))
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
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
@@
-304,7
+322,7
@@
def generate_episode(steps, size=64):
else:
xh += dx
yh += dy
else:
xh += dx
yh += dy
- change = True
+ nb_changes += 1
else:
x, y = xh, yh
xh += dx
else:
x, y = xh, yh
xh += dx
@@
-312,7
+330,7
@@
def generate_episode(steps, size=64):
if xh < 0 or xh > 1 or yh < 0 or yh > 1:
xh, yh = x, y
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
break
return frames, actions
@@
-326,12
+344,19
@@
def generate_episodes(nb, steps):
for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
frames, actions = generate_episode(steps)
all_frames += frames
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)
def create_data_and_processors(
return torch.cat(all_frames, 0).contiguous(), torch.cat(all_actions, 0)
def create_data_and_processors(
- nb_train_samples, nb_test_samples, mode, nb_steps, nb_epochs=10
+ nb_train_samples,
+ nb_test_samples,
+ mode,
+ nb_steps,
+ nb_epochs=10,
+ device=torch.device("cpu"),
+ device_storage=torch.device("cpu"),
+ logger=None,
):
assert mode in ["first_last"]
):
assert mode in ["first_last"]
@@
-339,30
+364,42
@@
def create_data_and_processors(
steps = [True] + [False] * (nb_steps + 1) + [True]
train_input, train_actions = generate_episodes(nb_train_samples, steps)
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
+ )
test_input, test_actions = generate_episodes(nb_test_samples, steps)
test_input, test_actions = generate_episodes(nb_test_samples, steps)
+ test_input, test_actions = test_input.to(device_storage), test_actions.to(
+ device_storage
+ )
encoder, quantizer, decoder = train_encoder(
encoder, quantizer, decoder = train_encoder(
- train_input, test_input, nb_epochs=nb_epochs
+ train_input,
+ test_input,
+ lambda_entropy=1.0,
+ nb_epochs=nb_epochs,
+ logger=logger,
+ device=device,
)
encoder.train(False)
quantizer.train(False)
decoder.train(False)
)
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 = []
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):
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)
)
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)
)
- * p
ow2
+ * p
).sum(-1)
seq.append(output)
).sum(-1)
seq.append(output)
@@
-371,9
+408,10
@@
def create_data_and_processors(
def seq2frame(input, batch_size=25, T=1e-2):
frames = []
def seq2frame(input, batch_size=25, T=1e-2):
frames = []
-
+ p = pow2.to(device)
for seq in input.split(batch_size):
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(
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(