# Written by Francois Fleuret <francois@fleuret.org>
-import math, os, tqdm
+import math, os, tqdm, warnings
import torch, torchvision
######################################################################
-import escape
+import greed
-class Escape(Task):
+class Greed(Task):
def __init__(
self,
nb_train_samples,
self.height = height
self.width = width
- states, actions, rewards = escape.generate_episodes(
+ states, actions, rewards = greed.generate_episodes(
nb_train_samples + nb_test_samples, height, width, T, nb_walls
)
- seq = escape.episodes2seq(states, actions, rewards)
+ seq = greed.episodes2seq(states, actions, rewards)
# seq = seq[:, seq.size(1) // 3 : 2 * seq.size(1) // 3]
self.train_input = seq[:nb_train_samples].to(self.device)
self.test_input = seq[nb_train_samples:].to(self.device)
+ self.state_len = self.height * self.width
+ self.index_lookahead_reward = 0
+ self.index_states = 1
+ self.index_action = self.state_len + 1
+ self.index_reward = self.state_len + 2
+ self.it_len = self.state_len + 3 # lookahead_reward / state / action / reward
+
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=desc
):
+ t = torch.arange(batch.size(1), device=batch.device)[None, :]
+ u = torch.randint(batch.size(1), (batch.size(0), 1), device=batch.device)
+ lr_mask = (t <= u).long() * (
+ t % self.it_len == self.index_lookahead_reward
+ ).long()
+
+ batch = lr_mask * greed.lookahead_reward2code(2) + (1 - lr_mask) * batch
yield batch
def vocabulary_size(self):
- return escape.nb_codes
+ return greed.nb_codes
def thinking_autoregression(
self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
- result = self.test_input[:250].clone()
- t = torch.arange(result.size(1), device=result.device)[None, :]
-
- state_len = self.height * self.width
- index_lookahead_reward = 0
- index_states = 1
- index_action = state_len + 1
- index_reward = state_len + 2
- it_len = state_len + 3 # lookahead_reward / state / action / reward
-
- result[:, it_len:] = -1
+ snapshots = []
def ar(result, ar_mask, logit_biases=None):
ar_mask = ar_mask.expand_as(result)
device=self.device,
progress_bar_desc=None,
)
+ warnings.warn("keeping thinking snapshots", RuntimeWarning)
+ snapshots.append(result[:10].detach().clone())
# Generate iteration after iteration
- optimistic_bias = result.new_zeros(escape.nb_codes, device=result.device)
- optimistic_bias[escape.lookahead_reward2code(-1)] = -math.log(1e1)
- optimistic_bias[escape.lookahead_reward2code(1)] = math.log(1e1)
+ result = self.test_input[:250].clone()
+ # Erase all the content but that of the first iteration
+ result[:, self.it_len :] = -1
+ # Set the lookahead_reward of the firs to UNKNOWN
+ result[:, self.index_lookahead_reward] = greed.lookahead_reward2code(2)
- snapshots = []
+ t = torch.arange(result.size(1), device=result.device)[None, :]
for u in tqdm.tqdm(
- range(it_len, result.size(1) - it_len + 1, it_len), desc="thinking"
+ range(0, result.size(1), self.it_len),
+ desc="thinking",
):
- # Generate the lookahead_reward and state
- ar_mask = (t >= u + index_lookahead_reward).long() * (
- t < u + index_states + state_len
+ # Generate the next state but keep the initial one, the
+ # lookahead_reward of previous iterations are set to
+ # UNKNOWN
+ if u > 0:
+ result[
+ :, u + self.index_lookahead_reward
+ ] = greed.lookahead_reward2code(2)
+ ar_mask = (t >= u + self.index_states).long() * (
+ t < u + self.index_states + self.state_len
+ ).long()
+ ar(result, ar_mask)
+
+ # Generate the action and reward with lookahead_reward to +1
+ result[:, u + self.index_lookahead_reward] = greed.lookahead_reward2code(1)
+ ar_mask = (t >= u + self.index_action).long() * (
+ t <= u + self.index_reward
).long()
ar(result, ar_mask)
- snapshots.append(result[:10].detach().clone())
- backup_lookahead_reward = result[:, u + index_lookahead_reward]
-
- # Re-generate the lookahead_reward
- ar_mask = (t == u + index_lookahead_reward).long()
- ar(result, ar_mask, logit_biases=optimistic_bias)
- snapshots.append(result[:10].detach().clone())
-
- # Generate the action and reward
- ar_mask = (t >= u + index_action).long() * (t <= u + index_reward).long()
- ar(result, ar_mask)
- snapshots.append(result[:10].detach().clone())
- result[:, u + index_lookahead_reward] = backup_lookahead_reward
+ # Set the lookahead_reward to UNKNOWN for the next iterations
+ result[:, u + self.index_lookahead_reward] = greed.lookahead_reward2code(2)
filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
with open(filename, "w") as f:
for n in range(10):
for s in snapshots:
- lr, s, a, r = escape.seq2episodes(
+ lr, s, a, r = greed.seq2episodes(
s[n : n + 1], self.height, self.width
)
- str = escape.episodes2str(
+ str = greed.episodes2str(
lr, s, a, r, unicode=True, ansi_colors=True
)
f.write(str)
# Saving the generated sequences
- lr, s, a, r = escape.seq2episodes(result, self.height, self.width)
- str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+ lr, s, a, r = greed.seq2episodes(result, self.height, self.width)
+ str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
with open(filename, "w") as f:
# Saving the ground truth
- lr, s, a, r = escape.seq2episodes(
+ lr, s, a, r = greed.seq2episodes(
result,
self.height,
self.width,
)
- str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+ str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
with open(filename, "w") as f:
# Saving the generated sequences
- lr, s, a, r = escape.seq2episodes(
+ lr, s, a, r = greed.seq2episodes(
result,
self.height,
self.width,
)
- str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+ str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
with open(filename, "w") as f:
projects / picoclvr.git / blobdiff