X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=870ab95e913e7597a07494ba40dd595200ae1f4c;hb=08b58304225e044a21419dd30302d985acc1824c;hp=6b6b8f2a5ed055c3f80473f70fa7b0ac87f6a526;hpb=621231cc5bb94f983c556a1b450b66067bec4165;p=picoclvr.git diff --git a/tasks.py b/tasks.py index 6b6b8f2..870ab95 100755 --- a/tasks.py +++ b/tasks.py @@ -5,7 +5,7 @@ # Written by Francois Fleuret -import math, os, tqdm +import math, os, tqdm, warnings import torch, torchvision @@ -1893,12 +1893,17 @@ class Escape(Task): states, actions, rewards = escape.generate_episodes( nb_train_samples + nb_test_samples, height, width, T, nb_walls ) - seq = escape.episodes2seq(states, actions, rewards, lookahead_delta=T) + seq = escape.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.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 + 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"} @@ -1910,22 +1915,22 @@ class Escape(Task): 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 * escape.lookahead_reward2code(2) + (1 - lr_mask) * batch yield batch def vocabulary_size(self): - return self.nb_codes + return escape.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_action = state_len - index_reward = state_len + 1 - index_lookahead_reward = state_len + 2 - it_len = state_len + 3 # state / action / reward / lookahead_reward + snapshots = [] def ar(result, ar_mask, logit_biases=None): ar_mask = ar_mask.expand_as(result) @@ -1940,40 +1945,59 @@ class Escape(Task): 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(self.nb_codes, device=result.device) - optimistic_bias[(-1) + escape.first_lookahead_rewards_code + 1] = math.log(1e-1) - optimistic_bias[(1) + escape.first_lookahead_rewards_code + 1] = math.log(1e1) + result = self.test_input[:250].clone() + result[:, self.it_len :] = -1 + result[:, self.index_lookahead_reward] = escape.lookahead_reward2code(2) + 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 pessimistically - ar_mask = (t < u).long() * (t % it_len == index_lookahead_reward).long() - ar(result, ar_mask, logit_biases=-optimistic_bias) - - # Generate the state - ar_mask = (t >= u).long() * (t < u + state_len).long() + # 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 + ] = escape.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] = escape.lookahead_reward2code(1) + ar_mask = (t >= u + self.index_action).long() * ( + t <= u + self.index_reward + ).long() ar(result, ar_mask) - # Generate the lookahead_reward optimistically - ar_mask = (t < u).long() * (t % it_len == index_lookahead_reward).long() - ar(result, ar_mask, logit_biases=optimistic_bias) + # Set the lookahead_reward to UNKNOWN for the next iterations + result[:, u + self.index_lookahead_reward] = escape.lookahead_reward2code(2) - # Generate the action and reward - ar_mask = (t >= u + index_action).long() * (t <= u + index_reward).long() - ar(result, ar_mask) + 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( + s[n : n + 1], self.height, self.width + ) + str = escape.episodes2str( + lr, s, a, r, unicode=True, ansi_colors=True + ) + f.write(str) + f.write("\n\n") # Saving the generated sequences - s, a, r, lr = escape.seq2episodes( - result, self.height, self.width, lookahead=True - ) - str = escape.episodes2str( - s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True - ) + lr, s, a, r = escape.seq2episodes(result, self.height, self.width) + str = escape.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: @@ -1987,12 +2011,12 @@ class Escape(Task): # Saving the ground truth - s, a, r, lr = escape.seq2episodes( - result, self.height, self.width, lookahead=True - ) - str = escape.episodes2str( - s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True + lr, s, a, r = escape.seq2episodes( + result, + self.height, + self.width, ) + str = escape.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: @@ -2019,12 +2043,12 @@ class Escape(Task): # Saving the generated sequences - s, a, r, lr = escape.seq2episodes( - result, self.height, self.width, lookahead=True - ) - str = escape.episodes2str( - s, a, r, lookahead_rewards=lr, unicode=True, ansi_colors=True + lr, s, a, r = escape.seq2episodes( + result, + self.height, + self.width, ) + str = escape.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: