X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=443419eb340704273b64152edadb1286aae50cbf;hb=refs%2Fheads%2Fmaster;hp=845b5b3399413ea257d3b216556d39bd270987ee;hpb=1b52b1cf1519ef3ce389f1b3ee63a89b93198fa7;p=picoclvr.git diff --git a/tasks.py b/tasks.py index 845b5b3..443419e 100755 --- a/tasks.py +++ b/tasks.py @@ -63,7 +63,7 @@ def masked_inplace_autoregression( class Task: - def batches(self, split="train"): + def batches(self, split="train", nb_to_use=-1, desc=None): pass def vocabulary_size(self): @@ -489,7 +489,7 @@ class PicoCLVR(Task): self.train_input = self.tensorize(self.train_descr) self.test_input = self.tensorize(self.test_descr) - def batches(self, split="train"): + 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( @@ -754,15 +754,17 @@ class Maze(Task): def compute_error( self, model, split="train", nb_to_use=-1, deterministic_synthesis=False ): + model_device = next(model.parameters()).device nb_total, nb_correct = 0, 0 count = torch.zeros( self.width * self.height, self.width * self.height, - device=self.device, + device=model_device, dtype=torch.int64, ) for input in self.batches(split, nb_to_use): + input = input.to(model_device) result = input.clone() ar_mask = result.new_zeros(result.size()) ar_mask[:, self.height * self.width :] = 1 @@ -836,7 +838,7 @@ class Maze(Task): eol = " " if j < count.size(1) - 1 else "\n" f.write(f"{count[i,j]}{eol}") - input = self.test_input[:48] + input = self.test_input[:48].to(next(model.parameters()).device) result = input.clone() ar_mask = result.new_zeros(result.size()) ar_mask[:, self.height * self.width :] = 1 @@ -1098,6 +1100,34 @@ class Stack(Task): device=self.device, ) + #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + for label, input in [ + ("train", self.train_input[:32]), + ("test", self.test_input[:32]), + ]: + output = model(BracketedSequence(input)).x + output = output.log_softmax(dim=-1) + filename = os.path.join( + result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt" + ) + with open(filename, "w") as f: + for n in range(input.size(0)): + s = stack.seq_to_str( + input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits + ) + for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")): + u = ( + " " * (10 - len(w)) + + w + + " " + + str(output[n][t][k].exp().item()) + + "\n" + ) + f.write(u) + f.write("\n") + logger(f"wrote {filename}") + #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + for n in range(result.size(0)): logger( f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" @@ -1685,7 +1715,7 @@ class Grid(Task): self.t_nul = self.token2id["#"] self.t_true = self.token2id["true"] self.t_false = self.token2id["false"] - self.t_pipe = self.token2id["|"] + # self.t_pipe = self.token2id["|"] # Tokenize the train and test sets self.train_input = self.str2tensor(self.train_descr) @@ -1694,7 +1724,7 @@ class Grid(Task): None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr) ) - def batches(self, split="train"): + 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( @@ -1823,7 +1853,7 @@ class QMLP(Task): self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 - def batches(self, split="train"): + 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( @@ -1880,6 +1910,7 @@ class Greed(Task): width, T, nb_walls, + nb_coins, logger=None, device=torch.device("cpu"), ): @@ -1887,23 +1918,27 @@ class Greed(Task): self.batch_size = batch_size self.device = device - self.height = height - self.width = width - states, actions, rewards = greed.generate_episodes( - nb_train_samples + nb_test_samples, height, width, T, nb_walls + self.world = greed.GreedWorld(height, width, T, nb_walls, nb_coins) + + states, actions, rewards = self.world.generate_episodes( + nb_train_samples + nb_test_samples ) - seq = greed.episodes2seq(states, actions, rewards) - # seq = seq[:, seq.size(1) // 3 : 2 * seq.size(1) // 3] + seq = self.world.episodes2seq(states, actions, rewards) 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 wipe_lookahead_rewards(self, batch): + 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.world.it_len == self.world.index_lookahead_reward + ).long() + + return ( + lr_mask * self.world.lookahead_reward2code(greed.REWARD_UNKNOWN) + + (1 - lr_mask) * batch + ) def batches(self, split="train", nb_to_use=-1, desc=None): assert split in {"train", "test"} @@ -1915,17 +1950,10 @@ class Greed(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 * greed.lookahead_reward2code(2) + (1 - lr_mask) * batch - yield batch + yield self.wipe_lookahead_rewards(batch) def vocabulary_size(self): - return greed.nb_codes + return self.world.nb_codes def thinking_autoregression( self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 @@ -1946,20 +1974,22 @@ class Greed(Task): progress_bar_desc=None, ) warnings.warn("keeping thinking snapshots", RuntimeWarning) - snapshots.append(result[:10].detach().clone()) + snapshots.append(result[:100].detach().clone()) # Generate iteration after iteration result = self.test_input[:250].clone() # Erase all the content but that of the first iteration - result[:, self.it_len :] = -1 + result[:, self.world.it_len :] = -1 # Set the lookahead_reward of the firs to UNKNOWN - result[:, self.index_lookahead_reward] = greed.lookahead_reward2code(2) + result[:, self.world.index_lookahead_reward] = self.world.lookahead_reward2code( + greed.REWARD_UNKNOWN + ) t = torch.arange(result.size(1), device=result.device)[None, :] for u in tqdm.tqdm( - range(0, result.size(1), self.it_len), + range(0, result.size(1), self.world.it_len), desc="thinking", ): # Generate the next state but keep the initial one, the @@ -1967,31 +1997,35 @@ class Greed(Task): # 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 + :, u + self.world.index_lookahead_reward + ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN) + ar_mask = (t >= u + self.world.index_states).long() * ( + t < u + self.world.index_states + self.world.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 + result[ + :, u + self.world.index_lookahead_reward + ] = self.world.lookahead_reward2code(greed.REWARD_PLUS) + ar_mask = (t >= u + self.world.index_reward).long() * ( + t <= u + self.world.index_action ).long() ar(result, ar_mask) # Set the lookahead_reward to UNKNOWN for the next iterations - result[:, u + self.index_lookahead_reward] = greed.lookahead_reward2code(2) + result[ + :, u + self.world.index_lookahead_reward + ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN) 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 n in range(snapshots[0].size(0)): for s in snapshots: - lr, s, a, r = greed.seq2episodes( - s[n : n + 1], self.height, self.width + lr, s, a, r = self.world.seq2episodes( + s[n : n + 1], ) - str = greed.episodes2str( + str = self.world.episodes2str( lr, s, a, r, unicode=True, ansi_colors=True ) f.write(str) @@ -1999,8 +2033,8 @@ class Greed(Task): # Saving the generated sequences - lr, s, a, r = greed.seq2episodes(result, self.height, self.width) - str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True) + lr, s, a, r = self.world.seq2episodes(result) + str = self.world.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: @@ -2010,16 +2044,14 @@ class Greed(Task): def produce_results( self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 ): - result = self.test_input[:250].clone() + result = self.wipe_lookahead_rewards(self.test_input[:250].clone()) # Saving the ground truth - lr, s, a, r = greed.seq2episodes( + lr, s, a, r = self.world.seq2episodes( result, - self.height, - self.width, ) - str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True) + str = self.world.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: @@ -2029,8 +2061,7 @@ class Greed(Task): # Re-generating from the first frame ar_mask = ( - torch.arange(result.size(1), device=result.device) - >= self.height * self.width + 3 + torch.arange(result.size(1), device=result.device) >= self.world.it_len ).long()[None, :] ar_mask = ar_mask.expand_as(result) result *= 1 - ar_mask # paraaaaanoiaaaaaaa @@ -2046,12 +2077,10 @@ class Greed(Task): # Saving the generated sequences - lr, s, a, r = greed.seq2episodes( + lr, s, a, r = self.world.seq2episodes( result, - self.height, - self.width, ) - str = greed.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True) + str = self.world.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: