X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=a4ef557d2adde2f7df591adba4d3efc6b31e7d5f;hb=12671bdc1a083514de11399041f74747a6ca601b;hp=e5d3a7e66a77c769d970da8c0268f7fee307b7b9;hpb=1eef58fd084437bbcd2041b946b468615e203dd8;p=picoclvr.git diff --git a/tasks.py b/tasks.py index e5d3a7e..a4ef557 100755 --- a/tasks.py +++ b/tasks.py @@ -71,7 +71,7 @@ class Task: class TaskFromFile(Task): - def tensorize(self, pairs): + def tensorize(self, pairs, shuffle): len_max = max([len(x[0]) for x in pairs]) input = torch.cat( @@ -98,6 +98,11 @@ class TaskFromFile(Task): 0, ).to("cpu") + if shuffle: + i = torch.randperm(input.size(0)) + input = input[i].contiguous() + pred_mask = pred_mask[i].contiguous() + return input, pred_mask # trim all the tensors in the tuple z to remove as much token from @@ -122,6 +127,7 @@ class TaskFromFile(Task): nb_train_samples, nb_test_samples, batch_size, + shuffle=False, device=torch.device("cpu"), ): self.batch_size = batch_size @@ -156,8 +162,12 @@ class TaskFromFile(Task): self.char2id = dict([(c, n) for n, c in enumerate(symbols)]) self.id2char = dict([(n, c) for c, n in self.char2id.items()]) - self.train_input, self.train_pred_masks = self.tensorize(train_pairs) - self.test_input, self.test_pred_masks = self.tensorize(test_pairs) + self.train_input, self.train_pred_masks = self.tensorize( + train_pairs, shuffle=shuffle + ) + self.test_input, self.test_pred_masks = self.tensorize( + test_pairs, shuffle=shuffle + ) def batches(self, split="train", nb_to_use=-1, desc=None): assert split in {"train", "test"} @@ -1851,3 +1861,159 @@ class QMLP(Task): ###################################################################### + +import escape + + +class Escape(Task): + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + height, + width, + T, + logger=None, + device=torch.device("cpu"), + ): + super().__init__() + + self.batch_size = batch_size + self.device = device + self.height = height + self.width = width + + states, actions, rewards = escape.generate_episodes( + nb_train_samples + nb_test_samples, height, width, 3 * T + ) + seq = escape.episodes2seq(states, actions, rewards, lookahead_delta=T) + 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 + + 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 + if nb_to_use > 0: + input = input[:nb_to_use] + if desc is None: + desc = f"epoch-{split}" + for batch in tqdm.tqdm( + input.split(self.batch_size), dynamic_ncols=True, desc=desc + ): + yield batch + + def vocabulary_size(self): + return self.nb_codes + + def thinking_autoregression( + self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 + ): + result = self.test_input[:100].clone() + t = torch.arange(result.size(1), device=result.device) + state_len = self.height * self.width + iteration_len = state_len + 3 + + def ar(): + masked_inplace_autoregression( + model, + self.batch_size, + result, + ar_mask, + deterministic_synthesis, + device=self.device, + ) + + for u in range( + iteration_len, result.size(1) - iteration_len + 1, iteration_len + ): + # Put a lookahead reward to -1, sample the next state + result[:, u - 1] = (-1) + 1 + escape.first_lookahead_rewards_code + ar_mask = (t >= u).long() * (t < u + state_len).long() + ar_mask = ar_mask[None, :] + ar_mask = ar_mask.expand_as(result) + result *= 1 - ar_mask + ar() + + # Put a lookahead reward to +1, sample the action and reward + result[:, u - 1] = (1) + 1 + escape.first_lookahead_rewards_code + ar_mask = (t >= state_len).long() * (t < state_len + 2).long() + ar_mask = ar_mask[None, :] + ar_mask = ar_mask.expand_as(result) + result *= 1 - ar_mask + ar() + + # 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 + ) + + filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt") + with open(filename, "w") as f: + f.write(str) + logger(f"wrote {filename}") + + def produce_results( + self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 + ): + result = self.test_input[:100].clone() + + # 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 + ) + + filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt") + with open(filename, "w") as f: + f.write(str) + logger(f"wrote {filename}") + + # Re-generating from the first frame + + ar_mask = ( + torch.arange(result.size(1), device=result.device) + >= self.height * self.width + 3 + ).long()[None, :] + ar_mask = ar_mask.expand_as(result) + result *= 1 - ar_mask # paraaaaanoiaaaaaaa + + masked_inplace_autoregression( + model, + self.batch_size, + result, + ar_mask, + deterministic_synthesis, + device=self.device, + ) + + # 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 + ) + + filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt") + with open(filename, "w") as f: + f.write(str) + logger(f"wrote {filename}") + + self.thinking_autoregression( + n_epoch, model, result_dir, logger, deterministic_synthesis, nmax + ) + + +######################################################################