X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=beaver.py;h=c3b7e09c1714199729a737a70725b3944ab787b7;hb=a0e547917131af0b353e3bf31a062c9b35c8dd18;hp=dfbb7b60a4cce0dd7fdc50cedfbd3cbb214570fe;hpb=39e24a2f9076db2d512791e723e7f2dc0275d99c;p=beaver.git diff --git a/beaver.py b/beaver.py index dfbb7b6..c3b7e09 100755 --- a/beaver.py +++ b/beaver.py @@ -172,10 +172,74 @@ def compute_perplexity(model, split="train"): def one_shot(gpt, task): t = gpt.training gpt.eval() - for input, targets in task.policy_batches(): - output = gpt(mygpt.BracketedSequence(input), with_readout = False).x + model = nn.Sequential( + nn.Linear(args.dim_model, args.dim_model), + nn.ReLU(), + nn.Linear(args.dim_model, args.dim_model), + nn.ReLU(), + nn.Linear(args.dim_model, 4), + ).to(device) + + for n_epoch in range(args.nb_epochs): + learning_rate = learning_rate_schedule[n_epoch] + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + + acc_train_loss, nb_train_samples = 0, 0 + for input, targets in task.policy_batches(split="train"): + output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x + output = model(output_gpt) + targets = targets * (input.unsqueeze(-1) == maze.v_empty) + output = output * (input.unsqueeze(-1) == maze.v_empty) + loss = ( + -(output.log_softmax(-1) * targets).sum() + / (input == maze.v_empty).sum() + + targets.xlogy(targets).sum() / (input == maze.v_empty).sum() + ) + acc_train_loss += loss.item() * input.size(0) + nb_train_samples += input.size(0) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + acc_test_loss, nb_test_samples = 0, 0 + for input, targets in task.policy_batches(split="test"): + output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x + output = model(output_gpt) + targets = targets * (input.unsqueeze(-1) == maze.v_empty) + output = output * (input.unsqueeze(-1) == maze.v_empty) + loss = ( + -(output.log_softmax(-1) * targets).sum() + / (input == maze.v_empty).sum() + + targets.xlogy(targets).sum() / (input == maze.v_empty).sum() + ) + acc_test_loss += loss.item() * input.size(0) + nb_test_samples += input.size(0) + + log_string( + f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}" + ) + + # ------------------- + input = task.test_input[:32, : task.height * task.width] + targets = task.test_policies[:32] + output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x + output = model(output_gpt) + losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1) + losses = losses * (input == maze.v_empty) + losses = losses / losses.max() + losses = losses.reshape(-1, args.maze_height, args.maze_width) + input = input.reshape(-1, args.maze_height, args.maze_width) + maze.save_image( + os.path.join(args.result_dir, f"oneshot_{n_epoch:04d}.png"), + mazes=input, + score_paths=losses, + ) + # ------------------- + gpt.train(t) + ###################################################################### @@ -226,7 +290,7 @@ class TaskMaze(Task): progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"), ) self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device)) - self.train_policies = train_policies.to(device) + self.train_policies = train_policies.flatten(-2).permute(0, 2, 1).to(device) test_mazes, test_paths, test_policies = maze.create_maze_data( nb_test_samples, @@ -236,7 +300,7 @@ class TaskMaze(Task): progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"), ) self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device)) - self.test_policies = test_policies.to(device) + self.test_policies = test_policies.flatten(-2).permute(0, 2, 1).to(device) self.nb_codes = self.train_input.max() + 1 @@ -255,7 +319,7 @@ class TaskMaze(Task): input = self.train_input if split == "train" else self.test_input targets = self.train_policies if split == "train" else self.test_policies input = input[:, : self.height * self.width] - targets = targets.flatten(-2) * (input != maze.v_wall)[:,None] + targets = targets * (input != maze.v_wall)[:, :, None] if nb_to_use > 0: input = input[:nb_to_use] @@ -315,10 +379,10 @@ class TaskMaze(Task): _, predicted_paths = self.seq2map(result) maze.save_image( os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"), - mazes, - paths, - predicted_paths, - maze.path_correctness(mazes, predicted_paths), + mazes=mazes, + target_paths=paths, + predicted_paths=predicted_paths, + path_correct=maze.path_correctness(mazes, predicted_paths), ) model.train(t) @@ -390,8 +454,6 @@ else: ###################################################################### -nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default - token_count = 0 for input in task.batches(split="train"): token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1)) @@ -431,7 +493,7 @@ if args.one_shot: ############################## -if nb_epochs_finished >= nb_epochs: +if nb_epochs_finished >= args.nb_epochs: n_epoch = nb_epochs_finished train_perplexity = compute_perplexity(model, split="train") test_perplexity = compute_perplexity(model, split="test") @@ -446,7 +508,7 @@ if nb_epochs_finished >= nb_epochs: ############################## -for n_epoch in range(nb_epochs_finished, nb_epochs): +for n_epoch in range(nb_epochs_finished, args.nb_epochs): learning_rate = learning_rate_schedule[n_epoch] log_string(f"learning_rate {learning_rate}")