X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=acecfdd311048fdbc1aedc49becae0b703783fbc;hb=b003cc9f89b7c3356f7d1e6c0c10b3dea249ef96;hp=f8e451be4f30fdfcbd5c9212b160214f4892c069;hpb=c253ddd45b809dd7389773f690a83366a17ccde6;p=picoclvr.git diff --git a/main.py b/main.py index f8e451b..acecfdd 100755 --- a/main.py +++ b/main.py @@ -92,6 +92,17 @@ parser.add_argument("--maze_width", type=int, default=21) parser.add_argument("--maze_nb_walls", type=int, default=15) +############################## +# Snake options + +parser.add_argument("--snake_height", type=int, default=6) + +parser.add_argument("--snake_width", type=int, default=8) + +parser.add_argument("--snake_nb_colors", type=int, default=3) + +parser.add_argument("--snake_length", type=int, default=400) + ###################################################################### args = parser.parse_args() @@ -488,7 +499,7 @@ class TaskMNIST(Task): masked_inplace_autoregression( model, self.batch_size, results, ar_mask, device=self.device ) - image_name = os.path.join(args.result_dir, f"result_mnist_{n_epoch:04d}.png") + image_name = os.path.join(args.result_dir, f"mnist_result_{n_epoch:04d}.png") torchvision.utils.save_image( 1 - results.reshape(-1, 1, 28, 28) / 255.0, image_name, @@ -608,7 +619,7 @@ class TaskMaze(Task): mazes, paths = self.seq2map(input) _, predicted_paths = self.seq2map(result) - filename = os.path.join(args.result_dir, f"result_{n_epoch:04d}.png") + filename = os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.png") maze.save_image( filename, mazes=mazes, @@ -627,7 +638,7 @@ class TaskMaze(Task): def generate_snake_sequences( nb, height, width, nb_colors, length, device=torch.device("cpu") ): - world = torch.randint(nb_colors, (nb, height, width), device=device) + worlds = torch.randint(nb_colors, (nb, height, width), device=device) # nb x 2 snake_position = torch.cat( ( @@ -636,17 +647,17 @@ def generate_snake_sequences( ), 1, ) - snake_direction = torch.randint(4, (nb, 1), device=device) - result = torch.empty(nb, 2*length, device=device, dtype=torch.int64) - count = torch.arange(nb, device=device) # [:,None] + snake_direction = torch.randint(4, (nb,), device=device) + sequences = torch.empty(nb, 2 * length, device=device, dtype=torch.int64) + i = torch.arange(nb, device=device) # [:,None] for l in range(length): # nb x 3 snake_next_direction = torch.cat( ( - (snake_direction - 1) % 4, - snake_direction, - (snake_direction + 1) % 4, + (snake_direction[:, None] - 1) % 4, + snake_direction[:, None], + (snake_direction[:, None] + 1) % 4, ), 1, ) @@ -668,25 +679,26 @@ def generate_snake_sequences( snake_next_position[:, :, 1] >= 0, snake_next_position[:, :, 1] < width ), ).float() - val = torch.rand_like(val) * val * torch.tensor([[1.,4.,1.]], device=device) + val = ( + torch.rand_like(val) * val * torch.tensor([[1.0, 4.0, 1.0]], device=device) + ) # nb - i = torch.arange(val.size(0), device=device) j = val.argmax(1) + snake_direction = snake_next_direction[i, j] - # nb x 1 - snake_direction = snake_next_direction[i[:, None], j[:, None]] - - result[:, 2*l] = world[count, snake_position[:, 0], snake_position[:, 1]] - result[:, 2*l+1] = snake_direction[:,0] + sequences[:, 2 * l] = worlds[i, snake_position[:, 0], snake_position[:, 1]] + 4 + sequences[:, 2 * l + 1] = snake_direction # nb x 2 - snake_position = snake_next_position[i[:, None], j[:, None]].squeeze(1) + snake_position = snake_next_position[i, j] + + return sequences, worlds - return result -generate_snake_sequences(nb=2, height=4, width=5, nb_colors=3, length=10) -exit(0) +# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20) +# exit(0) + class TaskSnake(Task): def __init__( @@ -705,10 +717,10 @@ class TaskSnake(Task): self.width = width self.device = device - self.train_input = generate_snake_sequences( + self.train_input, self.train_worlds = generate_snake_sequences( nb_train_samples, height, width, nb_colors, length, self.device ) - self.test_input = generate_snake_sequences( + self.test_input, self.test_worlds = generate_snake_sequences( nb_test_samples, height, width, nb_colors, length, self.device ) @@ -729,6 +741,44 @@ class TaskSnake(Task): def vocabulary_size(self): return self.nb_codes + def produce_results(self, n_epoch, model): + with torch.autograd.no_grad(): + t = model.training + model.eval() + + def compute_nb_correct(input): + result = input.clone() + i = torch.arange(result.size(1), device=result.device) + ar_mask = torch.logical_and(i >= i.size(0) // 2, i % 2 == 0)[ + None, : + ].long() + result *= 1 - ar_mask + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + + nb_total = ar_mask.sum() * input.size(0) + nb_correct = ((result == input).long() * ar_mask).sum() + + # nb_total = result.size(0) + # nb_correct = ((result - input).abs().sum(1) == 0).sum() + + return nb_total, nb_correct + + train_nb_total, train_nb_correct = compute_nb_correct(self.train_input) + + log_string( + f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%" + ) + + test_nb_total, test_nb_correct = compute_nb_correct(self.test_input) + + log_string( + f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + ) + + model.train(t) + ###################################################################### @@ -786,10 +836,10 @@ elif args.task == "snake": nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.batch_size, - height=6, - width=8, - nb_colors=5, - length=100, + height=args.snake_height, + width=args.snake_width, + nb_colors=args.snake_nb_colors, + length=args.snake_length, device=device, )