From a35b7a5ebee0b58fc76b64c13d7550eb71bc4567 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Tue, 20 Jun 2023 08:06:20 +0200 Subject: [PATCH 1/1] Update. --- main.py | 59 ++++++++++++++++++++++++++++++++++++--------------------- 1 file changed, 37 insertions(+), 22 deletions(-) diff --git a/main.py b/main.py index f8e451b..6e8ebff 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=100) + ###################################################################### args = parser.parse_args() @@ -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) + snake_direction = torch.randint(4, (nb,), device=device) + sequences = torch.empty(nb, 2 * length, device=device, dtype=torch.int64) count = 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 +679,29 @@ 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[count, snake_position[:, 0], snake_position[:, 1]] + 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 + # print(snake_position) + + +# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20) +# exit(0) -generate_snake_sequences(nb=2, height=4, width=5, nb_colors=3, length=10) -exit(0) class TaskSnake(Task): def __init__( @@ -705,10 +720,10 @@ 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 ) @@ -786,10 +801,10 @@ 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, ) -- 2.39.5