From: François Fleuret Date: Wed, 21 Jun 2023 18:10:29 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=cf7fcbb7a946c4d1f4d29a28e0eb04940d3b0f76;p=picoclvr.git Update. --- diff --git a/main.py b/main.py index 43d2900..9679236 100755 --- a/main.py +++ b/main.py @@ -39,7 +39,7 @@ parser.add_argument("--result_dir", type=str, default="results_default") parser.add_argument("--seed", type=int, default=0) -parser.add_argument("--nb_epochs", type=int, default=25) +parser.add_argument("--nb_epochs", type=int, default=None) parser.add_argument("--batch_size", type=int, default=None) @@ -100,7 +100,7 @@ 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_nb_colors", type=int, default=5) parser.add_argument("--snake_length", type=int, default=400) @@ -131,15 +131,19 @@ if args.seed >= 0: default_args = { "picoclvr": { + "nb_epochs": 25, "batch_size": 25, }, "mnist": { + "nb_epochs": 25, "batch_size": 10, }, "maze": { + "nb_epochs": 25, "batch_size": 25, }, "snake": { + "nb_epochs": 25, "batch_size": 20, }, } @@ -663,106 +667,7 @@ class TaskMaze(Task): ###################################################################### -def generate_snake_sequences( - nb, height, width, nb_colors, length, prompt_length, device=torch.device("cpu") -): - worlds = torch.randint(nb_colors, (nb, height, width), device=device) - nb_prior_visits = torch.zeros(nb, height, width, device=device) - - # nb x 2 - snake_position = torch.cat( - ( - torch.randint(height, (nb, 1), device=device), - torch.randint(width, (nb, 1), device=device), - ), - 1, - ) - snake_direction = torch.randint(4, (nb,), device=device) - sequences = torch.empty(nb, 2 * length, device=device, dtype=torch.int64) - sequences_prior_visits = torch.zeros( - 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[:, None] - 1) % 4, - snake_direction[:, None], - (snake_direction[:, None] + 1) % 4, - ), - 1, - ) - - # nb x 3 - vh = (snake_next_direction + 1) % 2 * (snake_next_direction - 1) - vw = snake_next_direction % 2 * (snake_next_direction - 2) - - # nb x 3 x 2 - snake_next_speed = torch.cat((vh[:, :, None], vw[:, :, None]), 2) - snake_next_position = snake_position[:, None, :] + snake_next_speed - - # nb x 3 - val = torch.logical_and( - torch.logical_and( - snake_next_position[:, :, 0] >= 0, snake_next_position[:, :, 0] < height - ), - torch.logical_and( - snake_next_position[:, :, 1] >= 0, snake_next_position[:, :, 1] < width - ), - ).float() - val = ( - # The multiplicative factors bias toward moving forward - torch.rand_like(val) - * val - * torch.tensor([[1.0, 2.0, 1.0]], device=device) - ) - - # nb - j = val.argmax(1) - snake_direction = snake_next_direction[i, j] - - sequences[:, 2 * l] = worlds[i, snake_position[:, 0], snake_position[:, 1]] + 4 - sequences_prior_visits[:, 2 * l] = nb_prior_visits[ - i, snake_position[:, 0], snake_position[:, 1] - ] - if l < prompt_length: - nb_prior_visits[i, snake_position[:, 0], snake_position[:, 1]] += 1 - sequences[:, 2 * l + 1] = snake_direction - - # nb x 2 - snake_position = snake_next_position[i, j] - - return sequences, sequences_prior_visits - - -# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20) -# exit(0) - - -def snake_solver(input, ar_mask): - for n in range(input.size(0)): - i, j, memory = 0, 0, {} - # print(input[n]) - # print(ar_mask[n]) - for l in range(input.size(1) // 2): - if ar_mask[n, 2 * l] == 1: - if memory.get((i, j)) is None: - input[n, 2 * l] = -1 - else: - input[n, 2 * l] = memory[(i, j)] - else: - # print(f'@3 {memory=}') - if memory.get((i, j)) is None: - memory[(i, j)] = input[n, 2 * l] - else: - assert memory[(i, j)] == input[n, 2 * l], f"n={n} l={l}" - # print(f'@1 {i=} {j=}') - d = input[n, 2 * l + 1].item() - i += (d + 1) % 2 * (d - 1) - j += d % 2 * (d - 2) - # print(f'@2 {i=} {j=}') +import snake class TaskSnake(Task): @@ -784,7 +689,7 @@ class TaskSnake(Task): self.device = device self.prompt_length = prompt_length - self.train_input, self.train_prior_visits = generate_snake_sequences( + self.train_input, self.train_prior_visits = snake.generate_sequences( nb_train_samples, height, width, @@ -793,7 +698,7 @@ class TaskSnake(Task): prompt_length, self.device, ) - self.test_input, self.test_prior_visits = generate_snake_sequences( + self.test_input, self.test_prior_visits = snake.generate_sequences( nb_test_samples, height, width, @@ -835,7 +740,7 @@ class TaskSnake(Task): ) result *= 1 - ar_mask - # snake_solver(result,ar_mask) + # snake.solver(result,ar_mask) masked_inplace_autoregression( model, self.batch_size, result, ar_mask, device=self.device diff --git a/snake.py b/snake.py new file mode 100755 index 0000000..eb46a07 --- /dev/null +++ b/snake.py @@ -0,0 +1,145 @@ +#!/usr/bin/env python + +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + +import torch, torchvision +import torch.nn.functional as F + + +def generate_sequences( + nb, height, width, nb_colors, length, prompt_length, device=torch.device("cpu") +): + worlds = torch.randint(nb_colors, (nb, height, width), device=device) + nb_prior_visits = torch.zeros(nb, height, width, device=device) + + # nb x 2 + snake_position = torch.cat( + ( + torch.randint(height, (nb, 1), device=device), + torch.randint(width, (nb, 1), device=device), + ), + 1, + ) + snake_direction = torch.randint(4, (nb,), device=device) + sequences = torch.empty(nb, 2 * length, device=device, dtype=torch.int64) + sequences_prior_visits = torch.zeros( + 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[:, None] - 1) % 4, + snake_direction[:, None], + (snake_direction[:, None] + 1) % 4, + ), + 1, + ) + + # nb x 3 + vh = (snake_next_direction + 1) % 2 * (snake_next_direction - 1) + vw = snake_next_direction % 2 * (snake_next_direction - 2) + + # nb x 3 x 2 + snake_next_speed = torch.cat((vh[:, :, None], vw[:, :, None]), 2) + snake_next_position = snake_position[:, None, :] + snake_next_speed + + # nb x 3 + val = torch.logical_and( + torch.logical_and( + snake_next_position[:, :, 0] >= 0, snake_next_position[:, :, 0] < height + ), + torch.logical_and( + snake_next_position[:, :, 1] >= 0, snake_next_position[:, :, 1] < width + ), + ).float() + val = ( + # The multiplicative factors bias toward moving forward + torch.rand_like(val) + * val + * torch.tensor([[1.0, 2.0, 1.0]], device=device) + ) + + # nb + j = val.argmax(1) + snake_direction = snake_next_direction[i, j] + + sequences[:, 2 * l] = worlds[i, snake_position[:, 0], snake_position[:, 1]] + 4 + sequences_prior_visits[:, 2 * l] = nb_prior_visits[ + i, snake_position[:, 0], snake_position[:, 1] + ] + if l < prompt_length: + nb_prior_visits[i, snake_position[:, 0], snake_position[:, 1]] += 1 + sequences[:, 2 * l + 1] = snake_direction + + # nb x 2 + snake_position = snake_next_position[i, j] + + return sequences, sequences_prior_visits + + +# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20) +# exit(0) + + +def solver(input, ar_mask): + for n in range(input.size(0)): + i, j, memory = 0, 0, {} + # print(input[n]) + # print(ar_mask[n]) + for l in range(input.size(1) // 2): + if ar_mask[n, 2 * l] == 1: + if memory.get((i, j)) is None: + input[n, 2 * l] = -1 + else: + input[n, 2 * l] = memory[(i, j)] + else: + # print(f'@3 {memory=}') + if memory.get((i, j)) is None: + memory[(i, j)] = input[n, 2 * l] + else: + assert memory[(i, j)] == input[n, 2 * l], f"n={n} l={l}" + # print(f'@1 {i=} {j=}') + d = input[n, 2 * l + 1].item() + i += (d + 1) % 2 * (d - 1) + j += d % 2 * (d - 2) + # print(f'@2 {i=} {j=}') + + +###################################################################### + +if __name__ == "__main__": + for n in range(16): + descr = generate(nb=1, height=12, width=16) + + print(nb_properties(descr, height=12, width=16)) + + with open(f"picoclvr_example_{n:02d}.txt", "w") as f: + for d in descr: + f.write(f"{d}\n\n") + + img = descr2img(descr, height=12, width=16) + if img.size(0) == 1: + img = F.pad(img, (1, 1, 1, 1), value=64) + + torchvision.utils.save_image( + img / 255.0, + f"picoclvr_example_{n:02d}.png", + padding=1, + nrow=4, + pad_value=0.8, + ) + + import time + + start_time = time.perf_counter() + descr = generate(nb=1000, height=12, width=16) + end_time = time.perf_counter() + print(f"{len(descr) / (end_time - start_time):.02f} samples per second") + +######################################################################