From 0cba1df2952a9f9b88b6e7aacfcddc17fbc35186 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Sat, 16 Sep 2023 12:24:21 +0200 Subject: [PATCH] Update. --- main.py | 22 ---------- snake.py | 47 ++++++--------------- tasks.py | 122 ------------------------------------------------------- 3 files changed, 12 insertions(+), 179 deletions(-) diff --git a/main.py b/main.py index 7197414..cd37b94 100755 --- a/main.py +++ b/main.py @@ -159,11 +159,6 @@ parser.add_argument("--expr_result_max", type=int, default=99) parser.add_argument("--expr_input_file", type=str, default=None) -############################## -# World options - -parser.add_argument("--world_vqae_nb_epochs", type=int, default=25) - ###################################################################### args = parser.parse_args() @@ -248,19 +243,12 @@ default_task_args = { "nb_train_samples": 50000, "nb_test_samples": 10000, }, - "mnist": { "model": "37M", "batch_size": 10, "nb_train_samples": 60000, "nb_test_samples": 10000, }, - "world": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 25000, - "nb_test_samples": 1000, - }, } if args.task in default_task_args: @@ -514,16 +502,6 @@ elif args.task == "grid": device=device, ) -elif args.task == "world": - task = tasks.World( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.batch_size, - vqae_nb_epochs=args.world_vqae_nb_epochs, - logger=log_string, - device=device, - ) - else: raise ValueError(f"Unknown task {args.task}") diff --git a/snake.py b/snake.py index 7c34941..8a16f9f 100755 --- a/snake.py +++ b/snake.py @@ -111,45 +111,22 @@ def solver(input, ar_mask): # print(f'@2 {i=} {j=}') +def seq2str(seq): + return "".join(["NESW123456789"[i] for i in seq]) + + ###################################################################### if __name__ == "__main__": - import cairo, numpy, math - - color_name2rgb = { - "red": [255, 0, 0], - "green": [0, 128, 0], - "blue": [0, 0, 255], - "yellow": [255, 255, 0], - "orange": [255, 128, 0], - "maroon": [128, 0, 0], - "dark_red": [139, 0, 0], - "brown": [165, 42, 42], - "firebrick": [178, 34, 34], - "crimson": [220, 20, 60], - "tomato": [255, 99, 71], - "coral": [255, 127, 80], - "indian_red": [205, 92, 92], - "light_coral": [240, 128, 128], - "dark_salmon": [233, 150, 122], - "salmon": [250, 128, 114], - } - - sequences, sequences_prior_visits, worlds, world_prior_visits = generate_sequences( - 8, 6, 8, 5, 20, 10 + train_input, train_prior_visits, _, _ = generate_sequences( + nb=20, + height=9, + width=12, + nb_colors=5, + length=50, + prompt_length=100, ) - delta = 16 - height, width = sequences.size(0) * 16, sequences.size(1) * 16 - pixel_map = torch.ByteTensor(width, height, 4).fill_(0).numpy() - surface = cairo.ImageSurface.create_for_data( - pixel_map, cairo.FORMAT_ARGB32, width, height - ) - ctx = cairo.Context(surface) - ctx.set_line_width(1.0) - - ctx.set_fill_rule(cairo.FILL_RULE_EVEN_ODD) - - ctx.fill() + print([seq2str(s) for s in train_input]) ###################################################################### diff --git a/tasks.py b/tasks.py index d787c59..183c3cf 100755 --- a/tasks.py +++ b/tasks.py @@ -1550,125 +1550,3 @@ class Grid(Task): ###################################################################### - -import world - - -class World(Task): - def __init__( - self, - nb_train_samples, - nb_test_samples, - batch_size, - vqae_nb_epochs, - logger=None, - device=torch.device("cpu"), - device_storage=torch.device("cpu"), - ): - super().__init__() - - self.batch_size = batch_size - self.device = device - - ( - train_frames, - train_action_seq, - test_frames, - test_action_seq, - self.frame2seq, - self.seq2frame, - ) = world.create_data_and_processors( - nb_train_samples, - nb_test_samples, - mode="first_last", - nb_steps=30, - nb_epochs=vqae_nb_epochs, - logger=logger, - device=device, - device_storage=device_storage, - ) - - train_frame_seq = self.frame2seq(train_frames).to(device_storage) - test_frame_seq = self.frame2seq(test_frames).to(device_storage) - - nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1 - nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1 - - self.len_frame_seq = train_frame_seq.size(1) - self.len_action_seq = train_action_seq.size(1) - self.nb_codes = nb_frame_codes + nb_action_codes - - train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1) - - train_action_seq += nb_frame_codes - self.train_input = torch.cat( - (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1 - ) - - test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1) - test_action_seq += nb_frame_codes - self.test_input = torch.cat( - (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 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.to(self.device) - - def vocabulary_size(self): - return self.nb_codes - - def produce_results( - self, n_epoch, model, result_dir, logger, deterministic_synthesis - ): - k = torch.arange( - 2 * self.len_frame_seq + self.len_action_seq, device=self.device - )[None, :] - - input = self.test_input[:64].to(self.device) - result = input.clone() - - ar_mask = ( - (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result) - ) - result *= 1 - ar_mask - - masked_inplace_autoregression( - model, - self.batch_size, - result, - ar_mask, - deterministic_synthesis, - device=self.device, - ) - - seq_start = input[:, : self.len_frame_seq] - seq_end = input[:, self.len_frame_seq + self.len_action_seq :] - seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :] - - result = torch.cat( - (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1 - ) - result = result.reshape(-1, result.size(-1)) - - frames = self.seq2frame(result) - image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png") - torchvision.utils.save_image( - frames.float() / (world.Box.nb_rgb_levels - 1), - image_name, - nrow=12, - padding=1, - pad_value=0.0, - ) - logger(f"wrote {image_name}") - - -###################################################################### -- 2.20.1