X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=183c3cfc0ff7faeae97a4ec2de2dd529ded3192b;hb=0cba1df2952a9f9b88b6e7aacfcddc17fbc35186;hp=d787c59e7dce1e70ec6a5b2b386c955b4536b018;hpb=6681907dcc86bf6e159925814d419f522e0e3300;p=picoclvr.git

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}")
-
-
-######################################################################