eliminate sdata issue

docs/INSTALL.md

@@ -40,7 +40,6 @@
     ```shell
     conda install -y pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia
     pip3 install -r requirements.txt
-    pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
     ```
 
 ---

docs/ISSUES.md

@@ -17,21 +17,7 @@
      1. Download [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14/tree/main) and [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/tree/main) in advance.
      2. Set *version* of FrozenCLIPEmbedder and FrozenOpenCLIPImageEmbedder in `vwm/modules/encoders/modules.py` to the new paths of `pytorch_model.bin`/`open_clip_pytorch_model.bin`.
 
-3. #### Datasets not yet available during training.
-
-   - Possible reason:
-
-     - The installed [sdata](https://github.com/Stability-AI/datapipelines) is not detected.
-
-   - Try this:
-
-     - Reinstall in the current project directory.
-
-       ````shell
-       pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
-       ````
-
-4. #### The shapes of linear layers cannot be multiplied at the cross-attention layers.
+3. #### The shapes of linear layers cannot be multiplied at the cross-attention layers.
 
    - Possible reason:
      - The dimension of cross-attention is not expended while the action conditions are injected, resulting in a mismatch.

vwm/data/dataset.py

@@ -1,87 +1,10 @@
 import random
-from typing import Optional
 
-import torchdata.datapipes.iter
-import webdataset as wds
-from omegaconf import DictConfig
 from pytorch_lightning import LightningDataModule
 from torch.utils.data import DataLoader, Dataset
 
 from .subsets import YouTubeDataset, NuScenesDataset
 
-try:
-    from sdata import create_dataset, create_dummy_dataset, create_loader
-except ImportError as e:
-    print("#" * 100)
-    print("Datasets not yet available")
-    print("To enable, we need to add stable-datasets as a submodule")
-    print("Please use ``git submodule update --init --recursive``")
-    print("and do ``pip install -e stable-datasets/`` from the root of this repo")
-    print("#" * 100)
-    exit(1)
-
-
-class StableDataModuleFromConfig(LightningDataModule):
-    def __init__(
-            self,
-            train: DictConfig,
-            validation: Optional[DictConfig] = None,
-            test: Optional[DictConfig] = None,
-            skip_val_loader: bool = False,
-            dummy: bool = False
-    ):
-        super().__init__()
-        self.train_config = train
-        assert (
-                "datapipeline" in self.train_config and "loader" in self.train_config
-        ), "Train config requires the fields `datapipeline` and `loader`"
-
-        self.val_config = validation
-        if not skip_val_loader:
-            if self.val_config is not None:
-                assert (
-                        "datapipeline" in self.val_config and "loader" in self.val_config
-                ), "Validation config requires the fields `datapipeline` and `loader`"
-            else:
-                print(
-                    "WARNING: no validation datapipeline defined, using that one from training"
-                )
-                self.val_config = train
-
-        self.test_config = test
-        if self.test_config is not None:
-            assert (
-                    "datapipeline" in self.test_config and "loader" in self.test_config
-            ), "Test config requires the fields `datapipeline` and `loader`"
-
-        self.dummy = dummy
-        if self.dummy:
-            print("#" * 100)
-            print("Using dummy dataset, hope you are debugging")
-            print("#" * 100)
-
-    def setup(self, stage: str) -> None:
-        print("Preparing datasets")
-        if self.dummy:
-            data_fn = create_dummy_dataset
-        else:
-            data_fn = create_dataset
-
-        self.train_data_pipeline = data_fn(**self.train_config.datapipeline)
-        if self.val_config:
-            self.val_data_pipeline = data_fn(**self.val_config.datapipeline)
-        if self.test_config:
-            self.test_data_pipeline = data_fn(**self.test_config.datapipeline)
-
-    def train_dataloader(self) -> torchdata.datapipes.iter.IterDataPipe:
-        return create_loader(self.train_data_pipeline, **self.train_config.loader)
-
-    def val_dataloader(self) -> wds.DataPipeline:
-        return create_loader(self.val_data_pipeline, **self.val_config.loader)
-
-    def test_dataloader(self) -> wds.DataPipeline:
-        return create_loader(self.test_data_pipeline, **self.test_config.loader)
-
 
 def dataset_mapping(subset_list: list, target_height: int, target_width: int, num_frames: int):
     datasets = list()