raw_image_data.py

import json
import math
import os
import random
from pathlib import Path

import numpy as np
import torch
from einops import rearrange
from torch.utils.data import Dataset as TorchDataset

from datasets.encode_openx_dataset import DATA_FREQ_TABLE
from genie.config import GenieConfig
from genie.st_mask_git import cosine_schedule


def normalize_actions(actions):
    """
    compute mean and std of actions. Normalize actions is done inside the network.
    """
    mean = np.mean(actions, axis=0).tolist()
    std = np.std(actions, axis=0).tolist()
    return actions, [mean, std]

class RawImageDataset(TorchDataset):
    """ Loads raw uint8 tokens as memmap-backed array """
    def __init__(
        self,
        data_dir,
        window_size,
        stride=1,
        filter_interrupts=True,
        filter_overlaps=False,
        use_actions=False,
        max_traj_num=1000000,
        compute_stride_from_freq_table=True,
        natural_hz=2,
        datio_noise_ratio=0.0,
        domain=None,
    ):
        """
        Args:
            data_dir: directory with the same format as `data/train_v0` and `data/val_v0`.
                Notably, has `video.bin` and `metadata.json`
            window_size: number of frames per "video" sequence
            stride: frame skip
            filter_interrupts: Under 3% of training frame sequences are the concatenation of two different clips.
                If filter_interrupts is True, will filter out these sequences using the segment ids.
            filter_overlaps: If False (default), one frame will appear in multiple examples;
                e.g. frame 0 might appear as the first frame in example 0 and also the second frame in example 15.
                If True, will filter out examples so that each frame appears at most once in the dataset.
            use_actions: If True, will load the actions from the `actions` folder for the models
        """
        data_dir = Path(data_dir)
        with open(data_dir / "metadata.json") as f:
            self.metadata = json.load(f)

        # TODO: assert not quantized in metadata
        shape = (self.metadata["num_images"], self.metadata["h"], self.metadata["w"], 3) #
        video_tokens_path, segment_ids_path, action_tokens_path = [data_dir / f"{name}.bin"
                                                                   for name in ["video", "segment_ids", "actions"]]

        token_dtype = np.dtype(self.metadata.get("token_dtype", "uint8"))
        self.data = np.memmap(video_tokens_path, mode="r", shape=shape, dtype=token_dtype)

        self.window_size, self.stride = window_size, stride
        self.datio_noise_ratio = datio_noise_ratio

        if domain is not None:  # TODO: remove
            self.name = domain
        else:
            self.name = self.metadata["name"]

        if compute_stride_from_freq_table:
            self.stride = max(DATA_FREQ_TABLE.get(self.name, 1) // natural_hz, 1)
        self.n_action = self.metadata.get("action_dim", 1) * (self.stride)

        # actions/ - a folder of action arrays stored in np.float32 format. For frame i,
        # the corresponding action is given by joint_pos[i], driving_command[i], neck_desired[i]
        if use_actions:
            actions = []

            # hack here for the separations in the 1x datasets
            for action_file in sorted((data_dir / "actions").iterdir()):
                actions.append(np.memmap(action_file, dtype=np.float32, mode="r").reshape(len(self.data), -1))

            self.actions = np.concatenate(actions, axis=-1)
            self.actions, self.action_stat = normalize_actions(self.actions)

        if os.path.isfile(segment_ids_path):
            self.segment_ids = np.memmap(
                segment_ids_path,
                dtype=np.int32,
                mode="r",
                shape=(self.metadata["num_images"],)
            )
        else:
            self.segment_ids = None
            if filter_interrupts:
                raise NotImplementedError("Cannot filter interrupted sequences without segment ids.")

        # Number of frames between the first and last frames of a video sequence (excluding one endpoint frame)
        self.video_len = (self.window_size - 1) * self.stride
        self.valid_start_inds = []

        for start_ind in range(len(self.data) - self.video_len - self.stride):
            # Assuming `segment_ids` is monotonically increasing, a sequence is interrupted (or too short)
            # if the first and last frames have different segment ids.
            if not (filter_interrupts and self.segment_ids[start_ind] != self.segment_ids[start_ind + self.video_len]):
                self.valid_start_inds.append(start_ind)

            if len(self.valid_start_inds) >= max_traj_num:
                break

        if filter_overlaps:
            # Instead of using a sliding window, use each frame at most once
            filtered_start_inds = []
            for start_ind in self.valid_start_inds:
                overlapping_start_inds = {start_ind - i * self.stride for i in range(1, self.window_size)}
                # all sequences from `overlapping_start_inds` will also contain `start_ind`,
                # so exclude sequence starting from `start_ind` if any of `overlapping_start_inds` is already being used
                for existing_start_ind in filtered_start_inds[-self.window_size * self.stride:]:
                    # Bound could be improved
                    if existing_start_ind in overlapping_start_inds:
                        break
                else:
                    filtered_start_inds.append(start_ind)

            self.valid_start_inds = filtered_start_inds
        print(f"Loaded {len(self)} sequences from {data_dir} {self.stride=} {self.window_size=} {self.n_action=}")

    def __len__(self):
        return len(self.valid_start_inds)

    def __getitem__(self, idx):
        """
        Returns a flattened sequence of tokens representing `self.window_size` frames,
        spaced `self.stride` apart.
        """
        start_ind = self.valid_start_inds[idx]
        x = self.data[start_ind : start_ind + self.video_len + 1 : self.stride].copy()
        x = torch.FloatTensor(x).float()

        # reconstructions since the input ids and the labels are the same
        attention_mask = torch.ones_like(x)
        data_dict = {
            "images": x,
            "labels": x,  # Do we need labels/attention mask?
            "attention_mask": attention_mask,
            "h": self.metadata["h"],
            "w": self.metadata["w"],
        }
        if hasattr(self, "actions"):
            # we want to have all actions within the stride to predict the next frame at the end of the stride
            # we will concatenate the actions from [window_size, d_action] to [window_size, d_action * stride]
            data_dict['action_ids'] = self.actions[start_ind:start_ind + self.video_len + self.stride].reshape(self.window_size, -1)
            data_dict['action_ids'] = torch.from_numpy(data_dict['action_ids'].astype(np.float32))

        data_dict["domain"] = self.name
        return data_dict