robomaster/data/dataset_image_video.py

import csv
import io
import json
import math
import os
import random
from threading import Thread

import albumentations
import cv2
import gc
import numpy as np
import torch
import torchvision.transforms as transforms

from func_timeout import func_timeout, FunctionTimedOut
from decord import VideoReader
from PIL import Image
from torch.utils.data import BatchSampler, Sampler
from torch.utils.data.dataset import Dataset
from contextlib import contextmanager

import tensorflow as tf
import tensorflow_datasets as tfds
from PIL import Image
from IPython import display
import tqdm

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
VIDEO_READER_TIMEOUT = 20

def dataset2path(dataset_name):
    if dataset_name == 'robo_net':
        version = '1.0.0'
    elif dataset_name == 'language_table':
        version = '0.0.1'
    else:
        version = '0.1.0'
    return f'/m2v_intern/fuxiao/Open-X-Embodiement/dataset/{dataset_name}/{version}'

def get_random_mask(shape):
    f, c, h, w = shape

    if f != 1:
        mask_index = np.random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], p=[0.05, 0.2, 0.2, 0.2, 0.05, 0.05, 0.05, 0.1, 0.05, 0.05]) 
    else:
        mask_index = np.random.choice([0, 1], p = [0.2, 0.8])
    mask = torch.zeros((f, 1, h, w), dtype=torch.uint8)

    if mask_index == 0:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围

        start_x = max(center_x - block_size_x // 2, 0)
        end_x = min(center_x + block_size_x // 2, w)
        start_y = max(center_y - block_size_y // 2, 0)
        end_y = min(center_y + block_size_y // 2, h)
        mask[:, :, start_y:end_y, start_x:end_x] = 1
    elif mask_index == 1:
        mask[:, :, :, :] = 1
    elif mask_index == 2:
        mask_frame_index = np.random.randint(1, 5)
        mask[mask_frame_index:, :, :, :] = 1
    elif mask_index == 3:
        mask_frame_index = np.random.randint(1, 5)
        mask[mask_frame_index:-mask_frame_index, :, :, :] = 1
    elif mask_index == 4:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围

        start_x = max(center_x - block_size_x // 2, 0)
        end_x = min(center_x + block_size_x // 2, w)
        start_y = max(center_y - block_size_y // 2, 0)
        end_y = min(center_y + block_size_y // 2, h)

        mask_frame_before = np.random.randint(0, f // 2)
        mask_frame_after = np.random.randint(f // 2, f)
        mask[mask_frame_before:mask_frame_after, :, start_y:end_y, start_x:end_x] = 1
    elif mask_index == 5:
        mask = torch.randint(0, 2, (f, 1, h, w), dtype=torch.uint8)
    elif mask_index == 6:
        num_frames_to_mask = random.randint(1, max(f // 2, 1))
        frames_to_mask = random.sample(range(f), num_frames_to_mask)

        for i in frames_to_mask:
            block_height = random.randint(1, h // 4)
            block_width = random.randint(1, w // 4)
            top_left_y = random.randint(0, h - block_height)
            top_left_x = random.randint(0, w - block_width)
            mask[i, 0, top_left_y:top_left_y + block_height, top_left_x:top_left_x + block_width] = 1
    elif mask_index == 7:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        a = torch.randint(min(w, h) // 8, min(w, h) // 4, (1,)).item()  # 长半轴
        b = torch.randint(min(h, w) // 8, min(h, w) // 4, (1,)).item()  # 短半轴

        for i in range(h):
            for j in range(w):
                if ((i - center_y) ** 2) / (b ** 2) + ((j - center_x) ** 2) / (a ** 2) < 1:
                    mask[:, :, i, j] = 1
    elif mask_index == 8:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        radius = torch.randint(min(h, w) // 8, min(h, w) // 4, (1,)).item()
        for i in range(h):
            for j in range(w):
                if (i - center_y) ** 2 + (j - center_x) ** 2 < radius ** 2:
                    mask[:, :, i, j] = 1
    elif mask_index == 9:
        for idx in range(f):
            if np.random.rand() > 0.5:
                mask[idx, :, :, :] = 1
    else:
        raise ValueError(f"The mask_index {mask_index} is not define")
    return mask

class ImageVideoSampler(BatchSampler):
    """A sampler wrapper for grouping images with similar aspect ratio into a same batch.

    Args:
        sampler (Sampler): Base sampler.
        dataset (Dataset): Dataset providing data information.
        batch_size (int): Size of mini-batch.
        drop_last (bool): If ``True``, the sampler will drop the last batch if
            its size would be less than ``batch_size``.
        aspect_ratios (dict): The predefined aspect ratios.
    """

    def __init__(self,
                sampler: Sampler,
                dataset: Dataset,
                batch_size: int,
                drop_last: bool = False
                ) -> None:
        if not isinstance(sampler, Sampler):
            raise TypeError('sampler should be an instance of ``Sampler``, '
                            f'but got {sampler}')
        if not isinstance(batch_size, int) or batch_size <= 0:
            raise ValueError('batch_size should be a positive integer value, '
                             f'but got batch_size={batch_size}')
        self.sampler = sampler
        self.dataset = dataset
        self.batch_size = batch_size
        self.drop_last = drop_last

        # buckets for each aspect ratio
        self.bucket = {'image':[], 'video':[]}

    def __iter__(self):
        for idx in self.sampler:
            content_type = self.dataset.dataset[idx].get('type', 'image')
            self.bucket[content_type].append(idx)

            # yield a batch of indices in the same aspect ratio group
            if len(self.bucket['video']) == self.batch_size:
                bucket = self.bucket['video']
                yield bucket[:]
                del bucket[:]
            elif len(self.bucket['image']) == self.batch_size:
                bucket = self.bucket['image']
                yield bucket[:]
                del bucket[:]

@contextmanager
def VideoReader_contextmanager(*args, **kwargs):
    vr = VideoReader(*args, **kwargs)
    try:
        yield vr
    finally:
        del vr
        gc.collect()

def get_video_reader_batch(video_reader, batch_index):
    frames = video_reader.get_batch(batch_index).asnumpy()
    return frames

def resize_frame(frame, target_short_side):
    h, w, _ = frame.shape
    if h < w:
        if target_short_side > h:
            return frame
        new_h = target_short_side
        new_w = int(target_short_side * w / h)
    else:
        if target_short_side > w:
            return frame
        new_w = target_short_side
        new_h = int(target_short_side * h / w)
    
    resized_frame = cv2.resize(frame, (new_w, new_h))
    return resized_frame

class ImageVideoDataset(Dataset):
    def __init__(
            self,
            data_root=None,
            video_sample_size_h=256, 
            video_sample_size_w=320, 
            video_sample_stride=4, 
            video_sample_n_frames=16,
            image_sample_size=512,
            text_drop_ratio=0.1,
            enable_bucket=False,
            video_length_drop_start=0.0, 
            video_length_drop_end=1.0,
            enable_inpaint=False,
        ):
        # Loading annotations from files
        print(f"loading dataset from {data_root} ...")
        self.data_root = data_root
        self.dataset = []

        b = tfds.builder_from_directory(builder_dir=dataset2path('fractal20220817_data'))
        ds = b.as_dataset(split='train')

        for i, batch in tqdm.tqdm(enumerate(ds), desc="Loading Open-X-Embodiement dataset"):
            episode = batch['steps']

        del dataset

        self.length = len(self.dataset)
        print(f"data scale: {self.length}")
        # TODO: enable bucket training
        self.enable_bucket   = enable_bucket
        self.text_drop_ratio = text_drop_ratio
        self.enable_inpaint  = enable_inpaint

        self.video_length_drop_start = video_length_drop_start
        self.video_length_drop_end = video_length_drop_end

        # Video params
        self.video_sample_stride    = video_sample_stride
        self.video_sample_n_frames  = video_sample_n_frames
        self.video_sample_size = (video_sample_size_h, video_sample_size_w)
        self.video_transforms = transforms.Compose(
            [
                transforms.Resize(min(self.video_sample_size)),
                transforms.CenterCrop(self.video_sample_size),
                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
            ]
        )

    def get_batch(self, idx):
        data_info = self.dataset[idx % len(self.dataset)]
        
        if data_info.get('type', 'image')=='video':
            video_id, text = data_info['file_path'], data_info['text']

            if self.data_root is None:
                video_dir = video_id
            else:
                video_dir = os.path.join(self.data_root, video_id)

            with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
                min_sample_n_frames = min(
                    self.video_sample_n_frames, 
                    int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
                )
                if min_sample_n_frames == 0:
                    raise ValueError(f"No Frames in video.")

                video_length = int(self.video_length_drop_end * len(video_reader))
                clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
                start_idx   = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
                batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)

                try:
                    sample_args = (video_reader, batch_index)
                    pixel_values = func_timeout(
                        VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
                    )
                    resized_frames = []
                    for i in range(len(pixel_values)):
                        frame = pixel_values[i]
                        resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
                        resized_frames.append(resized_frame)
                    pixel_values = np.array(resized_frames)
                except FunctionTimedOut:
                    raise ValueError(f"Read {idx} timeout.")
                except Exception as e:
                    raise ValueError(f"Failed to extract frames from video. Error is {e}.")

                if not self.enable_bucket:
                    pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
                    pixel_values = pixel_values / 255.
                    del video_reader
                else:
                    pixel_values = pixel_values

                if not self.enable_bucket:
                    pixel_values = self.video_transforms(pixel_values)
                
                # Random use no text generation
                if random.random() < self.text_drop_ratio:
                    text = ''
            return pixel_values, text, 'video'
        else:
            image_path, text = data_info['file_path'], data_info['text']
            if self.data_root is not None:
                image_path = os.path.join(self.data_root, image_path)
            image = Image.open(image_path).convert('RGB')
            if not self.enable_bucket:
                image = self.image_transforms(image).unsqueeze(0)
            else:
                image = np.expand_dims(np.array(image), 0)
            if random.random() < self.text_drop_ratio:
                text = ''
            return image, text, 'image'

    def __len__(self):
        return self.length
    
    def __getitem__(self, idx):
        data_info = self.dataset[idx % len(self.dataset)]
        data_type = data_info.get('type', 'image')
        while True:
            sample = {}
            try:
                data_info_local = self.dataset[idx % len(self.dataset)]
                data_type_local = data_info_local.get('type', 'image')
                if data_type_local != data_type:
                    raise ValueError("data_type_local != data_type")

                pixel_values, name, data_type = self.get_batch(idx)
                sample["pixel_values"] = pixel_values
                sample["text"] = name
                sample["data_type"] = data_type
                sample["idx"] = idx
                
                if len(sample) > 0:
                    break
            except Exception as e:
                print(e, self.dataset[idx % len(self.dataset)])
                idx = random.randint(0, self.length-1)

        if self.enable_inpaint and not self.enable_bucket:
            mask = get_random_mask(pixel_values.size())
            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
            sample["mask_pixel_values"] = mask_pixel_values
            sample["mask"] = mask

            clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
            clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
            sample["clip_pixel_values"] = clip_pixel_values

            ref_pixel_values = sample["pixel_values"][0].unsqueeze(0)
            if (mask == 1).all():
                ref_pixel_values = torch.ones_like(ref_pixel_values) * -1
            sample["ref_pixel_values"] = ref_pixel_values

        return sample