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import glob
import json
import os
import pickle
import random
import shutil
import tarfile
from functools import partial
import albumentations
import cv2
import numpy as np
import PIL
import torchvision.transforms.functional as TF
import yaml
from decord import VideoReader
from func_timeout import FunctionTimedOut, func_set_timeout
from omegaconf import OmegaConf
from PIL import Image
from torch.utils.data import BatchSampler, Dataset, Sampler
from tqdm import tqdm
from ..modules.image_degradation import (degradation_fn_bsr,
degradation_fn_bsr_light)
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
self.sampler_pos_start = 0
self.sampler_pos_reload = 0
self.num_samples_random = len(self.sampler)
# buckets for each aspect ratio
self.bucket = {'image':[], 'video':[]}
def set_epoch(self, epoch):
if hasattr(self.sampler, "set_epoch"):
self.sampler.set_epoch(epoch)
def __iter__(self):
for index_sampler, idx in enumerate(self.sampler):
if self.sampler_pos_reload != 0 and self.sampler_pos_reload < self.num_samples_random:
if index_sampler < self.sampler_pos_reload:
self.sampler_pos_start = (self.sampler_pos_start + 1) % self.num_samples_random
continue
elif index_sampler == self.sampler_pos_reload:
self.sampler_pos_reload = 0
content_type = self.dataset.data.get_type(idx)
bucket = self.bucket[content_type]
bucket.append(idx)
# yield a batch of indices in the same aspect ratio group
if len(self.bucket['video']) == self.batch_size:
yield self.bucket['video']
self.bucket['video'] = []
elif len(self.bucket['image']) == self.batch_size:
yield self.bucket['image']
self.bucket['image'] = []
self.sampler_pos_start = (self.sampler_pos_start + 1) % self.num_samples_random
class ImageVideoDataset(Dataset):
# update __getitem__() from ImageNetSR. If timeout for Pandas70M, throw exception.
# If caught exception(timeout or others), try another index until successful and return.
def __init__(self, size=None, video_size=128, video_len=25,
degradation=None, downscale_f=4, random_crop=True, min_crop_f=0.25, max_crop_f=1.,
s_t=None, slice_interval=None, data_root=None
):
"""
Imagenet Superresolution Dataloader
Performs following ops in order:
1. crops a crop of size s from image either as random or center crop
2. resizes crop to size with cv2.area_interpolation
3. degrades resized crop with degradation_fn
:param size: resizing to size after cropping
:param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
:param downscale_f: Low Resolution Downsample factor
:param min_crop_f: determines crop size s,
where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
:param max_crop_f: ""
:param data_root:
:param random_crop:
"""
self.base = self.get_base()
assert size
assert (size / downscale_f).is_integer()
self.size = size
self.LR_size = int(size / downscale_f)
self.min_crop_f = min_crop_f
self.max_crop_f = max_crop_f
assert(max_crop_f <= 1.)
self.center_crop = not random_crop
self.s_t = s_t
self.slice_interval = slice_interval
self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
self.video_rescaler = albumentations.SmallestMaxSize(max_size=video_size, interpolation=cv2.INTER_AREA)
self.video_len = video_len
self.video_size = video_size
self.data_root = data_root
self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
if degradation == "bsrgan":
self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
elif degradation == "bsrgan_light":
self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
else:
interpolation_fn = {
"cv_nearest": cv2.INTER_NEAREST,
"cv_bilinear": cv2.INTER_LINEAR,
"cv_bicubic": cv2.INTER_CUBIC,
"cv_area": cv2.INTER_AREA,
"cv_lanczos": cv2.INTER_LANCZOS4,
"pil_nearest": PIL.Image.NEAREST,
"pil_bilinear": PIL.Image.BILINEAR,
"pil_bicubic": PIL.Image.BICUBIC,
"pil_box": PIL.Image.BOX,
"pil_hamming": PIL.Image.HAMMING,
"pil_lanczos": PIL.Image.LANCZOS,
}[degradation]
self.pil_interpolation = degradation.startswith("pil_")
if self.pil_interpolation:
self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
else:
self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
interpolation=interpolation_fn)
def __len__(self):
return len(self.base)
def get_type(self, index):
return self.base[index].get('type', 'image')
def __getitem__(self, i):
@func_set_timeout(15) # time wait 3 seconds
def get_video_item(example):
if self.data_root is not None:
video_reader = VideoReader(os.path.join(self.data_root, example['file_path']))
else:
video_reader = VideoReader(example['file_path'])
video_length = len(video_reader)
if self.slice_interval == "rand":
slice_interval = np.random.choice([1, 2, 3])
else:
slice_interval = int(self.slice_interval)
clip_length = min(video_length, (self.video_len - 1) * slice_interval + 1)
start_idx = random.randint(0, video_length - clip_length)
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.video_len, dtype=int)
pixel_values = video_reader.get_batch(batch_index).asnumpy()
del video_reader
out_images = []
LR_out_images = []
min_side_len = min(pixel_values[0].shape[:2])
crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
crop_side_len = int(crop_side_len)
if self.center_crop:
self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
else:
self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
imgs = np.transpose(pixel_values, (1, 2, 3, 0))
imgs = self.cropper(image=imgs)["image"]
imgs = np.transpose(imgs, (3, 0, 1, 2))
for img in imgs:
image = self.video_rescaler(image=img)["image"]
out_images.append(image[None, :, :, :])
if self.pil_interpolation:
image_pil = PIL.Image.fromarray(image)
LR_image = self.degradation_process(image_pil)
LR_image = np.array(LR_image).astype(np.uint8)
else:
LR_image = self.degradation_process(image=image)["image"]
LR_out_images.append(LR_image[None, :, :, :])
example = {}
example['image'] = (np.concatenate(out_images) / 127.5 - 1.0).astype(np.float32)
example['LR_image'] = (np.concatenate(LR_out_images) / 127.5 - 1.0).astype(np.float32)
return example
example = self.base[i]
if example.get('type', 'image') == 'video':
while True:
try:
example = self.base[i]
return get_video_item(example)
except FunctionTimedOut:
print("stt catch: Function 'extract failed' timed out.")
i = random.randint(0, self.__len__() - 1)
except Exception as e:
print('stt catch', e)
i = random.randint(0, self.__len__() - 1)
elif example.get('type', 'image') == 'image':
while True:
try:
example = self.base[i]
if self.data_root is not None:
image = Image.open(os.path.join(self.data_root, example['file_path']))
else:
image = Image.open(example['file_path'])
image = image.convert("RGB")
image = np.array(image).astype(np.uint8)
min_side_len = min(image.shape[:2])
crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
crop_side_len = int(crop_side_len)
if self.center_crop:
self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
else:
self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
image = self.cropper(image=image)["image"]
image = self.image_rescaler(image=image)["image"]
if self.pil_interpolation:
image_pil = PIL.Image.fromarray(image)
LR_image = self.degradation_process(image_pil)
LR_image = np.array(LR_image).astype(np.uint8)
else:
LR_image = self.degradation_process(image=image)["image"]
example = {}
example["image"] = (image/127.5 - 1.0).astype(np.float32)
example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
return example
except Exception as e:
print("catch", e)
i = random.randint(0, self.__len__() - 1)
class CustomSRTrain(ImageVideoDataset):
def __init__(self, data_json_path, **kwargs):
self.data_json_path = data_json_path
super().__init__(**kwargs)
def get_base(self):
return [ann for ann in json.load(open(self.data_json_path))]
class CustomSRValidation(ImageVideoDataset):
def __init__(self, data_json_path, **kwargs):
self.data_json_path = data_json_path
super().__init__(**kwargs)
self.data_json_path = data_json_path
def get_base(self):
return [ann for ann in json.load(open(self.data_json_path))][:100] + \
[ann for ann in json.load(open(self.data_json_path))][-100:]
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