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Running
on
Zero
File size: 7,066 Bytes
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import json
import os
import random
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data.dataset import Dataset
class CC15M(Dataset):
def __init__(
self,
json_path,
video_folder=None,
resolution=512,
enable_bucket=False,
):
print(f"loading annotations from {json_path} ...")
self.dataset = json.load(open(json_path, 'r'))
self.length = len(self.dataset)
print(f"data scale: {self.length}")
self.enable_bucket = enable_bucket
self.video_folder = video_folder
resolution = tuple(resolution) if not isinstance(resolution, int) else (resolution, resolution)
self.pixel_transforms = transforms.Compose([
transforms.Resize(resolution[0]),
transforms.CenterCrop(resolution),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
def get_batch(self, idx):
video_dict = self.dataset[idx]
video_id, name = video_dict['file_path'], video_dict['text']
if self.video_folder is None:
video_dir = video_id
else:
video_dir = os.path.join(self.video_folder, video_id)
pixel_values = Image.open(video_dir).convert("RGB")
return pixel_values, name
def __len__(self):
return self.length
def __getitem__(self, idx):
while True:
try:
pixel_values, name = self.get_batch(idx)
break
except Exception as e:
print(e)
idx = random.randint(0, self.length-1)
if not self.enable_bucket:
pixel_values = self.pixel_transforms(pixel_values)
else:
pixel_values = np.array(pixel_values)
sample = dict(pixel_values=pixel_values, text=name)
return sample
class ImageEditDataset(Dataset):
def __init__(
self,
ann_path, data_root=None,
image_sample_size=512,
text_drop_ratio=0.1,
enable_bucket=False,
enable_inpaint=False,
return_file_name=False,
):
# Loading annotations from files
print(f"loading annotations from {ann_path} ...")
if ann_path.endswith('.csv'):
with open(ann_path, 'r') as csvfile:
dataset = list(csv.DictReader(csvfile))
elif ann_path.endswith('.json'):
dataset = json.load(open(ann_path))
self.data_root = data_root
self.dataset = 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.return_file_name = return_file_name
# Image params
self.image_sample_size = tuple(image_sample_size) if not isinstance(image_sample_size, int) else (image_sample_size, image_sample_size)
self.image_transforms = transforms.Compose([
transforms.Resize(min(self.image_sample_size)),
transforms.CenterCrop(self.image_sample_size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
])
def get_batch(self, idx):
data_info = self.dataset[idx % len(self.dataset)]
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:
raise ValueError("Not enable_bucket is not supported now. ")
else:
image = np.expand_dims(np.array(image), 0)
source_image_path = data_info.get('source_file_path', [])
source_image = []
if isinstance(source_image_path, list):
for _source_image_path in source_image_path:
if self.data_root is not None:
_source_image_path = os.path.join(self.data_root, _source_image_path)
_source_image = Image.open(_source_image_path).convert('RGB')
source_image.append(_source_image)
else:
if self.data_root is not None:
_source_image_path = os.path.join(self.data_root, source_image_path)
_source_image = Image.open(_source_image_path).convert('RGB')
source_image.append(_source_image)
if not self.enable_bucket:
raise ValueError("Not enable_bucket is not supported now. ")
else:
source_image = [np.array(_source_image) for _source_image in source_image]
if random.random() < self.text_drop_ratio:
text = ''
return image, source_image, text, 'image', image_path
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, source_pixel_values, name, data_type, file_path = self.get_batch(idx)
sample["pixel_values"] = pixel_values
sample["source_pixel_values"] = source_pixel_values
sample["text"] = name
sample["data_type"] = data_type
sample["idx"] = idx
if self.return_file_name:
sample["file_name"] = os.path.basename(file_path)
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
return sample
if __name__ == "__main__":
dataset = CC15M(
csv_path="./cc15m_add_index.json",
resolution=512,
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=0,)
for idx, batch in enumerate(dataloader):
print(batch["pixel_values"].shape, len(batch["text"])) |