| import random |
|
|
| import numpy as np |
| import torch |
| import torchvision |
| from PIL import Image |
| from tool.cleanfid.resize import build_resizer |
| import zipfile |
|
|
|
|
| class ResizeDataset(torch.utils.data.Dataset): |
| """ |
| A placeholder Dataset that enables parallelizing the resize operation |
| using multiple CPU cores |
| |
| files: list of all files in the folder |
| fn_resize: function that takes an np_array as input [0,255] |
| """ |
|
|
| def __init__(self, files, mode, size=(299, 299), fdir=None): |
| self.files = files |
| self.fdir = fdir |
| self.transforms = torchvision.transforms.ToTensor() |
| self.size = size |
| self.fn_resize = build_resizer(mode) |
| self.custom_image_tranform = lambda x: x |
|
|
| def __len__(self): |
| return len(self.files) |
|
|
| def __getitem__(self, i): |
| try: |
| path = str(self.files[i]) |
| if self.fdir is not None and '.zip' in self.fdir: |
| with zipfile.ZipFile(self.fdir).open(path, 'r') as f: |
| img_np = np.array(Image.open(f).convert('RGB')) |
| if ".npy" in path: |
| img_np = np.load(path) |
| else: |
| img_pil = Image.open(path).convert('RGB') |
| img_np = np.array(img_pil) |
| |
| img_np = self.custom_image_tranform(img_np) |
| |
| img_resized = self.fn_resize(img_np) |
|
|
| |
| if img_resized.dtype == "uint8": |
| img_t = self.transforms(np.array(img_resized))*255 |
| elif img_resized.dtype == "float32": |
| img_t = self.transforms(img_resized) |
| return img_t |
| except: |
| return self.__getitem__(np.random.randint(0, self.__len__() - 1)) |
|
|
|
|
| EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm', |
| 'tif', 'tiff', 'webp', 'npy'} |
|
|
