File size: 2,017 Bytes
88c4d74 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | from glob import glob
from torch.utils.data import Dataset
from PIL import Image
import math
import torch.nn.functional as F
def prepadding(x, factor=64):
_, _, h_ori, w_ori = x.shape
dh = factor * math.ceil(h_ori / factor) - h_ori
dw = factor * math.ceil(w_ori / factor) - w_ori
# 确保padding只在右侧和底部添加
x = F.pad(x, (0, dw, 0, dh))
return x, h_ori, w_ori
class MSCOCO(Dataset):
def __init__(self, root, transform, img_list=None):
assert root[-1] == '/', "root to COCO dataset should end with \'/\', not {}.".format(
root)
if img_list:
self.image_paths = []
with open(img_list, 'r') as r:
lines = r.read().splitlines()
for line in lines:
self.image_paths.append(root + line)
else:
self.image_paths = sorted(glob(root + "*.jpg"))
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
object: image.
"""
img_path = self.image_paths[index]
img = Image.open(img_path).convert('RGB')
if self.transform is not None:
img = self.transform(img)
return img
def __len__(self):
return len(self.image_paths)
class Kodak(Dataset):
def __init__(self, root, transform):
assert root[-1] == '/', "root to Kodak dataset should end with \'/\', not {}.".format(
root)
self.image_paths = sorted(glob(root + "*.png"))
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
object: image.
"""
img_path = self.image_paths[index]
img = Image.open(img_path).convert('RGB')
if self.transform is not None:
img = self.transform(img)
return img
def __len__(self):
return len(self.image_paths) |