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data_list.py
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#from __future__ import print_function, division
import torch
import numpy as np
import torchvision.transforms.functional as F
from torchvision import transforms
import random
from PIL import Image
from torch.utils.data import Dataset
import os
import os.path
from PIL import ImageFile
import random
from dda_model.util import get_cdm_file_name, get_expert_cdm_file_name
ImageFile.LOAD_TRUNCATED_IMAGES = True
def make_dataset(image_list, labels):
if labels:
len_ = len(image_list)
images = [(image_list[i].strip(), labels[i, :]) for i in range(len_)]
else:
if len(image_list[0].split()) > 2:
images = [(val.split()[0], np.array([int(la) for la in val.split()[1:]])) for val in image_list]
else:
images = [(val.split()[0], int(val.split()[1])) for val in image_list]
return images
def rgb_loader(path):
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def l_loader(path):
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('L')
class ImageList(Dataset):
def __init__(self, image_list, labels=None, transform=None, target_transform=None, mode='RGB', return_path=False,
return_cdm=False, cdm_path="", cdm_transform=None):
imgs = make_dataset(image_list, labels)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
"Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.imgs = imgs
self.return_path = return_path
self.transform = transform
self.target_transform = target_transform
self.cdm_transform = cdm_transform
self.return_cdm = return_cdm
self.cdm_path = cdm_path
if mode == 'RGB':
self.loader = rgb_loader
elif mode == 'L':
self.loader = l_loader
def __getitem__(self, index):
path, target = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if self.return_cdm:
cdm_file_name = get_cdm_file_name(path)
cdm_file_path = os.path.join(self.cdm_path, cdm_file_name)
cdm = self.loader(cdm_file_path)
if self.cdm_transform is not None:
cdm = self.cdm_transform(cdm)
if self.return_path:
if self.return_cdm:
return img, target, path, cdm
else:
return img, target, path
else:
if self.return_cdm:
return img, target, cdm
else:
return img, target
def __len__(self):
return len(self.imgs)
class ExpertImageList(Dataset):
def __init__(self, image_list, labels=None, transform=None, target_transform=None, mode='RGB', return_path=False, cdm_path="", cdm_transform=None, n_experts=1):
imgs = make_dataset(image_list, labels)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
"Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.imgs = imgs
self.return_path = return_path
self.transform = transform
self.target_transform = target_transform
self.cdm_transform = cdm_transform
self.cdm_path = cdm_path
self.n_experts = n_experts
if mode == 'RGB':
self.loader = rgb_loader
elif mode == 'L':
self.loader = l_loader
def __getitem__(self, index):
path, target = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
cdms = torch.zeros(self.n_experts, img.shape[0], img.shape[1], img.shape[2]).to(img.device)
# cdms = cdms.unsqueeze(0).expand(self.n_experts, -1, -1, -1)
for i in range(self.n_experts):
cdm_file_name = get_expert_cdm_file_name(path, i)
cdm_file_path = os.path.join(self.cdm_path, cdm_file_name)
cdm = self.loader(cdm_file_path)
if self.cdm_transform is not None:
cdm = self.cdm_transform(cdm)
cdms[i] = cdm
if self.return_path:
return img, target, path, cdms
else:
return img, target, cdms
def __len__(self):
return len(self.imgs)
class BundledImageList(Dataset):
def __init__(self, image_list, labels=None, ori_transform=None, cdm_transform=None, mode='RGB', return_path=False,
cdm_path="", bundled_transform=None, resized_crop_size=224, random_horizontal_flip=False):
imgs = make_dataset(image_list, labels)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
"Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.imgs = imgs
self.return_path = return_path
self.ori_transform = ori_transform
self.cdm_transform = cdm_transform
self.bundled_transform = bundled_transform
self.cdm_path = cdm_path
self.resized_crop_size = resized_crop_size
self.random_horizontal_flip = random_horizontal_flip
if mode == 'RGB':
self.loader = rgb_loader
elif mode == 'L':
self.loader = l_loader
def __getitem__(self, index):
path, target = self.imgs[index]
img = self.loader(path)
if self.ori_transform is not None:
img = self.ori_transform(img)
cdm_file_name = get_cdm_file_name(path)
cdm_file_path = os.path.join(self.cdm_path, cdm_file_name)
cdm = self.loader(cdm_file_path)
if self.cdm_transform is not None:
cdm = self.cdm_transform(cdm)
if self.random_horizontal_flip:
if random.random() > 0.5:
img = F.hflip(img)
cdm = F.hflip(cdm)
if self.resized_crop_size > 0:
# Perform random resized crop
i, j, h, w = transforms.RandomResizedCrop.get_params(
img, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.))
size = (self.resized_crop_size, self.resized_crop_size)
img = F.resized_crop(img, i, j, h, w, size)
cdm = F.resized_crop(cdm, i, j, h, w, size)
if self.bundled_transform is not None:
img = self.bundled_transform(img)
cdm = self.bundled_transform(cdm)
if self.return_path:
return img, target, path, cdm
else:
return img, target, cdm
def __len__(self):
return len(self.imgs)
class ImageValueList(Dataset):
def __init__(self, image_list, labels=None, transform=None, target_transform=None,
loader=rgb_loader):
imgs = make_dataset(image_list, labels)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
"Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.imgs = imgs
self.values = [1.0] * len(imgs)
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def set_values(self, values):
self.values = values
def __getitem__(self, index):
path, target = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.imgs)