torchvision.datasets.ImageFolder使用详解
一、数据集组织方式
ImageFolder是一个通用的数据加载器，它要求我们以下面这种格式来组织数据集的训练、验证或者测试图片。
root/1/xxx.png
root/1/xxy.png
root/1/xxz.png
. . .
root/2/12.png
. . .
root/3/123.png
. . .
root/4/356.png
. . .
对于上面的root，假设data文件夹在.py文件的同级目录中，那么root一般都是如下这种形式：./data/train 和 ./data/valid
二、ImageFolder参数详解
dataset=torchvision.datasets.ImageFolder(
root, transform=None,
target_transform=None,
loader=datasets.folder.default_loader,
is_valid_file=None)
参数详解：
- root：图片存储的根目录，即各类别文件夹所在目录的上一级目录。
- transform：对图片进行预处理的操作（函数），原始图片作为输入，返回一个转换后的图片。
- target_transform：对图片类别进行预处理的操作，输入为 target，输出对其的转换。 如果不传该参数，即对 target 不做任何转换，返回的顺序索引 0,1, 2…
- loader：表示数据集加载方式，通常默认加载方式即可。
- is_valid_file：获取图像文件的路径并检查该文件是否为有效文件的函数(用于检查损坏文件)
返回的dataset都有以下三种属性：
- self.classes：用一个 list 保存类别名称
- self.class_to_idx：类别对应的索引，与不做任何转换返回的 target 对应
- self.imgs：保存(img-path, class) tuple的 list
三、程序案例
from torchvision.datasets import ImageFolder
from torchvision import transforms
#加上transforms
normalize=transforms.Normalize(mean=[.5,.5,.5],std=[.5,.5,.5])
transform=transforms.Compose([
transforms.RandomCrop(180),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), #将图片转换为Tensor,归一化至[0,1]
normalize
])
dataset=ImageFolder('./data/train',transform=transform)
我们得到的dataset,它的结构就是[(img_data, class_id),(img_data, class_id),… ],下面我们打印第一个元素：
print(dataset[0])
输出：
(tensor([[[-0.5137, -0.4667, -0.4902, ..., -0.0980, -0.0980, -0.0902],
[-0.5922, -0.5529, -0.5059, ..., -0.0902, -0.0980, -0.0667],
[-0.5373, -0.5294, -0.4824, ..., -0.0588, -0.0824, -0.0196],
...,
[-0.3098, -0.3882, -0.3725, ..., -0.4353, -0.4510, -0.4196],
[-0.2863, -0.3647, -0.3725, ..., -0.4431, -0.4118, -0.4196],
[-0.3412, -0.3569, -0.3882, ..., -0.4667, -0.4588, -0.4196]],
[[-0.6157, -0.5686, -0.5922, ..., -0.2863, -0.2784, -0.2706],
[-0.6941, -0.6549, -0.6078, ..., -0.2784, -0.2784, -0.2471],
[-0.6392, -0.6314, -0.5843, ..., -0.2471, -0.2706, -0.2078],
...,
[-0.4431, -0.5059, -0.5059, ..., -0.5608, -0.5765, -0.5451],
[-0.4196, -0.4824, -0.5059, ..., -0.5686, -0.5373, -0.5451],
[-0.4745, -0.4902, -0.5294, ..., -0.5922, -0.5843, -0.5451]],
[[-0.6627, -0.6157, -0.6549, ..., -0.5059, -0.5216, -0.5137],
[-0.7412, -0.7020, -0.6706, ..., -0.4980, -0.5216, -0.4902],
[-0.6863, -0.6784, -0.6471, ..., -0.4667, -0.4902, -0.4275],
...,
[-0.6000, -0.6549, -0.6627, ..., -0.6784, -0.6941, -0.6627],
[-0.5765, -0.6314, -0.6471, ..., -0.6863, -0.6549, -0.6627],
[-0.6314, -0.6314, -0.6392, ..., -0.7098, -0.7020, -0.6627]]]), 0)
下面我们再看一下dataset的三个属性：
print(dataset.classes) #根据分的文件夹的名字来确定的类别
print(dataset.class_to_idx) #按顺序为这些类别定义索引为0,1...
print(dataset.imgs) #返回从所有文件夹中得到的图片的路径以及其类别
'''
输出：
['cat', 'dog']
{'cat': 0, 'dog': 1}
[('./data/train\\cat\\1.jpg', 0),
('./data/train\\cat\\2.jpg', 0),
('./data/train\\dog\\1.jpg', 1),
('./data/train\\dog\\2.jpg', 1)]
'''
自己编写datasets.ImageFolder
class CustomImageFolderDataset(datasets.ImageFolder):
def __init__(self,
root,
transform=None,
target_transform=None,
loader=datasets.folder.default_loader,
is_valid_file=None,
low_res_augmentation_prob=0.0,
crop_augmentation_prob=0.0,
photometric_augmentation_prob=0.0,
):
super(CustomImageFolderDataset, self).__init__(root,
transform=transform,
target_transform=target_transform,
loader=loader,
is_valid_file=is_valid_file)
self.root = root
self.low_res_augmentation_prob = low_res_augmentation_prob
self.crop_augmentation_prob = crop_augmentation_prob
self.photometric_augmentation_prob = photometric_augmentation_prob
self.random_resized_crop = transforms.RandomResizedCrop(size=(112, 112),
scale=(0.2, 1.0),
ratio=(0.75, 1.3333333333333333))
self.photometric = transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0)
self.tot_rot_try = 0
self.rot_success = 0
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if 'WebFace' in self.root:
# swap rgb to bgr since image is in rgb for webface
# 将 rgb 交换为 bgr，因为图像在 rgb 中用于 webface
sample = Image.fromarray(np.asarray(sample)[:,:,::-1])
sample, _ = self.augment(sample)
if self.transform is not None:
sample = self.transform(sample)
if self.target_transform is not None:
target = self.target_transform(target)
return sample, target
def augment(self, sample):
# crop with zero padding augmentation
if np.random.random() < self.crop_augmentation_prob:
# RandomResizedCrop augmentation
new = np.zeros_like(np.array(sample))
orig_W, orig_H = F._get_image_size(sample)
i, j, h, w = self.random_resized_crop.get_params(sample,
self.random_resized_crop.scale,
self.random_resized_crop.ratio)
cropped = F.crop(sample, i, j, h, w)
new[i:i+h,j:j+w, :] = np.array(cropped)
sample = Image.fromarray(new.astype(np.uint8))
crop_ratio = min(h, w) / max(orig_H, orig_W)
else:
crop_ratio = 1.0
# low resolution augmentation
if np.random.random() < self.low_res_augmentation_prob:
# low res augmentation
img_np, resize_ratio = low_res_augmentation(np.array(sample))
sample = Image.fromarray(img_np.astype(np.uint8))
else:
resize_ratio = 1
# photometric augmentation
if np.random.random() < self.photometric_augmentation_prob:
fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = \
self.photometric.get_params(self.photometric.brightness, self.photometric.contrast,
self.photometric.saturation, self.photometric.hue)
for fn_id in fn_idx:
if fn_id == 0 and brightness_factor is not None:
sample = F.adjust_brightness(sample, brightness_factor)
elif fn_id == 1 and contrast_factor is not None:
sample = F.adjust_contrast(sample, contrast_factor)
elif fn_id == 2 and saturation_factor is not None:
sample = F.adjust_saturation(sample, saturation_factor)
information_score = resize_ratio * crop_ratio
return sample, information_score