这个部分主要是教我们如何将我们自己的图片load进来，对数据进行resize，随机crop的一些操作，当中有两个依赖包 scikit-image 和 pandas, 使用conda install或者pip install应该很容易就可以安装上了
#导入对应的依赖包 from __future__ import print_function, division import os import torch import pandas as pd from skimage import io, transform import numpy as np import matplotlib.pyplot as plt from torch.utils.data import Dataset, DataLoader from torchvision import transforms, utils import warnings warnings.filterwarnings("ignore") plt.ion()需要下载对应数据集，放到./data/faces文件https://download.pytorch.org/tutorial/faces.zip 具体标注的格式如下：
# 使用pandas可以很容易的解析csv文件 landmarks_frame = pd.read_csv('./data/faces/face_landmarks.csv') n = 65 img_name = landmarks_frame.iloc[n, 0] landmarks = landmarks_frame.iloc[n, 1:].as_matrix() landmarks = landmarks.astype('float').reshape(-1, 2) print('Image name: {}'.format(img_name)) print('Landmarks shape: {}'.format(landmarks.shape)) print('First 4 landmarks: {}'.format(landmarks[:4])) Image name: person-7.jpg Landmarks shape: (68, 2) First 4 landmarks: [[32. 65.] [33. 76.] [34. 86.] [34. 97.]] def show_landmarks(image, landmarks): plt.imshow(image) plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r') plt.pause(0.001) plt.figure() show_landmarks(io.imread(os.path.join('./data/faces/', img_name)), landmarks) plt.show() class FaceLandmarksDataset(Dataset): def __init__(self, csv_file, root_dir, transform=None): self.landmarks_frame = pd.read_csv(csv_file) self.root_dir = root_dir self.transform = transform def __len__(self): return len(self.landmarks_frame) def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() img_name = os.path.join(self.root_dir, self.landmarks_frame.iloc[idx, 0]) #iloc基于位置的纯基于整数位置的索引,仅支持索引列位置,试图选择行位置将引发ValueError。 image = io.imread(img_name) landmarks = self.landmarks_frame.iloc[idx, 1:] landmarks = np.array([landmarks]) landmarks = landmarks.astype('float').reshape(-1, 2) sample = {'image':image, 'landmarks': landmarks} if self.transform: sample = self.transform(sample) return sample face_dataset = FaceLandmarksDataset(csv_file='./data/faces/face_landmarks.csv', root_dir='./data/faces/') fig = plt.figure() for i in range(len(face_dataset)): sample = face_dataset[i] print(i, sample['image'].shape, sample['landmarks'].shape) ax = plt.subplot(1, 4, i+1) #tight_layout会自动调整子图参数，使之填充整个图像区域 plt.tight_layout() ax.set_title('Sample #{}'.format(i)) ax.axis('off') #**kwargs 允许你将不定长度的键值对, 作为参数传递给一个函数。 如果你想要在一个函数里处理带名字的参数, 你应该使用**kwargs show_landmarks(**sample) if i == 3: plt.show() break 0 (324, 215, 3) (68, 2) 1 (500, 333, 3) (68, 2) 2 (250, 258, 3) (68, 2) 3 (434, 290, 3) (68, 2)这边建议还是在自己的IDE下面跑代码比较合适，如果使用jupyter notebook可能不会显示在同一个picture上。
class Rescale(object): """Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched to output_size keeping aspect ratio the same. """ def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) self.output_size = output_size def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] h, w = image.shape[:2] if isinstance(self.output_size, int): if h>w: new_h, new_w = self.output_size * h/w, self.output_size else: new_h, new_w = self.output_size, self.output_size*w/h else: new_h, new_w = self.output_size new_h, new_w = int(new_h), int(new_w) img = transform.resize(image, (new_h, new_w)) landmarks = landmarks * [new_w/w, new_h/h] return {'image': img, 'landmarks': landmarks} class RandomCrop(object): """Crop randomly the image in a sample. Args: output_size (tuple or int): Desired output size. If int, square crop is made. """ def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) if isinstance(output_size, int): self.output_size = (output_size, output_size) else: assert len(output_size) == 2 self.output_size = output_size def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] h, w = image.shape[:2] new_h, new_w = self.output_size top = np.random.randint(0, h-new_h) left = np.random.randint(0, w-new_w) image = image[top:top+new_h, left:left+new_w] landmarks = landmarks - [left, top] return {'image':image, 'landmarks':landmarks} class ToTensor(object): """Convert ndarrays in sample to Tensors.""" def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] # swap color axis because # numpy image: H x W x C # torch image: C X H X W image = image.transpose((2, 0, 1)) return {'image':torch.from_numpy(image), 'landmarks': torch.from_numpy(landmarks)} scale = Rescale(256) crop = RandomCrop(128) composed = transforms.Compose([Rescale(256), RandomCrop(224)]) fig = plt.figure() sample = face_dataset[65] for i, tsfrm in enumerate([scale, crop, composed]): transformed_sample = tsfrm(sample) ax = plt.subplot(1, 3, i + 1) plt.tight_layout() ax.set_title(type(tsfrm).__name__) show_landmarks(**transformed_sample) plt.show() transformed_dataset = FaceLandmarksDataset(csv_file='./data/faces/face_landmarks.csv', root_dir='./data/faces/', transform= transforms.Compose([Rescale(256), RandomCrop(224), ToTensor()])) for i in range(len(transformed_dataset)): sample = transformed_dataset[i] print(i, sample['image'].size(), sample['landmarks'].size()) if i == 3: break 0 torch.Size([3, 224, 224]) torch.Size([68, 2]) 1 torch.Size([3, 224, 224]) torch.Size([68, 2]) 2 torch.Size([3, 224, 224]) torch.Size([68, 2]) 3 torch.Size([3, 224, 224]) torch.Size([68, 2]) dataloader = DataLoader(transformed_dataset, batch_size=4, shuffle=True, num_workers=4) def show_landmarks_batch(sample_batched): images_batch, landmarks_batch = sample_batched['image'], sample_batched['landmarks'] batch_size = len(images_batch) im_size = images_batch.size(2) grid_border_size = 2 grid = utils.make_grid(images_batch) plt.imshow(grid.numpy().transpose(1, 2, 0)) for i in range(batch_size): plt.scatter(landmarks_batch[i, :, 0].numpy() + i*im_size+(i+1)*grid_border_size, landmarks_batch[i, :, 1].numpy()+grid_border_size, s=10, marker='.', c='r') plt.title('Batch from dataloader') for i_batch, sample_batched in enumerate(dataloader): print(i_batch, sample_batched['image'].size(), sample_batched['landmarks'].size()) if i_batch == 3: plt.figure() show_landmarks_batch(sample_batched) plt.axis('off') plt.ioff() plt.show() break 0 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) 1 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) 2 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) 3 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2])这样就学会了怎么利用pytorch读取数据，做数据增广等基本的预处理操作。
参考地址： https://pytorch.org/tutorials/beginner/data_loading_tutorial.html
