PyTorch 官方教程 Getting Started 第二部分 Data Loading and Processing Tutorial 笔记
1. Dataset 类
torch.utils.data.Dataset是用来表示数据集的抽象类。自己定制的数据集类应该继承Dataset，重写以下方法：
__len__len(dataset)返回 dataset 的 size.__getitem__支持索引，例如 dataset[i] 获得第 i 个 数据样本.
为 face landmarks dataset 创建 FaceLandmarksDataset 类。在__init__中读入 csv , 在__getitem__中读入图片。这样可以高效内存使用效率。
通过如下方式读取的单个数据样本是一个 dict {'image': image, 'landmarks': landmarks}。FaceLandmarksDataset 类接收可选参数 transform，可以对数据样本进行可行的操作。
class FaceLandmarksDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, csv_file, root_dir, transform=None):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
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):
img_name = os.path.join(self.root_dir,
self.landmarks_frame.iloc[idx, 0])
image = io.imread(img_name)
landmarks = self.landmarks_frame.iloc[idx, 1:].as_matrix()
landmarks = landmarks.astype('float').reshape(-1, 2)
sample = {'image': image, 'landmarks': landmarks}
if self.transform:
sample = self.transform(sample)
return sample
2. Transforms
自建三种 transforms:
Rescale：缩放图像RandomCrop：随机裁剪图像。图像增广。ToTensor：将NumPy图像转换为torch图像。
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))
# h and w are swapped for landmarks because for images,
# x and y axes are axis 1 and 0 respectively
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)}
3. Compose transforms
torchvision.transforms.Compose 可以将 transforms 操作组合使用。
scale = Rescale(256) # rescale the shorter side of the image to 25
crop = RandomCrop(128) # randomly crop a square of size 224 from it
composed = transforms.Compose([Rescale(256),
RandomCrop(224)])
# Apply each of the above transforms on sample.
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()
4. Afterword: Torchvision
torchvision 包提供许多常见数据集操作功能。ImageFolder对通用数据集操作很有用，它假设数据集组织方式为下列情况：
root/ants/xxx.png
root/ants/xxy.jpeg
root/ants/xxz.png
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root/bees/123.jpg
root/bees/nsdf3.png
root/bees/asd932_.png