规范化传递给 torch.transforms.Compose 函数的图像
Normalizing images passed to torch.transforms.Compose function
如何在 PyTorch 中找到要传递给 transforms.Normalize 函数的值？另外，在我的代码中，我应该完全执行 transforms.Normalize?
由于规范化数据集是一项众所周知的任务，我希望应该有某种脚本可以自动执行此操作。至少我在 PyTorch 论坛上找不到它。
transformed_dataset = MothLandmarksDataset(csv_file='moth_gt.csv',
root_dir='.',
transform=transforms.Compose([
Rescale(256),
RandomCrop(224),
transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
ToTensor()
]))
for i in range(len(transformed_dataset)):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['landmarks'].size())
if i == 3:
break
我知道这些当前值不属于我的数据集，不属于 ImageNet，但使用它们我实际上得到了一个错误：
TypeError Traceback (most recent call last)
<ipython-input-81-eb8dc46e0284> in <module>
10
11 for i in range(len(transformed_dataset)):
---> 12 sample = transformed_dataset[i]
13
14 print(i, sample['image'].size(), sample['landmarks'].size())
<ipython-input-48-9d04158922fb> in __getitem__(self, idx)
30
31 if self.transform:
---> 32 sample = self.transform(sample)
33
34 return sample
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, img)
59 def __call__(self, img):
60 for t in self.transforms:
---> 61 img = t(img)
62 return img
63
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, tensor)
210 Tensor: Normalized Tensor image.
211 """
--> 212 return F.normalize(tensor, self.mean, self.std, self.inplace)
213
214 def __repr__(self):
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/functional.py in normalize(tensor, mean, std, inplace)
278 """
279 if not torch.is_tensor(tensor):
--> 280 raise TypeError('tensor should be a torch tensor. Got {}.'.format(type(tensor)))
281
282 if tensor.ndimension() != 3:
TypeError: tensor should be a torch tensor. Got <class 'dict'>.
所以基本上是三个问题：
- 如何为我自己的自定义数据集找到与 ImageNet 中的均值和标准差相似的值？
- 如何传递这些值以及传递到哪里？我想我应该用 transforms.Compose 方法来做，但我可能错了。
- 我想我应该将 Normalize 应用于我的整个数据集，而不仅仅是训练集，对吗？
更新：
尝试此处提供的解决方案对我不起作用：https://discuss.pytorch.org/t/about-normalization-using-pre-trained-vgg16-networks/23560/6?u=mona_jalal
mean = 0.
std = 0.
nb_samples = 0.
for data in dataloader:
print(type(data))
batch_samples = data.size(0)
data.shape(0)
data = data.view(batch_samples, data.size(1), -1)
mean += data.mean(2).sum(0)
std += data.std(2).sum(0)
nb_samples += batch_samples
mean /= nb_samples
std /= nb_samples
错误是：
<class 'dict'>
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-51-e8ba3c8718bb> in <module>
5 for data in dataloader:
6 print(type(data))
----> 7 batch_samples = data.size(0)
8
9 data.shape(0)
AttributeError: 'dict' object has no attribute 'size'
这是打印（数据）结果：
{'image': tensor([[[[0.2961, 0.2941, 0.2941, ..., 0.2460, 0.2456, 0.2431],
[0.2953, 0.2977, 0.2980, ..., 0.2442, 0.2431, 0.2431],
[0.2941, 0.2941, 0.2980, ..., 0.2471, 0.2471, 0.2448],
...,
[0.3216, 0.3216, 0.3216, ..., 0.2482, 0.2471, 0.2471],
[0.3216, 0.3241, 0.3253, ..., 0.2471, 0.2471, 0.2450],
[0.3216, 0.3216, 0.3216, ..., 0.2471, 0.2452, 0.2431]],
[[0.2961, 0.2941, 0.2941, ..., 0.2460, 0.2456, 0.2431],
[0.2953, 0.2977, 0.2980, ..., 0.2442, 0.2431, 0.2431],
[0.2941, 0.2941, 0.2980, ..., 0.2471, 0.2471, 0.2448],
...,
[0.3216, 0.3216, 0.3216, ..., 0.2482, 0.2471, 0.2471],
[0.3216, 0.3241, 0.3253, ..., 0.2471, 0.2471, 0.2450],
[0.3216, 0.3216, 0.3216, ..., 0.2471, 0.2452, 0.2431]],
[[0.2961, 0.2941, 0.2941, ..., 0.2460, 0.2456, 0.2431],
[0.2953, 0.2977, 0.2980, ..., 0.2442, 0.2431, 0.2431],
[0.2941, 0.2941, 0.2980, ..., 0.2471, 0.2471, 0.2448],
...,
[0.3216, 0.3216, 0.3216, ..., 0.2482, 0.2471, 0.2471],
[0.3216, 0.3241, 0.3253, ..., 0.2471, 0.2471, 0.2450],
[0.3216, 0.3216, 0.3216, ..., 0.2471, 0.2452, 0.2431]]],
[[[0.3059, 0.3093, 0.3140, ..., 0.3373, 0.3363, 0.3345],
[0.3059, 0.3093, 0.3165, ..., 0.3412, 0.3389, 0.3373],
[0.3098, 0.3131, 0.3176, ..., 0.3450, 0.3412, 0.3412],
...,
[0.2931, 0.2966, 0.2931, ..., 0.2549, 0.2539, 0.2510],
[0.2902, 0.2902, 0.2902, ..., 0.2510, 0.2510, 0.2502],
[0.2864, 0.2900, 0.2863, ..., 0.2510, 0.2510, 0.2510]],
[[0.3059, 0.3093, 0.3140, ..., 0.3373, 0.3363, 0.3345],
[0.3059, 0.3093, 0.3165, ..., 0.3412, 0.3389, 0.3373],
[0.3098, 0.3131, 0.3176, ..., 0.3450, 0.3412, 0.3412],
...,
[0.2931, 0.2966, 0.2931, ..., 0.2549, 0.2539, 0.2510],
[0.2902, 0.2902, 0.2902, ..., 0.2510, 0.2510, 0.2502],
[0.2864, 0.2900, 0.2863, ..., 0.2510, 0.2510, 0.2510]],
[[0.3059, 0.3093, 0.3140, ..., 0.3373, 0.3363, 0.3345],
[0.3059, 0.3093, 0.3165, ..., 0.3412, 0.3389, 0.3373],
[0.3098, 0.3131, 0.3176, ..., 0.3450, 0.3412, 0.3412],
...,
[0.2931, 0.2966, 0.2931, ..., 0.2549, 0.2539, 0.2510],
[0.2902, 0.2902, 0.2902, ..., 0.2510, 0.2510, 0.2502],
[0.2864, 0.2900, 0.2863, ..., 0.2510, 0.2510, 0.2510]]],
[[[0.2979, 0.2980, 0.3015, ..., 0.2825, 0.2784, 0.2784],
[0.2980, 0.2980, 0.2980, ..., 0.2830, 0.2764, 0.2795],
[0.2980, 0.2980, 0.3012, ..., 0.2827, 0.2814, 0.2797],
...,
[0.3282, 0.3293, 0.3294, ..., 0.2238, 0.2235, 0.2235],
[0.3255, 0.3255, 0.3255, ..., 0.2240, 0.2235, 0.2229],
[0.3225, 0.3255, 0.3255, ..., 0.2216, 0.2235, 0.2223]],
[[0.2979, 0.2980, 0.3015, ..., 0.2825, 0.2784, 0.2784],
[0.2980, 0.2980, 0.2980, ..., 0.2830, 0.2764, 0.2795],
[0.2980, 0.2980, 0.3012, ..., 0.2827, 0.2814, 0.2797],
...,
[0.3282, 0.3293, 0.3294, ..., 0.2238, 0.2235, 0.2235],
[0.3255, 0.3255, 0.3255, ..., 0.2240, 0.2235, 0.2229],
[0.3225, 0.3255, 0.3255, ..., 0.2216, 0.2235, 0.2223]],
[[0.2979, 0.2980, 0.3015, ..., 0.2825, 0.2784, 0.2784],
[0.2980, 0.2980, 0.2980, ..., 0.2830, 0.2764, 0.2795],
[0.2980, 0.2980, 0.3012, ..., 0.2827, 0.2814, 0.2797],
...,
[0.3282, 0.3293, 0.3294, ..., 0.2238, 0.2235, 0.2235],
[0.3255, 0.3255, 0.3255, ..., 0.2240, 0.2235, 0.2229],
[0.3225, 0.3255, 0.3255, ..., 0.2216, 0.2235, 0.2223]]]],
dtype=torch.float64), 'landmarks': tensor([[[160.2964, 98.7339],
[223.0788, 72.5067],
[ 82.4163, 70.3733],
[152.3213, 137.7867]],
[[198.3194, 74.4341],
[273.7188, 118.7733],
[117.7113, 80.8000],
[182.0750, 107.2533]],
[[137.4789, 92.8523],
[174.9463, 40.3467],
[ 57.3013, 59.1200],
[129.3375, 131.6533]]], dtype=torch.float64)}
dataloader = DataLoader(transformed_dataset, batch_size=3,
shuffle=True, num_workers=4)
和
transformed_dataset = MothLandmarksDataset(csv_file='moth_gt.csv',
root_dir='.',
transform=transforms.Compose(
[
Rescale(256),
RandomCrop(224),
ToTensor()#,
##transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
## std = [ 0.229, 0.224, 0.225 ])
]
)
)
和
class MothLandmarksDataset(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):
if torch.is_tensor(idx):
idx = idx.tolist()
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:]
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
源代码错误
How to pass these values and where? I assume I should do it in
transforms.Compose method but I might be wrong.
在 MothLandmarksDataset 中，难怪它不起作用，因为您正试图将 Dict (sample) 传递给 torchvision.transforms，这需要 torch.Tensor 或 PIL.Image 作为输入。准确地说：
sample = {'image': image, 'landmarks': landmarks}
if self.transform:
sample = self.transform(sample)
您可以将sample["image"]传递给它，尽管您不应该。仅将此操作应用于 sample["image"] 会破坏其与 landmarks 的关系。你应该追求的是像 albumentations 库（参见 here）这样的东西，它可以以相同的方式转换 image 和 landmarks 以保持它们的关系。
torchvision 中也没有 Rescale 转换，也许你的意思是 Resize？
归一化的均值和方差
提供的代码没问题，但您必须像这样将数据解压缩到 torch.Tensor 中：
mean = 0.0
std = 0.0
nb_samples = 0.0
for data in dataloader:
images, landmarks = data["image"], data["landmarks"]
batch_samples = images.size(0)
images_data = images.view(batch_samples, images.size(1), -1)
mean += images_data.mean(2).sum(0)
std += images_data.std(2).sum(0)
nb_samples += batch_samples
mean /= nb_samples
std /= nb_samples
How to pass these values and where? I assume I should do it in
transforms.Compose method but I might be wrong.
这些值应传递给 torchvision.transforms.Normalize，仅应用于 sample["images"]，而不应用于 sample["landmarks"]。
I assume I should apply Normalize to my entire dataset not just the
training set, am I right?
您应该计算整个训练数据集的归一化值，并将这些计算出的值也应用于验证和测试。
如何在 PyTorch 中找到要传递给 transforms.Normalize 函数的值？另外，在我的代码中，我应该完全执行 transforms.Normalize?
由于规范化数据集是一项众所周知的任务，我希望应该有某种脚本可以自动执行此操作。至少我在 PyTorch 论坛上找不到它。
transformed_dataset = MothLandmarksDataset(csv_file='moth_gt.csv',
root_dir='.',
transform=transforms.Compose([
Rescale(256),
RandomCrop(224),
transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
ToTensor()
]))
for i in range(len(transformed_dataset)):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['landmarks'].size())
if i == 3:
break
我知道这些当前值不属于我的数据集，不属于 ImageNet，但使用它们我实际上得到了一个错误：
TypeError Traceback (most recent call last)
<ipython-input-81-eb8dc46e0284> in <module>
10
11 for i in range(len(transformed_dataset)):
---> 12 sample = transformed_dataset[i]
13
14 print(i, sample['image'].size(), sample['landmarks'].size())
<ipython-input-48-9d04158922fb> in __getitem__(self, idx)
30
31 if self.transform:
---> 32 sample = self.transform(sample)
33
34 return sample
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, img)
59 def __call__(self, img):
60 for t in self.transforms:
---> 61 img = t(img)
62 return img
63
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, tensor)
210 Tensor: Normalized Tensor image.
211 """
--> 212 return F.normalize(tensor, self.mean, self.std, self.inplace)
213
214 def __repr__(self):
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/functional.py in normalize(tensor, mean, std, inplace)
278 """
279 if not torch.is_tensor(tensor):
--> 280 raise TypeError('tensor should be a torch tensor. Got {}.'.format(type(tensor)))
281
282 if tensor.ndimension() != 3:
TypeError: tensor should be a torch tensor. Got <class 'dict'>.
所以基本上是三个问题：
- 如何为我自己的自定义数据集找到与 ImageNet 中的均值和标准差相似的值？
- 如何传递这些值以及传递到哪里？我想我应该用 transforms.Compose 方法来做，但我可能错了。
- 我想我应该将 Normalize 应用于我的整个数据集，而不仅仅是训练集，对吗？
更新：
尝试此处提供的解决方案对我不起作用：https://discuss.pytorch.org/t/about-normalization-using-pre-trained-vgg16-networks/23560/6?u=mona_jalal
mean = 0.
std = 0.
nb_samples = 0.
for data in dataloader:
print(type(data))
batch_samples = data.size(0)
data.shape(0)
data = data.view(batch_samples, data.size(1), -1)
mean += data.mean(2).sum(0)
std += data.std(2).sum(0)
nb_samples += batch_samples
mean /= nb_samples
std /= nb_samples
错误是：
<class 'dict'>
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-51-e8ba3c8718bb> in <module>
5 for data in dataloader:
6 print(type(data))
----> 7 batch_samples = data.size(0)
8
9 data.shape(0)
AttributeError: 'dict' object has no attribute 'size'
这是打印（数据）结果：
{'image': tensor([[[[0.2961, 0.2941, 0.2941, ..., 0.2460, 0.2456, 0.2431],
[0.2953, 0.2977, 0.2980, ..., 0.2442, 0.2431, 0.2431],
[0.2941, 0.2941, 0.2980, ..., 0.2471, 0.2471, 0.2448],
...,
[0.3216, 0.3216, 0.3216, ..., 0.2482, 0.2471, 0.2471],
[0.3216, 0.3241, 0.3253, ..., 0.2471, 0.2471, 0.2450],
[0.3216, 0.3216, 0.3216, ..., 0.2471, 0.2452, 0.2431]],
[[0.2961, 0.2941, 0.2941, ..., 0.2460, 0.2456, 0.2431],
[0.2953, 0.2977, 0.2980, ..., 0.2442, 0.2431, 0.2431],
[0.2941, 0.2941, 0.2980, ..., 0.2471, 0.2471, 0.2448],
...,
[0.3216, 0.3216, 0.3216, ..., 0.2482, 0.2471, 0.2471],
[0.3216, 0.3241, 0.3253, ..., 0.2471, 0.2471, 0.2450],
[0.3216, 0.3216, 0.3216, ..., 0.2471, 0.2452, 0.2431]],
[[0.2961, 0.2941, 0.2941, ..., 0.2460, 0.2456, 0.2431],
[0.2953, 0.2977, 0.2980, ..., 0.2442, 0.2431, 0.2431],
[0.2941, 0.2941, 0.2980, ..., 0.2471, 0.2471, 0.2448],
...,
[0.3216, 0.3216, 0.3216, ..., 0.2482, 0.2471, 0.2471],
[0.3216, 0.3241, 0.3253, ..., 0.2471, 0.2471, 0.2450],
[0.3216, 0.3216, 0.3216, ..., 0.2471, 0.2452, 0.2431]]],
[[[0.3059, 0.3093, 0.3140, ..., 0.3373, 0.3363, 0.3345],
[0.3059, 0.3093, 0.3165, ..., 0.3412, 0.3389, 0.3373],
[0.3098, 0.3131, 0.3176, ..., 0.3450, 0.3412, 0.3412],
...,
[0.2931, 0.2966, 0.2931, ..., 0.2549, 0.2539, 0.2510],
[0.2902, 0.2902, 0.2902, ..., 0.2510, 0.2510, 0.2502],
[0.2864, 0.2900, 0.2863, ..., 0.2510, 0.2510, 0.2510]],
[[0.3059, 0.3093, 0.3140, ..., 0.3373, 0.3363, 0.3345],
[0.3059, 0.3093, 0.3165, ..., 0.3412, 0.3389, 0.3373],
[0.3098, 0.3131, 0.3176, ..., 0.3450, 0.3412, 0.3412],
...,
[0.2931, 0.2966, 0.2931, ..., 0.2549, 0.2539, 0.2510],
[0.2902, 0.2902, 0.2902, ..., 0.2510, 0.2510, 0.2502],
[0.2864, 0.2900, 0.2863, ..., 0.2510, 0.2510, 0.2510]],
[[0.3059, 0.3093, 0.3140, ..., 0.3373, 0.3363, 0.3345],
[0.3059, 0.3093, 0.3165, ..., 0.3412, 0.3389, 0.3373],
[0.3098, 0.3131, 0.3176, ..., 0.3450, 0.3412, 0.3412],
...,
[0.2931, 0.2966, 0.2931, ..., 0.2549, 0.2539, 0.2510],
[0.2902, 0.2902, 0.2902, ..., 0.2510, 0.2510, 0.2502],
[0.2864, 0.2900, 0.2863, ..., 0.2510, 0.2510, 0.2510]]],
[[[0.2979, 0.2980, 0.3015, ..., 0.2825, 0.2784, 0.2784],
[0.2980, 0.2980, 0.2980, ..., 0.2830, 0.2764, 0.2795],
[0.2980, 0.2980, 0.3012, ..., 0.2827, 0.2814, 0.2797],
...,
[0.3282, 0.3293, 0.3294, ..., 0.2238, 0.2235, 0.2235],
[0.3255, 0.3255, 0.3255, ..., 0.2240, 0.2235, 0.2229],
[0.3225, 0.3255, 0.3255, ..., 0.2216, 0.2235, 0.2223]],
[[0.2979, 0.2980, 0.3015, ..., 0.2825, 0.2784, 0.2784],
[0.2980, 0.2980, 0.2980, ..., 0.2830, 0.2764, 0.2795],
[0.2980, 0.2980, 0.3012, ..., 0.2827, 0.2814, 0.2797],
...,
[0.3282, 0.3293, 0.3294, ..., 0.2238, 0.2235, 0.2235],
[0.3255, 0.3255, 0.3255, ..., 0.2240, 0.2235, 0.2229],
[0.3225, 0.3255, 0.3255, ..., 0.2216, 0.2235, 0.2223]],
[[0.2979, 0.2980, 0.3015, ..., 0.2825, 0.2784, 0.2784],
[0.2980, 0.2980, 0.2980, ..., 0.2830, 0.2764, 0.2795],
[0.2980, 0.2980, 0.3012, ..., 0.2827, 0.2814, 0.2797],
...,
[0.3282, 0.3293, 0.3294, ..., 0.2238, 0.2235, 0.2235],
[0.3255, 0.3255, 0.3255, ..., 0.2240, 0.2235, 0.2229],
[0.3225, 0.3255, 0.3255, ..., 0.2216, 0.2235, 0.2223]]]],
dtype=torch.float64), 'landmarks': tensor([[[160.2964, 98.7339],
[223.0788, 72.5067],
[ 82.4163, 70.3733],
[152.3213, 137.7867]],
[[198.3194, 74.4341],
[273.7188, 118.7733],
[117.7113, 80.8000],
[182.0750, 107.2533]],
[[137.4789, 92.8523],
[174.9463, 40.3467],
[ 57.3013, 59.1200],
[129.3375, 131.6533]]], dtype=torch.float64)}
dataloader = DataLoader(transformed_dataset, batch_size=3,
shuffle=True, num_workers=4)
和
transformed_dataset = MothLandmarksDataset(csv_file='moth_gt.csv',
root_dir='.',
transform=transforms.Compose(
[
Rescale(256),
RandomCrop(224),
ToTensor()#,
##transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
## std = [ 0.229, 0.224, 0.225 ])
]
)
)
和
class MothLandmarksDataset(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):
if torch.is_tensor(idx):
idx = idx.tolist()
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:]
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
源代码错误
How to pass these values and where? I assume I should do it in transforms.Compose method but I might be wrong.
在 MothLandmarksDataset 中，难怪它不起作用，因为您正试图将 Dict (sample) 传递给 torchvision.transforms，这需要 torch.Tensor 或 PIL.Image 作为输入。准确地说：
sample = {'image': image, 'landmarks': landmarks}
if self.transform:
sample = self.transform(sample)
您可以将sample["image"]传递给它，尽管您不应该。仅将此操作应用于 sample["image"] 会破坏其与 landmarks 的关系。你应该追求的是像 albumentations 库（参见 here）这样的东西，它可以以相同的方式转换 image 和 landmarks 以保持它们的关系。
torchvision 中也没有 Rescale 转换，也许你的意思是 Resize？
归一化的均值和方差
提供的代码没问题，但您必须像这样将数据解压缩到 torch.Tensor 中：
mean = 0.0
std = 0.0
nb_samples = 0.0
for data in dataloader:
images, landmarks = data["image"], data["landmarks"]
batch_samples = images.size(0)
images_data = images.view(batch_samples, images.size(1), -1)
mean += images_data.mean(2).sum(0)
std += images_data.std(2).sum(0)
nb_samples += batch_samples
mean /= nb_samples
std /= nb_samples
How to pass these values and where? I assume I should do it in transforms.Compose method but I might be wrong.
这些值应传递给 torchvision.transforms.Normalize，仅应用于 sample["images"]，而不应用于 sample["landmarks"]。
I assume I should apply Normalize to my entire dataset not just the training set, am I right?
您应该计算整个训练数据集的归一化值，并将这些计算出的值也应用于验证和测试。