timm: typeError: 'builtin_function_or_method' object is not subscriptable-CodePudding

I am implementing timm tutorials on data augmentation to increase the number of images of my dataset. According to their tutorials, I have implemented same code but it did not work.

Code

import numpy as np
import torch
from PIL import Image
from timm.data.transforms_factory import create_transform
a = create_transform(224, is_training=True)
print(a)
pets_image_paths = './download.png'
image = Image.open(pets_image_paths)
# We can convert this into a tensor, and transpose the channels into the format that PyTorch expects:
np_image = np.array(image, dtype=np.float32)
image = torch.as_tensor(np_image).transpose(2, 0)[None]
from timm.data.transforms import RandomResizedCropAndInterpolation
tfm = RandomResizedCropAndInterpolation(size=350, interpolation='random')
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 4, figsize=(10, 5))
for idx, im in enumerate([tfm(image) for i in range(4)]):
    ax[0, idx].imshow(im)
for idx, im in enumerate([tfm(image) for i in range(4)]):
    ax[1, idx].imshow(im)
fig.tight_layout()
plt.show()

Traceback

Traceback (most recent call last):
  File "/home/cvpr/PycharmProjects/timm_tutorials/9_augmentation.py", line 24, in <module>
    for idx, im in enumerate([tfm(image) for i in range(4)]):
  File "/home/cvpr/PycharmProjects/timm_tutorials/9_augmentation.py", line 24, in <listcomp>
    for idx, im in enumerate([tfm(image) for i in range(4)]):
  File "/home/cvpr/anaconda3/envs/timm_tutorials/lib/python3.8/site-packages/timm/data/transforms.py", line 181, in __call__
    i, j, h, w = self.get_params(img, self.scale, self.ratio)
  File "/home/cvpr/anaconda3/envs/timm_tutorials/lib/python3.8/site-packages/timm/data/transforms.py", line 143, in get_params
    area = img.size[0] * img.size[1]
TypeError: 'builtin_function_or_method' object is not subscriptable

CodePudding user response：

RandomResizedCropAndInterpolation expects the input to be an instance of PIL.Image and not torch.tensor. If you need to convert it to tensor, it has to be done later.

CodePudding user response：

As the previous answer mentions RandomResizedCropAndInterpolation expects PIL.Image.

You can look at timm docs in the Note:

Note: RandomResizedCropAndInterpolation expects the input to be an instance of PIL.Image and not torch.tensor.

So you can remove the Tensor convert:

import numpy as np
import torch
from PIL import Image
from timm.data.transforms_factory import create_transform
a = create_transform(224, is_training=True)
print(a)
pets_image_paths = './download.png'
image = Image.open(pets_image_paths)

from timm.data.transforms import RandomResizedCropAndInterpolation
tfm = RandomResizedCropAndInterpolation(size=350, interpolation='random')
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 4, figsize=(10, 5))
for idx, im in enumerate([tfm(image) for i in range(4)]):
    ax[0, idx].imshow(im)
for idx, im in enumerate([tfm(image) for i in range(4)]):
    ax[1, idx].imshow(im)
fig.tight_layout()
plt.show()