I had some problems using transforms.Resize((32, 32) because my network is designed for 33232 images, so I tried to use transforms.Resize((32, 32). But an error like this occurred:
TypeError: Unexpected type <class 'numpy.ndarray'>
To solve this problem, I tried to define a ResizeClass of my own:
class ResizeCustom(transforms.Resize):
def __init__(self, size, interpolation=Image.BILINEAR):
super(ResizeCustom, self).__init__(size, interpolation)
def __call__(self, img):
if isinstance(img, np.ndarray):
img = Image.fromarray(img)
return super(ResizeCustom, self).__call__(img)
However, the problem was not solved and an error like this appeared:
File "d:\Slef_Learning\MY_Project\WuYang\TDA_new_dataset\nets\net_out_tda.py", line 123, in images_to_matrix_lists
trainset, validation_dataset, test_dataset = MLclf.miniimagenet_clf_dataset(ratio_train=0.6, ratio_val=0.2, seed_value=None, shuffle=True, transform=self.train_transform, save_clf_data=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\envs\PyTorchGpu\Lib\site-packages\MLclf\MLclf.py", line 319, in miniimagenet_clf_dataset
data_feature_label_permutation_split = MLclf.miniimagenet_convert2classification(data_dir=data_dir, ratio_train=ratio_train, ratio_val=ratio_val, seed_value=seed_value, shuffle=shuffle, task_type='classical_or_meta', save_clf_data=save_clf_data, transform=transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\envs\PyTorchGpu\Lib\site-packages\MLclf\MLclf.py", line 193, in miniimagenet_convert2classification
data_feature_label['images'] = MLclf._feature_norm(data_feature_label['images'], transform=transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\envs\PyTorchGpu\Lib\site-packages\MLclf\MLclf.py", line 295, in _feature_norm
feature_output[i] = transform(feature_i)
~~~~~~~~~~~~~~^^^
RuntimeError: The expanded size of the tensor (84) must match the existing size (32) at non-singleton dimension 2. Target sizes: [3, 84, 84]. Tensor sizes: [3, 32, 32]
It seems that there are some problems with the size of the picture, but I don't understand where the problem occurs; especially this [3,84,84], isn't the original size of the picture 64*64?
I have also tried some bits and pieces, but they have no effect. Is there any solution?
I had some problems using
transforms.Resize((32, 32)because my network is designed for 33232 images, so I tried to usetransforms.Resize((32, 32). But an error like this occurred:To solve this problem, I tried to define a ResizeClass of my own:
However, the problem was not solved and an error like this appeared:
It seems that there are some problems with the size of the picture, but I don't understand where the problem occurs; especially this [3,84,84], isn't the original size of the picture 64*64?
I have also tried some bits and pieces, but they have no effect. Is there any solution?