Tensor permute
COLOR_BGR2RGB ) #convert BGR to RGB img = 255 - img #invert image if self. For instance, batchinputshapec (10, 32) indicates that the expected input will be batches of 10. Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model. join ( TRAIN, idx + '_' + str ( i ) + '.png' )) #read image img = cv2. For instance, (2, 1) permutes the first and second dimension of the input. choices ( range ( ntiles ), k = n ) #choose n from tiles #length of ids = n else : n = 2 * N ids = range ( min ( n, ntiles )) # imgs = for i in ids : img = cv2. values #tiles number is different per image #define how many images to be stacked# if self. You can always permute your tensor if you want to move the channel ordering. image_id #get name of image ntiles = fdict. df ) def _getitem_ ( self, idx ): y = self. transform = transform def _len_ ( self ): return len ( self. factor return inputs, outputsĬlass PANDADataset ( Dataset ): def _init_ ( self, df, fold = fold, train = True, transform = None ): self. factor = factor def _call_ ( self, sample ): inputs, outputs = sample inputs *= self. tensornpP np.transpose(tensornp, (2,1,3,4,0)) tensorptP tensorpt.permute(2,1,3,4,0) tensortfP tf.transpose(tensortf, (2,1,3,4,0)) This is how we. In this case we have to use the tensor.permute () attribute with PyTorch. from_numpy ( outputs ) class MulTransform (): def _init_ ( self, factor ): self. anspose supports only swapping of two axes and not more. len class ToTensor (): def _call_ ( self, sample ): inputs, outputs = sample return torch. transform ( sample ) return sample def _len_ ( self ): return self. y = xy ] def _getitem_ ( self, index ): sample = self. Share Improve this answer Follow answered at 15:55 iacob 15. tensor.view () reshapes the tensor (analogous to numpy.reshape) by reducing/expanding the size of each dimension (if one increases, the others must decrease). from_numpy ( xy ] ) #如果想在transform里转成tensor,那这里可以先保留array,比如 self. tensor.permute () permutes the order of the axes of a tensor. from_numpy ( xy ) #如果是xy是df,那就是xy.iloc.values self. Permute the dimensions of a tensor object: x z torch.randn(10,20,30) x.permute(2,0,1) np.permute() print('Permute dimensions:', x.shape. loadtxt ( './wine.csv', delimiter = ',', dtype = np. Import torch import torchvision import numpy as np from import Dataset import pandas as pd class WineDataset ( Dataset ): def _init_ ( self, transform = None ): #load csv as numpy, or convert to numpy array xy = np. contiguous (ntiguousformat) Tensor Returns a contiguous in memory tensor containing the same data as self tensor.