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inputing numpy array images into pytorch neural net


inputing numpy array images into pytorch neural net

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inputing numpy array images into pytorch neural net
Tag : python , By : terrestrial
Date : November 28 2020, 04:01 AM

Hope that helps The problem is that the input you give to your network is of type ByteTensor while only float operations are implemented for conv like operations. Try the following
my_img_tensor = my_img_tensor.type('torch.DoubleTensor')
# for converting to double tensor

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Inputing images into an array in Javascript and then displaying them in a table


Tag : javascript , By : Sanoran
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , How about using appendChild
for(var i = 1; i < 6; i++) {
    document.getElementById('d' + i).appendChild(diceB[i]);

NumPy/PyTorch extract subsets of images


Tag : python , By : user109127
Date : March 29 2020, 07:55 AM
Does that help Pretty simple really with view_as_windows from scikit-image, to get those sliding windowed views as a 6D array with the fourth axis being singleton. Then, use advanced-indexing to select the ones we want based off the y and x indices for indexing into the second and third axes of the windowed array to get our B.
Hence, the implementation would be -
from skimage.util.shape import view_as_windows

BSZ = 16, 16 # Blocksize
A6D = view_as_windows(A,(1,BSZ[0],BSZ[1]))
B_out = A6D[np.arange(N),y,x,0]
In [78]: A
Out[78]: 
array([[[ 5,  5,  3,  5,  3,  8],
        [ 5,  *2,  6,  2,  2,  4],
        [ 4,  3,  4,  9,  3,  8],
        [ 6,  3,  3, 10,  4,  5],
        [10,  2,  5,  7,  6,  7],
        [ 5,  4,  2,  5,  2, 10]],

       [[ 4,  9,  8,  4,  9,  8],
        [ 7, 10,  8,  2, 10,  9],
        [10,  *9,  3,  2,  4,  7],
        [ 5, 10,  8,  3,  5,  4],
        [ 6,  8,  2,  4, 10,  4],
        [ 2,  8,  6,  2,  7,  5]],

       [[ *4,  8,  7,  2,  9,  9],
        [ 2, 10,  2,  3,  8,  8],
        [10,  7,  5,  8,  2, 10],
        [ 7,  4, 10,  9,  6,  9],
        [ 3,  4,  9,  9, 10,  3],
        [ 6,  4, 10,  2,  6,  3]]])
In [79]: y
Out[79]: array([1, 2, 0])

In [80]: x
Out[80]: array([1, 1, 0])
In [81]: B
Out[81]: 
array([[[ 2,  6],
        [ 3,  4]],

       [[ 9,  3],
        [10,  8]],

       [[ 4,  8],
        [ 2, 10]]])

Pytorch tensor to numpy array


Tag : python , By : user165781
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a pytorch Tensor of size torch.Size([4, 3, 966, 1296]) , There are 4 dimensions of the tensor you want to convert.
[:, ::-1, :, :] 

Inputing 2d column array into 2d numpy array


Tag : development , By : phatfish
Date : March 29 2020, 07:55 AM
around this issue what is the "pythonic" way of inputting 2d column vector to a 2d numpy array (actual 2d array)? example problem below: , found it:
  sigma_points[:, [0]] = mu

pytorch compute pairwise difference: Incorrect result in NumPy vs PyTorch and different PyTorch versions


Tag : python , By : snapshooter
Date : March 29 2020, 07:55 AM
To fix the issue you can do The issue arises because of using PyTorch 0.1. If using PyTorch 1.0.1, the same operation of NumPy generalize to PyTorch without any modifications and issues. Here is a snapshot of the run in Colab.
>>> t1 = torch.from_numpy(a)
>>> t2 = torch.from_numpy(b)
>>> t1[np.newaxis, ...] - t2[:, np.newaxis, ...]

(0 ,.,.) = 
 -2 -2 -2
 -1  2  4
[torch.LongTensor of size 1x2x3]

>>> torch.__version__
'0.1.12_1'
>>> t1[:1, ] - t2
>>> tensor([[-2, -2, -2],   # t1_r1
            [-4, -1,  1]])  # t1_r2

>>> t1[1:, ] - t2
>>> tensor([[ 1,  1,  1],   # t2_r1
            [-1,  2,  4]])  # t2_r2
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