I'm trying to calculate the alpha values as explained here.
I have as argument a tensor with shape (1, 512, 14, 14). To calculate alpha values I need to calculate the average of all dimensions except the channel dimension, so the output will have the shape (1, k, 1, 1) which is essentialy (k,).
How can I do this in PyTorch?
Thanks!
You could permute the first and second axis to keep the channel dimension on dim=0, then flatten all other dimensions, and lastly, take the mean on that new axis:
x.permute(1, 0, 2, 3).flatten(start_dim=1).mean(dim=1)
Here are the shapes, step by step:
>>> x.permute(1, 0, 2, 3).shape
(512, 1, 14, 14)
>>> x.permute(1, 0, 2, 3).flatten(start_dim=1).shape
(512, 1, 196)
>>> x.permute(1, 0, 2, 3).flatten(start_dim=1).mean(dim=1).shape
(512,)
Related
I want to classify images using a pretrained model ResNet50 with Pytorch. I am faced with the problem of transferring data from the dataset to the model.
As far as I understood, it is necessary to transfer images for training in a tensor with the following dimension: (N, 4, 512,512), where N is the number of images, 4 is the number of channels, and 512 is the width and height of the picture. And also you need to pass "targets" as an array. Now I have a Pandas DataFrame with columns "Image", "Label". In the column "Images" I have a list of dimensions (512, 512, 4).
I tried writing data to an array, but it takes too long and takes up a lot of memory. Is there some other way to do this? So, my question is "How can I transfer data into model?"
This is part of my database:
Number
Image
label
0
[[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0...
0
1
[[[4, 3, 0, 18], [82, 0, 0, 27], [11, 4, 0, 20...
14
2
[[[2, 2, 0, 0], [1, 5, 0, 1], [0, 5, 0, 0], [2...
14
3
[[[7, 1, 0, 24], [31, 0, 0, 14], [23, 3, 0, 13...
3
...
...
...
I tried to do it in the following way:
x_train = []
y_train = []
for data in range(N):
x_train.append(train_df['Image'].iloc[data])
y_train.append(train_df['Label'].iloc[data])
x_train = torch.tensor(x_train)
y_train = torch.tensor(y_train)
y_train = y_train.view(-1)
x_train = x_train.permute(0, 3, 1, 2)
in the pyrr.Matrix docs it states:
Matrices are laid out in row-major format and can be loaded directly into OpenGL. To convert to column-major format, transpose the array using the numpy.array.T method.
creating a transformation matrix gives me:
Matrix44.from_translation( np.array([1,2,3]))
Matrix44([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[1, 2, 3, 1]])
If the layout is row-major, I would expect the output to be the transpose:
Matrix44([[1, 0, 0, 1],
[0, 1, 0, 2],
[0, 0, 1, 3],
[0, 0, 0, 1]])
I'm most likely confused (I come from C/OpenGL background), but could anyone please enlighten me?
Jonathan
I was writing down a great answer. But I found this really interesting link I invite you to read it !
This is a small resume :
If it's row-major matrix, then the translation is stored in the 3, 7, and 11th indices.
If it's column-major, then the translation is stored in the 12, 13, and 14th indices.
The difference behind the scene is the way to store the data. As it is 16 float in memory, those floats are contiguous in the memory. So you have to define if you either store them in 4 float x 4 columns or 4 float x 4 rows. And then it change the way you access and use it.
You can look at this link too.
I am using scipy and its cdist function to compute a distance matrix from an array of vectors.
import numpy as np
from scipy.spatial import distance
vectorList = [(0, 10), (4, 8), (9.0, 11.0), (14, 14), (16, 19), (25.5, 17.5), (35, 16)]
#Convert to numpy array
arr = np.array(vectorList)
#Computes distances matrix and set self-comparisons to NaN
d = distance.cdist(arr, arr)
np.fill_diagonal(d, None)
Let's say I want to return all the distances that are below a specific threshold (6 for example)
#Find pairs of vectors whose separation distance is < 6
id1, id2 = np.nonzero(d<6)
#id1 --> array([0, 1, 1, 2, 2, 3, 3, 4])
#id2 --> array([1, 0, 2, 1, 3, 2, 4, 3])
I now have 2 arrays of indices.
Question: how can I return the distances between these pairs of vectors as an array / list ?
4.47213595499958 #d[0][1]
4.47213595499958 #d[1][0]
5.830951894845301 #d[1][2]
5.830951894845301 #d[2][1]
5.830951894845301 #d[2][2]
5.830951894845301 #d[3][2]
5.385164807134504 #d[3][4]
5.385164807134504 #d[4][3]
d[id1][id2] returns a matrix, not a list, and the only way I found so far is to iterate over the distance matrix again which doesn't make sense.
np.array([d[i1][i2] for i1, i2 in zip(id1, id2)])
Use
d[id1, id2]
This is the form that numpy.nonzero example shows (i.e. a[np.nonzero(a > 3)]) which is different from the d[id1][id2] you are using.
See arrays.indexing for more details on numpy indexing.
I am trying to write some code (as part of a larger script) to develop numpy arrays of length n, which I can use to change the sign of an input list of length n, in all possible ways.
I am trying to produce all possible permutations of 1 and -1 of length n.
If I use itertools.permutations, it will not accept a repeat length greater than 2, because of not allowing repetitions. If I use itertools.combinations_with_replacement, then not all of the permutations are produced. I need "permutations_with_replacement".
I tried to use itertools.product, but I cannot get it to work.
Here is my code so far (n is an unknown number, depending on the length of the input list).
import numpy as np
import itertools
ones = [-1, 1]
multiplier = np.array([x for x in itertools.combinations_with_replacement(ones, n)])
Perhaps this is what you want?
>>> import itertools
>>> choices = [-1, 1]
>>> n = 3
>>> l = [choices] * n
>>> l
[[-1, 1], [-1, 1], [-1, 1]]
>>> list(itertools.product(*l))
[(-1, -1, -1), (-1, -1, 1), (-1, 1, -1), (-1, 1, 1), (1, -1, -1), (1, -1, 1), (1, 1, -1), (1, 1, 1)]
I have rather simple question but still couldn´t make it work.
I want a block diagonal n^2*n^2 matrix. The blocks are sparse n*n matrices with just the diagonal, first off diagonals and forth off diag. For the simple case of n=4 this can easily be done
datanew = ones((5,n1))
datanew[2] = -2*datanew[2]
diagsn = [-4,-1,0,1,4]
DD2 = sparse.spdiags(datanew,diagsn,n,n)
new = sparse.block_diag([DD2,DD2,DD2,DD2])
Since this only useful for small n's, is there a way better way to use block_diag? Thinking of n -> 1000
A simple way of constructing a long list of DD2 matrices, is with a list comprehension:
In [128]: sparse.block_diag([DD2 for _ in range(20)]).A
Out[128]:
array([[-2, 1, 0, ..., 0, 0, 0],
[ 1, -2, 1, ..., 0, 0, 0],
[ 0, 1, -2, ..., 0, 0, 0],
...,
[ 0, 0, 0, ..., -2, 1, 0],
[ 0, 0, 0, ..., 1, -2, 1],
[ 0, 0, 0, ..., 0, 1, -2]])
In [129]: _.shape
Out[129]: (80, 80)
At least in my version, block_diag wants a list of arrays, not *args:
In [133]: sparse.block_diag(DD2,DD2,DD2,DD2)
...
TypeError: block_diag() takes at most 3 arguments (4 given)
In [134]: sparse.block_diag([DD2,DD2,DD2,DD2])
Out[134]:
<16x16 sparse matrix of type '<type 'numpy.int32'>'
with 40 stored elements in COOrdinate format>
This probably isn't the fastest way to construct such a block diagonal array, but it's a start.
================
Looking at the code for sparse.block_mat I deduce that it does:
In [145]: rows=[]
In [146]: for i in range(4):
arow=[None]*4
arow[i]=DD2
rows.append(arow)
.....:
In [147]: rows
Out[147]:
[[<4x4 sparse matrix of type '<type 'numpy.int32'>'
with 10 stored elements (5 diagonals) in DIAgonal format>,
None,
None,
None],
[None,
<4x4 sparse matrix of type '<type 'numpy.int32'>'
...
None,
<4x4 sparse matrix of type '<type 'numpy.int32'>'
with 10 stored elements (5 diagonals) in DIAgonal format>]]
In other words, rows is a 'matrix' of None with DD2 along the diagonals. It then passes these to sparse.bmat.
In [148]: sparse.bmat(rows)
Out[148]:
<16x16 sparse matrix of type '<type 'numpy.int32'>'
with 40 stored elements in COOrdinate format>
bmat in turn collects the data,rows,cols from the coo format of all the input matricies, joins them into master arrays, and builds a new coo matrix from them.
So an alternative is to construct those 3 arrays directly.