About the parse_function about insightface_tf HOT 4 OPEN

In tensorflow, element-wise assign value to tensor is not allowed. If you want to do element-wise assign you can reference the arcface_loss or cosineface_losses which contains code for assignment by using a mask.

from insightface_tf.

I still do not understand how to do it, So I add cutout function
"images_train = cutout(images_train, args.cutout_length)"
after
"images_train, labels_train = sess.run(next_element)"

paper : https://arxiv.org/abs/1708.04552

def cutout(images,length):
w = 112
h = 112
length = length
images = images
for k in range(len(images)):
x1 = np.random.randint(w-length)
x2 = x1 + length
y1 = np.random.randint(h-length)
y2 = y1 + length
for i in range(x1,x2):
for j in range(y1,y2):
images[k][i][j][0] = 0
images[k][i][j][1] = 0
images[k][i][j][2] = 0
return images

from insightface_tf.

images[k][i][j][0] = 0 images[k][i][j][1] = 0 images[k][i][j][2] = 0
This is not allowed in tensorflow.

1. You should build a mask
2. Set the element in this mask to 1
3. Set the value of region which you want to use cutout to 0 in the mask.
4. Do element-wise multiple between the image and the mask.

from insightface_tf.

Thank you for the hint.
I am still new to TF.
By the way, the data type of images_train is numpy.ndarray, so I can do with it directly.
I have tried to use the mask in the pharse_function, but the cutout regions are the same in the same batch. Could you please tell me how to fix this?

I think tf map pharse_function by batch, so each batch is matched one pharse_function operation?

from insightface_tf.