from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import keras
import keras.layers as layers
from keras.models import Model
from keras.layers.core import Lambda
import encoder_models as EM
import cv2
import numpy as np
def GlobalAveragePooling2D_r(f):
def func(x):
repc = int(x.shape[4])
m = keras.backend.repeat_elements(f, repc, axis = 4)
x = layers.multiply([x, m])
repx = int(x.shape[2])
repy = int(x.shape[3])
x = (keras.backend.sum(x, axis=[2, 3], keepdims=True) / (keras.backend.sum(m, axis=[2, 3], keepdims=True)))
x = keras.backend.repeat_elements(x, repx, axis = 2)
x = keras.backend.repeat_elements(x, repy, axis = 3)
return x
return Lambda(func)
def Rep_mask(f):
def func(x):
x = keras.backend.repeat_elements(x, f, axis = 1)
return x
return Lambda(func)
def common_representation(x1, x2):
repc = int(x1.shape[1])
x2 = keras.layers.Reshape(target_shape=(1, np.int32(x2.shape[1]), np.int32(x2.shape[2]), np.int32(x2.shape[3]))) (x2)
x2 = Rep_mask(repc)(x2)
x = layers.concatenate([x1, x2], axis=4)
x = layers.TimeDistributed(layers.Conv2D(128, 3, padding = 'same', kernel_initializer = 'he_normal'))(x)
x = layers.TimeDistributed(layers.BatchNormalization(axis=3))(x)
x = layers.TimeDistributed(layers.Activation('relu'))(x)
return x

I’m new to Pytorch and I’m having some problems converting this code written in Keras to PyTorch. I have converted half of the code but I’m stuck in this part. Any help would be greatly appreciated.

Are you referring to the TimeDistributed layer? afaik there have been a few previous threads on how to implement this e.g., Timedistributed CNN - #2 by ilyes

Is layers.multiply() an elementwise mul? that corresponds to torch.mul. Similarly if keras.backend.sum corresponds to a reduction across some axes that corresponds to torch.sum.