# How to move feature maps across images using slicing?

I am trying to implement the online algorithm of this paper, which is on video classification. This work moves 1/8 of channel feature maps from each image, into the next image, after each convolution operation. The image of the operation has been attached here -

While trying to implement the same, I have succeeded in extracting out the first 1/8 channel feature maps, but I don’t know how to add them to the succeeding image. My code has been attached below -

``````import cv2
import gym
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

N = 1 # Batch Size
T = 5 # Time Steps. This means that there are 5 frames in the video
C = 3 # RGB Channels
H = 144 # Height
W = 144 # Width

foo = torch.randn(N*T, C, H, W)

print("Shape of foo = ", foo.shape)
#torch.Size([5, 3, 144, 144])

class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 8, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)

def forward(self, x):
x = F.relu(self.conv1(x))
print("Shape of x = ", x.shape)
# torch.Size([5, 8, 140, 140])
shape_extract = x[:, :1,:,:]
print("Shape of extract = ", shape_extract.shape)
# torch.Size([5, 1, 140, 140])
# 1/8 of the channels have been extracted out from above. But how do I transfer these channel features to the next image?

return x

net = Net()
output = net(foo)
``````

You could create e.g. a `dict` inside the custom model and store the output activations of the conv layers there (during the forward pass). In the second iteration you could then create the new activations by replacing parts of the conv output with the activations stored inside the model.

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