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# How to apply a 2D convolution operation in PyTorch?

We can apply a 2D convolution operation over an input image composed of several input planes using the **torch.nn.Conv2d()** module. It is implemented as a layer in a convolutional neural network (CNN). The input to a 2D convolution layer must be of size **[N,C,H,W]** where **N** is the batch size, C is the number of channels, H and W are the height and width of the input tensor.

Generally a 2D convolution operation is applied on the image tensors. For an RGB image, the number of channels is 3. The main feature of a convolution operation is the filter or kernel size and stride. This module supports **TensorFloat32**.

### Syntax

torch.nn.Conv2d(in_channels, out_channels, kernel_size)

### Parameters

**in_channels**– Number of channels in the input image.**out_channels**– Number of channels produced by the convolution operation.**kernel_size**– Size of the convolving kernel.

Along with the above three parameters, there are some optional parameters also such as **stride, padding, dilation,** etc. We will take examples of these parameters in detail in the following examples.

### Steps

You could use the following steps to apply a 2D convolution operation −

- Import the required library. In all the following examples, the required Python library is
**torch**. Make sure you have already installed it. To apply 2D convolution operation on an image, we need**torchvision**and**Pillow**as well.

import torch import torchvision from PIL import Image

Define the

**input**tensor or read the input image. If an input is an image, then we first convert it into a torch tensor.Define

**in_channels, out_channels, kernel_size,**and other parameters.Next define a convolution operation conv by passing the above-defined parameters to

**torch.nn.Conv2d()**.

conv = nn.Conv2d(in_channels, out_channels, kernel_size)

- Apply the convolution operation conv on the input tensor or image tensor.

output = conv(input)

- Next print the tensor after the convolution operation. If the input was an image tensor, then to visualize the image, we first convert the tensor obtained after convolution operation to a PIL image and then visualize the image.

Let's take a couple of examples to have a better understanding.

### Input Image

We will use the following image as the input file in Example 2.

## Example 1

In the following Python example, we perform 2D convolution operation on an input tensor. We apply different combinations of **kernel_size, stride, padding,** and **dilation**.

# Python 3 program to perform 2D convolution operation import torch import torch.nn as nn '''torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0) ''' in_channels = 2 out_channels = 3 kernel_size = 2 conv = nn.Conv2d(in_channels, out_channels, kernel_size) # conv = nn.Conv2d(2, 3, 2) '''input of size [N,C,H, W] N==>batch size, C==> number of channels, H==> height of input planes in pixels, W==> width in pixels. ''' # define the input with below info N=2 C=2 H=4 W=4 input = torch.empty(N,C,H,W).random_(256) print("Input Tensor:\n", input) print("Input Size:",input.size()) # Perform convolution operation output = conv(input) print("Output Tensor:\n", output) print("Output Size:",output.size()) # With square kernels (2,2) and equal stride conv = nn.Conv2d(2, 3, 2, stride=2) output = conv(input) print("Output Size:",output.size()) # non-square kernels and unequal stride and with padding conv = nn.Conv2d(2, 3, (2, 3), stride=(2, 1), padding=(2, 1)) output = conv(input) print("Output Size:",output.size()) # non-square kernels and unequal stride and with padding and dilation conv = nn.Conv2d(2, 3, (2, 3), stride=(2, 1), padding=(2, 1), dilation=(2, 1)) output = conv(input) print("Output Size:",output.size())

## Output

Input Tensor: tensor([[[[218., 190., 62., 113.], [244., 63., 207., 220.], [238., 110., 29., 131.], [ 65., 249., 183., 188.]], [[122., 250., 28., 126.], [ 10., 42., 4., 145.], [ 1., 122., 165., 189.], [ 59., 100., 1., 187.]]], [[[213., 18., 186., 162.], [121., 10., 107., 123.], [ 32., 129., 5., 227.], [ 76., 4., 196., 246.]], [[ 41., 191., 64., 195.], [146., 163., 39., 177.], [121., 84., 223., 144.], [ 44., 182., 25., 15.]]]]) Input Size: torch.Size([2, 2, 4, 4]) Output Tensor: tensor([[[[ 200.8638, 67.4519, 109.4424], [ 100.6047, 58.4399, 95.3557], [ 89.4536, 105.6236, 138.5873]], [[ -71.7612, -69.3269, 14.8537], [ -48.7640, -111.0042, -163.9681], [ -60.4490, 0.4771, -34.4785]], [[ -74.8413, -156.2264, -51.3553], [ -47.2120, -25.1986, -65.1617], [-109.8461, -68.7073, -47.6045]]], [[[ 90.5058, 51.1314, 138.2387], [ 62.8581, 62.5389, 56.5713], [ 78.0566, 57.6294, 143.0357]], [[-154.6399, -100.9079, -108.6138], [ -99.6024, -120.7665, -112.6453], [-107.5664, -76.9361, 17.8084]], [[ 23.9299, -95.5887, -51.7418], [ -46.8106, 15.3651, -66.4384], [ 2.1374, -65.6986, -144.9656]]]], grad_fn=<MkldnnConvolutionBackward>) Output Size: torch.Size([2, 3, 3, 3]) Output Size: torch.Size([2, 3, 2, 2]) Output Size: torch.Size([2, 3, 4, 4]) Output Size: torch.Size([2, 3, 3, 4])

## Example 2

In the following Python example, we perform 2D convolution operation on an input image. To apply 2D convolution, we first convert the image to a torch tensor and after convolution, again convert it to a PIL image for visualization.

# Python program to perform 2D convolution operation on an image # Import the required libraries import torch import torchvision from PIL import Image import torchvision.transforms as T # Read input image img = Image.open('dogncat.jpg') # convert the input image to torch tensor img = T.ToTensor()(img) print("Input image size:\n", img.size()) # size = [3, 466, 700] # unsqueeze the image to make it 4D tensor img = img.unsqueeze(0) # image size = [1, 3, 466, 700] # define convolution layer # conv = nn.Conv2d(in_channels, out_channels, kernel_size) conv = torch.nn.Conv2d(3, 3, 2) # apply convolution operation on image img = conv(img) # squeeze image to make it 3D img = img.squeeze(0) #now size is again [3, 466, 700] # convert image to PIL image img = T.ToPILImage()(img) # display the image after convolution img.show()

**Note** − You may get a different output image after the convolution operation because the weights initialized may be different at different runs.

## Output

Input image size: torch.Size([3, 525, 700]) Output image size: torch.Size([3, 524, 699])

Note that you may see some changes in the image obtained after each run because of the initialization of **weights** and **biases**.

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