Hello sir,

I tried to visualize the runtime performance improvement made by convolution layer which I implemented from scratch Vs torchscript version of convolution layer Vs torch.nn.conv2d() module for 100 iterations with input (128,3,28,28), out_channel =64, kernel size=3.

Convolution layer from scratch in CUDA -> 9.366 seconds

torchscript convolution layer from scratch in CUDA -> 6.636 seconds

torch.nn.conv2d() -> 475.614 milliseconds.

My code

```
class conv2D(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size):
super(conv2D,self).__init__()
self.weight = torch.nn.Parameter(torch.ones(out_channel,in_channel,kernel_size, kernel_size))
self.bias = torch.nn.Parameter(torch.zeros(out_channel))
self.kernel_size = kernel_size
self.in_channel = in_channel
self.out_channel = out_channel
def forward(self, image):
img_height = image.shape[3]
img_width = image.shape[2]
batch_size = image.shape[0]
out_height = img_height-self.kernel_size+1
out_width = img_width-self.kernel_size+1
output = torch.zeros(batch_size,self.out_channel,out_width,out_height)
for k in range(batch_size):
for i in range(out_height):
for j in range(out_width):
temp = torch.sum(image[k,:,j:j+self.kernel_size,i:i+self.kernel_size]*self.weight,dim=(1,2,3))
output[k,:,i,j]=torch.add(temp,self.bias)
return output
```

Scripting the model and running with a sample input to get an optimized graph

```
x = torch.ones(128,3,28,28).to("cuda")
c = conv2D(3,64,3).to("cuda")
c_s = torch.jit.script(c).to("cuda")
c_s(x)
```

Profiling both the scripted and normal method.

```
with torch.autograd.profiler.profile(use_cuda=True) as prof:
with torch.no_grad():
for i in range(100):
c(x)
print(prof.table())
```

```
with torch.autograd.profiler.profile(use_cuda=True) as prof:
with torch.no_grad():
for i in range(100):
c_s(x)
print(prof.table())
```

**Is there any problem in my approach? and how to optimize even more?**. I request you to help me with this problem.