Conv2d
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| import torch.nn as nn
from PIL import Image
from torchvision import transforms as T
img_path = [path to image]
img = Image.open(img_path)
transform = T.ToTensor()
img_tensor = transform(img).unsqueeze(0)
net = nn.Conv2d(3, 3, 3, stride=2)
out = net(img_tensor)
to_pil = T.ToPILImage()
print(out.shape)
out = out.squeeze(0)
print(out.shape)
img_pil = to_pil(out)
img_pil.show()
|
nn.Conv2d(3, 4, 3, stride=2)
nn.Conv2d(3, 3, 3, stride=2)
nn.Conv2d(3, 3, 3, stride=3)
3
nn.Conv2d(3, 3, 3, stride=3, padding=2)
MaxPool2d
padding < kernel size / 2
pad should be smaller than half of kernel size
nn.MaxPool2d(kernel size=8, stride=4, padding=1)
nn.MaxPool2d(kernel size=16, stride=4, padding=1)
nn.MaxPool2d(kernel size=32, stride=4, padding=1)
AvgPool2d
pad should be smaller than half of kernel size
nn.AvgPool2d(kernel size=8, stride=4, padding=1)
nn.AvgPool2d(kernel size=16, stride=4, padding=1)
nn.AvgPool2d(kernel size=32, stride=4, padding=1)