001 Conv2d、BatchNorm2d、MaxPool2d

Conv2d

https://pytorch.org/docs/stable/nn.html#conv2d

torch.nn.Conv2d(
  in_channels, out_channels, kernel_size,
  stride=1, padding=0, dilation=1, groups=1,
  bias=True, padding_mode='zeros'
)

• stride：卷积的步伐（Stride of the convolution）
• dilation：内核元素之间的间距（Spacing between kernel elements）

测试代码

import torch
import torch.nn as nn

def print_result(input, output, filter):
  print('------------------------------------------------------')
  print(input.size())
  print(filter)
  print(output.size())
  print('------------------------------------------------------')

# (batches, channels, H, W)
print('(batches, channels, H, W)')
input = torch.randn(1, 3, 128, 128)

print('base:')
m = nn.Conv2d(3, 6, 3)
output = m(input)
print_result(input, output, m)

print('padding:')
m = nn.Conv2d(6, 6, 3, padding=1)
input = output
output = m(input)
print_result(input, output, m)

print('stride:')
m = nn.Conv2d(6, 6, 3, stride=2)
input = output
output = m(input)
print_result(input, output, m)

print('dilation:')
m = nn.Conv2d(6, 6, 3, dilation=2)
input = output
output = m(input)
print_result(input, output, m)

(batches, channels, H, W)
base:
------------------------------------------------------
torch.Size([1, 3, 128, 128])
Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))
torch.Size([1, 6, 126, 126])
------------------------------------------------------
padding:
------------------------------------------------------
torch.Size([1, 6, 126, 126])
Conv2d(6, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
torch.Size([1, 6, 126, 126])
------------------------------------------------------
stride:
------------------------------------------------------
torch.Size([1, 6, 126, 126])
Conv2d(6, 6, kernel_size=(3, 3), stride=(2, 2))
torch.Size([1, 6, 62, 62])
------------------------------------------------------
dilation:
------------------------------------------------------
torch.Size([1, 6, 62, 62])
Conv2d(6, 6, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 2))
torch.Size([1, 6, 58, 58])
------------------------------------------------------

BatchNorm2d

https://pytorch.org/docs/stable/nn.html?highlight=batchnorm2d#torch.nn.BatchNorm2d

torch.nn.BatchNorm1d(
	num_features, eps=1e-05, momentum=0.1, affine=True, 
	track_running_stats=True
)

批量归一化

MaxPool2d

https://pytorch.org/docs/stable/nn.html?highlight=maxpool2d#torch.nn.MaxPool2d

torch.nn.MaxPool2d(
	kernel_size, stride=None, padding=0, dilation=1, 
	return_indices=False, ceil_mode=False
)