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guanjihuan 2024-04-03 16:22:28 +08:00
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2024.04.03_data_size_after_convolution_and_pooling/data_size_after_convolution.py Normal file
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"""
This code is supported by the website: https://www.guanjihuan.com
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/39320
"""
import torch
input_data = torch.randn(1, 1, 28, 28)
print('【有填充的情况，不同卷积核大小对输出数据的维度的影响】')
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=1, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为1）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=2, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为2）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为3）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=4, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为4）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=5, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为5）：", output_data.shape)
print()
print('【无填充的情况，不同卷积核大小对输出数据的维度的影响】')
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=1, stride=1, padding=0, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为1）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=2, stride=1, padding=0, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为2）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=0, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为3）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=4, stride=1, padding=0, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为4）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=5, stride=1, padding=0, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为5）：", output_data.shape)
print()
print('【不同步幅对输出数据的维度的影响（以卷积核大小为3，有填充的情况为例）】')
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（步幅为1）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=2, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（步幅为2）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=3, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（步幅为3）：", output_data.shape)
print()
print('【不同扩张率对输出数据的维度的影响（以步幅为1，有填充的情况为例）】')
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为3，扩张率为1）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1, dilation=2)
output_data = conv_layer(input_data)
print("维度（卷积核大小为3，扩张率为2）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1, dilation=3)
output_data = conv_layer(input_data)
print("维度（卷积核大小为3，扩张率为3）：", output_data.shape)
print()
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=4, stride=1, padding=1, dilation=1)
output_data = conv_layer(input_data)
print("维度（卷积核大小为4，扩张率为1）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=4, stride=1, padding=1, dilation=2)
output_data = conv_layer(input_data)
print("维度（卷积核大小为4，扩张率为2）：", output_data.shape)
conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=4, stride=1, padding=1, dilation=3)
output_data = conv_layer(input_data)
print("维度（卷积核大小为4，扩张率为3）：", output_data.shape)

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2024.04.03_data_size_after_convolution_and_pooling/data_size_after_pooling.py Normal file
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"""
This code is supported by the website: https://www.guanjihuan.com
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/39320
"""
import torch
input_data = torch.randn(1, 1, 28, 28)
print('【不同卷积核大小对输出数据的维度的影响】')
max_pool = torch.nn.MaxPool2d(kernel_size=1, stride=1)
output_data = max_pool(input_data)
print("维度（卷积核大小为1）：", output_data.shape)
max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=1)
output_data = max_pool(input_data)
print("维度（卷积核大小为2）：", output_data.shape)
max_pool = torch.nn.MaxPool2d(kernel_size=3, stride=1)
output_data = max_pool(input_data)
print("维度（卷积核大小为3）：", output_data.shape)
print()
print('【不同步幅对输出数据的维度的影响（以卷积核大小为2为例）】')
max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=1)
output_data = max_pool(input_data)
print("维度（步幅为1）：", output_data.shape)
max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
output_data = max_pool(input_data)
print("维度（步幅为2）：", output_data.shape)
max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=3)
output_data = max_pool(input_data)
print("维度（步幅为3）：", output_data.shape)