pytorch方法测试——卷积（一维）

import torch
import torch.nn as nn

m = nn.Conv1d(2, 5, 2, stride=2)
print(m)
input = torch.randn(2,2, 5)
print(input)
output = m(input)
print(m.weight)
print(m.bias)
print(output)
print(output.size())
print(m.weight[0][0][0] * input[0][0][0] + m.weight[0][0][1] * input[0][0][1] 

+ m.weight[0][1][0] * input[0][1][0] + m.weight[0][1][1] * input[0][1][1] + m.bias[0])

输出为：

Conv1d(2, 5, kernel_size=(2,), stride=(2,))
tensor([[[ 0.2060, -0.7951, -2.7771,  0.2033, -1.1268],
         [ 0.4578,  0.5288, -1.7435,  1.0188, -0.4615]],

        [[-0.7615, -2.3001,  1.3399,  0.9165, -0.0547],
         [ 2.1604, -0.8994, -1.2320,  0.2077,  1.5822]]])
Parameter containing:
tensor([[[ 0.4840,  0.2625],

         [ 0.4189, -0.1825]],

        [[-0.0461,  0.4118],
         [-0.2437,  0.0243]],

        [[-0.1979, -0.1668],
         [-0.3049, -0.3615]],

        [[ 0.3453,  0.4335],
         [ 0.4266, -0.3455]],

        [[ 0.1638,  0.2814],
         [-0.2100,  0.3497]]])
Parameter containing:
tensor([-0.4633,  0.3096, -0.4810,  0.1805,  0.3115])
tensor([[[-0.4770, -2.6703],
         [-0.1260,  0.9710],
         [-0.7199,  0.1979],
         [-0.0805, -1.7862],
         [ 0.2103,  0.6362]],

        [[-0.3663, -0.1284],
         [-1.1508,  0.9305],
         [-0.2801, -0.5985],
         [ 0.1528,  0.4432],
         [-1.2287,  1.1203]]])
torch.Size([2, 5, 2])

tensor(-0.4770)

结论：一维卷积第一个输出 = 

m.weight[0][0][0] * input[0][0][0] + m.weight[0][0][1] * input[0][0][1] 

+ m.weight[0][1][0] * input[0][1][0] + m.weight[0][1][1] * input[0][1][1] + m.bias[0]