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demo_5_1_conv.py
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demo_5_1_conv.py
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import torch
from torch import nn
import torch.optim as optim
import sys
# import graphviz
from pyvis.network import Network
class SimpleLayer(nn.Module):
def __init__(self,channel_in,channel_hid,channel_out):
super(SimpleLayer, self).__init__()
self.conv_1 = nn.Conv2d(channel_in, channel_hid ,3,1,0, bias=False) # 输入1,4,4 输出 1,2,2
self.conv_2 = nn.Conv2d(channel_hid, channel_out ,2,1,0, bias=False) # 输入1,2,2 输出 1,1,1
# self.fc_2 = nn.Linear(channel_out, channel_out, bias=False)
self.init_weights()
def init_weights(self):
# 使用torch.arange生成整数张量
self.conv_1.weight.data = (torch.arange(9)/10).float().view(self.conv_1.weight.shape)
self.conv_2.weight.data = (torch.arange(4)/10).float().view(self.conv_2.weight.shape)
def forward(self, x0):
x1 = self.conv_1(x0.float())
y1 = torch.sigmoid(x1)
x2 = self.conv_2(y1)
y2 = torch.sigmoid(x2)
return [x1,y1,x2,y2]
def manual_conv(input, weight):
batch_size, channels, height, width = input.size()
kernel_size = weight.size(2) # 获取卷积核的形状信息
padding = 0
stride = 1
# 计算卷积后的输出形状
output_height = (height - kernel_size + 2 * padding) // stride + 1
output_width = (width - kernel_size + 2 * padding) // stride + 1
# 创建输出张量
manual_x1 = torch.zeros(batch_size, 1, output_height, output_width)
d_w1 = torch.zeros([output_height, output_width,
weight.size(2),weight.size(3)])
d_x1 = torch.zeros([output_height, output_width,
input.size(2),input.size(3)])
# 卷积操作
for hy in range(output_height):
for wy in range(output_width):
manual_x1[0, 0, hy, wy] = torch.sum(input[0, 0, hy:hy + kernel_size, wy:wy + kernel_size] * weight)
"""
对于单层的来说,
每个w，每个位置了挪了多少个，相当于 output_height × output_width 次乘法计算，现在要做的就是把这些位置的内容求和，你就能得到这里w的梯度了
对于x，每个位置经历多少次卷积核的计算，把W求和，就能得到这里x的梯度了
"""
for hy in range(manual_x1.size(2)): # 输出y高度
for wy in range(manual_x1.size(3)): # 输出y宽度
d_w1[hy, wy,0:kernel_size, 0:kernel_size] = input[0, 0, hy:hy+kernel_size, wy:wy+kernel_size]
for hy in range(output_height): # 输出y高度
for wy in range(output_width): # 输出y宽度
d_x1[hy, wy, hy:hy + kernel_size, wy:wy + kernel_size] += weight[0,0,0:kernel_size, 0:kernel_size]
return [manual_x1,d_x1,d_w1]
if __name__ == '__main__':
input = torch.reshape(torch.arange(1,17), (1, 1, 4, 4))/10
model = SimpleLayer(1,1,1)
target = torch.tensor([1]).view(1,1,1,-1).to(torch.float32)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# sys.exit()
for epoch in range(2):
optimizer.zero_grad()
output = model(input)
loss = criterion(output[-1], target)
print(f'输入 {input.data} 输出 {output[-1].data} 目标 {target.data} 损失 {loss.item()}')
w1 = model.conv_1.weight.data # 1,1,3,3
w2 = model.conv_2.weight.data # 1,1,3,3
print('权重w1：',w1)
print('权重w2：',w2)
x0 = input.data
x1 = output[0].data # x0 > conv > x1 with w1
y1 = output[1].data # x1 > sig > y1
x2 = output[2].data # y1 > conv > x2 with w2
y2 = output[3].data # x2 > sig > y2
# 输出 y=f(x,w) dy/dx dy/dw
dy2_x2 = y2*(1-y2)
[manual_x2,dx2_y1,dx2_w2] = manual_conv(y1,w2)
dy1_x1 = y1*(1-y1)
[manual_x1,dx1_x0,dx1_w1] = manual_conv(x0,w1)
dy2_w2 = dy2_x2 * dx2_w2
dy2_x1 = dy2_x2 * dx2_y1 * dy1_x1
dy2_x1_reshape = dy2_x1.view(4)
dx1_w1_reshape = dx1_w1.view(4, 9)
dy2_w1 = torch.matmul(dy2_x1_reshape, dx1_w1_reshape).view(3, 3)
d_err = (output[-1].data - target.data) * 2
grad_w2 = d_err * dy2_w2
grad_w1 = d_err * dy2_w1
loss.backward()
print('梯度（自动）w2：',model.conv_2.weight.grad)
print('梯度 (手动)w2：',grad_w2)
print('梯度（自动）w1：',model.conv_1.weight.grad)
print('梯度 (手动)w1：',grad_w1)
print('权重（更新前）：',model.conv_1.weight.data)
print('权重（更新前）：',model.conv_2.weight.data)
# new_data = model.fc.weight.data[0, 0] - 0.01 * model.fc.weight.grad[0, 0]
new_data_1 = model.conv_1.weight.data - 0.01 * grad_w1
new_data_2 = model.conv_2.weight.data - 0.01 * grad_w2
print('权重（手动）w1：',new_data_1)
print('权重（手动）w2：',new_data_2)
optimizer.step()
print('权重（更新后）w1：',model.conv_1.weight.data)
print('权重（更新后）w2：',model.conv_2.weight.data)
print('---'*20)
break
# import torch
#
# # 创建两个张量
# tensor1 = torch.tensor([[[[1, 2]]]]) # 形状为(1, 1, 1, 2)
# tensor2 = torch.tensor([[[[1, 2, 3, 4]]]]) # 形状为(1, 1, 1, 4)
#
# # 调整张量的维度
# tensor1 = tensor1.view(2, 1)
# tensor2 = tensor2.view(1, 4)
#
# # 进行矩阵乘法
# result = torch.matmul(tensor1, tensor2)
#
# print(result)