torch之VGG块

酥酥 发布于 2022-04-17 83 次阅读

				
					import time
import torch
from torch import nn, optim

import sys
sys.path.append("..") 
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

print(torch.__version__)
print(device)
				
			
1.11.0+cu113
cuda

VGG块

				
					def vgg_block(num_convs, in_channels, out_channels):
    blk = []
    for i in range(num_convs):
        if i == 0:
            blk.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1))
        else:
            blk.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))
        blk.append(nn.ReLU())
    blk.append(nn.MaxPool2d(kernel_size=2, stride=2))
    return nn.Sequential(*blk)

VGG网络

				
					conv_arch = ((1, 1, 64), (1, 64, 128), (2, 128, 256), (2, 256, 512), (2, 512, 512))
fc_features = 512 * 7 * 7 # 根据卷积层的输出算出来的
fc_hidden_units = 4096 # 任意
				
			
				
					def vgg(conv_arch, fc_features, fc_hidden_units=4096):
        net = nn.Sequential()
    # 卷积层部分
        for i, (num_convs, in_channels, out_channels) in enumerate(conv_arch):
            net.add_module("vgg_block_" + str(i+1), vgg_block(num_convs, in_channels, out_channels))
    # 全连接层部分
            net.add_module("fc", nn.Sequential(d2l.FlattenLayer(),
                                 nn.Linear(fc_features, fc_hidden_units),
                                 nn.ReLU(),
                                 nn.Dropout(0.5),
                                 nn.Linear(fc_hidden_units, fc_hidden_units),
                                 nn.ReLU(),
                                 nn.Dropout(0.5),
                                 nn.Linear(fc_hidden_units, 10)
                                ))
        return net
				
			
				
					net = vgg(conv_arch, fc_features, fc_hidden_units)
X = torch.rand(1, 1, 224, 224)

# named_children获取一级子模块及其名字(named_modules会返回所有子模块,包括子模块的子模块)
for name, blk in net.named_children(): 
    X = blk(X)
    print(name, 'output shape: ', X.shape)
				
			
vgg_block_1 output shape:  torch.Size([1, 64, 112, 112])
vgg_block_2 output shape:  torch.Size([1, 128, 56, 56])
vgg_block_3 output shape:  torch.Size([1, 256, 28, 28])
vgg_block_4 output shape:  torch.Size([1, 512, 14, 14])
vgg_block_5 output shape:  torch.Size([1, 512, 7, 7])
fc output shape:  torch.Size([1, 10])
				
					ratio = 8
small_conv_arch = [(1, 1, 64//ratio), (1, 64//ratio, 128//ratio), (2, 128//ratio, 256//ratio), 
                   (2, 256//ratio, 512//ratio), (2, 512//ratio, 512//ratio)]
net = vgg(small_conv_arch, fc_features // ratio, fc_hidden_units // ratio)
print(net)
				
			
				
					batch_size = 64
# 如出现“out of memory”的报错信息，可减小batch_size或resize
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)

lr, num_epochs = 0.001, 5
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)
				
			
training on  cuda
epoch 1, loss 0.0101, train acc 0.755, test acc 0.859, time 255.9 sec
epoch 2, loss 0.0051, train acc 0.882, test acc 0.902, time 238.1 sec
epoch 3, loss 0.0043, train acc 0.900, test acc 0.908, time 225.5 sec
epoch 4, loss 0.0038, train acc 0.913, test acc 0.914, time 230.3 sec
epoch 5, loss 0.0035, train acc 0.919, test acc 0.918, time 153.9 sec