import torch
from torch import nn
from d2l import torch as d2l

def vgg_block(num_convs, in_channels, out_channels):
	layers = []
	for _ in range(num_convs):
		layers.append(nn.Conv2d(in_channels, out_channels, kernel_size = 3, padding=1))
		leyers.append(nn.ReLU())
		in_channels = out_channels
	layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
	return nn.Sequential(*layers)

conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))

def vgg(conv_arch):
	conv_blks = []
	in_channels = 1
	# 卷積層部分
	for (num_convs, out_channels) in conv_arch:
		conv_blks.append(vgg_block(num_convs, in_cannels, out_channels))
		in_channels = out_channels

	return nn.Sequential(
		*conv_blks, nn.Flatten(),
		# 全連接層部分
		nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),
		nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),
		nn.Linear(4096, 10)
	)

net = vgg(conv_arch)

X = torch.randn(size=(1, 1, 224, 224))
for blk in net:
	X = blk(X)
	print(blk.__class__.__name__, 'output shape: \t', X.shape)

Sequential output shape: torch.Size([1, 64, 112, 112])
Sequential output shape: torch.Size([1, 128, 56, 56])
Sequential output shape: torch.Size([1, 256, 28, 28])
Sequential output shape: torch.Size([1, 512, 14, 14])
Sequential output shape: torch.Size([1, 512, 7, 7])
Flatten output shape: torch.Size([1, 25088])
Linear output shape: torch.Size([1, 4096])
ReLU output shape: torch.Size([1, 4096])
Dropout output shape: torch.Size([1, 4096])
Linear output shape: torch.Size([1, 4096])
ReLU output shape: torch.Size([1, 4096])
Dropout output shape: torch.Size([1, 4096])
Linear output shape: torch.Size([1, 10])

ratio = 4
small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]
net = vgg(small_conv_arch)

lr, num_epochs, batch_size = 0.05, 10, 128

train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())

loss 0.175, train axx 0.935, test acc 0.917
2559.6 examples/sec on cuda:0