def __init__(self):
super(DenseNet, self).__init__()
# 卷积层部分
self.conv = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
num_channels, growth_rate = 64, 32 # num_channels为当前的通道数
num_convs_in_dense_blocks = [4, 4, 4, 4]
for i, num_convs in enumerate(num_convs_in_dense_blocks):
DB = DenseBlock(num_convs, num_channels, growth_rate)
self.conv.add_module("DenseBlosk_%d" % i, DB)
# 上一个稠密块的输出通道数
num_channels = DB.out_channels
# 在稠密块之间加入通道数减半的过渡层
if i != len(num_convs_in_dense_blocks) - 1:
self.conv.add_module(
"transition_block_%d" % i,
self.transition_block(num_channels, num_channels // 2))
num_channels = num_channels // 2
self.conv.add_module("BN", nn.BatchNorm2d(num_channels))
self.conv.add_module("relu", nn.ReLU())
self.fc = nn.Sequential(
utils.GlobalAvgPool2d(), utils.FlattenLayer(),
nn.Linear(num_channels,
10)) # GlobalAvgPool2d的输出: (Batch, num_channels, 1, 1)
def __init__(self, ):
super(ResNet, self).__init__()
# 卷积层部分
self.conv = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
self.conv.add_module("resnet_block1",
self.resnet_block(64, 64, 2, first_block=True))
self.conv.add_module("resnet_block2", self.resnet_block(64, 128, 2))
self.conv.add_module("resnet_block3", self.resnet_block(128, 256, 2))
self.conv.add_module("resnet_block4", self.resnet_block(256, 512, 2))
# 全连接层部分
self.fc = nn.Sequential(
utils.GlobalAvgPool2d(), utils.FlattenLayer(),
nn.Linear(512, 10)) # GlobalAvgPool2d的输出: (Batch, 512, 1, 1)
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
Inception(256, 128, (128, 192), (32, 96), 64),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
Inception(512, 160, (112, 224), (24, 64), 64),
Inception(512, 128, (128, 256), (24, 64), 64),
Inception(512, 112, (144, 288), (32, 64), 64),
Inception(528, 256, (160, 320), (32, 128), 128),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
Inception(832, 384, (192, 384), (48, 128), 128),
utils.GlobalAvgPool2d())
print('查看网络结构')
net = nn.Sequential(b1, b2, b3, b4, b5, utils.FlattenLayer(),
nn.Linear(1024, 10))
X = torch.rand(1, 1, 96, 96)
for blk in net.children():
X = blk(X)
print('output shape: ', X.shape)
print('获取和读取数据，这里缩减尺寸为 96')
batch_size = 256
train_iter, test_iter = utils.load_data_fashion_mnist(batch_size, resize=96)
print('训练模型，只 1 轮')
stride=2))
else:
blk.append(utils.Residual(out_channels, out_channels))
return nn.Sequential(*blk)
net = nn.Sequential(nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
net.add_module("resnet_block1", resnet_block(64, 64, 2, first_block=True))
net.add_module("resnet_block2", resnet_block(64, 128, 2))
net.add_module("resnet_block3", resnet_block(128, 256, 2))
net.add_module("resent_block4", resnet_block(256, 512, 2))
net.add_module("global_avg_pool", utils.GlobalAvgPool2d())
net.add_module("fc", nn.Sequential(utils.FlattenLayer(), nn.Linear(512, 10)))
# 训练模型
def train_with_data_aug(train_augs, test_augs, lr=0.001):
batch_size = 256
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
loss = torch.nn.CrossEntropyLoss()
train_iter = load_cifar10(True, train_augs, batch_size)
test_iter = load_cifar10(False, test_augs, batch_size)
utils.train(train_iter,
test_iter,
net,
loss,
optimizer,
for i, num_convs in enumerate(num_convs_in_dense_blocks):
DB = DenseBlock(num_convs, num_channels, growth_rate)
net.add_module("DenseBlosk_%d" % i, DB)
# 上一个稠密块的输出通道数
num_channels = DB.out_channels
# 在稠密块之间加入通道数减半的过渡层
if i != len(num_convs_in_dense_blocks) - 1:
net.add_module("transition_block_%d" % i,
transition_block(num_channels, num_channels // 2))
num_channels = num_channels // 2
net.add_module("BN", nn.BatchNorm2d(num_channels)) # 248
net.add_module("relu", nn.ReLU())
net.add_module(
"global_avg_pool",
d2l.GlobalAvgPool2d()) # GlobalAvgPool2d的输出: (Batch, num_channels, 1, 1)
net.add_module("fc",
nn.Sequential(d2l.FlattenLayer(), nn.Linear(num_channels, 10)))
X = torch.rand((1, 1, 96, 96))
for name, layer in net.named_children():
X = layer(X)
print(name, ' output shape:\t', X.shape)
def load_data_fashion_mnist(batch_size,
resize=None,
root='input/FashionMNIST2065'):
"""Download the fashion mnist dataset and then load into memory."""
trans = []
if resize:
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
Inception(256, 128, (128, 192), (32, 96), 64),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
Inception(512, 160, (112, 224), (24, 64), 64),
Inception(512, 128, (128, 256), (24, 64), 64),
Inception(512, 112, (144, 288), (32, 64), 64),
Inception(528, 256, (160, 320), (32, 128), 128),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
Inception(832, 384, (192, 384), (48, 128), 128),
d2l.GlobalAvgPool2d())
net = nn.Sequential(b1, b2, b3, b4, b5, d2l.FlattenLayer(),
nn.Linear(1024, 10))
net = nn.Sequential(b1, b2, b3, b4, b5, d2l.FlattenLayer(),
nn.Linear(1024, 10))
X = torch.rand(1, 1, 96, 96)
for blk in net.children():
X = blk(X)
print('output shape: ', X.shape)
batch_size = 16
# 如出现“out of memory”的报错信息，可减小batch_size或resize
def nin_block(in_channels, out_channels, kernel_size, stride, padding):
blk = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),
nn.ReLU(), nn.Conv2d(out_channels, out_channels, kernel_size=1),
nn.ReLU(), nn.Conv2d(out_channels, out_channels, kernel_size=1),
nn.ReLU())
return blk
net = nn.Sequential(nin_block(1, 96, kernel_size=11, stride=4, padding=0),
nn.MaxPool2d(kernel_size=3, stride=2),
nin_block(96, 256, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(kernel_size=3, stride=2),
nin_block(256, 384, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=3, stride=2), nn.Dropout(0.5),
nin_block(384, 10, kernel_size=3, stride=1, padding=1),
utils.GlobalAvgPool2d(), utils.FlattenLayer())
X = torch.rand(1, 1, 224, 224)
for name, blk in net.named_children():
X = blk(X)
print(name, 'output shape: ', X.shape)
# 训练模型
batch_size = 128
train_iter, test_iter = utils.load_data_fashion_mnist(batch_size, resize=224)
lr, num_epochs = 0.002, 5
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
utils.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device,
num_epochs)
for i, num_convs in enumerate(num_convs_in_dense_block):
DB = DenseBlock(num_convs, num_channels, growth_rate)
net.add_module('DenseBlock_%d' % i, DB)
# 上一个稠密块的输出通道数
num_channels = DB.out_channels
# 在稠密块之间加入通道数减半的过渡层
if i != len(num_convs_in_dense_block) - 1:
net.add_module('transition_block_%d' % i,
transition_block(num_channels, num_channels // 2))
num_channels = num_channels // 2
# 加入全局平均池化和全连接
net.add_module('BN', nn.BatchNorm2d(num_channels))
net.add_module('relu', nn.ReLU())
net.add_module('global_avg_pool', utils.GlobalAvgPool2d())
net.add_module(
'fc', nn.Sequential(utils.FlattenLayer(), nn.Linear(num_channels, 10)))
print('确保网络无误')
X = torch.rand((1, 1, 96, 96))
for name, layer in net.named_children():
X = layer(X)
print(name, 'output shape:\t', X.shape)
print('获取和读取数据')
batch_size = 256
train_iter, test_iter = utils.load_data_fashion_mnist(batch_size=batch_size,
resize=96)
print('训练模型')
assert in_channels == out_channels # 第一个模块的通道数同输入通道数一致
blk = []
for i in range(num_residuals):
if i == 0 and not first_block:
blk.append(Residual(in_channels, out_channels, use_1x1conv=True, stride=2))
else:
blk.append(Residual(out_channels, out_channels))
return nn.Sequential(*blk)
net.add_module("resnet_block1", resnet_block(64, 64, 2, first_block=True))
net.add_module("resnet_block2", resnet_block(64, 128, 2))
net.add_module("resnet_block3", resnet_block(128, 256, 2))
net.add_module("resnet_block4", resnet_block(256, 512, 2))
net.add_module("global_avg_pool", d2l.GlobalAvgPool2d()) # GlobalAvgPool2d的输出: (Batch, 512, 1, 1)
net.add_module("fc", nn.Sequential(d2l.FlattenLayer(), nn.Linear(512, 10)))
X = torch.rand((1, 1, 224, 224))
for name, layer in net.named_children():
X = layer(X)
print(name, ' output shape:\t', X.shape)
def load_data_fashion_mnist(batch_size, resize=None, root='input/FashionMNIST2065'):
"""Download the fashion mnist dataset and then load into memory."""
trans = []
if resize:
trans.append(torchvision.transforms.Resize(size=resize))
trans.append(torchvision.transforms.ToTensor())