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二十八、残差网络（ ResNet ）
import torch
import torchvision
import time
from torch import nn
from IPython import display
from torchvision import transforms
from torch.nn import functional as F
from torch.utils import data
import matplotlib.pyplot as plt
from matplotlib_inline import backend_inline
mydevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def accuracy(y_hat, y): # 定义一个函数来为预测正确的数量计数
"""计算预测正确的数量"""
if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:
y_hat = y_hat.argmax(axis=1)
cmp = y_hat.type(y.dtype) == y # bool 类型，若预测结果与实际结果一致，则为 True
return float(cmp.type(y.dtype).sum())
def evaluate_accuracy_gpu(net, data_iter, device=None):
"""使用GPU计算模型在数据集上的精度"""
if isinstance(net, nn.Module):
net.eval() # 设置为评估模式
if not device:
device = next(iter(net.parameters())).device
# 正确预测的数量，总预测的数量
metric = Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
# BERT微调所需的（之后将介绍）
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1]
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
axes.set_xscale(xscale), axes.set_yscale(yscale)
axes.set_xlim(xlim), axes.set_ylim(ylim)
if legend:
axes.legend(legend)
axes.grid()
class Accumulator: # 定义一个实用程序类 Accumulator，用于对多个变量进行累加
"""在n个变量上累加"""
def __init__(self, n):
self.data = [0.0] * n
def add(self, *args):
self.data = [a + float(b) for a, b in zip(self.data, args)]
def reset(self):
self.data = [0.0] * len(self.data)
def __getitem__(self, idx):
return self.data[idx]
class Animator: # 定义一个在动画中绘制数据的实用程序类 Animator
"""在动画中绘制数据"""
def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,
ylim=None, xscale='linear', yscale='linear',
fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,
figsize=(3.5, 2.5)):
# 增量地绘制多条线
if legend is None:
legend = []
backend_inline.set_matplotlib_formats('svg')
self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)
if nrows * ncols == 1:
self.axes = [self.axes, ]
# 使用lambda函数捕获参数
self.config_axes = lambda: set_axes(
self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
self.X, self.Y, self.fmts = None, None, fmts
def add(self, x, y):
# Add multiple data points into the figure
if not hasattr(y, "__len__"):
y = [y]
n = len(y)
if not hasattr(x, "__len__"):
x = [x] * n
if not self.X:
self.X = [[] for _ in range(n)]
if not self.Y:
self.Y = [[] for _ in range(n)]
for i, (a, b) in enumerate(zip(x, y)):
if a is not None and b is not None:
self.X[i].append(a)
self.Y[i].append(b)
self.axes[0].cla()
for x, y, fmt in zip(self.X, self.Y, self.fmts):
self.axes[0].plot(x, y, fmt)
self.config_axes()
display.display(self.fig)
# 通过以下两行代码实现了在PyCharm中显示动图
# plt.draw()
# plt.pause(interval=0.001)
display.clear_output(wait=True)
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
class Timer:
def __init__(self):
self.times = []
self.start()
def start(self):
self.tik = time.time()
def stop(self):
self.times.append(time.time() - self.tik)
return self.times[-1]
def sum(self):
"""Return the sum of time."""
return sum(self.times)
def load_data_fashion_mnist(batch_size, resize=None):
"""下载 Fashion-MNIST 数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
root="../data", train=True, transform=trans, download=False)
mnist_test = torchvision.datasets.FashionMNIST(
root="../data", train=False, transform=trans, download=False)
return (data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=4),
data.DataLoader(mnist_test, batch_size, shuffle=False,
num_workers=4))
def train(net, train_iter, test_iter, num_epochs, lr, device):
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
print('training on', torch.cuda.get_device_name(device))
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
animator = Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer, num_batches = Timer(), len(train_iter)
for epoch in range(num_epochs):
# 训练损失之和，训练准确率之和，样本数
metric = Accumulator(3)
net.train()
for i, (X, y) in enumerate(train_iter):
timer.start()
optimizer.zero_grad()
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], accuracy(y_hat, y), X.shape[0])
timer.stop()
train_l = metric[0] / metric[2]
train_acc = metric[1] / metric[2]
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(train_l, train_acc, None))
test_acc = evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
plt.title(f'loss {train_l:.3f}, train acc {train_acc:.3f}, test acc {test_acc:.3f}\n'
f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on {str(device)}')
plt.show()
class Residual(nn.Module):
def __init__(self, input_channels, num_channels,
use_1x1conv=False, strides=1):
super().__init__()
self.conv1 = nn.Conv2d(input_channels, num_channels,
kernel_size=3, padding=1, stride=strides)
self.conv2 = nn.Conv2d(num_channels, num_channels,
kernel_size=3, padding=1)
if use_1x1conv:
self.conv3 = nn.Conv2d(input_channels, num_channels,
kernel_size=1, stride=strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm2d(num_channels)
self.bn2 = nn.BatchNorm2d(num_channels)
def forward(self, X):
Y = F.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
Y += X
return F.relu(Y)
# 查看输入和输出形状一致的情况
blk = Residual(3, 3)
X = torch.rand(4, 3, 6, 6)
Y = blk(X)
print(f'查看输入和输出形状是否一致 : {Y.shape == X.shape}')
# 可以在增加输出通道数的同时，减半输出的高和宽
blk = Residual(3, 6, use_1x1conv=True, strides=2)
print(f'增加输出通道数，减半输出的高和宽 : {blk(X).shape}')
b1 = 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))
def resnet_block(input_channels, num_channels, num_residuals,
first_block=False):
blk = []
for i in range(num_residuals):
if i == 0 and not first_block:
blk.append(Residual(input_channels, num_channels,
use_1x1conv=True, strides=2))
else:
blk.append(Residual(num_channels, num_channels))
return blk
b2 = nn.Sequential(*resnet_block(64, 64, 2, first_block=True))
b3 = nn.Sequential(*resnet_block(64, 128, 2))
b4 = nn.Sequential(*resnet_block(128, 256, 2))
b5 = nn.Sequential(*resnet_block(256, 512, 2))
net = nn.Sequential(b1, b2, b3, b4, b5,
nn.AdaptiveAvgPool2d((1,1)),
nn.Flatten(), nn.Linear(512, 10))
X = torch.rand(size=(1, 1, 224, 224))
for layer in net:
X = layer(X)
print(layer.__class__.__name__, 'output shape:\t\t', X.shape)
lr, num_epochs, batch_size = 0.05, 5, 256
train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=96)
train(net, train_iter, test_iter, num_epochs, lr, mydevice)
二十九、稠密连接网络（ DenseNet ）
import torch
import torchvision
import time
from torch import nn
from IPython import display
from torchvision import transforms
from torch.nn import functional as F
from torch.utils import data
import matplotlib.pyplot as plt
from matplotlib_inline import backend_inline
mydevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def accuracy(y_hat, y): # 定义一个函数来为预测正确的数量计数
"""计算预测正确的数量"""
if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:
y_hat = y_hat.argmax(axis=1)
cmp = y_hat.type(y.dtype) == y # bool 类型，若预测结果与实际结果一致，则为 True
return float(cmp.type(y.dtype).sum())
def evaluate_accuracy_gpu(net, data_iter, device=None):
"""使用GPU计算模型在数据集上的精度"""
if isinstance(net, nn.Module):
net.eval() # 设置为评估模式
if not device:
device = next(iter(net.parameters())).device
# 正确预测的数量，总预测的数量
metric = Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
# BERT微调所需的（之后将介绍）
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1]
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
axes.set_xscale(xscale), axes.set_yscale(yscale)
axes.set_xlim(xlim), axes.set_ylim(ylim)
if legend:
axes.legend(legend)
axes.grid()
class Accumulator: # 定义一个实用程序类 Accumulator，用于对多个变量进行累加
"""在n个变量上累加"""
def __init__(self, n):
self.data = [0.0] * n
def add(self, *args):
self.data = [a + float(b) for a, b in zip(self.data, args)]
def reset(self):
self.data = [0.0] * len(self.data)
def __getitem__(self, idx):
return self.data[idx]
class Animator: # 定义一个在动画中绘制数据的实用程序类 Animator
"""在动画中绘制数据"""
def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,
ylim=None, xscale='linear', yscale='linear',
fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,
figsize=(3.5, 2.5)):
# 增量地绘制多条线
if legend is None:
legend = []
backend_inline.set_matplotlib_formats('svg')
self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)
if nrows * ncols == 1:
self.axes = [self.axes, ]
# 使用lambda函数捕获参数
self.config_axes = lambda: set_axes(
self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
self.X, self.Y, self.fmts = None, None, fmts
def add(self, x, y):
# Add multiple data points into the figure
if not hasattr(y, "__len__"):
y = [y]
n = len(y)
if not hasattr(x, "__len__"):
x = [x] * n
if not self.X:
self.X = [[] for _ in range(n)]
if not self.Y:
self.Y = [[] for _ in range(n)]
for i, (a, b) in enumerate(zip(x, y)):
if a is not None and b is not None:
self.X[i].append(a)
self.Y[i].append(b)
self.axes[0].cla()
for x, y, fmt in zip(self.X, self.Y, self.fmts):
self.axes[0].plot(x, y, fmt)
self.config_axes()
display.display(self.fig)
# 通过以下两行代码实现了在PyCharm中显示动图
# plt.draw()
# plt.pause(interval=0.001)
display.clear_output(wait=True)
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
class Timer:
def __init__(self):
self.times = []
self.start()
def start(self):
self.tik = time.time()
def stop(self):
self.times.append(time.time() - self.tik)
return self.times[-1]
def sum(self):
"""Return the sum of time."""
return sum(self.times)
def load_data_fashion_mnist(batch_size, resize=None):
"""下载 Fashion-MNIST 数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
root="../data", train=True, transform=trans, download=False)
mnist_test = torchvision.datasets.FashionMNIST(
root="../data", train=False, transform=trans, download=False)
return (data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=4),
data.DataLoader(mnist_test, batch_size, shuffle=False,
num_workers=4))
def train(net, train_iter, test_iter, num_epochs, lr, device):
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
print('training on', torch.cuda.get_device_name(device))
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
animator = Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer, num_batches = Timer(), len(train_iter)
for epoch in range(num_epochs):
# 训练损失之和，训练准确率之和，样本数
metric = Accumulator(3)
net.train()
for i, (X, y) in enumerate(train_iter):
timer.start()
optimizer.zero_grad()
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], accuracy(y_hat, y), X.shape[0])
timer.stop()
train_l = metric[0] / metric[2]
train_acc = metric[1] / metric[2]
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(train_l, train_acc, None))
test_acc = evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
plt.title(f'loss {train_l:.3f}, train acc {train_acc:.3f}, test acc {test_acc:.3f}\n'
f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on {str(device)}')
plt.show()
def conv_block(input_channels, num_channels):
return nn.Sequential(
nn.BatchNorm2d(input_channels), nn.ReLU(),
nn.Conv2d(input_channels, num_channels, kernel_size=3, padding=1))
class DenseBlock(nn.Module):
def __init__(self, num_convs, input_channels, num_channels):
super(DenseBlock, self).__init__()
layer = []
for i in range(num_convs):
layer.append(conv_block(
num_channels * i + input_channels, num_channels))
self.net = nn.Sequential(*layer)
def forward(self, X):
for blk in self.net:
Y = blk(X)
# 连接通道维度上每个块的输入和输出
X = torch.cat((X, Y), dim=1)
return X
blk = DenseBlock(2, 3, 10)
X = torch.randn(4, 3, 8, 8)
Y = blk(X)
print(f'得到通道数为 3 + 2 * 10 = 23 的输出 : {Y.shape}')
def transition_block(input_channels, num_channels):
return nn.Sequential(
nn.BatchNorm2d(input_channels), nn.ReLU(),
nn.Conv2d(input_channels, num_channels, kernel_size=1),
nn.AvgPool2d(kernel_size=2, stride=2))
blk = transition_block(23, 10)
print(f'输出的通道数减为 10，高和宽均减半 : {blk(Y).shape}')
b1 = 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为当前的通道数
num_channels, growth_rate = 64, 32
num_convs_in_dense_blocks = [4, 4, 4, 4]
blks = []
for i, num_convs in enumerate(num_convs_in_dense_blocks):
blks.append(DenseBlock(num_convs, num_channels, growth_rate))
# 上一个稠密块的输出通道数
num_channels += num_convs * growth_rate
# 在稠密块之间添加一个转换层，使通道数量减半
if i != len(num_convs_in_dense_blocks) - 1:
blks.append(transition_block(num_channels, num_channels // 2))
num_channels = num_channels // 2
net = nn.Sequential(
b1, *blks,
nn.BatchNorm2d(num_channels), nn.ReLU(),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(num_channels, 10))
lr, num_epochs, batch_size = 0.1, 5, 256
train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=96)
train(net, train_iter, test_iter, num_epochs, lr, mydevice)
文中部分知识参考：B 站 —— 跟李沐学AI；百度百科
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