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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_inlinemydevice = 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 Truereturn 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] * ndef 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, fmtsdef add(self, x, y):# Add multiple data points into the figureif not hasattr(y, "__len__"):y = [y]n = len(y)if not hasattr(x, "__len__"):x = [x] * nif 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 = Noneself.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 += Xreturn 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 blkb2 = 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.utils import data
import matplotlib.pyplot as plt
from matplotlib_inline import backend_inlinemydevice = 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 Truereturn 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] * ndef 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, fmtsdef add(self, x, y):# Add multiple data points into the figureif not hasattr(y, "__len__"):y = [y]n = len(y)if not hasattr(x, "__len__"):x = [x] * nif 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 Xblk = 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 // 2net = 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