● 【第一部分】 問題總結
● 【第二部分】 代碼練習
MobileNetV1
import torch
import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt import numpy as np import torch.optim as optim import class Block(nn.Module): '''Depthwise conv + Pointwise conv''' def __init__(self, in_planes, out_planes, stride=1): super(Block, self).__init__() # Depthwise 卷積，3*3 的卷積核，分為 in_planes，即各層單獨進行卷積 self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) # Pointwise 卷積，1*1 的卷積核 self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn2 = nn.BatchNorm2d(out_planes) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) return out #創建DataLoader # 使用GPU訓練，可以在菜單 "代碼執行工具" -> "更改運行時類型" 里進行設置 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2) testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2) #創建 MobileNetV1 網絡 class MobileNetV1(nn.Module): # (128,2) means conv planes=128, stride=2 cfg = [(64,1), (128,2), (128,1), (256,2), (256,1), (512,2), (512,1), (1024,2), (1024,1)] def __init__(self, num_classes=10): super(MobileNetV1, self).__init__() self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(32) self.layers = self._make_layers(in_planes=32) self.linear = nn.Linear(1024, num_classes) def _make_layers(self, in_planes): layers = [] for x in self.cfg: out_planes = x[0] stride = x[1] layers.append(Block(in_planes, out_planes, stride)) in_planes = out_planes return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layers(out) out = F.avg_pool2d(out, 2) out = out.view(out.size(0), -1) out = self.linear(out) return out #實例化網絡 # 網絡放到GPU上 net = MobileNetV1().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) #模型訓練 for epoch in range(10): # 重復多輪訓練 for i, (inputs, labels) in enumerate(trainloader): inputs = inputs.to(device) labels = labels.to(device) # 優化器梯度歸零 optimizer.zero_grad() # 正向傳播 + 反向傳播 + 優化 outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 輸出統計信息 if i % 100 == 0: print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item())) print('Finished Training') #模型測試 correct = 0 total = 0 for data in testloader: images, labels = data images, labels = images.to(device), labels.to(device) outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %.2f %%' % ( 100 * correct / total))
MobileNetV2
import torch
import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt import numpy as np import torch.optim as optim class Block(nn.Module): '''expand + depthwise + pointwise''' def __init__(self, in_planes, out_planes, expansion, stride): super(Block, self).__init__() self.stride = stride # 通過 expansion 增大 feature map 的數量 planes = expansion * in_planes self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, groups=planes, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn3 = nn.BatchNorm2d(out_planes) # 步長為 1 時，如果 in 和 out 的 feature map 通道不同，用一個卷積改變通道數 if stride == 1 and in_planes != out_planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(out_planes)) # 步長為 1 時，如果 in 和 out 的 feature map 通道相同，直接返回輸入 if stride == 1 and in_planes == out_planes: self.shortcut = nn.Sequential() def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) # 步長為1，加 shortcut 操作 if self.stride == 1: return out + self.shortcut(x) # 步長為2，直接輸出 else: return out #創建 MobileNetV2 網絡 class MobileNetV2(nn.Module): # (expansion, out_planes, num_blocks, stride) cfg = [(1, 16, 1, 1), (6, 24, 2, 1), (6, 32, 3, 2), (6, 64, 4, 2), (6, 96, 3, 1), (6, 160, 3, 2), (6, 320, 1, 1)] def __init__(self, num_classes=10): super(MobileNetV2, self).__init__() self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(32) self.layers = self._make_layers(in_planes=32) self.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False) self.bn2 = nn.BatchNorm2d(1280) self.linear = nn.Linear(1280, num_classes) def _make_layers(self, in_planes): layers = [] for expansion, out_planes, num_blocks, stride in self.cfg: strides = [stride] + [1]*(num_blocks-1) for stride in strides: layers.append(Block(in_planes, out_planes, expansion, stride)) in_planes = out_planes return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layers(out) out = F.relu(self.bn2(self.conv2(out))) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) return out #創建 DataLoader # 使用GPU訓練，可以在菜單 "代碼執行工具" -> "更改運行時類型" 里進行設置 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2) testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2) #實例化網絡 # 網絡放到GPU上 net = MobileNetV2().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) #模型訓練 for epoch in range(10): # 重復多輪訓練 for i, (inputs, labels) in enumerate(trainloader): inputs = inputs.to(device) labels = labels.to(device) # 優化器梯度歸零 optimizer.zero_grad() # 正向傳播 + 反向傳播 + 優化 outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 輸出統計信息 if i % 100 == 0: print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item())) print('Finished Training') #模型測試 correct = 0 total = 0 for data in testloader: images, labels = data images, labels = images.to(device), labels.to(device) outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %.2f %%' % ( 100 * correct / total))
