这篇教程PyTorch一小时掌握之迁移学习篇写得很实用,希望能帮到您。
概述迁移学习 (Transfer Learning) 是把已学训练好的模型参数用作新训练模型的起始参数. 迁移学习是深度学习中非常重要和常用的一个策略.
为什么使用迁移学习
更好的结果迁移学习 (Transfer Learning) 可以帮助我们得到更好的结果. 当我们手上的数据比较少的时候, 训练非常容易造成过拟合的现象. 使用迁移学习可以帮助我们通过更少的训练数据达到更好的效果. 使得模型的泛化能力更强, 训练过程更稳定.
节省时间迁移学习 (Transfer Learning) 可以帮助我们节省时间. 通过迁徙学习, 我们站在了巨人的肩膀上. 利用前人花大量时间训练好的参数, 能帮助我们在模型的训练上节省大把的时间.
加载模型首先我们需要加载模型, 并指定层数. 常用的模型有: - VGG
- ResNet
- SqueezeNet
- DenseNet
- Inception
- GoogLeNet
- ShuffleNet
- MobileNet
官网 API
ResNet152我们将使用 ResNet 152 和 CIFAR 100 来举例.
冻层实现def set_parameter_requires_grad(model, feature_extracting): """ 是否保留梯度, 实现冻层 :param model: 模型 :param feature_extracting: 是否冻层 :return: 无返回值 """ if feature_extracting: # 如果冻层 for param in model.parameters(): # 遍历每个权重参数 param.requires_grad = False # 保留梯度为False
模型初始化def initialize_model(model_name, num_classes, feature_exact, use_pretrained=True): """ 初始化模型 :param model_name: 模型名字 :param num_classes: 类别数 :param feature_exact: 是否冻层 :param use_pretrained: 是否下载模型 :return: 返回模型, """ model_ft = None if model_name == "resnet": """Resnet152""" # 加载模型 model_ft = models.resnet152(pretrained=use_pretrained) # 下载参数 set_parameter_requires_grad(model_ft, feature_exact) # 冻层 # 修改全连接层 num_features = model_ft.fc.in_features model_ft.fc = torch.nn.Sequential( torch.nn.Linear(num_features, num_classes), torch.nn.LogSoftmax(dim=1) ) # 返回初始化好的模型 return model_ft
获取需更新参数def parameter_to_update(model): """ 获取需要更新的参数 :param model: 模型 :return: 需要更新的参数列表 """ print("Params to learn") param_array = model.parameters() if feature_exact: param_array = [] for name, param, in model.named_parameters(): if param.requires_grad == True: param_array.append(param) print("/t", name) else: for name, param, in model.named_parameters(): if param.requires_grad == True: print("/t", name) return param_array
训练模型def train_model(model, dataloaders, citerion, optimizer, filename, num_epochs=25): # 获取起始时间 since = time.time() # 初始化参数 best_acc = 0 val_acc_history = [] train_acc_history = [] train_losses = [] valid_losses = [] LRs = [optimizer.param_groups[0]["lr"]] best_model_weights = copy.deepcopy(model.state_dict()) for epoch in range(num_epochs): print("Epoch {}/{}".format(epoch, num_epochs - 1)) print("-" * 10) # 训练和验证 for phase in ["train", "valid"]: if phase == "train": model.train() # 训练 else: model.eval() # 验证 running_loss = 0.0 running_corrects = 0 # 遍历数据 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # 梯度清零 optimizer.zero_grad() # 只有训练的时候计算和更新梯度 with torch.set_grad_enabled(phase == "train"): outputs = model(inputs) _, preds = torch.max(outputs, 1) # 计算损失 loss = criterion(outputs, labels) # 训练阶段更新权重 if phase == "train": loss.backward() optimizer.step() # 计算损失 running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / len(dataloaders[phase].dataset) epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset) time_eplased = time.time() - since print("Time elapsed {:.0f}m {:.0f}s".format(time_eplased // 60, time_eplased % 60)) print("{} Loss: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, epoch_acc)) # 得到最好的模型 if phase == "valid" and epoch_acc > best_acc: best_acc = epoch_acc best_model_weights = copy.deepcopy(model.state_dict()) state = { "state_dict": model.state_dict(), "best_acc": best_acc, "optimizer": optimizer.state_dict(), } torch.save(state, filename) if phase == "valid": val_acc_history.append(epoch_acc) valid_losses.append(epoch_loss) scheduler.step(epoch_loss) if phase == "train": train_acc_history.append(epoch_acc) train_losses.append(epoch_loss) print("Optimizer learning rate: {:.7f}".format(optimizer.param_groups[0]["lr"])) LRs.append(optimizer.param_groups[0]["lr"]) print() time_eplased = time.time() - since print("Training complete in {:.0f}m {:.0f}s".format(time_eplased // 60, time_eplased % 60)) print("Best val Acc: {:4f}".format(best_acc)) # 训练完后用最好的一次当做模型最终的结果 model.load_state_dict(best_model_weights) # 返回 return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs
获取数据def get_data(): """获取数据""" # 获取测试集 train = torchvision.datasets.CIFAR100(root="./mnt", train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), # 转换成张量 torchvision.transforms.Normalize((0.1307,), (0.3081,)) # 标准化 ])) train_loader = DataLoader(train, batch_size=batch_size) # 分割测试集 # 获取测试集 test = torchvision.datasets.CIFAR100(root="./mnt", train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), # 转换成张量 torchvision.transforms.Normalize((0.1307,), (0.3081,)) # 标准化 ])) test_loader = DataLoader(test, batch_size=batch_size) # 分割训练 data_loader = {"train": train_loader, "valid": test_loader} # 返回分割好的训练集和测试集 return data_loader
完整代码完整代码: import copyimport torchfrom torch.utils.data import DataLoaderimport timefrom torchsummary import summaryimport torchvisionimport torchvision.models as modelsdef set_parameter_requires_grad(model, feature_extracting): """ 是否保留梯度, 实现冻层 :param model: 模型 :param feature_extracting: 是否冻层 :return: 无返回值 """ if feature_extracting: # 如果冻层 for param in model.parameters(): # 遍历每个权重参数 param.requires_grad = False # 保留梯度为Falsedef initialize_model(model_name, num_classes, feature_exact, use_pretrained=True): """ 初始化模型 :param model_name: 模型名字 :param num_classes: 类别数 :param feature_exact: 是否冻层 :param use_pretrained: 是否下载模型 :return: 返回模型, """ model_ft = None if model_name == "resnet": """Resnet152""" # 加载模型 model_ft = models.resnet152(pretrained=use_pretrained) # 下载参数 set_parameter_requires_grad(model_ft, feature_exact) # 冻层 # 修改全连接层 num_features = model_ft.fc.in_features model_ft.fc = torch.nn.Sequential( torch.nn.Linear(num_features, num_classes), torch.nn.LogSoftmax(dim=1) ) # 返回初始化好的模型 return model_ftdef parameter_to_update(model): """ 获取需要更新的参数 :param model: 模型 :return: 需要更新的参数列表 """ print("Params to learn") param_array = model.parameters() if feature_exact: param_array = [] for name, param, in model.named_parameters(): if param.requires_grad == True: param_array.append(param) print("/t", name) else: for name, param, in model.named_parameters(): if param.requires_grad == True: print("/t", name) return param_arraydef train_model(model, dataloaders, citerion, optimizer, filename, num_epochs=25): # 获取起始时间 since = time.time() # 初始化参数 best_acc = 0 val_acc_history = [] train_acc_history = [] train_losses = [] valid_losses = [] LRs = [optimizer.param_groups[0]["lr"]] best_model_weights = copy.deepcopy(model.state_dict()) for epoch in range(num_epochs): print("Epoch {}/{}".format(epoch, num_epochs - 1)) print("-" * 10) # 训练和验证 for phase in ["train", "valid"]: if phase == "train": model.train() # 训练 else: model.eval() # 验证 running_loss = 0.0 running_corrects = 0 # 遍历数据 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # 梯度清零 optimizer.zero_grad() # 只有训练的时候计算和更新梯度 with torch.set_grad_enabled(phase == "train"): outputs = model(inputs) _, preds = torch.max(outputs, 1) # 计算损失 loss = criterion(outputs, labels) # 训练阶段更新权重 if phase == "train": loss.backward() optimizer.step() # 计算损失 running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / len(dataloaders[phase].dataset) epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset) time_eplased = time.time() - since print("Time elapsed {:.0f}m {:.0f}s".format(time_eplased // 60, time_eplased % 60)) print("{} Loss: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, epoch_acc)) # 得到最好的模型 if phase == "valid" and epoch_acc > best_acc: best_acc = epoch_acc best_model_weights = copy.deepcopy(model.state_dict()) state = { "state_dict": model.state_dict(), "best_acc": best_acc, "optimizer": optimizer.state_dict(), } torch.save(state, filename) if phase == "valid": val_acc_history.append(epoch_acc) valid_losses.append(epoch_loss) scheduler.step(epoch_loss) if phase == "train": train_acc_history.append(epoch_acc) train_losses.append(epoch_loss) print("Optimizer learning rate: {:.7f}".format(optimizer.param_groups[0]["lr"])) LRs.append(optimizer.param_groups[0]["lr"]) print() time_eplased = time.time() - since print("Training complete in {:.0f}m {:.0f}s".format(time_eplased // 60, time_eplased % 60)) print("Best val Acc: {:4f}".format(best_acc)) # 训练完后用最好的一次当做模型最终的结果 model.load_state_dict(best_model_weights) # 返回 return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRsdef get_data(): """获取数据""" # 获取测试集 train = torchvision.datasets.CIFAR100(root="./mnt", train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), # 转换成张量 torchvision.transforms.Normalize((0.1307,), (0.3081,)) # 标准化 ])) train_loader = DataLoader(train, batch_size=batch_size) # 分割测试集 # 获取测试集 test = torchvision.datasets.CIFAR100(root="./mnt", train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), # 转换成张量 torchvision.transforms.Normalize((0.1307,), (0.3081,)) # 标准化 ])) test_loader = DataLoader(test, batch_size=batch_size) # 分割训练 data_loader = {"train": train_loader, "valid": test_loader} # 返回分割好的训练集和测试集 return data_loader# 超参数filename = "checkpoint.pth" # 模型保存feature_exact = True # 冻层num_classes = 100 # 输出的类别数batch_size = 1024 # 一次训练的样本数目iteration_num = 10 # 迭代次数# 获取模型resnet152 = initialize_model( model_name="resnet", num_classes=num_classes, feature_exact=feature_exact, use_pretrained=True)# 是否使用GPU训练use_cuda = torch.cuda.is_available()device = torch.device("cuda" if use_cuda else "cpu")if use_cuda: resnet152.cuda() # GPU 计算print("是否使用 GPU 加速:", use_cuda)# 输出网络结构print(summary(resnet152, (3, 32, 32)))# 训练参数params_to_update = parameter_to_update(resnet152)# 优化器optimizer = torch.optim.Adam(params_to_update, lr=0.01)scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) # 学习率每10个epoch衰减到原来的1/10criterion = torch.nn.NLLLoss()if __name__ == "__main__": data_loader = get_data() resnet152, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model( model=resnet152, dataloaders=data_loader, citerion=criterion, optimizer=optimizer, num_epochs=iteration_num, filename=filename ) 输出结果: 是否使用 GPU 加速: True ---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 16, 16] 9,408 BatchNorm2d-2 [-1, 64, 16, 16] 128 ReLU-3 [-1, 64, 16, 16] 0 MaxPool2d-4 [-1, 64, 8, 8] 0 Conv2d-5 [-1, 64, 8, 8] 4,096 BatchNorm2d-6 [-1, 64, 8, 8] 128 ReLU-7 [-1, 64, 8, 8] 0 Conv2d-8 [-1, 64, 8, 8] 36,864 BatchNorm2d-9 [-1, 64, 8, 8] 128 ReLU-10 [-1, 64, 8, 8] 0 Conv2d-11 [-1, 256, 8, 8] 16,384 BatchNorm2d-12 [-1, 256, 8, 8] 512 Conv2d-13 [-1, 256, 8, 8] 16,384 BatchNorm2d-14 [-1, 256, 8, 8] 512 ReLU-15 [-1, 256, 8, 8] 0 Bottleneck-16 [-1, 256, 8, 8] 0 Conv2d-17 [-1, 64, 8, 8] 16,384 BatchNorm2d-18 [-1, 64, 8, 8] 128 ReLU-19 [-1, 64, 8, 8] 0 Conv2d-20 [-1, 64, 8, 8] 36,864 BatchNorm2d-21 [-1, 64, 8, 8] 128 ReLU-22 [-1, 64, 8, 8] 0 Conv2d-23 [-1, 256, 8, 8] 16,384 BatchNorm2d-24 [-1, 256, 8, 8] 512 ReLU-25 [-1, 256, 8, 8] 0 Bottleneck-26 [-1, 256, 8, 8] 0 Conv2d-27 [-1, 64, 8, 8] 16,384 BatchNorm2d-28 [-1, 64, 8, 8] 128 ReLU-29 [-1, 64, 8, 8] 0 Conv2d-30 [-1, 64, 8, 8] 36,864 BatchNorm2d-31 [-1, 64, 8, 8] 128 ReLU-32 [-1, 64, 8, 8] 0 Conv2d-33 [-1, 256, 8, 8] 16,384 BatchNorm2d-34 [-1, 256, 8, 8] 512 ReLU-35 [-1, 256, 8, 8] 0 Bottleneck-36 [-1, 256, 8, 8] 0 Conv2d-37 [-1, 128, 8, 8] 32,768 BatchNorm2d-38 [-1, 128, 8, 8] 256 ReLU-39 [-1, 128, 8, 8] 0 Conv2d-40 [-1, 128, 4, 4] 147,456 BatchNorm2d-41 [-1, 128, 4, 4] 256 ReLU-42 [-1, 128, 4, 4] 0 Conv2d-43 [-1, 512, 4, 4] 65,536 BatchNorm2d-44 [-1, 512, 4, 4] 1,024 Conv2d-45 [-1, 512, 4, 4] 131,072 BatchNorm2d-46 [-1, 512, 4, 4] 1,024 ReLU-47 [-1, 512, 4, 4] 0 Bottleneck-48 [-1, 512, 4, 4] 0 Conv2d-49 [-1, 128, 4, 4] 65,536 BatchNorm2d-50 [-1, 128, 4, 4] 256 ReLU-51 [-1, 128, 4, 4] 0 Conv2d-52 [-1, 128, 4, 4] 147,456 BatchNorm2d-53 [-1, 128, 4, 4] 256 ReLU-54 [-1, 128, 4, 4] 0 Conv2d-55 [-1, 512, 4, 4] 65,536 BatchNorm2d-56 [-1, 512, 4, 4] 1,024 ReLU-57 [-1, 512, 4, 4] 0 Bottleneck-58 [-1, 512, 4, 4] 0 Conv2d-59 [-1, 128, 4, 4] 65,536 BatchNorm2d-60 [-1, 128, 4, 4] 256 ReLU-61 [-1, 128, 4, 4] 0 Conv2d-62 [-1, 128, 4, 4] 147,456 BatchNorm2d-63 [-1, 128, 4, 4] 256 ReLU-64 [-1, 128, 4, 4] 0 Conv2d-65 [-1, 512, 4, 4] 65,536 BatchNorm2d-66 [-1, 512, 4, 4] 1,024 ReLU-67 [-1, 512, 4, 4] 0 Bottleneck-68 [-1, 512, 4, 4] 0 Conv2d-69 [-1, 128, 4, 4] 65,536 BatchNorm2d-70 [-1, 128, 4, 4] 256 ReLU-71 [-1, 128, 4, 4] 0 Conv2d-72 [-1, 128, 4, 4] 147,456 BatchNorm2d-73 [-1, 128, 4, 4] 256 ReLU-74 [-1, 128, 4, 4] 0 Conv2d-75 [-1, 512, 4, 4] 65,536 BatchNorm2d-76 [-1, 512, 4, 4] 1,024 ReLU-77 [-1, 512, 4, 4] 0 Bottleneck-78 [-1, 512, 4, 4] 0 Conv2d-79 [-1, 128, 4, 4] 65,536 BatchNorm2d-80 [-1, 128, 4, 4] 256 ReLU-81 [-1, 128, 4, 4] 0 Conv2d-82 [-1, 128, 4, 4] 147,456 BatchNorm2d-83 [-1, 128, 4, 4] 256 ReLU-84 [-1, 128, 4, 4] 0 Conv2d-85 [-1, 512, 4, 4] 65,536 BatchNorm2d-86 [-1, 512, 4, 4] 1,024 ReLU-87 [-1, 512, 4, 4] 0 Bottleneck-88 [-1, 512, 4, 4] 0 Conv2d-89 [-1, 128, 4, 4] 65,536 BatchNorm2d-90 [-1, 128, 4, 4] 256 ReLU-91 [-1, 128, 4, 4] 0 Conv2d-92 [-1, 128, 4, 4] 147,456 BatchNorm2d-93 [-1, 128, 4, 4] 256 ReLU-94 [-1, 128, 4, 4] 0 Conv2d-95 [-1, 512, 4, 4] 65,536 BatchNorm2d-96 [-1, 512, 4, 4] 1,024 ReLU-97 [-1, 512, 4, 4] 0 Bottleneck-98 [-1, 512, 4, 4] 0 Conv2d-99 [-1, 128, 4, 4] 65,536 BatchNorm2d-100 [-1, 128, 4, 4] 256 ReLU-101 [-1, 128, 4, 4] 0 Conv2d-102 [-1, 128, 4, 4] 147,456 BatchNorm2d-103 [-1, 128, 4, 4] 256 ReLU-104 [-1, 128, 4, 4] 0 Conv2d-105 [-1, 512, 4, 4] 65,536 BatchNorm2d-106 [-1, 512, 4, 4] 1,024 ReLU-107 [-1, 512, 4, 4] 0 Bottleneck-108 [-1, 512, 4, 4] 0 Conv2d-109 [-1, 128, 4, 4] 65,536 BatchNorm2d-110 [-1, 128, 4, 4] 256 ReLU-111 [-1, 128, 4, 4] 0 Conv2d-112 [-1, 128, 4, 4] 147,456 BatchNorm2d-113 [-1, 128, 4, 4] 256 ReLU-114 [-1, 128, 4, 4] 0 Conv2d-115 [-1, 512, 4, 4] 65,536 BatchNorm2d-116 [-1, 512, 4, 4] 1,024 ReLU-117 [-1, 512, 4, 4] 0 Bottleneck-118 [-1, 512, 4, 4] 0 Conv2d-119 [-1, 256, 4, 4] 131,072 BatchNorm2d-120 [-1, 256, 4, 4] 512 ReLU-121 [-1, 256, 4, 4] 0 Conv2d-122 [-1, 256, 2, 2] 589,824 BatchNorm2d-123 [-1, 256, 2, 2] 512 ReLU-124 [-1, 256, 2, 2] 0 Conv2d-125 [-1, 1024, 2, 2] 262,144 BatchNorm2d-126 [-1, 1024, 2, 2] 2,048 Conv2d-127 [-1, 1024, 2, 2] 524,288 BatchNorm2d-128 [-1, 1024, 2, 2] 2,048 ReLU-129 [-1, 1024, 2, 2] 0 Bottleneck-130 [-1, 1024, 2, 2] 0 Conv2d-131 [-1, 256, 2, 2] 262,144 BatchNorm2d-132 [-1, 256, 2, 2] 512 ReLU-133 [-1, 256, 2, 2] 0 Conv2d-134 [-1, 256, 2, 2] 589,824 BatchNorm2d-135 [-1, 256, 2, 2] 512 ReLU-136 [-1, 256, 2, 2] 0 Conv2d-137 [-1, 1024, 2, 2] 262,144 BatchNorm2d-138 [-1, 1024, 2, 2] 2,048 ReLU-139 [-1, 1024, 2, 2] 0 Bottleneck-140 [-1, 1024, 2, 2] 0 Conv2d-141 [-1, 256, 2, 2] 262,144 BatchNorm2d-142 [-1, 256, 2, 2] 512 ReLU-143 [-1, 256, 2, 2] 0 Conv2d-144 [-1, 256, 2, 2] 589,824 BatchNorm2d-145 [-1, 256, 2, 2] 512 ReLU-146 [-1, 256, 2, 2] 0 Conv2d-147 [-1, 1024, 2, 2] 262,144 BatchNorm2d-148 [-1, 1024, 2, 2] 2,048 ReLU-149 [-1, 1024, 2, 2] 0 Bottleneck-150 [-1, 1024, 2, 2] 0 Conv2d-151 [-1, 256, 2, 2] 262,144 BatchNorm2d-152 [-1, 256, 2, 2] 512 ReLU-153 [-1, 256, 2, 2] 0 Conv2d-154 [-1, 256, 2, 2] 589,824 BatchNorm2d-155 [-1, 256, 2, 2] 512 ReLU-156 [-1, 256, 2, 2] 0 Conv2d-157 [-1, 1024, 2, 2] 262,144 BatchNorm2d-158 [-1, 1024, 2, 2] 2,048 ReLU-159 [-1, 1024, 2, 2] 0 Bottleneck-160 [-1, 1024, 2, 2] 0 Conv2d-161 [-1, 256, 2, 2] 262,144 BatchNorm2d-162 [-1, 256, 2, 2] 512 ReLU-163 [-1, 256, 2, 2] 0 Conv2d-164 [-1, 256, 2, 2] 589,824 BatchNorm2d-165 [-1, 256, 2, 2] 512 ReLU-166 [-1, 256, 2, 2] 0 Conv2d-167 [-1, 1024, 2, 2] 262,144 BatchNorm2d-168 [-1, 1024, 2, 2] 2,048 ReLU-169 [-1, 1024, 2, 2] 0 Bottleneck-170 [-1, 1024, 2, 2] 0 Conv2d-171 [-1, 256, 2, 2] 262,144 BatchNorm2d-172 [-1, 256, 2, 2] 512 ReLU-173 [-1, 256, 2, 2] 0 Conv2d-174 [-1, 256, 2, 2] 589,824 BatchNorm2d-175 [-1, 256, 2, 2] 512 ReLU-176 [-1, 256, 2, 2] 0 Conv2d-177 [-1, 1024, 2, 2] 262,144 BatchNorm2d-178 [-1, 1024, 2, 2] 2,048 ReLU-179 [-1, 1024, 2, 2] 0 Bottleneck-180 [-1, 1024, 2, 2] 0 Conv2d-181 [-1, 256, 2, 2] 262,144 BatchNorm2d-182 [-1, 256, 2, 2] 512 ReLU-183 [-1, 256, 2, 2] 0 Conv2d-184 [-1, 256, 2, 2] 589,824 BatchNorm2d-185 [-1, 256, 2, 2] 512 ReLU-186 [-1, 256, 2, 2] 0 Conv2d-187 [-1, 1024, 2, 2] 262,144 BatchNorm2d-188 [-1, 1024, 2, 2] 2,048 ReLU-189 [-1, 1024, 2, 2] 0 Bottleneck-190 [-1, 1024, 2, 2] 0 Conv2d-191 [-1, 256, 2, 2] 262,144 BatchNorm2d-192 [-1, 256, 2, 2] 512 ReLU-193 [-1, 256, 2, 2] 0 Conv2d-194 [-1, 256, 2, 2] 589,824 BatchNorm2d-195 [-1, 256, 2, 2] 512 ReLU-196 [-1, 256, 2, 2] 0 Conv2d-197 [-1, 1024, 2, 2] 262,144 BatchNorm2d-198 [-1, 1024, 2, 2] 2,048 ReLU-199 [-1, 1024, 2, 2] 0 Bottleneck-200 [-1, 1024, 2, 2] 0 Conv2d-201 [-1, 256, 2, 2] 262,144 BatchNorm2d-202 [-1, 256, 2, 2] 512 ReLU-203 [-1, 256, 2, 2] 0 Conv2d-204 [-1, 256, 2, 2] 589,824 BatchNorm2d-205 [-1, 256, 2, 2] 512 ReLU-206 [-1, 256, 2, 2] 0 Conv2d-207 [-1, 1024, 2, 2] 262,144 BatchNorm2d-208 [-1, 1024, 2, 2] 2,048 ReLU-209 [-1, 1024, 2, 2] 0 Bottleneck-210 [-1, 1024, 2, 2] 0 Conv2d-211 [-1, 256, 2, 2] 262,144 BatchNorm2d-212 [-1, 256, 2, 2] 512 ReLU-213 [-1, 256, 2, 2] 0 Conv2d-214 [-1, 256, 2, 2] 589,824 BatchNorm2d-215 [-1, 256, 2, 2] 512 ReLU-216 [-1, 256, 2, 2] 0 Conv2d-217 [-1, 1024, 2, 2] 262,144 BatchNorm2d-218 [-1, 1024, 2, 2] 2,048 ReLU-219 [-1, 1024, 2, 2] 0 Bottleneck-220 [-1, 1024, 2, 2] 0 Conv2d-221 [-1, 256, 2, 2] 262,144 BatchNorm2d-222 [-1, 256, 2, 2] 512 ReLU-223 [-1, 256, 2, 2] 0 Conv2d-224 [-1, 256, 2, 2] 589,824 BatchNorm2d-225 [-1, 256, 2, 2] 512 ReLU-226 [-1, 256, 2, 2] 0 Conv2d-227 [-1, 1024, 2, 2] 262,144 BatchNorm2d-228 [-1, 1024, 2, 2] 2,048 ReLU-229 [-1, 1024, 2, 2] 0 Bottleneck-230 [-1, 1024, 2, 2] 0 Conv2d-231 [-1, 256, 2, 2] 262,144 BatchNorm2d-232 [-1, 256, 2, 2] 512 ReLU-233 [-1, 256, 2, 2] 0 Conv2d-234 [-1, 256, 2, 2] 589,824 BatchNorm2d-235 [-1, 256, 2, 2] 512 ReLU-236 [-1, 256, 2, 2] 0 Conv2d-237 [-1, 1024, 2, 2] 262,144 BatchNorm2d-238 [-1, 1024, 2, 2] 2,048 ReLU-239 [-1, 1024, 2, 2] 0 Bottleneck-240 [-1, 1024, 2, 2] 0 Conv2d-241 [-1, 256, 2, 2] 262,144 BatchNorm2d-242 [-1, 256, 2, 2] 512 ReLU-243 [-1, 256, 2, 2] 0 Conv2d-244 [-1, 256, 2, 2] 589,824 BatchNorm2d-245 [-1, 256, 2, 2] 512 ReLU-246 [-1, 256, 2, 2] 0 Conv2d-247 [-1, 1024, 2, 2] 262,144 BatchNorm2d-248 [-1, 1024, 2, 2] 2,048 ReLU-249 [-1, 1024, 2, 2] 0 Bottleneck-250 [-1, 1024, 2, 2] 0 Conv2d-251 [-1, 256, 2, 2] 262,144 BatchNorm2d-252 [-1, 256, 2, 2] 512 ReLU-253 [-1, 256, 2, 2] 0 Conv2d-254 [-1, 256, 2, 2] 589,824 BatchNorm2d-255 [-1, 256, 2, 2] 512 ReLU-256 [-1, 256, 2, 2] 0 Conv2d-257 [-1, 1024, 2, 2] 262,144 BatchNorm2d-258 [-1, 1024, 2, 2] 2,048 ReLU-259 [-1, 1024, 2, 2] 0 Bottleneck-260 [-1, 1024, 2, 2] 0 Conv2d-261 [-1, 256, 2, 2] 262,144 BatchNorm2d-262 [-1, 256, 2, 2] 512 ReLU-263 [-1, 256, 2, 2] 0 Conv2d-264 [-1, 256, 2, 2] 589,824 BatchNorm2d-265 [-1, 256, 2, 2] 512 ReLU-266 [-1, 256, 2, 2] 0 Conv2d-267 [-1, 1024, 2, 2] 262,144 BatchNorm2d-268 [-1, 1024, 2, 2] 2,048 ReLU-269 [-1, 1024, 2, 2] 0 Bottleneck-270 [-1, 1024, 2, 2] 0 Conv2d-271 [-1, 256, 2, 2] 262,144 BatchNorm2d-272 [-1, 256, 2, 2] 512 ReLU-273 [-1, 256, 2, 2] 0 Conv2d-274 [-1, 256, 2, 2] 589,824 BatchNorm2d-275 [-1, 256, 2, 2] 512 ReLU-276 [-1, 256, 2, 2] 0 Conv2d-277 [-1, 1024, 2, 2] 262,144 BatchNorm2d-278 [-1, 1024, 2, 2] 2,048 ReLU-279 [-1, 1024, 2, 2] 0 Bottleneck-280 [-1, 1024, 2, 2] 0 Conv2d-281 [-1, 256, 2, 2] 262,144 BatchNorm2d-282 [-1, 256, 2, 2] 512 ReLU-283 [-1, 256, 2, 2] 0 Conv2d-284 [-1, 256, 2, 2] 589,824 BatchNorm2d-285 [-1, 256, 2, 2] 512 ReLU-286 [-1, 256, 2, 2] 0 Conv2d-287 [-1, 1024, 2, 2] 262,144 BatchNorm2d-288 [-1, 1024, 2, 2] 2,048 ReLU-289 [-1, 1024, 2, 2] 0 Bottleneck-290 [-1, 1024, 2, 2] 0 Conv2d-291 [-1, 256, 2, 2] 262,144 BatchNorm2d-292 [-1, 256, 2, 2] 512 ReLU-293 [-1, 256, 2, 2] 0 Conv2d-294 [-1, 256, 2, 2] 589,824 BatchNorm2d-295 [-1, 256, 2, 2] 512 ReLU-296 [-1, 256, 2, 2] 0 Conv2d-297 [-1, 1024, 2, 2] 262,144 BatchNorm2d-298 [-1, 1024, 2, 2] 2,048 ReLU-299 [-1, 1024, 2, 2] 0 Bottleneck-300 [-1, 1024, 2, 2] 0 Conv2d-301 [-1, 256, 2, 2] 262,144 BatchNorm2d-302 [-1, 256, 2, 2] 512 ReLU-303 [-1, 256, 2, 2] 0 Conv2d-304 [-1, 256, 2, 2] 589,824 BatchNorm2d-305 [-1, 256, 2, 2] 512 ReLU-306 [-1, 256, 2, 2] 0 Conv2d-307 [-1, 1024, 2, 2] 262,144 BatchNorm2d-308 [-1, 1024, 2, 2] 2,048 ReLU-309 [-1, 1024, 2, 2] 0 Bottleneck-310 [-1, 1024, 2, 2] 0 Conv2d-311 [-1, 256, 2, 2] 262,144 BatchNorm2d-312 [-1, 256, 2, 2] 512 ReLU-313 [-1, 256, 2, 2] 0 Conv2d-314 [-1, 256, 2, 2] 589,824 BatchNorm2d-315 [-1, 256, 2, 2] 512 ReLU-316 [-1, 256, 2, 2] 0 Conv2d-317 [-1, 1024, 2, 2] 262,144 BatchNorm2d-318 [-1, 1024, 2, 2] 2,048 ReLU-319 [-1, 1024, 2, 2] 0 Bottleneck-320 [-1, 1024, 2, 2] 0 Conv2d-321 [-1, 256, 2, 2] 262,144 BatchNorm2d-322 [-1, 256, 2, 2] 512 ReLU-323 [-1, 256, 2, 2] 0 Conv2d-324 [-1, 256, 2, 2] 589,824 BatchNorm2d-325 [-1, 256, 2, 2] 512 ReLU-326 [-1, 256, 2, 2] 0 Conv2d-327 [-1, 1024, 2, 2] 262,144 BatchNorm2d-328 [-1, 1024, 2, 2] 2,048 ReLU-329 [-1, 1024, 2, 2] 0 Bottleneck-330 [-1, 1024, 2, 2] 0 Conv2d-331 [-1, 256, 2, 2] 262,144 BatchNorm2d-332 [-1, 256, 2, 2] 512 ReLU-333 [-1, 256, 2, 2] 0 Conv2d-334 [-1, 256, 2, 2] 589,824 BatchNorm2d-335 [-1, 256, 2, 2] 512 ReLU-336 [-1, 256, 2, 2] 0 Conv2d-337 [-1, 1024, 2, 2] 262,144 BatchNorm2d-338 [-1, 1024, 2, 2] 2,048 ReLU-339 [-1, 1024, 2, 2] 0 Bottleneck-340 [-1, 1024, 2, 2] 0 Conv2d-341 [-1, 256, 2, 2] 262,144 BatchNorm2d-342 [-1, 256, 2, 2] 512 ReLU-343 [-1, 256, 2, 2] 0 Conv2d-344 [-1, 256, 2, 2] 589,824 BatchNorm2d-345 [-1, 256, 2, 2] 512 ReLU-346 [-1, 256, 2, 2] 0 Conv2d-347 [-1, 1024, 2, 2] 262,144 BatchNorm2d-348 [-1, 1024, 2, 2] 2,048 ReLU-349 [-1, 1024, 2, 2] 0 Bottleneck-350 [-1, 1024, 2, 2] 0 Conv2d-351 [-1, 256, 2, 2] 262,144 BatchNorm2d-352 [-1, 256, 2, 2] 512 ReLU-353 [-1, 256, 2, 2] 0 Conv2d-354 [-1, 256, 2, 2] 589,824 BatchNorm2d-355 [-1, 256, 2, 2] 512 ReLU-356 [-1, 256, 2, 2] 0 Conv2d-357 [-1, 1024, 2, 2] 262,144 BatchNorm2d-358 [-1, 1024, 2, 2] 2,048 ReLU-359 [-1, 1024, 2, 2] 0 Bottleneck-360 [-1, 1024, 2, 2] 0 Conv2d-361 [-1, 256, 2, 2] 262,144 BatchNorm2d-362 [-1, 256, 2, 2] 512 ReLU-363 [-1, 256, 2, 2] 0 Conv2d-364 [-1, 256, 2, 2] 589,824 BatchNorm2d-365 [-1, 256, 2, 2] 512 ReLU-366 [-1, 256, 2, 2] 0 Conv2d-367 [-1, 1024, 2, 2] 262,144 BatchNorm2d-368 [-1, 1024, 2, 2] 2,048 ReLU-369 [-1, 1024, 2, 2] 0 Bottleneck-370 [-1, 1024, 2, 2] 0 Conv2d-371 [-1, 256, 2, 2] 262,144 BatchNorm2d-372 [-1, 256, 2, 2] 512 ReLU-373 [-1, 256, 2, 2] 0 Conv2d-374 [-1, 256, 2, 2] 589,824 BatchNorm2d-375 [-1, 256, 2, 2] 512 ReLU-376 [-1, 256, 2, 2] 0 Conv2d-377 [-1, 1024, 2, 2] 262,144 BatchNorm2d-378 [-1, 1024, 2, 2] 2,048 ReLU-379 [-1, 1024, 2, 2] 0 Bottleneck-380 [-1, 1024, 2, 2] 0 Conv2d-381 [-1, 256, 2, 2] 262,144 BatchNorm2d-382 [-1, 256, 2, 2] 512 ReLU-383 [-1, 256, 2, 2] 0 Conv2d-384 [-1, 256, 2, 2] 589,824 BatchNorm2d-385 [-1, 256, 2, 2] 512 ReLU-386 [-1, 256, 2, 2] 0 Conv2d-387 [-1, 1024, 2, 2] 262,144 BatchNorm2d-388 [-1, 1024, 2, 2] 2,048 ReLU-389 [-1, 1024, 2, 2] 0 Bottleneck-390 [-1, 1024, 2, 2] 0 Conv2d-391 [-1, 256, 2, 2] 262,144 BatchNorm2d-392 [-1, 256, 2, 2] 512 ReLU-393 [-1, 256, 2, 2] 0 Conv2d-394 [-1, 256, 2, 2] 589,824 BatchNorm2d-395 [-1, 256, 2, 2] 512 ReLU-396 [-1, 256, 2, 2] 0 Conv2d-397 [-1, 1024, 2, 2] 262,144 BatchNorm2d-398 [-1, 1024, 2, 2] 2,048 ReLU-399 [-1, 1024, 2, 2] 0 Bottleneck-400 [-1, 1024, 2, 2] 0 Conv2d-401 [-1, 256, 2, 2] 262,144 BatchNorm2d-402 [-1, 256, 2, 2] 512 ReLU-403 [-1, 256, 2, 2] 0 Conv2d-404 [-1, 256, 2, 2] 589,824 BatchNorm2d-405 [-1, 256, 2, 2] 512 ReLU-406 [-1, 256, 2, 2] 0 Conv2d-407 [-1, 1024, 2, 2] 262,144 BatchNorm2d-408 [-1, 1024, 2, 2] 2,048 ReLU-409 [-1, 1024, 2, 2] 0 Bottleneck-410 [-1, 1024, 2, 2] 0 Conv2d-411 [-1, 256, 2, 2] 262,144 BatchNorm2d-412 [-1, 256, 2, 2] 512 ReLU-413 [-1, 256, 2, 2] 0 Conv2d-414 [-1, 256, 2, 2] 589,824 BatchNorm2d-415 [-1, 256, 2, 2] 512 ReLU-416 [-1, 256, 2, 2] 0 Conv2d-417 [-1, 1024, 2, 2] 262,144 BatchNorm2d-418 [-1, 1024, 2, 2] 2,048 ReLU-419 [-1, 1024, 2, 2] 0 Bottleneck-420 [-1, 1024, 2, 2] 0 Conv2d-421 [-1, 256, 2, 2] 262,144 BatchNorm2d-422 [-1, 256, 2, 2] 512 ReLU-423 [-1, 256, 2, 2] 0 Conv2d-424 [-1, 256, 2, 2] 589,824 BatchNorm2d-425 [-1, 256, 2, 2] 512 ReLU-426 [-1, 256, 2, 2] 0 Conv2d-427 [-1, 1024, 2, 2] 262,144 BatchNorm2d-428 [-1, 1024, 2, 2] 2,048 ReLU-429 [-1, 1024, 2, 2] 0 Bottleneck-430 [-1, 1024, 2, 2] 0 Conv2d-431 [-1, 256, 2, 2] 262,144 BatchNorm2d-432 [-1, 256, 2, 2] 512 ReLU-433 [-1, 256, 2, 2] 0 Conv2d-434 [-1, 256, 2, 2] 589,824 BatchNorm2d-435 [-1, 256, 2, 2] 512 ReLU-436 [-1, 256, 2, 2] 0 Conv2d-437 [-1, 1024, 2, 2] 262,144 BatchNorm2d-438 [-1, 1024, 2, 2] 2,048 ReLU-439 [-1, 1024, 2, 2] 0 Bottleneck-440 [-1, 1024, 2, 2] 0 Conv2d-441 [-1, 256, 2, 2] 262,144 BatchNorm2d-442 [-1, 256, 2, 2] 512 ReLU-443 [-1, 256, 2, 2] 0 Conv2d-444 [-1, 256, 2, 2] 589,824 BatchNorm2d-445 [-1, 256, 2, 2] 512 ReLU-446 [-1, 256, 2, 2] 0 Conv2d-447 [-1, 1024, 2, 2] 262,144 BatchNorm2d-448 [-1, 1024, 2, 2] 2,048 ReLU-449 [-1, 1024, 2, 2] 0 Bottleneck-450 [-1, 1024, 2, 2] 0 Conv2d-451 [-1, 256, 2, 2] 262,144 BatchNorm2d-452 [-1, 256, 2, 2] 512 ReLU-453 [-1, 256, 2, 2] 0 Conv2d-454 [-1, 256, 2, 2] 589,824 BatchNorm2d-455 [-1, 256, 2, 2] 512 ReLU-456 [-1, 256, 2, 2] 0 Conv2d-457 [-1, 1024, 2, 2] 262,144 BatchNorm2d-458 [-1, 1024, 2, 2] 2,048 ReLU-459 [-1, 1024, 2, 2] 0 Bottleneck-460 [-1, 1024, 2, 2] 0 Conv2d-461 [-1, 256, 2, 2] 262,144 BatchNorm2d-462 [-1, 256, 2, 2] 512 ReLU-463 [-1, 256, 2, 2] 0 Conv2d-464 [-1, 256, 2, 2] 589,824 BatchNorm2d-465 [-1, 256, 2, 2] 512 ReLU-466 [-1, 256, 2, 2] 0 Conv2d-467 [-1, 1024, 2, 2] 262,144 BatchNorm2d-468 [-1, 1024, 2, 2] 2,048 ReLU-469 [-1, 1024, 2, 2] 0 Bottleneck-470 [-1, 1024, 2, 2] 0 Conv2d-471 [-1, 256, 2, 2] 262,144 BatchNorm2d-472 [-1, 256, 2, 2] 512 ReLU-473 [-1, 256, 2, 2] 0 Conv2d-474 [-1, 256, 2, 2] 589,824 BatchNorm2d-475 [-1, 256, 2, 2] 512 ReLU-476 [-1, 256, 2, 2] 0 Conv2d-477 [-1, 1024, 2, 2] 262,144 BatchNorm2d-478 [-1, 1024, 2, 2] 2,048 ReLU-479 [-1, 1024, 2, 2] 0 Bottleneck-480 [-1, 1024, 2, 2] 0 Conv2d-481 [-1, 512, 2, 2] 524,288 BatchNorm2d-482 [-1, 512, 2, 2] 1,024 ReLU-483 [-1, 512, 2, 2] 0 Conv2d-484 [-1, 512, 1, 1] 2,359,296 BatchNorm2d-485 [-1, 512, 1, 1] 1,024 ReLU-486 [-1, 512, 1, 1] 0 Conv2d-487 [-1, 2048, 1, 1] 1,048,576 BatchNorm2d-488 [-1, 2048, 1, 1] 4,096 Conv2d-489 [-1, 2048, 1, 1] 2,097,152 BatchNorm2d-490 [-1, 2048, 1, 1] 4,096 ReLU-491 [-1, 2048, 1, 1] 0 Bottleneck-492 [-1, 2048, 1, 1] 0 Conv2d-493 [-1, 512, 1, 1] 1,048,576 BatchNorm2d-494 [-1, 512, 1, 1] 1,024 ReLU-495 [-1, 512, 1, 1] 0 Conv2d-496 [-1, 512, 1, 1] 2,359,296 BatchNorm2d-497 [-1, 512, 1, 1] 1,024 ReLU-498 [-1, 512, 1, 1] 0 Conv2d-499 [-1, 2048, 1, 1] 1,048,576 BatchNorm2d-500 [-1, 2048, 1, 1] 4,096 ReLU-501 [-1, 2048, 1, 1] 0 Bottleneck-502 [-1, 2048, 1, 1] 0 Conv2d-503 [-1, 512, 1, 1] 1,048,576 BatchNorm2d-504 [-1, 512, 1, 1] 1,024 ReLU-505 [-1, 512, 1, 1] 0 Conv2d-506 [-1, 512, 1, 1] 2,359,296 BatchNorm2d-507 [-1, 512, 1, 1] 1,024 ReLU-508 [-1, 512, 1, 1] 0 Conv2d-509 [-1, 2048, 1, 1] 1,048,576 BatchNorm2d-510 [-1, 2048, 1, 1] 4,096 ReLU-511 [-1, 2048, 1, 1] 0 Bottleneck-512 [-1, 2048, 1, 1] 0 AdaptiveAvgPool2d-513 [-1, 2048, 1, 1] 0 Linear-514 [-1, 100] 204,900 LogSoftmax-515 [-1, 100] 0 ================================================================ Total params: 58,348,708 Trainable params: 204,900 Non-trainable params: 58,143,808 ---------------------------------------------------------------- Input size (MB): 0.01 Forward/backward pass size (MB): 12.40 Params size (MB): 222.58 Estimated Total Size (MB): 234.99 ---------------------------------------------------------------- None Params to learn fc.0.weight fc.0.bias Files already downloaded and verified Files already downloaded and verified Epoch 0/9 ---------- Time elapsed 0m 21s train Loss: 7.5111 Acc: 0.1484 Time elapsed 0m 26s valid Loss: 3.7821 Acc: 0.2493 /usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose. warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) Optimizer learning rate: 0.0100000 Epoch 1/9 ---------- Time elapsed 0m 47s train Loss: 2.9405 Acc: 0.3109 Time elapsed 0m 52s valid Loss: 3.2014 Acc: 0.2739 Optimizer learning rate: 0.0100000 Epoch 2/9 ---------- Time elapsed 1m 12s train Loss: 2.5866 Acc: 0.3622 Time elapsed 1m 17s valid Loss: 3.2239 Acc: 0.2787 Optimizer learning rate: 0.0100000 Epoch 3/9 ---------- Time elapsed 1m 38s train Loss: 2.4077 Acc: 0.3969 Time elapsed 1m 43s valid Loss: 3.2608 Acc: 0.2811 Optimizer learning rate: 0.0100000 Epoch 4/9 ---------- Time elapsed 2m 4s train Loss: 2.2742 Acc: 0.4263 Time elapsed 2m 9s valid Loss: 3.4260 Acc: 0.2689 Optimizer learning rate: 0.0100000 Epoch 5/9 ---------- Time elapsed 2m 29s train Loss: 2.1942 Acc: 0.4434 Time elapsed 2m 34s valid Loss: 3.4697 Acc: 0.2760 Optimizer learning rate: 0.0100000 Epoch 6/9 ---------- Time elapsed 2m 54s train Loss: 2.1369 Acc: 0.4583 Time elapsed 2m 59s valid Loss: 3.5391 Acc: 0.2744 Optimizer learning rate: 0.0100000 Epoch 7/9 ---------- Time elapsed 3m 20s train Loss: 2.0382 Acc: 0.4771 Time elapsed 3m 24s valid Loss: 3.5992 Acc: 0.2721 Optimizer learning rate: 0.0100000 Epoch 8/9 ---------- Time elapsed 3m 45s train Loss: 1.9776 Acc: 0.4939 Time elapsed 3m 50s valid Loss: 3.7533 Acc: 0.2685 Optimizer learning rate: 0.0100000 Epoch 9/9 ---------- Time elapsed 4m 11s train Loss: 1.9309 Acc: 0.5035 Time elapsed 4m 16s valid Loss: 3.9663 Acc: 0.2558 Optimizer learning rate: 0.0100000 Training complete in 4m 16s Best val Acc: 0.281100
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