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PyTorch一小時掌握之遷移學習篇

概述

遷移學習 (Transfer Learning) 是把已學訓練好的模型參數用作新訓練模型的起始參數. 遷移學習是深度學習中非常重要和常用的一個策略.

為什么使用遷移學習

更好的結果

遷移學習 (Transfer Learning) 可以幫助我們得到更好的結果.

當我們手上的數據比較少的時候, 訓練非常容易造成過擬合的現象. 使用遷移學習可以幫助我們通過更少的訓練數據達到更好的效果. 使得模型的泛化能力更強, 訓練過程更穩定.

節省時間

遷移學習 (Transfer Learning) 可以幫助我們節省時間.

通過遷徙學習, 我們站在了巨人的肩膀上. 利用前人花大量時間訓練好的參數, 能幫助我們在模型的訓練上節省大把的時間.

加載模型

首先我們需要加載模型, 并指定層數. 常用的模型有:

VGG
ResNet
SqueezeNet
DenseNet
Inception
GoogLeNet
ShuffleNet
MobileNet

官網 API

ResNet152

我們將使用 ResNet 152 和 CIFAR 100 來舉例.

凍層實現

def set_parameter_requires_grad(model, feature_extracting):
    """
    是否保留梯度, 實現凍層
    :param model: 模型
    :param feature_extracting: 是否凍層
    :return: 無返回值
    """
    if feature_extracting:  # 如果凍層
        for param in model.parameters():  # 遍歷每個權重參數
            param.requires_grad = False  # 保留梯度為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: 類別數
    :param feature_exact: 是否凍層
    :param use_pretrained: 是否下載模型
    :return: 返回模型,
    """

    model_ft = None

    if model_name == "resnet":
        """Resnet152"""

        # 加載模型
        model_ft = models.resnet152(pretrained=use_pretrained)  # 下載參數
        set_parameter_requires_grad(model_ft, feature_exact)  # 凍層

        # 修改全連接層
        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

獲取需更新參數

def parameter_to_update(model):
    """
    獲取需要更新的參數
    :param model: 模型
    :return: 需要更新的參數列表
    """

    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

訓練模型

def train_model(model, dataloaders, citerion, optimizer, filename, num_epochs=25):
    # 獲取起始時間
    since = time.time()

    # 初始化參數
    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)

        # 訓練和驗證
        for phase in ["train", "valid"]:
            if phase == "train":
                model.train()  # 訓練
            else:
                model.eval()  # 驗證

            running_loss = 0.0
            running_corrects = 0

            # 遍歷數據
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # 梯度清零
                optimizer.zero_grad()

                # 只有訓練的時候計算和更新梯度
                with torch.set_grad_enabled(phase == "train"):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)

                    # 計算損失
                    loss = criterion(outputs, labels)

                    # 訓練階段更新權重
                    if phase == "train":
                        loss.backward()
                        optimizer.step()

                # 計算損失
                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))

    # 訓練完后用最好的一次當做模型最終的結果
    model.load_state_dict(best_model_weights)

    # 返回
    return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs

獲取數據

def get_data():
    """獲取數據"""

    # 獲取測試集
    train = torchvision.datasets.CIFAR100(root="./mnt", train=True, download=True,
                                          transform=torchvision.transforms.Compose([
                                              torchvision.transforms.ToTensor(),  # 轉換成張量
                                              torchvision.transforms.Normalize((0.1307,), (0.3081,))  # 標準化
                                          ]))
    train_loader = DataLoader(train, batch_size=batch_size)  # 分割測試集

    # 獲取測試集
    test = torchvision.datasets.CIFAR100(root="./mnt", train=False, download=True,
                                         transform=torchvision.transforms.Compose([
                                             torchvision.transforms.ToTensor(),  # 轉換成張量
                                             torchvision.transforms.Normalize((0.1307,), (0.3081,))  # 標準化
                                         ]))
    test_loader = DataLoader(test, batch_size=batch_size)  # 分割訓練

    data_loader = {"train": train_loader, "valid": test_loader}

    # 返回分割好的訓練集和測試集
    return data_loader

完整代碼

完整代碼:

import copy
import torch
from torch.utils.data import DataLoader
import time
from torchsummary import summary
import torchvision
import torchvision.models as models


def set_parameter_requires_grad(model, feature_extracting):
    """
    是否保留梯度, 實現凍層
    :param model: 模型
    :param feature_extracting: 是否凍層
    :return: 無返回值
    """
    if feature_extracting:  # 如果凍層
        for param in model.parameters():  # 遍歷每個權重參數
            param.requires_grad = False  # 保留梯度為False


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: 類別數
    :param feature_exact: 是否凍層
    :param use_pretrained: 是否下載模型
    :return: 返回模型,
    """

    model_ft = None

    if model_name == "resnet":
        """Resnet152"""

        # 加載模型
        model_ft = models.resnet152(pretrained=use_pretrained)  # 下載參數
        set_parameter_requires_grad(model_ft, feature_exact)  # 凍層

        # 修改全連接層
        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


def parameter_to_update(model):
    """
    獲取需要更新的參數
    :param model: 模型
    :return: 需要更新的參數列表
    """

    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


def train_model(model, dataloaders, citerion, optimizer, filename, num_epochs=25):
    # 獲取起始時間
    since = time.time()

    # 初始化參數
    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)

        # 訓練和驗證
        for phase in ["train", "valid"]:
            if phase == "train":
                model.train()  # 訓練
            else:
                model.eval()  # 驗證

            running_loss = 0.0
            running_corrects = 0

            # 遍歷數據
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # 梯度清零
                optimizer.zero_grad()

                # 只有訓練的時候計算和更新梯度
                with torch.set_grad_enabled(phase == "train"):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)

                    # 計算損失
                    loss = criterion(outputs, labels)

                    # 訓練階段更新權重
                    if phase == "train":
                        loss.backward()
                        optimizer.step()

                # 計算損失
                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))

    # 訓練完后用最好的一次當做模型最終的結果
    model.load_state_dict(best_model_weights)

    # 返回
    return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs


def get_data():
    """獲取數據"""

    # 獲取測試集
    train = torchvision.datasets.CIFAR100(root="./mnt", train=True, download=True,
                                          transform=torchvision.transforms.Compose([
                                              torchvision.transforms.ToTensor(),  # 轉換成張量
                                              torchvision.transforms.Normalize((0.1307,), (0.3081,))  # 標準化
                                          ]))
    train_loader = DataLoader(train, batch_size=batch_size)  # 分割測試集

    # 獲取測試集
    test = torchvision.datasets.CIFAR100(root="./mnt", train=False, download=True,
                                         transform=torchvision.transforms.Compose([
                                             torchvision.transforms.ToTensor(),  # 轉換成張量
                                             torchvision.transforms.Normalize((0.1307,), (0.3081,))  # 標準化
                                         ]))
    test_loader = DataLoader(test, batch_size=batch_size)  # 分割訓練

    data_loader = {"train": train_loader, "valid": test_loader}

    # 返回分割好的訓練集和測試集
    return data_loader


# 超參數
filename = "checkpoint.pth"  # 模型保存
feature_exact = True  # 凍層
num_classes = 100  # 輸出的類別數
batch_size = 1024  # 一次訓練的樣本數目
iteration_num = 10  # 迭代次數

# 獲取模型
resnet152 = initialize_model(
    model_name="resnet",
    num_classes=num_classes,
    feature_exact=feature_exact,
    use_pretrained=True
)

# 是否使用GPU訓練
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
if use_cuda: resnet152.cuda()  # GPU 計算
print("是否使用 GPU 加速:", use_cuda)

# 輸出網絡結構
print(summary(resnet152, (3, 32, 32)))

# 訓練參數
params_to_update = parameter_to_update(resnet152)

# 優化器
optimizer = torch.optim.Adam(params_to_update, lr=0.01)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)  # 學習率每10個epoch衰減到原來的1/10
criterion = 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
    )

輸出結果:

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