使用resNet网络 进行图像分类（jupyter notebook）

• 这学期做了三次的CV把他贴出来，

• resNet网络的结构

import torch.nn as nn
import torch

class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
                               kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channel)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
                               kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channel)
        self.downsample = downsample
    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    """
    注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。
    但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，
    这么做的好处是能够在top1上提升大概0.5%的准确率。
    可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch
    """
    expansion = 4

    def __init__(self, in_channel, out_channel, stride=1, downsample=None,
                 groups=1, width_per_group=64):
        super(Bottleneck, self).__init__()

        width = int(out_channel * (width_per_group / 64.)) * groups

        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,
                               kernel_size=1, stride=1, bias=False)  # squeeze channels
        self.bn1 = nn.BatchNorm2d(width)
        # -----------------------------------------
        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,
                               kernel_size=3, stride=stride, bias=False, padding=1)
        self.bn2 = nn.BatchNorm2d(width)
        # -----------------------------------------
        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,
                               kernel_size=1, stride=1, bias=False)  # unsqueeze channels
        self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self,
                 block,
                 blocks_num,
                 num_classes=1000,
                 include_top=True,
                 groups=1,
                 width_per_group=64):
        super(ResNet, self).__init__()
        self.include_top = include_top
        self.in_channel = 64

        self.groups = groups
        self.width_per_group = width_per_group

        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
                               padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channel)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, blocks_num[0])
        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
        if self.include_top:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
            self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

    def _make_layer(self, block, channel, block_num, stride=1):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(channel * block.expansion))

        layers = []
        layers.append(block(self.in_channel,
                            channel,
                            downsample=downsample,
                            stride=stride,
                            groups=self.groups,
                            width_per_group=self.width_per_group))
        self.in_channel = channel * block.expansion

        for _ in range(1, block_num):
            layers.append(block(self.in_channel,
                                channel,
                                groups=self.groups,
                                width_per_group=self.width_per_group))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        if self.include_top:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x


def resnet34(num_classes=1000, include_top=True):
    # https://download.pytorch.org/models/resnet34-333f7ec4.pth
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)


def resnet50(num_classes=1000, include_top=True):
    # https://download.pytorch.org/models/resnet50-19c8e357.pth
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)


def resnet101(num_classes=1000, include_top=True):
    # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)


def resnext50_32x4d(num_classes=1000, include_top=True):
    # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
    groups = 32
    width_per_group = 4
    return ResNet(Bottleneck, [3, 4, 6, 3],
                  num_classes=num_classes,
                  include_top=include_top,
                  groups=groups,
                  width_per_group=width_per_group)


def resnext101_32x8d(num_classes=1000, include_top=True):
    # https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
    groups = 32
    width_per_group = 8
    return ResNet(Bottleneck, [3, 4, 23, 3],
                  num_classes=num_classes,
                  include_top=include_top,
                  groups=groups,
                  width_per_group=width_per_group)

• 工具类，进行数据的划分

# 工具类
import os
import random
import shutil
from shutil import copy2


def data_set_split(src_data_folder, target_data_folder, train_scale=0.8, val_scale=0.1, test_scale=0.1):
    '''
    读取源数据文件夹，生成划分好的文件夹，分为trian、val、test三个文件夹进行
    :param src_data_folder: 源文件夹 E:/biye/gogogo/note_book/torch_note/data/utils_test/data_split/src_data
    :param target_data_folder: 目标文件夹 E:/biye/gogogo/note_book/torch_note/data/utils_test/data_split/target_data
    :param train_scale: 训练集比例
    :param val_scale: 验证集比例
    :param test_scale: 测试集比例
    :return:
    '''
    print("开始数据集划分")
    class_names = os.listdir(src_data_folder)
    # 在目标目录下创建文件夹
    split_names = ['train', 'val', 'test']
    for split_name in split_names:
        split_path = os.path.join(target_data_folder, split_name)
        if os.path.isdir(split_path):
            pass
        else:
            os.mkdir(split_path)
        # 然后在split_path的目录下创建类别文件夹
        for class_name in class_names:
            class_split_path = os.path.join(split_path, class_name)
            if os.path.isdir(class_split_path):
                pass
            else:
                os.mkdir(class_split_path)

    # 按照比例划分数据集，并进行数据图片的复制
    # 首先进行分类遍历
    for class_name in class_names:
        current_class_data_path = os.path.join(src_data_folder, class_name)
        current_all_data = os.listdir(current_class_data_path)
        current_data_length = len(current_all_data)
        current_data_index_list = list(range(current_data_length))
        random.shuffle(current_data_index_list)

        train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name)
        val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name)
        test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name)
        train_stop_flag = current_data_length * train_scale
        val_stop_flag = current_data_length * (train_scale + val_scale)
        current_idx = 0
        train_num = 0
        val_num = 0
        test_num = 0
        for i in current_data_index_list:
            src_img_path = os.path.join(current_class_data_path, current_all_data[i])
            if current_idx <= train_stop_flag:
                copy2(src_img_path, train_folder)
                # print("{}复制到了{}".format(src_img_path, train_folder))
                train_num = train_num + 1
            elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag):
                copy2(src_img_path, val_folder)
                # print("{}复制到了{}".format(src_img_path, val_folder))
                val_num = val_num + 1
            else:
                copy2(src_img_path, test_folder)
                # print("{}复制到了{}".format(src_img_path, test_folder))
                test_num = test_num + 1

            current_idx = current_idx + 1

        print("*********************************{}*************************************".format(class_name))
        print(
            "{}类按照{}：{}：{}的比例划分完成，一共{}张图片".format(class_name, train_scale, val_scale, test_scale, current_data_length))
        print("训练集{}：{}张".format(train_folder, train_num))
        print("验证集{}：{}张".format(val_folder, val_num))
        print("测试集{}：{}张".format(test_folder, test_num))


if __name__ == '__main__':
    src_data_folder = "../input/jianghomework1/data/train"
    target_data_folder = "/kaggle/working"
    data_set_split(src_data_folder, target_data_folder)

• 进行训练

import os
import json

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from tqdm import tqdm

def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("using {} device.".format(device))
#     R_mean=  0.13128287041440617 G_mean=  0.15304898561393718 B_mean= 0.14544419562248562 stdR =  0.4799472429342831 stdG =  0.45038768037683824 stdB = 0.40085683947993256
    
    data_transform = {
        "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                     transforms.RandomHorizontalFlip(),
                                     transforms.ToTensor(),
                                     transforms.Normalize([0.471, 0.448, 0.408],[0.234, 0.239, 0.242])]),
        "val": transforms.Compose([transforms.Resize(256),
                                   transforms.CenterCrop(224),
                                   transforms.ToTensor(),
                                   transforms.Normalize([0.471, 0.448, 0.408],[0.234, 0.239, 0.242])])}
    train_db = datasets.ImageFolder(root='./train', transform=data_transform["train"])
    train_num = len(train_db)
    print(train_num) # 16000
    
    
    batch_size = 108
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('Using {} dataloader workers every process'.format(nw))    
    
    
    train_loader = torch.utils.data.DataLoader(train_db,
                                               batch_size=batch_size, shuffle=True,
                                               num_workers=nw)
    validate_dataset = datasets.ImageFolder(root='./val',transform=data_transform["val"])
    val_num = len(validate_dataset)
    validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                                  batch_size=batch_size, shuffle=False,
                                                  num_workers=nw)

    print("using {} images for training, {} images for validation.".format(train_num,
                                                                           val_num))
   
    net = resnet101()
    model_weight_path = "../input/resnet50/resnet101-5d3b4d8f.pth"
    net.load_state_dict(torch.load(model_weight_path, map_location=device))
    #for param in net.parameters():
    #     param.requires_grad = False
    in_channel = net.fc.in_features
    net.fc = nn.Linear(in_channel, 80)
    net.to(device)

    # define loss function
    loss_function = nn.CrossEntropyLoss()

    # construct an optimizer
    params = [p for p in net.parameters() if p.requires_grad]
    optimizer = optim.Adam(params, lr=0.0001)

    epochs = 15
    
    
    best_acc = 0.0
    save_path = './resNet101.pth'
    train_steps = len(train_loader)
    for epoch in range(epochs):
        # train
        net.train()
        running_loss = 0.0
        train_bar = tqdm(train_loader)
        for step, data in enumerate(train_bar):
            images, labels = data
            optimizer.zero_grad()
            logits = net(images.to(device))
            loss = loss_function(logits, labels.to(device))
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,epochs,loss)
        # validate
        net.eval()
        acc = 0.0  # accumulate accurate number / epoch
        with torch.no_grad():
            val_bar = tqdm(validate_loader)
            for val_data in val_bar:
                val_images, val_labels = val_data
                outputs = net(val_images.to(device))
                # loss = loss_function(outputs, test_labels)
                predict_y = torch.max(outputs, dim=1)[1]
                # print(val_labels)
                # print(predict_y)
                acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
                val_bar.desc = "valid epoch[{}/{}]".format(epoch + 1, epochs)
        val_accurate = acc / val_num
        print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' % (epoch + 1, running_loss / train_steps, val_accurate))

        if val_accurate > best_acc:
            best_acc = val_accurate
            torch.save(net.state_dict(), save_path)

    print('Finished Training')


if __name__ == '__main__':
    main()

• 最后的预测

import os
import json

import torch
from PIL import Image
from torchvision import transforms
# ../input/jianghomework1/data/val/1015.jpg
# ../input/wangshi1/data/val
# ../input/wangshi1/data/val
# ../input/wangshi1/data/val
def getValueList(i):
    arr=[]
    for i in range(i * 1000,(i + 1) * 1000):
        # print(i)
        temp = '../input/jianghomework1/data/val/' + str(i) +'.jpg'
        arr.append(temp)
    return arr
        

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

data_transform = transforms.Compose(
    [transforms.Resize(256),
     transforms.CenterCrop(224),
     transforms.ToTensor(),
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

# load image
model = resnet101(num_classes=80).to(device)
weights_path = "./resNet101.pth"
model.load_state_dict(torch.load(weights_path, map_location=device))

# prediction
model.eval()
LEN = 0


for i in range(10):
    img_path_list = getValueList(i)
    img_list = []
    for img_path in img_path_list:
        assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
        img = Image.open(img_path)
        if img.mode == 'L':
            img = img.convert('RGB')
        img = data_transform(img)
        img_list.append(img)

    batch_img = torch.stack(img_list, dim=0)

    with torch.no_grad():
        # predict class
        output = model(batch_img.to(device)).cpu()
        # print(output[0])
        predict = torch.softmax(output, dim=1)
        # print(predict)
        probs, classes = torch.max(predict, dim=1)
        # print(probs)
        # print(classes)
        txt = ['61', '6', '42', '23', '37', '49', '18', '16', '11', '64', '7', '63', '30', '40', '71', '32', '3', '51', '24', '1', '50', '79', '9', '77', '76', '50', '67', '15', '36', '79', '5', '7', '76', '15', '9', '27', '50', '35', '31', '26', '55', '30', '63', '6', '58', '7', '24', '57', '8', '45', '34', '33', '5', '59', '79', '19', '45', '77', '23', '49', '68', '16', '73', '29', '61', '63', '60', '61', '67', '54', '49', '33', '71', '55', '52', '72', '56', '11', '19', '11', '2', '6', '15', '19', '40', '30', '37', '11', '62', '53', '29', '32', '63', '69', '31', '14', '64', '68', '63', '8', '7', '72', '79', '38', '41', '63', '14', '34', '47', '40', '56', '54', '64', '74', '2', '77', '51', '8', '59', '60', '45', '40', '52', '58', '72', '76', '48', '51', '44', '24', '11', '49', '56', '61', '21', '4', '34', '76', '32', '64', '4', '44', '12', '59', '64', '17', '7', '42', '49', '29', '16', '53', '65', '4', '14', '49', '52', '63', '44', '53', '75', '8', '22', '18', '22', '4', '57', '8', '33', '65', '13', '74', '44', '14', '41', '13', '37', '22', '68', '4', '41', '35', '42', '42', '14', '62', '61', '47', '0', '77', '35', '68', '75', '4', '38', '66', '12', '51', '34', '13', '5', '64', '22', '29', '57', '3', '25', '46', '9', '48', '25', '47', '57', '36', '59', '1', '27', '28', '6', '14', '0', '66', '21', '23', '34', '13', '60', '29', '40', '48', '19', '73', '9', '38', '60', '54', '21', '42', '15', '45', '34', '36', '75', '77', '64', '8', '10', '34', '69', '17', '26', '50', '41', '64', '19', '72', '19', '13', '17', '6', '30', '57', '58', '53', '66', '24', '54', '61', '46', '12', '64', '31', '22', '22', '32', '4', '30', '21', '20', '78', '49', '70', '32', '41', '43', '46', '18', '41', '40', '66', '11', '3', '12', '69', '62', '65', '19', '18', '35', '73', '63', '62', '78', '51', '32', '26', '10', '71', '43', '35', '53', '36', '3', '28', '41', '34', '22', '21', '18', '38', '57', '16', '15', '74', '43', '34', '59', '77', '27', '51', '63', '59', '79', '19', '31', '18', '26', 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'24', '62', '56', '39', '51', '62', '9', '0', '40', '49', '21', '64', '30', '57', '45', '6', '52', '77', '38', '28', '35', '49', '14', '68', '36', '74', '52', '53', '36', '34', '12', '69', '19', '75', '41', '55', '39', '70', '36', '49', '27', '42', '51', '56', '5', '28', '19', '11', '7', '0', '22', '42', '9', '71', '18', '21', '6', '17', '18', '43', '61', '71', '65', '20', '70', '46', '7', '39', '26', '76', '22', '55', '67', '26', '47', '30', '65', '60', '25', '51', '13', '26', '55', '9', '33', '76', '8', '60', '40', '41', '11', '66', '28', '4', '37', '6', '28', '51', '10', '9', '58', '42', '20', '35', '76', '20', '14', '45', '25', '33', '49', '63', '13', '69', '15', '57', '54', '24', '16', '36', '38', '57', '68', '57', '22', '77', '61', '5', '62', '61', '76', '44', '64', '45', '50', '73', '5', '60', '79', '39', '20']
        dict = ['0','1','10','11','12','13','14','15','16','17','18','19','2','20','21','22','23','24','25','26','27','28','29','3','30','31','32','33','34','35','36','37','38','39','4','40','41','42','43','44','45','46','47','48','49','5','50','51','52','53','54','55','56','57','58','59','6','60','61','62','63','64','65','66','67','68','69','7','70','71','72','73','74','75','76','77','78','79','8','9']
        file = open("./96TESTA751.txt","a")
        for idx, (pro, cla) in enumerate(zip(probs, classes)):
            if txt[int(img_path_list[idx].split("/")[5].split(".")[0])] == dict[cla]:
                LEN = LEN + 1
            file.write(img_path_list[idx].split("/")[5]+" "+ dict[cla] +"\n")    
            print(img_path_list[idx].split("/")[5]+" "+ dict[cla] +"\n",end="")    
            # print("image: {}   class: {}  prob: {:.3}".format(img_path_list[idx],dict[cla],pro.numpy()))
        # print(LEN) 
        file.close()
print(LEN)