• 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 torchclass BasicBlock(nn.Module):expansion = 1def __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 = downsampledef forward(self, x):identity = xif 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 += identityout = self.relu(out)return outclass 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 = 4def __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.)) * groupsself.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,kernel_size=1, stride=1, bias=False)  # squeeze channelsself.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 channelsself.bn3 = nn.BatchNorm2d(out_channel*self.expansion)self.relu = nn.ReLU(inplace=True)self.downsample = downsampledef forward(self, x):identity = xif 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 += identityout = self.relu(out)return outclass 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_topself.in_channel = 64self.groups = groupsself.width_per_group = width_per_groupself.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 = Noneif 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.expansionfor _ 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 xdef resnet34(num_classes=1000, include_top=True):# https://download.pytorch.org/models/resnet34-333f7ec4.pthreturn 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.pthreturn 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.pthreturn 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.pthgroups = 32width_per_group = 4return 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.pthgroups = 32width_per_group = 8return 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 copy2def 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):passelse: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):passelse: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_scaleval_stop_flag = current_data_length * (train_scale + val_scale)current_idx = 0train_num = 0val_num = 0test_num = 0for 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 + 1elif (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 + 1else:copy2(src_img_path, test_folder)# print("{}复制到了{}".format(src_img_path, test_folder))test_num = test_num + 1current_idx = current_idx + 1print("*********************************{}*************************************".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 jsonimport torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from tqdm import tqdmdef 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 = 0.40085683947993256data_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) # 16000batch_size = 108nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workersprint('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 = Falsein_channel = net.fc.in_featuresnet.fc = nn.Linear(in_channel, 80)net.to(device)# define loss functionloss_function = nn.CrossEntropyLoss()# construct an optimizerparams = [p for p in net.parameters() if p.requires_grad]optimizer = optim.Adam(params, lr=0.0001)epochs = 15best_acc = 0.0save_path = './resNet101.pth'train_steps = len(train_loader)for epoch in range(epochs):# trainnet.train()running_loss = 0.0train_bar = tqdm(train_loader)for step, data in enumerate(train_bar):images, labels = dataoptimizer.zero_grad()logits = net(images.to(device))loss = loss_function(logits, labels.to(device))loss.backward()optimizer.step()# print statisticsrunning_loss += loss.item()train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,epochs,loss)# validatenet.eval()acc = 0.0  # accumulate accurate number / epochwith torch.no_grad():val_bar = tqdm(validate_loader)for val_data in val_bar:val_images, val_labels = val_dataoutputs = 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_numprint('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' % (epoch + 1, running_loss / train_steps, val_accurate))if val_accurate > best_acc:best_acc = val_accuratetorch.save(net.state_dict(), save_path)print('Finished Training')if __name__ == '__main__':main()

• 最后的预测

import os
import jsonimport 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 arrdevice = 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 = 0for 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 classoutput = 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', 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'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 + 1file.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)

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