零基础入门CV赛事（Baseline讲解） CV

零基础入门CV赛事：Baseline讲解 【零基础入门CV赛事（Baseline讲解）】
目录

零基础入门CV赛事：Baseline讲解

步骤1：定义好读取图像的Dataset
步骤2：定义好训练数据和验证数据的Dataset
步骤3：定义好字符分类模型，使用renset18的模型作为特征提取模块
步骤4：定义好训练、验证和预测模块
步骤5：迭代训练和验证模型
【参考资料】

Baseline思路：将不定长字符转换为定长字符的识别问题，并使用CNN完成训练和验证，具体包括以下几个步骤：

赛题数据读取（封装为Pytorch的Dataset和DataLoder）
构建CNN模型（使用Pytorch搭建）
模型训练与验证
模型结果预测

运行环境及安装示例
运行环境要求：Python2/3，Pytorch1.x，内存4G，有无GPU都可以。
下面给出python3.7+ torch1.3.1gpu版本的环境安装示例：

首先在Anaconda中创建一个专门用于本次天池练习赛的虚拟环境。

$conda create -n py37_torch131 python=3.7

激活环境，并安装pytorch1.3.1

$source activate py37_torch131 $conda install pytorch=1.3.1 torchvision cudatoolkit=10.0

通过下面的命令一键安装所需其它依赖库

$pip install jupyter tqdm opencv-python matplotlib pandas

启动notebook，即可开始baseline代码的学习

$jupyter-notebook

假设所有的赛题输入文件放在…/input/目录下，首先导入常用的包：

import os, sys, glob, shutil, json os.environ["CUDA_VISIBLE_DEVICES"] = '0' import cv2from PIL import Image import numpy as npfrom tqdm import tqdm, tqdm_notebookimport torch torch.manual_seed(0) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = Trueimport torchvision.models as models import torchvision.transforms as transforms import torchvision.datasets as datasets import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable from torch.utils.data.dataset import Dataset

步骤1：定义好读取图像的Dataset

class SVHNDataset(Dataset): def __init__(self, img_path, img_label, transform=None): self.img_path = img_path self.img_label = img_label if transform is not None: self.transform = transform else: self.transform = Nonedef __getitem__(self, index): img = Image.open(self.img_path[index]).convert('RGB')if self.transform is not None: img = self.transform(img)# 设置最长的字符长度为5个 lbl = np.array(self.img_label[index], dtype=np.int) lbl = list(lbl)+ (5 - len(lbl)) * [10] return img, torch.from_numpy(np.array(lbl[:5]))def __len__(self): return len(self.img_path)

步骤2：定义好训练数据和验证数据的Dataset

train_path = glob.glob('../input/train/*.png') train_path.sort() train_json = json.load(open('../input/train.json')) train_label = [train_json[x]['label'] for x in train_json] print(len(train_path), len(train_label))train_loader = torch.utils.data.DataLoader( SVHNDataset(train_path, train_label, transforms.Compose([ transforms.Resize((64, 128)), transforms.RandomCrop((60, 120)), transforms.ColorJitter(0.3, 0.3, 0.2), transforms.RandomRotation(5), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ])), batch_size=40, shuffle=True, num_workers=10, )val_path = glob.glob('../input/val/*.png') val_path.sort() val_json = json.load(open('../input/val.json')) val_label = [val_json[x]['label'] for x in val_json] print(len(val_path), len(val_label))val_loader = torch.utils.data.DataLoader( SVHNDataset(val_path, val_label, transforms.Compose([ transforms.Resize((60, 120)), # transforms.ColorJitter(0.3, 0.3, 0.2), # transforms.RandomRotation(5), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ])), batch_size=40, shuffle=False, num_workers=10, )

步骤3：定义好字符分类模型，使用renset18的模型作为特征提取模块

class SVHN_Model1(nn.Module): def __init__(self): super(SVHN_Model1, self).__init__()model_conv = models.resnet18(pretrained=True) model_conv.avgpool = nn.AdaptiveAvgPool2d(1) model_conv = nn.Sequential(*list(model_conv.children())[:-1]) self.cnn = model_convself.fc1 = nn.Linear(512, 11) self.fc2 = nn.Linear(512, 11) self.fc3 = nn.Linear(512, 11) self.fc4 = nn.Linear(512, 11) self.fc5 = nn.Linear(512, 11)def forward(self, img): feat = self.cnn(img) # print(feat.shape) feat = feat.view(feat.shape[0], -1) c1 = self.fc1(feat) c2 = self.fc2(feat) c3 = self.fc3(feat) c4 = self.fc4(feat) c5 = self.fc5(feat) return c1, c2, c3, c4, c5

步骤4：定义好训练、验证和预测模块

def train(train_loader, model, criterion, optimizer): # 切换模型为训练模式 model.train() train_loss = []for i, (input, target) in enumerate(train_loader): if use_cuda: input = input.cuda() target = target.cuda()c0, c1, c2, c3, c4 = model(input) loss = criterion(c0, target[:, 0]) + \ criterion(c1, target[:, 1]) + \ criterion(c2, target[:, 2]) + \ criterion(c3, target[:, 3]) + \ criterion(c4, target[:, 4])# loss /= 6 optimizer.zero_grad() loss.backward() optimizer.step()if i % 100 == 0: print(loss.item())train_loss.append(loss.item()) return np.mean(train_loss)def validate(val_loader, model, criterion): # 切换模型为预测模型 model.eval() val_loss = []# 不记录模型梯度信息 with torch.no_grad(): for i, (input, target) in enumerate(val_loader): if use_cuda: input = input.cuda() target = target.cuda()c0, c1, c2, c3, c4 = model(input) loss = criterion(c0, target[:, 0]) + \ criterion(c1, target[:, 1]) + \ criterion(c2, target[:, 2]) + \ criterion(c3, target[:, 3]) + \ criterion(c4, target[:, 4]) # loss /= 6 val_loss.append(loss.item()) return np.mean(val_loss)def predict(test_loader, model, tta=10): model.eval() test_pred_tta = None# TTA 次数 for _ in range(tta): test_pred = []with torch.no_grad(): for i, (input, target) in enumerate(test_loader): if use_cuda: input = input.cuda()c0, c1, c2, c3, c4 = model(input) output = np.concatenate([ c0.data.numpy(), c1.data.numpy(), c2.data.numpy(), c3.data.numpy(), c4.data.numpy()], axis=1) test_pred.append(output)test_pred = np.vstack(test_pred) if test_pred_tta is None: test_pred_tta = test_pred else: test_pred_tta += test_predreturn test_pred_tta

步骤5：迭代训练和验证模型

model = SVHN_Model1() criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), 0.001) best_loss = 1000.0use_cuda = False if use_cuda: model = model.cuda()for epoch in range(2): train_loss = train(train_loader, model, criterion, optimizer, epoch) val_loss = validate(val_loader, model, criterion)val_label = [''.join(map(str, x)) for x in val_loader.dataset.img_label] val_predict_label = predict(val_loader, model, 1) val_predict_label = np.vstack([ val_predict_label[:, :11].argmax(1), val_predict_label[:, 11:22].argmax(1), val_predict_label[:, 22:33].argmax(1), val_predict_label[:, 33:44].argmax(1), val_predict_label[:, 44:55].argmax(1), ]).T val_label_pred = [] for x in val_predict_label: val_label_pred.append(''.join(map(str, x[x!=10])))val_char_acc = np.mean(np.array(val_label_pred) == np.array(val_label))print('Epoch: {0}, Train loss: {1} \t Val loss: {2}'.format(epoch, train_loss, val_loss)) print(val_char_acc) # 记录下验证集精度 if val_loss < best_loss: best_loss = val_loss torch.save(model.state_dict(), './model.pt') 训练两个2 Epoch后，输出的训练日志为： Epoch: 0, Train loss: 3.1 Val loss: 3.4 验证集精度：0.3439 Epoch: 1, Train loss: 2.1 Val loss: 2.9 验证集精度：0.4346 步骤6：对测试集样本进行预测，生成提交文件 test_path = glob.glob('../input/test_a/*.png') test_path.sort() test_label = [[1]] * len(test_path) print(len(val_path), len(val_label))test_loader = torch.utils.data.DataLoader( SVHNDataset(test_path, test_label, transforms.Compose([ transforms.Resize((64, 128)), transforms.RandomCrop((60, 120)), # transforms.ColorJitter(0.3, 0.3, 0.2), # transforms.RandomRotation(5), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ])), batch_size=40, shuffle=False, num_workers=10, )test_predict_label = predict(test_loader, model, 1)test_label = [''.join(map(str, x)) for x in test_loader.dataset.img_label] test_predict_label = np.vstack([ test_predict_label[:, :11].argmax(1), test_predict_label[:, 11:22].argmax(1), test_predict_label[:, 22:33].argmax(1), test_predict_label[:, 33:44].argmax(1), test_predict_label[:, 44:55].argmax(1), ]).Ttest_label_pred = [] for x in test_predict_label: test_label_pred.append(''.join(map(str, x[x!=10])))import pandas as pd df_submit = pd.read_csv('../input/test_A_sample_submit.csv') df_submit['file_code'] = test_label_pred df_submit.to_csv('renset18.csv', index=None)

在训练完成2个Epoch后，模型在测试集上的成绩应该在0.33左右。
【参考资料】 Datawhale 零基础入门CV赛事