# -*- coding: utf-8 -*- """ Created on Mon Nov 7 16:24:57 2022 @author: lala """ # 引入需要的模块 import os import zipfile import random import json import paddle import sys import numpy as np from PIL import Image import matplotlib.pyplot as plt from paddle.io import Dataset random.seed(200) def unzip_data(src_path,target_path): if(not os.path.isdir(target_path + "Chinese Medicine")): z = zipfile.ZipFile(src_path, 'r') z.extractall(path=target_path) z.close() def get_data_list(target_path,train_list_path,eval_list_path): ''' 生成数据列表 ''' #存放所有类别的信息 class_detail = [] #获取所有类别保存的文件夹名称 data_list_path=target_path+"Chinese Medicine/" class_dirs = os.listdir(data_list_path) #总的图像数量 all_class_images = 0 #存放类别标签 class_label=0 #存放类别数目 class_dim = 0 #存储要写进eval.txt和train.txt中的内容 trainer_list=[] eval_list=[] #读取每个类别，['baihe', 'gouqi','jinyinhua','huaihua','dangshen'] for class_dir in class_dirs: if class_dir != ".DS_Store": class_dim += 1 #每个类别的信息 class_detail_list = {} eval_sum = 0 trainer_sum = 0 #统计每个类别有多少张图片 class_sum = 0 #获取类别路径 path = data_list_path + class_dir # 获取所有图片 img_paths = os.listdir(path) for img_path in img_paths: # 遍历文件夹下的每个图片 name_path = path + '/' + img_path # 每张图片的路径 if class_sum % 8 == 0: # 每8张图片取一个做验证数据 eval_sum += 1 # test_sum为测试数据的数目 eval_list.append(name_path + "\t%d" % class_label + "\n") else: trainer_sum += 1 trainer_list.append(name_path + "\t%d" % class_label + "\n")#trainer_sum测试数据的数目 class_sum += 1 #每类图片的数目 all_class_images += 1 #所有类图片的数目 # 说明的json文件的class_detail数据 class_detail_list['class_name'] = class_dir #类别名称 class_detail_list['class_label'] = class_label #类别标签 class_detail_list['class_eval_images'] = eval_sum #该类数据的测试集数目 class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目 class_detail.append(class_detail_list) #初始化标签列表 train_parameters['label_dict'][str(class_label)] = class_dir class_label += 1 #初始化分类数 train_parameters['class_dim'] = class_dim #乱序 random.shuffle(eval_list) with open(eval_list_path, 'a') as f: for eval_image in eval_list: f.write(eval_image) random.shuffle(trainer_list) with open(train_list_path, 'a') as f2: for train_image in trainer_list: f2.write(train_image) # 说明的json文件信息 readjson = {} readjson['all_class_name'] = data_list_path #文件父目录 readjson['all_class_images'] = all_class_images readjson['class_detail'] = class_detail jsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': ')) with open(train_parameters['readme_path'],'w') as f: f.write(jsons) print ('生成数据列表完成！') train_parameters = { "src_path":"/home/aistudio/data/data105575/Chinese Medicine.zip", #原始数据集路径 "target_path":"/home/aistudio/data/", #要解压的路径 "train_list_path": "/home/aistudio/data/train.txt", #train.txt路径 "eval_list_path": "/home/aistudio/data/eval.txt", #eval.txt路径 "label_dict":{}, #标签字典 "readme_path": "/home/aistudio/data/readme.json", #readme.json路径 "class_dim": -1, #分类数 } src_path=train_parameters['src_path'] target_path=train_parameters['target_path'] train_list_path=train_parameters['train_list_path'] eval_list_path=train_parameters['eval_list_path'] # 调用解压函数解压数据集 unzip_data(src_path,target_path) # 划分训练集与验证集，乱序，生成数据列表 #每次生成数据列表前，首先清空train.txt和eval.txt with open(train_list_path, 'w') as f: f.seek(0) f.truncate() with open(eval_list_path, 'w') as f: f.seek(0) f.truncate() #生成数据列表 get_data_list(target_path,train_list_path,eval_list_path) class dataset(Dataset): def __init__(self, data_path, mode='train'): """ 数据读取器 :param data_path: 数据集所在路径 :param mode: train or eval """ super().__init__() self.data_path = data_path self.img_paths = [] self.labels = [] if mode == 'train': with open(os.path.join(self.data_path, "train.txt"), "r", encoding="utf-8") as f: self.info = f.readlines() for img_info in self.info: img_path, label = img_info.strip().split('\t') self.img_paths.append(img_path) self.labels.append(int(label)) else: with open(os.path.join(self.data_path, "eval.txt"), "r", encoding="utf-8") as f: self.info = f.readlines() for img_info in self.info: img_path, label = img_info.strip().split('\t') self.img_paths.append(img_path) self.labels.append(int(label)) def __getitem__(self, index): """ 获取一组数据 :param index: 文件索引号 :return: """ # 第一步打开图像文件并获取label值 img_path = self.img_paths[index] img = Image.open(img_path) if img.mode != 'RGB': img = img.convert('RGB') img = img.resize((224, 224), Image.BILINEAR) #img = rand_flip_image(img) img = np.array(img).astype('float32') img = img.transpose((2, 0, 1)) / 255 label = self.labels[index] label = np.array([label], dtype="int64") return img, label def print_sample(self, index: int = 0): print("文件名", self.img_paths[index], "\t标签值", self.labels[index]) def __len__(self): return len(self.img_paths) #训练数据加载 train_dataset = dataset('/home/aistudio/data',mode='train') train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True) #评估数据加载 eval_dataset = dataset('/home/aistudio/data',mode='eval') eval_loader = paddle.io.DataLoader(eval_dataset, batch_size = 8, shuffle=False) # 定义卷积池化网络 class ConvPool(paddle.nn.Layer): '''卷积+池化''' def __init__(self, num_channels, num_filters, filter_size, pool_size, pool_stride, groups, conv_stride=1, conv_padding=1, ): super(ConvPool, self).__init__() # groups代表卷积层的数量 for i in range(groups): self.add_sublayer( #添加子层实例 'bb_%d' % i, paddle.nn.Conv2D( # layer in_channels=num_channels, #通道数 out_channels=num_filters, #卷积核个数 kernel_size=filter_size, #卷积核大小 stride=conv_stride, #步长 padding = conv_padding, #padding ) ) self.add_sublayer( 'relu%d' % i, paddle.nn.ReLU() ) num_channels = num_filters self.add_sublayer( 'Maxpool', paddle.nn.MaxPool2D( kernel_size=pool_size, #池化核大小 stride=pool_stride #池化步长 ) ) def forward(self, inputs): x = inputs for prefix, sub_layer in self.named_children(): # print(prefix,sub_layer) x = sub_layer(x) return x # VGG网络 class VGGNet(paddle.nn.Layer): def __init__(self): super(VGGNet, self).__init__() # 5个卷积池化操作 self.convpool01 = ConvPool( 3, 64, 3, 2, 2, 2) #3:通道数，64：卷积核个数，3:卷积核大小，2:池化核大小，2:池化步长，2:连续卷积个数 self.convpool02 = ConvPool( 64, 128, 3, 2, 2, 2) self.convpool03 = ConvPool( 128, 256, 3, 2, 2, 3) self.convpool04 = ConvPool( 256, 512, 3, 2, 2, 3) self.convpool05 = ConvPool( 512, 512, 3, 2, 2, 3) self.pool_5_shape = 512 * 7* 7 # 三个全连接层 self.fc01 = paddle.nn.Linear(self.pool_5_shape, 4096) self.drop1 = paddle.nn.Dropout(p=0.5) self.fc02 = paddle.nn.Linear(4096, 4096) self.drop2 = paddle.nn.Dropout(p=0.5) self.fc03 = paddle.nn.Linear(4096, train_parameters['class_dim']) def forward(self, inputs, label=None): # print('input_shape:', inputs.shape) #[8, 3, 224, 224] """前向计算""" out = self.convpool01(inputs) # print('convpool01_shape:', out.shape) #[8, 64, 112, 112] out = self.convpool02(out) # print('convpool02_shape:', out.shape) #[8, 128, 56, 56] out = self.convpool03(out) # print('convpool03_shape:', out.shape) #[8, 256, 28, 28] out = self.convpool04(out) # print('convpool04_shape:', out.shape) #[8, 512, 14, 14] out = self.convpool05(out) # print('convpool05_shape:', out.shape) #[8, 512, 7, 7] out = paddle.reshape(out, shape=[-1, 512*7*7]) out = self.fc01(out) out = self.drop1(out) out = self.fc02(out) out = self.drop2(out) out = self.fc03(out) if label is not None: acc = paddle.metric.accuracy(input=out, label=label) return out, acc else: return out # 折线图，用于观察训练过程中loss和acc的走势 def draw_process(title,color,iters,data,label): plt.title(title, fontsize=24) plt.xlabel("iter", fontsize=20) plt.ylabel(label, fontsize=20) plt.plot(iters, data,color=color,label=label) plt.legend() plt.grid() plt.show() # 参数配置，要保留之前数据集准备阶段配置的参数，所以使用update更新字典 train_parameters.update({ "input_size": [3, 224, 224], #输入图片的shape "num_epochs": 35, #训练轮数 "skip_steps": 10, #训练时输出日志的间隔 "save_steps": 100, #训练时保存模型参数的间隔 "learning_strategy": { #优化函数相关的配置 "lr": 0.0001 #超参数学习率 }, "checkpoints": "/home/aistudio/work/checkpoints" #保存的路径 }) model = VGGNet() model.train() # 配置loss函数 cross_entropy = paddle.nn.CrossEntropyLoss() # 配置参数优化器 optimizer = paddle.optimizer.Adam(learning_rate=train_parameters['learning_strategy']['lr'], parameters=model.parameters()) steps = 0 Iters, total_loss, total_acc = [], [], [] for epo in range(train_parameters['num_epochs']): for _, data in enumerate(train_loader()): steps += 1 x_data = data[0] y_data = data[1] predicts, acc = model(x_data, y_data) loss = cross_entropy(predicts, y_data) loss.backward() optimizer.step() optimizer.clear_grad() if steps % train_parameters["skip_steps"] == 0: Iters.append(steps) total_loss.append(loss.numpy()[0]) total_acc.append(acc.numpy()[0]) #打印中间过程 print('epo: {}, step: {}, loss is: {}, acc is: {}'\ .format(epo, steps, loss.numpy(), acc.numpy())) #保存模型参数 if steps % train_parameters["save_steps"] == 0: save_path = train_parameters["checkpoints"]+"/"+"save_dir_" + str(steps) + '.pdparams' print('save model to: ' + save_path) paddle.save(model.state_dict(),save_path) paddle.save(model.state_dict(),train_parameters["checkpoints"]+"/"+"save_dir_final.pdparams") draw_process("trainning loss","red",Iters,total_loss,"trainning loss") draw_process("trainning acc","green",Iters,total_acc,"trainning acc") # 模型评估 # 加载训练过程保存的最后一个模型 model__state_dict = paddle.load('work/checkpoints/save_dir_final.pdparams') model_eval = VGGNet() model_eval.set_state_dict(model__state_dict) model_eval.eval() accs = [] # 开始评估 for _, data in enumerate(eval_loader()): x_data = data[0] y_data = data[1] predicts = model_eval(x_data) acc = paddle.metric.accuracy(predicts, y_data) accs.append(acc.numpy()[0]) print('模型在验证集上的准确率为：',np.mean(accs)) def load_image(img_path): ''' 预测图片预处理 ''' img = Image.open(img_path) if img.mode != 'RGB': img = img.convert('RGB') img = img.resize((224, 224), Image.BILINEAR) img = np.array(img).astype('float32') img = img.transpose((2, 0, 1)) / 255 # HWC to CHW 及归一化 return img label_dic = train_parameters['label_dict'] import time # 加载训练过程保存的最后一个模型 model__state_dict = paddle.load('work/checkpoints/save_dir_final.pdparams') model_predict = VGGNet() model_predict.set_state_dict(model__state_dict) model_predict.eval() infer_imgs_path = os.listdir("infer") # print(infer_imgs_path) # 预测所有图片 for infer_img_path in infer_imgs_path: infer_img = load_image("infer/"+infer_img_path) infer_img = infer_img[np.newaxis,:, : ,:] #reshape(-1,3,224,224) infer_img = paddle.to_tensor(infer_img) result = model_predict(infer_img) lab = np.argmax(result.numpy()) print("样本: {},被预测为:{}".format(infer_img_path,label_dic[str(lab)])) img = Image.open("infer/"+infer_img_path) plt.imshow(img) plt.axis('off') plt.show() sys.stdout.flush() time.sleep(0.5)