diff --git a/Chinese Medicine.zip b/Chinese Medicine.zip deleted file mode 100644 index a11b4b6..0000000 Binary files a/Chinese Medicine.zip and /dev/null differ diff --git a/recognition.py b/recognition.py deleted file mode 100644 index 1cc7691..0000000 --- a/recognition.py +++ /dev/null @@ -1,406 +0,0 @@ -# -*- 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) \ No newline at end of file