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