paddle实现AlexNet

AlexNet结构 及 pytorch、tensorflow、keras、paddle实现ImageNet识别

环境

python3.6， paddlepaddle-gpu 1.6.3.post107

代码

# -*- coding: utf-8 -*- 
# @Time : 2020/1/21 11:18 
# @Author : Zhao HL
# @File : alexnet-paddle.py
import os, sys
from PIL import Image
import numpy as np
import pandas as pd
import paddle
from paddle import fluid
from paddle.fluid.layers import data, conv2d, pool2d, flatten, fc, cross_entropy, accuracy, mean
from my_utils import process_show, draw_loss_acc

# region parameters
# region paths
Data_path = "./data/my_imagenet"
Data_csv_path = "./data/my_imagenet.csv"
Model_path = 'model/'
Model_file_tf = "model/alexnet_tf.ckpt"
Model_file_keras = "model/alexnet_keras.h5"
Model_file_torch = "model/alexnet_torch.pth"
Model_file_paddle = "model/alexnet_paddle.model"
# endregion

# region image parameter
Img_size = 227
Img_chs = 3
Label_size = 1
Label_class = {'n02091244': 'Ibizan hound',
               'n02114548': 'white wolf',
               'n02138441': 'meerkat',
               'n03584254': 'iPod',
               'n03075370': 'combination lock',
               'n09256479': 'coral reef',
               'n03980874': 'poncho',
               'n02174001': 'rhinoceros beetle',
               'n03770439': 'miniskirt',
               'n03773504': 'missile'}
Labels_nums = len(Label_class)
# endregion

# region net parameter
Conv1_kernel_size = 11
Conv1_chs = 96
Conv2_kernel_size = 5
Conv2_chs = 256
Conv3_kernel_size = 3
Conv3_chs = 384
Conv4_kernel_size = 3
Conv4_chs = 384
Conv5_kernel_size = 3
Conv5_chs = 256
Flatten_size = 6 * 6 * 256
Fc1_size = 4096
Fc2_size = 4096
Fc3_size = Labels_nums
# endregion

# region hpyerparameter
Learning_rate = 1e-4
Batch_size = 32
Buffer_size = 256
Infer_size = 1
Epochs = 10
Train_num = 700
Train_batch_num = Train_num // Batch_size
Val_num = 100
Val_batch_num = Val_num // Batch_size
Test_num = 200
Test_batch_num = Test_num // Batch_size
# endregion
place = fluid.CUDAPlace(0) if fluid.cuda_places() else fluid.CPUPlace()
# endregion

class MyDataset():
    def __init__(self, root_path, batch_size, files_list=None,):
        self.root_path = root_path
        self.files_list = files_list if files_list else os.listdir(root_path)
        self.size = len(files_list)
        self.batch_size = batch_size

    def __len__(self):
        return self.size

    def dataset_reader(self):
        pass
        files_list = self.files_list if self.files_list is not None else os.listdir(self.root_path)

        def reader():
            np.random.shuffle(files_list)
            for file_name in files_list:
                label_str = file_name[:9]
                label = list(Label_class.keys()).index(label_str)
                img = Image.open(os.path.join(self.root_path, file_name))
                yield img, label
        return paddle.batch(paddle.reader.xmap_readers(self.transform, reader, 2, Buffer_size), batch_size=self.batch_size)

    def transform(self,sample):
        def Normalize(image, means, stds):
            for band in range(len(means)):
                image[:, :, band] = image[:, :, band] / 255.0
                image[:, :, band] = (image[:, :, band] - means[band]) / stds[band]
            image = np.transpose(image, [2, 1, 0])
            return image

        pass
        image, label = sample
        image = image.resize((Img_size, Img_size), Image.ANTIALIAS)
        image = Normalize(np.array(image).astype(np.float), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        return image, label

class AlexNet:
    def __init__(self, structShow=False):
        self.structShow = structShow
        self.image = data(shape=[Img_chs, Img_size, Img_size], dtype='float32', name='image')
        self.label = data(shape=[Label_size], dtype='int64', name='label')
        self.predict = self.get_alexNet()

    def get_alexNet(self):
        conv1 = conv2d(self.image, Conv1_chs, filter_size=Conv1_kernel_size, stride=4, padding=0, act='relu')
        pool1 = pool2d(conv1, 3, pool_stride=2, pool_type='max')

        conv2 = conv2d(pool1, Conv2_chs, filter_size=Conv2_kernel_size, padding=2, act='relu')
        pool2 = pool2d(conv2, 3, pool_stride=2, pool_type='max')

        conv3 = conv2d(pool2, Conv3_chs, filter_size=Conv3_kernel_size, padding=1, act='relu')
        conv4 = conv2d(conv3, Conv4_chs, filter_size=Conv4_kernel_size, padding=1, act='relu')
        conv5 = conv2d(conv4, Conv5_chs, filter_size=Conv5_kernel_size, padding=1, act='relu')
        pool3 = pool2d(conv5, 3, pool_stride=2, pool_type='max')

        flt = flatten(pool3, axis=1)
        fc1 = fc(flt, Fc1_size, act='relu')
        fc2 = fc(fc1, Fc2_size, act='relu')
        fc3 = fc(fc1, Fc3_size, act='softmax')

        if self.structShow:
            print(conv1.name, conv1.shape)
            print(pool1.name, pool1.shape)
            print(conv2.name, conv2.shape)
            print(pool2.name, pool2.shape)
            print(conv3.name, conv3.shape)
            print(conv4.name, conv4.shape)
            print(conv5.name, conv5.shape)
            print(pool3.name, pool3.shape)
            print(flt.name, flt.shape)
            print(fc1.name, fc1.shape)
            print(fc2.name, fc2.shape)
            print(fc3.name, fc3.shape)
        return fc3

def train():
    net = AlexNet(structShow=True)
    image, label, predict = net.image, net.label, net.predict
    feeder = fluid.DataFeeder(place=place, feed_list=[image, label])

    df = pd.read_csv(Data_csv_path, header=0, index_col=0)
    train_list = df[df['split'] == 'train']['filename'].tolist()
    val_list = df[df['split'] == 'val']['filename'].tolist()

    train_reader = MyDataset(Data_path, batch_size=Batch_size, files_list=train_list).dataset_reader()
    val_reader =  MyDataset(Data_path, batch_size=Batch_size, files_list=val_list).dataset_reader()

    loss = cross_entropy(input=predict, label=label)
    loss_mean = mean(loss)
    acc = accuracy(input=predict, label=label)
    optimizer = fluid.optimizer.AdamOptimizer(learning_rate=Learning_rate)
    optimizer.minimize(loss_mean)

    val_program = fluid.default_main_program().clone(for_test=True)
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    train_losses = np.ones(Epochs)
    train_accs = np.ones(Epochs)
    val_losses = np.ones(Epochs)
    val_accs = np.ones(Epochs)
    best_loss = float("inf")
    best_loss_epoch = 0
    for epoch in range(Epochs):
        print('Epoch %d/%d:' % (epoch + 1, Epochs))
        train_sum_loss = 0
        train_sum_acc = 0
        val_sum_loss = 0
        val_sum_acc = 0
        for batch_num, data in enumerate(train_reader()):
            train_loss, train_acc = exe.run(program=fluid.default_main_program(),  # 运行主程序
                                            feed=feeder.feed(data),  # 给模型喂入数据
                                            fetch_list=[loss_mean, acc])  # fetch 误差、准确率
            train_sum_loss += train_loss[0]
            train_sum_acc += train_acc[0]
            process_show(batch_num + 1, Train_num / Batch_size, train_acc, train_loss, prefix='train:')

        for batch_num, data in enumerate(val_reader()):
            val_loss, val_acc = exe.run(program=val_program,  # 执行训练程序
                                        feed=feeder.feed(data),  # 喂入数据
                                        fetch_list=[loss_mean, acc])  # fetch 误差、准确率
            val_sum_loss += val_loss[0]
            val_sum_acc += val_acc[0]
            process_show(batch_num + 1, Val_num / Batch_size, val_acc, val_loss, prefix='train:')

        train_sum_loss /= (Train_num // Batch_size)
        train_sum_acc /= (Train_num // Batch_size)
        val_sum_loss /= (Val_num // Batch_size)
        val_sum_acc /= (Val_num // Batch_size)

        train_losses[epoch] = train_sum_loss
        train_accs[epoch] = train_sum_acc
        val_losses[epoch] = val_sum_loss
        val_accs[epoch] = val_sum_acc
        print('average summary:\ntrain acc %.4f, loss %.4f ; val acc %.4f, loss %.4f'
              % (train_sum_acc, train_sum_loss, val_sum_acc, val_sum_loss))

        if val_sum_loss < best_loss:
            print('val_loss improve from %.4f to %.4f, model save to %s ! \n' % (
                best_loss, val_sum_loss, Model_file_paddle))
            best_loss = val_sum_loss
            best_loss_epoch = epoch + 1
            fluid.io.save_inference_model(Model_file_paddle,  # 保存推理model的路径
                                          ['image'],  # 推理（inference）需要 feed 的数据
                                          [predict],  # 保存推理（inference）结果的 Variables
                                          exe)  # executor 保存 inference model
        else:
            print('val_loss do not improve from %.4f \n' % (best_loss))
    print('best loss %.4f at epoch %d \n' % (best_loss, best_loss_epoch))
    draw_loss_acc(train_losses, train_accs, 'train')
    draw_loss_acc(val_losses, val_accs, 'val')


if __name__ == '__main__':
    pass
    # dataInfo_show(r'E:\_Python\01_deeplearning\03_AlexNet\data\my_imagenet',
    #               r'E:\_Python\01_deeplearning\03_AlexNet\data\my_imagenet.csv',
    #               r'E:\_Python\01_deeplearning\03_AlexNet\data\synset_words.txt')
    # dataset_divide(r'E:\_Python\01_deeplearning\03_AlexNet\data\my_imagenet.csv')
    train()

my_utils.py

# -*- coding: utf-8 -*- 
# @Time : 2020/1/21 11:39 
# @Author : Zhao HL
# @File : my_utils.py
import sys,os,random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
def process_show(num, nums, train_acc, train_loss, prefix='', suffix=''):
    rate = num / nums
    ratenum = int(round(rate, 2) * 100)
    bar = '\r%s batch %3d/%d:train accuracy %.4f, train loss %00.4f [%s%s]%.1f%% %s; ' % (
        prefix, num, nums, train_acc, train_loss, '#' * (ratenum//2), '_' * (50 - ratenum//2), ratenum, suffix)
    sys.stdout.write(bar)
    sys.stdout.flush()
    if num >= nums:
        print()

def dataInfo_show(data_path,csv_pth,cls_dic_path,shapesShow=True,classesShow=True):
    cls_dict = get_cls_dic(cls_dic_path)
    if classesShow:
        print('\n'+'*'*50)
        df = pd.read_csv(csv_pth)
        labels = df['label'].unique()
        label_cls = {label:cls_dict[label] for label in labels}
        print(label_cls)
        cls_count = df['label'].value_counts()
        cls_count = {cls_dict[k]:v for k,v in cls_count.items()}
        for k,v in cls_count.items():
            print(k,v)

    if shapesShow:
        print('\n'+'*'*50)
        shapes = []
        for filename in os.listdir(data_path):
            img = Image.open(os.path.join(data_path, filename))
            img = np.array(img)
            shapes.append(img.shape)
        shapes = pd.Series(shapes)
        print(shapes.value_counts())

def get_cls_dic(cls_dic_path):
    # 读取类标签字典，只取第一个逗号前的信息
    cls_df = pd.read_csv(cls_dic_path)
    cls_df['cls'] = cls_df['info'].apply(lambda x:x[:9]).tolist()
    cls_df['label'] = cls_df['info'].apply(lambda x: x[10:]).tolist()
    cls_df = cls_df.drop(columns=['info','other'])

    cls_dict = cls_df.set_index('cls').T.to_dict('list')
    cls_dict = {k:v[0] for k,v in cls_dict.items()}
    return cls_dict

def dataset_divide(csv_pth):
    cls_df = pd.read_csv(csv_pth, header=0,index_col=0)
    cls_df.insert(2,'split',None)
    filenames = list(cls_df['filename'])
    random.shuffle(filenames)
    train_num,train_val_num = int(len(filenames)*0.7),int(len(filenames)*0.8)
    train_names = filenames[:train_num]
    val_names = filenames[train_num:train_val_num]
    test_names = filenames[train_val_num:]
    cls_df.loc[cls_df['filename'].isin(train_names),'split'] = 'train'
    cls_df.loc[cls_df['filename'].isin(val_names), 'split'] = 'val'
    cls_df.loc[cls_df['filename'].isin(test_names), 'split'] = 'test'
    cls_df.to_csv(csv_pth)

def draw_loss_acc(loss,acc,type='',save_path=None):
    assert len(acc) == len(loss)
    x = [epoch for epoch in range(len(acc))]
    plt.subplot(2, 1, 1)
    plt.plot(x, acc, 'o-')
    plt.title(type+'  accuracy vs. epoches')
    plt.ylabel('accuracy')
    plt.subplot(2, 1, 2)
    plt.plot(x, loss, '.-')
    plt.xlabel(type+'  loss vs. epoches')
    plt.ylabel('loss')
    plt.show()
    if save_path:
        plt.savefig(os.path.join(save_path,type+"_acc_loss.png"))


if __name__ == '__main__':
    pass