用PaddlePaddle实现图像分类-SE_ResNeXt

原创
2020/04/17 13:24
阅读数 1.3K

项目简介

本项目基于paddle 实现了图像分类模型 SE_ResNeXt,建议使用GPU运行。动态图版本请查看:用PaddlePaddle实现图像分类-SE_ResNeXt(动态图版),具体介绍如下:

下载安装命令

## CPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/cpu paddlepaddle

## GPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/gpu paddlepaddle-gpu

ResNeXt:

VGG和ResNet的成功表明通过堆叠相同形状的Block的方法不仅可以减少超参数的数量,而且能取得SOTA的结果。而GoogleNet和Inception为代表的实践也表明,通过split-transform-merge策略进行精细的网络设计也能达到非常好的效果。ResNeXt的想法就是把这两种好的想法揉到一起。 ResNeXt并没有像GoogleNet系列那样进行split-transform-merge,而是如下图所示,对相同的子结构进行简单的重复,从而既做了split-transform-merge,同时也没有多少超参数量的增加。

上图中(a)结构是resnext的原始结构,(b)和(c)为(a)结构的等价表示,在实际实现中,采用实现起来相对简单的(c)结构,即通过分组卷积的形式来实现resnext的基础block。

Squeeze-and-Excitation Networks:

2017年Attention机制被提出,2018年很多的工作都是基于Attention机制进行的,SENet也可以看作是其中之一,该模块可以看作是channel-wies的Attention机制。如下图所示,SENet在常规的卷积之后增加了一条专门计算channel-wise scale的branch,然后把得到的值乘到相应的channel上。 

SE_ResNeXt:

SE_ResNeXt是将se模块应用在resnext中的residual block上得到的模型,其他结构参数配置可以了解ResNeXt网络结构。

参考链接:

[1] Res-Family: From ResNet to SE-ResNeXt
[2] Squeeze-and-Excitation Networks
[3] Aggregated Residual Transformations for Deep Neural Networks

数据介绍

使用公开鲜花据集,数据集压缩包里包含五个文件夹,每个文件夹一种花卉。分别是雏菊,蒲公英,玫瑰,向日葵,郁金香。每种各690-890张不等

In[1]
# 解压蔬菜数据集
!cd data/data2815 && unzip -q flower_photos.zip
In[2]
# 解压预训练模型参数 
!cd data/data6595 && unzip -q SE_ResNext50_32x4d_pretrained.zip
 

预处理数据,将其转化为需要的格式

In[3]
# 预处理数据,将其转化为标准格式。同时将数据拆分成两份,以便训练和计算预估准确率
import codecs
import os
import random
import shutil
from PIL import Image

train_ratio = 4.0 / 5

all_file_dir = 'data/data2815'
class_list = [c for c in os.listdir(all_file_dir) if os.path.isdir(os.path.join(all_file_dir, c)) and not c.endswith('Set') and not c.startswith('.')]
class_list.sort()
print(class_list)
train_image_dir = os.path.join(all_file_dir, "trainImageSet")
if not os.path.exists(train_image_dir):
    os.makedirs(train_image_dir)
    
eval_image_dir = os.path.join(all_file_dir, "evalImageSet")
if not os.path.exists(eval_image_dir):
    os.makedirs(eval_image_dir)

train_file = codecs.open(os.path.join(all_file_dir, "train.txt"), 'w')
eval_file = codecs.open(os.path.join(all_file_dir, "eval.txt"), 'w')

with codecs.open(os.path.join(all_file_dir, "label_list.txt"), "w") as label_list:
    label_id = 0
    for class_dir in class_list:
        label_list.write("{0}\t{1}\n".format(label_id, class_dir))
        image_path_pre = os.path.join(all_file_dir, class_dir)
        for file in os.listdir(image_path_pre):
            try:
                img = Image.open(os.path.join(image_path_pre, file))
                if random.uniform(0, 1) <= train_ratio:
                    shutil.copyfile(os.path.join(image_path_pre, file), os.path.join(train_image_dir, file))
                    train_file.write("{0}\t{1}\n".format(os.path.join(train_image_dir, file), label_id))
                else:
                    shutil.copyfile(os.path.join(image_path_pre, file), os.path.join(eval_image_dir, file))
                    eval_file.write("{0}\t{1}\n".format(os.path.join(eval_image_dir, file), label_id))
            except Exception as e:
                pass
                # 存在一些文件打不开,此处需要稍作清洗
        label_id += 1
            
train_file.close()
eval_file.close()
['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
 

模型训练主体

In[  ]
# -*- coding: UTF-8 -*-
"""
训练常用视觉基础网络,用于分类任务
需要将训练图片,类别文件 label_list.txt 放置在同一个文件夹下
程序会先读取 train.txt 文件获取类别数和图片数量
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import time
import math
import paddle
import paddle.fluid as fluid
import codecs
import logging

from paddle.fluid.initializer import MSRA
from paddle.fluid.initializer import Uniform
from paddle.fluid.param_attr import ParamAttr
from PIL import Image
from PIL import ImageEnhance

train_parameters = {
    "input_size": [3, 224, 224],
    "class_dim": -1,  # 分类数,会在初始化自定义 reader 的时候获得
    "image_count": -1,  # 训练图片数量,会在初始化自定义 reader 的时候获得
    "label_dict": {},
    "data_dir": "data/data2815",  # 训练数据存储地址
    "train_file_list": "train.txt",
    "label_file": "label_list.txt",
    "save_freeze_dir": "./freeze-model",
    "save_persistable_dir": "./persistable-params",
    "continue_train": False,        # 是否接着上一次保存的参数接着训练,优先级高于预训练模型
    "pretrained": True,            # 是否使用预训练的模型
    "pretrained_dir": "data/data6595/SE_ResNext50_32x4d_pretrained", 
    "mode": "train",
    "num_epochs": 20,
    "train_batch_size": 30,
    "mean_rgb": [127.5, 127.5, 127.5],  # 常用图片的三通道均值,通常来说需要先对训练数据做统计,此处仅取中间值
    "use_gpu": True,
    "dropout_seed": None,
    "image_enhance_strategy": {  # 图像增强相关策略
        "need_distort": True,  # 是否启用图像颜色增强
        "need_rotate": True,   # 是否需要增加随机角度
        "need_crop": True,      # 是否要增加裁剪
        "need_flip": True,      # 是否要增加水平随机翻转
        "hue_prob": 0.5,
        "hue_delta": 18,
        "contrast_prob": 0.5,
        "contrast_delta": 0.5,
        "saturation_prob": 0.5,
        "saturation_delta": 0.5,
        "brightness_prob": 0.5,
        "brightness_delta": 0.125
    },
    "early_stop": {
        "sample_frequency": 50,
        "successive_limit": 3,
        "good_acc1": 0.92
    },
    "rsm_strategy": {
        "learning_rate": 0.001,
        "lr_epochs": [20, 40, 60, 80, 100],
        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01, 0.002]
    },
    "momentum_strategy": {
        "learning_rate": 0.001,
        "lr_epochs": [20, 40, 60, 80, 100],
        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01, 0.002]
    },
    "sgd_strategy": {
        "learning_rate": 0.001,
        "lr_epochs": [20, 40, 60, 80, 100],
        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01, 0.002]
    },
    "adam_strategy": {
        "learning_rate": 0.002
    }
}


class SE_ResNeXt():
    def __init__(self, layers=50):
        self.params = train_parameters
        self.layers = layers

    def net(self, input, class_dim=1000):
        layers = self.layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)
        if layers == 50:
            cardinality = 32
            reduction_ratio = 16
            depth = [3, 4, 6, 3]
            num_filters = [128, 256, 512, 1024]

            conv = self.conv_bn_layer(
                input=input,
                num_filters=64,
                filter_size=7,
                stride=2,
                act='relu',
                name='conv1', )
            conv = fluid.layers.pool2d(
                input=conv,
                pool_size=3,
                pool_stride=2,
                pool_padding=1,
                pool_type='max')
        elif layers == 101:
            cardinality = 32
            reduction_ratio = 16
            depth = [3, 4, 23, 3]
            num_filters = [128, 256, 512, 1024]

            conv = self.conv_bn_layer(
                input=input,
                num_filters=64,
                filter_size=7,
                stride=2,
                act='relu',
                name="conv1", )
            conv = fluid.layers.pool2d(
                input=conv,
                pool_size=3,
                pool_stride=2,
                pool_padding=1,
                pool_type='max')
        elif layers == 152:
            cardinality = 64
            reduction_ratio = 16
            depth = [3, 8, 36, 3]
            num_filters = [128, 256, 512, 1024]

            conv = self.conv_bn_layer(
                input=input,
                num_filters=64,
                filter_size=3,
                stride=2,
                act='relu',
                name='conv1')
            conv = self.conv_bn_layer(
                input=conv,
                num_filters=64,
                filter_size=3,
                stride=1,
                act='relu',
                name='conv2')
            conv = self.conv_bn_layer(
                input=conv,
                num_filters=128,
                filter_size=3,
                stride=1,
                act='relu',
                name='conv3')
            conv = fluid.layers.pool2d(
                input=conv, pool_size=3, pool_stride=2, pool_padding=1, \
                pool_type='max')
        n = 1 if layers == 50 or layers == 101 else 3
        for block in range(len(depth)):
            n += 1
            for i in range(depth[block]):
                conv = self.bottleneck_block(
                    input=conv,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1,
                    cardinality=cardinality,
                    reduction_ratio=reduction_ratio,
                    name=str(n) + '_' + str(i + 1))

        pool = fluid.layers.pool2d(
            input=conv, pool_size=7, pool_type='avg', global_pooling=True)
        drop = fluid.layers.dropout(
            x=pool, dropout_prob=0.5, seed=self.params['dropout_seed'])
        stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
        out = fluid.layers.fc(
            input=drop,
            size=class_dim,
            act="softmax",
            param_attr=ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv),
                name='fc6_weights'),
            bias_attr=ParamAttr(name='fc6_offset'))
        return out

    def shortcut(self, input, ch_out, stride, name):
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            filter_size = 1
            return self.conv_bn_layer(
                input, ch_out, filter_size, stride, name='conv' + name + '_prj')
        else:
            return input

    def bottleneck_block(self,
                         input,
                         num_filters,
                         stride,
                         cardinality,
                         reduction_ratio,
                         name=None):
        conv0 = self.conv_bn_layer(
            input=input,
            num_filters=num_filters,
            filter_size=1,
            act='relu',
            name='conv' + name + '_x1')
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            groups=cardinality,
            act='relu',
            name='conv' + name + '_x2')
        conv2 = self.conv_bn_layer(
            input=conv1,
            num_filters=num_filters * 2,
            filter_size=1,
            act=None,
            name='conv' + name + '_x3')
        scale = self.squeeze_excitation(
            input=conv2,
            num_channels=num_filters * 2,
            reduction_ratio=reduction_ratio,
            name='fc' + name)

        short = self.shortcut(input, num_filters * 2, stride, name=name)

        return fluid.layers.elementwise_add(x=short, y=scale, act='relu')

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None,
                      name=None):
        conv = fluid.layers.conv2d(
            input=input,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=False,
            param_attr=ParamAttr(name=name + '_weights'), )
        bn_name = name + "_bn"
        return fluid.layers.batch_norm(
            input=conv,
            act=act,
            param_attr=ParamAttr(name=bn_name + '_scale'),
            bias_attr=ParamAttr(bn_name + '_offset'),
            moving_mean_name=bn_name + '_mean',
            moving_variance_name=bn_name + '_variance')

    def squeeze_excitation(self,
                           input,
                           num_channels,
                           reduction_ratio,
                           name=None):
        pool = fluid.layers.pool2d(
            input=input, pool_size=0, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        squeeze = fluid.layers.fc(
            input=pool,
            size=num_channels // reduction_ratio,
            act='relu',
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv),
                name=name + '_sqz_weights'),
            bias_attr=ParamAttr(name=name + '_sqz_offset'))
        stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
        excitation = fluid.layers.fc(
            input=squeeze,
            size=num_channels,
            act='sigmoid',
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv),
                name=name + '_exc_weights'),
            bias_attr=ParamAttr(name=name + '_exc_offset'))
        scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
        return scale


def init_log_config():
    """
    初始化日志相关配置
    :return:
    """
    global logger
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)
    log_path = os.path.join(os.getcwd(), 'logs')
    if not os.path.exists(log_path):
        os.makedirs(log_path)
    log_name = os.path.join(log_path, 'train.log')
    sh = logging.StreamHandler()
    fh = logging.FileHandler(log_name, mode='w')
    fh.setLevel(logging.DEBUG)
    formatter = logging.Formatter("%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s")
    fh.setFormatter(formatter)
    sh.setFormatter(formatter)
    logger.addHandler(sh)
    logger.addHandler(fh)


def init_train_parameters():
    """
    初始化训练参数,主要是初始化图片数量,类别数
    :return:
    """
    train_file_list = os.path.join(train_parameters['data_dir'], train_parameters['train_file_list'])
    label_list = os.path.join(train_parameters['data_dir'], train_parameters['label_file'])
    index = 0
    with codecs.open(label_list, encoding='utf-8') as flist:
        lines = [line.strip() for line in flist]
        for line in lines:
            parts = line.strip().split()
            train_parameters['label_dict'][parts[1]] = int(parts[0])
            index += 1
        train_parameters['class_dim'] = index
    with codecs.open(train_file_list, encoding='utf-8') as flist:
        lines = [line.strip() for line in flist]
        train_parameters['image_count'] = len(lines)


def resize_img(img, target_size):
    """
    强制缩放图片
    :param img:
    :param target_size:
    :return:
    """
    target_size = input_size
    img = img.resize((target_size[1], target_size[2]), Image.BILINEAR)
    return img


def random_crop(img, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
    w = 1. * aspect_ratio
    h = 1. / aspect_ratio

    bound = min((float(img.size[0]) / img.size[1]) / (w**2),
                (float(img.size[1]) / img.size[0]) / (h**2))
    scale_max = min(scale[1], bound)
    scale_min = min(scale[0], bound)

    target_area = img.size[0] * img.size[1] * np.random.uniform(scale_min,
                                                                scale_max)
    target_size = math.sqrt(target_area)
    w = int(target_size * w)
    h = int(target_size * h)

    i = np.random.randint(0, img.size[0] - w + 1)
    j = np.random.randint(0, img.size[1] - h + 1)

    img = img.crop((i, j, i + w, j + h))
    img = img.resize((train_parameters['input_size'][1], train_parameters['input_size'][2]), Image.BILINEAR)
    return img


def rotate_image(img):
    """
    图像增强,增加随机旋转角度
    """
    angle = np.random.randint(-14, 15)
    img = img.rotate(angle)
    return img


def random_brightness(img):
    """
    图像增强,亮度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['brightness_prob']:
        brightness_delta = train_parameters['image_enhance_strategy']['brightness_delta']
        delta = np.random.uniform(-brightness_delta, brightness_delta) + 1
        img = ImageEnhance.Brightness(img).enhance(delta)
    return img


def random_contrast(img):
    """
    图像增强,对比度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['contrast_prob']:
        contrast_delta = train_parameters['image_enhance_strategy']['contrast_delta']
        delta = np.random.uniform(-contrast_delta, contrast_delta) + 1
        img = ImageEnhance.Contrast(img).enhance(delta)
    return img


def random_saturation(img):
    """
    图像增强,饱和度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['saturation_prob']:
        saturation_delta = train_parameters['image_enhance_strategy']['saturation_delta']
        delta = np.random.uniform(-saturation_delta, saturation_delta) + 1
        img = ImageEnhance.Color(img).enhance(delta)
    return img


def random_hue(img):
    """
    图像增强,色度调整
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    if prob < train_parameters['image_enhance_strategy']['hue_prob']:
        hue_delta = train_parameters['image_enhance_strategy']['hue_delta']
        delta = np.random.uniform(-hue_delta, hue_delta)
        img_hsv = np.array(img.convert('HSV'))
        img_hsv[:, :, 0] = img_hsv[:, :, 0] + delta
        img = Image.fromarray(img_hsv, mode='HSV').convert('RGB')
    return img


def distort_color(img):
    """
    概率的图像增强
    :param img:
    :return:
    """
    prob = np.random.uniform(0, 1)
    # Apply different distort order
    if prob < 0.35:
        img = random_brightness(img)
        img = random_contrast(img)
        img = random_saturation(img)
        img = random_hue(img)
    elif prob < 0.7:
        img = random_brightness(img)
        img = random_saturation(img)
        img = random_hue(img)
        img = random_contrast(img)
    return img


def custom_image_reader(file_list, data_dir, mode):
    """
    自定义用户图片读取器,先初始化图片种类,数量
    :param file_list:
    :param data_dir:
    :param mode:
    :return:
    """
    with codecs.open(file_list) as flist:
        lines = [line.strip() for line in flist]

    def reader():
        np.random.shuffle(lines)
        for line in lines:
            if mode == 'train' or mode == 'val':
                img_path, label = line.split()
                img = Image.open(img_path)
                try:
                    if img.mode != 'RGB':
                        img = img.convert('RGB')
                    if train_parameters['image_enhance_strategy']['need_distort'] == True:
                        img = distort_color(img)
                    if train_parameters['image_enhance_strategy']['need_rotate'] == True:
                        img = rotate_image(img)
                    if train_parameters['image_enhance_strategy']['need_crop'] == True:
                        img = random_crop(img, train_parameters['input_size'])
                    if train_parameters['image_enhance_strategy']['need_flip'] == True:
                        mirror = int(np.random.uniform(0, 2))
                        if mirror == 1:
                            img = img.transpose(Image.FLIP_LEFT_RIGHT)
                    # HWC--->CHW && normalized
                    img = np.array(img).astype('float32')
                    img -= train_parameters['mean_rgb']
                    img = img.transpose((2, 0, 1))  # HWC to CHW
                    img *= 0.007843                 # 像素值归一化
                    yield img, int(label)
                except Exception as e:
                    pass                            # 以防某些图片读取处理出错,加异常处理
            elif mode == 'test':
                img_path = os.path.join(data_dir, line)
                img = Image.open(img_path)
                if img.mode != 'RGB':
                    img = img.convert('RGB')
                img = resize_img(img, train_parameters['input_size'])
                # HWC--->CHW && normalized
                img = np.array(img).astype('float32')
                img -= train_parameters['mean_rgb']
                img = img.transpose((2, 0, 1))  # HWC to CHW
                img *= 0.007843  # 像素值归一化
                yield img

    return reader


def optimizer_momentum_setting():
    """
    阶梯型的学习率适合比较大规模的训练数据
    """
    learning_strategy = train_parameters['momentum_strategy']
    batch_size = train_parameters["train_batch_size"]
    iters = train_parameters["image_count"] // batch_size
    lr = learning_strategy['learning_rate']

    boundaries = [i * iters for i in learning_strategy["lr_epochs"]]
    values = [i * lr for i in learning_strategy["lr_decay"]]
    learning_rate = fluid.layers.piecewise_decay(boundaries, values)
    optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
    return optimizer


def optimizer_rms_setting():
    """
    阶梯型的学习率适合比较大规模的训练数据
    """
    batch_size = train_parameters["train_batch_size"]
    iters = train_parameters["image_count"] // batch_size
    learning_strategy = train_parameters['rsm_strategy']
    lr = learning_strategy['learning_rate']

    boundaries = [i * iters for i in learning_strategy["lr_epochs"]]
    values = [i * lr for i in learning_strategy["lr_decay"]]

    optimizer = fluid.optimizer.RMSProp(
        learning_rate=fluid.layers.piecewise_decay(boundaries, values))

    return optimizer


def optimizer_sgd_setting():
    """
    loss下降相对较慢,但是最终效果不错,阶梯型的学习率适合比较大规模的训练数据
    """
    learning_strategy = train_parameters['sgd_strategy']
    batch_size = train_parameters["train_batch_size"]
    iters = train_parameters["image_count"] // batch_size
    lr = learning_strategy['learning_rate']

    boundaries = [i * iters for i in learning_strategy["lr_epochs"]]
    values = [i * lr for i in learning_strategy["lr_decay"]]
    learning_rate = fluid.layers.piecewise_decay(boundaries, values)
    optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
    return optimizer


def optimizer_adam_setting():
    """
    能够比较快速的降低 loss,但是相对后期乏力
    """
    learning_strategy = train_parameters['adam_strategy']
    learning_rate = learning_strategy['learning_rate']
    optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
    return optimizer


def load_params(exe, program):
    if train_parameters['continue_train'] and os.path.exists(train_parameters['save_persistable_dir']):
        logger.info('load params from retrain model')
        fluid.io.load_persistables(executor=exe,
                                   dirname=train_parameters['save_persistable_dir'],
                                   main_program=program)
    elif train_parameters['pretrained'] and os.path.exists(train_parameters['pretrained_dir']):
        logger.info('load params from pretrained model')
        def if_exist(var):
            return os.path.exists(os.path.join(train_parameters['pretrained_dir'], var.name))

        fluid.io.load_vars(exe, train_parameters['pretrained_dir'], main_program=program,
                           predicate=if_exist)


def train():
    train_prog = fluid.Program()
    train_startup = fluid.Program()
    logger.info("create prog success")
    logger.info("train config: %s", str(train_parameters))
    logger.info("build input custom reader and data feeder")
    file_list = os.path.join(train_parameters['data_dir'], "train.txt")
    mode = train_parameters['mode']
    batch_reader = paddle.batch(custom_image_reader(file_list, train_parameters['data_dir'], mode),
                                batch_size=train_parameters['train_batch_size'],
                                drop_last=False)
    batch_reader = paddle.reader.shuffle(batch_reader, train_parameters['train_batch_size'])
    place = fluid.CUDAPlace(0) if train_parameters['use_gpu'] else fluid.CPUPlace()
    # 定义输入数据的占位符
    img = fluid.data(name='img', shape=[-1] + train_parameters['input_size'], dtype='float32')
    label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
    feeder = fluid.DataFeeder(feed_list=[img, label], place=place)

    # 选取不同的网络
    logger.info("build newwork")
    model = SE_ResNeXt()
    out = model.net(input=img, class_dim=train_parameters['class_dim'])
    cost = fluid.layers.cross_entropy(out, label)
    avg_cost = fluid.layers.mean(x=cost)
    acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
    # 选取不同的优化器
    optimizer = optimizer_rms_setting()
    # optimizer = optimizer_momentum_setting()
    # optimizer = optimizer_sgd_setting()
    # optimizer = optimizer_adam_setting()
    optimizer.minimize(avg_cost)
    exe = fluid.Executor(place)

    main_program = fluid.default_main_program()
    exe.run(fluid.default_startup_program())
    train_fetch_list = [avg_cost.name, acc_top1.name, out.name]
    
    load_params(exe, main_program)

    # 训练循环主体
    stop_strategy = train_parameters['early_stop']
    successive_limit = stop_strategy['successive_limit']
    sample_freq = stop_strategy['sample_frequency']
    good_acc1 = stop_strategy['good_acc1']
    successive_count = 0
    stop_train = False
    total_batch_count = 0
    for pass_id in range(train_parameters["num_epochs"]):
        logger.info("current pass: %d, start read image", pass_id)
        batch_id = 0
        for step_id, data in enumerate(batch_reader()):
            t1 = time.time()
            # logger.info("data size:{0}".format(len(data)))
            loss, acc1, pred_ot = exe.run(main_program,
                                          feed=feeder.feed(data),
                                          fetch_list=train_fetch_list)
            t2 = time.time()
            batch_id += 1
            total_batch_count += 1
            period = t2 - t1
            loss = np.mean(np.array(loss))
            acc1 = np.mean(np.array(acc1))
            if batch_id % 10 == 0:
                logger.info("Pass {0}, trainbatch {1}, loss {2}, acc1 {3}, time {4}".format(pass_id, batch_id, loss, acc1,
                                                                                            "%2.2f sec" % period))
            # 简单的提前停止策略,认为连续达到某个准确率就可以停止了
            if acc1 >= good_acc1:
                successive_count += 1
                logger.info("current acc1 {0} meets good {1}, successive count {2}".format(acc1, good_acc1, successive_count))
                fluid.io.save_inference_model(dirname=train_parameters['save_freeze_dir'],
                                              feeded_var_names=['img'],
                                              target_vars=[out],
                                              main_program=main_program,
                                              executor=exe)
                if successive_count >= successive_limit:
                    logger.info("end training")
                    stop_train = True
                    break
            else:
                successive_count = 0

            # 通用的保存策略,减小意外停止的损失
            if total_batch_count % sample_freq == 0:
                logger.info("temp save {0} batch train result, current acc1 {1}".format(total_batch_count, acc1))
                fluid.io.save_persistables(dirname=train_parameters['save_persistable_dir'],
                                           main_program=main_program,
                                           executor=exe)
        if stop_train:
            break
    logger.info("training till last epcho, end training")
    fluid.io.save_persistables(dirname=train_parameters['save_persistable_dir'],
                                           main_program=main_program,
                                           executor=exe)
    fluid.io.save_inference_model(dirname=train_parameters['save_freeze_dir'],
                                              feeded_var_names=['img'],
                                              target_vars=[out],
                                              main_program=main_program,
                                              executor=exe)


if __name__ == '__main__':
    init_log_config()
    init_train_parameters()
    train()
2020-02-13 11:45:20,341-INFO: create prog success
2020-02-13 11:45:20,341 - <ipython-input-4-cf06ad10d681>[line:589] - INFO: create prog success
2020-02-13 11:45:20,344-INFO: train config: {'input_size': [3, 224, 224], 'class_dim': 5, 'image_count': 2919, 'label_dict': {'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}, 'data_dir': 'data/data2815', 'train_file_list': 'train.txt', 'label_file': 'label_list.txt', 'save_freeze_dir': './freeze-model', 'save_persistable_dir': './persistable-params', 'continue_train': False, 'pretrained': True, 'pretrained_dir': 'data/data6595/SE_ResNext50_32x4d_pretrained', 'mode': 'train', 'num_epochs': 20, 'train_batch_size': 30, 'mean_rgb': [127.5, 127.5, 127.5], 'use_gpu': True, 'dropout_seed': None, 'image_enhance_strategy': {'need_distort': True, 'need_rotate': True, 'need_crop': True, 'need_flip': True, 'hue_prob': 0.5, 'hue_delta': 18, 'contrast_prob': 0.5, 'contrast_delta': 0.5, 'saturation_prob': 0.5, 'saturation_delta': 0.5, 'brightness_prob': 0.5, 'brightness_delta': 0.125}, 'early_stop': {'sample_frequency': 50, 'successive_limit': 3, 'good_acc1': 0.92}, 'rsm_strategy': {'learning_rate': 0.001, 'lr_epochs': [20, 40, 60, 80, 100], 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'momentum_strategy': {'learning_rate': 0.001, 'lr_epochs': [20, 40, 60, 80, 100], 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'sgd_strategy': {'learning_rate': 0.001, 'lr_epochs': [20, 40, 60, 80, 100], 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'adam_strategy': {'learning_rate': 0.002}}
2020-02-13 11:45:20,344 - <ipython-input-4-cf06ad10d681>[line:590] - INFO: train config: {'input_size': [3, 224, 224], 'class_dim': 5, 'image_count': 2919, 'label_dict': {'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}, 'data_dir': 'data/data2815', 'train_file_list': 'train.txt', 'label_file': 'label_list.txt', 'save_freeze_dir': './freeze-model', 'save_persistable_dir': './persistable-params', 'continue_train': False, 'pretrained': True, 'pretrained_dir': 'data/data6595/SE_ResNext50_32x4d_pretrained', 'mode': 'train', 'num_epochs': 20, 'train_batch_size': 30, 'mean_rgb': [127.5, 127.5, 127.5], 'use_gpu': True, 'dropout_seed': None, 'image_enhance_strategy': {'need_distort': True, 'need_rotate': True, 'need_crop': True, 'need_flip': True, 'hue_prob': 0.5, 'hue_delta': 18, 'contrast_prob': 0.5, 'contrast_delta': 0.5, 'saturation_prob': 0.5, 'saturation_delta': 0.5, 'brightness_prob': 0.5, 'brightness_delta': 0.125}, 'early_stop': {'sample_frequency': 50, 'successive_limit': 3, 'good_acc1': 0.92}, 'rsm_strategy': {'learning_rate': 0.001, 'lr_epochs': [20, 40, 60, 80, 100], 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'momentum_strategy': {'learning_rate': 0.001, 'lr_epochs': [20, 40, 60, 80, 100], 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'sgd_strategy': {'learning_rate': 0.001, 'lr_epochs': [20, 40, 60, 80, 100], 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'adam_strategy': {'learning_rate': 0.002}}
2020-02-13 11:45:20,346-INFO: build input custom reader and data feeder
2020-02-13 11:45:20,346 - <ipython-input-4-cf06ad10d681>[line:591] - INFO: build input custom reader and data feeder
2020-02-13 11:45:20,349-INFO: build newwork
2020-02-13 11:45:20,349 - <ipython-input-4-cf06ad10d681>[line:605] - INFO: build newwork
2020-02-13 11:45:24,252-INFO: load params from pretrained model
2020-02-13 11:45:24,252 - <ipython-input-4-cf06ad10d681>[line:578] - INFO: load params from pretrained model
2020-02-13 11:45:24,604-INFO: current pass: 0, start read image
2020-02-13 11:45:24,604 - <ipython-input-4-cf06ad10d681>[line:634] - INFO: current pass: 0, start read image
2020-02-13 11:45:36,398-INFO: Pass 0, trainbatch 10, loss 1.7050864696502686, acc1 0.5, time 0.33 sec
2020-02-13 11:45:36,398 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 10, loss 1.7050864696502686, acc1 0.5, time 0.33 sec
2020-02-13 11:45:39,969-INFO: Pass 0, trainbatch 20, loss 1.2274528741836548, acc1 0.46666666865348816, time 0.54 sec
2020-02-13 11:45:39,969 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 20, loss 1.2274528741836548, acc1 0.46666666865348816, time 0.54 sec
2020-02-13 11:45:43,320-INFO: Pass 0, trainbatch 30, loss 1.240380048751831, acc1 0.6333333253860474, time 0.33 sec
2020-02-13 11:45:43,320 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 30, loss 1.240380048751831, acc1 0.6333333253860474, time 0.33 sec
2020-02-13 11:45:55,616-INFO: Pass 0, trainbatch 40, loss 1.2801538705825806, acc1 0.5, time 0.33 sec
2020-02-13 11:45:55,616 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 40, loss 1.2801538705825806, acc1 0.5, time 0.33 sec
2020-02-13 11:45:58,970-INFO: Pass 0, trainbatch 50, loss 1.2007794380187988, acc1 0.5666666626930237, time 0.34 sec
2020-02-13 11:45:58,970 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 50, loss 1.2007794380187988, acc1 0.5666666626930237, time 0.34 sec
2020-02-13 11:45:58,973-INFO: temp save 50 batch train result, current acc1 0.5666666626930237
2020-02-13 11:45:58,973 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 50 batch train result, current acc1 0.5666666626930237
2020-02-13 11:46:10,443-INFO: Pass 0, trainbatch 60, loss 0.7072051167488098, acc1 0.7666666507720947, time 0.34 sec
2020-02-13 11:46:10,443 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 60, loss 0.7072051167488098, acc1 0.7666666507720947, time 0.34 sec
2020-02-13 11:46:22,427-INFO: Pass 0, trainbatch 70, loss 0.7564895749092102, acc1 0.7333333492279053, time 0.34 sec
2020-02-13 11:46:22,427 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 70, loss 0.7564895749092102, acc1 0.7333333492279053, time 0.34 sec
2020-02-13 11:46:25,746-INFO: Pass 0, trainbatch 80, loss 0.5414671301841736, acc1 0.7333333492279053, time 0.33 sec
2020-02-13 11:46:25,746 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 80, loss 0.5414671301841736, acc1 0.7333333492279053, time 0.33 sec
2020-02-13 11:46:29,299-INFO: Pass 0, trainbatch 90, loss 0.8273724913597107, acc1 0.699999988079071, time 0.33 sec
2020-02-13 11:46:29,299 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 0, trainbatch 90, loss 0.8273724913597107, acc1 0.699999988079071, time 0.33 sec
2020-02-13 11:46:34,009-INFO: current pass: 1, start read image
2020-02-13 11:46:34,009 - <ipython-input-4-cf06ad10d681>[line:634] - INFO: current pass: 1, start read image
2020-02-13 11:46:42,880-INFO: temp save 100 batch train result, current acc1 0.7666666507720947
2020-02-13 11:46:42,880 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 100 batch train result, current acc1 0.7666666507720947
2020-02-13 11:46:48,708-INFO: Pass 1, trainbatch 10, loss 0.4975849688053131, acc1 0.8333333134651184, time 0.34 sec
2020-02-13 11:46:48,708 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 10, loss 0.4975849688053131, acc1 0.8333333134651184, time 0.34 sec
2020-02-13 11:46:52,282-INFO: Pass 1, trainbatch 20, loss 0.5479647517204285, acc1 0.7666666507720947, time 0.33 sec
2020-02-13 11:46:52,282 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 20, loss 0.5479647517204285, acc1 0.7666666507720947, time 0.33 sec
2020-02-13 11:46:53,964-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:46:53,964 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:46:56,923-INFO: Pass 1, trainbatch 30, loss 0.5903249382972717, acc1 0.7333333492279053, time 0.34 sec
2020-02-13 11:46:56,923 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 30, loss 0.5903249382972717, acc1 0.7333333492279053, time 0.34 sec
2020-02-13 11:47:08,670-INFO: Pass 1, trainbatch 40, loss 0.7165757417678833, acc1 0.6666666865348816, time 0.59 sec
2020-02-13 11:47:08,670 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 40, loss 0.7165757417678833, acc1 0.6666666865348816, time 0.59 sec
2020-02-13 11:47:11,370-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:47:11,370 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:47:13,129-INFO: Pass 1, trainbatch 50, loss 0.36270684003829956, acc1 0.8666666746139526, time 0.34 sec
2020-02-13 11:47:13,129 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 50, loss 0.36270684003829956, acc1 0.8666666746139526, time 0.34 sec
2020-02-13 11:47:13,796-INFO: temp save 150 batch train result, current acc1 0.6666666865348816
2020-02-13 11:47:13,796 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 150 batch train result, current acc1 0.6666666865348816
2020-02-13 11:47:19,953-INFO: Pass 1, trainbatch 60, loss 0.6333877444267273, acc1 0.7333333492279053, time 0.39 sec
2020-02-13 11:47:19,953 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 60, loss 0.6333877444267273, acc1 0.7333333492279053, time 0.39 sec
2020-02-13 11:47:32,101-INFO: Pass 1, trainbatch 70, loss 0.753383219242096, acc1 0.699999988079071, time 0.34 sec
2020-02-13 11:47:32,101 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 70, loss 0.753383219242096, acc1 0.699999988079071, time 0.34 sec
2020-02-13 11:47:35,453-INFO: Pass 1, trainbatch 80, loss 0.3543848991394043, acc1 0.9333333373069763, time 0.33 sec
2020-02-13 11:47:35,453 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 80, loss 0.3543848991394043, acc1 0.9333333373069763, time 0.33 sec
2020-02-13 11:47:35,456-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:47:35,456 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:47:37,146-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 2
2020-02-13 11:47:37,146 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 2
2020-02-13 11:47:39,198-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:47:39,198 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:47:42,606-INFO: Pass 1, trainbatch 90, loss 0.6844303607940674, acc1 0.7666666507720947, time 0.35 sec
2020-02-13 11:47:42,606 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 1, trainbatch 90, loss 0.6844303607940674, acc1 0.7666666507720947, time 0.35 sec
2020-02-13 11:47:47,262-INFO: current pass: 2, start read image
2020-02-13 11:47:47,262 - <ipython-input-4-cf06ad10d681>[line:634] - INFO: current pass: 2, start read image
2020-02-13 11:47:57,042-INFO: temp save 200 batch train result, current acc1 0.8333333134651184
2020-02-13 11:47:57,042 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 200 batch train result, current acc1 0.8333333134651184
2020-02-13 11:48:00,749-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:48:00,749 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:48:03,507-INFO: Pass 2, trainbatch 10, loss 0.36411359906196594, acc1 0.8333333134651184, time 0.34 sec
2020-02-13 11:48:03,507 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 10, loss 0.36411359906196594, acc1 0.8333333134651184, time 0.34 sec
2020-02-13 11:48:04,495-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:48:04,495 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:48:08,201-INFO: Pass 2, trainbatch 20, loss 0.8488689661026001, acc1 0.699999988079071, time 0.33 sec
2020-02-13 11:48:08,201 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 20, loss 0.8488689661026001, acc1 0.699999988079071, time 0.33 sec
2020-02-13 11:48:11,816-INFO: Pass 2, trainbatch 30, loss 0.45844316482543945, acc1 0.8333333134651184, time 0.36 sec
2020-02-13 11:48:11,816 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 30, loss 0.45844316482543945, acc1 0.8333333134651184, time 0.36 sec
2020-02-13 11:48:26,313-INFO: Pass 2, trainbatch 40, loss 0.34117022156715393, acc1 0.8333333134651184, time 0.33 sec
2020-02-13 11:48:26,313 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 40, loss 0.34117022156715393, acc1 0.8333333134651184, time 0.33 sec
2020-02-13 11:48:26,643-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:48:26,643 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:48:30,957-INFO: Pass 2, trainbatch 50, loss 0.4207676947116852, acc1 0.8333333134651184, time 0.34 sec
2020-02-13 11:48:30,957 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 50, loss 0.4207676947116852, acc1 0.8333333134651184, time 0.34 sec
2020-02-13 11:48:31,291-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:48:31,291 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:48:33,604-INFO: temp save 250 batch train result, current acc1 0.699999988079071
2020-02-13 11:48:33,604 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 250 batch train result, current acc1 0.699999988079071
2020-02-13 11:48:38,760-INFO: Pass 2, trainbatch 60, loss 0.3204272985458374, acc1 0.8333333134651184, time 0.33 sec
2020-02-13 11:48:38,760 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 60, loss 0.3204272985458374, acc1 0.8333333134651184, time 0.33 sec
2020-02-13 11:48:50,547-INFO: Pass 2, trainbatch 70, loss 0.951206386089325, acc1 0.7333333492279053, time 0.33 sec
2020-02-13 11:48:50,547 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 70, loss 0.951206386089325, acc1 0.7333333492279053, time 0.33 sec
2020-02-13 11:48:52,552-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:48:52,552 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:48:55,189-INFO: Pass 2, trainbatch 80, loss 0.5782159566879272, acc1 0.7666666507720947, time 0.62 sec
2020-02-13 11:48:55,189 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 80, loss 0.5782159566879272, acc1 0.7666666507720947, time 0.62 sec
2020-02-13 11:48:58,545-INFO: Pass 2, trainbatch 90, loss 0.6623353958129883, acc1 0.7666666507720947, time 0.33 sec
2020-02-13 11:48:58,545 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 2, trainbatch 90, loss 0.6623353958129883, acc1 0.7666666507720947, time 0.33 sec
2020-02-13 11:49:03,432-INFO: current pass: 3, start read image
2020-02-13 11:49:03,432 - <ipython-input-4-cf06ad10d681>[line:634] - INFO: current pass: 3, start read image
2020-02-13 11:49:14,224-INFO: temp save 300 batch train result, current acc1 0.800000011920929
2020-02-13 11:49:14,224 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 300 batch train result, current acc1 0.800000011920929
2020-02-13 11:49:18,036-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:18,036 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:20,050-INFO: Pass 3, trainbatch 10, loss 0.637872576713562, acc1 0.7666666507720947, time 0.36 sec
2020-02-13 11:49:20,050 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 10, loss 0.637872576713562, acc1 0.7666666507720947, time 0.36 sec
2020-02-13 11:49:22,079-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:49:22,079 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:49:24,519-INFO: Pass 3, trainbatch 20, loss 0.19275659322738647, acc1 0.9666666388511658, time 0.33 sec
2020-02-13 11:49:24,519 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 20, loss 0.19275659322738647, acc1 0.9666666388511658, time 0.33 sec
2020-02-13 11:49:24,523-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:49:24,523 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:49:28,304-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:28,304 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:30,656-INFO: Pass 3, trainbatch 30, loss 0.722159206867218, acc1 0.7666666507720947, time 0.62 sec
2020-02-13 11:49:30,656 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 30, loss 0.722159206867218, acc1 0.7666666507720947, time 0.62 sec
2020-02-13 11:49:42,422-INFO: Pass 3, trainbatch 40, loss 0.5116085410118103, acc1 0.699999988079071, time 0.35 sec
2020-02-13 11:49:42,422 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 40, loss 0.5116085410118103, acc1 0.699999988079071, time 0.35 sec
2020-02-13 11:49:46,065-INFO: Pass 3, trainbatch 50, loss 0.32186874747276306, acc1 0.8666666746139526, time 0.34 sec
2020-02-13 11:49:46,065 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 50, loss 0.32186874747276306, acc1 0.8666666746139526, time 0.34 sec
2020-02-13 11:49:46,731-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:46,731 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:48,457-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:48,457 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:50,534-INFO: temp save 350 batch train result, current acc1 0.8999999761581421
2020-02-13 11:49:50,534 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 350 batch train result, current acc1 0.8999999761581421
2020-02-13 11:49:54,067-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:54,067 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:49:56,159-INFO: Pass 3, trainbatch 60, loss 0.3668791949748993, acc1 0.8333333134651184, time 0.35 sec
2020-02-13 11:49:56,159 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 60, loss 0.3668791949748993, acc1 0.8333333134651184, time 0.35 sec
2020-02-13 11:50:08,509-INFO: Pass 3, trainbatch 70, loss 0.37396901845932007, acc1 0.8333333134651184, time 0.35 sec
2020-02-13 11:50:08,509 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 70, loss 0.37396901845932007, acc1 0.8333333134651184, time 0.35 sec
2020-02-13 11:50:10,199-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:10,199 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:11,965-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:11,965 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:14,328-INFO: Pass 3, trainbatch 80, loss 0.18938438594341278, acc1 0.9333333373069763, time 0.33 sec
2020-02-13 11:50:14,328 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 80, loss 0.18938438594341278, acc1 0.9333333373069763, time 0.33 sec
2020-02-13 11:50:14,332-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:14,332 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:18,008-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:18,008 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:20,153-INFO: Pass 3, trainbatch 90, loss 0.38485482335090637, acc1 0.8333333134651184, time 0.36 sec
2020-02-13 11:50:20,153 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 3, trainbatch 90, loss 0.38485482335090637, acc1 0.8333333134651184, time 0.36 sec
2020-02-13 11:50:23,148-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:23,148 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:24,855-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:24,855 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:27,252-INFO: current pass: 4, start read image
2020-02-13 11:50:27,252 - <ipython-input-4-cf06ad10d681>[line:634] - INFO: current pass: 4, start read image
2020-02-13 11:50:38,291-INFO: temp save 400 batch train result, current acc1 0.8666666746139526
2020-02-13 11:50:38,291 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 400 batch train result, current acc1 0.8666666746139526
2020-02-13 11:50:42,339-INFO: Pass 4, trainbatch 10, loss 0.6877737045288086, acc1 0.7666666507720947, time 0.34 sec
2020-02-13 11:50:42,339 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 10, loss 0.6877737045288086, acc1 0.7666666507720947, time 0.34 sec
2020-02-13 11:50:45,713-INFO: Pass 4, trainbatch 20, loss 0.24820978939533234, acc1 0.9333333373069763, time 0.34 sec
2020-02-13 11:50:45,713 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 20, loss 0.24820978939533234, acc1 0.9333333373069763, time 0.34 sec
2020-02-13 11:50:45,716-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:45,716 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:48,383-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:48,383 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:51,159-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:51,159 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:50:52,844-INFO: Pass 4, trainbatch 30, loss 0.3058304190635681, acc1 0.8333333134651184, time 0.66 sec
2020-02-13 11:50:52,844 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 30, loss 0.3058304190635681, acc1 0.8333333134651184, time 0.66 sec
2020-02-13 11:51:03,011-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:51:03,011 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:51:05,718-INFO: Pass 4, trainbatch 40, loss 0.39489877223968506, acc1 0.8666666746139526, time 0.33 sec
2020-02-13 11:51:05,718 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 40, loss 0.39489877223968506, acc1 0.8666666746139526, time 0.33 sec
2020-02-13 11:51:09,369-INFO: Pass 4, trainbatch 50, loss 0.5701739192008972, acc1 0.7666666507720947, time 0.33 sec
2020-02-13 11:51:09,369 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 50, loss 0.5701739192008972, acc1 0.7666666507720947, time 0.33 sec
2020-02-13 11:51:12,310-INFO: temp save 450 batch train result, current acc1 0.7333333492279053
2020-02-13 11:51:12,310 - <ipython-input-4-cf06ad10d681>[line:669] - INFO: temp save 450 batch train result, current acc1 0.7333333492279053
2020-02-13 11:51:20,854-INFO: Pass 4, trainbatch 60, loss 0.28663089871406555, acc1 0.8999999761581421, time 0.35 sec
2020-02-13 11:51:20,854 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 60, loss 0.28663089871406555, acc1 0.8999999761581421, time 0.35 sec
2020-02-13 11:51:29,662-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:51:29,662 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:51:34,046-INFO: Pass 4, trainbatch 70, loss 0.414912611246109, acc1 0.8666666746139526, time 0.34 sec
2020-02-13 11:51:34,046 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 70, loss 0.414912611246109, acc1 0.8666666746139526, time 0.34 sec
2020-02-13 11:51:37,061-INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:51:37,061 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9333333373069763 meets good 0.92, successive count 1
2020-02-13 11:51:38,448-INFO: Pass 4, trainbatch 80, loss 0.18134334683418274, acc1 0.9666666388511658, time 0.34 sec
2020-02-13 11:51:38,448 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 80, loss 0.18134334683418274, acc1 0.9666666388511658, time 0.34 sec
2020-02-13 11:51:38,452-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 2
2020-02-13 11:51:38,452 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 2
2020-02-13 11:51:41,175-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:51:41,175 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:51:44,527-INFO: Pass 4, trainbatch 90, loss 0.24817109107971191, acc1 0.8999999761581421, time 0.60 sec
2020-02-13 11:51:44,527 - <ipython-input-4-cf06ad10d681>[line:650] - INFO: Pass 4, trainbatch 90, loss 0.24817109107971191, acc1 0.8999999761581421, time 0.60 sec
2020-02-13 11:51:46,822-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:51:46,822 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:51:48,520-INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
2020-02-13 11:51:48,520 - <ipython-input-4-cf06ad10d681>[line:654] - INFO: current acc1 0.9666666388511658 meets good 0.92, successive count 1
In[5]
from __future__ import absolute_import  
from __future__ import division  
from __future__ import print_function  
  
import os  
import numpy as np  
import random  
import time  
import codecs  
import sys  
import functools  
import math  
import paddle  
import paddle.fluid as fluid  
from paddle.fluid import core  
from paddle.fluid.param_attr import ParamAttr  
from PIL import Image, ImageEnhance  
  
target_size = [3, 224, 224]  
mean_rgb = [127.5, 127.5, 127.5]  
data_dir = "data/data2815"  
eval_file = "eval.txt"  
use_gpu = True  
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()  
exe = fluid.Executor(place)  
save_freeze_dir = "./freeze-model"  
[inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(dirname=save_freeze_dir, executor=exe)  
# print(fetch_targets)  
  
  
def crop_image(img, target_size):  
    width, height = img.size  
    w_start = (width - target_size[2]) / 2  
    h_start = (height - target_size[1]) / 2  
    w_end = w_start + target_size[2]  
    h_end = h_start + target_size[1]  
    img = img.crop((w_start, h_start, w_end, h_end))  
    return img  
  
  
def resize_img(img, target_size):  
    ret = img.resize((target_size[1], target_size[2]), Image.BILINEAR)  
    return ret  
  
  
def read_image(img_path):  
    img = Image.open(img_path)  
    if img.mode != 'RGB':  
        img = img.convert('RGB')  
    img = crop_image(img, target_size)  
    img = np.array(img).astype('float32')  
    img -= mean_rgb  
    img = img.transpose((2, 0, 1))  # HWC to CHW  
    img *= 0.007843  
    img = img[np.newaxis,:]  
    return img  
  
  
def infer(image_path):  
    tensor_img = read_image(image_path)  
    label = exe.run(inference_program, feed={feed_target_names[0]: tensor_img}, fetch_list=fetch_targets)  
    return np.argmax(label)  
  
  
def eval_all():  
    eval_file_path = os.path.join(data_dir, eval_file)  
    total_count = 0  
    right_count = 0  
    with codecs.open(eval_file_path, encoding='utf-8') as flist:   
        lines = [line.strip() for line in flist]  
        t1 = time.time()  
        for line in lines:  
            total_count += 1  
            parts = line.strip().split()  
            result = infer(parts[0])  
            # print("infer result:{0} answer:{1}".format(result, parts[1]))  
            if str(result) == parts[1]:  
                right_count += 1  
        period = time.time() - t1  
        print("total eval count:{0} cost time:{1} predict accuracy:{2}".format(total_count, "%2.2f sec" % period, right_count / total_count))  
  
  
if __name__ == '__main__':  
    eval_all()
total eval count:751 cost time:41.82 sec predict accuracy:0.877496671105193

点击链接,使用AI Studio一键上手实践项目吧:https://aistudio.baidu.com/aistudio/projectdetail/169410 

下载安装命令

## CPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/cpu paddlepaddle

## GPU版本安装命令
pip install -f https://paddlepaddle.org.cn/pip/oschina/gpu paddlepaddle-gpu

>> 访问 PaddlePaddle 官网,了解更多相关内容

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