【paddlepaddle速成】paddlepaddle图像分类从模型自定义到测试

这一次我们讲讲paddlepadle这个百度开源的机器学习框架，一个图像分类任务从训练到测试出结果的全流程。

将涉及到paddlepaddle和visualdl，git如下：https://github.com/PaddlePaddle

相关的代码、数据都在我们 Git 上，希望大家 Follow 一下这个 Git 项目，后面会持续更新不同框架下的任务。

https://github.com/longpeng2008/LongPeng_ML_Course

01paddlepaddle是什么

正所谓google有tensorflow，facebook有pytorch，amazon有mxnet，作为国内机器学习的先驱，百度也有PaddlePaddle，其中Paddle即Parallel Distributed Deep Learning(并行分布式深度学习)，整体使用起来与tensorflow非常类似。

sudo pip install paddlepaddle

安装就是一条命令，话不多说上代码。

02paddlepaddle训练

训练包括三部分，数据的定义，网络的定义，以及可视化和模型的存储。

2.1 数据定义

定义一个图像分类任务的dataset如下：

from multiprocessing import cpu_count

import paddle.v2 as paddle

class Dataset:

def __init__(self,cropsize,resizesize):

self.cropsize = cropsize

self.resizesize = resizesize

def train_mapper(self,sample):

img, label = sample

img = paddle.image.load_image(img)

img = paddle.image.simple_transform(img, self.resizesize, self.cropsize, True)

#print "train_mapper",img.shape,label

return img.flatten().astype('float32'), label

def test_mapper(self,sample):

img, label = sample

img = paddle.image.load_image(img)

img = paddle.image.simple_transform(img, self.resizesize, self.cropsize, False)

#print "test_mapper",img.shape,label

return img.flatten().astype('float32'), label

def train_reader(self,train_list, buffered_size=1024):

def reader():

with open(train_list, 'r') as f:

lines = [line.strip() for line in f.readlines()]

print "len of train dataset=",len(lines)

for line in lines:

img_path, lab = line.strip().split(' ')

yield img_path, int(lab)

return paddle.reader.xmap_readers(self.train_mapper, reader,

cpu_count(), buffered_size)

def test_reader(self,test_list, buffered_size=1024):

def reader():

with open(test_list, 'r') as f:

lines = [line.strip() for line in f.readlines()]

print "len of val dataset=",len(lines)

for line in lines:

img_path, lab = line.strip().split(' ')

yield img_path, int(lab)

return paddle.reader.xmap_readers(self.test_mapper, reader,

cpu_count(), buffered_size)

从上面代码可以看出:

(1) 使用了paddle.image.load_image进行图片的读取， paddle.image.simple_transform进行了简单的图像变换，这里只有图像crop操作，更多的使用可以参考API。

(2) 使用了paddle.reader.xmap_readers进行数据的映射。

2.2 网络定义

# coding=utf-8 import paddle.fluid as fluid def simplenet(input): # 定义卷积块 conv1 = fluid.layers.conv2d(input=input, num_filters=12,stride=2,padding=1,filter_size=3,act="relu") bn1 = fluid.layers.batch_norm(input=conv1) conv2 = fluid.layers.conv2d(input=bn1, num_filters=12,stride=2,padding=1,filter_size=3,act="relu") bn2 = fluid.layers.batch_norm(input=conv2) conv3 = fluid.layers.conv2d(input=bn2, num_filters=12,stride=2,padding=1,filter_size=3,act="relu") bn3 = fluid.layers.batch_norm(input=conv3) fc1 = fluid.layers.fc(input=bn3, size=128, act=None) return fc1,conv1

与之前的caffe，pytorch，tensorflow框架一样，定义了一个3层卷积与2层全连接的网络。为了能够更好的进行可视化，我们使用了PaddlePaddle Fluid，Fluid的设计也是用来让用户像Pytorch和Tensorflow Eager Execution一样可以执行动态计算而不需要创建图。

2.3可视化

paddlepaddle有与之配套使用的可视化框架，即visualdl。

visualdl是百度数据可视化实验室发布的深度学习可视化平台，它的定位与tensorboard很像，可视化内容包含了向量，参数直方图分布，模型结构，图像等功能，以后我们会详细给大家讲述，这次直接在代码中展示如何使用。

安装使用pip install --upgrade visualdl，使用下面的命令可以查看官方例子：

vdl_create_scratch_log

visualDL --logdir ./scratch_log --port 8080

http://127.0.0.1:8080

下面是loss和直方图的查看

在咱们项目中，具体使用方法如下

# 首先定义相关变量 # 创建VisualDL，并指定log存储路径 logdir = "./logs" logwriter = LogWriter(logdir, sync_cycle=10) # 创建loss的趋势图 with logwriter.mode("train") as writer: loss_scalar = writer.scalar("loss") # 创建acc的趋势图 with logwriter.mode("train") as writer: acc_scalar = writer.scalar("acc") # 定义输出频率 num_samples = 4 # 创建卷积层和输出图像的图形化展示 with logwriter.mode("train") as writer: conv_image = writer.image("conv_image", num_samples, 1) input_image = writer.image("input_image", num_samples, 1) # 创建可视化的训练模型结构 with logwriter.mode("train") as writer: param1_histgram = writer.histogram("param1", 100)

然后在训练过程中进行记录，这是完整的训练代码，红色部分就是记录结果。

# coding=utf-8 import numpy as np import os import paddle.fluid as fluid import paddle.fluid.framework as framework import paddle.v2 as paddle from paddle.fluid.initializer import NormalInitializer from paddle.fluid.param_attr import ParamAttr from visualdl import LogWriter from dataset import Dataset from net_fluid import simplenet # 创建VisualDL，并指定当前该项目的VisualDL的路径 logdir = "./logs" logwriter = LogWriter(logdir, sync_cycle=10) # 创建loss的趋势图 with logwriter.mode("train") as writer: loss_scalar = writer.scalar("loss") # 创建acc的趋势图 with logwriter.mode("train") as writer: acc_scalar = writer.scalar("acc") # 定义输出频率 num_samples = 4 # 创建卷积层和输出图像的图形化展示 with logwriter.mode("train") as writer: conv_image = writer.image("conv_image", num_samples, 1) input_image = writer.image("input_image", num_samples, 1) # 创建可视化的训练模型结构 with logwriter.mode("train") as writer: param1_histgram = writer.histogram("param1", 100) def train(use_cuda, learning_rate, num_passes, BATCH_SIZE=128): class_dim = 2 image_shape = [3, 48, 48] image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') net, conv1 = simplenet(image) # 获取全连接输出 predict = fluid.layers.fc( input=net, size=class_dim, act='softmax', param_attr=ParamAttr(name="param1", initializer=NormalInitializer())) # 获取损失 cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(x=cost) # 计算batch，从而来求平均的准确率 batch_size = fluid.layers.create_tensor(dtype='int64') print "batchsize=",batch_size batch_acc = fluid.layers.accuracy(input=predict, label=label, total=batch_size) # 定义优化方法 optimizer = fluid.optimizer.Momentum( learning_rate=learning_rate, momentum=0.9, regularization=fluid.regularizer.L2Decay(5 * 1e-5)) opts = optimizer.minimize(avg_cost) # 是否使用GPU place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() # 创建调试器 exe = fluid.Executor(place) # 初始化调试器 exe.run(fluid.default_startup_program()) # 保存结果 model_save_dir = "./models" # 获取训练数据 resizesize = 60 cropsize = 48 mydata = Dataset(cropsize=cropsize,resizesize=resizesize) mydatareader = mydata.train_reader(train_list='./all_shuffle_train.txt') train_reader = paddle.batch(reader=paddle.reader.shuffle(reader=mydatareader,buf_size=50000),batch_size=128) # 指定数据和label的对应关系 feeder = fluid.DataFeeder(place=place, feed_list=[image, label]) step = 0 sample_num = 0 start_up_program = framework.default_startup_program() param1_var = start_up_program.global_block().var("param1") accuracy = fluid.average.WeightedAverage() # 开始训练，使用循环的方式来指定训多少个Pass for pass_id in range(num_passes): # 从训练数据中按照一个个batch来读取数据 accuracy.reset() for batch_id, data in enumerate(train_reader()): loss, conv1_out, param1, acc, weight = exe.run(fluid.default_main_program(), feed=feeder.feed(data), fetch_list=[avg_cost, conv1, param1_var, batch_acc,batch_size]) accuracy.add(value=acc, weight=weight) pass_acc = accuracy.eval() # 重新启动图形化展示组件 if sample_num == 0: input_image.start_sampling() conv_image.start_sampling() # 获取taken idx1 = input_image.is_sample_taken() idx2 = conv_image.is_sample_taken() # 保证它们的taken是一样的 assert idx1 == idx2 idx = idx1 if idx != -1: # 加载输入图像的数据数据 image_data = data[0][0] input_image_data = np.transpose( image_data.reshape(image_shape), axes=[1, 2, 0]) input_image.set_sample(idx, input_image_data.shape, input_image_data.flatten()) # 加载卷积数据 conv_image_data = conv1_out[0][0] conv_image.set_sample(idx, conv_image_data.shape, conv_image_data.flatten()) # 完成输出一次 sample_num += 1 if sample_num % num_samples == 0: input_image.finish_sampling() conv_image.finish_sampling() sample_num = 0 # 加载趋势图的数据 loss_scalar.add_record(step, loss) acc_scalar.add_record(step, acc) # 添加模型结构数据 param1_histgram.add_record(step, param1.flatten())

# 输出训练日志 print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(pass_acc)) step += 1 model_path = os.path.join(model_save_dir,str(pass_id)) if not os.path.exists(model_save_dir): os.mkdir(model_save_dir) fluid.io.save_inference_model(model_path,['image'],[predict],exe) if __name__ == '__main__': # 开始训练 train(use_cuda=False, learning_rate=0.005, num_passes=300)

2.4训练结果

看看acc和loss的曲线，可见已经收敛

03paddlepaddle测试

训练的时候使用了fluid，测试的时候也需要定义调试器，加载训练好的模型，完整的代码如下

# encoding:utf-8 import sys import numpy as np import paddle.v2 as paddle from PIL import Image import os import cv2 # coding=utf-8 import numpy as np import paddle.fluid as fluid import paddle.fluid.framework as framework import paddle.v2 as paddle from paddle.fluid.initializer import NormalInitializer from paddle.fluid.param_attr import ParamAttr from visualdl import LogWriter from net_fluid import simplenet if __name__ == "__main__": # 开始预测 type_size = 2 testsize = 48 imagedir = sys.argv[1] images = os.listdir(imagedir)

# 定义调试器 save_dirname = "./models/299" exe = fluid.Executor(fluid.CPUPlace()) inference_scope = fluid.core.Scope() with fluid.scope_guard(inference_scope): # 加载模型

[inference_program,feed_target_names,fetch_targets] = fluid.io.load_inference_model(save_dirname,exe) predicts = np.zeros((type_size,1)) for image in images: imagepath = os.path.join(imagedir,image) img = paddle.image.load_image(imagepath) img = paddle.image.simple_transform(img,testsize,testsize,False) img = img[np.newaxis,:] #print img.shape results = np.argsort(-exe.run(inference_program,feed={feed_target_names[0]:img}, fetch_list=fetch_targets)[0]) label = results[0][0] predicts[label] += 1 print predicts

由于所有框架的测试流程都差不多，所以就不对每一部分进行解释了，大家可以自行去看代码。