入门示例
pdpd静态图大体上是如下这样一个过程，先有个印象，具体参考代码理解
首先定义前向网络（定义模型时需要占位），指标计算(label,loss,outs)，优化器
train函数：
设置paddle的数据读取对象reader，类似torch的dataloader，reader会一次提供多列数据
设置exe，即执行器，类似一个session？
初始化结果
设置program(main,start,test)，主函数，启动程序 （pd以Program的形式动态描述计算过程）
设置优化目标（最小化loss结果）
train循环：
设置feeder，数据提供器，可以理解为占位
exe.run(start_program) #初始化program，编译为c++形式
for _ in range(epoch):
for data in reader():
#开始训练program
outs = exe.run(main_program,
feed = feeder.feed(data),#往占位填数据，前向输入的变量
fetch_list = []) #结果名字的列表
fluid.io.save_inference_model() #保存模型
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License from __future__ import print_function import os import argparse import paddle import paddle.fluid as fluid import numpy import sys from resnet import resnet_cifar10 def parse_args(): #定义参数 parser = argparse.ArgumentParser("image_classification") parser.add_argument( '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.') parser.add_argument( '--use_gpu', type=bool, default=0, help='whether to use gpu') parser.add_argument( '--num_epochs', type=int, default=1, help='number of epoch') args = parser.parse_args() return args def inference_network(): #定义前向网络 # The image is 32 * 32 with RGB representation. data_shape = [3, 32, 32] images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32') predict = resnet_cifar10(images, 32) # predict = vgg_bn_drop(images) # un-comment to use vgg net return predict def train_network(predict): #定义结果指标 label = fluid.layers.data(name='label', shape=[1], dtype='int64') cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) accuracy = fluid.layers.accuracy(input=predict, label=label) return [avg_cost, accuracy] def optimizer_program(): return fluid.optimizer.Adam(learning_rate=0.001) def train(use_cuda, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() BATCH_SIZE = 128 #设置paddle的数据读取对象reader，类似torch的dataloader if args.enable_ce: train_reader = paddle.batch( paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE) test_reader = paddle.batch( paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) else: test_reader = paddle.batch( paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.cifar.train10(), buf_size=128 * 100), batch_size=BATCH_SIZE) #定义feed的对象 feed_order = ['pixel', 'label'] #设置主程序，startup以及main、test，训练前exe.run(start_program); #训练时传入exe.run(main_program)，测试时exe.run(test_program) main_program = fluid.default_main_program() start_program = fluid.default_startup_program() if args.enable_ce: main_program.random_seed = 90 start_program.random_seed = 90 #初始化结果 predict = inference_network() avg_cost, acc = train_network(predict) # Test program test_program = main_program.clone(for_test=True) optimizer = optimizer_program() optimizer.minimize(avg_cost) #设置执行器exe exe = fluid.Executor(place) EPOCH_NUM = args.num_epochs # For training test cost def train_test(program, reader): count = 0 feed_var_list = [ program.global_block().var(var_name) for var_name in feed_order ] #定义feed列表 # 定义前向数据占位feeder feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place) test_exe = fluid.Executor(place) accumulated = len([avg_cost, acc]) * [0] for tid, test_data in enumerate(reader()): avg_cost_np = test_exe.run( program=program, feed=feeder_test.feed(test_data), fetch_list=[avg_cost, acc]) accumulated = [ x[0] + x[1][0] for x in zip(accumulated, avg_cost_np) ] count += 1 return [x / count for x in accumulated] # main train loop. def train_loop(): feed_var_list_loop = [ main_program.global_block().var(var_name) for var_name in feed_order ] #定义前向数据占位feeder feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place) exe.run(start_program) #网络参数初始化 step = 0 for pass_id in range(EPOCH_NUM): for step_id, data_train in enumerate(train_reader()): avg_loss_value = exe.run(#使用data填充前传数据的占位feeder，填入结果名字到fetch_list main_program, feed=feeder.feed(data_train), fetch_list=[avg_cost, acc]) if step_id % 100 == 0: print(" Pass %d, Batch %d, Cost %f, Acc %f" % ( step_id, pass_id, avg_loss_value[0], avg_loss_value[1])) else: sys.stdout.write('.') sys.stdout.flush() step += 1 #做验证 avg_cost_test, accuracy_test = train_test( test_program, reader=test_reader) print(' Test with Pass {0}, Loss {1:2.2}, Acc {2:2.2}'.format( pass_id, avg_cost_test, accuracy_test)) #保存模型参数 if params_dirname is not None: fluid.io.save_inference_model(params_dirname, ["pixel"], [predict], exe) if args.enable_ce and pass_id == EPOCH_NUM - 1: print("kpis train_cost %f" % avg_loss_value[0]) print("kpis train_acc %f" % avg_loss_value[1]) print("kpis test_cost %f" % avg_cost_test) print("kpis test_acc %f" % accuracy_test) train_loop() def infer(use_cuda, params_dirname=None): from PIL import Image place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() def load_image(infer_file): im = Image.open(infer_file) im = im.resize((32, 32), Image.ANTIALIAS) im = numpy.array(im).astype(numpy.float32) # The storage order of the loaded image is W(width), # H(height), C(channel). PaddlePaddle requires # the CHW order, so transpose them. im = im.transpose((2, 0, 1)) # CHW im = im / 255.0 # Add one dimension to mimic the list format. im = numpy.expand_dims(im, axis=0) return im cur_dir = os.path.dirname(os.path.realpath(__file__)) img = load_image(cur_dir + '/image/dog.png') with fluid.scope_guard(inference_scope): # Use fluid.io.load_inference_model to obtain the inference program desc, # the feed_target_names (the names of variables that will be feeded # data using feed operators), and the fetch_targets (variables that # we want to obtain data from using fetch operators). #加载模型 [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(params_dirname, exe) # Construct feed as a dictionary of {feed_target_name: feed_target_data} # and results will contain a list of data corresponding to fetch_targets. results = exe.run( inference_program, feed={feed_target_names[0]: img}, fetch_list=fetch_targets) # infer label label_list = [ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" ] print("infer results: %s" % label_list[numpy.argmax(results[0])]) def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return save_path = "image_classification_resnet.inference.model" train(use_cuda=use_cuda, params_dirname=save_path) infer(use_cuda=use_cuda, params_dirname=save_path) if __name__ == '__main__': # For demo purpose, the training runs on CPU # Please change accordingly. args = parse_args() use_cuda = args.use_gpu main(use_cuda)