# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # 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 argparse import paddle.v2.fluid as fluid import paddle.v2 as paddle import sys import numpy def parse_arg(): parser = argparse.ArgumentParser() parser.add_argument( "nn_type", help="The neural network type, in ['mlp', 'conv']", type=str, choices=['mlp', 'conv']) parser.add_argument( "--parallel", help='Run in parallel or not', default=False, action="store_true") parser.add_argument( "--use_cuda", help="Run the program by using CUDA", default=False, action="store_true") return parser.parse_args() BATCH_SIZE = 64 def loss_net(hidden, label): prediction = fluid.layers.fc(input=hidden, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=prediction, label=label) avg_loss = fluid.layers.mean(x=loss) acc = fluid.layers.accuracy(input=prediction, label=label) return prediction, avg_loss, acc def mlp(img, label): hidden = fluid.layers.fc(input=img, size=200, act='tanh') hidden = fluid.layers.fc(input=hidden, size=200, act='tanh') return loss_net(hidden, label) def conv_net(img, label): conv_pool_1 = fluid.nets.simple_img_conv_pool( input=img, filter_size=5, num_filters=20, pool_size=2, pool_stride=2, act="relu") conv_pool_2 = fluid.nets.simple_img_conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, pool_size=2, pool_stride=2, act="relu") return loss_net(conv_pool_2, label) def train(args, save_dirname=None): print("recognize digits with args: {0}".format(" ".join(sys.argv[1:]))) img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') if args.nn_type == 'mlp': net_conf = mlp else: net_conf = conv_net if args.parallel: places = fluid.layers.get_places() pd = fluid.layers.ParallelDo(places) with pd.do(): img_ = pd.read_input(img) label_ = pd.read_input(label) prediction, avg_loss, acc = net_conf(img_, label_) for o in [avg_loss, acc]: pd.write_output(o) avg_loss, acc = pd() # get mean loss and acc through every devices. avg_loss = fluid.layers.mean(x=avg_loss) acc = fluid.layers.mean(x=acc) else: prediction, avg_loss, acc = net_conf(img, label) test_program = fluid.default_main_program().clone() optimizer = fluid.optimizer.Adam(learning_rate=0.001) optimizer.minimize(avg_loss) place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=500), batch_size=BATCH_SIZE) test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) feeder = fluid.DataFeeder(feed_list=[img, label], place=place) PASS_NUM = 100 for pass_id in range(PASS_NUM): for batch_id, data in enumerate(train_reader()): # train a mini-batch, fetch nothing exe.run(feed=feeder.feed(data)) if (batch_id + 1) % 10 == 0: acc_set = [] avg_loss_set = [] for test_data in test_reader(): acc_np, avg_loss_np = exe.run(program=test_program, feed=feeder.feed(test_data), fetch_list=[acc, avg_loss]) acc_set.append(float(acc_np)) avg_loss_set.append(float(avg_loss_np)) # get test acc and loss acc_val = numpy.array(acc_set).mean() avg_loss_val = numpy.array(avg_loss_set).mean() if float(acc_val) > 0.85: # test acc > 85% if save_dirname is not None: fluid.io.save_inference_model(save_dirname, ["img"], [prediction], exe) return else: print( 'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'. format(pass_id, batch_id + 1, float(avg_loss_val), float(acc_val))) def infer(args, save_dirname=None): if save_dirname is None: return place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) # 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). [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(save_dirname, exe) if args.nn_type == 'mlp': tensor_img = numpy.random.rand(1, 28, 28).astype("float32") else: tensor_img = numpy.random.rand(1, 1, 28, 28).astype("float32") # 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]: tensor_img}, fetch_list=fetch_targets) print("infer results: ", results[0]) if __name__ == '__main__': args = parse_arg() if not args.use_cuda and not args.parallel: save_dirname = "recognize_digits_" + args.nn_type + ".inference.model" else: save_dirname = None train(args, save_dirname) infer(args, save_dirname)