# Copyright (c) 2018 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 paddle.fluid.core as core import math import os import sys import unittest import numpy import paddle import paddle.fluid as fluid from paddle.fluid.layers.device import get_places 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(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_1 = fluid.layers.batch_norm(conv_pool_1) 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(nn_type, use_cuda, parallel, save_dirname=None, save_full_dirname=None, model_filename=None, params_filename=None, is_local=True): if use_cuda and not fluid.core.is_compiled_with_cuda(): return img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') if nn_type == 'mlp': net_conf = mlp else: net_conf = conv_net if parallel: raise NotImplementedError() else: prediction, avg_loss, acc = net_conf(img, label) test_program = fluid.default_main_program().clone(for_test=True) optimizer = fluid.optimizer.Adam(learning_rate=0.001) optimizer.minimize(avg_loss) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) 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) def train_loop(main_program): exe.run(fluid.default_startup_program()) 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(main_program, 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 ) > 0.2: # Smaller value to increase CI speed if save_dirname is not None: fluid.io.save_inference_model( save_dirname, ["img"], [prediction], exe, model_filename=model_filename, params_filename=params_filename) if save_full_dirname is not None: fluid.io.save_inference_model( save_full_dirname, [], [], exe, model_filename=model_filename, params_filename=params_filename, export_for_deployment=False) 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))) if math.isnan(float(avg_loss_val)): sys.exit("got NaN loss, training failed.") raise AssertionError("Loss of recognize digits is too large") if is_local: train_loop(fluid.default_main_program()) else: port = os.getenv("PADDLE_PSERVER_PORT", "6174") pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip... eplist = [] for ip in pserver_ips.split(","): eplist.append(':'.join([ip, port])) pserver_endpoints = ",".join(eplist) # ip:port,ip:port... trainers = int(os.getenv("PADDLE_TRAINERS")) current_endpoint = os.getenv("POD_IP") + ":" + port trainer_id = int(os.getenv("PADDLE_TRAINER_ID")) training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER") t = fluid.DistributeTranspiler() t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers) if training_role == "PSERVER": pserver_prog = t.get_pserver_program(current_endpoint) pserver_startup = t.get_startup_program(current_endpoint, pserver_prog) exe.run(pserver_startup) exe.run(pserver_prog) elif training_role == "TRAINER": train_loop(t.get_trainer_program()) def infer(use_cuda, save_dirname=None, model_filename=None, params_filename=None): if save_dirname is None: return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() 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). [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model( save_dirname, exe, model_filename, params_filename) # The input's dimension of conv should be 4-D or 5-D. # Use normilized image pixels as input data, which should be in the range [-1.0, 1.0]. batch_size = 1 tensor_img = numpy.random.uniform( -1.0, 1.0, [batch_size, 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]) def main(use_cuda, parallel, nn_type, combine): save_dirname = None save_full_dirname = None model_filename = None params_filename = None if not use_cuda and not parallel: save_dirname = "recognize_digits_" + nn_type + ".inference.model" save_full_dirname = "recognize_digits_" + nn_type + ".train.model" if combine == True: model_filename = "__model_combined__" params_filename = "__params_combined__" # call train() with is_local argument to run distributed train train( nn_type=nn_type, use_cuda=use_cuda, parallel=parallel, save_dirname=save_dirname, save_full_dirname=save_full_dirname, model_filename=model_filename, params_filename=params_filename) infer( use_cuda=use_cuda, save_dirname=save_dirname, model_filename=model_filename, params_filename=params_filename) class TestRecognizeDigits(unittest.TestCase): pass def inject_test_method(use_cuda, parallel, nn_type, combine): def __impl__(self): prog = fluid.Program() startup_prog = fluid.Program() scope = fluid.core.Scope() with fluid.scope_guard(scope): with fluid.program_guard(prog, startup_prog): main(use_cuda, parallel, nn_type, combine) fn = 'test_{0}_{1}_{2}_{3}'.format(nn_type, 'cuda' if use_cuda else 'cpu', 'parallel' if parallel else 'normal', 'combine' if combine else 'separate') setattr(TestRecognizeDigits, fn, __impl__) def inject_all_tests(): for use_cuda in (False, True): if use_cuda and not core.is_compiled_with_cuda(): continue for parallel in (False, True): for nn_type in ('mlp', 'conv'): inject_test_method(use_cuda, parallel, nn_type, True) # Two unit-test for saving parameters as separate files inject_test_method(False, False, 'mlp', False) inject_test_method(False, False, 'conv', False) inject_all_tests() if __name__ == '__main__': unittest.main()