From 2ac46d538da103929e58c0500ca0baca391f39bc Mon Sep 17 00:00:00 2001 From: gongweibao Date: Fri, 19 Jan 2018 20:28:13 +0800 Subject: [PATCH] Add distribution implement of image classification. (#7687) Add distribution implement of image classification --- .../notest_dist_image_classification.py | 173 ++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py diff --git a/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py new file mode 100644 index 0000000000..218dea31e1 --- /dev/null +++ b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py @@ -0,0 +1,173 @@ +#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 sys + +import paddle.v2 as paddle +import paddle.v2.fluid as fluid +import os +import sys + +TRAINERS = 5 +BATCH_SIZE = 128 +PASS_NUM = 100 + + +def resnet_cifar10(input, depth=32): + def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'): + tmp = fluid.layers.conv2d( + input=input, + filter_size=filter_size, + num_filters=ch_out, + stride=stride, + padding=padding, + act=None, + bias_attr=False) + return fluid.layers.batch_norm(input=tmp, act=act) + + def shortcut(input, ch_in, ch_out, stride): + if ch_in != ch_out: + return conv_bn_layer(input, ch_out, 1, stride, 0, None) + else: + return input + + def basicblock(input, ch_in, ch_out, stride): + tmp = conv_bn_layer(input, ch_out, 3, stride, 1) + tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None) + short = shortcut(input, ch_in, ch_out, stride) + return fluid.layers.elementwise_add(x=tmp, y=short, act='relu') + + def layer_warp(block_func, input, ch_in, ch_out, count, stride): + tmp = block_func(input, ch_in, ch_out, stride) + for i in range(1, count): + tmp = block_func(tmp, ch_out, ch_out, 1) + return tmp + + assert (depth - 2) % 6 == 0 + n = (depth - 2) / 6 + conv1 = conv_bn_layer( + input=input, ch_out=16, filter_size=3, stride=1, padding=1) + res1 = layer_warp(basicblock, conv1, 16, 16, n, 1) + res2 = layer_warp(basicblock, res1, 16, 32, n, 2) + res3 = layer_warp(basicblock, res2, 32, 64, n, 2) + pool = fluid.layers.pool2d( + input=res3, pool_size=8, pool_type='avg', pool_stride=1) + return pool + + +def vgg16_bn_drop(input): + def conv_block(input, num_filter, groups, dropouts): + return fluid.nets.img_conv_group( + input=input, + pool_size=2, + pool_stride=2, + conv_num_filter=[num_filter] * groups, + conv_filter_size=3, + conv_act='relu', + conv_with_batchnorm=True, + conv_batchnorm_drop_rate=dropouts, + pool_type='max') + + conv1 = conv_block(input, 64, 2, [0.3, 0]) + conv2 = conv_block(conv1, 128, 2, [0.4, 0]) + conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0]) + conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0]) + conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0]) + + drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5) + fc1 = fluid.layers.fc(input=drop, size=512, act=None) + bn = fluid.layers.batch_norm(input=fc1, act='relu') + drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5) + fc2 = fluid.layers.fc(input=drop2, size=512, act=None) + return fc2 + + +classdim = 10 +data_shape = [3, 32, 32] + +images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32') +label = fluid.layers.data(name='label', shape=[1], dtype='int64') + +net_type = "vgg" +if len(sys.argv) >= 2: + net_type = sys.argv[1] + +if net_type == "vgg": + print("train vgg net") + net = vgg16_bn_drop(images) +elif net_type == "resnet": + print("train resnet") + net = resnet_cifar10(images, 32) +else: + raise ValueError("%s network is not supported" % net_type) + +predict = fluid.layers.fc(input=net, size=classdim, act='softmax') +cost = fluid.layers.cross_entropy(input=predict, label=label) +avg_cost = fluid.layers.mean(x=cost) + +optimizer = fluid.optimizer.Adam(learning_rate=0.001) +optimize_ops, params_grads = optimizer.minimize(avg_cost) + +accuracy = fluid.evaluator.Accuracy(input=predict, label=label) + +train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.cifar.train10(), buf_size=128 * 10), + batch_size=BATCH_SIZE) + +place = fluid.CPUPlace() +exe = fluid.Executor(place) + +t = fluid.DistributeTranspiler() +# all parameter server endpoints list for spliting parameters +pserver_endpoints = os.getenv("PSERVERS") +# server endpoint for current node +current_endpoint = os.getenv("SERVER_ENDPOINT") +# run as trainer or parameter server +training_role = os.getenv("TRAINING_ROLE", + "TRAINER") # get the training role: trainer/pserver +t.transpile( + optimize_ops, params_grads, pservers=pserver_endpoints, trainers=TRAINERS) + +if training_role == "PSERVER": + if not current_endpoint: + print("need env SERVER_ENDPOINT") + exit(1) + print("start pserver at:", current_endpoint) + 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) + print("pserver run end") +elif training_role == "TRAINER": + print("start trainer") + trainer_prog = t.get_trainer_program() + feeder = fluid.DataFeeder(place=place, feed_list=[images, label]) + exe.run(fluid.default_startup_program()) + for pass_id in range(PASS_NUM): + accuracy.reset(exe) + for data in train_reader(): + loss, acc = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost] + accuracy.metrics) + pass_acc = accuracy.eval(exe) + print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str( + pass_acc)) + # this model is slow, so if we can train two mini batch, we think it works properly. + print("trainer run end") +else: + print("environment var TRAINER_ROLE should be TRAINER os PSERVER") +exit(1) -- GitLab