Add distribution implement of image classification. (#7687)
Add distribution implement of image classificationadd_depthwiseConv_op_gpu
gongweibao
7 years ago
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#Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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from __future__ import print_function
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import sys
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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import os
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import sys
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TRAINERS = 5
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BATCH_SIZE = 128
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PASS_NUM = 100
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def resnet_cifar10(input, depth=32):
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def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
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tmp = fluid.layers.conv2d(
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input=input,
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filter_size=filter_size,
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num_filters=ch_out,
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stride=stride,
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padding=padding,
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act=None,
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bias_attr=False)
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return fluid.layers.batch_norm(input=tmp, act=act)
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def shortcut(input, ch_in, ch_out, stride):
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if ch_in != ch_out:
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return conv_bn_layer(input, ch_out, 1, stride, 0, None)
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else:
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return input
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def basicblock(input, ch_in, ch_out, stride):
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tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
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tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
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short = shortcut(input, ch_in, ch_out, stride)
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return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
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def layer_warp(block_func, input, ch_in, ch_out, count, stride):
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tmp = block_func(input, ch_in, ch_out, stride)
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for i in range(1, count):
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tmp = block_func(tmp, ch_out, ch_out, 1)
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return tmp
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assert (depth - 2) % 6 == 0
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n = (depth - 2) / 6
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conv1 = conv_bn_layer(
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input=input, ch_out=16, filter_size=3, stride=1, padding=1)
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res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
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res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
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res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
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pool = fluid.layers.pool2d(
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input=res3, pool_size=8, pool_type='avg', pool_stride=1)
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return pool
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def vgg16_bn_drop(input):
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def conv_block(input, num_filter, groups, dropouts):
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return fluid.nets.img_conv_group(
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input=input,
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pool_size=2,
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pool_stride=2,
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conv_num_filter=[num_filter] * groups,
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conv_filter_size=3,
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conv_act='relu',
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conv_with_batchnorm=True,
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conv_batchnorm_drop_rate=dropouts,
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pool_type='max')
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conv1 = conv_block(input, 64, 2, [0.3, 0])
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conv2 = conv_block(conv1, 128, 2, [0.4, 0])
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conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
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conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
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conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
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drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
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fc1 = fluid.layers.fc(input=drop, size=512, act=None)
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bn = fluid.layers.batch_norm(input=fc1, act='relu')
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drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
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fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
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return fc2
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classdim = 10
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data_shape = [3, 32, 32]
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images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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net_type = "vgg"
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if len(sys.argv) >= 2:
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net_type = sys.argv[1]
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if net_type == "vgg":
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print("train vgg net")
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net = vgg16_bn_drop(images)
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elif net_type == "resnet":
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print("train resnet")
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net = resnet_cifar10(images, 32)
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else:
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raise ValueError("%s network is not supported" % net_type)
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predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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optimizer = fluid.optimizer.Adam(learning_rate=0.001)
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optimize_ops, params_grads = optimizer.minimize(avg_cost)
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accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.cifar.train10(), buf_size=128 * 10),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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t = fluid.DistributeTranspiler()
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# all parameter server endpoints list for spliting parameters
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pserver_endpoints = os.getenv("PSERVERS")
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# server endpoint for current node
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current_endpoint = os.getenv("SERVER_ENDPOINT")
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# run as trainer or parameter server
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training_role = os.getenv("TRAINING_ROLE",
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"TRAINER") # get the training role: trainer/pserver
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t.transpile(
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optimize_ops, params_grads, pservers=pserver_endpoints, trainers=TRAINERS)
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if training_role == "PSERVER":
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if not current_endpoint:
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print("need env SERVER_ENDPOINT")
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exit(1)
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print("start pserver at:", current_endpoint)
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pserver_prog = t.get_pserver_program(current_endpoint)
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pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
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exe.run(pserver_startup)
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exe.run(pserver_prog)
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print("pserver run end")
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elif training_role == "TRAINER":
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print("start trainer")
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trainer_prog = t.get_trainer_program()
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feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
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exe.run(fluid.default_startup_program())
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for pass_id in range(PASS_NUM):
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accuracy.reset(exe)
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for data in train_reader():
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loss, acc = exe.run(trainer_prog,
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feed=feeder.feed(data),
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fetch_list=[avg_cost] + accuracy.metrics)
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pass_acc = accuracy.eval(exe)
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print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
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pass_acc))
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# this model is slow, so if we can train two mini batch, we think it works properly.
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print("trainer run end")
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else:
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print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
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exit(1)
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