lilong12
5 years ago
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# Copyright (c) 2018 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 numpy as np
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import argparse
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import time
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import math
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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from paddle.fluid import core
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import unittest
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from multiprocessing import Process
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import os
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import signal
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from functools import reduce
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from test_dist_base import TestDistRunnerBase, runtime_main
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import paddle.distributed.fleet as fleet
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paddle.enable_static()
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DTYPE = "float32"
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paddle.dataset.mnist.fetch()
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# Fix seed for test
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fluid.default_startup_program().random_seed = 1
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fluid.default_main_program().random_seed = 1
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def cnn_model(data):
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=data,
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filter_size=5,
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num_filters=20,
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pool_size=2,
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pool_stride=2,
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act="relu",
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param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
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value=0.01)))
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conv_pool_2 = fluid.nets.simple_img_conv_pool(
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input=conv_pool_1,
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filter_size=5,
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num_filters=50,
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pool_size=2,
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pool_stride=2,
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act="relu",
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param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
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value=0.01)))
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SIZE = 10
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input_shape = conv_pool_2.shape
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param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
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scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
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predict = fluid.layers.fc(
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input=conv_pool_2,
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size=SIZE,
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act="softmax",
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01)))
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return predict
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class TestDistMnist2x2(TestDistRunnerBase):
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def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
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# Input data
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device_id = 0
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if dist_strategy:
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fleet.init(is_collective=True)
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with fluid.device_guard("gpu:0"):
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images = fluid.layers.data(
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name='pixel', shape=[1, 28, 28], dtype=DTYPE)
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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if dist_strategy:
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data_loader = fluid.io.DataLoader.from_generator(
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feed_list=[images, label],
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capacity=64,
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use_double_buffer=False,
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iterable=False)
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# Train program
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predict = cnn_model(images)
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with fluid.device_guard("gpu:0"):
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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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# Evaluator
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with fluid.device_guard("gpu:0"):
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batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
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batch_acc = fluid.layers.accuracy(
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input=predict, label=label, total=batch_size_tensor)
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inference_program = fluid.default_main_program().clone()
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base_lr = self.lr
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passes = [30, 60, 80, 90]
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steps_per_pass = 10
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bd = [steps_per_pass * p for p in passes]
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lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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lr_val = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
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opt = fluid.optimizer.Momentum(learning_rate=lr_val, momentum=0.9)
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# Reader
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train_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=batch_size)
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test_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=batch_size)
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if dist_strategy:
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strategy = fleet.DistributedStrategy()
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strategy.pipeline = True
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dist_opt = fleet.distributed_optimizer(
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optimizer=opt, strategy=strategy)
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dist_opt.minimize(avg_cost)
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else:
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opt.minimize(avg_cost)
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if dist_strategy:
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return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict, data_loader
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else:
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return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
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if __name__ == "__main__":
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runtime_main(TestDistMnist2x2)
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