You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
64 lines
2.2 KiB
64 lines
2.2 KiB
# Copyright (c) 2020 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
|
|
import paddle.fluid as fluid
|
|
from paddle.distributed.fleet.meta_optimizers import FP16AllReduceOptimizer as FP16AllReduce
|
|
from test_dist_base import TestDistRunnerBase, runtime_main
|
|
from dist_mnist import cnn_model
|
|
|
|
DTYPE = "float32"
|
|
paddle.dataset.mnist.fetch()
|
|
|
|
# Fix seed for test
|
|
fluid.default_startup_program().random_seed = 1
|
|
fluid.default_main_program().random_seed = 1
|
|
|
|
|
|
class TestDistMnist2x2(TestDistRunnerBase):
|
|
def get_model(self, batch_size=2):
|
|
# Input data
|
|
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
|
|
# Train program
|
|
predict = cnn_model(images)
|
|
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
|
|
# Evaluator
|
|
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
|
|
batch_acc = fluid.layers.accuracy(
|
|
input=predict, label=label, total=batch_size_tensor)
|
|
|
|
inference_program = fluid.default_main_program().clone()
|
|
# Optimization
|
|
opt = fluid.optimizer.MomentumOptimizer(
|
|
learning_rate=0.001, momentum=0.9)
|
|
opt = FP16AllReduce(opt)
|
|
|
|
# Reader
|
|
train_reader = paddle.batch(
|
|
paddle.dataset.mnist.test(), batch_size=batch_size)
|
|
test_reader = paddle.batch(
|
|
paddle.dataset.mnist.test(), batch_size=batch_size)
|
|
opt.minimize(avg_cost)
|
|
return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
|
|
|
|
|
|
if __name__ == "__main__":
|
|
runtime_main(TestDistMnist2x2)
|