wehub-resource-sync
135 lines
4.0 KiB
Python
135 lines
4.0 KiB
Python
# 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 functools import reduce
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import nets
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from test_dist_base import TestDistRunnerBase, runtime_main
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import paddle
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from paddle import base
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from paddle.incubate.distributed.fleet.collective import 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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base.default_startup_program().random_seed = 1
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base.default_main_program().random_seed = 1
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def cnn_model(data):
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conv_pool_1 = 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=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.01)
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),
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)
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conv_pool_2 = 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=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.01)
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),
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)
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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 = paddle.static.nn.fc(
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x=conv_pool_2,
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size=SIZE,
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activation="softmax",
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weight_attr=base.param_attr.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.01)
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),
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)
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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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images = paddle.static.data(
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name='pixel', shape=[-1, 1, 28, 28], dtype=DTYPE
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)
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label = paddle.static.data(name='label', shape=[-1, 1], dtype='int64')
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# Train program
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predict = cnn_model(images)
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cost = paddle.nn.functional.cross_entropy(
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input=predict, label=label, reduction='none', use_softmax=False
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)
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avg_cost = paddle.mean(x=cost)
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# Evaluator
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batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
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batch_acc = paddle.static.accuracy(
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input=predict, label=label, total=batch_size_tensor
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)
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inference_program = base.default_main_program().clone()
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# Optimization
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# TODO(typhoonzero): fix distributed adam optimizer
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# opt = paddle.optimizer.Adam(
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# learning_rate=0.001, beta1=0.9, beta2=0.999)
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if not use_dgc:
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opt = paddle.optimizer.Momentum(learning_rate=self.lr, momentum=0.9)
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else:
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opt = paddle.distributed.fleet.meta_optimizers.DGCMomentumOptimizer(
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learning_rate=self.lr, momentum=0.9, rampup_begin_step=2
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)
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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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)
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test_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=batch_size
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)
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if dist_strategy:
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dist_opt = fleet.distributed_optimizer(
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optimizer=opt, strategy=dist_strategy
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)
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_, param_grads = dist_opt.minimize(avg_cost)
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else:
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opt.minimize(avg_cost)
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return (
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inference_program,
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avg_cost,
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train_reader,
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test_reader,
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batch_acc,
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predict,
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)
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if __name__ == "__main__":
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runtime_main(TestDistMnist2x2)
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