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115 lines
3.6 KiB
115 lines
3.6 KiB
# Copyright (c) 2020 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 unittest
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from paddle.fluid.backward import calc_gradient
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import numpy as np
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class ConvBNLayer(fluid.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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use_cudnn=False):
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super(ConvBNLayer, self).__init__()
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self._conv = fluid.dygraph.Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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bias_attr=False,
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use_cudnn=use_cudnn)
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self._batch_norm = fluid.dygraph.BatchNorm(num_filters, act=act)
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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def create_program():
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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x = fluid.data(name='img', shape=[-1, 3, 224, 224])
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x.stop_gradient = False
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x = fluid.layers.prelu(x, mode="channel")
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conv = ConvBNLayer(
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num_channels=3,
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num_filters=3,
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filter_size=1,
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act='relu',
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use_cudnn=True)
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y = conv(x) + x
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loss = fluid.layers.reduce_sum(y)
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sgd = fluid.optimizer.SGD(learning_rate=0.01)
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sgd.minimize(loss)
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return loss, main, startup, conv._conv.weight
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class TestInplaceAddto(unittest.TestCase):
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def test_result(self):
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def run_program(enable_addto):
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np.random.seed(10)
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paddle.manual_seed(10)
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paddle.framework.random._manual_program_seed(10)
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if fluid.core.is_compiled_with_cuda():
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fluid.set_flags({"FLAGS_cudnn_deterministic": True})
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fluid.set_flags({"FLAGS_max_inplace_grad_add": 2})
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loss, main, startup, w = create_program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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strategy = fluid.BuildStrategy()
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strategy.enable_addto = enable_addto
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compiled = fluid.CompiledProgram(main).with_data_parallel(
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loss_name=loss.name, build_strategy=strategy)
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exe.run(startup)
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img = np.random.uniform(-128, 128,
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[8, 3, 224, 224]).astype(np.float32)
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for i in range(2):
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res = exe.run(compiled,
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feed={'img': img},
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fetch_list=[loss.name, w.name])
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return res
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res1, w1 = run_program(True)
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res2, w2 = run_program(False)
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print(res1, res2)
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self.assertTrue(np.array_equal(res1, res2))
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if __name__ == "__main__":
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unittest.main()
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