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[MLU] fix sync_bn of mlu and add unittests (PaddlePaddle#45707)
* [MLU] fix sync_bn of mlu and add unittests * [MLU] remove redunant code of pytest
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python/paddle/fluid/tests/unittests/mlu/parallel_dygraph_sync_batch_norm.py
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| # Copyright (c) 2022 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. | ||
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| from __future__ import print_function | ||
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| import os | ||
| import contextlib | ||
| import unittest | ||
| import numpy as np | ||
| import six | ||
| import pickle | ||
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| import paddle | ||
| import paddle.fluid as fluid | ||
| import paddle.fluid.dygraph as dygraph | ||
| from paddle.fluid import core | ||
| from paddle.fluid.optimizer import SGDOptimizer | ||
| from paddle.nn import Conv2D, Linear, SyncBatchNorm | ||
| from paddle.fluid.dygraph.base import to_variable | ||
| import sys | ||
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| sys.path.append("..") | ||
| from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase | ||
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| class TestLayer(fluid.dygraph.Layer): | ||
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| def __init__(self, | ||
| num_channels, | ||
| num_filters, | ||
| filter_size, | ||
| stride=1, | ||
| groups=1, | ||
| act=None): | ||
| super(TestLayer, self).__init__() | ||
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| self._conv = Conv2D(in_channels=num_channels, | ||
| out_channels=num_filters, | ||
| kernel_size=filter_size, | ||
| stride=stride, | ||
| padding=(filter_size - 1) // 2, | ||
| groups=groups, | ||
| bias_attr=False) | ||
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| self._sync_batch_norm = SyncBatchNorm(num_filters) | ||
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| self._conv2 = Conv2D(in_channels=num_filters, | ||
| out_channels=num_filters, | ||
| kernel_size=filter_size, | ||
| stride=stride, | ||
| padding=(filter_size - 1) // 2, | ||
| groups=groups, | ||
| bias_attr=False) | ||
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| self._sync_batch_norm2 = SyncBatchNorm(num_filters, | ||
| weight_attr=False, | ||
| bias_attr=False) | ||
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| def forward(self, inputs): | ||
| y = self._conv(inputs) | ||
| y = self._sync_batch_norm(y) | ||
| y = self._conv2(y) | ||
| y = self._sync_batch_norm2(y) | ||
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| return y | ||
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| class TestSyncBatchNorm(TestParallelDyGraphRunnerBase): | ||
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| def get_model(self): | ||
| model = TestLayer(3, 64, 7) | ||
| train_reader = paddle.batch(paddle.dataset.flowers.test(use_xmap=False), | ||
| batch_size=32, | ||
| drop_last=True) | ||
| opt = fluid.optimizer.Adam(learning_rate=1e-3, | ||
| parameter_list=model.parameters()) | ||
| return model, train_reader, opt | ||
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| def run_one_loop(self, model, opt, data): | ||
| batch_size = len(data) | ||
| dy_x_data = np.array([x[0].reshape(3, 224, 224) | ||
| for x in data]).astype('float32') | ||
| img = to_variable(dy_x_data) | ||
| img.stop_gradient = False | ||
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| out = model(img) | ||
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| out = paddle.mean(out) | ||
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| return out | ||
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| if __name__ == "__main__": | ||
| runtime_main(TestSyncBatchNorm) |
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