wehub-resource-sync
102 lines
2.7 KiB
Python
102 lines
2.7 KiB
Python
# 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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import numpy as np
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from legacy_test.test_dist_base import (
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TestParallelDyGraphRunnerBase,
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runtime_main,
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)
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import paddle
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from paddle.nn import Conv2D, SyncBatchNorm
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class TestLayer(paddle.nn.Layer):
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def __init__(
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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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):
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super().__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_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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bias_attr=False,
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)
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self._sync_batch_norm = SyncBatchNorm(num_filters)
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self._conv2 = Conv2D(
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in_channels=num_filters,
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out_channels=num_filters,
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kernel_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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bias_attr=False,
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)
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self._sync_batch_norm2 = SyncBatchNorm(
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num_filters, weight_attr=False, bias_attr=False
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)
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._sync_batch_norm(y)
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y = self._conv2(y)
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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):
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model = TestLayer(3, 64, 7)
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train_reader = paddle.batch(
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paddle.dataset.flowers.test(use_xmap=False),
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batch_size=32,
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drop_last=True,
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)
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opt = paddle.optimizer.Adam(
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learning_rate=1e-3, parameters=model.parameters()
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)
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return model, train_reader, opt
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def run_one_loop(self, model, opt, data):
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batch_size = len(data)
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dy_x_data = np.array([x[0].reshape(3, 224, 224) for x in data]).astype(
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'float32'
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)
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img = paddle.to_tensor(dy_x_data)
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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__":
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runtime_main(TestSyncBatchNorm)
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