wehub-resource-sync
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
# Copyright (c) 2021 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 unittest
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import numpy as np
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from op_test_ipu import IPUOpTest
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import paddle
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import paddle.static
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from paddle.optimizer.lr import LRScheduler
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class LR_New(LRScheduler):
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def __init__(self, learning_rate=1e-5, last_epoch=-1, verbose=False):
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super().__init__(learning_rate, last_epoch, verbose)
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def get_lr(self):
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self.base_lr = self.base_lr + 1e-4
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self.last_epoch = self.last_epoch + 1
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return self.base_lr
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class TestConvNet(IPUOpTest):
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@IPUOpTest.static_graph
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def build_model(self):
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image = paddle.static.data(
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name='image', shape=[1, 3, 10, 10], dtype='float32'
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)
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conv1 = paddle.nn.Conv2D(
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in_channels=image.shape[1],
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out_channels=3,
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kernel_size=3,
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bias_attr=False,
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)(image)
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loss = paddle.mean(conv1)
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opt = paddle.optimizer.Lamb(learning_rate=LR_New())
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opt.minimize(loss)
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self.feed_list = [image.name]
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self.fetch_list = [loss]
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def run_model(self, run_ipu=True):
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self.build_model()
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if run_ipu:
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place = paddle.IPUPlace()
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(self.startup_prog)
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if run_ipu:
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ipu_strategy = paddle.static.IpuStrategy()
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ipu_strategy.set_graph_config(is_training=True)
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program = paddle.static.IpuCompiledProgram(
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self.main_prog, ipu_strategy=ipu_strategy
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).compile(self.feed_list, self.fetch_list)
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else:
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program = self.main_prog
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result = []
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for _ in range(100):
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if hasattr(program, "lr_scheduler"):
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program.lr_scheduler.step()
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loss_res = exe.run(
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program, feed=self.feed, fetch_list=self.fetch_list
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)
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result.append(loss_res)
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return np.array(result)
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def test_training(self):
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data = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed = {'image': data}
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# cpu and ipu dimension mismatch, cpu:(100, 1, 1), ipu:(100, 1)
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ipu_loss = self.run_model(True).flatten()
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cpu_loss = self.run_model(False).flatten()
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np.testing.assert_allclose(ipu_loss, cpu_loss, rtol=1e-05, atol=1e-10)
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if __name__ == "__main__":
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unittest.main()
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