wehub-resource-sync
137 lines
3.8 KiB
Python
137 lines
3.8 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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class TestBase(IPUOpTest):
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def setUp(self):
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self.set_atol()
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self.set_training()
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self.set_data_feed()
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self.set_feed_attr()
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self.set_op_attrs()
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def set_atol(self):
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self.atol = 3e-6
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self.rtol = 1e-6
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self.atol_fp16 = 4e-3
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self.rtol_fp16 = 1e-3
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def set_data_feed(self):
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data = np.random.uniform(size=[1, 8, 10, 10])
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self.feed_fp32 = {'in_0': data.astype(np.float32)}
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self.feed_fp16 = {'in_0': data.astype(np.float16)}
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def set_feed_attr(self):
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self.feed_shape = [x.shape for x in self.feed_fp32.values()]
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self.feed_list = list(self.feed_fp32.keys())
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def set_op_attrs(self):
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self.attrs = {
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"num_groups": 8,
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"epsilon": 1e-05,
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"data_layout": 'NCHW',
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}
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@IPUOpTest.static_graph
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def build_model(self):
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x = paddle.static.data(
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name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
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)
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if "data_layout" in self.attrs and self.attrs["data_layout"] == "NHWC":
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index = 3
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else:
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index = 1
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if self.is_training:
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ch = self.feed_shape[0][1]
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conv1 = paddle.nn.Conv2D(
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in_channels=x.shape[1],
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out_channels=ch,
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kernel_size=3,
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bias_attr=False,
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)(x)
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scale = paddle.ParamAttr(trainable=True)
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bias = paddle.ParamAttr(trainable=True)
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out = paddle.nn.GroupNorm(
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num_channels=conv1.shape[index],
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weight_attr=scale,
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bias_attr=bias,
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**self.attrs,
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)(conv1)
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loss = paddle.mean(out)
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adam = paddle.optimizer.Adam(learning_rate=1e-2)
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adam.minimize(loss)
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self.fetch_list = [loss]
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else:
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out = paddle.nn.GroupNorm(
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x.shape[index], weight_attr=True, bias_attr=True, **self.attrs
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)(x)
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self.fetch_list = [out]
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def run_model(self, exec_mode):
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self.run_op_test(exec_mode)
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def test(self):
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for m in IPUOpTest.ExecutionMode:
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if not self.skip_mode(m):
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self.build_model()
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self.run_model(m)
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self.check()
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class TestCase1(TestBase):
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def set_op_attrs(self):
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self.attrs = {
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"num_groups": 4,
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"epsilon": 1e-05,
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"data_layout": 'NCHW',
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}
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class TestTrainCase1(TestBase):
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def set_training(self):
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self.is_training = True
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self.epoch = 20
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@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
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class TestTrainCase2(TestBase):
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def set_atol(self):
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self.atol = 7e-4
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self.rtol = 1e-6
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self.atol_fp16 = 1e-2
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self.rtol_fp16 = 1e-2
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def set_op_attrs(self):
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self.attrs = {
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"num_groups": 4,
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"epsilon": 1e-05,
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"data_layout": 'NCHW',
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}
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def set_training(self):
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self.is_training = True
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self.epoch = 20
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if __name__ == "__main__":
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unittest.main()
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