wehub-resource-sync
142 lines
4.2 KiB
Python
142 lines
4.2 KiB
Python
# Copyright (c) 2022 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_feed()
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self.set_op_attrs()
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def set_feed(self):
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data = np.random.uniform(size=[5, 4, 2, 3])
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self.feed_fp32 = {'x': data.astype(np.float32)}
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self.feed_fp16 = {'x': data.astype(np.float16)}
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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 = {"pad": [1, 2, 3, 4]}
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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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pad = paddle.nn.functional.pad(x, **self.attrs)
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self.fetch_list = [pad.name]
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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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@unittest.skip("Do not support `pad` as a tensor")
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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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@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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const_attrs = {
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'name': 'y',
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'shape': [4],
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'dtype': 'int32',
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'value': 2,
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}
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y = paddle.tensor.fill_constant(**const_attrs)
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pad = paddle.nn.functional.pad(x, pad=y)
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self.fetch_list = [pad.name]
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class TestCase2(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [2, 5], "data_format": "NCL"}
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def set_feed(self):
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data = np.random.uniform(size=[4, 2, 3])
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self.feed_fp32 = {'x': data.astype(np.float32)}
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self.feed_fp16 = {'x': data.astype(np.float16)}
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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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class TestCase3(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [2, 5, 2, 3, 6, 3], "data_format": "NCDHW"}
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def set_feed(self):
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data = np.random.uniform(size=[2, 3, 4, 2, 3])
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self.feed_fp32 = {'x': data.astype(np.float32)}
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self.feed_fp16 = {'x': data.astype(np.float16)}
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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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class TestCase4(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [2, 2, 1, 1], "mode": "reflect"}
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@unittest.skip("replicate mode is not supported")
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class TestCase5(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [1, 2, 3, 4], "mode": "replicate"}
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@unittest.skip("circular mode is not supported")
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class TestCase6(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [1, 2, 3, 4], "mode": "circular"}
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@unittest.skip("Only support NCL, NCHW, NCDHW")
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class TestCase7(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [1, 2], "data_format": "NLC"}
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@unittest.skip("Only support NCL, NCHW, NCDHW")
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class TestCase8(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [1, 2, 3, 4], "data_format": "NHWC"}
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@unittest.skip("Only support NCL, NCHW, NCDHW")
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class TestCase9(TestBase):
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def set_op_attrs(self):
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self.attrs = {"pad": [1, 2, 3, 4, 1, 3], "data_format": "NDHWC"}
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if __name__ == "__main__":
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unittest.main()
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