wehub-resource-sync
140 lines
4.0 KiB
Python
140 lines
4.0 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_feed_attr()
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self.set_op_attrs()
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def set_atol(self):
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self.atol = 1e-6
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self.rtol = 1e-6
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self.atol_fp16 = 1e-3
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self.rtol_fp16 = 1e-3
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def set_feed(self):
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data1 = np.random.uniform(size=[10])
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data2 = np.random.uniform(size=[20])
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self.feed_fp32 = {
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'x': data1.astype(np.float32),
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'y': data2.astype(np.float32),
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}
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self.feed_fp16 = {
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'x': data1.astype(np.float16),
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'y': data2.astype(np.float16),
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}
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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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self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
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def set_op_attrs(self):
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self.attrs = {}
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self.attrs['axis'] = [0, 1]
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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],
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shape=self.feed_shape[0],
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dtype=self.feed_dtype[0],
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)
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y = paddle.static.data(
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name=self.feed_list[1],
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shape=self.feed_shape[1],
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dtype=self.feed_dtype[1],
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)
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r1, r2 = paddle.meshgrid(x, y)
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self.fetch_list = [r1.name, r2.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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for k, v in self.output_dict.items():
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self.output_dict[k] = np.concatenate([vv.flatten() for vv in v])
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self.check()
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class TestCase1(TestBase):
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def set_feed(self):
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data1 = np.random.uniform(size=[10])
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data2 = np.random.uniform(size=[20])
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data3 = np.random.uniform(size=[30])
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self.feed_fp32 = {
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'x': data1.astype(np.float32),
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'y': data2.astype(np.float32),
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'z': data3.astype(np.float32),
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}
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self.feed_fp16 = {
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'x': data1.astype(np.float16),
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'y': data2.astype(np.float16),
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'z': data3.astype(np.float16),
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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],
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shape=self.feed_shape[0],
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dtype=self.feed_dtype[0],
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)
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y = paddle.static.data(
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name=self.feed_list[1],
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shape=self.feed_shape[1],
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dtype=self.feed_dtype[1],
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)
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z = paddle.static.data(
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name=self.feed_list[2],
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shape=self.feed_shape[2],
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dtype=self.feed_dtype[2],
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)
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r1, r2, r3 = paddle.meshgrid(x, y, z)
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self.fetch_list = [r1.name, r2.name, r3.name]
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class TestCase2(TestBase):
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def set_feed(self):
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data1 = np.random.uniform(size=[100])
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data2 = np.random.uniform(size=[200])
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self.feed_fp32 = {
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'x': data1.astype(np.int32),
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'y': data2.astype(np.int32),
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}
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self.feed_fp16 = {
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'x': data1.astype(np.int32),
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'y': data2.astype(np.int32),
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}
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if __name__ == "__main__":
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unittest.main()
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