wehub-resource-sync
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
# Copyright (c) 2023 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 utils import compare_legacy_with_pt
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import paddle
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class TestSparseIsnan(unittest.TestCase):
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"""
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Test the API paddle.sparse.isnan on some sparse tensors.
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x: sparse tensor, out: sparse tensor
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"""
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def to_sparse(self, x, format):
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if format == 'coo':
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return x.detach().to_sparse_coo(sparse_dim=x.ndim)
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elif format == 'csr':
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return x.detach().to_sparse_csr()
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def check_result(self, x_shape, format, data_type="float32"):
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raw_inp = np.random.randint(-100, 100, x_shape)
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mask = np.random.randint(0, 2, x_shape)
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inp_x = (raw_inp * mask).astype(data_type)
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inp_x[inp_x > 0] = np.nan
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np_out = np.isnan(inp_x[inp_x != 0])
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dense_x = paddle.to_tensor(inp_x)
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sp_x = self.to_sparse(dense_x, format)
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sp_out = paddle.sparse.isnan(sp_x)
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sp_out_values = sp_out.values().numpy()
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np.testing.assert_allclose(np_out, sp_out_values, rtol=1e-05)
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def test_isnan_shape(self):
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self.check_result([20], 'coo')
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self.check_result([4, 5], 'coo')
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self.check_result([4, 5], 'csr')
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self.check_result([8, 16, 32], 'coo')
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self.check_result([8, 16, 32], 'csr')
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def test_isnan_dtype(self):
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self.check_result([4, 5], 'coo', "float32")
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self.check_result([4, 5], 'csr', "float32")
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self.check_result([8, 16, 32], 'coo', "float64")
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self.check_result([8, 16, 32], 'csr', "float64")
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class TestStatic(unittest.TestCase):
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@compare_legacy_with_pt
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def test(self):
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paddle.enable_static()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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indices = paddle.static.data(
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name='indices', shape=[2, 3], dtype='int32'
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)
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values = paddle.static.data(
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name='values', shape=[3], dtype='float32'
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)
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dense_shape = [3, 3]
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sp_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
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sp_y = paddle.sparse.isnan(sp_x)
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out = sp_y.to_dense()
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print(
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"before in exe, global program: ",
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paddle.base.default_main_program,
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flush=True,
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)
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exe = paddle.static.Executor()
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indices_data = [[0, 1, 2], [1, 2, 0]]
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values_data = np.array([1.0, float("nan"), 3.0]).astype('float32')
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fetch = exe.run(
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feed={'indices': indices_data, 'values': values_data},
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fetch_list=[out],
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return_numpy=True,
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)
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correct_out = np.array(
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[
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[False, False, False],
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[False, False, True],
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[False, False, False],
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]
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).astype('float32')
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np.testing.assert_allclose(correct_out, fetch[0], rtol=1e-5)
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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