wehub-resource-sync
137 lines
4.1 KiB
Python
137 lines
4.1 KiB
Python
# Copyright (c) 2020 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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from unittest import TestCase
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import numpy as np
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from op_test import get_places
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import paddle
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import paddle.base.dygraph as dg
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import paddle.nn.functional as F
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class TestFunctionalConv1DError(TestCase):
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def setUp(self):
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self.input = []
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self.filter = []
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self.bias = None
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 0
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self.data_format = "NCL"
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def dygraph_case(self):
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with dg.guard():
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x = paddle.to_tensor(self.input, dtype=paddle.float32)
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w = paddle.to_tensor(self.filter, dtype=paddle.float32)
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b = (
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None
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if self.bias is None
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else paddle.to_tensor(self.bias, dtype=paddle.float32)
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)
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y = F.conv1d_transpose(
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x,
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w,
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b,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format,
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)
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def test_exception(self):
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with self.assertRaises(ValueError):
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self.dygraph_case()
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class TestFunctionalConv1DErrorCase1(TestFunctionalConv1DError):
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def setUp(self):
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self.input = np.random.randn(1, 3, 3)
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self.filter = np.random.randn(3, 3, 1)
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self.bias = None
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 0
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self.data_format = "NCL"
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class TestFunctionalConv1DErrorCase2(TestFunctionalConv1DError):
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def setUp(self):
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self.input = np.random.randn(1, 3, 3)
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self.filter = np.random.randn(3)
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self.bias = None
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.data_format = "NCL"
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class TestFunctionalConv1DTranspose_ZeroSize(TestCase):
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def init_data(self):
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self.input = np.random.randn(0, 1, 2)
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self.filter = np.random.randn(1, 1, 2)
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self.np_out = np.zeros([0, 1, 3])
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def setUp(self):
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self.init_data()
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self.bias = None
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self.padding = 0
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self.stride = 1
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self.dilation = 1
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self.groups = 1
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self.data_format = "NCL"
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self.places = get_places()
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def test_dygraph(self):
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for place in self.places:
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with dg.guard(place):
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input = paddle.to_tensor(self.input)
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input.stop_gradient = False
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filter = paddle.to_tensor(self.filter)
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filter.stop_gradient = False
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y = F.conv1d_transpose(
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input,
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filter,
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self.bias,
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padding=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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groups=self.groups,
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data_format=self.data_format,
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)
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np.testing.assert_allclose(y.numpy(), self.np_out)
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loss = y.sum()
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loss.backward()
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np.testing.assert_allclose(input.grad.shape, input.shape)
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np.testing.assert_allclose(filter.grad, np.zeros(filter.shape))
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class TestFunctionalConv1DTranspose_ZeroSize2(
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TestFunctionalConv1DTranspose_ZeroSize
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):
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def init_data(self):
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self.input = np.random.randn(2, 3, 2)
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self.filter = np.random.randn(3, 0, 3)
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self.np_out = np.zeros([2, 0, 4])
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if __name__ == "__main__":
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unittest.main()
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