# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from unittest import TestCase import numpy as np import paddle import paddle.fluid.dygraph as dg import paddle.nn.functional as F class TestFunctionalConv1DError(TestCase): def setUp(self): self.input = [] self.filter = [] self.bias = None self.padding = 0 self.stride = 1 self.dilation = 1 self.groups = 1 self.data_format = "NCL" def dygraph_case(self): with dg.guard(): x = dg.to_variable(self.input, dtype=paddle.float32) w = dg.to_variable(self.filter, dtype=paddle.float32) b = ( None if self.bias is None else dg.to_variable(self.bias, dtype=paddle.float32) ) y = F.conv1d_transpose( x, w, b, padding=self.padding, stride=self.stride, dilation=self.dilation, groups=self.groups, data_format=self.data_format, ) def test_exception(self): with self.assertRaises(ValueError): self.dygraph_case() class TestFunctionalConv1DErrorCase1(TestFunctionalConv1DError): def setUp(self): self.input = np.random.randn(1, 3, 3) self.filter = np.random.randn(3, 3, 1) self.bias = None self.padding = 0 self.stride = 1 self.dilation = 1 self.groups = 0 self.data_format = "NCL" class TestFunctionalConv1DErrorCase2(TestFunctionalConv1DError): def setUp(self): self.input = np.random.randn(1, 3, 3) self.filter = np.random.randn(3) self.bias = None self.padding = 0 self.stride = 1 self.dilation = 1 self.groups = 1 self.data_format = "NCL" class TestFunctionalConv1DErrorCase3(TestFunctionalConv1DError): def setUp(self): self.input = np.random.randn(6, 0, 6) self.filter = np.random.randn(6, 0, 0) self.bias = None self.padding = 0 self.stride = 1 self.dilation = 1 self.groups = 1 self.data_format = "NCL" if __name__ == "__main__": unittest.main()