76b4f47a76b0ba64475112e28847f777e36a0d30,theano/tensor/nnet/tests/test_abstract_conv.py,BaseTestConv2d,test_gradinput_arbitrary_output_shapes,#BaseTestConv2d#,468

Before Change

                min_output_shape = self.get_output_shape(
                    input_shape, filter_shape, self.default_subsamples,
                    border_mode, self.default_filters_dilations)
                if not all(o >= min_o for (o, min_o) in zip(output_shape, min_output_shape)):
                    continue
                for provide_shape in self.provide_shape:
                    yield (self.tcase_gi,
                           input_shape,
                           filter_shape,

After Change

                computed_shape = get_conv_output_shape(
                    input_shape, filter_shape, border_mode, self.default_subsamples, self.default_filters_dilations)
                // is this a valid combination?
                if tuple(computed_shape) == output_shape:
                    yield (self.tcase_gi,
                           input_shape,
                           filter_shape,
                           output_shape,
                           self.default_subsamples,
                           border_mode,
                           True,
                           True,
                           self.default_filters_dilations,
                           False)
                else:
                    // expect an error
                    yield (self.tcase_gi,
                           input_shape,
                           filter_shape,
                           output_shape,
                           self.default_subsamples,
                           border_mode,
                           True,
                           True,
                           self.default_filters_dilations,
                           True)

    def test_gradinput_impossible_output_shapes(self):
        for i in range(1, 20):
            for k in range(1, 10):
                for border_mode in ("valid", "half", "full", (0, 2)):

Instances