test_unpooling_2d.py 文件源码

def check_forward(self, x_data):
        x = chainer.Variable(x_data)
        y = functions.unpooling_2d(x, self.ksize, outsize=self.outsize,
                                   cover_all=self.cover_all)
        self.assertEqual(y.data.dtype, self.dtype)
        y_data = cuda.to_cpu(y.data)

        self.assertEqual(self.gy.shape, y_data.shape)
        for i in six.moves.range(self.N):
            for c in six.moves.range(self.n_channels):
                outsize = self.outsize or self.expected_outsize
                assert y_data.shape[2:] == outsize
                if outsize == (5, 2):
                    expect = numpy.zeros(outsize, dtype=self.dtype)
                    expect[:2, :] = self.x[i, c, 0, 0]
                    expect[2:4, :] = self.x[i, c, 1, 0]
                elif outsize == (4, 2):
                    expect = numpy.array([
                        [self.x[i, c, 0, 0], self.x[i, c, 0, 0]],
                        [self.x[i, c, 0, 0], self.x[i, c, 0, 0]],
                        [self.x[i, c, 1, 0], self.x[i, c, 1, 0]],
                        [self.x[i, c, 1, 0], self.x[i, c, 1, 0]],
                    ])
                elif outsize == (3, 1):
                    expect = numpy.array([
                        [self.x[i, c, 0, 0]],
                        [self.x[i, c, 0, 0]],
                        [self.x[i, c, 1, 0]],
                    ])
                else:
                    raise ValueError('Unsupported outsize: {}'.format(outsize))
                gradient_check.assert_allclose(expect, y_data[i, c])