9db32ceedaef0873f99b4a7f400e705dd6d64173,theano/sandbox/cuda/tests/test_gemmcorr3d.py,TestCorr3DMM,run_gradinput,#TestCorr3DMM#Any#Any#Any#,260

Before Change

        // Get bottom shape using convTransp3D
        bottom_shape = res_ref.shape
        bottom_val = numpy.random.random(bottom_shape).astype("float32")
        bottom = shared(bottom_val)

        weight = gpu_contiguous(filters.dimshuffle(0, 4, 1, 2, 3))
        top = gpu_contiguous(inputs.dimshuffle(0, 4, 1, 2, 3))
        if (subsample == (1, 1, 1)):
            conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(
                kern=weight, topgrad=top)
        else:
            conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(
                kern=weight, topgrad=top,
                shape=bottom.shape[1:4])
        conv_gemm = conv_gemm.dimshuffle(0, 2, 3, 4, 1)
        f = theano.function([], conv_gemm, mode=mode_with_gpu)

        res = f()

After Change

        bottom_height = (inputs_shape[1] - 1) * subsample[0] + filters_shape[1]
        bottom_width = (inputs_shape[2] - 1) * subsample[1] + filters_shape[2]
        bottom_depth = (inputs_shape[3] - 1) * subsample[2] + filters_shape[3]
        bottom_shape = theano.shared(numpy.array([bottom_height, bottom_width, bottom_depth]))

        weight = gpu_contiguous(filters.dimshuffle(0, 4, 1, 2, 3))
        top = gpu_contiguous(inputs.dimshuffle(0, 4, 1, 2, 3))
        if (subsample == (1, 1, 1)):
            conv_ref = Corr3dMM_gradInputs(subsample=subsample)(
                kern=weight, topgrad=top)
            conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(
                kern=weight, topgrad=top)
        else:
            conv_ref = Corr3dMM_gradInputs(subsample=subsample)(
                kern=weight, topgrad=top,
                shape=bottom_shape)
            conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(
                kern=weight, topgrad=top,
                shape=bottom_shape)

Instances