def run_gradinput(self, inputs_shape, filters_shape, subsample=(1, 1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = shared(inputs_val)
filters = shared(filters_val)
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)
f_ref = theano.function([], conv_ref)
f = theano.function([], conv_gemm, mode=mode_with_gpu)
res_ref = f_ref()
res = f()
utt.assert_allclose(res_ref, res)
def run_gradinput(self, inputs_shape, filters_shape, subsample=(1, 1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = shared(inputs_val)
filters = shared(filters_val)
bias = shared(numpy.zeros(filters_shape[4]).astype('float32'))
conv = theano.tensor.nnet.convTransp3D(W=filters,
b=bias,
d=subsample,
H=inputs)
f_ref = theano.function([], conv)
res_ref = f_ref()
# 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()
utt.assert_allclose(res_ref, res)