def visit(self, op, *args):
self.computation.set_op_rank(op)
# op.args[0] : delta
# op.args[1] : data batch
# op.args[2] (optional) : dbias
# op.fprop.args[0] : data batch
# op.fprop.args[1] : filters
# op.fprop.conv_params : forward params
delta = args[0]
data = args[1]
filters = op.fprop.args[1]
conv_params = op.fprop.conv_params
"""
print(delta.axes)
print(filters.axes)
print(data.axes)
print(conv_params)
"""
ngraph_update_conv = PyngConvolutionBackpropFilters(
self.computation.lookup_cpp_op(data),
Shape(list(filters.axes.lengths)),
self.computation.lookup_cpp_op(delta),
Strides([conv_params['str_h'], conv_params['str_w']]),
Strides([conv_params['dil_h'], conv_params['dil_w']]),
CoordinateDiff([conv_params['pad_h'], conv_params['pad_w']]),
CoordinateDiff([conv_params['pad_h'], conv_params['pad_w']]),
Strides([1, 1]))
ngraph_update_conv.name = op.name.replace(
'/', '_') + "_ConvolutionBackpropFilters"
self.computation.register_cpp_op(op,
ngraph_update_conv,
set_name=False)
def test_convolutionBackpropFilters():
element_type = Type.f32
image_shape = Shape([1, 1, 10, 10])
filter_shape = Shape([1, 1, 3, 3])
output_shape = Shape([1, 1, 17, 17])
image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
filter_arr = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)
window_strides = [1, 1]
window_dilation = [1, 1]
padding_below = [0, 0]
padding_above = [0, 0]
data_dilation = [2, 2]
output_arr = convolution2d(image_arr[0][0], filter_arr[0][0],
window_strides, window_dilation, padding_below,
padding_above,
data_dilation).reshape(1, 1, 17, 17)
A = Parameter(element_type, image_shape)
B = Parameter(element_type, output_shape)
parameter_list = [A, B]
function = Function(
NodeVector([
ConvolutionBackpropFilters(A, filter_shape, B,
Strides(window_strides),
Strides(window_dilation),
CoordinateDiff(padding_below),
CoordinateDiff(padding_above),
Strides(data_dilation))
]), parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, image_shape)
b = backend.make_primary_tensor_view(element_type, output_shape)
a.write(util.numpy_to_c(image_arr), 0, 10 * 10 * 4)
b.write(util.numpy_to_c(output_arr), 0, 17 * 17 * 4)
result_arr = np.zeros(3 * 3, dtype=np.float32).reshape(1, 1, 3, 3)
result = backend.make_primary_tensor_view(element_type, Shape([1, 1, 3,
3]))
result.write(util.numpy_to_c(result_arr), 0, 3 * 3 * 4)
cf.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 3 * 3 * 4)
result_arr_ref = np.array([[[[923832, 413952, 939870.],
[425832, 190752, 432960.],
[1084212, 485232, 1100250.]]]])
assert np.allclose(result_arr, result_arr_ref)
