def test_2layer_deconv(deconv_n4_hw4_c1_5x5):
cf1 = ConvParams(**deconv_n4_hw4_c1_5x5)
# 2nd layer filter
fshape2 = (4, 3, 3)
str_h = str_w = 2
K, R, S = fshape2
C, D, H, W, N = cf1.dimO # 2nd layer input dimensions
cf2 = ConvParams(C=C, D=D, H=H, W=W, N=N, K=K, R=R, S=S, str_h=str_h, str_w=str_w, deconv=True)
# randomly initialize
input_value = rng.uniform(-0.5, 0.5, cf1.ax_i)
filter1_value = rng.uniform(-0.5, 0.5, cf1.ax_f)
filter2_value = rng.uniform(-0.5, 0.5, cf2.ax_f)
error_value = rng.uniform(-0.5, 0.5, cf2.ax_o)
inputs = ng.placeholder(cf1.ax_i)
filters1 = ng.placeholder(cf1.ax_f)
filters2 = ng.placeholder(cf2.ax_f)
errors = ng.placeholder(cf2.ax_o)
out1 = ng.deconvolution(cf1.conv_params, inputs, filters1, axes=cf1.ax_o)
out2 = ng.deconvolution(cf2.conv_params, out1, filters2, axes=cf2.ax_o)
bprop_out = ng.deriv(out2, inputs, errors)
updat_out2 = ng.deriv(out2, filters2, errors)
updat_out1 = ng.deriv(out2, filters1, errors)
with executor([out1, out2, bprop_out, updat_out1, updat_out2],
inputs, filters1, filters2, errors) as conv_executor:
out1_ng, out2_ng, gradI_ng, gradF1_ng, gradF2_ng = \
conv_executor(input_value, filter1_value, filter2_value, error_value)
# Compute reference with NumPy
# fprop
out1_np = reference_deconv_fprop(cf1.conv_params, input_value, filter1_value)
out2_np = reference_deconv_fprop(cf2.conv_params, out1_np, filter2_value)
# bprop
gradI2_np, gradF2_np = reference_deconv_bprop(cf2.conv_params,
error_value,
out1_np,
filter2_value)
gradI1_np, gradF1_np = reference_deconv_bprop(cf1.conv_params,
gradI2_np,
input_value,
filter1_value)
# Compare fprop
assert np.allclose(out1_ng, out1_np, rtol=0.01, atol=0)
assert np.allclose(out2_ng, out2_np, rtol=0.01, atol=0)
# Compare bprop
assert np.allclose(gradI_ng, gradI1_np, rtol=0.01, atol=0)
# Compare update
assert np.allclose(gradF1_ng, gradF1_np, rtol=0.01, atol=0)
assert np.allclose(gradF2_ng, gradF2_np, rtol=0.01, atol=0)
def test_deconv(transformer_factory, deconv_n4_hw4_c1_5x5):
cf = ConvParams(**deconv_n4_hw4_c1_5x5)
# randomly initialize
input_value = rng.uniform(-0.5, 0.5, cf.ax_i)
filter_value = rng.uniform(-0.5, 0.5, cf.ax_f)
error_value = rng.uniform(-0.5, 0.5, cf.ax_o)
inputs = ng.placeholder(cf.ax_i)
filters = ng.placeholder(cf.ax_f)
errors = ng.placeholder(cf.ax_o)
output = ng.deconvolution(cf.conv_params, inputs, filters, axes=cf.ax_o)
bprop_out = ng.deriv(output, inputs, errors)
updat_out = ng.deriv(output, filters, errors)
with executor([output, bprop_out, updat_out], inputs, filters,
errors) as conv_executor:
result_ng, gradI_ng, gradF_ng = conv_executor(input_value,
filter_value,
error_value)
# Compute reference with NumPy
result_np = reference_deconv_fprop(cf.conv_params, input_value,
filter_value)
gradI_np, gradF_np = reference_deconv_bprop(cf.conv_params, error_value,
input_value, filter_value)
# Compare fprop
assert np.allclose(result_ng, result_np, rtol=0.1, atol=0)
# Compare bprop
assert np.allclose(gradI_ng, gradI_np, rtol=0.1, atol=0)
# Compare update
assert np.allclose(gradF_ng, gradF_np, rtol=0.1, atol=0)
def make_convolution_op(onnx_node, ng_inputs, transpose=False):
# type: (NodeWrapper, List[TensorOp], bool) -> Op
"""
Create an ngraph convolution or deconvolution Op based on an ONNX node.
:param onnx_node: wrapped ONNX node for Conv of ConvTranspose op
:param ng_inputs: ngraph TensorOp input tensors
:param transpose: should this be a transposed convolution?
:return: ngraph Op for convolution or deconvolution
"""
if len(ng_inputs) == 3:
x, weights, bias = ng_inputs
elif len(ng_inputs) == 2:
x, weights = ng_inputs
bias = ng.constant(0)
else:
raise ValueError(
'Conv node (%s): unexpected number of input values: %d.',
onnx_node.name, len(ng_inputs))
# Reorder x axes from ONNX convention (N, C, H, W, D) to ngraph (C, D, H, W, N)
# Reorder weights axes from ONNX (K, J, R, S, T) to ngraph (J, T, R, S, K)
# Axis names follow https://ngraph.nervanasys.com/index.html/axes.html
if len(x.axes) == 4: # 2D convolution
x = reorder_axes(x, 'NCHW', 'CDHWN')
weights = reorder_axes(weights, 'KJRS', 'JTRSK')
elif len(x.axes) == 5: # 3D convolution
x = reorder_axes(x, 'NCHWD', 'CDHWN')
weights = reorder_axes(weights, 'KJRST', 'JTRSK')
else:
raise NotImplementedError(
'Conv node (%s): only 2D and 3D convolutions are supported.',
onnx_node.name)
groups = onnx_node.get_attribute_value('group', 1)
if groups != 1:
raise NotImplementedError(
'Conv node (%s): `group` attribute value %d not supported.',
onnx_node.name, groups)
# Prepare ngraph convolution operation
conv_params = get_conv_params(onnx_node)
output_axes = make_conv_output_axes(x, weights, conv_params)
if transpose:
conv = ng.deconvolution(conv_params, x, weights, axes=output_axes)
else:
conv = ng.convolution(conv_params, x, weights, axes=output_axes)
conv = cast_to_pos_axes(conv) + bias
# ONNX output should have axes in the order N, C, H, W, D
conv = reorder_axes(conv, 'CDHWN', 'NCHWD')
if len(ng_inputs[0].axes
) == 4: # 2D convolution, slice away the D axis from output
conv = ng.tensor_slice(conv, [
slice(None), slice(None),
slice(None), slice(None), 0
])
return conv
