def test_extract_padding_params_for_conv_layer(pads, output):
out = extract_padding_params_for_conv_layer(pads)
if isinstance(output, nn.Module):
s = len(pads) // 2
inp = np.random.rand(*s * [3])
expected_out = pad_impl(inp, np.array(pads), "constant", 0)
infered_out = out(torch.from_numpy(inp)).numpy()
assert (expected_out == infered_out).all()
assert output._get_name() == out._get_name()
assert output.padding == out.padding
assert output.value == out.value
else:
assert out == output
def extract_attributes(node):
"""Extract onnx attributes. Map onnx feature naming to pytorch."""
kwargs = {}
for attr in node.attribute:
if attr.name == "dilations":
kwargs["dilation"] = extract_attr_values(attr)
elif attr.name == "group":
kwargs["groups"] = extract_attr_values(attr)
elif attr.name == "kernel_shape":
kwargs["kernel_size"] = extract_attr_values(attr)
elif attr.name == "pads":
params = extract_attr_values(attr)
if node.op_type == "Pad":
kwargs["padding"] = extract_padding_params(params)
else:
# Works for Conv, MaxPooling and other layers from convert_layer func
kwargs["padding"] = extract_padding_params_for_conv_layer(
params)
elif attr.name == "strides":
kwargs["stride"] = extract_attr_values(attr)
elif attr.name == "axis" and node.op_type == "Flatten":
kwargs["start_dim"] = extract_attr_values(attr)
elif attr.name == "axis" or attr.name == "axes":
v = extract_attr_values(attr)
if isinstance(v, (tuple, list)) and len(v) == 1:
kwargs["dim"] = v[0]
else:
kwargs["dim"] = v
elif attr.name == "keepdims":
kwargs["keepdim"] = bool(extract_attr_values(attr))
elif attr.name == "epsilon":
kwargs["eps"] = extract_attr_values(attr)
elif attr.name == "momentum":
kwargs["momentum"] = extract_attr_values(attr)
elif attr.name == "ceil_mode":
kwargs["ceil_mode"] = bool(extract_attr_values(attr))
elif attr.name == "value":
kwargs["constant"] = extract_attr_values(attr)
elif attr.name == "perm":
kwargs["dims"] = extract_attr_values(attr)
elif attr.name == "split":
kwargs["split_size_or_sections"] = extract_attr_values(attr)
elif attr.name == "spatial":
kwargs["spatial"] = extract_attr_values(
attr) # Batch norm parameter
elif attr.name == "to":
kwargs["dtype"] = TENSOR_PROTO_MAPPING[extract_attr_values(
attr)].lower()
elif attr.name == "mode":
kwargs["mode"] = extract_attr_values(attr)
elif attr.name == "transB":
kwargs["transpose_weight"] = not extract_attr_values(attr)
elif attr.name == "transA":
kwargs["transpose_activation"] = bool(extract_attr_values(attr))
elif attr.name == "alpha" and node.op_type == "LeakyRelu":
kwargs["negative_slope"] = extract_attr_values(attr)
elif attr.name == "alpha":
kwargs["weight_multiplier"] = extract_attr_values(attr)
elif attr.name == "beta":
kwargs["bias_multiplier"] = extract_attr_values(attr)
elif attr.name == "starts":
kwargs["starts"] = extract_attr_values(attr)
elif attr.name == "ends":
kwargs["ends"] = extract_attr_values(attr)
elif attr.name == "coordinate_transformation_mode":
arg = extract_attr_values(attr)
if arg == "align_corners":
kwargs["align_corners"] = True
else:
warnings.warn(
"Pytorch's interpolate uses no coordinate_transformation_mode={}. "
"Result might differ.".format(arg))
elif node.op_type == "Resize":
# These parameters are not used, warn in Resize operator
kwargs[attr.name] = extract_attr_values(attr)
elif attr.name == "auto_pad":
value = extract_attr_values(attr)
if value == "NOTSET":
pass
else:
raise NotImplementedError(
"auto_pad={} functionality not implemented.".format(value))
else:
raise NotImplementedError(
"Extraction of attribute {} not implemented.".format(
attr.name))
return kwargs