def extract(cls, node):
attr = get_mxnet_layer_attrs(node.symbol_dict)
kernel = attr.tuple("kernel", int, None)
stride = attr.tuple("stride", int,
tuple(np.ones(len(kernel), dtype=np.int64)))
padding = attr.tuple("pad", int,
tuple(np.zeros(len(kernel), dtype=np.int64)))
dilate = attr.tuple("dilate", int,
tuple(np.ones(len(kernel), dtype=np.int64)))
num_deformable_group = attr.int("num_deformable_group", 1)
num_group = attr.int("num_group", 1)
output = attr.int("num_filter", None)
bias_term = attr.str("no_bias", 'False') == 'False'
final_dilations = int64_array([1, 1, *[d for d in dilate]
]) if dilate is not None else None
node_attrs = {
'op': __class__.op,
'bias_addable': True,
'bias_term': bias_term,
'pad': int64_array([[0, 0], [0, 0],
*[[pad, pad] for pad in padding]]),
'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
'dilation': final_dilations,
'output_spatial_shape': None,
'output_shape': None,
'stride': int64_array([1, 1, *[s for s in stride]]),
'group': num_group,
'deformable_group': num_deformable_group,
'output': output,
'kernel_spatial': int64_array([k for k in kernel]),
'bilinear_interpolation_pad': True,
'input_feature_channel': 1,
'output_feature_channel': 0,
'kernel_spatial_idx': None,
'reshape_kernel': True,
'weights_index': 2,
'in_ports_count': 4,
'spatial_dims': None,
'channel_dims': int64_array([1]),
'batch_dims': int64_array([0]),
'layout': 'NCHW',
}
# update the attributes of the node
DeformableConvolution.update_node_stat(node, node_attrs)
return cls.enabled
def extract(cls, node):
# Extract pads attribute
# In case if pads is not specified it will be set in default (1) in infer function
pads = onnx_attr(node,
'pads',
'ints',
default=None,
dst_type=lambda x: int64_array(x))
assert pads is None or len(pads) % 2 == 0
final_pad = None
if pads is not None:
pads = pads.reshape([2, -1])
pads = np.transpose(pads)
final_pad = int64_array([[0, 0], [0, 0], *pads])
# Extract dilations attribute
# In case if dilations is not specified it will be set in default (1) in infer function
dilations = onnx_attr(node,
'dilations',
'ints',
default=None,
dst_type=lambda x: int64_array(x))
final_dilations = int64_array([1, 1, *dilations
]) if dilations is not None else None
# Extract dilations attribute
# In case if dilations is not specified it will be set in default (1) in infer function
strides = onnx_attr(node,
'strides',
'ints',
default=None,
dst_type=lambda x: int64_array(x))
final_strides = int64_array([1, 1, *strides
]) if strides is not None else None
kernel_shape = onnx_attr(node, 'kernel_shape', 'ints', default=None)
auto_pad = onnx_attr(node,
'auto_pad',
's',
default=None,
dst_type=get_onnx_autopad)
group = onnx_attr(node,
'group',
'i',
default=1,
dst_type=lambda x: int64_array(x))
deformable_groups = onnx_attr(node,
'deformable_groups',
'i',
default=1)
attrs = {
'op':
__class__.op,
'auto_pad':
auto_pad,
'bias_addable':
False,
'bias_term':
False,
'pad':
final_pad,
'pad_spatial_shape':
int64_array(pads) if pads is not None else None,
'dilation':
final_dilations,
'output_spatial_shape':
None,
'output_shape':
None,
'stride':
final_strides,
'group':
group,
'deformable_group':
deformable_groups,
'output':
None,
'weights_index':
2,
'kernel_spatial':
int64_array(kernel_shape) if kernel_shape is not None else None,
'input_feature_channel':
1,
'output_feature_channel':
0,
'kernel_spatial_idx':
None, # Will be calculated in infer function (np.array([2, 3]))
'spatial_dims':
None, # Will be calculated in infer function
'channel_dims':
int64_array([1]),
'batch_dims':
int64_array([0]),
'layout':
'NCHW'
}
# update the attributes of the node
DeformableConvolution.update_node_stat(node, attrs)
return cls.enabled