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: np.array(x, dtype=np.int64))
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 = np.array([[0, 0], [0, 0], *pads], dtype=np.int64)
# 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: np.array(x, dtype=np.int64))
final_dilations = np.array([1, 1, *dilations], dtype=np.int64) 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: np.array(x, dtype=np.int64))
final_strides = np.array([1, 1, *strides], dtype=np.int64) 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: np.array(x, dtype=np.int64))
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': np.array(pads, dtype=np.int64) 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': np.array(kernel_shape, dtype=np.int64) 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': np.array([1], dtype=np.int64),
'batch_dims': np.array([0], dtype=np.int64),
'layout': 'NCHW'
}
# update the attributes of the node
DeformableConvolution.update_node_stat(node, attrs)
return cls.enabled
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 = np.array(
[1, 1, *[d for d in dilate]],
dtype=np.int64) if dilate is not None else None
node_attrs = {
'op':
__class__.op,
'bias_addable':
True,
'bias_term':
bias_term,
'pad':
np.array([[0, 0], [0, 0], *[[pad, pad] for pad in padding]],
dtype=np.int64),
'pad_spatial_shape':
np.array([[pad, pad] for pad in padding], dtype=np.int64),
'dilation':
final_dilations,
'output_spatial_shape':
None,
'output_shape':
None,
'stride':
np.array([1, 1, *[s for s in stride]], dtype=np.int64),
'group':
num_group,
'deformable_group':
num_deformable_group,
'output':
output,
'kernel_spatial':
np.array([k for k in kernel], dtype=np.int64),
'input_feature_channel':
1,
'output_feature_channel':
0,
'kernel_spatial_idx':
None,
'reshape_kernel':
True,
'weights_index':
2,
'spatial_dims':
None,
'channel_dims':
np.array([1], dtype=np.int64),
'batch_dims':
np.array([0], dtype=np.int64),
'layout':
'NCHW',
}
# update the attributes of the node
DeformableConvolution.update_node_stat(node, node_attrs)
return cls.enabled