def extract(cls, node):
# borders: leftBorder, topBorder, rightBorder, bottomBordes
borders = onnx_attr(node,
'border',
'ints',
default=None,
dst_type=int64_array)
scale = onnx_attr(node,
'scale',
'ints',
default=None,
dst_type=int64_array)
# Crop reference: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Crop
if len(borders) != 4:
log.error(
'ONNX Crop layer {} should take exactly 4 borders instead of {}'
.format(node.name, len(borders)))
return False
attrs = {'axis': int64_array([2, 3])}
if scale is not None:
attrs.update({
'dim': scale,
'offset': int64_array([borders[1], borders[0]])
})
else:
attrs.update({
'crop_begin': int64_array([borders[1], borders[0]]),
'crop_end': int64_array([borders[3], borders[2]])
})
Crop.update_node_stat(node, attrs)
return CropFrontExtractor.enabled
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
offset = attrs.tuple("offset", int, ())
axis = attrs.int("num_args", 0)
node_attrs = {
'axis': axis,
'offset': list(offset),
'dim': None,
}
Crop.update_node_stat(node, node_attrs)
return cls.enabled
def extract(cls, node):
proto_layer = node.pb
param = proto_layer.crop_param
mapping_rule = {
'axis': param.axis,
'offset': param.offset,
'dim': None, # set in infer
'infer': crop_infer
}
# update the attributes of the node
Crop.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
pb = node.parameters
mapping_rule = {
'dim': pb['dim'],
'offset': pb['offset'],
'axis': pb['axis'],
'layout': 'NCHW'
}
Crop.update_node_stat(node, attrs=mapping_rule)
return cls.enabled