def extract(cls, node):
attrs = {
'data_type': tf_dtype_extractor(node.pb.attr["dtype"].type),
'shape': tf_tensor_shape(node.pb.attr["shape"].shape),
'identity': True,
'infer': lambda node: copy_shape_infer(node, value_infer=copy_value),
}
Op.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
shapes = node.pb.attr['shapes'].list.shape
tf_types = node.pb.attr['component_types'].list.type
extracted_types = []
for t in tf_types:
extracted_types.append(tf_dtype_extractor(t))
result_shapes = []
for shape_pb in shapes:
shape = shape_pb.dim
if len(shape) == 3:
result_shapes.append(int64_array([1, shape[0].size, shape[1].size, shape[2].size]))
else:
result_shapes.append(int64_array([dim.size for dim in shape]))
Op.update_node_stat(node, {'shapes': result_shapes, 'types': extracted_types})
return cls.enabled
def extract(cls, node):
shapes = node.pb.attr['output_shapes'].list.shape
tf_types = node.pb.attr['output_types'].list.type
extracted_types = []
for t in tf_types:
extracted_types.append(tf_dtype_extractor(t))
result_shapes = []
for shape_pb in shapes:
result_shapes.append(tf_tensor_shape(shape_pb))
Op.update_node_stat(
node, {
'shapes': result_shapes,
'types': extracted_types,
'out_ports_count': 1
})
return cls.enabled
def extract(cls, node):
narrow_range = node.pb.attr['narrow_range'].b
num_bits = node.pb.attr['num_bits'].i
levels = 2**num_bits - int(narrow_range)
# we prepare this operation to be converted to FakeQuantize op,
# but input reconnection is needed, so we don't set infer function and type attribute
Op.update_node_stat(
node, {
'op': 'FakeQuantWithMinMaxVars',
'levels': levels,
'narrow_range': narrow_range,
'num_bits': num_bits
})
return cls.enabled
def extract(cls, node):
attrs = get_attrs(node)
Op.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
Op.update_node_stat(node, {'op': 'FakeConst'})
return cls.enabled