def test_copy_op_attrs(self):
"""copy_op_attrs should only copy attrs supported by the target node"""
# copy_op_attrs is used to transfer attrs from a fused op node
# (e.g. _FusedConv2D) to a standalone op (e.g. Conv2D)
# any additional attrs of the fused op need to be ignored
fused_op_str = '{"name":"model/conv2d/BiasAdd",'\
+ '"op":"_FusedConv2D","input":["input",'\
+ '"model/conv2d/Conv2D/ReadVariableOp",'\
+ '"model/conv2d/BiasAdd/ReadVariableOp",'\
+ '"model/p_re_lu/Neg"],"device":"/device:CPU:0",' \
+ '"attr":{"dilations":{"list":{"i":["1","1","1","1"]}},'\
+ '"T":{"type":"DT_FLOAT"},"data_format":{"s":"TkhXQw=="},'\
+ '"strides":{"list":{"i":["1","1","1","1"]}},'\
+ '"use_cudnn_on_gpu":{"b":true},'\
+ '"explicit_paddings":{"list":{}},'\
+ '"num_args":{"i":"2"},"epsilon":{"f":0},'\
+ '"padding":{"s":"VkFMSUQ="},'\
+ '"fused_ops":{"list":{"s":["Qmlhc0FkZA==","UHJlbHU="]}}}}'
fused_op = testutils.node_proto_from_json(fused_op_str)
node = rewrite.make_op_node('Conv2D', fused_op.input[0:2])
rewrite.copy_op_attrs(source=fused_op, target=node)
op_def = rewrite.get_op_def(node.op)
allowed = set(attr.name for attr in op_def.attr)
forbidden = any(attr for attr in node.attr if attr not in allowed)
self.assertFalse(forbidden)
# randomply check for some of the expected attributes
self.assertTrue('padding' in node.attr)
self.assertTrue('strides' in node.attr)
def _split_fused_op(node: util.NodeDef, input_node_map: util.NameToNode,
_) -> util.NodeList:
# Possible fused Conv2D patterns are:
# • Conv2D + BiasAdd
# • Conv2D + BiasAdd + <Activation>
# • Conv2D + FusedBatchNorm + <Activation>
# • Conv2D + Squeeze + BiasAdd
#
# Fused MatMul only has one pattern:
# • MatMul + BiasAdd + <Activation>
#
# FusedBatchNorm and Squeeze are not relevant for inference and thus never
# present in (optimised) frozen graphs generated by tfjs converter.
# This leaves us with Conv2D|MatMul + BiasAdd + <Activation> as the only
# remaining possible variants.
#
# For compatibility reasons with quantised TFLite models, we optionally
# split Conv2D + BiasAdd as well.
#
# We return [Conv2D|MatMul, BiasAdd|BiasAddV1, <Activation>].
# Unsupported <Activation>-nodes will be dealt with in a separate step
fused_op_name = node.op[6:] # remove the '_Fused'-prefix
fused_ops = list(s.decode('utf-8') for s in node.attr['fused_ops'].list.s)
inputs = list(node.input)
names_used = set()
def node_name(node_index):
name = generate_name_from(inputs[node_index], input_node_map)
if name in names_used:
name = generate_name_from(name,
input_node_map,
suffix=fused_ops[node_index - 2])
names_used.add(name)
return name
fused_op = util.make_op_node(fused_op_name, inputs[0:2], node_name(1))
fused_op = util.copy_op_attrs(source=node, target=fused_op)
bias_add = util.make_op_node(fused_ops[0], [fused_op, inputs[2]],
node_name(2))
bias_add = util.copy_op_attrs(source=node, target=bias_add)
have_activation = len(fused_ops) > 1
if have_activation:
# Prelu activation has additional input; reuse original name in all
# cases
name = node_name(3) if len(inputs) > 3 else node.name
activation = util.make_op_node(fused_ops[1], [bias_add] + inputs[3:],
name)
return [fused_op, bias_add, activation]
else:
return [fused_op, bias_add]
def _split_fused_depthwise(node: util.NodeDef, input_node_map: util.NameToNode,
weight_mods: util.WeightModifiers) -> util.NodeList:
"""Decompose fused op into DepthwiseConv2dNative + BiasAdd [+ Activation]
"""
fused_ops = list(s.decode('utf-8') for s in node.attr['fused_ops'].list.s)
inputs = node.input
names_used = set()
def node_name(node_index):
"""Return unique node names for sub-operations by appending fused-op"""
i = min(node_index, len(inputs)-1) # PReLU has 4 inputs, others only 3
name = generate_name_from(inputs[i], input_node_map)
if name in names_used:
name = generate_name_from(name, input_node_map,
suffix=fused_ops[node_index-2])
names_used.add(name)
return name
op = 'DepthwiseConv2dNative'
depthwise = util.make_op_node(op, inputs[0:2], node_name(1))
depthwise = util.copy_op_attrs(source=node, target=depthwise)
op = fused_ops[0]
bias_add = util.make_op_node(op, [depthwise, inputs[2]], node_name(2))
bias_add = util.copy_op_attrs(source=node, target=bias_add)
node_list = [depthwise, bias_add]
if len(fused_ops) > 1:
# we have an activation function
op = fused_ops[1]
input_nodes = [bias_add] + inputs[3:]
if util.get_op_def(op) is None:
# unsupported activation function - just copy type attribute
dtype = depthwise.attr['T'].type
activation = util.make_op_node(op, input_nodes, node_name(3),
dtype)
else:
# supported activation function - copy applicable attributes
activation = util.make_op_node(op, input_nodes, node_name(3))
activation = util.copy_op_attrs(source=node, target=activation)
node_list.append(activation)
return node_list
def _split_fused_op(node: util.NodeDef, input_node_map: util.NameToNode,
_) -> util.NodeList:
# Possible fused Conv2D patterns are:
# • Conv2D + BiasAdd
# • Conv2D + BiasAdd + <Activation>
# • Conv2D + FusedBatchNorm + <Activation>
# • Conv2D + Squeeze + BiasAdd
#
# Fused MatMul only has one pattern:
# • MatMul + BiasAdd + <Activation>
#
# FusedBatchNorm and Squeeze are not relevant for inference and thus never
# present in (optimised) frozen graphs generated by tfjs converter.
# This leaves us with Conv2D|MatMul + BiasAdd + <Activation> as the only
# remaining possible variants, since Conv2D + BiasAdd doesn't need to be
# split.
#
# We return [Conv2D|MatMul, BiasAdd|BiasAddV1, <Activation>].
# Unsupported <Activation>-nodes will be dealt with in a separate step
fused_op_name = node.op[6:] # remove the '_Fused'-prefix
fused_ops = list(s.decode('utf-8') for s in node.attr['fused_ops'].list.s)
inputs = list(node.input)
names_used = set()
def node_name(i):
name = util.generate_name_from(inputs[i], input_node_map)
if name in names_used:
# avoid name collisions by adding the name of the fused operation
# (the first name is always unique)
name = util.generate_name_from(name,
input_node_map,
suffix=fused_ops[i - 2])
names_used.add(name)
return name
fused_op = util.make_op_node(fused_op_name, inputs[0:2], node_name(1))
fused_op = util.copy_op_attrs(source=node, target=fused_op)
bias_add = util.make_op_node(fused_ops[0], [fused_op, inputs[2]],
node_name(2))
activation = util.make_op_node(fused_ops[1], [bias_add] + inputs[3:],
node_name(3))
return [fused_op, bias_add, activation]