def extract_const_nodes(nodes):
"""Takes a list of nodes and extract the weights. Return weight manifest
object.
Args:
nodes: list of tf.NodeDef TensorFlow NodeDef proto object.
"""
constants = [node for node in nodes if node.op == 'Const']
const_inputs = {}
# removed the conditional inputs for constants
for const in constants:
const_inputs[const.name] = const.input[:]
del const.input[:]
const_manifest = []
for const in constants:
const_manifest.append({
'name':
const.name,
'data':
graph_rewrite_util.values_from_const(const)
})
# Restore the conditional inputs
const.input[:] = const_inputs[const.name]
# Remove the binary array from tensor and save it to the external file.
for field_name in CLEARED_TENSOR_FIELDS:
const.attr["value"].tensor.ClearField(field_name)
return const_manifest
def _create_alpha_node(neg_alpha_op, updated_alpha):
if neg_alpha_op.name not in updated_alpha:
alpha_value = -graph_rewrite_util.values_from_const(neg_alpha_op)
neg_alpha_op.attr['value'].CopyFrom(
attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
alpha_value, alpha_value.dtype.type, alpha_value.shape)))
updated_alpha.append(neg_alpha_op.name)
def testFusePrelu(self):
layers = [
tf.keras.layers.PReLU(
alpha_initializer=tf.initializers.constant(0.25)),
tf.keras.layers.PReLU(
alpha_initializer=tf.initializers.constant(0.25))
]
model = tf.keras.Sequential(layers)
tf.keras.backend.set_learning_phase(0)
input_tensor = tf.constant([1.0, 1.0])
@tf.function
def execute_model(tensor):
return model(tensor)
graph = tf_saved_model_conversion_v2._freeze_saved_model_v2(
execute_model.get_concrete_function(input_tensor))
graph_def = graph.as_graph_def()
for node in graph_def.node:
if node.op == 'Conv2D':
node.device = "/CPU:0"
config = config_pb2.ConfigProto()
rewriter_config = config.graph_options.rewrite_options
rewriter_config.optimizers[:] = [
'pruning', 'constfold', 'arithmetic', 'dependency', 'pruning',
'remap', 'constfold', 'arithmetic', 'dependency'
]
for output in ['Identity']:
graph.add_to_collection('train_op',
graph.get_operation_by_name(output))
signature = meta_graph_pb2.SignatureDef()
graph_def = tf_saved_model_conversion_v2._run_grappler(
config, graph_def, graph, signature)
optimized_graph_def = fuse_prelu.fuse_ops_for_prelu(graph_def)
prelu_op_count = 0
value = None
for node in optimized_graph_def.node:
self.assertNotEqual("Relu", node.op)
if node.op == 'Prelu':
prelu_op_count += 1
if node.op == 'Const':
value = graph_rewrite_util.values_from_const(node)
self.assertEqual(prelu_op_count, 2)
self.assertEqual(value, [0.25])
def extract_weights(graph_def,
output_graph,
tf_version,
signature_def,
quantization_dtype=None,
weight_shard_size_bytes=1024 * 1024 * 4):
"""Takes a Python GraphDef object and extract the weights.
Args:
graph_def: tf.GraphDef TensorFlow GraphDef proto object, which represents
the model topology.
tf_version: Tensorflow version of the input graph.
signature_def: the SignatureDef of the inference graph.
quantization_dtype: An optional numpy dtype to quantize weights to for
compression. Only np.uint8 and np.uint16 are supported.
weight_shard_size_bytes: Shard size (in bytes) of the weight files.
The size of each weight file will be <= this value.
"""
constants = [node for node in graph_def.node if node.op == 'Const']
const_inputs = {}
# removed the conditional inputs for constants
for const in constants:
const_inputs[const.name] = const.input[:]
del const.input[:]
print('Writing weight file ' + output_graph + '...')
const_manifest = []
for const in constants:
const_manifest.append({
'name':
const.name,
'data':
graph_rewrite_util.values_from_const(const)
})
# Restore the conditional inputs
const.input[:] = const_inputs[const.name]
# Remove the binary array from tensor and save it to the external file.
for field_name in CLEARED_TENSOR_FIELDS:
const.attr["value"].tensor.ClearField(field_name)
write_artifacts(MessageToDict(graph_def), [const_manifest],
output_graph,
tf_version,
signature_def,
quantization_dtype=quantization_dtype,
weight_shard_size_bytes=weight_shard_size_bytes)
def fold_batch_norms(input_graph_def):
"""Removes batch normalization ops by folding them into convolutions.
Batch normalization during training has multiple dynamic parameters that are
updated, but once the graph is finalized these become constants. That means
there's an opportunity to reduce the computations down to a scale and
addition, rather than the more expensive multiple ops, and even bake the
scaling into the convolution weights. This function identifies the typical
pattern of batch normalization subgraphs, and performs the transformation to
fold the computations down into a simpler form. It currently only supports
batch normalization that's performed by the BatchNormWithGlobalNormalization
FusedBatchNorm and FusedBatchNormV3 ops, and will need to be extended in the
future to handle the newer style.
Args:
input_graph_def: A GraphDef containing a model.
Returns:
Modified graph with BN ops removed, and modified weights.
Raises:
ValueError: If the graph is badly formed with duplicate node names.
"""
input_node_map = {}
for node in input_graph_def.node:
if node.name not in input_node_map:
input_node_map[node.name] = node
else:
raise ValueError("Duplicate node names detected for ", node.name)
nodes_to_skip = {}
new_ops = []
for node in input_graph_def.node:
if (node.op not in ("BatchNormWithGlobalNormalization",
"FusedBatchNorm", "FusedBatchNormV3")):
continue
bias = None
conv_op = graph_rewrite_util.node_from_map(
input_node_map,
node.input[INPUT_ORDER[node.op].index("conv_op")])
# There might be an Add/BiasAdd op between the conv and the batchnorm,
# which we can fold into the mean param of the batchnorm.
if conv_op.op in ['BiasAdd', 'Add', 'AddV2']:
add_op = conv_op
# Follow the first input of the add to get to the conv.
conv_op = graph_rewrite_util.node_from_map(
input_node_map, add_op.input[0])
bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[1])
if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
# Follow the second input of the add to get to the conv.
conv_op = graph_rewrite_util.node_from_map(
input_node_map, add_op.input[1])
bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[0])
if bias and bias.op != 'Const':
tf_logging.warning("The bias %s after the conv %s was not a constant. "
"Maybe because freeze_graph wasn't "
"run first?" % (bias.name, conv_op.name))
continue
if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
tf_logging.warning("Didn't find expected Conv2D or DepthwiseConv2dNative"
" input to '%s'" % node.name)
continue
weights_op = graph_rewrite_util.node_from_map(
input_node_map, conv_op.input[1])
if weights_op.op != "Const":
tf_logging.warning("Didn't find expected conv Constant input to '%s',"
" found %s instead. Maybe because freeze_graph wasn't"
" run first?" % (conv_op.name, weights_op))
continue
weights = graph_rewrite_util.values_from_const(weights_op)
if conv_op.op == "Conv2D":
channel_count = weights.shape[3]
elif conv_op.op == "DepthwiseConv2dNative":
channel_count = weights.shape[2] * weights.shape[3]
mean_op = graph_rewrite_util.node_from_map(
input_node_map,
node.input[INPUT_ORDER[node.op].index("mean_op")])
if mean_op.op != "Const":
tf_logging.warning("Didn't find expected mean Constant input to '%s',"
" found %s instead. Maybe because freeze_graph wasn't"
" run first?" % (node.name, mean_op))
continue
mean_value = graph_rewrite_util.values_from_const(mean_op)
if bias is not None:
# Adjust the mean of the batchnorm based on the add op in-between the conv
# and the batchnorm.
mean_value = mean_value - graph_rewrite_util.values_from_const(bias)
if mean_value.shape != (channel_count,):
tf_logging.warning("Incorrect shape for mean, found %s, expected %s,"
" for node %s" % (str(mean_value.shape), str(
(channel_count,)), node.name))
continue
var_op = graph_rewrite_util.node_from_map(
input_node_map,
node.input[INPUT_ORDER[node.op].index("var_op")])
if var_op.op != "Const":
tf_logging.warning("Didn't find expected var Constant input to '%s',"
" found %s instead. Maybe because freeze_graph wasn't"
" run first?" % (node.name, var_op))
continue
var_value = graph_rewrite_util.values_from_const(var_op)
if var_value.shape != (channel_count,):
tf_logging.warning("Incorrect shape for var, found %s, expected %s,"
" for node %s" % (str(var_value.shape), str(
(channel_count,)), node.name))
continue
beta_op = graph_rewrite_util.node_from_map(
input_node_map,
node.input[INPUT_ORDER[node.op].index("beta_op")])
if beta_op.op != "Const":
tf_logging.warning("Didn't find expected beta Constant input to '%s',"
" found %s instead. Maybe because freeze_graph wasn't"
" run first?" % (node.name, beta_op))
continue
beta_value = graph_rewrite_util.values_from_const(beta_op)
if beta_value.shape != (channel_count,):
tf_logging.warning("Incorrect shape for beta, found %s, expected %s,"
" for node %s" % (str(beta_value.shape), str(
(channel_count,)), node.name))
continue
gamma_op = graph_rewrite_util.node_from_map(
input_node_map,
node.input[INPUT_ORDER[node.op].index("gamma_op")])
if gamma_op.op != "Const":
tf_logging.warning("Didn't find expected gamma Constant input to '%s',"
" found %s instead. Maybe because freeze_graph wasn't"
" run first?" % (node.name, gamma_op))
continue
gamma_value = graph_rewrite_util.values_from_const(gamma_op)
if gamma_value.shape != (channel_count,):
tf_logging.warning("Incorrect shape for gamma, found %s, expected %s,"
" for node %s" % (str(gamma_value.shape), str(
(channel_count,)), node.name))
continue
variance_epsilon_value = node.attr[EPSILON_ATTR[node.op]].f
nodes_to_skip[node.name] = True
nodes_to_skip[weights_op.name] = True
nodes_to_skip[conv_op.name] = True
if bias is not None:
nodes_to_skip[add_op.name] = True
if scale_after_normalization(node):
scale_value = (
(1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value)) *
gamma_value)
else:
scale_value = (
1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value))
offset_value = (-mean_value * scale_value) + beta_value
scaled_weights = np.copy(weights)
it = np.nditer(
scaled_weights, flags=["multi_index"], op_flags=["readwrite"])
if conv_op.op == "Conv2D":
while not it.finished:
current_scale = scale_value[it.multi_index[3]]
it[0] *= current_scale
it.iternext()
elif conv_op.op == "DepthwiseConv2dNative":
channel_multiplier = weights.shape[3]
while not it.finished:
current_scale = scale_value[it.multi_index[2] * channel_multiplier +
it.multi_index[3]]
it[0] *= current_scale
it.iternext()
scaled_weights_op = node_def_pb2.NodeDef()
scaled_weights_op.op = "Const"
scaled_weights_op.name = conv_op.name + '_weights'
scaled_weights_op.attr["dtype"].CopyFrom(weights_op.attr["dtype"])
scaled_weights_op.attr["value"].CopyFrom(
attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
scaled_weights, weights.dtype.type, weights.shape)))
# Replace the weights node with scaled weights node
for i, weights_node in enumerate(conv_op.input):
if weights_node == weights_op.name:
conv_op.input[i] = scaled_weights_op.name
new_conv_op = node_def_pb2.NodeDef()
new_conv_op.CopyFrom(conv_op)
offset_op = node_def_pb2.NodeDef()
offset_op.op = "Const"
offset_op.name = conv_op.name + "_bn_offset"
offset_op.attr["dtype"].CopyFrom(mean_op.attr["dtype"])
offset_op.attr["value"].CopyFrom(
attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
offset_value, mean_value.dtype.type, offset_value.shape)))
bias_add_op = node_def_pb2.NodeDef()
bias_add_op.op = "BiasAdd"
bias_add_op.name = node.name
bias_add_op.attr["T"].CopyFrom(conv_op.attr["T"])
bias_add_op.attr["data_format"].CopyFrom(conv_op.attr["data_format"])
bias_add_op.input.extend([new_conv_op.name, offset_op.name])
new_ops.extend([scaled_weights_op, new_conv_op, offset_op, bias_add_op])
result_graph_def = graph_pb2.GraphDef()
for node in input_graph_def.node:
if node.name in nodes_to_skip:
continue
new_node = node_def_pb2.NodeDef()
new_node.CopyFrom(node)
retained_input = []
for input_node in new_node.input:
if not input_node.startswith('^') or input_node[1:] not in nodes_to_skip:
retained_input.append(input_node)
new_node.input[:] = retained_input
result_graph_def.node.extend([new_node])
result_graph_def.node.extend(new_ops)
result_graph_def.versions.CopyFrom(input_graph_def.versions)
return result_graph_def
