def main(args):
# If output_model path is relative and in cwd, make it absolute from root
output_model = FLAGS.output_model
if str(Path(output_model).parent) == '.':
output_model = str((Path.cwd() / output_model))
output_fld = Path(output_model).parent
output_model_name = Path(output_model).name
output_model_stem = Path(output_model).stem
output_model_pbtxt_name = output_model_stem + '.pbtxt'
# Create output directory if it does not exist
Path(output_model).parent.mkdir(parents=True, exist_ok=True)
if FLAGS.channels_first:
K.set_image_data_format('channels_first')
else:
K.set_image_data_format('channels_last')
custom_object_dict = get_custom_objects()
model = load_input_model(FLAGS.input_model, FLAGS.input_model_json,
FLAGS.input_model_yaml, custom_objects=custom_object_dict)
# TODO(amirabdi): Support networks with multiple inputs
orig_output_node_names = [node.op.name for node in model.outputs]
if FLAGS.output_nodes_prefix:
num_output = len(orig_output_node_names)
pred = [None] * num_output
converted_output_node_names = [None] * num_output
# Create dummy tf nodes to rename output
for i in range(num_output):
converted_output_node_names[i] = '{}{}'.format(
FLAGS.output_nodes_prefix, i)
pred[i] = tf.identity(model.outputs[i],
name=converted_output_node_names[i])
else:
converted_output_node_names = orig_output_node_names
logging.info('Converted output node names are: %s',
str(converted_output_node_names))
sess = K.get_session()
if FLAGS.output_meta_ckpt:
saver = tf.train.Saver()
saver.save(sess, str(output_fld / output_model_stem))
if FLAGS.save_graph_def:
tf.train.write_graph(sess.graph.as_graph_def(), str(output_fld),
output_model_pbtxt_name, as_text=True)
logging.info('Saved the graph definition in ascii format at %s',
str(Path(output_fld) / output_model_pbtxt_name))
if FLAGS.quantize:
from tensorflow.tools.graph_transforms import TransformGraph
transforms = ["quantize_weights", "quantize_nodes"]
transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [],
converted_output_node_names,
transforms)
constant_graph = graph_util.convert_variables_to_constants(
sess,
transformed_graph_def,
converted_output_node_names)
else:
constant_graph = graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(),
converted_output_node_names)
graph_io.write_graph(constant_graph, str(output_fld), output_model_name,
as_text=False)
logging.info('Saved the freezed graph at %s',
str(Path(output_fld) / output_model_name))
def __init__(self,
meta_file,
checkpoint_file,
dest_nodes,
inputShape=None,
in_nodes=None):
super(TensorflowParser, self).__init__()
# load model files into TensorFlow graph
if meta_file:
model = TensorflowParser._load_meta(meta_file)
if checkpoint_file:
self.ckpt_data = TensorflowParser._load_weights(checkpoint_file)
self.weight_loaded = True
# extract subgraph using in_nodes and dest_nodes
if in_nodes != None and inputShape != None:
from tensorflow.python.tools import strip_unused_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.platform import gfile
model = strip_unused_lib.strip_unused(
input_graph_def=model,
input_node_names=in_nodes,
output_node_names=dest_nodes,
placeholder_type_enum=dtypes.float32.as_datatype_enum)
input_list = [None]
for i in range(len(inputShape)):
input_list.append(tensorflow.Dimension(inputShape[i]))
tensor_input = tensorflow.TensorShape(input_list)
# Build network graph
self.tf_graph = TensorflowGraph(model)
for node in self.tf_graph.model.node:
if node.name in in_nodes:
node.attr['shape'].shape.CopyFrom(tensor_input.as_proto())
node.attr['_output_shapes'].list.shape.pop(
) #unknown_rank pop
node.attr['_output_shapes'].list.shape.extend(
[tensor_input.as_proto()])
# extract subgraph using dest_nodes
elif dest_nodes != None:
from tensorflow.python.framework.graph_util import extract_sub_graph
model = extract_sub_graph(model, dest_nodes)
# Get input node name
if not in_nodes:
in_nodes = []
for node in model.node:
if node.op == 'Placeholder':
in_nodes.append(node.name)
# Graph Transform
transforms = ["fold_constants(ignore_errors=true)"]
transformed_graph_def = TransformGraph(model, in_nodes, dest_nodes,
transforms)
in_type_list = {}
in_shape_list = {}
for n in transformed_graph_def.node:
if n.name in in_nodes:
in_type_list[n.name] = n.attr['dtype'].type
in_node_shape = n.attr['shape'].shape
in_node_shape_str = self._shapeToStr(in_node_shape)
in_shape_list[n.name] = in_node_shape_str
dtype = tensorflow.float32
with tensorflow.Graph().as_default() as g:
input_map = {}
for in_node in in_nodes:
if in_type_list[in_node] == 1 or in_type_list[in_node] == 0:
dtype = tensorflow.float32
elif in_type_list[in_node] == 3:
dtype = tensorflow.int32
elif in_type_list[in_node] == 10:
dtype = tensorflow.bool
x = tensorflow.placeholder(dtype, shape=in_shape_list[in_node])
input_map[in_node] = x
tensorflow.import_graph_def(transformed_graph_def,
name='',
input_map=input_map)
with tensorflow.Session(graph=g) as sess:
tempdir = tempfile.mkdtemp()
meta_graph_def = tensorflow.train.export_meta_graph(
filename=os.path.join(tempdir, 'my-model.meta'))
model = meta_graph_def.graph_def
shutil.rmtree(tempdir)
self.tf_graph = TensorflowGraph(model)
self.tf_graph.build()
process_graph(self.tf_graph, self.ckpt_data)
pred = [None]*num_output
pred_node_names = [None]*num_output
for i in range(num_output):
pred_node_names[i] = args.output_node_prefix+str(i)
pred[i] = tf.identity(net_model.outputs[i], name=pred_node_names[i])
print('output nodes names are: ', pred_node_names)
# [optional] write graph definition in ascii
sess = K.get_session()
if args.graph_def:
f = args.output_graphdef_file
tf.train.write_graph(sess.graph.as_graph_def(), output_fld, f, as_text=True)
print('saved the graph definition in ascii format at: ', osp.join(output_fld, f))
# convert variables to constants and save
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
from tensorflow.tools.graph_transforms import TransformGraph
if args.quantize:
transforms = ["quantize_weights", "quantize_nodes"]
transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [], pred_node_names, transforms)
constant_graph = graph_util.convert_variables_to_constants(sess, transformed_graph_def, pred_node_names)
else:
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)
graph_io.write_graph(constant_graph, output_fld, args.output_model_file, as_text=False)
print('saved the freezed graph (ready for inference) at: ', osp.join(output_fld, args.output_model_file))
def optimize_graph(params):
config = tf.ConfigProto(device_count={'GPU': 0}, allow_soft_placement=True)
init_checkpoint = params.ckpt_dir
tf.logging.info('build graph...')
# input placeholders, not sure if they are friendly to XLA
input_ids = tf.placeholder(tf.int32, (None, params.max_seq_len),
'input_ids')
input_mask = tf.placeholder(tf.int32, (None, params.max_seq_len),
'input_mask')
input_type_ids = tf.placeholder(tf.int32, (None, params.max_seq_len),
'segment_ids')
jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
with jit_scope():
features = {}
features['input_ids'] = input_ids
features['input_mask'] = input_mask
features['segment_ids'] = input_type_ids
model = BertMultiTask(params)
hidden_feature = model.body(features, tf.estimator.ModeKeys.PREDICT)
pred = model.top(features, hidden_feature,
tf.estimator.ModeKeys.PREDICT)
output_tensors = [pred[k] for k in pred]
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tmp_g = tf.get_default_graph().as_graph_def()
input_node_names = ['input_ids', 'input_mask', 'segment_ids']
output_node_names = [
'%s_top/%s_predict' %
(params.share_top[problem], params.share_top[problem])
for problem in params.problem_list
]
transforms = [
'remove_nodes(op=Identity)',
'fold_constants(ignore_errors=true)',
'fold_batch_norms',
# 'quantize_weights',
# 'quantize_nodes',
'merge_duplicate_nodes',
'strip_unused_nodes',
'sort_by_execution_order'
]
with tf.Session(config=config) as sess:
tf.logging.info('load parameters from checkpoint...')
sess.run(tf.global_variables_initializer())
tf.logging.info('freeze...')
tmp_g = tf.graph_util.convert_variables_to_constants(
sess, tmp_g, [n.name[:-2] for n in output_tensors])
tmp_g = TransformGraph(tmp_g, input_node_names, output_node_names,
transforms)
tmp_file = os.path.join(params.ckpt_dir, 'export_model')
tf.logging.info('write graph to a tmp file: %s' % tmp_file)
with tf.gfile.GFile(tmp_file, 'wb') as f:
f.write(tmp_g.SerializeToString())
return tmp_file
input_node_names = [the_inp.name.split(':')[0]]
output_node_names = [the_out.name.split(':')[0]]
print("Input node names:", input_node_names)
print("Output node names:", output_node_names)
graph_def = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_node_names)
#%%
from tensorflow.tools.graph_transforms import TransformGraph
graph_def = TransformGraph(graph_def, input_node_names, output_node_names, [
'strip_unused_nodes()',
'remove_nodes(op=Identity, op=CheckNumerics)',
'fold_constants()',
'fold_old_batch_norms',
'fold_batch_norms',
'round_weights(num_steps=128)',
])
#%%
with open('batched_elmo128.pb', 'wb') as f:
f.write(graph_def.SerializeToString())
#%%
newg = tf.Graph()
with newg.as_default():
tf.import_graph_def(graph_def)
with gfile.FastGFile(FREEZED_PATH, 'wb') as f:
f.write(optimized_graph_def.SerializeToString())
print("Starting graph optimization ... ")
transforms = [
'strip_unused_nodes(type=float, shape="1,160,576,3")',
'remove_nodes(op=Identity, op=CheckNumerics)',
'fold_constants(ignore_errors=false)',
'fold_batch_norms',
'remove_device',
'fold_old_batch_norms',
'fold_constants(ignore_errors=false)',
'round_weights(num_steps=256)',
]
# 'fuse_convolutions',
for transform in transforms:
print("Starting transform: `%s` ... " % transform)
optimized_graph_def = TransformGraph(optimized_graph_def,
[INPUT_TENSOR_NAME],
[FINAL_TENSOR_NAME], [transform])
tf.summary.FileWriter('opt_log', graph_def=optimized_graph_def)
print("Wrote optimized graph to `%s` ... " % 'opt_log')
with gfile.FastGFile(OPTIMIZED_PATH, 'wb') as f:
f.write(optimized_graph_def.SerializeToString())
print("Done! Wrote results to `%s`." % 'tf_files')
def fix_fp16_modelerror(model_path,
targetop_name_expectedfp32=None,
targetop_input_expectedfp32=None,
targetop_name_expectedfp16=None,
targetop_input_expectedfp16=None,
input_names=[],
output_names=[]):
"""
If you find errors like the following, you can try the following solutions
e.g.1
error1:
Input 1 of node tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/CropAndResize was passed half from
tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/transform_fpcoor_for_tf/concat:0 incompatible with expected float.
error2:
Input 1 of node tower-pred-0/multilevel_roi_align/roi_level3/roi_align/crop_and_resize/CropAndResize was passed half from
tower-pred-0/multilevel_roi_align/roi_level3/roi_align/crop_and_resize/transform_fpcoor_for_tf/concat:0 incompatible with expected float.
opexpected_type is 'fp32', opinput_type is 'fp16'
targetop_name_expectedfp32: ['tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/CropAndResize']
targetop_input_expectedfp32: ['tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/transform_fpcoor_for_tf/concat']
e.g.2
error1:
Input 0 of node tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/transpose_1 was passed float from
tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/CropAndResize:0 incompatible with expected half.
opexpected_type is 'fp16', opinput_type is 'fp32'
targetop_name_expectedfp16: ['tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/transpose_1']
targetop_input_expectedfp16: ['tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/CropAndResize']
"""
source_graph_def = convert_graph_all2fp16(model_path,
target_type='fp16',
first_modify=False)
for node in source_graph_def.node:
## add cast node, opexpected_type='fp32', opinput_type='fp16'
if node.name in targetop_input_expectedfp32: #'tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/transform_fpcoor_for_tf/concat'
new_node_ = source_graph_def.node.add()
new_node_.name = node.name + '_cast'
new_node_.op = "Cast"
new_node_.input.append(node.name)
new_node_.attr["SrcT"].type = types_pb2.DT_HALF
new_node_.attr["DstT"].type = types_pb2.DT_FLOAT
new_node_.attr["Truncate"].b = False
continue
## add cast node, opexpected_type='fp16', opinput_type='fp32'
if node.name in targetop_input_expectedfp16: #'tower-pred-0/multilevel_roi_align/roi_level2/roi_align/crop_and_resize/transform_fpcoor_for_tf/concat'
new_node_ = source_graph_def.node.add()
new_node_.name = node.name + '_cast'
new_node_.op = "Cast"
new_node_.input.append(node.name)
new_node_.attr["SrcT"].type = types_pb2.DT_FLOAT
new_node_.attr["DstT"].type = types_pb2.DT_HALF
new_node_.attr["Truncate"].b = False
continue
for node in source_graph_def.node:
# convert targetop'input to cast node op name
if (node.name in targetop_name_expectedfp32) or (
node.name in targetop_name_expectedfp16):
for i, name in enumerate(node.input):
if (name in targetop_input_expectedfp32) or (
name in targetop_input_expectedfp16):
node.input[i] = name + '_cast'
continue
# transform graph
if output_names:
if not input_names:
input_names = []
transforms = ["strip_unused_nodes"]
source_graph_def = TransformGraph(source_graph_def, input_names,
output_names, transforms)
# write graph_def to model
tf.io.write_graph(source_graph_def,
logdir=args.savepath,
name=args.outpbname + '-new.pb',
as_text=False)
print("Converting new pb done ...")
def optimize_graph_for_inference(model_dir, input_node_names,
output_node_names):
input_graph_path = os.path.join(model_dir, 'graph.pbtxt')
input_checkpoint = tf.train.latest_checkpoint(model_dir)
input_binary = False
clear_devices = True
print("Loading Graph `{}` ...".format(input_graph_path))
print("Loading Checkpoint `{}` ...".format(input_checkpoint))
print("Freezing Graph ...")
# https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/freeze_graph.py#L286-L301
frozen_graph_def = freeze_graph.freeze_graph(
input_graph=input_graph_path, # A `GraphDef` file to load.
input_saver="", # A TensorFlow Saver file.
input_binary=
input_binary, # A Bool. True means input_graph is .pb, False indicates .pbtxt.
input_checkpoint=input_checkpoint,
output_node_names=",".join(
output_node_names
), # The name(s) of the output nodes, comma separated.
restore_op_name="", # Unused
filename_tensor_name="", # Unused
output_graph=os.path.join(
"/tmp", 'frozen_saved_model.pb'
), # String where to write the frozen `GraphDef`.
clear_devices=
clear_devices, # A Bool whether to remove device specifications.
initializer_nodes=
"", # Comma separated list of initializer nodes to run before freezing.
variable_names_whitelist=
"", # The set of variable names to convert (optional, bydefault, all variables are converted)
variable_names_blacklist=
"", # The set of variable names to omit converting to constants (optional)
input_meta_graph=None, # A `MetaGraphDef` file to load (optional).
input_saved_model_dir=
None, # Path to the dir with TensorFlow 'SavedModel' file and variables (optional).
saved_model_tags=tag_constants.
SERVING, # Group of comma separated tag(s) of the MetaGraphDef to load, in string format.
checkpoint_version=saver_pb2.SaverDef.
V2 # Tensorflow variable file format (saver_pb2.SaverDef.V1 or saver_pb2.SaverDef.V2
)
print("Optimizing Graph for Inference ...")
optimized_frozen_graph = optimize_for_inference_lib.optimize_for_inference(
frozen_graph_def,
input_node_names, # an array of the input node(s)
output_node_names, # an array of output nodes
tf.float32.as_datatype_enum)
transforms = [
'remove_nodes(op=Identity)', 'merge_duplicate_nodes',
'strip_unused_nodes', 'fold_constants(ignore_errors=true)',
'fold_batch_norms'
]
print("Applying Graph Transformations ...")
return TransformGraph(
optimized_frozen_graph,
input_node_names, # an array of the input node(s)
output_node_names, # an array of output nodes
transforms)
import tensorflow as tf
from tensorflow.python.tools import optimize_for_inference_lib
from tensorflow.tools.graph_transforms import TransformGraph
output_names = ['conv2d_input', 'conv2d/kernel/Initializer/random_uniform/shape', 'conv2d/kernel/Initializer/random_uniform/min', 'conv2d/kernel/Initializer/random_uniform/max', 'conv2d/kernel/Initializer/random_uniform/RandomUniform', 'conv2d/kernel/Initializer/random_uniform/sub', 'conv2d/kernel/Initializer/random_uniform/mul', 'conv2d/kernel/Initializer/random_uniform', 'conv2d/kernel', 'conv2d/kernel/IsInitialized/VarIsInitializedOp', 'conv2d/kernel/Assign', 'conv2d/kernel/Read/ReadVariableOp', 'conv2d/bias/Initializer/zeros', 'conv2d/bias', 'conv2d/bias/IsInitialized/VarIsInitializedOp', 'conv2d/bias/Assign', 'conv2d/bias/Read/ReadVariableOp', 'conv2d/dilation_rate', 'conv2d/Conv2D/ReadVariableOp', 'conv2d/Conv2D', 'conv2d/BiasAdd/ReadVariableOp', 'conv2d/BiasAdd', 'activation/Relu', 'max_pooling2d/MaxPool', 'conv2d_1/kernel/Initializer/random_uniform/shape', 'conv2d_1/kernel/Initializer/random_uniform/min', 'conv2d_1/kernel/Initializer/random_uniform/max', 'conv2d_1/kernel/Initializer/random_uniform/RandomUniform', 'conv2d_1/kernel/Initializer/random_uniform/sub', 'conv2d_1/kernel/Initializer/random_uniform/mul', 'conv2d_1/kernel/Initializer/random_uniform', 'conv2d_1/kernel', 'conv2d_1/kernel/IsInitialized/VarIsInitializedOp', 'conv2d_1/kernel/Assign', 'conv2d_1/kernel/Read/ReadVariableOp', 'conv2d_1/bias/Initializer/zeros', 'conv2d_1/bias', 'conv2d_1/bias/IsInitialized/VarIsInitializedOp', 'conv2d_1/bias/Assign', 'conv2d_1/bias/Read/ReadVariableOp', 'conv2d_1/dilation_rate', 'conv2d_1/Conv2D/ReadVariableOp', 'conv2d_1/Conv2D', 'conv2d_1/BiasAdd/ReadVariableOp', 'conv2d_1/BiasAdd', 'activation_1/Relu', 'max_pooling2d_1/MaxPool', 'flatten/Shape', 'flatten/strided_slice/stack', 'flatten/strided_slice/stack_1', 'flatten/strided_slice/stack_2', 'flatten/strided_slice', 'flatten/Reshape/shape/1', 'flatten/Reshape/shape', 'flatten/Reshape', 'dense/kernel/Initializer/random_uniform/shape', 'dense/kernel/Initializer/random_uniform/min', 'dense/kernel/Initializer/random_uniform/max', 'dense/kernel/Initializer/random_uniform/RandomUniform', 'dense/kernel/Initializer/random_uniform/sub', 'dense/kernel/Initializer/random_uniform/mul', 'dense/kernel/Initializer/random_uniform', 'dense/kernel', 'dense/kernel/IsInitialized/VarIsInitializedOp', 'dense/kernel/Assign', 'dense/kernel/Read/ReadVariableOp', 'dense/bias/Initializer/zeros', 'dense/bias', 'dense/bias/IsInitialized/VarIsInitializedOp', 'dense/bias/Assign', 'dense/bias/Read/ReadVariableOp', 'dense/MatMul/ReadVariableOp', 'dense/MatMul', 'dense/BiasAdd/ReadVariableOp', 'dense/BiasAdd', 'dense_1/kernel/Initializer/random_uniform/shape', 'dense_1/kernel/Initializer/random_uniform/min', 'dense_1/kernel/Initializer/random_uniform/max', 'dense_1/kernel/Initializer/random_uniform/RandomUniform', 'dense_1/kernel/Initializer/random_uniform/sub', 'dense_1/kernel/Initializer/random_uniform/mul', 'dense_1/kernel/Initializer/random_uniform', 'dense_1/kernel', 'dense_1/kernel/IsInitialized/VarIsInitializedOp', 'dense_1/kernel/Assign', 'dense_1/kernel/Read/ReadVariableOp', 'dense_1/bias/Initializer/zeros', 'dense_1/bias', 'dense_1/bias/IsInitialized/VarIsInitializedOp', 'dense_1/bias/Assign', 'dense_1/bias/Read/ReadVariableOp', 'dense_1/MatMul/ReadVariableOp', 'dense_1/MatMul', 'dense_1/BiasAdd/ReadVariableOp', 'dense_1/BiasAdd', 'activation_2/Sigmoid', 'activation_2_target', 'total/Initializer/zeros', 'total', 'total/IsInitialized/VarIsInitializedOp', 'total/Assign', 'total/Read/ReadVariableOp', 'count/Initializer/zeros', 'count', 'count/IsInitialized/VarIsInitializedOp', 'count/Assign', 'count/Read/ReadVariableOp', 'metrics/acc/Cast/x', 'metrics/acc/Greater', 'metrics/acc/Cast_1', 'metrics/acc/Equal', 'metrics/acc/Cast_2', 'metrics/acc/Mean/reduction_indices', 'metrics/acc/Mean', 'metrics/acc/Const', 'metrics/acc/Sum', 'metrics/acc/AssignAddVariableOp', 'metrics/acc/ReadVariableOp', 'metrics/acc/Size', 'metrics/acc/Cast_3', 'metrics/acc/AssignAddVariableOp_1', 'metrics/acc/ReadVariableOp_1', 'metrics/acc/div_no_nan/ReadVariableOp', 'metrics/acc/div_no_nan/ReadVariableOp_1', 'metrics/acc/div_no_nan', 'metrics/acc/Identity', 'loss/activation_2_loss/Const', 'loss/activation_2_loss/logistic_loss/zeros_like', 'loss/activation_2_loss/logistic_loss/GreaterEqual', 'loss/activation_2_loss/logistic_loss/Select', 'loss/activation_2_loss/logistic_loss/Neg', 'loss/activation_2_loss/logistic_loss/Select_1', 'loss/activation_2_loss/logistic_loss/mul', 'loss/activation_2_loss/logistic_loss/sub', 'loss/activation_2_loss/logistic_loss/Exp', 'loss/activation_2_loss/logistic_loss/Log1p', 'loss/activation_2_loss/logistic_loss', 'loss/activation_2_loss/Mean/reduction_indices', 'loss/activation_2_loss/Mean', 'loss/activation_2_loss/weighted_loss/Cast/x', 'loss/activation_2_loss/weighted_loss/broadcast_weights/assert_broadcastable/weights/shape', 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with tf.gfile.FastGFile('TF1Model_Frozen_Graph.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
graph_def = optimize_for_inference_lib.optimize_for_inference(graph_def, ['Placeholder'], output_names, tf.float32.as_datatype_enum)
graph_def = TransformGraph(graph_def, ['Placeholder'], output_names, ['remove_nodes(op=PlaceholderWithDefault)', 'sort_by_execution_order'])
with tf.gfile.FastGFile('TF1Model_Consts_Fused.pb', 'wb') as f:
f.write(graph_def.SerializeToString())
def convert_graph_all2fp16(model_path,
target_type='fp16',
first_modify=True,
input_names=None,
output_names=None):
if target_type == 'fp16':
dtype = types_pb2.DT_HALF
elif target_type == 'fp64':
dtype = types_pb2.DT_DOUBLE
else:
dtype = types_pb2.DT_FLOAT
source_sess = load_graph(model_path)
source_graph_def = source_sess.graph.as_graph_def()
target_graph_def = graph_pb2.GraphDef()
target_graph_def.versions.CopyFrom(source_graph_def.versions)
for node in source_graph_def.node:
# fused batch norm node
if node.op == "FusedBatchNorm":
rewrite_batch_norm_node_v2(node,
target_graph_def,
target_type=target_type)
continue
# replicate node
new_node = target_graph_def.node.add()
new_node.op = node.op
new_node.name = node.name
new_node.input.extend(node.input)
attrs = list(node.attr.keys())
# keep batch norm params node
if ("BatchNorm" in node.name) or ('batch_normalization' in node.name) \
or ('bn/gamma' in node.name) or ('bn/beta' in node.name) or ('bn/mean' in node.name) or ('bn/variance' in node.name): ## make sure FusedBatchNormV2'inputs scale/offset/mean/variance will be U/float type
for attr in attrs:
new_node.attr[attr].CopyFrom(node.attr[attr])
continue
# replace dtype in node attr with target dtype
for attr in attrs:
# keep special node in fp32
if node.name in keep_fp32_node_name:
new_node.attr[attr].CopyFrom(node.attr[attr])
continue
if node.attr[attr].type == types_pb2.DT_FLOAT:
# modify node dtype
node.attr[attr].type = dtype
if attr == "value":
tensor = node.attr[attr].tensor
if tensor.dtype == types_pb2.DT_FLOAT:
# if float_val exists
if tensor.float_val:
float_val = tf.make_ndarray(node.attr[attr].tensor)
new_node.attr[attr].tensor.CopyFrom(
tf.make_tensor_proto(float_val, dtype=dtype))
continue
# if tensor content exists
if tensor.tensor_content:
tensor_shape = [
x.size for x in tensor.tensor_shape.dim
]
tensor_weights = tf.make_ndarray(tensor)
# reshape tensor
tensor_weights = np.reshape(tensor_weights,
tensor_shape)
tensor_proto = tf.make_tensor_proto(tensor_weights,
dtype=dtype)
new_node.attr[attr].tensor.CopyFrom(tensor_proto)
continue
new_node.attr[attr].CopyFrom(node.attr[attr])
if not first_modify:
return target_graph_def
else:
# transform graph
if output_names:
if not input_names:
input_names = []
transforms = ["strip_unused_nodes"]
target_graph_def = TransformGraph(target_graph_def, input_names,
output_names, transforms)
# write graph_def to model
tf.io.write_graph(target_graph_def,
logdir=args.savepath,
name=args.outpbname + '.pb',
as_text=False)
print("Converting done ...")
def __init__(self, option, src_model_file):
# Keep in lexicographical order
self._op_converters = {
TFOpType.Abs.name: self.convert_elementwise,
TFOpType.Add.name: self.convert_add,
TFOpType.AddV2.name: self.convert_add,
TFOpType.ArgMax.name: self.convert_argmax,
TFOpType.AvgPool.name: self.convert_pooling,
TFOpType.BatchMatMul.name: self.convert_matmul,
TFOpType.BatchMatMulV2.name: self.convert_matmul,
TFOpType.BatchToSpaceND.name: self.convert_space_batch,
TFOpType.BiasAdd.name: self.convert_biasadd,
TFOpType.Cast.name: self.convert_cast,
TFOpType.ConcatV2.name: self.convert_concat,
TFOpType.Const.name: self.convert_nop,
TFOpType.Conv2D.name: self.convert_conv2d,
TFOpType.Conv2DBackpropInput.name: self.convert_conv2d,
TFOpType.Cumsum.name: self.convert_cumsum,
TFOpType.DepthwiseConv2dNative.name: self.convert_conv2d,
TFOpType.DepthToSpace.name: self.convert_space_depth,
TFOpType.Div.name: self.convert_elementwise,
TFOpType.Elu.name: self.convert_activation,
TFOpType.Equal.name: self.convert_elementwise,
TFOpType.ExpandDims.name: self.convert_expand_dims,
TFOpType.ExtractImagePatches.name:
self.convert_extract_image_patches,
TFOpType.FakeQuantWithMinMaxVars.name: self.convert_fake_quantize,
TFOpType.FakeQuantWithMinMaxArgs.name: self.convert_fake_quantize,
TFOpType.Fill.name: self.convert_fill,
TFOpType.FloorDiv.name: self.convert_elementwise,
TFOpType.FusedBatchNorm.name: self.convert_fused_batchnorm,
TFOpType.FusedBatchNormV2.name: self.convert_fused_batchnorm,
TFOpType.FusedBatchNormV3.name: self.convert_fused_batchnorm,
TFOpType.Gather.name: self.convert_gather,
TFOpType.GatherV2.name: self.convert_gather,
TFOpType.Identity.name: self.convert_identity,
TFOpType.LeakyRelu.name: self.convert_activation,
TFOpType.MatMul.name: self.convert_matmul,
TFOpType.Max.name: self.convert_reduce,
TFOpType.Maximum.name: self.convert_elementwise,
TFOpType.MaxPool.name: self.convert_pooling,
TFOpType.Mean.name: self.convert_reduce,
TFOpType.Min.name: self.convert_reduce,
TFOpType.Minimum.name: self.convert_elementwise,
TFOpType.MirrorPad.name: self.convert_pad,
TFOpType.Mul.name: self.convert_elementwise,
TFOpType.Neg.name: self.convert_elementwise,
TFOpType.NotEqual.name: self.convert_elementwise,
TFOpType.OneHot.name: self.convert_one_hot,
TFOpType.Pack.name: self.convert_stack,
TFOpType.Pad.name: self.convert_pad,
TFOpType.PadV2.name: self.convert_pad,
TFOpType.Placeholder.name: self.convert_nop,
TFOpType.Pow.name: self.convert_elementwise,
TFOpType.Prod.name: self.convert_reduce,
TFOpType.Sub.name: self.convert_elementwise,
TFOpType.RealDiv.name: self.convert_elementwise,
TFOpType.SquaredDifference.name: self.convert_elementwise,
TFOpType.Square.name: self.convert_elementwise,
TFOpType.Rsqrt.name: self.convert_elementwise,
TFOpType.Relu.name: self.convert_activation,
TFOpType.Relu6.name: self.convert_activation,
TFOpType.Tanh.name: self.convert_activation,
TFOpType.Reshape.name: self.convert_reshape,
TFOpType.ResizeBicubic.name: self.convert_resize_bicubic,
TFOpType.ResizeBilinear.name: self.convert_resize_bilinear,
TFOpType.ResizeNearestNeighbor.name:
self.convert_resize_nearest_neighbor,
TFOpType.ReverseV2.name: self.convert_reverse,
TFOpType.Select.name: self.convert_select,
TFOpType.Shape.name: self.convert_shape,
TFOpType.Sigmoid.name: self.convert_activation,
TFOpType.Sign.name: self.convert_elementwise,
TFOpType.Slice.name: self.convert_slice,
TFOpType.Softmax.name: self.convert_softmax,
TFOpType.SpaceToBatchND.name: self.convert_space_batch,
TFOpType.SpaceToDepth.name: self.convert_space_depth,
TFOpType.Split.name: self.convert_split,
TFOpType.SplitV.name: self.convert_splitv,
TFOpType.Sqrt.name: self.convert_elementwise,
TFOpType.Squeeze.name: self.convert_squeeze,
TFOpType.Stack.name: self.convert_stack,
TFOpType.StridedSlice.name: self.convert_stridedslice,
TFOpType.Sum.name: self.convert_reduce,
TFOpType.Tile.name: self.convert_tile,
TFOpType.Transpose.name: self.convert_transpose,
TFOpType.Unpack.name: self.convert_unstack,
TFOpType.Unstack.name: self.convert_unstack,
}
self._option = option
self._mace_net_def = mace_pb2.NetDef()
ConverterUtil.set_filter_format(self._mace_net_def, DataFormat.HWIO)
ConverterUtil.add_data_format_arg(self._mace_net_def, DataFormat.NHWC)
ConverterUtil.set_framework_type(self._mace_net_def,
FrameworkType.TENSORFLOW.value)
# import tensorflow graph
tf_graph_def = tf.GraphDef()
with tf.gfile.Open(src_model_file, 'rb') as f:
tf_graph_def.ParseFromString(f.read())
self._placeholders = {}
self._skip_tensor = set()
self._output_shape = {}
print("Run transform_graph: %s" % TFTransformGraphOptions)
try:
print("output keys: ", option.output_nodes.keys())
transformed_graph_def = TransformGraph(tf_graph_def,
option.input_nodes.keys(),
option.output_nodes.keys(),
TFTransformGraphOptions)
except Exception as ex:
print("Failed to transform graph using tf tool: %s" % ex)
transformed_graph_def = tf_graph_def
# To check optimized model, uncomment following code.
# tf.io.write_graph(
# transformed_graph_def,
# ".",
# os.path.basename(src_model_file)[:-3] + "_opt.pb",
# as_text=False
# )
self.add_shape_info(transformed_graph_def)
# reset default graph to clear earlier import
tf.reset_default_graph()
with tf.Session() as session:
with session.graph.as_default() as graph:
tf.import_graph_def(transformed_graph_def, name='')
self._tf_graph = graph
self.update_output_shapes(session)
# we have polluted graph with 'shape' ops, so reset it and reload it
# again
tf.reset_default_graph()
with tf.Session() as session:
with session.graph.as_default() as graph:
tf.import_graph_def(transformed_graph_def, name='')
self._tf_graph = graph
def main(config):
print(config)
# Build Networks
tf.reset_default_graph()
photo_ph = tf.placeholder(tf.float32, [1, None, None, 1]) # input grayscale image, normalized by 0~1
is_training = tf.constant(False) # Always False in testing
ops = build_networks(config, photo_ph, is_training)
print(ops)
tfconfig = tf.ConfigProto()
tfconfig.gpu_options.allow_growth = True
sess = tf.Session(config=tfconfig)
sess.run(tf.global_variables_initializer())
# load model
saver = tf.train.Saver()
print('Load trained models...')
if os.path.isdir(config.model):
checkpoint = tf.train.latest_checkpoint(config.model)
model_dir = config.model
else:
checkpoint = config.model
model_dir = os.path.dirname(config.model)
if checkpoint is not None:
print('Checkpoint', os.path.basename(checkpoint))
print("[{}] Resuming...".format(time.asctime()))
saver.restore(sess, checkpoint)
else:
raise ValueError('Cannot load model from {}'.format(model_dir))
print('Done.')
output_graph="export/normal.pb"
print(tf.all_variables())
#with tf.gfile.GFile(output_graph, "wb") as f:
# f.write(sess.graph.as_graph_def().SerializeToString())
#tf.train.write_graph(sess.graph.as_graph_def(), 'export/', 'normaltxt.pbtxt',as_text=True)
input_graph_def = sess.graph.as_graph_def()
output_nodes_names=[ops['kpts'].op.name,ops['feats'].op.name,ops['scale_maps'].op.name,ops['kpts_scale'].op.name,ops['degree_maps'].op.name,ops['kpts_ori'].op.name]
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session
input_graph_def, # input_graph_def is useful for retrieving the nodes
output_nodes_names
)
# output_graph_def.save("export/frozen2.pb")
output_graph_name="export/frozen.pb"
#with tf.gfile.GFile(output_graph_name, "wb") as f:
# f.write(output_graph_def.SerializeToString())
#tf.train.write_graph(output_graph_def, 'export/', 'frozen.pbtxt',as_text=True)
inp_node = ['Placeholder']
optimize_graph_def = optimize_for_inference_lib.optimize_for_inference(output_graph_def, inp_node, output_nodes_names,
tf.float32.as_datatype_enum)
optimize_graph_def = TransformGraph(optimize_graph_def, inp_node, output_nodes_names, ["sort_by_execution_order"])
removeUnusedNodesAndAttrs(to_remove, optimize_graph_def)
output_graph_name="export/optimize.pb"
with tf.gfile.GFile(output_graph_name, "wb") as f:
f.write(optimize_graph_def.SerializeToString())
tf.train.write_graph(optimize_graph_def, "export/", 'optimize.pbtxt', as_text=True)
def save_tf_image(keras_model,
odir,
model_file='',
graph_file='',
quantize=False,
num_output=1,
output_node_prefix='output_node',
backend='tf'):
"""
Helper function to save TF model, see
https://github.com/amir-abdi/keras_to_tensorflow/blob/master/keras_to_tensorflow.py
quantize: if set to True, use the quantize feature of Tensorflow
(https://www.tensorflow.org/performance/quantization)
num_output: this value has nothing to do with the number of classes, batch_size, etc.,
and it is mostly equal to 1. If the network is a **multi-stream network**
(forked network with multiple outputs), set the value to the number of outputs.
output_node_prefix: the prefix to use for output nodes
"""
from keras.models import load_model
from keras import backend as K
import tensorflow as tf
if not os.path.isdir(odir):
os.mkdir(odir)
# Load keras model and rename output
K.set_learning_phase(0)
if backend == 'theano':
K.set_image_data_format('channels_first')
else:
K.set_image_data_format('channels_last')
# load keras model
net_model = load_model(keras_model)
if not model_file:
model_file = 'model-%s.pb' % (time.strftime('%Y%m%d', time.gmtime()))
pred = [None] * num_output
pred_node_names = [None] * num_output
for i in range(num_output):
pred_node_names[i] = output_node_prefix + str(i)
pred[i] = tf.identity(net_model.outputs[i], name=pred_node_names[i])
print('output nodes names are: ', pred_node_names)
# [optional] write graph definition in ascii
sess = K.get_session()
if graph_file:
tf.train.write_graph(sess.graph.as_graph_def(),
odir,
graph_file,
as_text=True)
print('saved the graph definition in ascii format at: ',
os.path.join(odir, graph_file))
# convert variables to constants and save
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
if quantize: # https://www.tensorflow.org/performance/quantization
from tensorflow.tools.graph_transforms import TransformGraph
transforms = ["quantize_weights", "quantize_nodes"]
transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [],
pred_node_names, transforms)
constant_graph = graph_util.convert_variables_to_constants(
sess, transformed_graph_def, pred_node_names)
else:
constant_graph = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(), pred_node_names)
graph_io.write_graph(constant_graph, odir, model_file, as_text=False)
graph_io.write_graph(constant_graph,
odir,
model_file + 'txt',
as_text=True)
print('saved TF model %s' % os.path.join(odir, model_file))
def append_postprocessing_op(frozen_graph_def, max_detections,
max_classes_per_detection, nms_score_threshold,
nms_iou_threshold, num_classes, scale_values):
"""Appends postprocessing custom op.
Args:
frozen_graph_def: Frozen GraphDef for SSD model after freezing the
checkpoint
max_detections: Maximum number of detections (boxes) to show
max_classes_per_detection: Number of classes to display per detection
nms_score_threshold: Score threshold used in Non-maximal suppression in
post-processing
nms_iou_threshold: Intersection-over-union threshold used in Non-maximal
suppression in post-processing
num_classes: number of classes in SSD detector
scale_values: scale values is a dict with following key-value pairs
{y_scale: 10, x_scale: 10, h_scale: 5, w_scale: 5} that are used in decode
centersize boxes
Returns:
transformed_graph_def: Frozen GraphDef with postprocessing custom op
appended
TFLite_Detection_PostProcess custom op node has four outputs:
detection_boxes: a float32 tensor of shape [1, num_boxes, 4] with box
locations
detection_classes: a float32 tensor of shape [1, num_boxes]
with class indices
detection_scores: a float32 tensor of shape [1, num_boxes]
with class scores
num_boxes: a float32 tensor of size 1 containing the number of detected
boxes
"""
new_output = frozen_graph_def.node.add()
new_output.op = 'TFLite_Detection_PostProcess'
new_output.name = 'TFLite_Detection_PostProcess'
new_output.attr['_output_quantized'].CopyFrom(
attr_value_pb2.AttrValue(b=True))
new_output.attr['max_detections'].CopyFrom(
attr_value_pb2.AttrValue(i=max_detections))
new_output.attr['max_classes_per_detection'].CopyFrom(
attr_value_pb2.AttrValue(i=max_classes_per_detection))
new_output.attr['nms_score_threshold'].CopyFrom(
attr_value_pb2.AttrValue(f=nms_score_threshold.pop()))
new_output.attr['nms_iou_threshold'].CopyFrom(
attr_value_pb2.AttrValue(f=nms_iou_threshold.pop()))
new_output.attr['num_classes'].CopyFrom(
attr_value_pb2.AttrValue(i=num_classes))
new_output.attr['y_scale'].CopyFrom(
attr_value_pb2.AttrValue(f=scale_values['y_scale'].pop()))
new_output.attr['x_scale'].CopyFrom(
attr_value_pb2.AttrValue(f=scale_values['x_scale'].pop()))
new_output.attr['h_scale'].CopyFrom(
attr_value_pb2.AttrValue(f=scale_values['h_scale'].pop()))
new_output.attr['w_scale'].CopyFrom(
attr_value_pb2.AttrValue(f=scale_values['w_scale'].pop()))
new_output.input.extend(
['raw_outputs/box_encodings', 'raw_outputs/class_predictions', 'anchors'])
# Transform the graph to append new postprocessing op
input_names = []
output_names = ['TFLite_Detection_PostProcess']
transforms = ['strip_unused_nodes']
transformed_graph_def = TransformGraph(frozen_graph_def, input_names,
output_names, transforms)
return transformed_graph_def
# Save a graph
graph_def = sess.graph.as_graph_def()
# Freeze graph. Replaces variables to constants.
graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph_def, out_nodes)
# Optimize graph. Removes training-only ops, unused nodes.
graph_def = optimize_for_inference_lib.optimize_for_inference(
graph_def, inp_nodes, out_nodes, dtype.as_datatype_enum)
# Fuse constant operations.
transforms = ["fold_constants(ignore_errors=True)"]
if args.quantize:
transforms += ["quantize_weights(minimum_size=0)"]
transforms += ["sort_by_execution_order"]
graph_def = TransformGraph(graph_def, inp_nodes, out_nodes, transforms)
# By default, float16 weights are stored in repeated tensor's field called
# `half_val`. It has type int32 with leading zeros for unused bytes.
# This type is encoded by Varint that means only 7 bits are used for value
# representation but the last one is indicated the end of encoding. This way
# float16 might takes 1 or 2 or 3 bytes depends on value. To impove compression,
# we replace all `half_val` values to `tensor_content` using only 2 bytes for everyone.
for node in graph_def.node:
if 'value' in node.attr:
halfs = node.attr["value"].tensor.half_val
if not node.attr["value"].tensor.tensor_content and halfs:
node.attr["value"].tensor.tensor_content = struct.pack(
'H' * len(halfs), *halfs)
node.attr["value"].tensor.ClearField('half_val')
def __init__(self, option, src_model_file):
self._op_converters = {
TFOpType.Conv2D.name: self.convert_conv2d,
TFOpType.DepthwiseConv2dNative.name: self.convert_conv2d,
TFOpType.Conv2DBackpropInput.name: self.convert_conv2d,
TFOpType.BiasAdd.name: self.convert_biasadd,
TFOpType.Add.name: self.convert_add,
TFOpType.Sub.name: self.convert_elementwise,
TFOpType.Mul.name: self.convert_elementwise,
TFOpType.Div.name: self.convert_elementwise,
TFOpType.Min.name: self.convert_elementwise,
TFOpType.Minimum.name: self.convert_elementwise,
TFOpType.Max.name: self.convert_elementwise,
TFOpType.Maximum.name: self.convert_elementwise,
TFOpType.Neg.name: self.convert_elementwise,
TFOpType.Abs.name: self.convert_elementwise,
TFOpType.Pow.name: self.convert_elementwise,
TFOpType.RealDiv.name: self.convert_elementwise,
TFOpType.SquaredDifference.name: self.convert_elementwise,
TFOpType.Square.name: self.convert_elementwise,
TFOpType.Rsqrt.name: self.convert_elementwise,
TFOpType.Equal.name: self.convert_elementwise,
TFOpType.Relu.name: self.convert_activation,
TFOpType.LeakyRelu.name: self.convert_activation,
TFOpType.Relu6.name: self.convert_activation,
TFOpType.Tanh.name: self.convert_activation,
TFOpType.Sigmoid.name: self.convert_activation,
TFOpType.Fill.name: self.convert_fill,
TFOpType.FusedBatchNorm.name: self.convert_fused_batchnorm,
TFOpType.AvgPool.name: self.convert_pooling,
TFOpType.MaxPool.name: self.convert_pooling,
TFOpType.MatMul.name: self.convert_matmul,
TFOpType.BatchMatMul.name: self.convert_matmul,
TFOpType.Identity.name: self.convert_identity,
TFOpType.Reshape.name: self.convert_reshape,
TFOpType.Shape.name: self.convert_shape,
TFOpType.ExpandDims.name: self.convert_expand_dims,
TFOpType.Squeeze.name: self.convert_squeeze,
TFOpType.Transpose.name: self.convert_transpose,
TFOpType.Softmax.name: self.convert_softmax,
TFOpType.ResizeBicubic.name: self.convert_resize_bicubic,
TFOpType.ResizeBilinear.name: self.convert_resize_bilinear,
TFOpType.ResizeNearestNeighbor.name:
self.convert_resize_nearest_neighbor, # noqa
TFOpType.Placeholder.name: self.convert_nop,
TFOpType.SpaceToBatchND.name: self.convert_space_batch,
TFOpType.BatchToSpaceND.name: self.convert_space_batch,
TFOpType.DepthToSpace.name: self.convert_space_depth,
TFOpType.SpaceToDepth.name: self.convert_space_depth,
TFOpType.Pad.name: self.convert_pad,
TFOpType.ConcatV2.name: self.convert_concat,
TFOpType.Mean.name: self.convert_mean,
TFOpType.Const.name: self.convert_nop,
TFOpType.Gather.name: self.convert_gather,
TFOpType.StridedSlice.name: self.convert_stridedslice,
TFOpType.Slice.name: self.convert_slice,
TFOpType.ReverseV2.name: self.convert_reverse,
TFOpType.Pack.name: self.convert_stack,
TFOpType.Stack.name: self.convert_stack,
TFOpType.Unpack.name: self.convert_unstack,
TFOpType.Unstack.name: self.convert_unstack,
TFOpType.Cast.name: self.convert_cast,
TFOpType.ArgMax.name: self.convert_argmax,
TFOpType.Split.name: self.convert_split,
TFOpType.FakeQuantWithMinMaxVars.name: self.convert_fake_quantize,
TFOpType.FloorDiv.name: self.convert_elementwise,
TFOpType.Sqrt.name: self.convert_elementwise,
TFOpType.MirrorPad.name: self.convert_pad,
}
self._option = option
self._mace_net_def = mace_pb2.NetDef()
ConverterUtil.set_filter_format(self._mace_net_def, FilterFormat.HWIO)
# import tensorflow graph
tf_graph_def = tf.GraphDef()
with tf.gfile.Open(src_model_file, 'rb') as f:
tf_graph_def.ParseFromString(f.read())
self._placeholders = {}
print("Run transform_graph: %s" %
TFTransformGraphOptions[option.device])
try:
print("output keys: ", option.output_nodes.keys())
transformed_graph_def = TransformGraph(
tf_graph_def, option.input_nodes.keys(),
option.output_nodes.keys(),
TFTransformGraphOptions[option.device])
except Exception as ex:
print("Failed to transform graph using tf tool: %s" % ex)
transformed_graph_def = tf_graph_def
# To check optimized model, uncomment following code.
# tf.io.write_graph(
# transformed_graph_def,
# ".",
# os.path.basename(src_model_file)[:-3] + "_opt.pb",
# as_text=False
# )
self.add_shape_info(transformed_graph_def)
with tf.Session() as session:
with session.graph.as_default() as graph:
tf.import_graph_def(transformed_graph_def, name='')
self._tf_graph = graph
self._skip_tensor = set()
self._output_shape_list = []
self._output_shape_op_list = []
# Node with which prefixes should be removed
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Postprocessor/',
'Preprocessor/map')
# Read the graph.
with tf.gfile.FastGFile(args.input, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
inpNames = ['image_tensor']
outNames = [
'num_detections', 'detection_scores', 'detection_boxes',
'detection_classes'
]
graph_def = TransformGraph(graph_def, inpNames, outNames,
['sort_by_execution_order'])
def getUnconnectedNodes():
unconnected = []
for node in graph_def.node:
unconnected.append(node.name)
for inp in node.input:
if inp in unconnected:
unconnected.remove(inp)
return unconnected
# Detect unfused batch normalization nodes and fuse them.
def fuse_batch_normalization():
# Add_0 <-- moving_variance, add_y
