import h5py
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from tensorflow.keras.models import load_model, Model
from scipy.io import loadmat
import tensorflow.compat.v1.keras.backend as K
K.set_image_data_format('channels_first')
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
# 先定义我们读取keras模型权重的函数
def print_keras_wegiths(weight_file_path):
# 读取weights h5文件返回File类
f = h5py.File(weight_file_path)
try:
# 读取各层的名称以及包含层信息的Group类
for layer, g in f.items():
print(" {}".format(layer))
print(" Attributes:")
# 输出储存在Group类中的attrs信息，一般是各层的weights和bias及他们的名称
for key, value in g.attrs.items():
print(" {}: {}".format(key, value))
finally:
f.close()
def keras_to_tensorflow(args):
# If output_model path is relative and in cwd, make it absolute from root
output_model = args.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 args.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(args.input_model,
args.input_model_json,
args.input_model_yaml,
custom_objects=custom_object_dict)
# TODO(amirabdi): Support networks with multiple inputs
orig_output_node_names = [
node.name.split(':')[0] for node in model.outputs
]
#orig_output_node_names = [node.op.name for node in model.outputs]
if args.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(
args.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 args.output_meta_ckpt:
saver = tf.train.Saver()
saver.save(sess, str(output_fld / output_model_stem))
if args.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 args.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))
