# allow_growth=True, # it will cause fragmentation.
per_process_gpu_memory_fraction=0.1)))
graph_def = graph.as_graph_def()
with sess.graph.as_default():
sess.run(tf.global_variables_initializer())
tf.keras.backend.set_session(session=sess)
# with tf.gfile.GFile(COMPILE_MODEL_PATH.replace('.pb', '_{}.pb'.format(int(0.95 * 10000))), "rb") as f:
# graph_def_file = f.read()
# graph_def.ParseFromString(graph_def_file)
# print('{}.meta'.format(tf_checkpoint))
model = NeuralNetwork(model_conf, RunMode.Predict, model_conf.neu_cnn,
model_conf.neu_recurrent)
model.build_graph()
saver = tf.train.Saver(var_list=tf.global_variables())
"""从项目中加载最后一次训练的网络参数"""
saver.restore(sess,
tf.train.latest_checkpoint(model_conf.model_root_path))
# _ = tf.import_graph_def(graph_def, name="")
"""定义操作符"""
dense_decoded_op = sess.graph.get_tensor_by_name("dense_decoded:0")
x_op = sess.graph.get_tensor_by_name('input:0')
"""固定网络"""
sess.graph.finalize()
true_count = 0
false_count = 0
def testing(self, image_dir, limit=None):
graph = tf.Graph()
sess = tf.Session(
graph=graph,
config=tf.ConfigProto(
# allow_soft_placement=True,
# log_device_placement=True,
gpu_options=tf.GPUOptions(
allocator_type='BFC',
# allow_growth=True, # it will cause fragmentation.
per_process_gpu_memory_fraction=0.1)))
with sess.graph.as_default():
sess.run(tf.global_variables_initializer())
tf.keras.backend.set_session(session=sess)
model = NeuralNetwork(self.model_conf, RunMode.Predict,
self.model_conf.neu_cnn,
self.model_conf.neu_recurrent)
model.build_graph()
saver = tf.train.Saver(var_list=tf.global_variables())
"""从项目中加载最后一次训练的网络参数"""
saver.restore(
sess,
tf.train.latest_checkpoint(self.model_conf.model_root_path))
# _ = tf.import_graph_def(graph_def, name="")
"""定义操作符"""
dense_decoded_op = sess.graph.get_tensor_by_name("dense_decoded:0")
x_op = sess.graph.get_tensor_by_name('input:0')
"""固定网络"""
sess.graph.finalize()
true_count = 0
false_count = 0
"""
以下为根据路径调用预测函数输出结果的demo
"""
# Fill in your own sample path
dir_list = os.listdir(image_dir)
random.shuffle(dir_list)
lines = []
for i, p in enumerate(dir_list):
n = os.path.join(image_dir, p)
if limit and i > limit:
break
with open(n, "rb") as f:
b = f.read()
batch = self.get_image_batch(b)
st = time.time()
predict_text = self.predict_func(
batch,
sess,
dense_decoded_op,
x_op,
)
et = time.time()
# t = p.split(".")[0].lower() == predict_text.lower()
# csv_output = "{},{}".format(p.split(".")[0], predict_text)
# lines.append(csv_output)
# print(csv_output)
is_mark = '_' in p
if is_mark:
if 'LOWER' in self.model_conf.category_param:
label = p.split("_")[0].lower()
t = label == predict_text.lower()
elif 'UPPER' in self.model_conf.category_param:
label = p.split("_")[0].upper()
t = label == predict_text.upper()
else:
label = p.split("_")[0]
t = label == predict_text
# Used to verify test sets
if t:
true_count += 1
else:
false_count += 1
print(i, p, label, predict_text, t,
true_count / (true_count + false_count),
(et - st) * 1000)
else:
print(i, p, predict_text,
true_count / (true_count + false_count),
(et - st) * 1000)