class TaggingModel():
def __init__(self, configs):
tag_id_file = configs.get('tag_id_file', None)
model_pb = configs.get('model_pb', None)
extract_text = configs.get('extract_text', False)
if tag_id_file is None:
raise
else:
self.label_name_dict = get_label_name_dict(tag_id_file, None)
if model_pb is None:
raise
else:
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
meta_graph_def = tf.saved_model.loader.load(
self.sess, [tf.saved_model.tag_constants.SERVING], model_pb)
signature_def = meta_graph_def.signature_def
self.signature = signature_def[tf.saved_model.signature_constants.
DEFAULT_SERVING_SIGNATURE_DEF_KEY]
batch_size = configs.get('video_feats_extractor_batch_size', 8)
imgfeat_extractor = configs.get('imgfeat_extractor', 'Youtube8M')
self.feat_extractor = MultiModalFeatureExtract(
batch_size=batch_size,
imgfeat_extractor=imgfeat_extractor,
extract_video=True,
extract_audio=True,
extract_text=extract_text)
def image_preprocess(self, image, rescale=224):
#resize to 224 and normlize to 0-1, then perform f(x)= 2*(x-0.5)
if isinstance(image, type(None)):
print("WARNING: test input image is None")
return np.zeros((rescale, rescale, 3))
if image.shape[0] != rescale:
image = cv2.resize(image, (rescale, rescale))
image = 2 * (image / (np.max(image) + 1e-10) - 0.5)
return image
def text_preprocess(self, txt, max_len=128):
tokens = ['[CLS]'] + tokokenizer.tokenize(txt)
ids = tokokenizer.convert_tokens_to_ids(tokens)
ids = ids[:max_len]
ids = ids + [0] * (max_len - len(ids))
return ids
def preprocess(self, feat_dict, max_frame_num=300):
ret_dict = {}
for feat_type in feat_dict:
if feat_type == 'video':
feats = np.zeros((max_frame_num, len(feat_dict['video'][0])))
valid_num = min(max_frame_num, len(feat_dict['video']))
feats[:valid_num] = feat_dict['video']
elif feat_type == 'audio':
feats = np.zeros((max_frame_num, len(feat_dict['audio'][0])))
valid_num = min(max_frame_num, len(feat_dict['audio']))
feats[:valid_num] = feat_dict['audio']
elif feat_type == 'text':
feats = self.text_preprocess(feat_dict['text'], 128)
elif feat_type == 'image':
feats = self.image_preprocess(feat_dict['image'])
else:
raise
ret_dict[feat_type] = feats
return ret_dict
def inference(self, test_file):
with self.sess.as_default() as sess:
start_time = time.time()
feat_dict = self.feat_extractor.extract_feat(test_file, save=False)
end_time = time.time()
print("feature extract cost time: {} sec".format(end_time -
start_time))
if 'text' in feat_dict:
print(feat_dict['text'])
else:
feat_dict['text'] = ""
feat_dict_preprocess = self.preprocess(feat_dict)
feed_dict = {}
# Get input tensor.
for key in feat_dict:
if key in self.signature.inputs:
feed_dict[self.signature.inputs[key].name] = [
feat_dict_preprocess[key]
]
if 'video_frames_num' in self.signature.inputs:
feed_dict[self.signature.inputs['video_frames_num'].name] = [
len(feat_dict['video'])
]
if 'audio_frames_num' in self.signature.inputs:
feed_dict[self.signature.inputs['audio_frames_num'].name] = [
len(feat_dict['audio'])
]
# Get output tensor.
class_indexes = self.signature.outputs['class_indexes'].name
predictions = self.signature.outputs['predictions'].name
#video_embedding = self.signature.outputs['video_embedding'].name #(Optional)
start_time = time.time()
class_indexes, predictions = sess.run([class_indexes, predictions],
feed_dict)
end_time = time.time()
print("multi-modal tagging model forward cost time: {} sec".format(
end_time - start_time))
labels = [
self.label_name_dict[index] for index in class_indexes[0]
]
scores = predictions[0]
return labels, scores
class TaggingModel(): # 输入参数是config字典
def __init__(self, configs):
tag_id_file = configs.get('tag_id_file', None) # 返回指定键的值 equals to tag_id_file = config['tag_id_file']
model_pb = configs.get('model_pb', None)
if tag_id_file is None:
raise # 手动异常 报错Error
else:
self.label_name_dict = get_label_name_dict(tag_id_file, None) # 返回到一个字典 label_name_dict:{'label': int(id)}
if model_pb is None:
raise
else: # 产生一个sess变量和一个signature变量
config = tf.ConfigProto(allow_soft_placement=True) # 允许tensorflow自动分配设备
config.gpu_options.allow_growth = True # 用于动态申请显存，从少到多慢慢增加gpu容量
self.sess = tf.Session(config=config) # tf.Session 三个参数：target graph config
meta_graph_def = tf.saved_model.loader.load(self.sess, [tf.saved_model.tag_constants.SERVING], model_pb) # 加载一个保存的模型
signature_def = meta_graph_def.signature_def # A dictionary mapping signature names to functions.
self.signature = signature_def[tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] # key: tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
batch_size = configs.get('video_feats_extractor_batch_size', 8) # 默认值是8 actually新创建Key: video_feats_extractor_batch_size 提取特征
imgfeat_extractor = configs.get('imgfeat_extractor', 'Youtube8M') # 默认是string: 'Youtube8M' as 图像特征提取器
self.feat_extractor = MultiModalFeatureExtract(batch_size=batch_size, imgfeat_extractor= imgfeat_extractor,
extract_video = True, extract_audio = True, extract_text = False) # 特征提取器 as a class
def image_preprocess(self, image, rescale=224):
#resize to 224 and normlize to 0-1, then perform f(x)= 2*(x-0.5)
if isinstance(image, type(None)):
print("WARNING: test input image is None")
return np.zeros((rescale, rescale, 3))
if image.shape[0] !=rescale:
image = cv2.resize(image, (rescale, rescale))
image = 2*(image/(np.max(image)+1e-10) - 0.5)
return image
def text_preprocess(self, txt,max_len=128):
tokens = ['[CLS]'] + tokokenizer.tokenize(txt)
ids = tokokenizer.convert_tokens_to_ids(tokens)
ids = ids[:max_len]
ids = ids + [0]*(max_len-len(ids))
return ids
def preprocess(self, feat_dict, max_frame_num=300):
ret_dict = {}
for feat_type in feat_dict:
if feat_type=='video':
feats = np.zeros((max_frame_num, len(feat_dict['video'][0])))
valid_num = min(max_frame_num, len(feat_dict['video']))
feats[:valid_num] = feat_dict['video']
elif feat_type=='audio':
feats = np.zeros((max_frame_num, len(feat_dict['audio'][0])))
valid_num = min(max_frame_num, len(feat_dict['audio']))
feats[:valid_num] = feat_dict['audio']
elif feat_type=='text':
feats = self.text_preprocess(feat_dict['text'], 128)
elif feat_type == 'image':
feats = self.image_preprocess(feat_dict['image'])
else:
raise
ret_dict[feat_type] = feats
return ret_dict
def load_multimodal_feat(self, test_file, feat_dir):
assert os.path.exists(feat_dir)
feat_dict = {}
# load video feat
video_id = os.path.basename(test_file).split('.m')[0]
feat_dict['video'] = np.load(os.path.join(feat_dir,'video_npy' ,'Youtube8M', 'tagging', video_id + '.npy'))
feat_dict['audio'] = np.load(os.path.join(feat_dir, 'audio_npy', 'Vggish', 'tagging', video_id + '.npy'))
feat_dict['image'] = cv2.imread(os.path.join(feat_dir, 'image_jpg','tagging', video_id + '.jpg'), 1)
feat_dict['text'] = open(os.path.join(feat_dir, 'text_txt', 'tagging', video_id + '.txt')).read()
return feat_dict
def inference(self, test_file, load_feat=False, feat_dir=None):
tf.reset_default_graph()
with self.sess.as_default() as sess:
if load_feat == False:
start_time = time.time()
feat_dict = self.feat_extractor.extract_feat(test_file, save=False)
end_time = time.time()
print("feature extract cost time: {} sec".format(end_time -start_time))
else:
feat_dict = self.load_multimodal_feat(test_file, feat_dir)
if 'text' in feat_dict:
# print(feat_dict['text'])
pass
else:
feat_dict['text'] = ""
feat_dict_preprocess = self.preprocess(feat_dict)
feed_dict ={}
# Get input tensor.
for key in feat_dict:
if key in self.signature.inputs:
feed_dict[self.signature.inputs[key].name] = [feat_dict_preprocess[key]]
if 'video_frames_num' in self.signature.inputs:
feed_dict[self.signature.inputs['video_frames_num'].name] = [len(feat_dict['video'])]
if 'audio_frames_num' in self.signature.inputs:
feed_dict[self.signature.inputs['audio_frames_num'].name] = [len(feat_dict['audio'])]
# Get output tensor.
class_indexes = self.signature.outputs['class_indexes'].name
predictions = self.signature.outputs['predictions'].name
#video_embedding = self.signature.outputs['video_embedding'].name #(Optional)
start_time = time.time()
class_indexes,predictions = sess.run([class_indexes,predictions], feed_dict) #
end_time = time.time()
print("multi-modal tagging model forward cost time: {} sec".format(end_time -start_time))
labels=[self.label_name_dict[index] for index in class_indexes[0]]
scores = predictions[0]
return labels, scores