# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(os.path.join(__dir__, ''))
import numpy as np
import tarfile
import requests
from tqdm import tqdm
import shutil
from paddle import inference
from paddle.inference import Config, create_predictor
from tools.utils import ppTSM_Inference_helper
__all__ = ['PaddleVideo']
# path of download model and data
BASE_DIR = os.path.expanduser("~/.paddlevideo_inference/")
BASE_INFERENCE_MODEL_DIR = os.path.join(BASE_DIR, 'inference_model')
BASE_VIDEOS_DIR = os.path.join(BASE_DIR, 'videos')
# support Models
MODELS = {
'ppTSM':
'https://videotag.bj.bcebos.com/PaddleVideo/InferenceModel/ppTSM_infer.tar',
'ppTSM_v2':
'https://videotag.bj.bcebos.com/PaddleVideo/InferenceModel/ppTSM_v2_infer.tar'
}
MODEL_NAMES = list(MODELS.keys())
def parse_args(mMain=True, add_help=True):
"""
Args:
mMain: bool. True for command args, False for python interface
"""
import argparse
def str2bool(v):
return v.lower() in ("true", "t", "1")
if mMain == True:
# general params
parser = argparse.ArgumentParser(add_help=add_help)
parser.add_argument("--model_name", type=str, default='')
parser.add_argument("-v", "--video_file", type=str, default='')
parser.add_argument("--use_gpu", type=str2bool, default=True)
# params for decode and sample
parser.add_argument("--num_seg", type=int, default=16)
# params for preprocess
parser.add_argument("--short_size", type=int, default=256)
parser.add_argument("--target_size", type=int, default=224)
# params for predict
parser.add_argument("--model_file", type=str, default='')
parser.add_argument("--params_file", type=str)
parser.add_argument("-b", "--batch_size", type=int, default=1)
parser.add_argument("--use_fp16", type=str2bool, default=False)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--gpu_mem", type=int, default=8000)
parser.add_argument("--top_k", type=int, default=1)
parser.add_argument("--enable_mkldnn", type=bool, default=False)
parser.add_argument("--label_name_path", type=str, default='')
return parser.parse_args()
else:
return argparse.Namespace(model_name='',
video_file='',
use_gpu=True,
num_seg=16,
short_size=256,
target_size=224,
model_file='',
params_file='',
batch_size=1,
use_fp16=False,
ir_optim=True,
use_tensorrt=False,
gpu_mem=8000,
top_k=1,
enable_mkldnn=False,
label_name_path='')
def parse_file_paths(input_path: str) -> list:
if os.path.isfile(input_path):
files = [
input_path,
]
else:
files = os.listdir(input_path)
files = [
file for file in files
if (file.endswith(".avi") or file.endswith(".mp4"))
]
files = [os.path.join(input_path, file) for file in files]
return files
def download_with_progressbar(url, save_path):
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(save_path, 'wb') as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
raise Exception("Something went wrong while downloading models")
def download_inference_model(model_storage_directory, url):
# using custom model
tar_file_name_list = [
'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel'
]
if not os.path.exists(
os.path.join(model_storage_directory,
'inference.pdiparams')) or not os.path.exists(
os.path.join(model_storage_directory,
'inference.pdmodel')):
tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
print('download {} to {}'.format(url, tmp_path))
os.makedirs(model_storage_directory, exist_ok=True)
download_with_progressbar(url, tmp_path) #download
#save to directory
with tarfile.open(tmp_path, 'r') as tarObj:
for member in tarObj.getmembers():
filename = None
for tar_file_name in tar_file_name_list:
if tar_file_name in member.name:
filename = tar_file_name
if filename is None:
continue
file = tarObj.extractfile(member)
with open(os.path.join(model_storage_directory, filename),
'wb') as f:
f.write(file.read())
os.remove(tmp_path)
def create_paddle_predictor(args):
config = Config(args.model_file, args.params_file)
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
else:
config.disable_gpu()
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
config.disable_glog_info()
config.switch_ir_optim(args.ir_optim) # default true
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=Config.Precision.Half
if args.use_fp16 else Config.Precision.Float32,
max_batch_size=args.batch_size)
config.enable_memory_optim()
# use zero copy
config.switch_use_feed_fetch_ops(False)
predictor = create_predictor(config)
return predictor
def load_label_name_dict(path):
result = {}
if not os.path.exists(path):
print(
'Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!'
)
else:
for line in open(path, 'r'):
partition = line.split('\n')[0].partition(' ')
try:
result[int(partition[0])] = str(partition[-1])
except:
result = {}
break
return result
class PaddleVideo(object):
def __init__(self, **kwargs):
print(
'\nInference models that Paddle provides are listed as follows:\n{}'
.format(MODEL_NAMES), '\n')
process_params = parse_args(mMain=False, add_help=False)
process_params.__dict__.update(**kwargs)
if not os.path.exists(process_params.model_file):
if process_params.model_name is None:
raise Exception('Please input model name that you want to use!')
if process_params.model_name in MODEL_NAMES:
url = MODELS[process_params.model_name]
download_path = os.path.join(BASE_INFERENCE_MODEL_DIR,
process_params.model_name)
if not os.path.exists(download_path):
os.makedirs(download_path)
#create pretrained model download_path
download_inference_model(model_storage_directory=download_path,
url=url)
process_params.model_file = os.path.join(
download_path, 'inference.pdmodel')
process_params.params_file = os.path.join(
download_path, 'inference.pdiparams')
process_params.label_name_path = os.path.join(
__dir__, '../data/k400/Kinetics-400_label_list.txt')
else:
raise Exception(
'If you want to use your own model, Please input model_file as model path!'
)
else:
print('Using user-specified model and params!')
print("process params are as follows: \n{}".format(process_params))
self.label_name_dict = load_label_name_dict(
process_params.label_name_path)
self.args = process_params
self.predictor = create_paddle_predictor(process_params)
def predict(self, video):
"""
predict label of video with paddlevideo
Args:
video:input video for clas, support single video , internet url, folder path containing series of videos
Returns:
list[dict:{videoname: "",class_ids: [], scores: [], label_names: []}],if label name path is None,label names will be empty
"""
video_list = []
assert isinstance(video, (str))
# get input_tensor and output_tensor
input_names = self.predictor.get_input_names()
output_names = self.predictor.get_output_names()
input_tensor_list = []
output_tensor_list = []
for item in input_names:
input_tensor_list.append(self.predictor.get_input_handle(item))
for item in output_names:
output_tensor_list.append(self.predictor.get_output_handle(item))
if isinstance(video, str):
# download internet video
if video.startswith('http'):
if not os.path.exists(BASE_VIDEOS_DIR):
os.makedirs(BASE_VIDEOS_DIR)
video_path = os.path.join(BASE_VIDEOS_DIR, 'tmp.mp4')
download_with_progressbar(video, video_path)
print("Current using video from Internet:{}, renamed as: {}".
format(video, video_path))
video = video_path
files = parse_file_paths(video)
else:
print('Please input legal video!')
# Inferencing process
InferenceHelper = ppTSM_Inference_helper(
num_seg=self.args.num_seg,
short_size=self.args.short_size,
target_size=self.args.target_size,
top_k=self.args.top_k)
batch_num = self.args.batch_size
for st_idx in range(0, len(files), batch_num):
ed_idx = min(st_idx + batch_num, len(files))
# Pre process batched input
batched_inputs = InferenceHelper.preprocess_batch(
files[st_idx:ed_idx])
# run inference
for i in range(len(input_tensor_list)):
input_tensor_list[i].copy_from_cpu(batched_inputs[i])
self.predictor.run()
batched_outputs = []
for j in range(len(output_tensor_list)):
batched_outputs.append(output_tensor_list[j].copy_to_cpu())
results_list = InferenceHelper.postprocess(batched_outputs,
print_output=False,
return_result=True)
for res in results_list:
classes = res["topk_class"]
label_names = []
if len(self.label_name_dict) != 0:
label_names = [self.label_name_dict[c] for c in classes]
res["label_names"] = label_names
print("Current video file: {0}".format(res["video_id"]))
print("\ttop-{0} classes: {1}".format(len(res["topk_class"]),
res["topk_class"]))
print("\ttop-{0} scores: {1}".format(len(res["topk_scores"]),
res["topk_scores"]))
print("\ttop-{0} label names: {1}".format(
len(res["label_names"]), res["label_names"]))
def main():
# for cmd
args = parse_args(mMain=True)
clas_engine = PaddleVideo(**(args.__dict__))
clas_engine.predict(args.video_file)
if __name__ == '__main__':
main()