diff --git a/deploy/pipeline/config/infer_cfg_pphuman.yml b/deploy/pipeline/config/infer_cfg_pphuman.yml index 49587314f7819c37cdec2132d9c527389abec3d2..80e8a5e0a3950593ed3c2e8548b45aa9e729a411 100644 --- a/deploy/pipeline/config/infer_cfg_pphuman.yml +++ b/deploy/pipeline/config/infer_cfg_pphuman.yml @@ -5,28 +5,28 @@ visual: True warmup_frame: 50 DET: - model_dir: output_inference/mot_ppyoloe_l_36e_pipeline/ + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip batch_size: 1 MOT: - model_dir: output_inference/mot_ppyoloe_l_36e_pipeline/ + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip tracker_config: deploy/pipeline/config/tracker_config.yml batch_size: 1 basemode: "idbased" enable: False KPT: - model_dir: output_inference/dark_hrnet_w32_256x192/ + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip batch_size: 8 ATTR: - model_dir: output_inference/PPLCNet_x1_0_person_attribute_945_infer/ + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.zip batch_size: 8 basemode: "idbased" enable: False VIDEO_ACTION: - model_dir: output_inference/ppTSM + model_dir: https://videotag.bj.bcebos.com/PaddleVideo-release2.3/ppTSM_fight.zip batch_size: 1 frame_len: 8 sample_freq: 7 @@ -36,7 +36,7 @@ VIDEO_ACTION: enable: False SKELETON_ACTION: - model_dir: output_inference/STGCN + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip batch_size: 1 max_frames: 50 display_frames: 80 @@ -45,7 +45,7 @@ SKELETON_ACTION: enable: False ID_BASED_DETACTION: - model_dir: output_inference/ppyoloe_crn_s_80e_smoking_visdrone + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip batch_size: 8 basemode: "idbased" threshold: 0.6 @@ -54,7 +54,7 @@ ID_BASED_DETACTION: enable: False ID_BASED_CLSACTION: - model_dir: output_inference/PPHGNet_tiny_calling_halfbody + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip batch_size: 8 basemode: "idbased" threshold: 0.8 @@ -63,7 +63,7 @@ ID_BASED_CLSACTION: enable: False REID: - model_dir: output_inference/reid_model/ + model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip batch_size: 16 basemode: "idbased" enable: False diff --git a/deploy/pipeline/docs/tutorials/QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/QUICK_STARTED.md index 87a3fe015004b7c789d5dce57385061bd93788ac..5404485e39f00259cf4450caf17421dd74a8db4e 100644 --- a/deploy/pipeline/docs/tutorials/QUICK_STARTED.md +++ b/deploy/pipeline/docs/tutorials/QUICK_STARTED.md @@ -40,7 +40,9 @@ PP-Human提供了目标检测、属性识别、行为识别、ReID预训练模 | 行为识别 | 视频输入 行为识别 | 准确率: 96.43 | 单人2.7ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | | ReID | 视频输入 跨镜跟踪 | mAP: 98.8 | 单人1.5ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) | -下载模型后，解压至`./output_inference`文件夹 +下载模型后，解压至`./output_inference`文件夹。 + +在配置文件中，模型路径默认为模型的下载路径，如果用户不修改，则在推理时会自动下载对应的模型。 **注意：** diff --git a/deploy/pipeline/docs/tutorials/action.md b/deploy/pipeline/docs/tutorials/action.md index 883b030a74515fd7795b6fc0b792131d1d254545..b4710cfbaa908f10fa90343ad7210c017ff6944d 100644 --- a/deploy/pipeline/docs/tutorials/action.md +++ b/deploy/pipeline/docs/tutorials/action.md @@ -47,7 +47,7 @@ SKELETON_ACTION: # 基于骨骼点的行为识别模型配置 ``` ### 使用方法 -1. 从上表链接中下载模型并解压到```./output_inference```路径下。 +1. 从上表链接中下载模型并解压到```./output_inference```路径下。默认自动下载模型，如果手动下载，需要修改模型文件夹为模型存放路径。 2. 目前行为识别模块仅支持视频输入，根据期望开启的行为识别方案类型，设置infer_cfg_pphuman.yml中`SKELETON_ACTION`的enable: True, 然后启动命令如下： ```python python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ diff --git a/deploy/pipeline/docs/tutorials/attribute.md b/deploy/pipeline/docs/tutorials/attribute.md index 299cdf42f5a2993107e6a3b5e684da56cb585c7a..ed28b33690bc8f9ee91b54b7a12af17f1b247a0f 100644 --- a/deploy/pipeline/docs/tutorials/attribute.md +++ b/deploy/pipeline/docs/tutorials/attribute.md @@ -17,7 +17,7 @@ ## 使用方法 -1. 从上表链接中下载模型并解压到```PaddleDetection/output_inference```路径下，并且设置```deploy/pipeline/config/infer_cfg_pphuman.yml```中`ATTR`的enable: True +1. 从上表链接中下载模型并解压到```PaddleDetection/output_inference```路径下，并修改配置文件中模型路径，也可默认自动下载模型。设置```deploy/pipeline/config/infer_cfg_pphuman.yml```中`ATTR`的enable: True `infer_cfg_pphuman.yml`中配置项说明： ``` diff --git a/deploy/pipeline/docs/tutorials/mot.md b/deploy/pipeline/docs/tutorials/mot.md index 05960fe6ec186daa2520e58108c2a95fcf725627..a5648afed9667844438d6fe166f195de5e36f27b 100644 --- a/deploy/pipeline/docs/tutorials/mot.md +++ b/deploy/pipeline/docs/tutorials/mot.md @@ -14,7 +14,7 @@ ## 使用方法 -1. 从上表链接中下载模型并解压到```./output_inference```路径下 +1. 从上表链接中下载模型并解压到```./output_inference```路径下，并修改配置文件中模型路径。默认为自动下载模型，无需做改动。 2. 图片输入时，是纯检测任务，启动命令如下 ```python python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ diff --git a/deploy/pipeline/docs/tutorials/mtmct.md b/deploy/pipeline/docs/tutorials/mtmct.md index 54e407ab873771d0b393496a0c02b700954e90df..2236d9355dce05698b492f778622450c5db3ac16 100644 --- a/deploy/pipeline/docs/tutorials/mtmct.md +++ b/deploy/pipeline/docs/tutorials/mtmct.md @@ -7,7 +7,7 @@ PP-Human跨镜头跟踪模块主要目的在于提供一套简洁、高效的跨 ## 使用方法 -1. 下载模型 [REID模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) 并解压到```./output_inference```路径下, MOT模型请参考[mot说明](./mot.md)文件下载。 +1. 下载模型 [REID模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) 并解压到```./output_inference```路径下，修改配置文件中模型路径。也可简单起见直接用默认配置，自动下载模型。 MOT模型请参考[mot说明](./mot.md)文件下载。 2. 跨镜头跟踪模式下，要求输入的多个视频放在同一目录下，同时开启infer_cfg_pphuman.yml 中的REID选择中的enable=True, 命令如下： ```python diff --git a/deploy/pipeline/download.py b/deploy/pipeline/download.py new file mode 100644 index 0000000000000000000000000000000000000000..f243838b74310f7cdfc5035f7c17d54985d8de85 --- /dev/null +++ b/deploy/pipeline/download.py @@ -0,0 +1,313 @@ +# Copyright (c) 2022 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, sys +import os.path as osp +import hashlib +import requests +import shutil +import tqdm +import time +import tarfile +import zipfile +from paddle.utils.download import _get_unique_endpoints + +PPDET_WEIGHTS_DOWNLOAD_URL_PREFIX = 'https://paddledet.bj.bcebos.com/' + +DOWNLOAD_RETRY_LIMIT = 3 + +WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/infer_weights") + + +def is_url(path): + """ + Whether path is URL. + Args: + path (string): URL string or not. + """ + return path.startswith('http://') \ + or path.startswith('https://') \ + or path.startswith('ppdet://') + + +def parse_url(url): + url = url.replace("ppdet://", PPDET_WEIGHTS_DOWNLOAD_URL_PREFIX) + return url + + +def map_path(url, root_dir, path_depth=1): + # parse path after download to decompress under root_dir + assert path_depth > 0, "path_depth should be a positive integer" + dirname = url + for _ in range(path_depth): + dirname = osp.dirname(dirname) + fpath = osp.relpath(url, dirname) + + zip_formats = ['.zip', '.tar', '.gz'] + for zip_format in zip_formats: + fpath = fpath.replace(zip_format, '') + return osp.join(root_dir, fpath) + + +def _md5check(fullname, md5sum=None): + if md5sum is None: + return True + + md5 = hashlib.md5() + with open(fullname, 'rb') as f: + for chunk in iter(lambda: f.read(4096), b""): + md5.update(chunk) + calc_md5sum = md5.hexdigest() + + if calc_md5sum != md5sum: + return False + return True + + +def _check_exist_file_md5(filename, md5sum, url): + return _md5check(filename, md5sum) + + +def _download(url, path, md5sum=None): + """ + Download from url, save to path. + url (str): download url + path (str): download to given path + """ + if not osp.exists(path): + os.makedirs(path) + + fname = osp.split(url)[-1] + fullname = osp.join(path, fname) + retry_cnt = 0 + + while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum, + url)): + if retry_cnt < DOWNLOAD_RETRY_LIMIT: + retry_cnt += 1 + else: + raise RuntimeError("Download from {} failed. " + "Retry limit reached".format(url)) + + # NOTE: windows path join may incur \, which is invalid in url + if sys.platform == "win32": + url = url.replace('\\', '/') + + req = requests.get(url, stream=True) + if req.status_code != 200: + raise RuntimeError("Downloading from {} failed with code " + "{}!".format(url, req.status_code)) + + # For protecting download interupted, download to + # tmp_fullname firstly, move tmp_fullname to fullname + # after download finished + tmp_fullname = fullname + "_tmp" + total_size = req.headers.get('content-length') + with open(tmp_fullname, 'wb') as f: + if total_size: + for chunk in tqdm.tqdm( + req.iter_content(chunk_size=1024), + total=(int(total_size) + 1023) // 1024, + unit='KB'): + f.write(chunk) + else: + for chunk in req.iter_content(chunk_size=1024): + if chunk: + f.write(chunk) + shutil.move(tmp_fullname, fullname) + return fullname + + +def _download_dist(url, path, md5sum=None): + env = os.environ + if 'PADDLE_TRAINERS_NUM' in env and 'PADDLE_TRAINER_ID' in env: + trainer_id = int(env['PADDLE_TRAINER_ID']) + num_trainers = int(env['PADDLE_TRAINERS_NUM']) + if num_trainers <= 1: + return _download(url, path, md5sum) + else: + fname = osp.split(url)[-1] + fullname = osp.join(path, fname) + lock_path = fullname + '.download.lock' + + if not osp.isdir(path): + os.makedirs(path) + + if not osp.exists(fullname): + from paddle.distributed import ParallelEnv + unique_endpoints = _get_unique_endpoints(ParallelEnv() + .trainer_endpoints[:]) + with open(lock_path, 'w'): # touch + os.utime(lock_path, None) + if ParallelEnv().current_endpoint in unique_endpoints: + _download(url, path, md5sum) + os.remove(lock_path) + else: + while os.path.exists(lock_path): + time.sleep(0.5) + return fullname + else: + return _download(url, path, md5sum) + + +def _move_and_merge_tree(src, dst): + """ + Move src directory to dst, if dst is already exists, + merge src to dst + """ + if not osp.exists(dst): + shutil.move(src, dst) + elif osp.isfile(src): + shutil.move(src, dst) + else: + for fp in os.listdir(src): + src_fp = osp.join(src, fp) + dst_fp = osp.join(dst, fp) + if osp.isdir(src_fp): + if osp.isdir(dst_fp): + _move_and_merge_tree(src_fp, dst_fp) + else: + shutil.move(src_fp, dst_fp) + elif osp.isfile(src_fp) and \ + not osp.isfile(dst_fp): + shutil.move(src_fp, dst_fp) + + +def _decompress(fname): + """ + Decompress for zip and tar file + """ + + # For protecting decompressing interupted, + # decompress to fpath_tmp directory firstly, if decompress + # successed, move decompress files to fpath and delete + # fpath_tmp and remove download compress file. + fpath = osp.split(fname)[0] + fpath_tmp = osp.join(fpath, 'tmp') + if osp.isdir(fpath_tmp): + shutil.rmtree(fpath_tmp) + os.makedirs(fpath_tmp) + + if fname.find('tar') >= 0: + with tarfile.open(fname) as tf: + tf.extractall(path=fpath_tmp) + elif fname.find('zip') >= 0: + with zipfile.ZipFile(fname) as zf: + zf.extractall(path=fpath_tmp) + elif fname.find('.txt') >= 0: + return + else: + raise TypeError("Unsupport compress file type {}".format(fname)) + + for f in os.listdir(fpath_tmp): + src_dir = osp.join(fpath_tmp, f) + dst_dir = osp.join(fpath, f) + _move_and_merge_tree(src_dir, dst_dir) + + shutil.rmtree(fpath_tmp) + os.remove(fname) + + +def _decompress_dist(fname): + env = os.environ + if 'PADDLE_TRAINERS_NUM' in env and 'PADDLE_TRAINER_ID' in env: + trainer_id = int(env['PADDLE_TRAINER_ID']) + num_trainers = int(env['PADDLE_TRAINERS_NUM']) + if num_trainers <= 1: + _decompress(fname) + else: + lock_path = fname + '.decompress.lock' + from paddle.distributed import ParallelEnv + unique_endpoints = _get_unique_endpoints(ParallelEnv() + .trainer_endpoints[:]) + # NOTE(dkp): _decompress_dist always performed after + # _download_dist, in _download_dist sub-trainers is waiting + # for download lock file release with sleeping, if decompress + # prograss is very fast and finished with in the sleeping gap + # time, e.g in tiny dataset such as coco_ce, spine_coco, main + # trainer may finish decompress and release lock file, so we + # only craete lock file in main trainer and all sub-trainer + # wait 1s for main trainer to create lock file, for 1s is + # twice as sleeping gap, this waiting time can keep all + # trainer pipeline in order + # **change this if you have more elegent methods** + if ParallelEnv().current_endpoint in unique_endpoints: + with open(lock_path, 'w'): # touch + os.utime(lock_path, None) + _decompress(fname) + os.remove(lock_path) + else: + time.sleep(1) + while os.path.exists(lock_path): + time.sleep(0.5) + else: + _decompress(fname) + + +def get_path(url, root_dir=WEIGHTS_HOME, md5sum=None, check_exist=True): + """ Download from given url to root_dir. + if file or directory specified by url is exists under + root_dir, return the path directly, otherwise download + from url and decompress it, return the path. + url (str): download url + root_dir (str): root dir for downloading + md5sum (str): md5 sum of download package + """ + # parse path after download to decompress under root_dir + fullpath = map_path(url, root_dir) + + # For same zip file, decompressed directory name different + # from zip file name, rename by following map + decompress_name_map = {"ppTSM_fight": "ppTSM", } + for k, v in decompress_name_map.items(): + if fullpath.find(k) >= 0: + fullpath = osp.join(osp.split(fullpath)[0], v) + + if osp.exists(fullpath) and check_exist: + if not osp.isfile(fullpath) or \ + _check_exist_file_md5(fullpath, md5sum, url): + return fullpath, True + else: + os.remove(fullpath) + + fullname = _download_dist(url, root_dir, md5sum) + + # new weights format which postfix is 'pdparams' not + # need to decompress + if osp.splitext(fullname)[-1] not in ['.pdparams', '.yml']: + _decompress_dist(fullname) + + return fullpath, False + + +def get_weights_path(url): + """Get weights path from WEIGHTS_HOME, if not exists, + download it from url. + """ + url = parse_url(url) + path, _ = get_path(url, WEIGHTS_HOME) + return path + + +def auto_download_model(model_path): + # auto download + if is_url(model_path): + weight = get_weights_path(model_path) + return weight + return None + + +if __name__ == "__main__": + model_path = "https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip" + auto_download_model(model_path) diff --git a/deploy/pipeline/pipeline.py b/deploy/pipeline/pipeline.py index 55842fa377a872c1e8df8e25e38b2332b0bb7220..98329a469652e8f055b54ee77aae55f0cd931861 100644 --- a/deploy/pipeline/pipeline.py +++ b/deploy/pipeline/pipeline.py @@ -51,6 +51,8 @@ from pphuman.mtmct import mtmct_process from ppvehicle.vehicle_plate import PlateRecognizer from ppvehicle.vehicle_attr import VehicleAttr +from download import auto_download_model + class Pipeline(object): """ @@ -166,6 +168,45 @@ class Pipeline(object): self.predictor.run(self.input) +def get_model_dir(cfg): + # auto download inference model + model_dir_dict = {} + for key in cfg.keys(): + if type(cfg[key]) == dict and \ + ("enable" in cfg[key].keys() and cfg[key]['enable'] + or "enable" not in cfg[key].keys()): + + if "model_dir" in cfg[key].keys(): + model_dir = cfg[key]["model_dir"] + downloaded_model_dir = auto_download_model(model_dir) + if downloaded_model_dir: + model_dir = downloaded_model_dir + model_dir_dict[key] = model_dir + print(key, " model dir:", model_dir) + elif key == "VEHICLE_PLATE": + det_model_dir = cfg[key]["det_model_dir"] + downloaded_det_model_dir = auto_download_model(det_model_dir) + if downloaded_det_model_dir: + det_model_dir = downloaded_det_model_dir + model_dir_dict["det_model_dir"] = det_model_dir + print("det_model_dir model dir:", det_model_dir) + + rec_model_dir = cfg[key]["rec_model_dir"] + downloaded_rec_model_dir = auto_download_model(rec_model_dir) + if downloaded_rec_model_dir: + rec_model_dir = downloaded_rec_model_dir + model_dir_dict["rec_model_dir"] = rec_model_dir + print("rec_model_dir model dir:", rec_model_dir) + elif key == "MOT": # for idbased and skeletonbased actions + model_dir = cfg[key]["model_dir"] + downloaded_model_dir = auto_download_model(model_dir) + if downloaded_model_dir: + model_dir = downloaded_model_dir + model_dir_dict[key] = model_dir + + return model_dir_dict + + class PipePredictor(object): """ Predictor in single camera @@ -292,9 +333,12 @@ class PipePredictor(object): self.file_name = None self.collector = DataCollector() + # auto download inference model + model_dir_dict = get_model_dir(self.cfg) + if not is_video: det_cfg = self.cfg['DET'] - model_dir = det_cfg['model_dir'] + model_dir = model_dir_dict['DET'] batch_size = det_cfg['batch_size'] self.det_predictor = Detector( model_dir, device, run_mode, batch_size, trt_min_shape, @@ -302,7 +346,7 @@ class PipePredictor(object): enable_mkldnn) if self.with_human_attr: attr_cfg = self.cfg['ATTR'] - model_dir = attr_cfg['model_dir'] + model_dir = model_dir_dict['ATTR'] batch_size = attr_cfg['batch_size'] basemode = attr_cfg['basemode'] self.modebase[basemode] = True @@ -313,7 +357,7 @@ class PipePredictor(object): if self.with_vehicle_attr: vehicleattr_cfg = self.cfg['VEHICLE_ATTR'] - model_dir = vehicleattr_cfg['model_dir'] + model_dir = model_dir_dict['VEHICLE_ATTR'] batch_size = vehicleattr_cfg['batch_size'] color_threshold = vehicleattr_cfg['color_threshold'] type_threshold = vehicleattr_cfg['type_threshold'] @@ -327,7 +371,7 @@ class PipePredictor(object): else: if self.with_human_attr: attr_cfg = self.cfg['ATTR'] - model_dir = attr_cfg['model_dir'] + model_dir = model_dir_dict['ATTR'] batch_size = attr_cfg['batch_size'] basemode = attr_cfg['basemode'] self.modebase[basemode] = True @@ -337,7 +381,7 @@ class PipePredictor(object): enable_mkldnn) if self.with_idbased_detaction: idbased_detaction_cfg = self.cfg['ID_BASED_DETACTION'] - model_dir = idbased_detaction_cfg['model_dir'] + model_dir = model_dir_dict['ID_BASED_DETACTION'] batch_size = idbased_detaction_cfg['batch_size'] basemode = idbased_detaction_cfg['basemode'] threshold = idbased_detaction_cfg['threshold'] @@ -363,7 +407,7 @@ class PipePredictor(object): if self.with_idbased_clsaction: idbased_clsaction_cfg = self.cfg['ID_BASED_CLSACTION'] - model_dir = idbased_clsaction_cfg['model_dir'] + model_dir = model_dir_dict['ID_BASED_CLSACTION'] batch_size = idbased_clsaction_cfg['batch_size'] basemode = idbased_clsaction_cfg['basemode'] threshold = idbased_clsaction_cfg['threshold'] @@ -389,7 +433,7 @@ class PipePredictor(object): if self.with_skeleton_action: skeleton_action_cfg = self.cfg['SKELETON_ACTION'] - skeleton_action_model_dir = skeleton_action_cfg['model_dir'] + skeleton_action_model_dir = model_dir_dict['SKELETON_ACTION'] skeleton_action_batch_size = skeleton_action_cfg['batch_size'] skeleton_action_frames = skeleton_action_cfg['max_frames'] display_frames = skeleton_action_cfg['display_frames'] @@ -414,7 +458,7 @@ class PipePredictor(object): if self.modebase["skeletonbased"]: kpt_cfg = self.cfg['KPT'] - kpt_model_dir = kpt_cfg['model_dir'] + kpt_model_dir = model_dir_dict['KPT'] kpt_batch_size = kpt_cfg['batch_size'] self.kpt_predictor = KeyPointDetector( kpt_model_dir, @@ -439,7 +483,7 @@ class PipePredictor(object): if self.with_vehicle_attr: vehicleattr_cfg = self.cfg['VEHICLE_ATTR'] - model_dir = vehicleattr_cfg['model_dir'] + model_dir = model_dir_dict['VEHICLE_ATTR'] batch_size = vehicleattr_cfg['batch_size'] color_threshold = vehicleattr_cfg['color_threshold'] type_threshold = vehicleattr_cfg['type_threshold'] @@ -452,7 +496,7 @@ class PipePredictor(object): if self.with_mtmct: reid_cfg = self.cfg['REID'] - model_dir = reid_cfg['model_dir'] + model_dir = model_dir_dict['REID'] batch_size = reid_cfg['batch_size'] basemode = reid_cfg['basemode'] self.modebase[basemode] = True @@ -464,7 +508,7 @@ class PipePredictor(object): if self.with_mot or self.modebase["idbased"] or self.modebase[ "skeletonbased"]: mot_cfg = self.cfg['MOT'] - model_dir = mot_cfg['model_dir'] + model_dir = model_dir_dict['MOT'] tracker_config = mot_cfg['tracker_config'] batch_size = mot_cfg['batch_size'] basemode = mot_cfg['basemode'] @@ -491,7 +535,7 @@ class PipePredictor(object): basemode = video_action_cfg['basemode'] self.modebase[basemode] = True - video_action_model_dir = video_action_cfg['model_dir'] + video_action_model_dir = model_dir_dict['VIDEO_ACTION'] video_action_batch_size = video_action_cfg['batch_size'] short_size = video_action_cfg["short_size"] target_size = video_action_cfg["target_size"] diff --git a/deploy/pipeline/pphuman/video_action_infer.py b/deploy/pipeline/pphuman/video_action_infer.py index 8a75c7b8ba52ef2599217c022487ffdbc6a170f0..cb12bd71b6c421e7105e3e2ed60ce2f9a519e1bb 100644 --- a/deploy/pipeline/pphuman/video_action_infer.py +++ b/deploy/pipeline/pphuman/video_action_infer.py @@ -118,8 +118,7 @@ class VideoActionRecognizer(object): } if run_mode in precision_map.keys(): self.config.enable_tensorrt_engine( - max_batch_size=self.batch_size, - precision_mode=precision_map[run_mode]) + max_batch_size=8, precision_mode=precision_map[run_mode]) self.config.enable_memory_optim() # use zero copy