diff --git a/configs/mot/mcfairmot/README.md b/configs/mot/mcfairmot/README.md index 1e63acce691970be860a64a42748bb9c37427d4a..555aee9fecd5a03c91c5fd3500e5f9b5c6d38e3c 100644 --- a/configs/mot/mcfairmot/README.md +++ b/configs/mot/mcfairmot/README.md @@ -44,6 +44,13 @@ PP-tracking provides an AI studio public project tutorial. Please refer to this - MOTA is the average MOTA of 4 catecories in the VisDrone Vehicle dataset, and this dataset is extracted from the VisDrone2019 MOT dataset, here we provide the download [link](https://bj.bcebos.com/v1/paddledet/data/mot/visdrone_mcmot_vehicle.zip). - The tracker used in MCFairMOT model here is ByteTracker. +### MCFairMOT off-line quantization results on VisDrone Vehicle val-set +| Model | Compression Strategy | Prediction Delay（T4） |Prediction Delay（V100）| Model Configuration File |Compression Algorithm Configuration File | +| :--------------| :------- | :------: | :----: | :----: | :----: | +| DLA-34 | baseline | 41.3 | 21.9 |[Configuration File](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)| - | +| DLA-34 | off-line quantization | 37.8 | 21.2 |[Configuration File](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)|[Configuration File](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.3/configs/slim/post_quant/mcfairmot_ptq.yml)| + + ## Getting Start ### 1. Training @@ -95,6 +102,14 @@ python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/mc - The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images. - Each line of the tracking results txt file is `frame,id,x1,y1,w,h,score,cls_id,-1,-1`. +### 6. Off-line quantization + +The offline quantization model is calibrated using the VisDrone Vehicle val-set, running as: +```bash +CUDA_VISIBLE_DEVICES=0 python3.7 tools/post_quant.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml --slim_config=configs/slim/post_quant/mcfairmot_ptq.yml +``` +**Notes:** + - Offline quantization uses the VisDrone Vehicle val-set dataset and a 4-class vehicle tracking model by default. ## Citations ``` diff --git a/configs/mot/mcfairmot/README_cn.md b/configs/mot/mcfairmot/README_cn.md index c2f60fee161191193e5a2002d948290e26e25d05..184045584a455cc8b2443a9d5541e12732e625a9 100644 --- a/configs/mot/mcfairmot/README_cn.md +++ b/configs/mot/mcfairmot/README_cn.md @@ -43,6 +43,12 @@ PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking - MOTA是VisDrone Vehicle数据集4类车辆目标的平均MOTA, 该数据集是VisDrone数据集中抽出4类车辆类别组成的，此处提供数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/mot/visdrone_mcmot_vehicle.zip)。 - MCFairMOT模型此处使用的跟踪器是使用的ByteTracker。 +### MCFairMOT 在VisDrone Vehicle val-set上离线量化结果 +| 骨干网络 | 压缩策略 | 预测时延（T4） |预测时延（V100）| 配置文件 |压缩算法配置文件 | +| :--------------| :------- | :------: | :----: | :----: | :----: | +| DLA-34 | baseline | 41.3 | 21.9 |[配置文件](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)| - | +| DLA-34 | 离线量化 | 37.8 | 21.2 |[配置文件](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.3/configs/slim/post_quant/mcfairmot_ptq.yml)| + ## 快速开始 ### 1. 训练 @@ -93,6 +99,14 @@ python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/mc - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 - 多类别跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,cls_id,-1,-1`。 +### 6. 离线量化 + +使用 VisDrone Vehicle val-set 对离线量化模型进行校准，运行方式： +```bash +CUDA_VISIBLE_DEVICES=0 python3.7 tools/post_quant.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml --slim_config=configs/slim/post_quant/mcfairmot_ptq.yml +``` +**注意:** + - 离线量化默认使用的是VisDrone Vehicle val-set数据集以及4类车辆跟踪模型。 ## 引用 ``` diff --git a/configs/slim/post_quant/mcfairmot_ptq.yml b/configs/slim/post_quant/mcfairmot_ptq.yml new file mode 100644 index 0000000000000000000000000000000000000000..7ab8e38b9715aa10e5d38a84fa15a033c9ee919f --- /dev/null +++ b/configs/slim/post_quant/mcfairmot_ptq.yml @@ -0,0 +1,10 @@ +weights: https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.pdparams +slim: PTQ + +PTQ: + ptq_config: { + 'activation_quantizer': 'HistQuantizer', + 'upsample_bins': 127, + 'hist_percent': 0.999} + quant_batch_num: 10 + fuse: True diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index cf9028b100bf7714f5be4556e3419de7073c570f..8a447132d89419e6c8bb8e4f7785a45cbd0148cf 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -73,6 +73,10 @@ class Trainer(object): logger.error('DeepSORT has no need of training on mot dataset.') sys.exit(1) + if cfg.architecture == 'FairMOT' and self.mode == 'eval': + images = self.parse_mot_images(cfg) + self.dataset.set_images(images) + if self.mode == 'train': self.loader = create('{}Reader'.format(self.mode.capitalize()))( self.dataset, cfg.worker_num) @@ -114,14 +118,17 @@ class Trainer(object): # EvalDataset build with BatchSampler to evaluate in single device # TODO: multi-device evaluate if self.mode == 'eval': - self._eval_batch_sampler = paddle.io.BatchSampler( - self.dataset, batch_size=self.cfg.EvalReader['batch_size']) - reader_name = '{}Reader'.format(self.mode.capitalize()) - # If metric is VOC, need to be set collate_batch=False. - if cfg.metric == 'VOC': - cfg[reader_name]['collate_batch'] = False - self.loader = create(reader_name)(self.dataset, cfg.worker_num, - self._eval_batch_sampler) + if cfg.architecture == 'FairMOT': + self.loader = create('EvalMOTReader')(self.dataset, 0) + else: + self._eval_batch_sampler = paddle.io.BatchSampler( + self.dataset, batch_size=self.cfg.EvalReader['batch_size']) + reader_name = '{}Reader'.format(self.mode.capitalize()) + # If metric is VOC, need to be set collate_batch=False. + if cfg.metric == 'VOC': + cfg[reader_name]['collate_batch'] = False + self.loader = create(reader_name)(self.dataset, cfg.worker_num, + self._eval_batch_sampler) # TestDataset build after user set images, skip loader creation here # build optimizer in train mode @@ -759,3 +766,28 @@ class Trainer(object): flops = flops(self.model, input_spec) / (1000**3) logger.info(" Model FLOPs : {:.6f}G. (image shape is {})".format( flops, input_data['image'][0].unsqueeze(0).shape)) + + def parse_mot_images(self, cfg): + import glob + # for quant + dataset_dir = cfg['EvalMOTDataset'].dataset_dir + data_root = cfg['EvalMOTDataset'].data_root + data_root = '{}/{}'.format(dataset_dir, data_root) + seqs = os.listdir(data_root) + seqs.sort() + all_images = [] + for seq in seqs: + infer_dir = os.path.join(data_root, seq) + assert infer_dir is None or os.path.isdir(infer_dir), \ + "{} is not a directory".format(infer_dir) + images = set() + exts = ['jpg', 'jpeg', 'png', 'bmp'] + exts += [ext.upper() for ext in exts] + for ext in exts: + images.update(glob.glob('{}/*.{}'.format(infer_dir, ext))) + images = list(images) + images.sort() + assert len(images) > 0, "no image found in {}".format(infer_dir) + all_images.extend(images) + logger.info("Found {} inference images in total.".format(len(images))) + return all_images \ No newline at end of file