mirror of https://github.com/FoundationVision/GLEE
178 lines
6.7 KiB
Markdown
178 lines
6.7 KiB
Markdown
# Tutorial for Testing (Updating)
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GLEE can be directly tested on classic detection and segmentation datasets locally. For some video datasets, the results need to be submitted to Codalab for evaluation. Additionally, certain datasets such as TAO, BURST, and OmniLabel require evaluation using additional tools. We will continue to update the evaluation tutorials for all datasets reported in the paper here.
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## Detection，Instance Segmentation，REC&RES
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GLEE can directly perform evaluations on COCO, Objects365, LVIS, and the RefCOCO series based on Detectron2. Typically, the Stage 2 yaml config file can be used, with manual adjustments made for the dataset to be inferred and the selection of weights to be downloaded from the [MODEL_ZOO.md](MODEL_ZOO.md).
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To inference on COCO:
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```bash
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# Lite
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Lite/Stage2_joint_training_CLIPteacher_R50.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Lite_joint.pth DATASETS.TEST '("coco_2017_val",)'
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# Plus
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Plus/Stage2_joint_training_CLIPteacher_SwinL.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Plus_joint.pth DATASETS.TEST '("coco_2017_val",)'
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# Pro
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Pro/Stage2_joint_training_CLIPteacher_EVA02L.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Pro_joint.pth DATASETS.TEST '("coco_2017_val",)'
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```
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Replace `"path/to/downloaded/weights"` with the actual path to the pretrained model weights and use `"DATASETS.TEST"` to specific the dataset you wish to evaluate on.
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`'("coco_2017_val",)'` can be replace by :
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```bash
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# Lite
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Lite/Stage2_joint_training_CLIPteacher_R50.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Lite_joint.pth DATASETS.TEST
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'("coco_2017_val",)'
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'("lvis_v1_minival",)'
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'("lvis_v1_val",)'
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'("objects365_v2_val",)'
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'("refcoco-unc-val",)'
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'("refcoco-unc-testA",)'
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'("refcoco-unc-testB",)'
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'("refcocoplus-unc-val",)'
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'("refcocoplus-unc-testA",)'
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'("refcocoplus-unc-testB",)'
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'("refcocog-umd-val",)'
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'("refcocog-umd-test",)'
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# Alternatively, to infer across all tasks at once:
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'("coco_2017_val","lvis_v1_minival","lvis_v1_val","objects365_v2_val","refcoco-unc-val","refcoco-unc-testA","refcoco-unc-testB","refcocoplus-unc-val","refcocoplus-unc-testA","refcocoplus-unc-testB","refcocog-umd-val","refcocog-umd-test",)'
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```
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# Video Tasks
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### Youtube-VIS, OVIS
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1. Run the inference scripts:
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```
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# YTVIS19 GLEE-Lite
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/ytvis19_base.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth
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# YTVIS19 GLEE-Plus
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/ytvis19_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth
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# ovis GLEE-Lite
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/ovis_base.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Lite_joint.pth
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# ovis GLEE-Plus
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/ovis_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth
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```
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2. Submit the results.zip to online servers.
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### TAO, BURST
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#### 1. Data preparation
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TAO and BURST share the same video frames.
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First, download the validation set zip files (2-TAO_VAL.zip, 2_AVA_HACS_VAL_e49d8f78098a8ffb3769617570a20903.zip) and unzip them from https://motchallenge.net/tao_download.php.
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Then, download our preprocessed YTVIS format (COCO-like) annotation files from huggingface:
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https://huggingface.co/spaces/Junfeng5/GLEE_demo/tree/main/annotations/TAO
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And organize them as below:
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```
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${GLEE_ROOT}
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-- datasets
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-- TAO
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--burst_annotations
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-- TAO_val_withlabel_ytvisformat.json
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-- val
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-- all_classes.json
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-- ...
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--TAO_annotations
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-- validation_ytvisfmt.json
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-- validation.json
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-- frames
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-- val
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-- ArgoVerse
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-- ava
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-- ...
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```
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#### 2. TAO
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1. Run the inference scripts:
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```
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/TAO_Lite.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Lite_joint.pth
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/TAO_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth
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```
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2. For TAO, we use teta as our evaluate metric (for more details, please refer to https://github.com/SysCV/tet/blob/main/teta/README.md)
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3. Install teta and run evaluation:
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```
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git clone https://github.com/SysCV/tet.git
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cd tet/teta/
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pip install -r requirements.txt
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pip install -e .
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# eval
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python3 scripts/run_tao.py --METRICS TETA --TRACKERS_TO_EVAL TETer --GT_FOLDER /path/to/${GLEE_ROOT}/datasets/TAO/TAO_annotations/validation.json --TRACKER_SUB_FOLDER /path/to/${GLEE_ROOT}/GLEE_TAO_Lite_640p/inference/results.json
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```
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#### 3. BURST
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1. Run the inference scripts:
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```
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/BURST_Lite.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Lite_joint.pth
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python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/BURST_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth
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```
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2. Download eval tools from https://github.com/Ali2500/BURST-benchmark and https://github.com/JonathonLuiten/TrackEval:
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```
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mkdir burst_tools
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cd burst_tools
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git clone https://github.com/Ali2500/BURST-benchmark.git
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git clone https://github.com/JonathonLuiten/TrackEval.git
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wget https://huggingface.co/spaces/Junfeng5/GLEE_demo/resolve/main/annotations/convert_ytvis2tao.py
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```
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3. Run eval codes:
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```
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# first convert ytvis format results to TAO/BURST results
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python3 convert_ytvis2tao.py --results path/to/GLEE_BURST_Lite_720p/inference/results.json --refer /path/to/${GLEE_ROOT}/datasets/TAO/burst_annotations/val/all_classes.json
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cd BURST-benchmark
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export TRACKEVAL_DIR=/path/to/burst_tools/TrackEval/
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python3 burstapi/eval/run.py --pred ../converted_tao_results.json --gt ../../burst_annotations/val/ --task class_guided
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```
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# Omnilabel and ODinW (Updating)
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