!pip3 install git+https://github.com/MiXaiLL76/faster_coco_eval
!mkdir -pv data/coco/
!wget -P data/coco/ http://images.cocodataset.org/annotations/annotations_trainval2017.zip
!wget -P data/coco/ http://images.cocodataset.org/zips/val2017.zip
!unzip data/coco/annotations_trainval2017.zip -d data/coco/
!unzip data/coco/val2017.zip -d data/coco/
!rm -rf data/coco/*.zip
yolo3_model_path = "https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth"
!wget -P model {yolo3_model_path}
import os.path as osp
_BASE_CONFIG_DIR = "configs/"
CONFIG_FILE = "yolo/yolov3_d53_320_273e_coco.py"
CHECKPOINT_FILE = "model/yolov3_d53_320_273e_coco-421362b6.pth"
WORK_DIR = "."
use_cpu = False
cfg_path = osp.join(_BASE_CONFIG_DIR, CONFIG_FILE)
print(f"{cfg_path=}")
cfg_path='configs/yolo/yolov3_d53_320_273e_coco.py'
_dop = ""
if use_cpu:
_dop += f" --gpu-id -1 "
!python3 test.py \
{cfg_path} \
{CHECKPOINT_FILE} \
--format-only {_dop}\
--cfg-options data.test.ann_file=data/coco/annotations/instances_val2017.json \
data.test.img_prefix=data/coco/val2017 \
--out yolo_result.pkl
%%time
!python3 eval_metric.py {cfg_path} yolo_result.pkl \
--eval bbox \
--cfg-options data.test.ann_file=data/coco/annotations/instances_val2017.json
loading annotations into memory...
Done (t=0.28s)
creating index...
index created!
Data uploaded for 1.057 sec.
Evaluating bbox...
Loading and preparing results...
DONE (t=0.47s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=16.69s).
Accumulating evaluation results...
DONE (t=3.02s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.279
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.491
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.283
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.105
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.301
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.438
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.395
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.395
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.395
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.185
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.574
OrderedDict([('bbox_mAP', 0.279), ('bbox_mAP_50', 0.491), ('bbox_mAP_75', 0.283), ('bbox_mAP_s', 0.105), ('bbox_mAP_m', 0.301), ('bbox_mAP_l', 0.438), ('bbox_mAP_copypaste', '0.279 0.491 0.283 0.105 0.301 0.438')])
Data validate for 22.854 sec.
CPU times: user 160 ms, sys: 22.4 ms, total: 183 ms
Wall time: 26 s
%%time
!python3 eval_metric.py {cfg_path} yolo_result.pkl \
--eval bbox \
--cfg-options data.test.ann_file=data/coco/annotations/instances_val2017.json data.test.type='FasterCocoDataset'
loading annotations into memory...
Done (t=0.28s)
creating index...
index created!
Data uploaded for 1.065 sec.
Evaluating bbox...
Loading and preparing results...
DONE (t=0.47s)
creating index...
index created!
OrderedDict([('bbox_mAP', 0.279), ('bbox_mAP_50', 0.491), ('bbox_mAP_75', 0.283), ('bbox_mAP_s', 0.105), ('bbox_mAP_m', 0.301), ('bbox_mAP_l', 0.438), ('bbox_mAP_copypaste', '0.279 0.491 0.283 0.105 0.301 0.438')])
Data validate for 8.714 sec.
CPU times: user 91.8 ms, sys: 5.82 ms, total: 97.6 ms
Wall time: 11.8 s
yoloact_model_path = "https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth"
!wget -P model {yoloact_model_path}
CONFIG_FILE = "yolact/yolact_r50_1x8_coco.py"
CHECKPOINT_FILE = "model/yolact_r50_1x8_coco_20200908-f38d58df.pth"
cfg_path = osp.join(_BASE_CONFIG_DIR, CONFIG_FILE)
print(f"{cfg_path=}")
cfg_path='configs/yolact/yolact_r50_1x8_coco.py'
_dop = ""
if use_cpu:
_dop += f" --gpu-id -1 "
!python3 test.py \
{cfg_path} \
{CHECKPOINT_FILE} \
--format-only {_dop}\
--cfg-options data.test.ann_file=data/coco/annotations/instances_val2017.json \
data.test.img_prefix=data/coco/val2017 \
--out yoloact_result.pkl
%%time
!python3 eval_metric.py {cfg_path} yoloact_result.pkl \
--eval segm \
--cfg-options data.test.ann_file=data/coco/annotations/instances_val2017.json
loading annotations into memory...
Done (t=0.28s)
creating index...
index created!
Data uploaded for 2.235 sec.
Evaluating segm...
/opt/conda/lib/python3.9/site-packages/mmdet/datasets/coco.py:470: UserWarning: The key "bbox" is deleted for more accurate mask AP of small/medium/large instances since v2.12.0. This does not change the overall mAP calculation.
warnings.warn(
Loading and preparing results...
DONE (t=1.21s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=22.02s).
Accumulating evaluation results...
/opt/conda/lib/python3.9/site-packages/pycocotools/cocoeval.py:378: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
DONE (t=3.49s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.486
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.296
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.100
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.315
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.465
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.392
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.176
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.439
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.568
OrderedDict([('segm_mAP', 0.29), ('segm_mAP_50', 0.486), ('segm_mAP_75', 0.296), ('segm_mAP_s', 0.1), ('segm_mAP_m', 0.315), ('segm_mAP_l', 0.465), ('segm_mAP_copypaste', '0.290 0.486 0.296 0.100 0.315 0.465')])
Data validate for 35.356 sec.
CPU times: user 235 ms, sys: 22 ms, total: 257 ms
Wall time: 39.6 s
%%time
!python3 eval_metric.py {cfg_path} yoloact_result.pkl \
--eval segm \
--cfg-options data.test.ann_file=data/coco/annotations/instances_val2017.json data.test.type='FasterCocoDataset'
loading annotations into memory...
Done (t=0.28s)
creating index...
index created!
Data uploaded for 2.305 sec.
Evaluating segm...
/home/rdl/storage/eval/coco_fast.py:105: UserWarning: The key "bbox" is deleted for more accurate mask AP of small/medium/large instances since v2.12.0. This does not change the overall mAP calculation.
warnings.warn(
Loading and preparing results...
DONE (t=1.25s)
creating index...
index created!
OrderedDict([('segm_mAP', 0.29), ('segm_mAP_50', 0.486), ('segm_mAP_75', 0.296), ('segm_mAP_s', 0.1), ('segm_mAP_m', 0.315), ('segm_mAP_l', 0.465), ('segm_mAP_copypaste', '0.290 0.486 0.296 0.100 0.315 0.465')])
Data validate for 18.403 sec.
CPU times: user 103 ms, sys: 52.8 ms, total: 156 ms
Wall time: 22.7 s