mAP Update (#176)
* updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates
This commit is contained in:
Glenn Jocher
GitHub
parent
f2cb840123
commit
c0cacc45a1
95
README.md
95
README.md
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@ -30,6 +30,7 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
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# Tutorials
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# Tutorials
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* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
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* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
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* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
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* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
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* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
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* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
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@ -67,13 +68,16 @@ HS**V** Intensity | +/- 50%
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https://cloud.google.com/deep-learning-vm/
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https://cloud.google.com/deep-learning-vm/
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**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
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**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
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**CPU platform:** Intel Skylake
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**CPU platform:** Intel Skylake
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**GPUs:** 1-4x P100 ($0.493/hr), 1-8x V100 ($0.803/hr)
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**GPUs:** K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
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**HDD:** 100 GB SSD
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**HDD:** 100 GB SSD
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**Dataset:** COCO train 2014
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**Dataset:** COCO train 2014
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GPUs | `batch_size` | batch time | epoch time | epoch cost
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GPUs | `batch_size` | batch time | epoch time | epoch cost
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--- |---| --- | --- | ---
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--- |---| --- | --- | ---
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<i></i> | (images) | (s/batch) | |
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<i></i> | (images) | (s/batch) | |
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1 K80 | 16 | 1.43s | 175min | $0.58
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1 P4 | 8 | 0.51s | 125min | $0.58
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1 T4 | 16 | 0.78s | 94min | $0.55
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1 P100 | 16 | 0.39s | 48min | $0.39
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1 P100 | 16 | 0.39s | 48min | $0.39
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2 P100 | 32 | 0.48s | 29min | $0.47
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2 P100 | 32 | 0.48s | 29min | $0.47
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4 P100 | 64 | 0.65s | 20min | $0.65
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4 P100 | 64 | 0.65s | 20min | $0.65
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@ -108,13 +112,32 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
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- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
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- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
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- Use `test.py --weights weights/latest.pt` to test the latest training results.
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- Use `test.py --weights weights/latest.pt` to test the latest training results.
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- Compare to official darknet results from https://arxiv.org/abs/1804.02767.
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- Compare to darknet published results https://arxiv.org/abs/1804.02767.
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<i></i> | ultralytics/yolov3 | darknet
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<!---
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%<i></i> | ultralytics/yolov3 fastest 5:52@416 (`pycocotools`) | darknet
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--- | --- | ---
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--- | --- | ---
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YOLOv3-320 | 51.3 | 51.5
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YOLOv3-320 | 51.9 (51.4) | 51.5
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YOLOv3-416 | 54.9 | 55.3
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YOLOv3-416 | 55.0 (54.9) | 55.3
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YOLOv3-608 | 57.9 | 57.9
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YOLOv3-608 | 57.5 (57.8) | 57.9
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<i></i> | ultralytics/yolov3 MERGE 7:15@416 (`pycocotools`) | darknet
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--- | --- | ---
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YOLOv3-320 | 52.3 (51.7) | 51.5
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YOLOv3-416 | 55.4 (55.3) | 55.3
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YOLOv3-608 | 57.9 (58.1) | 57.9
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<i></i> | ultralytics/yolov3 MERGE+earlier_pred4 8:34@416 (`pycocotools`) | darknet
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--- | --- | ---
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YOLOv3-320 | 52.3 (51.8) | 51.5
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YOLOv3-416 | 55.5 (55.4) | 55.3
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YOLOv3-608 | 57.9 (58.2) | 57.9
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--->
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<i></i> | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) with `pycocotools` | [darknet/yolov3](https://arxiv.org/abs/1804.02767)
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--- | --- | ---
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YOLOv3-320 | 51.8 | 51.5
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YOLOv3-416 | 55.4 | 55.3
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YOLOv3-608 | 58.2 | 57.9
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``` bash
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``` bash
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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@ -123,34 +146,42 @@ sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd co
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cd yolov3
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cd yolov3
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python3 test.py --save-json --conf-thres 0.001 --img-size 416
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python3 test.py --save-json --conf-thres 0.001 --img-size 416
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Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
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Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308
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Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549
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Image Total P R mAP
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310
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Calculating mAP: 100%|█████████████████████████████████| 157/157 [08:34<00:00, 2.53s/it]
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
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5000 5000 0.0896 0.756 0.555
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.317
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.452
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.411
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.435
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.477
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.587
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python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
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python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
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Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
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Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328
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Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579
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Image Total P R mAP
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335
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Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00, 1.55s/it]
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
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5000 5000 0.0966 0.786 0.579
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.582
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.437
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577
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```
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```
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# Contact
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# Contact
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23
detect.py
23
detect.py
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@ -14,7 +14,7 @@ def detect(
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output='output', # output folder
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output='output', # output folder
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img_size=416,
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img_size=416,
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conf_thres=0.3,
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conf_thres=0.3,
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nms_thres=0.45,
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nms_thres=0.5,
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save_txt=False,
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save_txt=False,
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save_images=True,
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save_images=True,
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webcam=False
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webcam=False
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@ -29,9 +29,6 @@ def detect(
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# Load weights
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# Load weights
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if weights.endswith('.pt'): # pytorch format
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if weights.endswith('.pt'): # pytorch format
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if weights.endswith('yolov3.pt') and not os.path.exists(weights):
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if platform in ('darwin', 'linux'): # linux/macos
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os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights)
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model.load_state_dict(torch.load(weights, map_location=device)['model'])
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model.load_state_dict(torch.load(weights, map_location=device)['model'])
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else: # darknet format
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else: # darknet format
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_ = load_darknet_weights(model, weights)
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_ = load_darknet_weights(model, weights)
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torch.onnx.export(model, img, 'weights/model.onnx', verbose=True)
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torch.onnx.export(model, img, 'weights/model.onnx', verbose=True)
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return
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return
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pred = model(img)
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pred = model(img)
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pred = pred[pred[:, :, 4] > conf_thres] # remove boxes < threshold
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detections = non_max_suppression(pred, conf_thres, nms_thres)[0]
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if len(pred) > 0:
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# Run NMS on predictions
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detections = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0]
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if len(detections) > 0:
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# Rescale boxes from 416 to true image size
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# Rescale boxes from 416 to true image size
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scale_coords(img_size, detections[:, :4], im0.shape).round()
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scale_coords(img_size, detections[:, :4], im0.shape).round()
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# Print results to screen
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# Print results to screen
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unique_classes = detections[:, -1].cpu().unique()
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for c in detections[:, -1].unique():
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for c in unique_classes:
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n = (detections[:, -1] == c).sum()
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n = (detections[:, -1].cpu() == c).sum()
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print('%g %ss' % (n, classes[int(c)]), end=', ')
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print('%g %ss' % (n, classes[int(c)]), end=', ')
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# Draw bounding boxes and labels of detections
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# Draw bounding boxes and labels of detections
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for *xyxy, conf, cls_conf, cls in detections:
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for *xyxy, conf, cls_conf, cls in detections:
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if save_txt: # Write to file
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if save_txt: # Write to file
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with open(save_path + '.txt', 'a') as file:
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with open(save_path + '.txt', 'a') as file:
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file.write(('%g ' * 6 + '\n') % (*xyxy, cls, cls_conf * conf))
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file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
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# Add bbox to the image
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# Add bbox to the image
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label = '%s %.2f' % (classes[int(cls)], conf)
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label = '%s %.2f' % (classes[int(cls)], conf)
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parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
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parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
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parser.add_argument('--images', type=str, default='data/samples', help='path to images')
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parser.add_argument('--images', type=str, default='data/samples', help='path to images')
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parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
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parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
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parser.add_argument('--conf-thres', type=float, default=0.50, help='object confidence threshold')
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parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
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parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
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parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
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opt = parser.parse_args()
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opt = parser.parse_args()
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print(opt)
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print(opt)
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@ -1,5 +1,7 @@
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import os
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import os
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import torch.nn.functional as F
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from utils.parse_config import *
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from utils.parse_config import *
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from utils.utils import *
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from utils.utils import *
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@ -158,6 +160,8 @@ class YOLOLayer(nn.Module):
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p[..., 2:4] = torch.exp(p[..., 2:4]) * self.anchor_wh # wh yolo method
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p[..., 2:4] = torch.exp(p[..., 2:4]) * self.anchor_wh # wh yolo method
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# p[..., 2:4] = ((torch.sigmoid(p[..., 2:4]) * 2) ** 2) * self.anchor_wh # wh power method
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# p[..., 2:4] = ((torch.sigmoid(p[..., 2:4]) * 2) ** 2) * self.anchor_wh # wh power method
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p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf
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p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf
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p[..., 5:] = torch.sigmoid(p[..., 5:]) # p_class
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# p[..., 5:] = F.softmax(p[..., 5:], dim=4) # p_class
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p[..., :4] *= self.stride
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p[..., :4] *= self.stride
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# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
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# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
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131
test.py
131
test.py
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@ -1,6 +1,5 @@
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import argparse
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import argparse
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import json
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import json
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import time
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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||||||
|
|
@ -12,18 +11,18 @@ from utils.utils import *
|
||||||
def test(
|
def test(
|
||||||
cfg,
|
cfg,
|
||||||
data_cfg,
|
data_cfg,
|
||||||
weights,
|
weights=None,
|
||||||
batch_size=16,
|
batch_size=16,
|
||||||
img_size=416,
|
img_size=416,
|
||||||
iou_thres=0.5,
|
iou_thres=0.5,
|
||||||
conf_thres=0.3,
|
conf_thres=0.1,
|
||||||
nms_thres=0.45,
|
nms_thres=0.5,
|
||||||
save_json=False,
|
save_json=False,
|
||||||
model=None
|
model=None
|
||||||
):
|
):
|
||||||
|
if model is None:
|
||||||
device = torch_utils.select_device()
|
device = torch_utils.select_device()
|
||||||
|
|
||||||
if model is None:
|
|
||||||
# Initialize model
|
# Initialize model
|
||||||
model = Darknet(cfg, img_size).to(device)
|
model = Darknet(cfg, img_size).to(device)
|
||||||
|
|
||||||
|
|
@ -35,11 +34,14 @@ def test(
|
||||||
|
|
||||||
if torch.cuda.device_count() > 1:
|
if torch.cuda.device_count() > 1:
|
||||||
model = nn.DataParallel(model)
|
model = nn.DataParallel(model)
|
||||||
|
else:
|
||||||
|
device = next(model.parameters()).device
|
||||||
|
|
||||||
# Configure run
|
# Configure run
|
||||||
data_cfg = parse_data_cfg(data_cfg)
|
data_cfg = parse_data_cfg(data_cfg)
|
||||||
nC = int(data_cfg['classes']) # number of classes (80 for COCO)
|
|
||||||
test_path = data_cfg['valid']
|
test_path = data_cfg['valid']
|
||||||
|
if (os.sep + 'coco' + os.sep) in test_path: # COCO dataset probable
|
||||||
|
save_json = True # use pycocotools
|
||||||
|
|
||||||
# Dataloader
|
# Dataloader
|
||||||
dataset = LoadImagesAndLabels(test_path, img_size=img_size)
|
dataset = LoadImagesAndLabels(test_path, img_size=img_size)
|
||||||
|
|
@ -50,104 +52,111 @@ def test(
|
||||||
collate_fn=dataset.collate_fn)
|
collate_fn=dataset.collate_fn)
|
||||||
|
|
||||||
model.eval()
|
model.eval()
|
||||||
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
|
seen = 0
|
||||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||||
mP, mR, mAPs, TP, jdict = [], [], [], [], []
|
mP, mR, mAP, mAPj = 0.0, 0.0, 0.0, 0.0
|
||||||
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
|
jdict, tdict, stats, AP, AP_class = [], [], [], [], []
|
||||||
coco91class = coco80_to_coco91_class()
|
coco91class = coco80_to_coco91_class()
|
||||||
for imgs, targets, paths, shapes in tqdm(dataloader):
|
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc='Calculating mAP')):
|
||||||
t = time.time()
|
|
||||||
targets = targets.to(device)
|
targets = targets.to(device)
|
||||||
imgs = imgs.to(device)
|
imgs = imgs.to(device)
|
||||||
|
|
||||||
output = model(imgs)
|
output = model(imgs)
|
||||||
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
|
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
|
||||||
|
|
||||||
# Compute average precision for each sample
|
# Per image
|
||||||
for si, detections in enumerate(output):
|
for si, pred in enumerate(output):
|
||||||
|
image_id = int(Path(paths[si]).stem.split('_')[-1])
|
||||||
labels = targets[targets[:, 0] == si, 1:]
|
labels = targets[targets[:, 0] == si, 1:]
|
||||||
seen += 1
|
seen += 1
|
||||||
|
|
||||||
if detections is None:
|
if pred is None:
|
||||||
# If there are labels but no detections mark as zero AP
|
|
||||||
if len(labels) != 0:
|
|
||||||
mP.append(0), mR.append(0), mAPs.append(0)
|
|
||||||
continue
|
continue
|
||||||
|
|
||||||
# Get detections sorted by decreasing confidence scores
|
|
||||||
detections = detections[(-detections[:, 4]).argsort()]
|
|
||||||
|
|
||||||
if save_json:
|
if save_json:
|
||||||
|
# add to json pred dictionary
|
||||||
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
|
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
|
||||||
box = detections[:, :4].clone() # xyxy
|
box = pred[:, :4].clone() # xyxy
|
||||||
scale_coords(img_size, box, shapes[si]) # to original shape
|
scale_coords(img_size, box, shapes[si]) # to original shape
|
||||||
box = xyxy2xywh(box) # xywh
|
box = xyxy2xywh(box) # xywh
|
||||||
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||||
|
for di, d in enumerate(pred):
|
||||||
# add to json dictionary
|
|
||||||
for di, d in enumerate(detections):
|
|
||||||
jdict.append({
|
jdict.append({
|
||||||
'image_id': int(Path(paths[si]).stem.split('_')[-1]),
|
'image_id': image_id,
|
||||||
'category_id': coco91class[int(d[6])],
|
'category_id': coco91class[int(d[6])],
|
||||||
'bbox': [float3(x) for x in box[di]],
|
'bbox': [float3(x) for x in box[di]],
|
||||||
'score': float3(d[4] * d[5])
|
'score': float(d[4])
|
||||||
})
|
})
|
||||||
|
|
||||||
|
# if len(labels) > 0:
|
||||||
|
# # add to json targets dictionary
|
||||||
|
# # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], ...
|
||||||
|
# box = labels[:, 1:].clone()
|
||||||
|
# box[:, [0, 2]] *= shapes[si][1] # scale width
|
||||||
|
# box[:, [1, 3]] *= shapes[si][0] # scale height
|
||||||
|
# box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||||
|
# for di, d in enumerate(labels):
|
||||||
|
# tdict.append({
|
||||||
|
# 'segmentation': [[]],
|
||||||
|
# 'iscrowd': 0,
|
||||||
|
# 'image_id': image_id,
|
||||||
|
# 'category_id': coco91class[int(d[0])],
|
||||||
|
# 'id': seen,
|
||||||
|
# 'bbox': [float3(x) for x in box[di]],
|
||||||
|
# 'area': float3(box[di][2:4].prod())
|
||||||
|
# })
|
||||||
|
|
||||||
# If no labels add number of detections as incorrect
|
# If no labels add number of detections as incorrect
|
||||||
correct = []
|
correct = []
|
||||||
|
detected = []
|
||||||
if len(labels) == 0:
|
if len(labels) == 0:
|
||||||
# correct.extend([0 for _ in range(len(detections))])
|
# correct.extend([0 for _ in range(len(detections))])
|
||||||
mP.append(0), mR.append(0), mAPs.append(0)
|
|
||||||
continue
|
continue
|
||||||
else:
|
else:
|
||||||
# Extract target boxes as (x1, y1, x2, y2)
|
# Extract target boxes as (x1, y1, x2, y2)
|
||||||
target_box = xywh2xyxy(labels[:, 1:5]) * img_size
|
target_box = xywh2xyxy(labels[:, 1:5]) * img_size
|
||||||
target_cls = labels[:, 0]
|
target_cls = labels[:, 0]
|
||||||
|
|
||||||
detected = []
|
for *pred_box, conf, cls_conf, cls_pred in pred:
|
||||||
for *pred_box, conf, cls_conf, cls_pred in detections:
|
if cls_pred not in target_cls:
|
||||||
|
correct.append(0)
|
||||||
|
continue
|
||||||
|
|
||||||
# Best iou, index between pred and targets
|
# Best iou, index between pred and targets
|
||||||
iou, bi = bbox_iou(pred_box, target_box).max(0)
|
iou, bi = bbox_iou(pred_box, target_box).max(0)
|
||||||
|
|
||||||
# If iou > threshold and class is correct mark as correct
|
# If iou > threshold and class is correct mark as correct
|
||||||
if iou > iou_thres and cls_pred == target_cls[bi] and bi not in detected:
|
if iou > iou_thres and bi not in detected:
|
||||||
correct.append(1)
|
correct.append(1)
|
||||||
detected.append(bi)
|
detected.append(bi)
|
||||||
else:
|
else:
|
||||||
correct.append(0)
|
correct.append(0)
|
||||||
|
|
||||||
# Compute Average Precision (AP) per class
|
# Convert to Numpy
|
||||||
AP, AP_class, R, P = ap_per_class(tp=np.array(correct),
|
tp = np.array(correct)
|
||||||
conf=detections[:, 4].cpu().numpy(),
|
conf = pred[:, 4].cpu().numpy()
|
||||||
pred_cls=detections[:, 6].cpu().numpy(),
|
pred_cls = pred[:, 6].cpu().numpy()
|
||||||
target_cls=target_cls.cpu().numpy())
|
target_cls = target_cls.cpu().numpy()
|
||||||
|
stats.append((tp, conf, pred_cls, target_cls))
|
||||||
|
|
||||||
# Accumulate AP per class
|
# Compute means
|
||||||
AP_accum_count += np.bincount(AP_class, minlength=nC)
|
stats_np = [np.concatenate(x, 0) for x in list(zip(*stats))]
|
||||||
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
|
if len(stats_np):
|
||||||
|
AP, AP_class, R, P = ap_per_class(*stats_np)
|
||||||
|
mP, mR, mAP = P.mean(), R.mean(), AP.mean()
|
||||||
|
|
||||||
# Compute mean AP across all classes in this image, and append to image list
|
# Print P, R, mAP
|
||||||
mP.append(P.mean())
|
print(('%11s%11s' + '%11.3g' * 3) % (seen, len(dataset), mP, mR, mAP))
|
||||||
mR.append(R.mean())
|
|
||||||
mAPs.append(AP.mean())
|
|
||||||
|
|
||||||
# Means of all images
|
|
||||||
mean_P = np.mean(mP)
|
|
||||||
mean_R = np.mean(mR)
|
|
||||||
mean_mAP = np.mean(mAPs)
|
|
||||||
|
|
||||||
# Print image mAP and running mean mAP
|
|
||||||
print(('%11s%11s' + '%11.3g' * 4 + 's') %
|
|
||||||
(seen, len(dataset), mean_P, mean_R, mean_mAP, time.time() - t))
|
|
||||||
|
|
||||||
# Print mAP per class
|
# Print mAP per class
|
||||||
|
if len(stats_np):
|
||||||
print('\nmAP Per Class:')
|
print('\nmAP Per Class:')
|
||||||
for i, c in enumerate(load_classes(data_cfg['names'])):
|
names = load_classes(data_cfg['names'])
|
||||||
if AP_accum_count[i]:
|
for c, a in zip(AP_class, AP):
|
||||||
print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i])))
|
print('%15s: %-.4f' % (names[c], a))
|
||||||
|
|
||||||
# Save JSON
|
# Save JSON
|
||||||
if save_json:
|
if save_json and mAP and len(jdict):
|
||||||
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files]
|
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files]
|
||||||
with open('results.json', 'w') as file:
|
with open('results.json', 'w') as file:
|
||||||
json.dump(jdict, file)
|
json.dump(jdict, file)
|
||||||
|
|
@ -157,16 +166,20 @@ def test(
|
||||||
|
|
||||||
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||||
cocoGt = COCO('../coco/annotations/instances_val2014.json') # initialize COCO ground truth api
|
cocoGt = COCO('../coco/annotations/instances_val2014.json') # initialize COCO ground truth api
|
||||||
cocoDt = cocoGt.loadRes('results.json') # initialize COCO detections api
|
cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api
|
||||||
|
|
||||||
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
|
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
|
||||||
cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images
|
cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images
|
||||||
cocoEval.evaluate()
|
cocoEval.evaluate()
|
||||||
cocoEval.accumulate()
|
cocoEval.accumulate()
|
||||||
cocoEval.summarize()
|
cocoEval.summarize()
|
||||||
|
mAP = cocoEval.stats[1] # update mAP to pycocotools mAP
|
||||||
|
|
||||||
|
# F1 score = harmonic mean of precision and recall
|
||||||
|
# F1 = 2 * (mP * mR) / (mP + mR)
|
||||||
|
|
||||||
# Return mAP
|
# Return mAP
|
||||||
return mean_P, mean_R, mean_mAP
|
return mP, mR, mAP
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
|
|
@ -176,8 +189,8 @@ if __name__ == '__main__':
|
||||||
parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
|
parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
|
||||||
parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
|
parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
|
||||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
|
||||||
parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
|
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
|
||||||
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
|
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
|
||||||
parser.add_argument('--img-size', type=int, default=416, help='size of each image dimension')
|
parser.add_argument('--img-size', type=int, default=416, help='size of each image dimension')
|
||||||
opt = parser.parse_args()
|
opt = parser.parse_args()
|
||||||
|
|
|
||||||
12
train.py
12
train.py
|
|
@ -40,7 +40,7 @@ def train(
|
||||||
|
|
||||||
# Optimizer
|
# Optimizer
|
||||||
lr0 = 0.001 # initial learning rate
|
lr0 = 0.001 # initial learning rate
|
||||||
optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9, weight_decay=0.0005)
|
optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=0.9, weight_decay=0.0005)
|
||||||
|
|
||||||
cutoff = -1 # backbone reaches to cutoff layer
|
cutoff = -1 # backbone reaches to cutoff layer
|
||||||
start_epoch = 0
|
start_epoch = 0
|
||||||
|
|
@ -119,9 +119,9 @@ def train(
|
||||||
if plot_images:
|
if plot_images:
|
||||||
fig = plt.figure(figsize=(10, 10))
|
fig = plt.figure(figsize=(10, 10))
|
||||||
for ip in range(batch_size):
|
for ip in range(batch_size):
|
||||||
labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size
|
boxes = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy().T * img_size
|
||||||
plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
|
plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
|
||||||
plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-')
|
plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
|
||||||
plt.axis('off')
|
plt.axis('off')
|
||||||
fig.tight_layout()
|
fig.tight_layout()
|
||||||
fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi)
|
fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi)
|
||||||
|
|
@ -170,7 +170,7 @@ def train(
|
||||||
best_loss = mloss['total']
|
best_loss = mloss['total']
|
||||||
|
|
||||||
# Save training results
|
# Save training results
|
||||||
save = True
|
save = False
|
||||||
if save:
|
if save:
|
||||||
# Save latest checkpoint
|
# Save latest checkpoint
|
||||||
checkpoint = {'epoch': epoch,
|
checkpoint = {'epoch': epoch,
|
||||||
|
|
@ -190,11 +190,11 @@ def train(
|
||||||
|
|
||||||
# Calculate mAP
|
# Calculate mAP
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
|
results = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, model=model)
|
||||||
|
|
||||||
# Write epoch results
|
# Write epoch results
|
||||||
with open('results.txt', 'a') as file:
|
with open('results.txt', 'a') as file:
|
||||||
file.write(s + '%11.3g' * 3 % (P, R, mAP) + '\n')
|
file.write(s + '%11.3g' * 3 % results + '\n') # append P, R, mAP
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
|
|
|
||||||
24
utils/gcp.sh
24
utils/gcp.sh
|
|
@ -10,8 +10,8 @@ sudo reboot now
|
||||||
|
|
||||||
# Re-clone
|
# Re-clone
|
||||||
sudo rm -rf yolov3
|
sudo rm -rf yolov3
|
||||||
git clone https://github.com/ultralytics/yolov3 # master
|
# git clone https://github.com/ultralytics/yolov3 # master
|
||||||
# git clone -b multi_gpu --depth 1 https://github.com/ultralytics/yolov3 # branch
|
git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch
|
||||||
cp -r weights yolov3
|
cp -r weights yolov3
|
||||||
cp -r cocoapi/PythonAPI/pycocotools yolov3
|
cp -r cocoapi/PythonAPI/pycocotools yolov3
|
||||||
cd yolov3
|
cd yolov3
|
||||||
|
|
@ -26,11 +26,11 @@ python3 train.py --resume
|
||||||
python3 detect.py
|
python3 detect.py
|
||||||
|
|
||||||
# Test
|
# Test
|
||||||
python3 detect.py --save-json --conf-thres 0.001 --img-size 416
|
python3 test.py --save-json
|
||||||
|
|
||||||
# Git pull
|
# Git pull
|
||||||
git pull https://github.com/ultralytics/yolov3 # master
|
git pull https://github.com/ultralytics/yolov3 # master
|
||||||
git pull https://github.com/ultralytics/yolov3 multi_gpu # branch
|
git pull https://github.com/ultralytics/yolov3 map_update # branch
|
||||||
|
|
||||||
# Test Darknet training
|
# Test Darknet training
|
||||||
python3 test.py --weights ../darknet/backup/yolov3.backup
|
python3 test.py --weights ../darknet/backup/yolov3.backup
|
||||||
|
|
@ -40,10 +40,16 @@ gsutil cp yolov3/weights/latest1gpu.pt gs://ultralytics
|
||||||
|
|
||||||
# Copy latest.pt FROM bucket
|
# Copy latest.pt FROM bucket
|
||||||
gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
|
gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
|
||||||
wget https://storage.googleapis.com/ultralytics/latest.pt -O weights/latest.pt
|
wget https://storage.googleapis.com/ultralytics/yolov3/latest_v1_0.pt -O weights/latest_v1_0.pt
|
||||||
|
wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/best_v1_0.pt
|
||||||
|
|
||||||
# Trade Studies
|
# Debug/Development
|
||||||
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
|
sudo rm -rf yolov3
|
||||||
|
# git clone https://github.com/ultralytics/yolov3 # master
|
||||||
|
git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch
|
||||||
cp -r weights yolov3
|
cp -r weights yolov3
|
||||||
cd yolov3 && python3 train.py --batch-size 16 --epochs 1
|
cp -r cocoapi/PythonAPI/pycocotools yolov3
|
||||||
sudo shutdown
|
cd yolov3
|
||||||
|
|
||||||
|
#git pull https://github.com/ultralytics/yolov3 map_update # branch
|
||||||
|
python3 test.py --img-size 320
|
||||||
|
|
|
||||||
153
utils/utils.py
153
utils/utils.py
|
|
@ -7,7 +7,6 @@ import matplotlib.pyplot as plt
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
import torch.nn as nn
|
import torch.nn as nn
|
||||||
import torch.nn.functional as F
|
|
||||||
|
|
||||||
from utils import torch_utils
|
from utils import torch_utils
|
||||||
|
|
||||||
|
|
@ -106,10 +105,10 @@ def xyxy2xywh(x):
|
||||||
def xywh2xyxy(x):
|
def xywh2xyxy(x):
|
||||||
# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
|
# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
|
||||||
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
|
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
|
||||||
y[:, 0] = (x[:, 0] - x[:, 2] / 2)
|
y[:, 0] = x[:, 0] - x[:, 2] / 2
|
||||||
y[:, 1] = (x[:, 1] - x[:, 3] / 2)
|
y[:, 1] = x[:, 1] - x[:, 3] / 2
|
||||||
y[:, 2] = (x[:, 0] + x[:, 2] / 2)
|
y[:, 2] = x[:, 0] + x[:, 2] / 2
|
||||||
y[:, 3] = (x[:, 1] + x[:, 3] / 2)
|
y[:, 3] = x[:, 1] + x[:, 3] / 2
|
||||||
return y
|
return y
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -142,25 +141,25 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
|
||||||
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
|
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
|
||||||
|
|
||||||
# Find unique classes
|
# Find unique classes
|
||||||
unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))
|
unique_classes = np.unique(target_cls)
|
||||||
|
|
||||||
# Create Precision-Recall curve and compute AP for each class
|
# Create Precision-Recall curve and compute AP for each class
|
||||||
ap, p, r = [], [], []
|
ap, p, r = [], [], []
|
||||||
for c in unique_classes:
|
for c in unique_classes:
|
||||||
i = pred_cls == c
|
i = pred_cls == c
|
||||||
n_gt = sum(target_cls == c) # Number of ground truth objects
|
n_gt = (target_cls == c).sum() # Number of ground truth objects
|
||||||
n_p = sum(i) # Number of predicted objects
|
n_p = i.sum() # Number of predicted objects
|
||||||
|
|
||||||
if (n_p == 0) and (n_gt == 0):
|
if n_p == 0 and n_gt == 0:
|
||||||
continue
|
continue
|
||||||
elif (n_p == 0) or (n_gt == 0):
|
elif n_p == 0 or n_gt == 0:
|
||||||
ap.append(0)
|
ap.append(0)
|
||||||
r.append(0)
|
r.append(0)
|
||||||
p.append(0)
|
p.append(0)
|
||||||
else:
|
else:
|
||||||
# Accumulate FPs and TPs
|
# Accumulate FPs and TPs
|
||||||
fpc = np.cumsum(1 - tp[i])
|
fpc = (1 - tp[i]).cumsum()
|
||||||
tpc = np.cumsum(tp[i])
|
tpc = (tp[i]).cumsum()
|
||||||
|
|
||||||
# Recall
|
# Recall
|
||||||
recall_curve = tpc / (n_gt + 1e-16)
|
recall_curve = tpc / (n_gt + 1e-16)
|
||||||
|
|
@ -328,15 +327,18 @@ def build_targets(model, targets):
|
||||||
return txy, twh, tcls, indices
|
return txy, twh, tcls, indices
|
||||||
|
|
||||||
|
|
||||||
|
# @profile
|
||||||
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||||
"""
|
"""
|
||||||
Removes detections with lower object confidence score than 'conf_thres'
|
Removes detections with lower object confidence score than 'conf_thres'
|
||||||
Non-Maximum Suppression to further filter detections.
|
Non-Maximum Suppression to further filter detections.
|
||||||
Returns detections with shape:
|
Returns detections with shape:
|
||||||
(x1, y1, x2, y2, object_conf, class_score, class_pred)
|
(x1, y1, x2, y2, object_conf, class_conf, class)
|
||||||
"""
|
"""
|
||||||
|
|
||||||
output = [None for _ in range(len(prediction))]
|
min_wh = 2 # (pixels) minimum box width and height
|
||||||
|
|
||||||
|
output = [None] * len(prediction)
|
||||||
for image_i, pred in enumerate(prediction):
|
for image_i, pred in enumerate(prediction):
|
||||||
# Experiment: Prior class size rejection
|
# Experiment: Prior class size rejection
|
||||||
# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
|
# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
|
||||||
|
|
@ -352,56 +354,53 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||||
# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
|
# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
|
||||||
|
|
||||||
# Filter out confidence scores below threshold
|
# Filter out confidence scores below threshold
|
||||||
class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)
|
class_conf, class_pred = pred[:, 5:].max(1)
|
||||||
v = pred[:, 4] > conf_thres
|
# pred[:, 4] *= class_conf
|
||||||
v = v.nonzero().squeeze()
|
|
||||||
if len(v.shape) == 0:
|
|
||||||
v = v.unsqueeze(0)
|
|
||||||
|
|
||||||
pred = pred[v]
|
i = (pred[:, 4] > conf_thres) & (pred[:, 2] > min_wh) & (pred[:, 3] > min_wh)
|
||||||
class_prob = class_prob[v]
|
pred = pred[i]
|
||||||
class_pred = class_pred[v]
|
|
||||||
|
|
||||||
# If none are remaining => process next image
|
# If none are remaining => process next image
|
||||||
nP = pred.shape[0]
|
if len(pred) == 0:
|
||||||
if not nP:
|
|
||||||
continue
|
continue
|
||||||
|
|
||||||
# From (center x, center y, width, height) to (x1, y1, x2, y2)
|
# Select predicted classes
|
||||||
|
class_conf = class_conf[i]
|
||||||
|
class_pred = class_pred[i].unsqueeze(1).float()
|
||||||
|
|
||||||
|
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
|
||||||
pred[:, :4] = xywh2xyxy(pred[:, :4])
|
pred[:, :4] = xywh2xyxy(pred[:, :4])
|
||||||
|
pred[:, 4] *= class_conf # improves mAP from 0.549 to 0.551
|
||||||
|
|
||||||
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred)
|
# Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
|
||||||
detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1)
|
pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
|
||||||
# Iterate through all predicted classes
|
|
||||||
unique_labels = detections[:, -1].cpu().unique().to(prediction.device)
|
|
||||||
|
|
||||||
nms_style = 'OR' # 'OR' (default), 'AND', 'MERGE' (experimental)
|
# Get detections sorted by decreasing confidence scores
|
||||||
for c in unique_labels:
|
pred = pred[(-pred[:, 4]).argsort()]
|
||||||
# Get the detections with class c
|
|
||||||
dc = detections[detections[:, -1] == c]
|
det_max = []
|
||||||
# Sort the detections by maximum object confidence
|
nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental)
|
||||||
_, conf_sort_index = torch.sort(dc[:, 4] * dc[:, 5], descending=True)
|
for c in pred[:, -1].unique():
|
||||||
dc = dc[conf_sort_index]
|
dc = pred[pred[:, -1] == c] # select class c
|
||||||
dc = dc[:min(len(dc), 100)] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
|
dc = dc[:min(len(dc), 100)] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
|
||||||
|
|
||||||
# Non-maximum suppression
|
# Non-maximum suppression
|
||||||
det_max = []
|
|
||||||
ind = list(range(len(dc)))
|
|
||||||
if nms_style == 'OR': # default
|
if nms_style == 'OR': # default
|
||||||
while len(ind):
|
# METHOD1
|
||||||
j = ind[0]
|
# ind = list(range(len(dc)))
|
||||||
det_max.append(dc[j:j + 1]) # save highest conf detection
|
# while len(ind):
|
||||||
reject = bbox_iou(dc[j], dc[ind]) > nms_thres
|
# j = ind[0]
|
||||||
[ind.pop(i) for i in reversed(reject.nonzero())]
|
# det_max.append(dc[j:j + 1]) # save highest conf detection
|
||||||
# while dc.shape[0]: # SLOWER METHOD
|
# reject = (bbox_iou(dc[j], dc[ind]) > nms_thres).nonzero()
|
||||||
# det_max.append(dc[:1]) # save highest conf detection
|
# [ind.pop(i) for i in reversed(reject)]
|
||||||
# if len(dc) == 1: # Stop if we're at the last detection
|
|
||||||
# break
|
|
||||||
# iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
|
||||||
# dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
|
||||||
|
|
||||||
# Image Total P R mAP
|
# METHOD2
|
||||||
# 4964 5000 0.629 0.594 0.586
|
while dc.shape[0]:
|
||||||
|
det_max.append(dc[:1]) # save highest conf detection
|
||||||
|
if len(dc) == 1: # Stop if we're at the last detection
|
||||||
|
break
|
||||||
|
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
||||||
|
dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
||||||
|
|
||||||
elif nms_style == 'AND': # requires overlap, single boxes erased
|
elif nms_style == 'AND': # requires overlap, single boxes erased
|
||||||
while len(dc) > 1:
|
while len(dc) > 1:
|
||||||
|
|
@ -411,22 +410,16 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||||
dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
||||||
|
|
||||||
elif nms_style == 'MERGE': # weighted mixture box
|
elif nms_style == 'MERGE': # weighted mixture box
|
||||||
while len(dc) > 0:
|
while len(dc):
|
||||||
iou = bbox_iou(dc[0], dc[0:]) # iou with other boxes
|
i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes
|
||||||
i = iou > nms_thres
|
weights = dc[i, 4:5]
|
||||||
|
|
||||||
weights = dc[i, 4:5] * dc[i, 5:6]
|
|
||||||
dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
|
dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
|
||||||
det_max.append(dc[:1])
|
det_max.append(dc[:1])
|
||||||
dc = dc[iou < nms_thres]
|
dc = dc[i == 0]
|
||||||
|
|
||||||
# Image Total P R mAP
|
if len(det_max):
|
||||||
# 4964 5000 0.633 0.598 0.589 # normal
|
det_max = torch.cat(det_max) # concatenate
|
||||||
|
output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort
|
||||||
if len(det_max) > 0:
|
|
||||||
det_max = torch.cat(det_max)
|
|
||||||
# Add max detections to outputs
|
|
||||||
output[image_i] = det_max if output[image_i] is None else torch.cat((output[image_i], det_max))
|
|
||||||
|
|
||||||
return output
|
return output
|
||||||
|
|
||||||
|
|
@ -463,20 +456,42 @@ def coco_only_people(path='../coco/labels/val2014/'):
|
||||||
print(labels.shape[0], file)
|
print(labels.shape[0], file)
|
||||||
|
|
||||||
|
|
||||||
def plot_results(start=0):
|
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
|
||||||
|
# Compares the two methods for width-height anchor multiplication
|
||||||
|
# https://github.com/ultralytics/yolov3/issues/168
|
||||||
|
x = np.arange(-4.0, 4.0, .1)
|
||||||
|
ya = np.exp(x)
|
||||||
|
yb = (torch.sigmoid(torch.from_numpy(x)).numpy() * 2)
|
||||||
|
|
||||||
|
fig = plt.figure(figsize=(6, 3), dpi=150)
|
||||||
|
plt.plot(x, ya, '.-', label='yolo method')
|
||||||
|
plt.plot(x, yb ** 2, '.-', label='^2 power method')
|
||||||
|
plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method')
|
||||||
|
plt.xlim(left=-4, right=4)
|
||||||
|
plt.ylim(bottom=0, top=6)
|
||||||
|
plt.xlabel('input')
|
||||||
|
plt.ylabel('output')
|
||||||
|
plt.legend()
|
||||||
|
fig.tight_layout()
|
||||||
|
fig.savefig('comparison.jpg', dpi=fig.dpi)
|
||||||
|
|
||||||
|
|
||||||
|
def plot_results(start=0): # from utils.utils import *; plot_results()
|
||||||
# Plot YOLO training results file 'results.txt'
|
# Plot YOLO training results file 'results.txt'
|
||||||
# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
|
# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
|
||||||
# from utils.utils import *; plot_results()
|
|
||||||
|
|
||||||
fig = plt.figure(figsize=(14, 7))
|
fig = plt.figure(figsize=(14, 7))
|
||||||
s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
|
s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
|
||||||
for f in sorted(glob.glob('results*.txt')):
|
for f in sorted(glob.glob('results*.txt')):
|
||||||
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T # column 11 is mAP
|
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11, 12]).T # column 11 is mAP
|
||||||
x = range(1, results.shape[1])
|
x = range(start, results.shape[1])
|
||||||
for i in range(8):
|
for i in range(8):
|
||||||
plt.subplot(2, 4, i + 1)
|
plt.subplot(2, 4, i + 1)
|
||||||
plt.plot(results[i, x[start:]], marker='.', label=f)
|
plt.plot(x, results[i, x], marker='.', label=f)
|
||||||
plt.title(s[i])
|
plt.title(s[i])
|
||||||
if i == 0:
|
if i == 0:
|
||||||
plt.legend()
|
plt.legend()
|
||||||
|
if i == 7:
|
||||||
|
plt.plot(x, results[i + 1, x], marker='.', label=f)
|
||||||
fig.tight_layout()
|
fig.tight_layout()
|
||||||
|
fig.savefig('results.jpg', dpi=fig.dpi)
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue