revert test module to confuse users...

.github/workflows/ci-testing.yml

@@ -71,9 +71,9 @@ jobs:
         # detect custom
         python detect.py --weights runs/exp0/weights/last.pt --device $di
         # test official
-        python eval.py --weights weights/${{ matrix.yolo5-model }}.pt --device $di --batch-size 1
+        python test.py --weights weights/${{ matrix.yolo5-model }}.pt --device $di --batch-size 1
         # test custom
-        python eval.py --weights runs/exp0/weights/last.pt --device $di  --batch-size 1
+        python test.py --weights runs/exp0/weights/last.pt --device $di  --batch-size 1
         # inspect
         python models/yolo.py --cfg models/${{ matrix.yolo5-model }}.yaml
         # export

README.md

@@ -27,8 +27,8 @@ This repository represents Ultralytics open-source research into future object d
 
 
 ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.  
-** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python eval.py --data coco.yaml --img 736 --conf 0.001`  
-** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img.  Reproduce by `python eval.py --data coco.yaml --img 640 --conf 0.1`  
+** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 736 --conf 0.001`  
+** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img.  Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1`  
 ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). 
 
 

eval.py → test.py

@@ -233,7 +233,7 @@ def test(data,
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(prog='eval.py')
+    parser = argparse.ArgumentParser(prog='test.py')
     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
     parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')

train.py

@@ -7,7 +7,7 @@ import torch.optim.lr_scheduler as lr_scheduler
 import torch.utils.data
 from torch.utils.tensorboard import SummaryWriter
 
-import eval  # import eval.py to get mAP after each epoch
+import test  # import test.py to get mAP after each epoch
 from models.yolo import Model
 from utils import google_utils
 from utils.datasets import *
@@ -291,7 +291,7 @@ def train(hyp):
         ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride'])
         final_epoch = epoch + 1 == epochs
         if not opt.notest or final_epoch:  # Calculate mAP
-            results, maps, times = eval.test(opt.data,
+            results, maps, times = test.test(opt.data,
                                              batch_size=batch_size,
                                              imgsz=imgsz_test,
                                              save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),

tutorial.ipynb

@@ -236,7 +236,7 @@
       },
       "source": [
         "# Run YOLOv5x on COCO val2017\n",
-        "!python eval.py --weights yolov5x.pt --data coco.yaml --img 672"
+        "!python test.py --weights yolov5x.pt --data coco.yaml --img 672"
       ],
       "execution_count": null,
       "outputs": [
@@ -319,7 +319,7 @@
       },
       "source": [
         "# Run YOLOv5s on COCO test-dev2017 with argument --task test\n",
-        "!python eval.py --weights yolov5s.pt --data ./data/coco.yaml --task test"
+        "!python test.py --weights yolov5s.pt --data ./data/coco.yaml --task test"
       ],
       "execution_count": null,
       "outputs": []
@@ -717,7 +717,7 @@
         "for x in best*\n",
         "do\n",
         "  gsutil cp gs://*/*/*/$x.pt .\n",
-        "  python eval.py --weights $x.pt --data coco.yaml --img 672\n",
+        "  python test.py --weights $x.pt --data coco.yaml --img 672\n",
         "done"
       ],
       "execution_count": null,
@@ -744,8 +744,8 @@
         "  do\n",
         "    python detect.py --weights $x.pt --device $di  # detect official\n",
         "    python detect.py --weights runs/exp0/weights/last.pt --device $di  # detect custom\n",
-        "    python eval.py --weights $x.pt --device $di # test official\n",
-        "    python eval.py --weights runs/exp0/weights/last.pt --device $di # test custom\n",
+        "    python test.py --weights $x.pt --device $di # test official\n",
+        "    python test.py --weights runs/exp0/weights/last.pt --device $di # test custom\n",
         "  done\n",
         "  python models/yolo.py --cfg $x.yaml # inspect\n",
         "  python models/export.py --weights $x.pt --img 640 --batch 1 # export\n",

utils/utils.py

@@ -1087,7 +1087,7 @@ def plot_targets_txt():  # from utils.utils import *; plot_targets_txt()
 
 
 def plot_study_txt(f='study.txt', x=None):  # from utils.utils import *; plot_study_txt()
-    # Plot study.txt generated by eval.py
+    # Plot study.txt generated by test.py
     fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
     ax = ax.ravel()