updates

README.md

@@ -50,7 +50,7 @@ https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw
 
 **Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
 
-**Resume Training:** `python3 train.py --resume` to resume training from `weights/latest.pt`.
+**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
 
 Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day)**.
 
@@ -136,8 +136,9 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
 
 # mAP
 
-- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
+- `test.py --weights weights/yolov3.weights` to test official YOLOv3 weights.
-- Use `test.py --weights weights/latest.pt` to test the latest training results.
+- `test.py --weights weights/last.pt` to test most recent checkpoint.
+- `test.py --weights weights/best.pt` to test best checkpoint.
 - Compare to darknet published results https://arxiv.org/abs/1804.02767.
 
 <!---

train.py

@@ -45,7 +45,7 @@ def train(cfg,
     # Initialize
     init_seeds()
     weights = 'weights' + os.sep
-    latest = weights + 'latest.pt'
+    last = weights + 'last.pt'
     best = weights + 'best.pt'
     device = torch_utils.select_device()
     multi_scale = opt.multi_scale
@@ -79,10 +79,10 @@ def train(cfg,
             for p in model.parameters():
                 p.requires_grad = True if p.shape[0] == nf else False
 
-        else:  # resume from latest.pt
+        else:  # resume from last.pt
             if opt.bucket:
-                os.system('gsutil cp gs://%s/latest.pt %s' % (opt.bucket, latest))  # download from bucket
+                os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last))  # download from bucket
-            chkpt = torch.load(latest, map_location=device)  # load checkpoint
+            chkpt = torch.load(last, map_location=device)  # load checkpoint
             model.load_state_dict(chkpt['model'])
 
         if chkpt['optimizer'] is not None:
@@ -273,10 +273,10 @@ def train(cfg,
                              model) is nn.parallel.DistributedDataParallel else model.state_dict(),
                          'optimizer': optimizer.state_dict()}
 
-            # Save latest checkpoint
+            # Save last checkpoint
-            torch.save(chkpt, latest)
+            torch.save(chkpt, last)
             if opt.bucket:
-                os.system('gsutil cp %s gs://%s' % (latest, opt.bucket))  # upload to bucket
+                os.system('gsutil cp %s gs://%s' % (last, opt.bucket))  # upload to bucket
 
             # Save best checkpoint
             if best_fitness == fitness:

utils/datasets.py

@@ -181,9 +181,13 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                 s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
                 np.savetxt(sp, s, fmt='%g')
 
-            with open(sp, 'r') as f:  # read existing shapefile
+            try:
-                s = np.array([x.split() for x in f.read().splitlines()], dtype=np.float64)
+                with open(sp, 'r') as f:  # read existing shapefile
-                assert len(s) == n, 'Shapefile error. Please delete %s and rerun' % sp  # TODO: auto-delete shapefile
+                    s = np.array([x.split() for x in f.read().splitlines()], dtype=np.float64)
+                    assert len(s) == n, 'Shapefile out of sync'
+            except:
+                os.remove(sp)
+                print('Shapefile deleted: %s. Please rerun again.' % sp)
 
             # Sort by aspect ratio
             ar = s[:, 1] / s[:, 0]  # aspect ratio

utils/gcp.sh

@@ -35,12 +35,12 @@ git pull https://github.com/ultralytics/yolov3 test  # branch
 # Test Darknet training
 python3 test.py --weights ../darknet/backup/yolov3.backup
 
-# Copy latest.pt TO bucket
+# Copy last.pt TO bucket
-gsutil cp yolov3/weights/latest1gpu.pt gs://ultralytics
+gsutil cp yolov3/weights/last1gpu.pt gs://ultralytics
 
-# Copy latest.pt FROM bucket
+# Copy last.pt FROM bucket
-gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
+gsutil cp gs://ultralytics/last.pt yolov3/weights/last.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/last_v1_0.pt -O weights/last_v1_0.pt
 wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/best_v1_0.pt
 
 # Reproduce tutorials
@@ -94,7 +94,7 @@ python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/ba
 
 # Debug/Development
 python3 train.py --data data/coco.data --img-size 320 --single-scale --batch-size 64 --accumulate 1 --epochs 1 --evolve --giou
-python3 test.py --weights weights/latest.pt --cfg cfg/yolov3-spp.cfg --img-size 320
+python3 test.py --weights weights/last.pt --cfg cfg/yolov3-spp.cfg --img-size 320
 
 gsutil cp evolve.txt gs://ultralytics
 sudo shutdown