updates

data/coco_1cls.data

@@ -1,6 +0,0 @@
-classes=80
-train=../coco/coco_1cls.txt
-valid=../coco/coco_1cls.txt
-names=data/coco.names
-backup=backup/
-eval=coco

data/coco_1img.data

@@ -1,6 +0,0 @@
-classes=80
-train=../coco/coco_1img.txt
-valid=../coco/coco_1img.txt
-names=data/coco.names
-backup=backup/
-eval=coco

train.py

@@ -20,7 +20,9 @@ def train(
         accumulate=1,
         multi_scale=False,
         freeze_backbone=False,
-        num_workers=4
+        num_workers=4,
+        transfer=False  # Transfer learning (train only YOLO layers)
+
 ):
     weights = 'weights' + os.sep
     latest = weights + 'latest.pt'
@@ -46,14 +48,26 @@ def train(
     cutoff = -1  # backbone reaches to cutoff layer
     start_epoch = 0
     best_loss = float('inf')
+    yl = get_yolo_layers(model)  # yolo layers
+    nf = int(model.module_defs[yl[0] - 1]['filters'])  # yolo layer size (i.e. 255)
+
     if resume:  # Load previously saved PyTorch model
-        checkpoint = torch.load(latest, map_location=device)  # load checkpoint
+        if transfer:  # Transfer learning
-        model.load_state_dict(checkpoint['model'])
+            chkpt = torch.load(weights + 'yolov3-tiny.pt', map_location=device)
-        start_epoch = checkpoint['epoch'] + 1
+            model.load_state_dict(
-        if checkpoint['optimizer'] is not None:
+                {k: v for k, v in chkpt['model'].items() if (int(k.split('.')[1]) + 1) not in yl}, strict=False)
-            optimizer.load_state_dict(checkpoint['optimizer'])
+            for (name, p) in model.named_parameters():
-            best_loss = checkpoint['best_loss']
+                p.requires_grad = True if p.shape[0] == nf else False
-        del checkpoint
+
+        else:  # resume from latest.pt
+            chkpt = torch.load(latest, map_location=device)  # load checkpoint
+            model.load_state_dict(chkpt['model'])
+
+        start_epoch = chkpt['epoch'] + 1
+        if chkpt['optimizer'] is not None:
+            optimizer.load_state_dict(chkpt['optimizer'])
+            best_loss = chkpt['best_loss']
+        del chkpt
 
     else:  # Initialize model with backbone (optional)
         if cfg.endswith('yolov3.cfg'):
@@ -61,10 +75,6 @@ def train(
         elif cfg.endswith('yolov3-tiny.cfg'):
             cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
 
-    # Transfer learning (train only YOLO layers)
-    # for (name, p) in model.named_parameters():
-    #     p.requires_grad = True if p.shape[0] == 255 else False
-
     # Set scheduler (reduce lr at epoch 250)
     scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[250], gamma=0.1, last_epoch=start_epoch - 1)
 
@@ -174,22 +184,22 @@ def train(
         save = True
         if save:
             # Save latest checkpoint
-            checkpoint = {'epoch': epoch,
+            chkpt = {'epoch': epoch,
                      'best_loss': best_loss,
                      'model': model.module.state_dict() if type(
                          model) is nn.parallel.DistributedDataParallel else model.state_dict(),
                      'optimizer': optimizer.state_dict()}
-            torch.save(checkpoint, latest)
+            torch.save(chkpt, latest)
 
             # Save best checkpoint
             if best_loss == mloss['total']:
-                torch.save(checkpoint, best)
+                torch.save(chkpt, best)
 
-            # Save backup weights every 10 epochs (optional)
+            # Save backup every 10 epochs (optional)
             if epoch > 0 and epoch % 10 == 0:
-                torch.save(checkpoint, weights + 'backup%g.pt' % epoch)
+                torch.save(chkpt, weights + 'backup%g.pt' % epoch)
 
-            del checkpoint
+            del chkpt
 
         # Calculate mAP
         with torch.no_grad():
@@ -210,6 +220,7 @@ if __name__ == '__main__':
     parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
     parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
     parser.add_argument('--resume', action='store_true', help='resume training flag')
+    parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
     parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
     parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
     parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
@@ -224,7 +235,8 @@ if __name__ == '__main__':
         opt.cfg,
         opt.data_cfg,
         img_size=opt.img_size,
-        resume=opt.resume,
+        resume=True or opt.resume or opt.transfer,
+        transfer=True or opt.transfer,
         epochs=opt.epochs,
         batch_size=opt.batch_size,
         accumulate=opt.accumulate,

utils/gcp.sh

@@ -45,11 +45,14 @@ wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/b
 
 # Debug/Development
 sudo rm -rf yolov3
-# git clone https://github.com/ultralytics/yolov3  # master
+git clone https://github.com/ultralytics/yolov3  # master
-git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3  # branch
+# git clone -b hyperparameter_search --depth 1 https://github.com/ultralytics/yolov3 hyperparameter_search  # branch
 cp -r weights yolov3
 cp -r cocoapi/PythonAPI/pycocotools yolov3
 cd yolov3
 
-#git pull https://github.com/ultralytics/yolov3 map_update  # branch
+git pull https://github.com/ultralytics/yolov3 #hyperparameter_search  # branch
-python3 test.py --img-size 320
+python3 train.py --data-cfg data/coco_1cls.data
+python3 train.py --data-cfg data/coco_1img.data
+
+

utils/utils.py

@@ -295,12 +295,11 @@ def build_targets(model, targets):
 
     txy, twh, tcls, indices = [], [], [], []
     for i, layer in enumerate(get_yolo_layers(model)):
-        nG = model.module_list[layer][0].nG  # grid size
+        layer = model.module_list[layer][0]
-        anchor_vec = model.module_list[layer][0].anchor_vec
 
         # iou of targets-anchors
-        gwh = targets[:, 4:6] * nG
+        gwh = targets[:, 4:6] * layer.nG
-        iou = [wh_iou(x, gwh) for x in anchor_vec]
+        iou = [wh_iou(x, gwh) for x in layer.anchor_vec]
         iou, a = torch.stack(iou, 0).max(0)  # best iou and anchor
 
         # reject below threshold ious (OPTIONAL, increases P, lowers R)
@@ -313,7 +312,7 @@ def build_targets(model, targets):
 
         # Indices
         b, c = t[:, :2].long().t()  # target image, class
-        gxy = t[:, 2:4] * nG
+        gxy = t[:, 2:4] * layer.nG
         gi, gj = gxy.long().t()  # grid_i, grid_j
         indices.append((b, a, gj, gi))
 
@@ -321,11 +320,12 @@ def build_targets(model, targets):
         txy.append(gxy - gxy.floor())
 
         # Width and height
-        twh.append(torch.log(gwh / anchor_vec[a]))  # yolo method
+        twh.append(torch.log(gwh / layer.anchor_vec[a]))  # yolo method
         # twh.append(torch.sqrt(gwh / anchor_vec[a]) / 2)  # power method
 
         # Class
         tcls.append(c)
+        assert c.max() <= layer.nC, 'Target classes exceed model classes'
 
     return txy, twh, tcls, indices