Remove deprecated --arc architecture options, implement --arc default for all cases

models.py

@@ -7,7 +7,7 @@ from utils.utils import *
 ONNX_EXPORT = False
 
 
-def create_modules(module_defs, img_size, arc):
+def create_modules(module_defs, img_size):
     # Constructs module list of layer blocks from module configuration in module_defs
 
     hyperparams = module_defs.pop(0)
@@ -250,11 +250,11 @@ class YOLOLayer(nn.Module):
 class Darknet(nn.Module):
     # YOLOv3 object detection model
 
-    def __init__(self, cfg, img_size=(416, 416), arc='default'):
+    def __init__(self, cfg, img_size=(416, 416)):
         super(Darknet, self).__init__()
 
         self.module_defs = parse_model_cfg(cfg)
-        self.module_list, self.routs = create_modules(self.module_defs, img_size, arc)
+        self.module_list, self.routs = create_modules(self.module_defs, img_size)
         self.yolo_layers = get_yolo_layers(self)
 
         # Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346

train.py

@@ -32,7 +32,7 @@ hyp = {'giou': 3.54,  # giou loss gain
        'lrf': -4.,  # final LambdaLR learning rate = lr0 * (10 ** lrf)
        'momentum': 0.937,  # SGD momentum
        'weight_decay': 0.000484,  # optimizer weight decay
-       'fl_gamma': 1.5,  # focal loss gamma
+       'fl_gamma': 0.0,  # focal loss gamma (efficientDet default is gamma=1.5)
        'hsv_h': 0.0138,  # image HSV-Hue augmentation (fraction)
        'hsv_s': 0.678,  # image HSV-Saturation augmentation (fraction)
        'hsv_v': 0.36,  # image HSV-Value augmentation (fraction)
@@ -77,7 +77,7 @@ def train():
         os.remove(f)
 
     # Initialize model
-    model = Darknet(cfg, arc=opt.arc).to(device)
+    model = Darknet(cfg).to(device)
 
     # Optimizer
     pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
@@ -192,7 +192,6 @@ def train():
 
     # Model parameters
     model.nc = nc  # attach number of classes to model
-    model.arc = opt.arc  # attach yolo architecture
     model.hyp = hyp  # attach hyperparameters to model
     model.gr = 0.0  # giou loss ratio (obj_loss = 1.0 or giou)
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
@@ -406,7 +405,6 @@ if __name__ == '__main__':
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
     parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
     parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='initial weights path')
-    parser.add_argument('--arc', type=str, default='default', help='yolo architecture')  # default, uCE, uBCE
     parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
     parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
     parser.add_argument('--adam', action='store_true', help='use adam optimizer')

utils/utils.py

@@ -377,7 +377,6 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     lcls, lbox, lobj = ft([0]), ft([0]), ft([0])
     tcls, tbox, indices, anchor_vec = build_targets(model, targets)
     h = model.hyp  # hyperparameters
-    arc = model.arc  # architecture
     red = 'mean'  # Loss reduction (sum or mean)
 
     # Define criteria
@@ -388,8 +387,9 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     cp, cn = smooth_BCE(eps=0.0)
 
     # focal loss
-    if 'F' in arc:
-        BCEcls, BCEobj = FocalLoss(BCEcls, h['fl_gamma']), FocalLoss(BCEobj, h['fl_gamma'])
+    g = h['fl_gamma']  # focal loss gamma
+    if g > 0:
+        BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
     # Compute losses
     np, ng = 0, 0  # number grid points, targets