Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

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

This commit is contained in:
Glenn Jocher 2020-03-16 20:46:25 -07:00
parent 77c6c01970
commit 448c4a6e1f
3 changed files with 8 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
models.py
View File

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

6
train.py
View File

@ -32,7 +32,7 @@ hyp = {'giou': 3.54, # giou loss gain
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
'momentum': 0.937, # SGD momentum 'momentum': 0.937, # SGD momentum
'weight_decay': 0.000484, # optimizer weight decay '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_h': 0.0138, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction) 'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction) 'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
@ -77,7 +77,7 @@ def train():
os.remove(f) os.remove(f)
# Initialize model # Initialize model
model = Darknet(cfg, arc=opt.arc).to(device) model = Darknet(cfg).to(device)
# Optimizer # Optimizer
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
@ -192,7 +192,6 @@ def train():
# Model parameters # Model parameters
model.nc = nc # attach number of classes to model model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
model.gr = 0.0 # giou loss ratio (obj_loss = 1.0 or giou) 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 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('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') 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('--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('--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('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
parser.add_argument('--adam', action='store_true', help='use adam optimizer') parser.add_argument('--adam', action='store_true', help='use adam optimizer')

6
utils/utils.py
View File

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