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

updates 

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher 2019-09-02 11:59:13 +02:00
parent 71938356c8
commit 8f913ba82a
1 changed files with 5 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
train.py
View File

@ -23,7 +23,7 @@ hyp = {'giou': 1.582, # giou loss gain
'obj': 21.35, # obj loss gain (*=80 for uBCE with 80 classes) 'obj': 21.35, # obj loss gain (*=80 for uBCE with 80 classes)
'obj_pw': 3.941, # obj BCELoss positive_weight 'obj_pw': 3.941, # obj BCELoss positive_weight
'iou_t': 0.2635, # iou training threshold 'iou_t': 0.2635, # iou training threshold
'lr0': 0.002324, # initial learning rate 'lr0': 0.002324, # initial learning rate (SGD=1E-3, Adam=9E-5)
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
'momentum': 0.97, # SGD momentum 'momentum': 0.97, # SGD momentum
'weight_decay': 0.0004569, # optimizer weight decay 'weight_decay': 0.0004569, # optimizer weight decay
@ -80,7 +80,7 @@ def train():
# optimizer = optim.Adam(pg0, lr=hyp['lr0']) # optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1) # optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) # optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
del pg0, pg1 del pg0, pg1
@ -259,8 +259,8 @@ def train():
# Compute loss # Compute loss
loss, loss_items = compute_loss(pred, targets, model) loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss): if not torch.isfinite(loss):
print('WARNING: non-finite loss, skipping batch ', loss_items) print('WARNING: non-finite loss, ending training ', loss_items)
continue return results
# Scale loss by nominal batch_size of 64 # Scale loss by nominal batch_size of 64
loss *= batch_size / 64 loss *= batch_size / 64
@ -378,6 +378,7 @@ if __name__ == '__main__':
parser.add_argument('--weights', type=str, default='', help='initial weights') # i.e. weights/darknet.53.conv.74 parser.add_argument('--weights', type=str, default='', help='initial weights') # i.e. weights/darknet.53.conv.74
parser.add_argument('--arc', type=str, default='defaultpw', help='yolo architecture') # defaultpw, uCE, uBCE parser.add_argument('--arc', type=str, default='defaultpw', help='yolo architecture') # defaultpw, uCE, uBCE
parser.add_argument('--prebias', action='store_true', help='transfer-learn yolo biases prior to training') parser.add_argument('--prebias', action='store_true', help='transfer-learn yolo biases prior to training')
parser.add_argument('--var', type=float, help='debug variable')
opt = parser.parse_args() opt = parser.parse_args()
opt.weights = 'weights/last.pt' if opt.resume else opt.weights opt.weights = 'weights/last.pt' if opt.resume else opt.weights
print(opt) print(opt)