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

updates

This commit is contained in:
Glenn Jocher 2019-07-10 19:48:29 +02:00
parent 88a2c71a9f
commit a9e42a16f1
2 changed files with 9 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
train.py
View File

@ -31,8 +31,8 @@ def train(
data_cfg,
img_size=416,
epochs=100, # 500200 batches at bs 16, 117263 images = 273 epochs
batch_size=8,
accumulate=8, # effective bs = batch_size * accumulate = 8 * 8 = 64
batch_size=16,
accumulate=4, # effective bs = batch_size * accumulate = 8 * 8 = 64
freeze_backbone=False,
):
init_seeds()
@ -302,8 +302,8 @@ def print_mutation(hyp, results):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=8, help='batch size')
parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate before optimizing')
parser.add_argument('--batch-size', type=int, default=16, help='batch size')
parser.add_argument('--accumulate', type=int, default=4, help='number of batches to accumulate before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path')
parser.add_argument('--single-scale', action='store_true', help='train at fixed size (no multi-scale)')

10
utils/utils.py
View File

@ -281,7 +281,7 @@ def compute_loss(p, targets, model, giou_loss=True): # predictions, targets, mo
MSE = nn.MSELoss()
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
# CE = nn.CrossEntropyLoss() # (weight=model.class_weights)
CE = nn.CrossEntropyLoss() # (weight=model.class_weights)
# Compute losses
bs = p[0].shape[0] # batch size
@ -304,10 +304,10 @@ def compute_loss(p, targets, model, giou_loss=True): # predictions, targets, mo
lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy loss
lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss
tclsm = torch.zeros_like(pi[..., 5:])
tclsm[range(len(b)), tcls[i]] = 1.0
lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE)
# lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # cls loss (CE)
# tclsm = torch.zeros_like(pi[..., 5:])
# tclsm[range(len(b)), tcls[i]] = 1.0
# lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE)
lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # cls loss (CE)
# Append targets to text file
# with open('targets.txt', 'a') as file: