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

multi-scale update

This commit is contained in:
Glenn Jocher 2020-04-12 18:22:54 -07:00
parent 46726dad13
commit 1038b0d269
1 changed files with 16 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
train.py
View File

@ -56,23 +56,27 @@ if hyp['fl_gamma']:
def train(): def train():
cfg = opt.cfg cfg = opt.cfg
data = opt.data data = opt.data
img_size, img_size_test = opt.img_size if len(opt.img_size) == 2 else opt.img_size * 2 # train, test sizes
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64 accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights weights = opt.weights # initial training weights
imgsz_min, imgsz_max, img_size_test = opt.img_size # img sizes (min, max, test)
# Initialize # Image Sizes
gs = 32 # (pixels) grid size gs = 64 # (pixels) grid size
assert math.fmod(img_size, gs) == 0, '--img-size must be a %g-multiple' % gs assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
init_seeds() opt.multi_scale |= imgsz_min != imgsz_max # multi if different (min, max)
if opt.multi_scale: if opt.multi_scale:
img_sz_min = round(img_size / gs / 1.5) + 1 if imgsz_min == imgsz_max:
img_sz_max = round(img_size / gs * 1.5) imgsz_min //= 1.5
img_size = img_sz_max * gs # initiate with maximum multi_scale size imgsz_max //= 0.667
print('Using multi-scale %g - %g' % (img_sz_min * gs, img_size)) grid_min, grid_max = imgsz_min // gs, imgsz_max // gs
imgsz_max = grid_max * gs # initialize with maximum multi_scale size
print('Using multi-scale %g - %g' % (grid_min * gs, imgsz_max))
img_size = imgsz_max
# Configure run # Configure run
init_seeds()
data_dict = parse_data_cfg(data) data_dict = parse_data_cfg(data)
train_path = data_dict['train'] train_path = data_dict['train']
test_path = data_dict['valid'] test_path = data_dict['valid']
@ -248,7 +252,7 @@ def train():
# Multi-Scale training # Multi-Scale training
if opt.multi_scale: if opt.multi_scale:
if ni / accumulate % 1 == 0: #  adjust img_size (67% - 150%) every 1 batch if ni / accumulate % 1 == 0: #  adjust img_size (67% - 150%) every 1 batch
img_size = random.randrange(img_sz_min, img_sz_max + 1) * gs img_size = random.randrange(grid_min, grid_max + 1) * gs
sf = img_size / max(imgs.shape[2:]) # scale factor sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1: if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to 32-multiple) ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to 32-multiple)
@ -387,7 +391,7 @@ if __name__ == '__main__':
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path') parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
parser.add_argument('--multi-scale', action='store_true', help='adjust (67%% - 150%%) img_size every 10 batches') parser.add_argument('--multi-scale', action='store_true', help='adjust (67%% - 150%%) img_size every 10 batches')
parser.add_argument('--img-size', nargs='+', type=int, default=[416], help='train and test image-sizes') parser.add_argument('--img-size', nargs='+', type=int, default=[512], help='[min_train, max-train, test] img sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', action='store_true', help='resume training from last.pt') parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
@ -403,6 +407,7 @@ if __name__ == '__main__':
opt = parser.parse_args() opt = parser.parse_args()
opt.weights = last if opt.resume else opt.weights opt.weights = last if opt.resume else opt.weights
print(opt) print(opt)
opt.img_size.extend([opt.img_size[-1]] * (3 - len(opt.img_size))) # extend to 3 sizes (min, max, test)
device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size) device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
if device.type == 'cpu': if device.type == 'cpu':
mixed_precision = False mixed_precision = False