Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-06-13 18:13:30 +02:00
parent 19d2232665
commit 8f609246db
1 changed files with 4 additions and 3 deletions
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7
train.py
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@ -74,8 +74,9 @@ def train(
device = torch_utils.select_device()
torch.backends.cudnn.benchmark = True # possibly unsuitable for multiscale
img_size_test = img_size # image size for testing
multi_scale = not opt.single_scale
if opt.multi_scale:
if multi_scale:
img_size_min = round(img_size / 32 / 1.5)
img_size_max = round(img_size / 32 * 1.5)
img_size = img_size_max * 32 # initiate with maximum multi_scale size
@ -203,7 +204,7 @@ def train(
targets = targets.to(device)
# Multi-Scale training
if opt.multi_scale:
if multi_scale:
if (i + 1 + nb * epoch) % 10 == 0: #  adjust (67% - 150%) every 10 batches
img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32
print('img_size = %g' % img_size)
@ -310,7 +311,7 @@ if __name__ == '__main__':
parser.add_argument('--accumulate', type=int, default=8, 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('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
parser.add_argument('--single-scale', action='store_true', help='train at fixed size (no multi-scale)')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--transfer', action='store_true', help='transfer learning flag')