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car-detection-bayes
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parameterize grid size

This commit is contained in:
Glenn Jocher 2020-04-08 10:14:33 -07:00
parent 9f41b7601a
commit 97780cfdb4
1 changed files with 8 additions and 6 deletions
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14
train.py
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@ -63,12 +63,14 @@ def train():
weights = opt.weights # initial training weights
# Initialize
gs = 32 # (pixels) grid size
assert math.fmod(img_size, gs) == 0, '--img-size must be a %g-multiple' % gs
init_seeds()
if opt.multi_scale:
img_sz_min = round(img_size / 32 / 1.5)
img_sz_max = round(img_size / 32 * 1.5)
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
img_sz_min = round(img_size / gs / 1.5) + 1
img_sz_max = round(img_size / gs * 1.5)
img_size = img_sz_max * gs # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * gs, img_size))
# Configure run
data_dict = parse_data_cfg(data)
@ -246,10 +248,10 @@ def train():
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 1 == 0: #  adjust img_size (67% - 150%) every 1 batch
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
img_size = random.randrange(img_sz_min, img_sz_max + 1) * gs
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / 32.) * 32 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)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Run model