Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-02-05 20:35:54 -08:00
parent ec942bd23c
commit d778bf3c7a
1 changed files with 16 additions and 16 deletions
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32
train.py
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@ -244,22 +244,6 @@ def train():
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
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)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Plot images with bounding boxes
if ni == 0:
fname = 'train_batch%g.png' % i
plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
if tb_writer:
tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
# Hyperparameter burn-in
# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
# if ni <= n_burn:
@ -271,6 +255,22 @@ def train():
# x['lr'] = hyp['lr0'] * g
# x['weight_decay'] = hyp['weight_decay'] * g
# Plot images with bounding boxes
if ni == 0:
fname = 'train_batch%g.png' % i
plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
if tb_writer:
tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
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)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Run model
pred = model(imgs)