Add train.py ``--img-size` floor (#4099)
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train.py
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train.py
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@ -207,7 +207,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# Image sizes
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# Image sizes
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gs = max(int(model.stride.max()), 32) # grid size (max stride)
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gs = max(int(model.stride.max()), 32) # grid size (max stride)
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nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
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nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
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imgsz = check_img_size(opt.imgsz, gs) # verify imgsz is gs-multiple
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imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
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# DP mode
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# DP mode
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if cuda and RANK == -1 and torch.cuda.device_count() > 1:
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if cuda and RANK == -1 and torch.cuda.device_count() > 1:
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@ -181,11 +181,11 @@ def check_requirements(requirements='requirements.txt', exclude=()):
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print(emojis(s)) # emoji-safe
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print(emojis(s)) # emoji-safe
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def check_img_size(img_size, s=32):
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def check_img_size(img_size, s=32, floor=0):
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# Verify img_size is a multiple of stride s
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# Verify img_size is a multiple of stride s
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new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
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new_size = max(make_divisible(img_size, int(s)), floor) # ceil gs-multiple
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if new_size != img_size:
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if new_size != img_size:
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print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
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print(f'WARNING: --img-size {img_size} must be multiple of max stride {s}, updating to {new_size}')
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return new_size
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return new_size
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