updates
This commit is contained in:
Glenn Jocher
parent
c0095c2bc9
commit
eca1a25dcd
2
train.py
2
train.py
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@ -446,7 +446,7 @@ if __name__ == '__main__':
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mixed_precision = False
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# scale hyp['obj'] by img_size (evolved at 320)
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hyp['obj'] *= opt.img_size / 320.
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# hyp['obj'] *= opt.img_size / 320.
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tb_writer = None
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if not opt.evolve: # Train normally
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@ -367,7 +367,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
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tcls, tbox, indices, anchor_vec = build_targets(model, targets)
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h = model.hyp # hyperparameters
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arc = model.arc # # (default, uCE, uBCE) detection architectures
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red = 'mean' # Loss reduction (sum or mean)
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red = 'sum' # Loss reduction (sum or mean)
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# Define criteria
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]), reduction=red)
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@ -399,7 +399,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
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pbox = torch.cat((pxy, pwh), 1) # predicted box
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giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
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lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean() # giou loss
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tobj[b, a, gj, gi] = 1.0 # giou.detach().type(tobj.dtype)
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tobj[b, a, gj, gi] = giou.detach().type(tobj.dtype)
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if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes)
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t = torch.zeros_like(ps[:, 5:]) # targets
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@ -739,11 +739,28 @@ def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
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shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
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def kmean_anchors(path='data/coco64.txt', n=9, img_size=(320, 640)):
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# from utils.utils import *; _ = kmean_anchors(n=9)
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def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)):
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# from utils.utils import *; _ = kmean_anchors()
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# Produces a list of target kmeans suitable for use in *.cfg files
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from utils.datasets import LoadImagesAndLabels
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from scipy.cluster.vq import kmeans
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thr = 0.20 # IoU threshold
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def print_results(thr, wh, k):
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k = k[np.argsort(k.prod(1))] # sort small to large
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iou = wh_iou(torch.Tensor(wh), torch.Tensor(k))
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max_iou, min_iou = iou.max(1)[0], iou.min(1)[0]
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bpr, aat = (max_iou > thr).float().mean(), (iou > thr).float().mean() * n # best possible recall, anch > thr
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print('%.2f iou_thr: %.3f best possible recall, %.2f anchors > thr' % (thr, bpr, aat))
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print('kmeans anchors (n=%g, img_size=%s, IoU=%.3f/%.3f/%.3f-min/mean/best): ' %
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(n, img_size, min_iou.mean(), iou.mean(), max_iou.mean()), end='')
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for i, x in enumerate(k):
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print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
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return k
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def fitness(thr, wh, k): # mutation fitness
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iou = wh_iou(wh, torch.Tensor(k)).max(1)[0] # max iou
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bpr = (iou > thr).float().mean() # best possible recall
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return iou.mean() * 0.80 + bpr * 0.20 # weighted combination
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# Get label wh
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wh = []
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@ -754,11 +771,18 @@ def kmean_anchors(path='data/coco64.txt', n=9, img_size=(320, 640)):
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wh = np.concatenate(wh, 0).repeat(nr, axis=0) # augment 10x
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wh *= np.random.uniform(img_size[0], img_size[1], size=(wh.shape[0], 1)) # normalized to pixels (multi-scale)
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# Kmeans calculation
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print('Running kmeans on %g points...' % len(wh))
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s = wh.std(0) # sigmas for whitening
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k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
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k = k[np.argsort(k.prod(1))] * s # sort small to large
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# Darknet yolov3.cfg anchors
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if n == 9:
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k = np.array([[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]])
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k = print_results(thr, wh, k)
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else:
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# Kmeans calculation
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from scipy.cluster.vq import kmeans
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print('Running kmeans on %g points...' % len(wh))
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s = wh.std(0) # sigmas for whitening
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k, dist = kmeans(wh / s, n, iter=20) # points, mean distance
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k *= s
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k = print_results(thr, wh, k)
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# # Plot
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# k, d = [None] * 20, [None] * 20
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@ -768,21 +792,17 @@ def kmean_anchors(path='data/coco64.txt', n=9, img_size=(320, 640)):
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# ax = ax.ravel()
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# ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
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# Measure IoUs
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iou = wh_iou(torch.Tensor(wh), torch.Tensor(k))
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min_iou, max_iou = iou.min(1)[0], iou.max(1)[0]
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for x in [0.10, 0.20, 0.30]: # iou thresholds
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print('%.2f iou_thr: %.3f best possible recall, %.2f anchors > thr' %
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(x, (max_iou > x).float().mean(), (iou > x).float().mean() * n)) # BPR (best possible recall)
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# Evolve
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wh = torch.Tensor(wh)
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f, ng = fitness(thr, wh, k), 1000 # fitness, generations
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for _ in tqdm(range(ng), desc='Evolving anchors'):
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kg = (k.copy() * (1 + np.random.random() * np.random.randn(*k.shape) * 0.20)).clip(min=2.0)
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fg = fitness(thr, wh, kg)
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if fg > f:
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f, k = fg, kg.copy()
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print(fg, list(k.round().reshape(-1)))
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k = print_results(thr, wh, k)
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# Print
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print('kmeans anchors (n=%g, img_size=%s, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %
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(n, img_size, min_iou.mean(), iou.mean(), max_iou.mean()), end='')
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for i, x in enumerate(k):
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print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
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# Plot
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# plt.hist(biou.numpy().ravel(), 100)
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return k
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