From 785bfec286b7367492097d4ce3a99eb27cc7c504 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 2 Feb 2020 09:19:44 -0800 Subject: [PATCH] updates --- utils/utils.py | 32 +++++++++++++++++++------------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/utils/utils.py b/utils/utils.py index f78534da..4bf0080a 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -751,22 +751,22 @@ def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)): from utils.datasets import LoadImagesAndLabels thr = 0.20 # IoU threshold - def print_results(thr, wh, k): + def print_results(wh, k): k = k[np.argsort(k.prod(1))] # sort small to large iou = wh_iou(torch.Tensor(wh), torch.Tensor(k)) - max_iou, min_iou = iou.max(1)[0], iou.min(1)[0] + max_iou = iou.max(1)[0] bpr, aat = (max_iou > thr).float().mean(), (iou > thr).float().mean() * n # best possible recall, anch > thr print('%.2f iou_thr: %.3f best possible recall, %.2f anchors > thr' % (thr, bpr, aat)) - print('kmeans anchors (n=%g, img_size=%s, IoU=%.3f/%.3f/%.3f-min/mean/best): ' % - (n, img_size, min_iou.mean(), iou.mean(), max_iou.mean()), end='') + print('n=%g, img_size=%s, IoU_all=%.3f/%.3f-mean/best, IoU>thr=%.3f-mean: ' % + (n, img_size, iou.mean(), max_iou.mean(), iou[iou > thr].mean()), end='') for i, x in enumerate(k): print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg return k - def fitness(thr, wh, k): # mutation fitness - iou = wh_iou(wh, torch.Tensor(k)).max(1)[0] # max iou - bpr = (iou > thr).float().mean() # best possible recall - return iou.mean() * bpr # product + def fitness(wh, k): # mutation fitness + iou = wh_iou(wh, torch.Tensor(k)) # iou + max_iou = iou.max(1)[0] + return max_iou.mean() # product # Get label wh wh = [] @@ -776,6 +776,7 @@ def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)): wh.append(l[:, 3:5] * (s / s.max())) # image normalized to letterbox normalized wh wh = np.concatenate(wh, 0).repeat(nr, axis=0) # augment 10x wh *= np.random.uniform(img_size[0], img_size[1], size=(wh.shape[0], 1)) # normalized to pixels (multi-scale) + wh = wh[(wh > 2.0).all(1)] # remove below threshold boxes (< 2 pixels wh) # Darknet yolov3.cfg anchors use_darknet = False @@ -788,7 +789,7 @@ def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)): s = wh.std(0) # sigmas for whitening k, dist = kmeans(wh / s, n, iter=30) # points, mean distance k *= s - k = print_results(thr, wh, k) + k = print_results(wh, k) # # Plot # k, d = [None] * 20, [None] * 20 @@ -797,21 +798,26 @@ def kmean_anchors(path='../coco/train2017.txt', n=9, img_size=(320, 640)): # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # ax = ax.ravel() # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.tight_layout() + # fig.savefig('wh.png', dpi=200) # Evolve npr = np.random wh = torch.Tensor(wh) - f, sh, ng, mp, s = fitness(thr, wh, k), k.shape, 1000, 0.9, 0.1 # fitness, generations, mutation probability, sigma + f, sh, ng, mp, s = fitness(wh, k), k.shape, 1000, 0.9, 0.1 # fitness, generations, mutation prob, sigma for _ in tqdm(range(ng), desc='Evolving anchors'): v = np.ones(sh) while (v == 1).all(): # mutate until a change occurs (prevent duplicates) v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) # 98.6, 61.6 kg = (k.copy() * v).clip(min=2.0) - fg = fitness(thr, wh, kg) + fg = fitness(wh, kg) if fg > f: f, k = fg, kg.copy() - print_results(thr, wh, k) - k = print_results(thr, wh, k) + print_results(wh, k) + k = print_results(wh, k) return k