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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-01-02 11:09:10 -08:00
parent 8841c4980c
commit 0883d2fda1
1 changed files with 10 additions and 8 deletions
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utils/utils.py
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@ -739,29 +739,31 @@ def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
def kmean_anchors(path='data/coco64.txt', n=12, img_size=(320, 640)): def kmean_anchors(path='data/coco64.txt', n=9, img_size=(320, 640)):
# from utils.utils import *; _ = kmean_anchors(n=9) # from utils.utils import *; _ = kmean_anchors(n=9)
# Produces a list of target kmeans suitable for use in *.cfg files # Produces a list of target kmeans suitable for use in *.cfg files
from utils.datasets import LoadImagesAndLabels from utils.datasets import LoadImagesAndLabels
from scipy import cluster from scipy.cluster.vq import kmeans
# Get label wh # Get label wh
wh = [] wh = []
dataset = LoadImagesAndLabels(path, augment=True, rect=True, cache_labels=True) dataset = LoadImagesAndLabels(path, augment=True, rect=True, cache_labels=True)
nr = 1 if img_size[0] == img_size[1] else 10 # number augmentation repetitions
for s, l in zip(dataset.shapes, dataset.labels): for s, l in zip(dataset.shapes, dataset.labels):
wh.append(l[:, 3:5] * (s / s.max())) # image normalized to letterbox normalized wh wh.append(l[:, 3:5] * (s / s.max())) # image normalized to letterbox normalized wh
wh = np.concatenate(wh, 0).repeat(10, axis=0) # augment 10x 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 *= np.random.uniform(img_size[0], img_size[1], size=(wh.shape[0], 1)) # normalized to pixels (multi-scale)
# Kmeans calculation # Kmeans calculation
print('Running kmeans...') print('Running kmeans...')
k, dist = cluster.vq.kmeans(wh, n) # points, mean distance s = wh.std(0) # sigmas for whitening
k = k[np.argsort(k.prod(1))] # sort small to large k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
k = k[np.argsort(k.prod(1))] * s # sort small to large
# # Plot # # Plot
# k, d = [None] * 20, [None] * 20 # k, d = [None] * 20, [None] * 20
# for i in tqdm(range(1, 21)): # for i in tqdm(range(1, 21)):
# k[i-1], d[i-1] = cluster.vq.kmeans(wh, i) # points, mean distance # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance
# fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # fig, ax = plt.subplots(1, 2, figsize=(14, 7))
# ax = ax.ravel() # ax = ax.ravel()
# ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
@ -769,8 +771,8 @@ def kmean_anchors(path='data/coco64.txt', n=12, img_size=(320, 640)):
# Measure IoUs # Measure IoUs
iou = wh_iou(torch.Tensor(wh), torch.Tensor(k)) iou = wh_iou(torch.Tensor(wh), torch.Tensor(k))
min_iou, max_iou = iou.min(1)[0], iou.max(1)[0] min_iou, max_iou = iou.min(1)[0], iou.max(1)[0]
for x in [0.10, 0.15, 0.20, 0.25, 0.30, 0.35]: # iou thresholds for x in [0.10, 0.20, 0.30]: # iou thresholds
print('%.2f iou_thr: %.3f best possible recall, %.1f anchors > thr' % print('%.2f iou_thr: %.3f best possible recall, %.2f anchors > thr' %
(x, (max_iou > x).float().mean(), (iou > x).float().mean() * n)) # BPR (best possible recall) (x, (max_iou > x).float().mean(), (iou > x).float().mean() * n)) # BPR (best possible recall)
# Print # Print