def eval(dataloader, head_detector):
"""
Given the dataloader of the test split compute the
average corLoc of the dataset using the head detector
model given as the argument to the function.
"""
test_img_num = 0
test_corrLoc = 0.0
for _, (img, bbox_, scale) in enumerate(dataloader):
scale = at.scalar(scale)
img, bbox = img.cuda().float(), bbox_.cuda()
img, bbox = Variable(img), Variable(bbox)
pred_bboxes_, _ = head_detector.predict(img, scale, mode='evaluate')
gt_bboxs = at.tonumpy(bbox_)[0]
pred_bboxes_ = at.tonumpy(pred_bboxes_)
if pred_bboxes_.shape[0] == 0:
test_img_num += 1
continue
else:
ious = bbox_iou(pred_bboxes_, gt_bboxs)
max_ious = ious.max(axis=1)
corr_preds = np.where(max_ious >= 0.5)[0]
num_boxs = gt_bboxs.shape[0]
num_corr_preds = len(corr_preds)
test_corrLoc += num_corr_preds / num_boxs
test_img_num += 1
return test_corrLoc / test_img_num
def _calc_ious(self, anchor, bbox, inside_index):
ious = bbox_iou(anchor, bbox)
argmax_ious = ious.argmax(axis=1)
max_ious = ious[np.arange(len(inside_index)), argmax_ious]
gt_argmax_ious = ious.argmax(axis=0)
gt_max_ious = ious[gt_argmax_ious, np.arange(ious.shape[1])]
gt_argmax_ious = np.where(ious == gt_max_ious)[0]
return argmax_ious, max_ious, gt_argmax_ious
def eval(dataloader, head_detector):
test_img_num = 0
test_corrLoc = 0.0
for _, (img, bbox_, scale) in enumerate(dataloader):
scale = at.scalar(scale)
img, bbox = img.cuda().float(), bbox_.cuda()
img, bbox = Variable(img), Variable(bbox)
pred_bboxes_, _ = head_detector.predict(img, scale, mode='evaluate')
gt_bboxs = at.tonumpy(bbox_)[0]
pred_bboxes_ = at.tonumpy(pred_bboxes_)
if pred_bboxes_.shape[0] == 0:
test_img_num += 1
continue
else:
ious = bbox_iou(pred_bboxes_, gt_bboxs)
max_ious = ious.max(axis=1)
corr_preds = np.where(max_ious >= 0.5)[0]
num_boxs = gt_bboxs.shape[0]
num_corr_preds = len(corr_preds)
test_corrLoc += num_corr_preds / num_boxs
test_img_num += 1
return test_corrLoc / test_img_num