def initial_finetune(self, img, detection_box):
self.stopwatch.start('initial_finetune')
t = time.time()
# generate samples
pos_num, neg_num = ADNetConf.g()['initial_finetune'][
'pos_num'], ADNetConf.g()['initial_finetune']['neg_num']
pos_boxes, neg_boxes = detection_box.get_posneg_samples(self.imgwh,
pos_num,
neg_num,
use_whole=True)
pos_lb_action = BoundingBox.get_action_labels(pos_boxes, detection_box)
feats = self._get_features([
commons.extract_region(img, box) for i, box in enumerate(pos_boxes)
])
for box, feat in zip(pos_boxes, feats):
box.feat = feat
feats = self._get_features([
commons.extract_region(img, box) for i, box in enumerate(neg_boxes)
])
for box, feat in zip(neg_boxes, feats):
box.feat = feat
# train_fc_finetune_hem
self._finetune_fc(img, pos_boxes, neg_boxes, pos_lb_action,
ADNetConf.get()['initial_finetune']['learning_rate'],
ADNetConf.get()['initial_finetune']['iter'])
self.histories.append((pos_boxes, neg_boxes, pos_lb_action,
np.copy(img), self.iteration))
_logger.info('ADNetRunner.initial_finetune t=%.3f' % t)
self.stopwatch.stop('initial_finetune')
def _finetune_fc(self, img, pos_boxes, neg_boxes, pos_lb_action, learning_rate, iter, iter_score=1):
BATCHSIZE = ADNetConf.g()['minibatch_size']
def get_img(idx, posneg):
if isinstance(img, tuple):
return img[posneg][idx]
return img
pos_samples = [commons.extract_region(get_img(i, 0), box) for i, box in enumerate(pos_boxes)]
neg_samples = [commons.extract_region(get_img(i, 1), box) for i, box in enumerate(neg_boxes)]
# pos_feats, neg_feats = self._get_features(pos_samples), self._get_features(neg_samples)
# commons.imshow_grid('pos', pos_samples[-50:], 10, 5)
# commons.imshow_grid('neg', neg_samples[-50:], 10, 5)
cv2.waitKey(1)
for i in range(iter):
batch_idxs = commons.random_idxs(len(pos_boxes), BATCHSIZE)
batch_feats = [x.feat for x in commons.choices_by_idx(pos_boxes, batch_idxs)]
batch_lb_action = commons.choices_by_idx(pos_lb_action, batch_idxs)
self.persistent_sess.run(
self.adnet.weighted_grads_op1,
feed_dict={
self.adnet.layer_feat: batch_feats,
self.adnet.label_tensor: batch_lb_action,
self.adnet.action_history_tensor: np.zeros(shape=(BATCHSIZE, 1, 1, 110)),
self.learning_rate_placeholder: learning_rate,
self.tensor_is_training: True
}
)
if i % iter_score == 0:
# training score auxiliary(fc2)
# -- hard score example mining
scores = []
for batch_neg in commons.chunker([x.feat for x in neg_boxes], ADNetRunner.MAX_BATCHSIZE):
scores_batch = self.persistent_sess.run(
self.adnet.layer_scores,
feed_dict={
self.adnet.layer_feat: batch_neg,
self.adnet.action_history_tensor: np.zeros(shape=(len(batch_neg), 1, 1, 110)),
self.learning_rate_placeholder: learning_rate,
self.tensor_is_training: False
}
)
scores.extend(scores_batch)
desc_order_idx = [i[0] for i in sorted(enumerate(scores), reverse=True, key=lambda x:x[1][1])]
# -- train
batch_feats_neg = [x.feat for x in commons.choices_by_idx(neg_boxes, desc_order_idx[:BATCHSIZE])]
self.persistent_sess.run(
self.adnet.weighted_grads_op2,
feed_dict={
self.adnet.layer_feat: batch_feats + batch_feats_neg,
self.adnet.class_tensor: [1]*len(batch_feats) + [0]*len(batch_feats_neg),
self.adnet.action_history_tensor: np.zeros(shape=(len(batch_feats)+len(batch_feats_neg), 1, 1, 110)),
self.learning_rate_placeholder: learning_rate,
self.tensor_is_training: True
}
)
def initial_finetune(self, img, detection_box):
# print("Start initial_finetune1")
# generate samples
pos_num, neg_num = ADNetConf.g()['initial_finetune'][
'pos_num'], ADNetConf.g()['initial_finetune']['neg_num']
# print("Ending initial_finetune1")
pos_boxes, neg_boxes = detection_box.get_posneg_samples(self.imgwh,
pos_num,
neg_num,
use_whole=True)
# print("Ending initial_finetune133")
pos_lb_action = BoundingBox.get_action_labels(pos_boxes, detection_box)
# print("Ending initial_finetune44")
feats = self._get_features([
commons.extract_region(img, box) for i, box in enumerate(pos_boxes)
])
for box, feat in zip(pos_boxes, feats):
box.feat = feat
feats = self._get_features([
commons.extract_region(img, box) for i, box in enumerate(neg_boxes)
])
for box, feat in zip(neg_boxes, feats):
box.feat = feat
# print("Ending initial_finetune2")
# train_fc_finetune_hem
self._finetune_fc(img, pos_boxes, neg_boxes, pos_lb_action,
ADNetConf.get()['initial_finetune']['learning_rate'],
ADNetConf.get()['initial_finetune']['iter'])
self.histories.append((pos_boxes, neg_boxes, pos_lb_action,
np.copy(img), self.iteration))
def redetection_by_sampling(self, prev_box, img):
"""
default redetection method
"""
imgwh = Coordinate.get_imgwh(img)
translation_f = min(1.5, 0.6 * 1.15**self.failed_cnt)
candidates = prev_box.gen_noise_samples(imgwh, 'gaussian', ADNetConf.g()['redetection']['samples'],
gaussian_translation_f=translation_f)
scores = []
for c_batch in commons.chunker(candidates, ADNetRunner.MAX_BATCHSIZE):
samples = [commons.extract_region(img, box) for box in c_batch]
classes = self.persistent_sess.run(
self.adnet.layer_scores,
feed_dict={
self.adnet.input_tensor: samples,
self.adnet.action_history_tensor: [commons.onehot_flatten(self.action_histories_old)]*len(c_batch),
self.tensor_is_training: False
}
)
scores.extend([x[1] for x in classes])
top5_idx = [i[0] for i in sorted(enumerate(scores), reverse=True, key=lambda x: x[1])][:5]
mean_score = sum([scores[x] for x in top5_idx]) / 5.0
if mean_score >= self.latest_score:
mean_box = candidates[0]
for i in range(1, 5):
mean_box += candidates[i]
return mean_box / 5.0, mean_score
return None, 0.0
def img_label(lb_dir_dict):
img_label = {}
train_img = {}
test_img = {}
for key in lb_dir_dict:
imgs = []
img_dir = glob.glob(lb_dir_dict[key]+'/*.bmp')
for idx, dir in enumerate(img_dir):
print("Read: ", dir)
img = commons.imread(dir)
img = commons.extract_region(img)
imgs.append(img)
img_label[key] = imgs
train_size = int(len(imgs)*0.8)
train_img[key] = imgs[:train_size]
test_img[key] = imgs[train_size:]
return img_label, train_img, test_img
def tracking(self, img, curr_bbox):
self.iteration += 1
is_tracked = True
boxes = []
self.latest_score = -1
self.stopwatch.start('tracking.do_action')
for track_i in range(ADNetConf.get()['predict']['num_action']):
patch = commons.extract_region(img, curr_bbox)
# forward with image & action history
actions, classes = self.persistent_sess.run(
[self.adnet.layer_actions, self.adnet.layer_scores],
feed_dict={
self.adnet.input_tensor: [patch],
self.adnet.action_history_tensor:
[commons.onehot_flatten(self.action_histories)],
self.tensor_is_training:
False
})
latest_score = classes[0][1]
if latest_score < ADNetConf.g()['predict']['thresh_fail']:
is_tracked = False
self.action_histories_old = np.copy(self.action_histories)
self.action_histories = np.insert(self.action_histories, 0,
12)[:-1]
break
else:
self.failed_cnt = 0
self.latest_score = latest_score
# move box
action_idx = np.argmax(actions[0])
self.action_histories = np.insert(self.action_histories, 0,
action_idx)[:-1]
prev_bbox = curr_bbox
curr_bbox = curr_bbox.do_action(self.imgwh, action_idx)
if action_idx != ADNetwork.ACTION_IDX_STOP:
if prev_bbox == curr_bbox:
print('action idx', action_idx)
print(prev_bbox)
print(curr_bbox)
raise Exception('box not moved.')
# oscillation check
if action_idx != ADNetwork.ACTION_IDX_STOP and curr_bbox in boxes:
action_idx = ADNetwork.ACTION_IDX_STOP
if action_idx == ADNetwork.ACTION_IDX_STOP:
break
boxes.append(curr_bbox)
self.stopwatch.stop('tracking.do_action')
# redetection when tracking failed
new_score = 0.0
if not is_tracked:
self.failed_cnt += 1
# run redetection callback function
new_box, new_score = self.callback_redetection(curr_bbox, img)
if new_box is not None:
curr_bbox = new_box
patch = commons.extract_region(img, curr_bbox)
_logger.debug('redetection success=%s' %
(str(new_box is not None)))
# save samples
if is_tracked or new_score > ADNetConf.g(
)['predict']['thresh_success']:
self.stopwatch.start('tracking.save_samples.roi')
imgwh = Coordinate.get_imgwh(img)
pos_num, neg_num = ADNetConf.g(
)['finetune']['pos_num'], ADNetConf.g()['finetune']['neg_num']
pos_boxes, neg_boxes = curr_bbox.get_posneg_samples(
imgwh,
pos_num,
neg_num,
use_whole=False,
pos_thresh=ADNetConf.g()['finetune']['pos_thresh'],
neg_thresh=ADNetConf.g()['finetune']['neg_thresh'],
uniform_translation_f=2,
uniform_scale_f=5)
self.stopwatch.stop('tracking.save_samples.roi')
self.stopwatch.start('tracking.save_samples.feat')
feats = self._get_features([
commons.extract_region(img, box)
for i, box in enumerate(pos_boxes)
])
for box, feat in zip(pos_boxes, feats):
box.feat = feat
feats = self._get_features([
commons.extract_region(img, box)
for i, box in enumerate(neg_boxes)
])
for box, feat in zip(neg_boxes, feats):
box.feat = feat
pos_lb_action = BoundingBox.get_action_labels(pos_boxes, curr_bbox)
self.histories.append((pos_boxes, neg_boxes, pos_lb_action,
np.copy(img), self.iteration))
# clear old ones
self.histories = self.histories[-ADNetConf.g(
)['finetune']['long_term']:]
self.stopwatch.stop('tracking.save_samples.feat')
# online finetune
if self.iteration % ADNetConf.g(
)['finetune']['interval'] == 0 or is_tracked is False:
img_pos, img_neg = [], []
pos_boxes, neg_boxes, pos_lb_action = [], [], []
pos_term = 'long_term' if is_tracked else 'short_term'
for i in range(ADNetConf.g()['finetune'][pos_term]):
if i >= len(self.histories):
break
pos_boxes.extend(self.histories[-(i + 1)][0])
pos_lb_action.extend(self.histories[-(i + 1)][2])
img_pos.extend([self.histories[-(i + 1)][3]] *
len(self.histories[-(i + 1)][0]))
for i in range(ADNetConf.g()['finetune']['short_term']):
if i >= len(self.histories):
break
neg_boxes.extend(self.histories[-(i + 1)][1])
img_neg.extend([self.histories[-(i + 1)][3]] *
len(self.histories[-(i + 1)][1]))
self.stopwatch.start('tracking.online_finetune')
self._finetune_fc((img_pos, img_neg), pos_boxes, neg_boxes,
pos_lb_action,
ADNetConf.get()['finetune']['learning_rate'],
ADNetConf.get()['finetune']['iter'])
_logger.debug('finetuned')
self.stopwatch.stop('tracking.online_finetune')
visualizer.image('patch', patch)
# cv2.imshow('patch', patch)
return curr_bbox