def get_batch(self):
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
if self.has_rpn:
data, label, im_info = get_rpn_testbatch(roidb, self.cfg)
else:
data, label, im_info = get_rcnn_testbatch(roidb, self.cfg)
self.data = [[mx.nd.array(idata[name]) for name in self.data_name] for idata in data]
self.im_info = im_info
def get_batch(self):
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
if self.has_rpn:
data, label, im_info = get_rpn_testbatch(roidb, self.cfg)
else:
data, label, im_info = get_rcnn_testbatch(roidb, self.cfg)
self.data = [[mx.nd.array(idata[name]) for name in self.data_name]
for idata in data]
self.im_info = im_info
def get_batch(self):
cur_roidb = self.roidb[self.cur_roidb_index].copy()
cur_roidb['image'] = cur_roidb['pattern'] % self.cur_frameid
# update seg_id
cur_roidb['frame_seg_id'] = self.cur_frameid
self.cur_seg_len = cur_roidb['frame_seg_len']
data, label, im_info = get_rpn_testbatch([cur_roidb], self.cfg)
if self.cur_frameid == 0: # new video
self.key_frame_flag = 0
else: # normal frame
self.key_frame_flag = 2
self.data = [[mx.nd.array(idata[name]) for name in self.data_name]
for idata in data]
self.im_info = im_info
def get_batch_individual(self, cur_from=None):
if cur_from is None:
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
if self.has_rpn:
data, label, im_info = get_rpn_testbatch(roidb, self.cfg)
else:
data, label, im_info = get_rcnn_testbatch(roidb, self.cfg)
self.lock_data.acquire()
self.data = [mx.nd.array(data[name]) for name in self.data_name]
self.im_info = im_info
self.lock_data.release()
return data, im_info
def get_batch(self):
cur_roidb = self.roidb[self.cur_roidb_index].copy()
cur_roidb['image'] = cur_roidb['pattern'] % self.cur_frameid
self.cur_seg_len = cur_roidb['frame_seg_len']
data, label, im_info = get_rpn_testbatch([cur_roidb], self.cfg)
if self.cur_frameid == 0: # new video
self.key_frame_flag = 0
else: # normal frame
self.key_frame_flag = 2
extend_data = [{'data': data[0]['data'] ,
'im_info': data[0]['im_info'],
'data_cache': data[0]['data'],
'feat_cache': data[0]['data']}]
self.data = [[mx.nd.array(extend_data[i][name]) for name in self.data_name] for i in xrange(len(data))]
self.im_info = im_info
def get_batch(self):
cur_roidb = self.roidb[self.cur_roidb_index].copy()
cur_roidb['image'] = cur_roidb['pattern'] % self.cur_frameid
self.cur_seg_len = cur_roidb['frame_seg_len']
data, label, im_info = get_rpn_testbatch([cur_roidb], self.cfg,
self.cur_frameid)
if self.key_frameid == self.cur_frameid: # key frame
self.data_key_old = self.data_key.copy(
) if self.data_key is not None else data[0]['data'].copy()
self.data_key = data[0]['data'].copy()
if self.key_frameid == 0:
self.data_key_old = data[0]['data'].copy()
self.key_frame_flag = 0
else:
self.key_frame_flag = 1
elif self.cur_frameid + 1 == self.cur_seg_len: #key frame
self.data_key_old = self.data_key.copy(
) if self.data_key is not None else data[0]['data'].copy()
self.data_key = data[0]['data'].copy()
self.key_frame_flag = 1
else:
self.key_frame_flag = 2
extend_data = [{
'data':
data[0]['data'],
'im_info':
data[0]['im_info'],
'data_key':
self.data_key,
'data_key_old':
self.data_key_old,
'motion_vector':
data[0]['motion_vector'],
'res_diff':
data[0]['res_diff'],
'feat_key_old':
np.zeros((1, self.cfg.network.DFF_FEAT_DIM, 1, 1)),
'feat_key':
np.zeros((1, self.cfg.network.DFF_FEAT_DIM, 1, 1))
}]
self.data = [[
mx.nd.array(extend_data[i][name]) for name in self.data_name
] for i in xrange(len(data))]
self.im_info = im_info
def get_batch(self):
cur_roidb = self.roidb[self.cur_roidb_index].copy()
cur_roidb['image'] = cur_roidb['pattern'] % self.cur_frameid
self.cur_seg_len = cur_roidb['frame_seg_len']
data, label, im_info = get_rpn_testbatch([cur_roidb], self.cfg)
if self.key_frameid == self.cur_frameid: # key frame
self.data_key = data[0]['data'].copy()
if self.key_frameid == 0:
self.key_frame_flag = 0
else:
self.key_frame_flag = 1
else:
self.key_frame_flag = 2
extend_data = [{'data': data[0]['data'],
'im_info': data[0]['im_info'],
'data_key': self.data_key,
'feat_key': np.zeros((1,self.cfg.network.DFF_FEAT_DIM,1,1))}]
self.data = [[mx.nd.array(extend_data[i][name]) for name in self.data_name] for i in xrange(len(data))]
self.im_info = im_info
def get_init_batch(self):
cur_roidb = self.roidb[self.cur_roidb_index].copy()
cur_roidb['image'] = cur_roidb['pattern'] % self.cur_frameid
self.cur_seg_len = cur_roidb['frame_seg_len']
data, label, im_info = get_rpn_testbatch([cur_roidb], self.cfg)
if self.cur_frameid == 0: # new frame
self.key_frame_flag = 0
else: # normal frame
self.key_frame_flag = 2
feat_stride = float(self.cfg.network.RCNN_FEAT_STRIDE)
extend_data = [{'data': data[0]['data'] ,
'im_info': data[0]['im_info'],
'data_cache': np.zeros((6, 3, max([v[0] for v in self.cfg.SCALES]), max([v[1] for v in self.cfg.SCALES]))),
'feat_cache': np.zeros((6, self.cfg.network.FGFA_FEAT_DIM,
np.ceil(max([v[0] for v in self.cfg.SCALES]) / feat_stride).astype(np.int),
np.ceil(max([v[1] for v in self.cfg.SCALES]) / feat_stride).astype(np.int)))}]
self.data = [[mx.nd.array(extend_data[i][name]) for name in self.data_name] for i in xrange(len(data))]
self.im_info = im_info
def get_init_batch(self):
cur_roidb = self.roidb[self.cur_roidb_index].copy()
if 'frame_seg_len' in cur_roidb:
self.cur_seg_len = cur_roidb['frame_seg_len']
elif 'video_len' in cur_roidb:
self.cur_seg_len = cur_roidb['video_len']
else:
assert False, 'unknown video length type'
if self.cur_frameid == 0: # new video
self.key_frame_flag = 0
self.video_index = np.arange(self.cur_seg_len)
if self.video_shuffle:
np.random.shuffle(self.video_index)
else: # normal frame
self.key_frame_flag = 2
if self.video_shuffle:
if 'frame_ids' in cur_roidb:
cur_roidb['image'] = cur_roidb['frame_ids'][self.video_index[
self.cur_frameid]]
elif 'pattern' in cur_roidb:
cur_roidb['image'] = cur_roidb['pattern'] % self.video_index[
self.cur_frameid]
else:
assert False, 'unknown video pattern for get image index'
else:
if 'frame_ids' in cur_roidb:
cur_roidb['image'] = cur_roidb['frame_ids'][self.cur_frameid]
elif 'pattern' in cur_roidb:
cur_roidb['image'] = cur_roidb['pattern'] % self.cur_frameid
else:
assert False, 'unknown video pattern for get image index'
data, label, im_info = get_rpn_testbatch([cur_roidb], self.cfg)
# feat_stride = float(self.cfg.network.RCNN_FEAT_STRIDE)
feat_stride = float(self.cfg.network.RPN_FEAT_STRIDE)
H = np.ceil(max([v[0] for v in self.cfg.SCALES]) / feat_stride).astype(
np.int)
W = np.ceil(max([v[1] for v in self.cfg.SCALES]) / feat_stride).astype(
np.int)
T = self.cfg.TEST.KEY_FRAME_INTERVAL * 2 + 1
extend_data = [{
'data':
data[0]['data'],
'im_info':
data[0]['im_info'],
'data_cache':
np.zeros((T, 3, max([v[0] for v in self.cfg.SCALES]),
max([v[1] for v in self.cfg.SCALES]))),
'feat_cache':
np.zeros((T, self.cfg.network.FGFA_FEAT_DIM, H, W))
}]
self.data = [[
mx.nd.array(extend_data[i][name]) for name in self.data_name
] for i in xrange(len(data))]
self.im_info = im_info
if isinstance(cur_roidb['image'], int):
import os
self.img_name = os.path.join(cur_roidb['video_id'].split('_')[0], cur_roidb['video_id'], \
str(cur_roidb['image']) + '.jpg')
else:
self.img_name = cur_roidb['image']
