def get_batch(self):
# slice roidb
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)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack(
[batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
def get_batch(self):
# slice roidb
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)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
def parfetch(self, iroidb):
# get testing data for multigpu
data, label = get_rcnn_batch(iroidb, self.cfg)
# add gt_boxes to data for learn nms
if 'gt_boxes' in data:
data['gt_boxes'] = data['gt_boxes'][np.newaxis, :, :]
return {'data': data, 'label': label}
def parfetch(self, iroidb):
data, label = get_rcnn_batch(iroidb, self.cfg)
return {'data': data, 'label': label}
def parfetch(self, iroidb):
data, label = get_rcnn_batch(iroidb, self.cfg)
return {'data': data, 'label': label}
