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 slice
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 testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = minibatch.get_rpn_batch(iroidb)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = minibatch.assign_anchor(feat_shape, label['gt_boxes'],
data['im_info'], self.feat_stride,
self.anchor_scales,
self.anchor_ratios,
self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack(
[batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
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.mode == 'test':
self.data, self.label = minibatch.get_minibatch(roidb, self.num_classes, self.mode)
else:
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)
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = minibatch.get_minibatch(iroidb, self.num_classes, self.mode)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# assign anchor for label
label = minibatch.assign_anchor(feat_shape, label['gt_boxes'], data['im_info'],
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
del data['im_info']
new_label_list.append(label)
all_data = dict()
for key in ['data']:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
all_label['label'] = tensor_vstack([batch['label'] for batch in new_label_list], pad=-1)
for key in ['bbox_target', 'bbox_inside_weight', 'bbox_outside_weight']:
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list])
self.data = [mx.nd.array(all_data['data'])]
self.label = [mx.nd.array(all_label['label']),
mx.nd.array(all_label['bbox_target']),
mx.nd.array(all_label['bbox_inside_weight']),
mx.nd.array(all_label['bbox_outside_weight'])]
def infer_shape(self, max_data_shape=None, max_label_shape=None):
""" Return maximum data and label shape for single gpu """
if max_data_shape is None:
max_data_shape = []
if max_label_shape is None:
max_label_shape = []
max_shapes = dict(max_data_shape + max_label_shape)
im_info = [[max_shapes['data'][2], max_shapes['data'][3], 1.0]]
_, feat_shape, _ = self.feat_sym.infer_shape(**max_shapes)
label = minibatch.assign_anchor(feat_shape[0], np.zeros(
(0, 5)), im_info, self.feat_stride, self.anchor_scales,
self.anchor_ratios,
self.allowed_border)
label = [label[k] for k in self.label_name]
label_shape = [(k, v.shape) for k, v in zip(self.label_name, label)]
return max_data_shape, label_shape
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.mode == 'test':
self.data, self.label = minibatch.get_minibatch(
roidb, self.num_classes, self.mode)
else:
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)
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = minibatch.get_minibatch(iroidb, self.num_classes,
self.mode)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# assign anchor for label
label = minibatch.assign_anchor(feat_shape, label['gt_boxes'],
data['im_info'],
self.feat_stride,
self.anchor_scales,
self.anchor_ratios,
self.allowed_border)
del data['im_info']
new_label_list.append(label)
all_data = dict()
for key in ['data']:
all_data[key] = tensor_vstack(
[batch[key] for batch in data_list])
all_label = dict()
all_label['label'] = tensor_vstack(
[batch['label'] for batch in new_label_list], pad=-1)
for key in [
'bbox_target', 'bbox_inside_weight', 'bbox_outside_weight'
]:
all_label[key] = tensor_vstack(
[batch[key] for batch in new_label_list])
self.data = [mx.nd.array(all_data['data'])]
self.label = [
mx.nd.array(all_label['label']),
mx.nd.array(all_label['bbox_target']),
mx.nd.array(all_label['bbox_inside_weight']),
mx.nd.array(all_label['bbox_outside_weight'])
]