def par_assign_anchor_wrapper(cfg, iroidb, feat_sym, feat_strides, anchor_scales, anchor_ratios, allowed_border):
data, rpn_label = get_rpn_batch(iroidb, cfg)
data_shape = {k:v.shape for k,v in data.items()}
del data_shape['im_info']
data['gt_boxes'] = rpn_label['gt_boxes'][np.newaxis,:,:]
feat_shape = [y[1] for y in [x.infer_shape(**data_shape) for x in feat_sym]]
label = assign_pyramid_anchor(feat_shape, rpn_label['gt_boxes'],data['im_info'],cfg,
feat_strides, anchor_scales, anchor_ratios, allowed_border)
return {'data':data,'label':label}
def get_batch_parallel(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)]
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
slices = _split_input_slice(self.batch_size, work_load_list)
max_data = {}
max_label = {}
data_lst = []
rpn_label_lst = []
for idx, islice in enumerate(slices):
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, rpn_label = get_rpn_batch(iroidb, self.cfg)
data['gt_boxes'] = rpn_label['gt_boxes'][np.newaxis,:,:]
data_shape = {k:list(v.shape) for k,v in data.items()}
if max_data == {} :
max_data = data_shape
else:
#max_data = {k:np.where(max_data[k]>v,max_data[k],v) for k,v in data_shape.items() }
for k,v in data_shape.items():
max_data[k] = np.where(np.array(max_data[k])>np.array(data_shape[k]),np.array(max_data[k]),np.array(data_shape[k]))
data_lst.append(data)
rpn_label_lst.append(rpn_label)
for k,v in max_data.items():
max_data[k][0] = self.batch_size
self.data = [mx.nd.zeros(tuple(max_data['data'])),mx.nd.zeros(tuple(max_data['im_info'])),mx.nd.full(tuple(max_data['gt_boxes']),-1)]
del max_data['im_info']
del max_data['gt_boxes']
max_data = {k:tuple(v) for k,v in max_data.items()}
all_label = {}
for idx, islice in enumerate(slices):
feat_shape = [y[1] for y in [x.infer_shape(**max_data) for x in self.feat_sym]]
d = data_lst[idx]
self.data[0][idx,:d['data'].shape[1],:d['data'].shape[2],:d['data'].shape[3]] = d['data'][0]
self.data[1][idx,:d['im_info'].shape[1]] = d['im_info'][0]
self.data[2][idx,:d['gt_boxes'].shape[1],:d['gt_boxes'].shape[2]] = d['gt_boxes'][0]
label = assign_pyramid_anchor(feat_shape, rpn_label_lst[idx]['gt_boxes'],data_lst[idx]['im_info'],self.cfg,
self.feat_strides, self.anchor_scales, self.anchor_ratios, self.allowed_border)
if all_label == {}:
all_label = label
else:
for k,v in label.items():
all_label[k] = np.vstack([all_label[k],v])
self.label = [mx.nd.array(v) for k,v in all_label.items()]
def par_assign_anchor_wrapper(cfg, iroidb, feat_sym, feat_strides, anchor_scales, anchor_ratios, allowed_border):
# get testing data for multigpu
data, rpn_label = get_rpn_batch(iroidb, cfg)
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
# add gt_boxes to data for e2e
data['gt_boxes'] = rpn_label['gt_boxes'][np.newaxis, :, :]
feat_shape = [y[1] for y in [x.infer_shape(**data_shape) for x in feat_sym]]
label = assign_pyramid_anchor(feat_shape, rpn_label['gt_boxes'], data['im_info'], cfg,
feat_strides, anchor_scales, anchor_ratios, allowed_border)
return {'data': data, 'label': label}
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)
input_batch_size = max_shapes['data'][0]
im_info = [[max_shapes['data'][2], max_shapes['data'][3], 1.0]]
feat_shape = [y[1] for y in [x.infer_shape(**max_shapes) for x in self.feat_sym]]
label = assign_pyramid_anchor(feat_shape, np.zeros((0, 5)), im_info, self.cfg,
self.feat_strides, self.anchor_scales, self.anchor_ratios, self.allowed_border)
label = [label[k] for k in self.label_name]
label_shape = [(k, tuple([input_batch_size] + list(v.shape[1:]))) for k, v in zip(self.label_name, label)]
return max_data_shape, label_shape
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)
input_batch_size = max_shapes['data'][0]
im_info = [[max_shapes['data'][2], max_shapes['data'][3], 1.0]]
feat_shape = [y[1] for y in [x.infer_shape(**max_shapes) for x in self.feat_sym]]
label = assign_pyramid_anchor(feat_shape, np.zeros((0, 5)), im_info, self.cfg,
self.feat_strides, self.anchor_scales, self.anchor_ratios, self.allowed_border)
label = [label[k] for k in self.label_name]
label_shape = [(k, tuple([input_batch_size] + list(v.shape[1:]))) for k, v in zip(self.label_name, label)]
return max_data_shape, label_shape
