async def async_simple_test_rpn(self, x, img_metas):
sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
async with completed(__name__,
'rpn_head_forward',
sleep_interval=sleep_interval):
rpn_outs = self(x)
proposal_list = self.get_bboxes(*rpn_outs, img_metas=img_metas)
return proposal_list
async def async_test_rpn(self, x, img_meta, rpn_test_cfg):
sleep_interval = rpn_test_cfg.pop("async_sleep_interval", 0.025)
async with completed(__name__,
"rpn_head_forward",
sleep_interval=sleep_interval):
rpn_outs = self.rpn_head(x)
proposal_inputs = rpn_outs + (img_meta, rpn_test_cfg)
proposal_list = self.rpn_head.get_bboxes(*proposal_inputs)
return proposal_list
async def async_test_mask(
self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False,
mask_test_cfg=None,
):
# image shape of the first image in the batch (only one)
ori_shape = img_metas[0]["ori_shape"]
scale_factor = img_metas[0]["scale_factor"]
if det_bboxes.shape[0] == 0:
segm_result = [[]
for _ in range(self.mask_head.num_classes - 1)]
else:
_bboxes = det_bboxes[:, :
4] * scale_factor if rescale else det_bboxes
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor(
x[:len(self.mask_roi_extractor.featmap_strides)],
mask_rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
if mask_test_cfg and mask_test_cfg.get("async_sleep_interval"):
sleep_interval = mask_test_cfg["async_sleep_interval"]
else:
sleep_interval = 0.035
async with completed(__name__,
"mask_head_forward",
sleep_interval=sleep_interval):
mask_pred = self.mask_head(mask_feats)
segm_result = self.mask_head.get_seg_masks(
mask_pred,
_bboxes,
det_labels,
self.test_cfg.rcnn,
ori_shape,
scale_factor,
rescale,
)
return segm_result
async def async_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False,
mask_test_cfg=None):
"""Asynchronized test for mask head without augmentation."""
# image shape of the first image in the batch (only one)
ori_shape = img_metas[0]['ori_shape']
scale_factor = img_metas[0]['scale_factor']
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
if rescale and not isinstance(scale_factor,
(float, torch.Tensor)):
scale_factor = det_bboxes.new_tensor(scale_factor)
_bboxes = (
det_bboxes[:, :4] *
scale_factor if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor(
x[:len(self.mask_roi_extractor.featmap_strides)],
mask_rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
sleep_interval = mask_test_cfg['async_sleep_interval']
else:
sleep_interval = 0.035
async with completed(
__name__,
'mask_head_forward',
sleep_interval=sleep_interval):
mask_pred = self.mask_head(mask_feats)
segm_result = self.mask_head.get_seg_masks(
mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape,
scale_factor, rescale)
return segm_result
async def async_test_mask(self,
x,
img_meta,
det_bboxes,
det_labels,
rescale=False,
mask_test_cfg=None):
# image shape of the first image in the batch (only one)
ori_shape = img_meta[0]['ori_shape']
scale_factor = img_meta[0]['scale_factor']
if det_bboxes.shape[0] == 0:
segm_result = [[]
for _ in range(self.mask_head.num_classes - 1)]
else:
# 如果simple_get_bboxes恢复图片原尺寸，这里再变回来（feature map尺寸）=? 因为要roi??
_bboxes = (det_bboxes[:, :4] *
scale_factor if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor(
x[:len(self.mask_roi_extractor.featmap_strides)],
mask_rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
sleep_interval = mask_test_cfg['async_sleep_interval']
else:
sleep_interval = 0.035
async with completed(__name__,
"mask_head_forward",
sleep_interval=sleep_interval):
mask_pred = self.mask_head(mask_feats)
segm_result = self.mask_head.get_seg_masks(
mask_pred, _bboxes, det_labels, self.test_cfg.rcnn,
ori_shape, scale_factor, rescale)
return segm_result
