def extract_feats(self, img, img_metas):
"""Extract features for `img` during testing.
Args:
img (Tensor): of shape (1, C, H, W) encoding input image.
Typically these should be mean centered and std scaled.
img_metas (list[dict]): list of image information dict where each
dict has: 'img_shape', 'scale_factor', 'flip', and may also
contain 'filename', 'ori_shape', 'pad_shape', and
'img_norm_cfg'. For details on the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`.
Returns:
list[Tensor]: Multi level feature maps of `img`.
"""
key_frame_interval = self.test_cfg.get('key_frame_interval', 10)
frame_id = img_metas[0].get('frame_id', -1)
assert frame_id >= 0
is_key_frame = False if frame_id % key_frame_interval else True
if is_key_frame:
self.memo = Dict()
self.memo.img = img
x = self.detector.extract_feat(img)
self.memo.feats = x
else:
flow_img = torch.cat((img, self.memo.img), dim=1)
flow = self.motion(flow_img, img_metas)
x = []
for i in range(len(self.memo.feats)):
x_single = flow_warp_feats(self.memo.feats[i], flow)
x.append(x_single)
return x
def forward_train(self,
img,
img_metas,
gt_bboxes,
gt_labels,
ref_img,
ref_img_metas,
ref_gt_bboxes,
ref_gt_labels,
gt_instance_ids=None,
gt_bboxes_ignore=None,
gt_masks=None,
proposals=None,
ref_gt_instance_ids=None,
ref_gt_bboxes_ignore=None,
ref_gt_masks=None,
ref_proposals=None,
**kwargs):
"""
Args:
img (Tensor): of shape (N, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box.
ref_img (Tensor): of shape (N, 1, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
1 denotes there is only one reference image for each input
image.
ref_img_metas (list[list[dict]]): The first list only has one
element. The second list contains reference image information
dict where each dict has: 'img_shape', 'scale_factor', 'flip',
and may also contain 'filename', 'ori_shape', 'pad_shape', and
'img_norm_cfg'. For details on the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`.
ref_gt_bboxes (list[Tensor]): The list only has one Tensor. The
Tensor contains ground truth bboxes for each reference image
with shape (num_all_ref_gts, 5) in
[ref_img_id, tl_x, tl_y, br_x, br_y] format. The ref_img_id
start from 0, and denotes the id of reference image for each
key image.
ref_gt_labels (list[Tensor]): The list only has one Tensor. The
Tensor contains class indices corresponding to each reference
box with shape (num_all_ref_gts, 2) in
[ref_img_id, class_indice].
gt_instance_ids (None | list[Tensor]): specify the instance id for
each ground truth bbox.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : true segmentation masks for each box
used if the architecture supports a segmentation task.
proposals (None | Tensor) : override rpn proposals with custom
proposals. Use when `with_rpn` is False.
ref_gt_instance_ids (None | list[Tensor]): specify the instance id
for each ground truth bboxes of reference images.
ref_gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes of reference images can be ignored when computing the
loss.
ref_gt_masks (None | Tensor) : True segmentation masks for each
box of reference image used if the architecture supports a
segmentation task.
ref_proposals (None | Tensor) : override rpn proposals with custom
proposals of reference images. Use when `with_rpn` is False.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
assert len(img) == 1, \
'Dff video detectors only support 1 batch size per gpu for now.'
is_video_data = img_metas[0]['is_video_data']
flow_img = torch.cat((img, ref_img[:, 0]), dim=1)
flow = self.motion(flow_img, img_metas)
ref_x = self.detector.extract_feat(ref_img[:, 0])
x = []
for i in range(len(ref_x)):
x_single = flow_warp_feats(ref_x[i], flow)
if not is_video_data:
x_single = 0 * x_single + ref_x[i]
x.append(x_single)
losses = dict()
# Two stage detector
if hasattr(self.detector, 'roi_head'):
# RPN forward and loss
if self.detector.with_rpn:
proposal_cfg = self.detector.train_cfg.get(
'rpn_proposal', self.detector.test_cfg.rpn)
rpn_losses, proposal_list = \
self.detector.rpn_head.forward_train(
x,
img_metas,
gt_bboxes,
gt_labels=None,
gt_bboxes_ignore=gt_bboxes_ignore,
proposal_cfg=proposal_cfg)
losses.update(rpn_losses)
else:
proposal_list = proposals
roi_losses = self.detector.roi_head.forward_train(
x, img_metas, proposal_list, gt_bboxes, gt_labels,
gt_bboxes_ignore, gt_masks, **kwargs)
losses.update(roi_losses)
# Single stage detector
elif hasattr(self.detector, 'bbox_head'):
bbox_losses = self.detector.bbox_head.forward_train(
x, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore)
losses.update(bbox_losses)
else:
raise TypeError('detector must has roi_head or bbox_head.')
return losses