import torch
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_head
from .single_stage import SingleStageDetector
[docs]@DETECTORS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
segm_head,
mask_head,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg,
test_cfg, pretrained)
self.segm_head = build_head(segm_head)
self.mask_head = build_head(mask_head)
self.init_segm_mask_weights()
[docs] def init_segm_mask_weights(self):
"""Initialize weights of the YOLACT semg head and YOLACT mask head."""
self.segm_head.init_weights()
self.mask_head.init_weights()
[docs] def forward_dummy(self, img):
"""Used for computing network flops.
See `mmdetection/tools/analysis_tools/get_flops.py`
"""
raise NotImplementedError
[docs] def forward_train(self,
img,
img_metas,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""
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
`mmdet/datasets/pipelines/formatting.py:Collect`.
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
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.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# convert Bitmap mask or Polygon Mask to Tensor here
gt_masks = [
gt_mask.to_tensor(dtype=torch.uint8, device=img.device)
for gt_mask in gt_masks
]
x = self.extract_feat(img)
cls_score, bbox_pred, coeff_pred = self.bbox_head(x)
bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels,
img_metas)
losses, sampling_results = self.bbox_head.loss(
*bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
segm_head_outs = self.segm_head(x[0])
loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels)
losses.update(loss_segm)
mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas,
sampling_results)
loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes,
img_metas, sampling_results)
losses.update(loss_mask)
# check NaN and Inf
for loss_name in losses.keys():
assert torch.isfinite(torch.stack(losses[loss_name]))\
.all().item(), '{} becomes infinite or NaN!'\
.format(loss_name)
return losses
[docs] def simple_test(self, img, img_metas, rescale=False):
"""Test function without test time augmentation."""
x = self.extract_feat(img)
cls_score, bbox_pred, coeff_pred = self.bbox_head(x)
bbox_inputs = (cls_score, bbox_pred,
coeff_pred) + (img_metas, self.test_cfg, rescale)
det_bboxes, det_labels, det_coeffs = self.bbox_head.get_bboxes(
*bbox_inputs)
bbox_results = [
bbox2result(det_bbox, det_label, self.bbox_head.num_classes)
for det_bbox, det_label in zip(det_bboxes, det_labels)
]
num_imgs = len(img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
segm_results = [[[] for _ in range(self.mask_head.num_classes)]
for _ in range(num_imgs)]
else:
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i][:, :4]
for i in range(len(det_bboxes))
]
mask_preds = self.mask_head(x[0], det_coeffs, _bboxes, img_metas)
# apply mask post-processing to each image individually
segm_results = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[] for _ in range(self.mask_head.num_classes)])
else:
segm_result = self.mask_head.get_seg_masks(
mask_preds[i], det_labels[i], img_metas[i], rescale)
segm_results.append(segm_result)
return list(zip(bbox_results, segm_results))
[docs] def aug_test(self, imgs, img_metas, rescale=False):
"""Test with augmentations."""
raise NotImplementedError