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# Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa
from typing import List, Tuple
import torch
import torch.nn.functional as F
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox2roi
from mmdet.utils import ConfigType, InstanceList
from ..task_modules.samplers import SamplingResult
from ..utils import empty_instances
from .standard_roi_head import StandardRoIHead
@MODELS.register_module()
class PointRendRoIHead(StandardRoIHead):
"""`PointRend <https://arxiv.org/abs/1912.08193>`_."""
def __init__(self, point_head: ConfigType, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
assert self.with_bbox and self.with_mask
self.init_point_head(point_head)
def init_point_head(self, point_head: ConfigType) -> None:
"""Initialize ``point_head``"""
self.point_head = MODELS.build(point_head)
def mask_loss(self, x: Tuple[Tensor],
sampling_results: List[SamplingResult], bbox_feats: Tensor,
batch_gt_instances: InstanceList) -> dict:
"""Run forward function and calculate loss for mask head and point head
in training."""
mask_results = super().mask_loss(
x=x,
sampling_results=sampling_results,
bbox_feats=bbox_feats,
batch_gt_instances=batch_gt_instances)
mask_point_results = self._mask_point_loss(
x=x,
sampling_results=sampling_results,
mask_preds=mask_results['mask_preds'],
batch_gt_instances=batch_gt_instances)
mask_results['loss_mask'].update(
loss_point=mask_point_results['loss_point'])
return mask_results
def _mask_point_loss(self, x: Tuple[Tensor],
sampling_results: List[SamplingResult],
mask_preds: Tensor,
batch_gt_instances: InstanceList) -> dict:
"""Run forward function and calculate loss for point head in
training."""
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
rel_roi_points = self.point_head.get_roi_rel_points_train(
mask_preds, pos_labels, cfg=self.train_cfg)
rois = bbox2roi([res.pos_bboxes for res in sampling_results])
fine_grained_point_feats = self._get_fine_grained_point_feats(
x, rois, rel_roi_points)
coarse_point_feats = point_sample(mask_preds, rel_roi_points)
mask_point_pred = self.point_head(fine_grained_point_feats,
coarse_point_feats)
loss_and_target = self.point_head.loss_and_target(
point_pred=mask_point_pred,
rel_roi_points=rel_roi_points,
sampling_results=sampling_results,
batch_gt_instances=batch_gt_instances,
cfg=self.train_cfg)
return loss_and_target
def _mask_point_forward_test(self, x: Tuple[Tensor], rois: Tensor,
label_preds: Tensor,
mask_preds: Tensor) -> Tensor:
"""Mask refining process with point head in testing.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
rois (Tensor): shape (num_rois, 5).
label_preds (Tensor): The predication class for each rois.
mask_preds (Tensor): The predication coarse masks of
shape (num_rois, num_classes, small_size, small_size).
Returns:
Tensor: The refined masks of shape (num_rois, num_classes,
large_size, large_size).
"""
refined_mask_pred = mask_preds.clone()
for subdivision_step in range(self.test_cfg.subdivision_steps):
refined_mask_pred = F.interpolate(
refined_mask_pred,
scale_factor=self.test_cfg.scale_factor,
mode='bilinear',
align_corners=False)
# If `subdivision_num_points` is larger or equal to the
# resolution of the next step, then we can skip this step
num_rois, channels, mask_height, mask_width = \
refined_mask_pred.shape
if (self.test_cfg.subdivision_num_points >=
self.test_cfg.scale_factor**2 * mask_height * mask_width
and
subdivision_step < self.test_cfg.subdivision_steps - 1):
continue
point_indices, rel_roi_points = \
self.point_head.get_roi_rel_points_test(
refined_mask_pred, label_preds, cfg=self.test_cfg)
fine_grained_point_feats = self._get_fine_grained_point_feats(
x=x, rois=rois, rel_roi_points=rel_roi_points)
coarse_point_feats = point_sample(mask_preds, rel_roi_points)
mask_point_pred = self.point_head(fine_grained_point_feats,
coarse_point_feats)
point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
refined_mask_pred = refined_mask_pred.reshape(
num_rois, channels, mask_height * mask_width)
refined_mask_pred = refined_mask_pred.scatter_(
2, point_indices, mask_point_pred)
refined_mask_pred = refined_mask_pred.view(num_rois, channels,
mask_height, mask_width)
return refined_mask_pred
def _get_fine_grained_point_feats(self, x: Tuple[Tensor], rois: Tensor,
rel_roi_points: Tensor) -> Tensor:
"""Sample fine grained feats from each level feature map and
concatenate them together.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
rois (Tensor): shape (num_rois, 5).
rel_roi_points (Tensor): A tensor of shape (num_rois, num_points,
2) that contains [0, 1] x [0, 1] normalized coordinates of the
most uncertain points from the [mask_height, mask_width] grid.
Returns:
Tensor: The fine grained features for each points,
has shape (num_rois, feats_channels, num_points).
"""
assert rois.shape[0] > 0, 'RoI is a empty tensor.'
num_imgs = x[0].shape[0]
fine_grained_feats = []
for idx in range(self.mask_roi_extractor.num_inputs):
feats = x[idx]
spatial_scale = 1. / float(
self.mask_roi_extractor.featmap_strides[idx])
point_feats = []
for batch_ind in range(num_imgs):
# unravel batch dim
feat = feats[batch_ind].unsqueeze(0)
inds = (rois[:, 0].long() == batch_ind)
if inds.any():
rel_img_points = rel_roi_point_to_rel_img_point(
rois=rois[inds],
rel_roi_points=rel_roi_points[inds],
img=feat.shape[2:],
spatial_scale=spatial_scale).unsqueeze(0)
point_feat = point_sample(feat, rel_img_points)
point_feat = point_feat.squeeze(0).transpose(0, 1)
point_feats.append(point_feat)
fine_grained_feats.append(torch.cat(point_feats, dim=0))
return torch.cat(fine_grained_feats, dim=1)
def predict_mask(self,
x: Tuple[Tensor],
batch_img_metas: List[dict],
results_list: InstanceList,
rescale: bool = False) -> InstanceList:
"""Perform forward propagation of the mask head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
batch_img_metas (list[dict]): List of image information.
results_list (list[:obj:`InstanceData`]): Detection results of
each image.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
Returns:
list[:obj:`InstanceData`]: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
# don't need to consider aug_test.
bboxes = [res.bboxes for res in results_list]
mask_rois = bbox2roi(bboxes)
if mask_rois.shape[0] == 0:
results_list = empty_instances(
batch_img_metas,
mask_rois.device,
task_type='mask',
instance_results=results_list,
mask_thr_binary=self.test_cfg.mask_thr_binary)
return results_list
mask_results = self._mask_forward(x, mask_rois)
mask_preds = mask_results['mask_preds']
# split batch mask prediction back to each image
num_mask_rois_per_img = [len(res) for res in results_list]
mask_preds = mask_preds.split(num_mask_rois_per_img, 0)
# refine mask_preds
mask_rois = mask_rois.split(num_mask_rois_per_img, 0)
mask_preds_refined = []
for i in range(len(batch_img_metas)):
labels = results_list[i].labels
x_i = [xx[[i]] for xx in x]
mask_rois_i = mask_rois[i]
mask_rois_i[:, 0] = 0
mask_pred_i = self._mask_point_forward_test(
x_i, mask_rois_i, labels, mask_preds[i])
mask_preds_refined.append(mask_pred_i)
# TODO: Handle the case where rescale is false
results_list = self.mask_head.predict_by_feat(
mask_preds=mask_preds_refined,
results_list=results_list,
batch_img_metas=batch_img_metas,
rcnn_test_cfg=self.test_cfg,
rescale=rescale)
return results_list
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