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import logging
import sys
import torch
from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
logger = logging.getLogger(__name__)
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import completed
class BBoxTestMixin(object):
if sys.version_info >= (3, 7):
async def async_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False,
bbox_semaphore=None,
global_lock=None):
"""Asynchronized test for box head without augmentation."""
rois = bbox2roi(proposals)
roi_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
roi_feats = self.shared_head(roi_feats)
sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017)
async with completed(
__name__, 'bbox_head_forward',
sleep_interval=sleep_interval):
cls_score, bbox_pred = self.bbox_head(roi_feats)
img_shape = img_metas[0]['img_shape']
scale_factor = img_metas[0]['scale_factor']
det_bboxes, det_labels = self.bbox_head.get_bboxes(
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=rcnn_test_cfg)
return det_bboxes, det_labels
def simple_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False):
"""Test only det bboxes without augmentation.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
img_metas (list[dict]): Image meta info.
proposals (Tensor or List[Tensor]): Region proposals.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
rescale (bool): If True, return boxes in original image space.
Default: False.
Returns:
tuple[list[Tensor], list[Tensor]]: The first list contains
the boxes of the corresponding image in a batch, each
tensor has the shape (num_boxes, 5) and last dimension
5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor
in the second list is the labels with shape (num_boxes, ).
The length of both lists should be equal to batch_size.
"""
# get origin input shape to support onnx dynamic input shape
if torch.onnx.is_in_onnx_export():
assert len(
img_metas
) == 1, 'Only support one input image while in exporting to ONNX'
img_shapes = img_metas[0]['img_shape_for_onnx']
else:
img_shapes = tuple(meta['img_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# The length of proposals of different batches may be different.
# In order to form a batch, a padding operation is required.
if isinstance(proposals, list):
# padding to form a batch
max_size = max([proposal.size(0) for proposal in proposals])
for i, proposal in enumerate(proposals):
supplement = proposal.new_full(
(max_size - proposal.size(0), proposal.size(1)), 0)
proposals[i] = torch.cat((supplement, proposal), dim=0)
rois = torch.stack(proposals, dim=0)
else:
rois = proposals
batch_index = torch.arange(
rois.size(0), device=rois.device).float().view(-1, 1, 1).expand(
rois.size(0), rois.size(1), 1)
rois = torch.cat([batch_index, rois[..., :4]], dim=-1)
batch_size = rois.shape[0]
num_proposals_per_img = rois.shape[1]
# Eliminate the batch dimension
rois = rois.view(-1, 5)
bbox_results = self._bbox_forward(x, rois)
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
# Recover the batch dimension
rois = rois.reshape(batch_size, num_proposals_per_img, -1)
cls_score = cls_score.reshape(batch_size, num_proposals_per_img, -1)
if not torch.onnx.is_in_onnx_export():
# remove padding
supplement_mask = rois[..., -1] == 0
cls_score[supplement_mask, :] = 0
# bbox_pred would be None in some detector when with_reg is False,
# e.g. Grid R-CNN.
if bbox_pred is not None:
# the bbox prediction of some detectors like SABL is not Tensor
if isinstance(bbox_pred, torch.Tensor):
bbox_pred = bbox_pred.reshape(batch_size,
num_proposals_per_img, -1)
if not torch.onnx.is_in_onnx_export():
bbox_pred[supplement_mask, :] = 0
else:
# TODO: Looking forward to a better way
# For SABL
bbox_preds = self.bbox_head.bbox_pred_split(
bbox_pred, num_proposals_per_img)
# apply bbox post-processing to each image individually
det_bboxes = []
det_labels = []
for i in range(len(proposals)):
# remove padding
supplement_mask = proposals[i][..., -1] == 0
for bbox in bbox_preds[i]:
bbox[supplement_mask] = 0
det_bbox, det_label = self.bbox_head.get_bboxes(
rois[i],
cls_score[i],
bbox_preds[i],
img_shapes[i],
scale_factors[i],
rescale=rescale,
cfg=rcnn_test_cfg)
det_bboxes.append(det_bbox)
det_labels.append(det_label)
return det_bboxes, det_labels
else:
bbox_pred = None
return self.bbox_head.get_bboxes(
rois,
cls_score,
bbox_pred,
img_shapes,
scale_factors,
rescale=rescale,
cfg=rcnn_test_cfg)
def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
"""Test det bboxes with test time augmentation."""
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(feats, img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
# TODO more flexible
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip, flip_direction)
rois = bbox2roi([proposals])
bbox_results = self._bbox_forward(x, rois)
bboxes, scores = self.bbox_head.get_bboxes(
rois,
bbox_results['cls_score'],
bbox_results['bbox_pred'],
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
return det_bboxes, det_labels
class MaskTestMixin(object):
if sys.version_info >= (3, 7):
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
def simple_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False):
"""Simple test for mask head without augmentation."""
# image shapes of images in the batch
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# The length of proposals of different batches may be different.
# In order to form a batch, a padding operation is required.
if isinstance(det_bboxes, list):
# padding to form a batch
max_size = max([bboxes.size(0) for bboxes in det_bboxes])
for i, (bbox, label) in enumerate(zip(det_bboxes, det_labels)):
supplement_bbox = bbox.new_full(
(max_size - bbox.size(0), bbox.size(1)), 0)
supplement_label = label.new_full((max_size - label.size(0), ),
0)
det_bboxes[i] = torch.cat((supplement_bbox, bbox), dim=0)
det_labels[i] = torch.cat((supplement_label, label), dim=0)
det_bboxes = torch.stack(det_bboxes, dim=0)
det_labels = torch.stack(det_labels, dim=0)
batch_size = det_bboxes.size(0)
num_proposals_per_img = det_bboxes.shape[1]
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
det_bboxes = det_bboxes[..., :4]
if rescale:
if not isinstance(scale_factors[0], float):
scale_factors = det_bboxes.new_tensor(scale_factors)
det_bboxes = det_bboxes * scale_factors.unsqueeze(1)
batch_index = torch.arange(
det_bboxes.size(0), device=det_bboxes.device).float().view(
-1, 1, 1).expand(det_bboxes.size(0), det_bboxes.size(1), 1)
mask_rois = torch.cat([batch_index, det_bboxes], dim=-1)
mask_rois = mask_rois.view(-1, 5)
mask_results = self._mask_forward(x, mask_rois)
mask_pred = mask_results['mask_pred']
# Recover the batch dimension
mask_preds = mask_pred.reshape(batch_size, num_proposals_per_img,
*mask_pred.shape[1:])
# apply mask post-processing to each image individually
segm_results = []
for i in range(batch_size):
mask_pred = mask_preds[i]
det_bbox = det_bboxes[i]
det_label = det_labels[i]
# remove padding
supplement_mask = det_bbox[..., -1] != 0
mask_pred = mask_pred[supplement_mask]
det_bbox = det_bbox[supplement_mask]
det_label = det_label[supplement_mask]
if det_label.shape[0] == 0:
segm_results.append([[]
for _ in range(self.mask_head.num_classes)
])
else:
segm_result = self.mask_head.get_seg_masks(
mask_pred, det_bbox, det_label, self.test_cfg,
ori_shapes[i], scale_factors[i], rescale)
segm_results.append(segm_result)
return segm_results
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
"""Test for mask head with test time augmentation."""
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
aug_masks = []
for x, img_meta in zip(feats, img_metas):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip, flip_direction)
mask_rois = bbox2roi([_bboxes])
mask_results = self._mask_forward(x, mask_rois)
# convert to numpy array to save memory
aug_masks.append(
mask_results['mask_pred'].sigmoid().cpu().numpy())
merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head.get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
self.test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return segm_result
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