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import torch
import numpy as np
from mmcv.runner import force_fp32, auto_fp16
from mmcv.runner import get_dist_info
from mmcv.runner.fp16_utils import cast_tensor_type
from mmdet.models import DETECTORS
from mmdet3d.core import bbox3d2result
from mmdet3d.models.detectors.mvx_two_stage import MVXTwoStageDetector
from .utils import GridMask, pad_multiple, GpuPhotoMetricDistortion
@DETECTORS.register_module()
class SparseBEV(MVXTwoStageDetector):
def __init__(self,
data_aug=None,
stop_prev_grad=0,
pts_voxel_layer=None,
pts_voxel_encoder=None,
pts_middle_encoder=None,
pts_fusion_layer=None,
img_backbone=None,
pts_backbone=None,
img_neck=None,
pts_neck=None,
pts_bbox_head=None,
img_roi_head=None,
img_rpn_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
super(SparseBEV, self).__init__(pts_voxel_layer, pts_voxel_encoder,
pts_middle_encoder, pts_fusion_layer,
img_backbone, pts_backbone, img_neck, pts_neck,
pts_bbox_head, img_roi_head, img_rpn_head,
train_cfg, test_cfg, pretrained)
self.data_aug = data_aug
self.stop_prev_grad = stop_prev_grad
self.color_aug = GpuPhotoMetricDistortion()
self.grid_mask = GridMask(ratio=0.5, prob=0.7)
self.use_grid_mask = True
self.memory = {}
self.queue = queue.Queue()
@auto_fp16(apply_to=('img'), out_fp32=True)
def extract_img_feat(self, img):
if self.use_grid_mask:
img = self.grid_mask(img)
img_feats = self.img_backbone(img)
if isinstance(img_feats, dict):
img_feats = list(img_feats.values())
if self.with_img_neck:
img_feats = self.img_neck(img_feats)
return img_feats
def extract_feat(self, img, img_metas):
if isinstance(img, list):
img = torch.stack(img, dim=0)
assert img.dim() == 5
B, N, C, H, W = img.size()
img = img.view(B * N, C, H, W)
img = img.float()
# move some augmentations to GPU
if self.data_aug is not None:
if 'img_color_aug' in self.data_aug and self.data_aug['img_color_aug'] and self.training:
img = self.color_aug(img)
if 'img_norm_cfg' in self.data_aug:
img_norm_cfg = self.data_aug['img_norm_cfg']
norm_mean = torch.tensor(img_norm_cfg['mean'], device=img.device)
norm_std = torch.tensor(img_norm_cfg['std'], device=img.device)
if img_norm_cfg['to_rgb']:
img = img[:, [2, 1, 0], :, :] # BGR to RGB
img = img - norm_mean.reshape(1, 3, 1, 1)
img = img / norm_std.reshape(1, 3, 1, 1)
for b in range(B):
img_shape = (img.shape[2], img.shape[3], img.shape[1])
img_metas[b]['img_shape'] = [img_shape for _ in range(N)]
img_metas[b]['ori_shape'] = [img_shape for _ in range(N)]
if 'img_pad_cfg' in self.data_aug:
img_pad_cfg = self.data_aug['img_pad_cfg']
img = pad_multiple(img, img_metas, size_divisor=img_pad_cfg['size_divisor'])
input_shape = img.shape[-2:]
# update real input shape of each single img
for img_meta in img_metas:
img_meta.update(input_shape=input_shape)
if self.training and self.stop_prev_grad > 0:
H, W = input_shape
img = img.reshape(B, -1, 6, C, H, W)
img_grad = img[:, :self.stop_prev_grad]
img_nograd = img[:, self.stop_prev_grad:]
all_img_feats = [self.extract_img_feat(img_grad.reshape(-1, C, H, W))]
with torch.no_grad():
self.eval()
for k in range(img_nograd.shape[1]):
all_img_feats.append(self.extract_img_feat(img_nograd[:, k].reshape(-1, C, H, W)))
self.train()
img_feats = []
for lvl in range(len(all_img_feats[0])):
C, H, W = all_img_feats[0][lvl].shape[1:]
img_feat = torch.cat([feat[lvl].reshape(B, -1, 6, C, H, W) for feat in all_img_feats], dim=1)
img_feat = img_feat.reshape(-1, C, H, W)
img_feats.append(img_feat)
else:
img_feats = self.extract_img_feat(img)
img_feats_reshaped = []
for img_feat in img_feats:
BN, C, H, W = img_feat.size()
img_feats_reshaped.append(img_feat.view(B, int(BN / B), C, H, W))
return img_feats_reshaped
def forward_pts_train(self,
pts_feats,
gt_bboxes_3d,
gt_labels_3d,
img_metas,
gt_bboxes_ignore=None):
"""Forward function for point cloud branch.
Args:
pts_feats (list[torch.Tensor]): Features of point cloud branch
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
boxes for each sample.
gt_labels_3d (list[torch.Tensor]): Ground truth labels for
boxes of each sampole
img_metas (list[dict]): Meta information of samples.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
boxes to be ignored. Defaults to None.
Returns:
dict: Losses of each branch.
"""
outs = self.pts_bbox_head(pts_feats, img_metas)
loss_inputs = [gt_bboxes_3d, gt_labels_3d, outs]
losses = self.pts_bbox_head.loss(*loss_inputs)
return losses
@force_fp32(apply_to=('img', 'points'))
def forward(self, return_loss=True, **kwargs):
"""Calls either forward_train or forward_test depending on whether
return_loss=True.
Note this setting will change the expected inputs. When
`return_loss=True`, img and img_metas are single-nested (i.e.
torch.Tensor and list[dict]), and when `resturn_loss=False`, img and
img_metas should be double nested (i.e. list[torch.Tensor],
list[list[dict]]), with the outer list indicating test time
augmentations.
"""
if return_loss:
return self.forward_train(**kwargs)
else:
return self.forward_test(**kwargs)
def forward_train(self,
points=None,
img_metas=None,
gt_bboxes_3d=None,
gt_labels_3d=None,
gt_labels=None,
gt_bboxes=None,
img=None,
proposals=None,
gt_bboxes_ignore=None,
img_depth=None,
img_mask=None):
"""Forward training function.
Args:
points (list[torch.Tensor], optional): Points of each sample.
Defaults to None.
img_metas (list[dict], optional): Meta information of each sample.
Defaults to None.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`], optional):
Ground truth 3D boxes. Defaults to None.
gt_labels_3d (list[torch.Tensor], optional): Ground truth labels
of 3D boxes. Defaults to None.
gt_labels (list[torch.Tensor], optional): Ground truth labels
of 2D boxes in images. Defaults to None.
gt_bboxes (list[torch.Tensor], optional): Ground truth 2D boxes in
images. Defaults to None.
img (torch.Tensor optional): Images of each sample with shape
(N, C, H, W). Defaults to None.
proposals ([list[torch.Tensor], optional): Predicted proposals
used for training Fast RCNN. Defaults to None.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
2D boxes in images to be ignored. Defaults to None.
Returns:
dict: Losses of different branches.
"""
img_feats = self.extract_feat(img, img_metas)
for i in range(len(img_metas)):
img_metas[i]['gt_bboxes_3d'] = gt_bboxes_3d[i]
img_metas[i]['gt_labels_3d'] = gt_labels_3d[i]
losses = self.forward_pts_train(img_feats, gt_bboxes_3d, gt_labels_3d, img_metas, gt_bboxes_ignore)
return losses
def forward_test(self, img_metas, img=None, **kwargs):
for var, name in [(img_metas, 'img_metas')]:
if not isinstance(var, list):
raise TypeError('{} must be a list, but got {}'.format(
name, type(var)))
img = [img] if img is None else img
return self.simple_test(img_metas[0], img[0], **kwargs)
def simple_test_pts(self, x, img_metas, rescale=False):
outs = self.pts_bbox_head(x, img_metas)
bbox_list = self.pts_bbox_head.get_bboxes(outs, img_metas[0], rescale=rescale)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results
def simple_test(self, img_metas, img=None, rescale=False):
world_size = get_dist_info()[1]
if world_size == 1: # online
return self.simple_test_online(img_metas, img, rescale)
else: # offline
return self.simple_test_offline(img_metas, img, rescale)
def simple_test_offline(self, img_metas, img=None, rescale=False):
img_feats = self.extract_feat(img=img, img_metas=img_metas)
bbox_list = [dict() for _ in range(len(img_metas))]
bbox_pts = self.simple_test_pts(img_feats, img_metas, rescale=rescale)
for result_dict, pts_bbox in zip(bbox_list, bbox_pts):
result_dict['pts_bbox'] = pts_bbox
return bbox_list
def simple_test_online(self, img_metas, img=None, rescale=False):
self.fp16_enabled = False
assert len(img_metas) == 1 # batch_size = 1
B, N, C, H, W = img.shape
img = img.reshape(B, N//6, 6, C, H, W)
img_filenames = img_metas[0]['filename']
num_frames = len(img_filenames) // 6
# assert num_frames == img.shape[1]
img_shape = (H, W, C)
img_metas[0]['img_shape'] = [img_shape for _ in range(len(img_filenames))]
img_metas[0]['ori_shape'] = [img_shape for _ in range(len(img_filenames))]
img_metas[0]['pad_shape'] = [img_shape for _ in range(len(img_filenames))]
img_feats_list, img_metas_list = [], []
# extract feature frame by frame
for i in range(num_frames):
img_indices = list(np.arange(i * 6, (i + 1) * 6))
img_metas_curr = [{}]
for k in img_metas[0].keys():
if isinstance(img_metas[0][k], list):
img_metas_curr[0][k] = [img_metas[0][k][i] for i in img_indices]
if img_filenames[img_indices[0]] in self.memory:
# found in memory
img_feats_curr = self.memory[img_filenames[img_indices[0]]]
else:
# extract feature and put into memory
img_feats_curr = self.extract_feat(img[:, i], img_metas_curr)
self.memory[img_filenames[img_indices[0]]] = img_feats_curr
self.queue.put(img_filenames[img_indices[0]])
while self.queue.qsize() >= 16: # avoid OOM
pop_key = self.queue.get()
self.memory.pop(pop_key)
img_feats_list.append(img_feats_curr)
img_metas_list.append(img_metas_curr)
# reorganize
feat_levels = len(img_feats_list[0])
img_feats_reorganized = []
for j in range(feat_levels):
feat_l = torch.cat([img_feats_list[i][j] for i in range(len(img_feats_list))], dim=0)
feat_l = feat_l.flatten(0, 1)[None, ...]
img_feats_reorganized.append(feat_l)
img_metas_reorganized = img_metas_list[0]
for i in range(1, len(img_metas_list)):
for k, v in img_metas_list[i][0].items():
if isinstance(v, list):
img_metas_reorganized[0][k].extend(v)
img_feats = img_feats_reorganized
img_metas = img_metas_reorganized
img_feats = cast_tensor_type(img_feats, torch.half, torch.float32)
# run detector
bbox_list = [dict() for _ in range(1)]
bbox_pts = self.simple_test_pts(img_feats, img_metas, rescale=rescale)
for result_dict, pts_bbox in zip(bbox_list, bbox_pts):
result_dict['pts_bbox'] = pts_bbox
return bbox_list
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