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V1.2 #34

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578e0d4
modify future trajs issue
ruiqi-song May 27, 2025
223f9f5
v1.2
ruiqi-song May 27, 2025
752b1d7
delete .file
ruiqi-song May 27, 2025
4c364db
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ruiqi-song May 27, 2025
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107 changes: 25 additions & 82 deletions projects/mmdet3d_plugin/GenAD/GenAD_head.py
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Original file line number Diff line number Diff line change
Expand Up @@ -524,6 +524,7 @@ def forward(self,
gt_labels_3d=None,
gt_attr_labels=None,
ego_fut_trajs=None,
gt_bboxes_3d=None,
):
"""Forward function.
Args:
Expand Down Expand Up @@ -755,12 +756,12 @@ def forward(self,
# generator for planning & motion
current_states = torch.cat((motion_hs.permute(1, 0, 2), ca_motion_query.reshape(batch_size, -1, self.embed_dims)), dim=2)
distribution_comp = {}
# states = torch.randn((2, 1, 64, 200, 200), device=motion_hs.device)
# future_distribution_inputs = torch.randn((2, 5, 6, 200, 200), device=motion_hs.device)
noise = None

if self.training:
future_distribution_inputs = self.get_future_labels(gt_labels_3d, gt_attr_labels,
ego_fut_trajs, motion_hs.device)
ego_fut_trajs, outputs_classes,
outputs_coords, gt_bboxes_3d)
else:
future_distribution_inputs = None

Expand Down Expand Up @@ -798,12 +799,7 @@ def forward(self,
agent_trajs = torch.stack(motion_fut_trajs_list, dim=3)
agent_trajs = agent_trajs.reshape(batch_size, 1, self.agent_dim, self.fut_mode, -1)

# future_hs = future_states_hs[:, :, 0:self.agent_dim * self.fut_mode, :].reshape(
# batch_size, self.agent_dim, self.fut_mode, -1)
# current_hs = current_states[:, 0:self.agent_dim * self.fut_mode, :].reshape(
# batch_size, self.agent_dim, self.fut_mode, -1)
#
# motion_cls_hs = torch.cat((future_hs, current_hs), dim=-1)

motion_cls_hs = torch.cat((future_states_hs[:, :, 0:self.agent_dim * self.fut_mode, :].
reshape(batch_size, self.agent_dim, self.fut_mode, -1),
current_states[:, 0:self.agent_dim * self.fut_mode, :].
Expand All @@ -820,8 +816,6 @@ def forward(self,
outputs_coords = torch.stack(outputs_coords)
outputs_trajs = agent_trajs.permute(1, 0, 2, 3, 4)
outputs_trajs_classes = torch.stack(outputs_trajs_classes)
# outputs_trajs = outputs_trajs.repeat(outputs_coords.shape[0], 1, 1, 1, 1)
# outputs_trajs_classes = outputs_trajs_classes.repeat(outputs_coords.shape[0], 1, 1, 1)

outs = {
'bev_embed': bev_embed,
Expand Down Expand Up @@ -1987,73 +1981,6 @@ def distribution_forward(self, present_features, future_distribution_inputs=None

return sample, output_distribution

def get_future_labels(self, gt_labels_3d, gt_attr_labels, ego_fut_trajs, device):

"""get_future_label.
Args:
gt_labels_3d: agent future 3d labels
gt_attr_labels: agent future 3d labels
ego_fut_trajs: ego future trajectory.
device: gpu device id
Returns:
gt_trajs: [B, A, T, 2]
"""

agent_dim = 300
veh_list = [0, 1, 3, 4]
mapped_class_names = [
'car', 'truck', 'construction_vehicle', 'bus',
'trailer', 'barrier', 'motorcycle', 'bicycle',
'pedestrian', 'traffic_cone'
]
ignore_list = ['construction_vehicle', 'barrier',
'traffic_cone', 'motorcycle', 'bicycle']

batch_size = len(gt_labels_3d)

# gt_label = gt_labels_3d[0]
# gt_attr_label = gt_attr_labels[0]

gt_fut_trajs_bz_list = []

for bz in range(batch_size):
gt_fut_trajs_list = []
gt_label = gt_labels_3d[bz]
gt_attr_label = gt_attr_labels[bz]
for i in range(gt_label.shape[0]):
gt_label[i] = 0 if gt_label[i] in veh_list else gt_label[i]
box_name = mapped_class_names[gt_label[i]]
if box_name in ignore_list:
continue
gt_fut_masks = gt_attr_label[i][self.fut_ts * 2:self.fut_ts * 3]
num_valid_ts = sum(gt_fut_masks == 1)
gt_fut_traj = gt_attr_label[i][:self.fut_ts * 2].reshape(-1, 2)
gt_fut_traj = gt_fut_traj[:num_valid_ts]
if gt_fut_traj.shape[0] == 0:
gt_fut_traj = torch.zeros([self.fut_ts - gt_fut_traj.shape[0], 2], device=device)
if gt_fut_traj.shape[0] < self.fut_ts:
gt_fut_traj = torch.cat(
(gt_fut_traj, torch.zeros([self.fut_ts - gt_fut_traj.shape[0], 2], device=device)), 0)
gt_fut_trajs_list.append(gt_fut_traj)

if len(gt_fut_trajs_list) != 0 & len(gt_fut_trajs_list) < agent_dim:
gt_fut_trajs = torch.cat(
(torch.stack(gt_fut_trajs_list),
torch.zeros([agent_dim - len(gt_fut_trajs_list), self.fut_ts, 2], device=device)), 0)
else:
gt_fut_trajs = torch.zeros([agent_dim, self.fut_ts, 2], device=device)

gt_fut_trajs_bz_list.append(gt_fut_trajs)

if len(gt_fut_trajs_bz_list) != 0:
gt_trajs = torch.cat((torch.stack(gt_fut_trajs_bz_list).repeat(1, 6, 1, 1), ego_fut_trajs), dim=1)
else:
gt_trajs = ego_fut_trajs
# future_states = gt_trajs.reshape(batch_size, gt_trajs.shape[1], -1)

# [bz, a, t, 2]
return gt_trajs.reshape(batch_size, gt_trajs.shape[1], -1)

def future_states_predict(self, batch_size, sample, hidden_states, current_states):
"""get_future_label.
Args:
Expand Down Expand Up @@ -2082,8 +2009,24 @@ def future_states_predict(self, batch_size, sample, hidden_states, current_state

return states_hs, future_states_hs





def get_future_labels(self, gt_labels_3d, gt_attr_labels, ego_fut_trajs, outputs_classes, outputs_coords, gt_bboxes_3d):
temp_cls_scores = torch.stack(outputs_classes)[-1]
temp_bbox_preds = torch.stack(outputs_coords)[-1]
bz = temp_cls_scores.size(0)
temp_cls_scores_list = [temp_cls_scores[i] for i in range(bz)]
temp_bbox_preds_list = [temp_bbox_preds[i] for i in range(bz)]
device = gt_labels_3d[0].device
gt_bboxes_list = [torch.cat(
(gt_bboxes.gravity_center, gt_bboxes.tensor[:, 3:]),
dim=1).to(device) for gt_bboxes in gt_bboxes_3d]
cls_reg_targets = self.get_targets(temp_cls_scores_list, temp_bbox_preds_list,
gt_bboxes_list, gt_labels_3d,
gt_attr_labels, None)
(_, _, _, _, traj_targets_list, traj_weights_list, _, _, _) = cls_reg_targets
agent_targets_gt = torch.stack(traj_targets_list) # [bz, 300, 12]
agent_targets_gt = agent_targets_gt.repeat_interleave(6, dim=1)
ego_target_gt = ego_fut_trajs.flatten(2) # # [bz, 1, 12]
all_target_gt = torch.cat([agent_targets_gt, ego_target_gt], dim=1) # [bz, 301, 12]

return all_target_gt