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GDRN.py
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GDRN.py
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import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import load_checkpoint
from detectron2.utils.events import get_event_storage
from core.utils.pose_utils import quat2mat_torch
from core.utils.rot_reps import ortho6d_to_mat_batch
from core.utils import quaternion_lf, lie_algebra
from core.utils.solver_utils import build_optimizer_with_params
from ..losses.coor_cross_entropy import CrossEntropyHeatmapLoss
from ..losses.l2_loss import L2Loss
from ..losses.pm_loss import PyPMLoss
from ..losses.rot_loss import angular_distance, rot_l2_loss
from .cdpn_rot_head_region import RotWithRegionHead
from .cdpn_trans_head import TransHeadNet
# pnp net variants
from .conv_pnp_net import ConvPnPNet
from .model_utils import compute_mean_re_te, get_mask_prob
from .point_pnp_net import PointPnPNet, SimplePointPnPNet
from .pose_from_pred import pose_from_pred
from .pose_from_pred_centroid_z import pose_from_pred_centroid_z
from .pose_from_pred_centroid_z_abs import pose_from_pred_centroid_z_abs
from .resnet_backbone import ResNetBackboneNet, resnet_spec
logger = logging.getLogger(__name__)
class GDRN(nn.Module):
def __init__(self, cfg, backbone, rot_head_net, trans_head_net=None, pnp_net=None):
super().__init__()
assert cfg.MODEL.CDPN.NAME == "GDRN", cfg.MODEL.CDPN.NAME
self.backbone = backbone
self.rot_head_net = rot_head_net
self.pnp_net = pnp_net
self.trans_head_net = trans_head_net
self.cfg = cfg
self.concat = cfg.MODEL.CDPN.ROT_HEAD.ROT_CONCAT
self.r_out_dim, self.mask_out_dim, self.region_out_dim = get_xyz_mask_region_out_dim(cfg)
# uncertainty multi-task loss weighting
# https://github.com/Hui-Li/multi-task-learning-example-PyTorch/blob/master/multi-task-learning-example-PyTorch.ipynb
# a = log(sigma^2)
# L*exp(-a) + a or L*exp(-a) + log(1+exp(a))
# self.log_vars = nn.Parameter(torch.tensor([0, 0], requires_grad=True, dtype=torch.float32).cuda())
if cfg.MODEL.CDPN.USE_MTL:
self.loss_names = [
"mask",
"coor_x",
"coor_y",
"coor_z",
"coor_x_bin",
"coor_y_bin",
"coor_z_bin",
"region",
"PM_R",
"PM_xy",
"PM_z",
"PM_xy_noP",
"PM_z_noP",
"PM_T",
"PM_T_noP",
"centroid",
"z",
"trans_xy",
"trans_z",
"trans_LPnP",
"rot",
"bind",
]
for loss_name in self.loss_names:
self.register_parameter(
f"log_var_{loss_name}", nn.Parameter(torch.tensor([0.0], requires_grad=True, dtype=torch.float32))
)
def forward(
self,
x,
gt_xyz=None,
gt_xyz_bin=None,
gt_mask_trunc=None,
gt_mask_visib=None,
gt_mask_obj=None,
gt_region=None,
gt_allo_quat=None,
gt_ego_quat=None,
gt_allo_rot6d=None,
gt_ego_rot6d=None,
gt_ego_rot=None,
gt_points=None,
sym_infos=None,
gt_trans=None,
gt_trans_ratio=None,
roi_classes=None,
roi_coord_2d=None,
roi_cams=None,
roi_centers=None,
roi_whs=None,
roi_extents=None,
resize_ratios=None,
do_loss=False,
):
cfg = self.cfg
r_head_cfg = cfg.MODEL.CDPN.ROT_HEAD
t_head_cfg = cfg.MODEL.CDPN.TRANS_HEAD
pnp_net_cfg = cfg.MODEL.CDPN.PNP_NET
# x.shape [bs, 3, 256, 256]
if self.concat:
features, x_f64, x_f32, x_f16 = self.backbone(x) # features.shape [bs, 2048, 8, 8]
# joints.shape [bs, 1152, 64, 64]
mask, coor_x, coor_y, coor_z, region = self.rot_head_net(features, x_f64, x_f32, x_f16)
else:
features = self.backbone(x) # features.shape [bs, 2048, 8, 8]
# joints.shape [bs, 1152, 64, 64]
mask, coor_x, coor_y, coor_z, region = self.rot_head_net(features)
# TODO: remove this trans_head_net
# trans = self.trans_head_net(features)
device = x.device
bs = x.shape[0]
num_classes = r_head_cfg.NUM_CLASSES
out_res = cfg.MODEL.CDPN.BACKBONE.OUTPUT_RES
if r_head_cfg.ROT_CLASS_AWARE:
assert roi_classes is not None
coor_x = coor_x.view(bs, num_classes, self.r_out_dim // 3, out_res, out_res)
coor_x = coor_x[torch.arange(bs).to(device), roi_classes]
coor_y = coor_y.view(bs, num_classes, self.r_out_dim // 3, out_res, out_res)
coor_y = coor_y[torch.arange(bs).to(device), roi_classes]
coor_z = coor_z.view(bs, num_classes, self.r_out_dim // 3, out_res, out_res)
coor_z = coor_z[torch.arange(bs).to(device), roi_classes]
if r_head_cfg.MASK_CLASS_AWARE:
assert roi_classes is not None
mask = mask.view(bs, num_classes, self.mask_out_dim, out_res, out_res)
mask = mask[torch.arange(bs).to(device), roi_classes]
if r_head_cfg.REGION_CLASS_AWARE:
assert roi_classes is not None
region = region.view(bs, num_classes, self.region_out_dim, out_res, out_res)
region = region[torch.arange(bs).to(device), roi_classes]
# -----------------------------------------------
# get rot and trans from pnp_net
# NOTE: use softmax for bins (the last dim is bg)
if coor_x.shape[1] > 1 and coor_y.shape[1] > 1 and coor_z.shape[1] > 1:
coor_x_softmax = F.softmax(coor_x[:, :-1, :, :], dim=1)
coor_y_softmax = F.softmax(coor_y[:, :-1, :, :], dim=1)
coor_z_softmax = F.softmax(coor_z[:, :-1, :, :], dim=1)
coor_feat = torch.cat([coor_x_softmax, coor_y_softmax, coor_z_softmax], dim=1)
else:
coor_feat = torch.cat([coor_x, coor_y, coor_z], dim=1) # BCHW
if pnp_net_cfg.WITH_2D_COORD:
assert roi_coord_2d is not None
coor_feat = torch.cat([coor_feat, roi_coord_2d], dim=1)
# NOTE: for region, the 1st dim is bg
region_softmax = F.softmax(region[:, 1:, :, :], dim=1)
mask_atten = None
if pnp_net_cfg.MASK_ATTENTION != "none":
mask_atten = get_mask_prob(cfg, mask)
region_atten = None
if pnp_net_cfg.REGION_ATTENTION:
region_atten = region_softmax
pred_rot_, pred_t_ = self.pnp_net(
coor_feat, region=region_atten, extents=roi_extents, mask_attention=mask_atten
)
if pnp_net_cfg.R_ONLY: # override trans pred
pred_t_ = self.trans_head_net(features)
# convert pred_rot to rot mat -------------------------
rot_type = pnp_net_cfg.ROT_TYPE
if rot_type in ["ego_quat", "allo_quat"]:
pred_rot_m = quat2mat_torch(pred_rot_)
elif rot_type in ["ego_log_quat", "allo_log_quat"]:
pred_rot_m = quat2mat_torch(quaternion_lf.qexp(pred_rot_))
elif rot_type in ["ego_lie_vec", "allo_lie_vec"]:
pred_rot_m = lie_algebra.lie_vec_to_rot(pred_rot_)
elif rot_type in ["ego_rot6d", "allo_rot6d"]:
pred_rot_m = ortho6d_to_mat_batch(pred_rot_)
else:
raise RuntimeError(f"Wrong pred_rot_ dim: {pred_rot_.shape}")
# convert pred_rot_m and pred_t to ego pose -----------------------------
if pnp_net_cfg.TRANS_TYPE == "centroid_z":
pred_ego_rot, pred_trans = pose_from_pred_centroid_z(
pred_rot_m,
pred_centroids=pred_t_[:, :2],
pred_z_vals=pred_t_[:, 2:3], # must be [B, 1]
roi_cams=roi_cams,
roi_centers=roi_centers,
resize_ratios=resize_ratios,
roi_whs=roi_whs,
eps=1e-4,
is_allo="allo" in pnp_net_cfg.ROT_TYPE,
z_type=pnp_net_cfg.Z_TYPE,
# is_train=True
is_train=do_loss, # TODO: sometimes we need it to be differentiable during test
)
elif pnp_net_cfg.TRANS_TYPE == "centroid_z_abs":
# abs 2d obj center and abs z
pred_ego_rot, pred_trans = pose_from_pred_centroid_z_abs(
pred_rot_m,
pred_centroids=pred_t_[:, :2],
pred_z_vals=pred_t_[:, 2:3], # must be [B, 1]
roi_cams=roi_cams,
eps=1e-4,
is_allo="allo" in pnp_net_cfg.ROT_TYPE,
# is_train=True
is_train=do_loss, # TODO: sometimes we need it to be differentiable during test
)
elif pnp_net_cfg.TRANS_TYPE == "trans":
# TODO: maybe denormalize trans
pred_ego_rot, pred_trans = pose_from_pred(
pred_rot_m, pred_t_, eps=1e-4, is_allo="allo" in pnp_net_cfg.ROT_TYPE, is_train=do_loss
)
else:
raise ValueError(f"Unknown pnp_net trans type: {pnp_net_cfg.TRANS_TYPE}")
if not do_loss: # test
out_dict = {"rot": pred_ego_rot, "trans": pred_trans}
if cfg.TEST.USE_PNP:
# TODO: move the pnp/ransac inside forward
out_dict.update({"mask": mask, "coor_x": coor_x, "coor_y": coor_y, "coor_z": coor_z, "region": region})
else:
out_dict = {}
assert (
(gt_xyz is not None)
and (gt_trans is not None)
and (gt_trans_ratio is not None)
and (gt_region is not None)
)
mean_re, mean_te = compute_mean_re_te(pred_trans, pred_ego_rot, gt_trans, gt_ego_rot)
vis_dict = {
"vis/error_R": mean_re,
"vis/error_t": mean_te * 100, # cm
"vis/error_tx": np.abs(pred_trans[0, 0].detach().item() - gt_trans[0, 0].detach().item()) * 100, # cm
"vis/error_ty": np.abs(pred_trans[0, 1].detach().item() - gt_trans[0, 1].detach().item()) * 100, # cm
"vis/error_tz": np.abs(pred_trans[0, 2].detach().item() - gt_trans[0, 2].detach().item()) * 100, # cm
"vis/tx_pred": pred_trans[0, 0].detach().item(),
"vis/ty_pred": pred_trans[0, 1].detach().item(),
"vis/tz_pred": pred_trans[0, 2].detach().item(),
"vis/tx_net": pred_t_[0, 0].detach().item(),
"vis/ty_net": pred_t_[0, 1].detach().item(),
"vis/tz_net": pred_t_[0, 2].detach().item(),
"vis/tx_gt": gt_trans[0, 0].detach().item(),
"vis/ty_gt": gt_trans[0, 1].detach().item(),
"vis/tz_gt": gt_trans[0, 2].detach().item(),
"vis/tx_rel_gt": gt_trans_ratio[0, 0].detach().item(),
"vis/ty_rel_gt": gt_trans_ratio[0, 1].detach().item(),
"vis/tz_rel_gt": gt_trans_ratio[0, 2].detach().item(),
}
loss_dict = self.gdrn_loss(
cfg=self.cfg,
out_mask=mask,
gt_mask_trunc=gt_mask_trunc,
gt_mask_visib=gt_mask_visib,
gt_mask_obj=gt_mask_obj,
out_x=coor_x,
out_y=coor_y,
out_z=coor_z,
gt_xyz=gt_xyz,
gt_xyz_bin=gt_xyz_bin,
out_region=region,
gt_region=gt_region,
out_trans=pred_trans,
gt_trans=gt_trans,
out_rot=pred_ego_rot,
gt_rot=gt_ego_rot,
out_centroid=pred_t_[:, :2], # TODO: get these from trans head
out_trans_z=pred_t_[:, 2],
gt_trans_ratio=gt_trans_ratio,
gt_points=gt_points,
sym_infos=sym_infos,
extents=roi_extents,
# roi_classes=roi_classes,
)
if cfg.MODEL.CDPN.USE_MTL:
for _name in self.loss_names:
if f"loss_{_name}" in loss_dict:
vis_dict[f"vis_lw/{_name}"] = torch.exp(-getattr(self, f"log_var_{_name}")).detach().item()
for _k, _v in vis_dict.items():
if "vis/" in _k or "vis_lw/" in _k:
if isinstance(_v, torch.Tensor):
_v = _v.item()
vis_dict[_k] = _v
storage = get_event_storage()
storage.put_scalars(**vis_dict)
return out_dict, loss_dict
return out_dict
def gdrn_loss(
self,
cfg,
out_mask,
gt_mask_trunc,
gt_mask_visib,
gt_mask_obj,
out_x,
out_y,
out_z,
gt_xyz,
gt_xyz_bin,
out_region,
gt_region,
out_rot=None,
gt_rot=None,
out_trans=None,
gt_trans=None,
out_centroid=None,
out_trans_z=None,
gt_trans_ratio=None,
gt_points=None,
sym_infos=None,
extents=None,
):
r_head_cfg = cfg.MODEL.CDPN.ROT_HEAD
t_head_cfg = cfg.MODEL.CDPN.TRANS_HEAD
pnp_net_cfg = cfg.MODEL.CDPN.PNP_NET
loss_dict = {}
gt_masks = {"trunc": gt_mask_trunc, "visib": gt_mask_visib, "obj": gt_mask_obj}
# rot xyz loss ----------------------------------
if not r_head_cfg.FREEZE:
xyz_loss_type = r_head_cfg.XYZ_LOSS_TYPE
gt_mask_xyz = gt_masks[r_head_cfg.XYZ_LOSS_MASK_GT]
if xyz_loss_type == "L1":
loss_func = nn.L1Loss(reduction="sum")
loss_dict["loss_coor_x"] = loss_func(
out_x * gt_mask_xyz[:, None], gt_xyz[:, 0:1] * gt_mask_xyz[:, None]
) / gt_mask_xyz.sum().float().clamp(min=1.0)
loss_dict["loss_coor_y"] = loss_func(
out_y * gt_mask_xyz[:, None], gt_xyz[:, 1:2] * gt_mask_xyz[:, None]
) / gt_mask_xyz.sum().float().clamp(min=1.0)
loss_dict["loss_coor_z"] = loss_func(
out_z * gt_mask_xyz[:, None], gt_xyz[:, 2:3] * gt_mask_xyz[:, None]
) / gt_mask_xyz.sum().float().clamp(min=1.0)
elif xyz_loss_type == "CE_coor":
gt_xyz_bin = gt_xyz_bin.long()
loss_func = CrossEntropyHeatmapLoss(reduction="sum", weight=None) # r_head_cfg.XYZ_BIN+1
loss_dict["loss_coor_x"] = loss_func(
out_x * gt_mask_xyz[:, None], gt_xyz_bin[:, 0] * gt_mask_xyz.long()
) / gt_mask_xyz.sum().float().clamp(min=1.0)
loss_dict["loss_coor_y"] = loss_func(
out_y * gt_mask_xyz[:, None], gt_xyz_bin[:, 1] * gt_mask_xyz.long()
) / gt_mask_xyz.sum().float().clamp(min=1.0)
loss_dict["loss_coor_z"] = loss_func(
out_z * gt_mask_xyz[:, None], gt_xyz_bin[:, 2] * gt_mask_xyz.long()
) / gt_mask_xyz.sum().float().clamp(min=1.0)
else:
raise NotImplementedError(f"unknown xyz loss type: {xyz_loss_type}")
loss_dict["loss_coor_x"] *= r_head_cfg.XYZ_LW
loss_dict["loss_coor_y"] *= r_head_cfg.XYZ_LW
loss_dict["loss_coor_z"] *= r_head_cfg.XYZ_LW
# mask loss ----------------------------------
if not r_head_cfg.FREEZE:
mask_loss_type = r_head_cfg.MASK_LOSS_TYPE
gt_mask = gt_masks[r_head_cfg.MASK_LOSS_GT]
if mask_loss_type == "L1":
loss_dict["loss_mask"] = nn.L1Loss(reduction="mean")(out_mask[:, 0, :, :], gt_mask)
elif mask_loss_type == "BCE":
loss_dict["loss_mask"] = nn.BCEWithLogitsLoss(reduction="mean")(out_mask[:, 0, :, :], gt_mask)
elif mask_loss_type == "CE":
loss_dict["loss_mask"] = nn.CrossEntropyLoss(reduction="mean")(out_mask, gt_mask.long())
else:
raise NotImplementedError(f"unknown mask loss type: {mask_loss_type}")
loss_dict["loss_mask"] *= r_head_cfg.MASK_LW
# roi region loss --------------------
if not r_head_cfg.FREEZE:
region_loss_type = r_head_cfg.REGION_LOSS_TYPE
gt_mask_region = gt_masks[r_head_cfg.REGION_LOSS_MASK_GT]
if region_loss_type == "CE":
gt_region = gt_region.long()
loss_func = nn.CrossEntropyLoss(reduction="sum", weight=None) # r_head_cfg.XYZ_BIN+1
loss_dict["loss_region"] = loss_func(
out_region * gt_mask_region[:, None], gt_region * gt_mask_region.long()
) / gt_mask_region.sum().float().clamp(min=1.0)
else:
raise NotImplementedError(f"unknown region loss type: {region_loss_type}")
loss_dict["loss_region"] *= r_head_cfg.REGION_LW
# point matching loss ---------------
if pnp_net_cfg.PM_LW > 0:
assert (gt_points is not None) and (gt_trans is not None) and (gt_rot is not None)
loss_func = PyPMLoss(
loss_type=pnp_net_cfg.PM_LOSS_TYPE,
beta=pnp_net_cfg.PM_SMOOTH_L1_BETA,
reduction="mean",
loss_weight=pnp_net_cfg.PM_LW,
norm_by_extent=pnp_net_cfg.PM_NORM_BY_EXTENT,
symmetric=pnp_net_cfg.PM_LOSS_SYM,
disentangle_t=pnp_net_cfg.PM_DISENTANGLE_T,
disentangle_z=pnp_net_cfg.PM_DISENTANGLE_Z,
t_loss_use_points=pnp_net_cfg.PM_T_USE_POINTS,
r_only=pnp_net_cfg.PM_R_ONLY,
)
loss_pm_dict = loss_func(
pred_rots=out_rot,
gt_rots=gt_rot,
points=gt_points,
pred_transes=out_trans,
gt_transes=gt_trans,
extents=extents,
sym_infos=sym_infos,
)
loss_dict.update(loss_pm_dict)
# rot_loss ----------
if pnp_net_cfg.ROT_LW > 0:
if pnp_net_cfg.ROT_LOSS_TYPE == "angular":
loss_dict["loss_rot"] = angular_distance(out_rot, gt_rot)
elif pnp_net_cfg.ROT_LOSS_TYPE == "L2":
loss_dict["loss_rot"] = rot_l2_loss(out_rot, gt_rot)
else:
raise ValueError(f"Unknown rot loss type: {pnp_net_cfg.ROT_LOSS_TYPE}")
loss_dict["loss_rot"] *= pnp_net_cfg.ROT_LW
# centroid loss -------------
if pnp_net_cfg.CENTROID_LW > 0:
assert (
pnp_net_cfg.TRANS_TYPE == "centroid_z"
), "centroid loss is only valid for predicting centroid2d_rel_delta"
if pnp_net_cfg.CENTROID_LOSS_TYPE == "L1":
loss_dict["loss_centroid"] = nn.L1Loss(reduction="mean")(out_centroid, gt_trans_ratio[:, :2])
elif pnp_net_cfg.CENTROID_LOSS_TYPE == "L2":
loss_dict["loss_centroid"] = L2Loss(reduction="mean")(out_centroid, gt_trans_ratio[:, :2])
elif pnp_net_cfg.CENTROID_LOSS_TYPE == "MSE":
loss_dict["loss_centroid"] = nn.MSELoss(reduction="mean")(out_centroid, gt_trans_ratio[:, :2])
else:
raise ValueError(f"Unknown centroid loss type: {pnp_net_cfg.CENTROID_LOSS_TYPE}")
loss_dict["loss_centroid"] *= pnp_net_cfg.CENTROID_LW
# z loss ------------------
if pnp_net_cfg.Z_LW > 0:
if pnp_net_cfg.Z_TYPE == "REL":
gt_z = gt_trans_ratio[:, 2]
elif pnp_net_cfg.Z_TYPE == "ABS":
gt_z = gt_trans[:, 2]
else:
raise NotImplementedError
if pnp_net_cfg.Z_LOSS_TYPE == "L1":
loss_dict["loss_z"] = nn.L1Loss(reduction="mean")(out_trans_z, gt_z)
elif pnp_net_cfg.Z_LOSS_TYPE == "L2":
loss_dict["loss_z"] = L2Loss(reduction="mean")(out_trans_z, gt_z)
elif pnp_net_cfg.Z_LOSS_TYPE == "MSE":
loss_dict["loss_z"] = nn.MSELoss(reduction="mean")(out_trans_z, gt_z)
else:
raise ValueError(f"Unknown z loss type: {pnp_net_cfg.Z_LOSS_TYPE}")
loss_dict["loss_z"] *= pnp_net_cfg.Z_LW
# trans loss ------------------
if pnp_net_cfg.TRANS_LW > 0:
if pnp_net_cfg.TRANS_LOSS_DISENTANGLE:
# NOTE: disentangle xy/z
if pnp_net_cfg.TRANS_LOSS_TYPE == "L1":
loss_dict["loss_trans_xy"] = nn.L1Loss(reduction="mean")(out_trans[:, :2], gt_trans[:, :2])
loss_dict["loss_trans_z"] = nn.L1Loss(reduction="mean")(out_trans[:, 2], gt_trans[:, 2])
elif pnp_net_cfg.TRANS_LOSS_TYPE == "L2":
loss_dict["loss_trans_xy"] = L2Loss(reduction="mean")(out_trans[:, :2], gt_trans[:, :2])
loss_dict["loss_trans_z"] = L2Loss(reduction="mean")(out_trans[:, 2], gt_trans[:, 2])
elif pnp_net_cfg.TRANS_LOSS_TYPE == "MSE":
loss_dict["loss_trans_xy"] = nn.MSELoss(reduction="mean")(out_trans[:, :2], gt_trans[:, :2])
loss_dict["loss_trans_z"] = nn.MSELoss(reduction="mean")(out_trans[:, 2], gt_trans[:, 2])
else:
raise ValueError(f"Unknown trans loss type: {pnp_net_cfg.TRANS_LOSS_TYPE}")
loss_dict["loss_trans_xy"] *= pnp_net_cfg.TRANS_LW
loss_dict["loss_trans_z"] *= pnp_net_cfg.TRANS_LW
else:
if pnp_net_cfg.TRANS_LOSS_TYPE == "L1":
loss_dict["loss_trans_LPnP"] = nn.L1Loss(reduction="mean")(out_trans, gt_trans)
elif pnp_net_cfg.TRANS_LOSS_TYPE == "L2":
loss_dict["loss_trans_LPnP"] = L2Loss(reduction="mean")(out_trans, gt_trans)
elif pnp_net_cfg.TRANS_LOSS_TYPE == "MSE":
loss_dict["loss_trans_LPnP"] = nn.MSELoss(reduction="mean")(out_trans, gt_trans)
else:
raise ValueError(f"Unknown trans loss type: {pnp_net_cfg.TRANS_LOSS_TYPE}")
loss_dict["loss_trans_LPnP"] *= pnp_net_cfg.TRANS_LW
# bind loss (R^T@t)
if pnp_net_cfg.get("BIND_LW", 0.0) > 0.0:
pred_bind = torch.bmm(out_rot.permute(0, 2, 1), out_trans.view(-1, 3, 1)).view(-1, 3)
gt_bind = torch.bmm(gt_rot.permute(0, 2, 1), gt_trans.view(-1, 3, 1)).view(-1, 3)
if pnp_net_cfg.BIND_LOSS_TYPE == "L1":
loss_dict["loss_bind"] = nn.L1Loss(reduction="mean")(pred_bind, gt_bind)
elif pnp_net_cfg.BIND_LOSS_TYPE == "L2":
loss_dict["loss_bind"] = L2Loss(reduction="mean")(pred_bind, gt_bind)
elif pnp_net_cfg.CENTROID_LOSS_TYPE == "MSE":
loss_dict["loss_bind"] = nn.MSELoss(reduction="mean")(pred_bind, gt_bind)
else:
raise ValueError(f"Unknown bind loss (R^T@t) type: {pnp_net_cfg.BIND_LOSS_TYPE}")
loss_dict["loss_bind"] *= pnp_net_cfg.BIND_LW
if cfg.MODEL.CDPN.USE_MTL:
for _k in loss_dict:
_name = _k.replace("loss_", "log_var_")
cur_log_var = getattr(self, _name)
loss_dict[_k] = loss_dict[_k] * torch.exp(-cur_log_var) + torch.log(1 + torch.exp(cur_log_var))
return loss_dict
def get_xyz_mask_region_out_dim(cfg):
r_head_cfg = cfg.MODEL.CDPN.ROT_HEAD
t_head_cfg = cfg.MODEL.CDPN.TRANS_HEAD
xyz_loss_type = r_head_cfg.XYZ_LOSS_TYPE
mask_loss_type = r_head_cfg.MASK_LOSS_TYPE
if xyz_loss_type in ["MSE", "L1", "L2", "SmoothL1"]:
r_out_dim = 3
elif xyz_loss_type in ["CE_coor", "CE"]:
r_out_dim = 3 * (r_head_cfg.XYZ_BIN + 1)
else:
raise NotImplementedError(f"unknown xyz loss type: {xyz_loss_type}")
if mask_loss_type in ["L1", "BCE"]:
mask_out_dim = 1
elif mask_loss_type in ["CE"]:
mask_out_dim = 2
else:
raise NotImplementedError(f"unknown mask loss type: {mask_loss_type}")
region_out_dim = r_head_cfg.NUM_REGIONS + 1
# at least 2 regions (with bg, at least 3 regions)
assert region_out_dim > 2, region_out_dim
return r_out_dim, mask_out_dim, region_out_dim
def build_model_optimizer(cfg):
backbone_cfg = cfg.MODEL.CDPN.BACKBONE
r_head_cfg = cfg.MODEL.CDPN.ROT_HEAD
t_head_cfg = cfg.MODEL.CDPN.TRANS_HEAD
pnp_net_cfg = cfg.MODEL.CDPN.PNP_NET
if "resnet" in backbone_cfg.ARCH:
params_lr_list = []
# backbone net
block_type, layers, channels, name = resnet_spec[backbone_cfg.NUM_LAYERS]
backbone_net = ResNetBackboneNet(
block_type, layers, backbone_cfg.INPUT_CHANNEL, freeze=backbone_cfg.FREEZE, rot_concat=r_head_cfg.ROT_CONCAT
)
if backbone_cfg.FREEZE:
for param in backbone_net.parameters():
with torch.no_grad():
param.requires_grad = False
else:
params_lr_list.append(
{
"params": filter(lambda p: p.requires_grad, backbone_net.parameters()),
"lr": float(cfg.SOLVER.BASE_LR),
}
)
# rotation head net -----------------------------------------------------
r_out_dim, mask_out_dim, region_out_dim = get_xyz_mask_region_out_dim(cfg)
rot_head_net = RotWithRegionHead(
cfg,
channels[-1],
r_head_cfg.NUM_LAYERS,
r_head_cfg.NUM_FILTERS,
r_head_cfg.CONV_KERNEL_SIZE,
r_head_cfg.OUT_CONV_KERNEL_SIZE,
rot_output_dim=r_out_dim,
mask_output_dim=mask_out_dim,
freeze=r_head_cfg.FREEZE,
num_classes=r_head_cfg.NUM_CLASSES,
rot_class_aware=r_head_cfg.ROT_CLASS_AWARE,
mask_class_aware=r_head_cfg.MASK_CLASS_AWARE,
num_regions=r_head_cfg.NUM_REGIONS,
region_class_aware=r_head_cfg.REGION_CLASS_AWARE,
norm=r_head_cfg.NORM,
num_gn_groups=r_head_cfg.NUM_GN_GROUPS,
)
if r_head_cfg.FREEZE:
for param in rot_head_net.parameters():
with torch.no_grad():
param.requires_grad = False
else:
params_lr_list.append(
{
"params": filter(lambda p: p.requires_grad, rot_head_net.parameters()),
"lr": float(cfg.SOLVER.BASE_LR),
}
)
# translation head net --------------------------------------------------------
if not t_head_cfg.ENABLED:
trans_head_net = None
assert not pnp_net_cfg.R_ONLY, "if pnp_net is R_ONLY, trans_head must be enabled!"
else:
trans_head_net = TransHeadNet(
channels[-1], # the channels of backbone output layer
t_head_cfg.NUM_LAYERS,
t_head_cfg.NUM_FILTERS,
t_head_cfg.CONV_KERNEL_SIZE,
t_head_cfg.OUT_CHANNEL,
freeze=t_head_cfg.FREEZE,
norm=t_head_cfg.NORM,
num_gn_groups=t_head_cfg.NUM_GN_GROUPS,
)
if t_head_cfg.FREEZE:
for param in trans_head_net.parameters():
with torch.no_grad():
param.requires_grad = False
else:
params_lr_list.append(
{
"params": filter(lambda p: p.requires_grad, trans_head_net.parameters()),
"lr": float(cfg.SOLVER.BASE_LR) * t_head_cfg.LR_MULT,
}
)
# -----------------------------------------------
if r_head_cfg.XYZ_LOSS_TYPE in ["CE_coor", "CE"]:
pnp_net_in_channel = r_out_dim - 3
else:
pnp_net_in_channel = r_out_dim
if pnp_net_cfg.WITH_2D_COORD:
pnp_net_in_channel += 2
if pnp_net_cfg.REGION_ATTENTION:
pnp_net_in_channel += r_head_cfg.NUM_REGIONS
if pnp_net_cfg.MASK_ATTENTION in ["concat"]: # do not add dim for none/mul
pnp_net_in_channel += 1
if pnp_net_cfg.ROT_TYPE in ["allo_quat", "ego_quat"]:
rot_dim = 4
elif pnp_net_cfg.ROT_TYPE in ["allo_log_quat", "ego_log_quat", "allo_lie_vec", "ego_lie_vec"]:
rot_dim = 3
elif pnp_net_cfg.ROT_TYPE in ["allo_rot6d", "ego_rot6d"]:
rot_dim = 6
else:
raise ValueError(f"Unknown ROT_TYPE: {pnp_net_cfg.ROT_TYPE}")
pnp_head_cfg = pnp_net_cfg.PNP_HEAD_CFG
pnp_head_type = pnp_head_cfg.pop("type")
if pnp_head_type == "ConvPnPNet":
pnp_head_cfg.update(
nIn=pnp_net_in_channel,
rot_dim=rot_dim,
num_regions=r_head_cfg.NUM_REGIONS,
featdim=128,
num_layers=3,
mask_attention_type=pnp_net_cfg.MASK_ATTENTION,
)
pnp_net = ConvPnPNet(**pnp_head_cfg)
elif pnp_head_type == "PointPnPNet":
pnp_head_cfg.update(nIn=pnp_net_in_channel, rot_dim=rot_dim, num_regions=r_head_cfg.NUM_REGIONS)
pnp_net = PointPnPNet(**pnp_head_cfg)
elif pnp_head_type == "SimplePointPnPNet":
pnp_head_cfg.update(
nIn=pnp_net_in_channel,
rot_dim=rot_dim,
mask_attention_type=pnp_net_cfg.MASK_ATTENTION,
# num_regions=r_head_cfg.NUM_REGIONS,
)
pnp_net = SimplePointPnPNet(**pnp_head_cfg)
else:
raise ValueError(f"Unknown pnp head type: {pnp_head_type}")
if pnp_net_cfg.FREEZE:
for param in pnp_net.parameters():
with torch.no_grad():
param.requires_grad = False
else:
params_lr_list.append(
{
"params": filter(lambda p: p.requires_grad, pnp_net.parameters()),
"lr": float(cfg.SOLVER.BASE_LR) * pnp_net_cfg.LR_MULT,
}
)
# ================================================
# CDPN (Coordinates-based Disentangled Pose Network)
model = GDRN(cfg, backbone_net, rot_head_net, trans_head_net=trans_head_net, pnp_net=pnp_net)
if cfg.MODEL.CDPN.USE_MTL:
params_lr_list.append(
{
"params": filter(
lambda p: p.requires_grad,
[_param for _name, _param in model.named_parameters() if "log_var" in _name],
),
"lr": float(cfg.SOLVER.BASE_LR),
}
)
# get optimizer
optimizer = build_optimizer_with_params(cfg, params_lr_list)
if cfg.MODEL.WEIGHTS == "":
## backbone initialization
backbone_pretrained = cfg.MODEL.CDPN.BACKBONE.get("PRETRAINED", "")
if backbone_pretrained == "":
logger.warning("Randomly initialize weights for backbone!")
else:
# initialize backbone with official ImageNet weights
logger.info(f"load backbone weights from: {backbone_pretrained}")
load_checkpoint(model.backbone, backbone_pretrained, strict=False, logger=logger)
model.to(torch.device(cfg.MODEL.DEVICE))
return model, optimizer