def __init__(self, use_xyz=True, mode='TRAIN'):
super().__init__()
self.training_mode = (mode == 'TRAIN')
MODEL = importlib.import_module(cfg.RPN.BACKBONE)
self.backbone_net = MODEL.get_model(input_channels=int(
cfg.RPN.USE_INTENSITY),
use_xyz=use_xyz)
# classification branch
cls_layers = []
pre_channel = cfg.RPN.FP_MLPS[0][-1]
for k in range(0, cfg.RPN.CLS_FC.__len__()):
cls_layers.append(
pt_utils.Conv1d(pre_channel,
cfg.RPN.CLS_FC[k],
bn=cfg.RPN.USE_BN))
pre_channel = cfg.RPN.CLS_FC[k]
cls_layers.append(pt_utils.Conv1d(pre_channel, 1, activation=None))
if cfg.RPN.DP_RATIO >= 0:
cls_layers.insert(1, nn.Dropout(cfg.RPN.DP_RATIO))
self.rpn_cls_layer = nn.Sequential(*cls_layers)
# regression branch
per_loc_bin_num = int(cfg.RPN.LOC_SCOPE / cfg.RPN.LOC_BIN_SIZE) * 2
if cfg.RPN.LOC_XZ_FINE:
reg_channel = per_loc_bin_num * 4 + cfg.RPN.NUM_HEAD_BIN * 2 + 3
else:
reg_channel = per_loc_bin_num * 2 + cfg.RPN.NUM_HEAD_BIN * 2 + 3
reg_channel += 1 # reg y
reg_layers = []
pre_channel = cfg.RPN.FP_MLPS[0][-1]
for k in range(0, cfg.RPN.REG_FC.__len__()):
reg_layers.append(
pt_utils.Conv1d(pre_channel,
cfg.RPN.REG_FC[k],
bn=cfg.RPN.USE_BN))
pre_channel = cfg.RPN.REG_FC[k]
reg_layers.append(
pt_utils.Conv1d(pre_channel, reg_channel, activation=None))
if cfg.RPN.DP_RATIO >= 0:
reg_layers.insert(1, nn.Dropout(cfg.RPN.DP_RATIO))
self.rpn_reg_layer = nn.Sequential(*reg_layers)
if cfg.RPN.LOSS_CLS == 'DiceLoss':
self.rpn_cls_loss_func = loss_utils.DiceLoss(ignore_target=-1)
elif cfg.RPN.LOSS_CLS == 'SigmoidFocalLoss':
self.rpn_cls_loss_func = loss_utils.SigmoidFocalClassificationLoss(
alpha=cfg.RPN.FOCAL_ALPHA[0], gamma=cfg.RPN.FOCAL_GAMMA)
elif cfg.RPN.LOSS_CLS == 'BinaryCrossEntropy':
self.rpn_cls_loss_func = F.binary_cross_entropy
else:
raise NotImplementedError
self.proposal_layer = ProposalLayer(mode=mode)
self.init_weights()
def __init__(self, use_xyz=True, mode='TRAIN'):
super().__init__()
self.training_mode = (mode == 'TRAIN')
MODEL = importlib.import_module(cfg.RPN.BACKBONE)
self.backbone_net = MODEL.get_model(input_channels=int(
cfg.RPN.USE_INTENSITY),
use_xyz=use_xyz)
# here Conv1d is almost the same as torch Conv1d
# for torch Conv1d see https://pytorch.org/docs/stable/nn.html#conv1d
# here we use the Conv1d so we can do two levels of batch calculation
# the first level is at the level of the scenes and the second is at the level of th points
# The input to both heads is a (B, C, N) shaped tensor.
# C is number of channels (i.e. the number of features each point has) (it is apparently 128)
# N is the number of points in one scene
# this way we regress the output values of all the points using a single run of a Conv1d layer
# Notice the output has the form: classification head (B,1,N) , regression head (B,9,N)
# since the kernel_size is 1 the output is a linear combination of channels just like a simple linear regression plus a bias
# in the case of the regression head, each of the 9 outputs has its own set of weights and biases.
# notice the output is the result of the regression/classification for all the points not just a single one.
# classification branch
cls_layers = []
pre_channel = cfg.RPN.FP_MLPS[0][-1] # = 128
for k in range(0, cfg.RPN.CLS_FC.__len__()):
# input is 128 output is also 128
cls_layers.append(
pt_utils.Conv1d(
pre_channel, cfg.RPN.CLS_FC[k],
bn=cfg.RPN.USE_BN)) # bn is batch normalization
pre_channel = cfg.RPN.CLS_FC[k]
cls_layers.append(pt_utils.Conv1d(pre_channel, 1, activation=None)
) # sigmoid is applied in the loss function not here
# this ends up being:
# 1st layer 128 inputs to 128 outputs
# 2nd layer 128 to 1
if cfg.RPN.DP_RATIO >= 0:
cls_layers.insert(1, nn.Dropout(cfg.RPN.DP_RATIO))
self.rpn_cls_layer = nn.Sequential(*cls_layers)
# it adds a dropout layer with ratio 0.5
# regression branch
# we will do a normal regression for all the 9 parameters (x,y,z, w,h,l , rx,ry,rz) of our bboxes
reg_channel = 9
reg_layers = []
pre_channel = cfg.RPN.FP_MLPS[0][-1] # = 128
for k in range(0, cfg.RPN.REG_FC.__len__()): # cfg.RPN.REG_FC = [128]
reg_layers.append(
pt_utils.Conv1d(pre_channel,
cfg.RPN.REG_FC[k],
bn=cfg.RPN.USE_BN))
pre_channel = cfg.RPN.REG_FC[k]
reg_layers.append(
pt_utils.Conv1d(pre_channel, reg_channel, activation=None))
#if you use binning and classification the activation of this last layer is applied in the loss instead
# see /lib/utils/loss_utils.py "get_reg_loss" it uses BinaryCrossEntropy which applies a softmax (I need to change this !)
if cfg.RPN.DP_RATIO >= 0:
reg_layers.insert(1, nn.Dropout(cfg.RPN.DP_RATIO))
self.rpn_reg_layer = nn.Sequential(*reg_layers)
# this ends up being:
# 1st layer 128 inputs to 128 outputs
# 2nd layer 128 to 9 outputs
# it adds a dropout layer with ratio 0.5
if cfg.RPN.LOSS_CLS == 'DiceLoss':
self.rpn_cls_loss_func = loss_utils.DiceLoss(ignore_target=-1)
elif cfg.RPN.LOSS_CLS == 'SigmoidFocalLoss':
self.rpn_cls_loss_func = loss_utils.SigmoidFocalClassificationLoss(
alpha=cfg.RPN.FOCAL_ALPHA[0], gamma=cfg.RPN.FOCAL_GAMMA)
elif cfg.RPN.LOSS_CLS == 'BinaryCrossEntropy':
self.rpn_cls_loss_func = F.binary_cross_entropy
else:
raise NotImplementedError
# proposal layer is only used in RCNN and not in RPN
self.proposal_layer = ProposalLayer(mode=mode)
self.init_weights()