def __init__(self, input_channels=6, do_interpolation=False):
super().__init__()
print('PNv2_ASAP-1')
self.do_interpolation = do_interpolation
self.SA_modules = nn.ModuleList()
channel_in = input_channels
skip_channel_list = [input_channels]
for k in range(NPOINTS.__len__()):
mlps = MLPS[k].copy()
channel_out = 0
for idx in range(mlps.__len__()):
mlps[idx] = [channel_in] + mlps[idx]
channel_out += mlps[idx][-1]
self.SA_modules.append(
PointnetSAModuleMSG(npoint=NPOINTS[k],
radii=RADIUS[k],
nsamples=NSAMPLE[k],
mlps=mlps,
use_xyz=True,
bn=True))
skip_channel_list.append(channel_out)
channel_in = channel_out
self.tem_sa1 = PointnetSAModule(mlp=MLP1,
npoint=NPOINT1,
nsample=NSAMPLE1,
radius=RADIUS1)
self.fuse_layer1_atten = nn.Conv1d(MLP1[-1] * 2,
2,
kernel_size=1,
stride=1)
self.fuse_layer1 = nn.Conv1d(MLP1[-1], 384, kernel_size=1, stride=1)
self.tem_fp1 = PointnetFPModule(mlp=FP_MLP1)
self.tem_dropout = nn.Dropout(DP_RATIO)
self.FP_modules = nn.ModuleList()
for k in range(FP_MLPS.__len__()):
pre_channel = FP_MLPS[
k + 1][-1] if k + 1 < len(FP_MLPS) else channel_out
self.FP_modules.append(
PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] +
FP_MLPS[k]))
cls_layers = []
pre_channel = FP_MLPS[0][-1]
for k in range(0, CLS_FC.__len__()):
cls_layers.append(pt_utils.Conv1d(pre_channel, CLS_FC[k], bn=True))
pre_channel = CLS_FC[k]
cls_layers.append(pt_utils.Conv1d(pre_channel, 20, activation=None))
cls_layers.insert(1, nn.Dropout(0.5))
self.cls_layer = nn.Sequential(*cls_layers)
def __init__(self, input_channels=6):
super().__init__()
self.SA_modules = nn.ModuleList()
channel_in = input_channels
skip_channel_list = [input_channels]
for k in range(NPOINTS.__len__()): # k = 0,1,2,3
mlps = MLPS[k].copy() # mlps=MLPS[0], MLPS[1], MLPS[2], MLPS[3]
channel_out = 0
for idx in range(mlps.__len__()): # idx = 0,1
mlps[idx] = [channel_in] + mlps[
idx] #mlps=[0] = [0, 16, 16, 32] mlps[1] = [0, 32, 32, 64]
channel_out += mlps[idx][-1] # channel_out = 32+64 = 96
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=NPOINTS[k],
radii=RADIUS[k],
nsamples=NSAMPLE[k],
mlps=mlps, #[[0, 16, 16, 32], [0, 32, 32, 64]]
use_xyz=True,
bn=True))
skip_channel_list.append(channel_out)
channel_in = channel_out
self.FP_modules = nn.ModuleList()
for k in range(FP_MLPS.__len__()):
pre_channel = FP_MLPS[
k + 1][-1] if k + 1 < len(FP_MLPS) else channel_out
self.FP_modules.append(
PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] +
FP_MLPS[k]))
cls_layers = []
pre_channel = FP_MLPS[0][-1]
for k in range(0, CLS_FC.__len__()):
cls_layers.append(pt_utils.Conv1d(pre_channel, CLS_FC[k], bn=True))
pre_channel = CLS_FC[k]
cls_layers.append(pt_utils.Conv1d(pre_channel, 1, activation=None))
cls_layers.insert(1, nn.Dropout(0.5))
self.cls_layer = nn.Sequential(*cls_layers)