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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchinfo
from torch.utils.checkpoint import checkpoint
import time
#import matplotlib.pyplot as plt
from torch.autograd import Function
from torch.autograd.functional import jacobian
device = 'cuda'
from tqdm.notebook import trange, tqdm
#from chamferdist import ChamferDistance
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import os
import sys
sys.path.insert(0,'../robot/')
from pointconv_util import *
scale = 1.0
class ShuffleLayer1D(nn.Module):
"""
ShuffleLayer (ShuffleNet) for swapping channels in groups.
"""
def __init__(self, groups):
super().__init__()
self.groups = groups
def forward(self, x):
batchsize, num_channels, width = x.size()
channels_per_group = num_channels // self.groups
# reshape
x = x.view(batchsize, self.groups, channels_per_group, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, width)
return x
def knn(x, k):
inner = -2*torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x**2, dim=1, keepdim=True)
pairwise_distance = -xx - inner - xx.transpose(2, 1)
idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k)
return idx
def get_graph_feature(x, k=20, idx=None, dim9=False):
batch_size = x.size(0)
num_points = x.size(2)
x = x.view(batch_size, -1, num_points)
#print(x.shape)
if idx is None:
if dim9 == False:
idx = knn(x, k=k) # (batch_size, num_points, k)
else:
idx = knn(x[:, 128:], k=k)
device = torch.device('cuda')
idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1)*num_points
idx = idx + idx_base
idx = idx.view(-1)
_, num_dims, _ = x.size()
x = x.transpose(2, 1).contiguous() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points)
feature = x.view(batch_size*num_points, -1)[idx, :]
feature = feature.view(batch_size, num_points, k, num_dims)
x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
feature = torch.cat((feature-x, x), dim=3).permute(0, 3, 1, 2).contiguous()
return feature # (batch_size, 2*num_dims, num_points, k)
class DGCNN_semseg_s3dis(nn.Module):
def __init__(self, args):
super(DGCNN_semseg_s3dis, self).__init__()
self.args = args
self.k = args.k
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm2d(64)
self.bn5 = nn.BatchNorm2d(64)
self.bn6 = nn.BatchNorm1d(args.emb_dims)
self.bn7 = nn.BatchNorm1d(512)
self.bn8 = nn.BatchNorm1d(512)
self.conv1 = nn.Sequential(nn.Conv2d(6+128*2, 64, kernel_size=1, bias=False),
self.bn1,
nn.LeakyReLU(negative_slope=0.2))
self.conv2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=False),
self.bn2,
nn.LeakyReLU(negative_slope=0.2))
self.conv3 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
self.bn3,
nn.LeakyReLU(negative_slope=0.2))
self.conv4 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=False),
self.bn4,
nn.LeakyReLU(negative_slope=0.2))
self.conv5 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
self.bn5,
nn.LeakyReLU(negative_slope=0.2))
self.conv6 = nn.Sequential(nn.Conv1d(192, args.emb_dims, kernel_size=1, bias=False),
self.bn6,
nn.LeakyReLU(negative_slope=0.2))
self.conv7 = nn.Sequential(nn.Conv1d(1216, 512, kernel_size=1, bias=False),
self.bn7,
nn.LeakyReLU(negative_slope=0.2))
self.conv8 = nn.Sequential(nn.Conv1d(512, 512, kernel_size=1, bias=False),
self.bn8,
nn.LeakyReLU(negative_slope=0.2))
#self.dp1 = nn.Dropout(p=args.dropout)
self.conv9 = nn.Sequential(nn.Conv1d(512, 512, kernel_size=1),nn.ReLU(),nn.Conv1d(512,16,1))
#self.out = nn.Sequential(nn.Conv1d(512,512,1),nn.GroupNorm(4,512),nn.ReLU(),nn.Conv1d(512,3*2048,1),nn.Tanh())
def forward(self, x):
batch_size = x.size(0)
num_points = x.size(2)
x = get_graph_feature(x, k=self.k, dim9=True) # (batch_size, 9, num_points) -> (batch_size, 9*2, num_points, k)
x = self.conv1(x) # (batch_size, 9*2, num_points, k) -> (batch_size, 64, num_points, k)
x = self.conv2(x) # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points, k)
x1 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
x = get_graph_feature(x1, k=self.k) # (batch_size, 64, num_points) -> (batch_size, 64*2, num_points, k)
x = self.conv3(x) # (batch_size, 64*2, num_points, k) -> (batch_size, 64, num_points, k)
x = self.conv4(x) # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points, k)
x2 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
x = get_graph_feature(x2, k=self.k) # (batch_size, 64, num_points) -> (batch_size, 64*2, num_points, k)
x = self.conv5(x) # (batch_size, 64*2, num_points, k) -> (batch_size, 64, num_points, k)
x3 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
x = torch.cat((x1, x2, x3), dim=1) # (batch_size, 64*3, num_points)
x = self.conv6(x) # (batch_size, 64*3, num_points) -> (batch_size, emb_dims, num_points)
x = x.max(dim=-1, keepdim=True)[0] # (batch_size, emb_dims, num_points) -> (batch_size, emb_dims, 1)
x = x.repeat(1, 1, num_points) # (batch_size, 1024, num_points)
x = torch.cat((x, x1, x2, x3), dim=1) # (batch_size, 1024+64*3, num_points)
x = self.conv7(x) # (batch_size, 1024+64*3, num_points) -> (batch_size, 512, num_points)
x = self.conv8(x) # (batch_size, 512, num_points) -> (batch_size, 256, num_points)
#x = self.dp1(x)
x = self.conv9(x) # (batch_size, 256, num_points) -> (batch_size, 13, num_points)
return x
def get_graph_feature1(x, k=20, idx=None, dim9=False):
batch_size = x.size(0)
num_points = x.size(2)
x = x.view(batch_size, -1, num_points)
#print(x.shape)
if idx is None:
if dim9 == False:
idx = knn(x, k=k) # (batch_size, num_points, k)
else:
idx = knn(x[:, -3:], k=k)
device = torch.device('cuda')
idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1)*num_points
idx = idx + idx_base
idx = idx.view(-1)
_, num_dims, _ = x.size()
x = x.transpose(2, 1).contiguous() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points)
feature = x.view(batch_size*num_points, -1)[idx, :]
feature = feature.view(batch_size, num_points, k, num_dims)
x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
feature = torch.cat((feature-x, x), dim=3).permute(0, 3, 1, 2).contiguous()
return feature,idx # (batch_size, 2*num_dims, num_points, k)
class DGCNN_lean(nn.Module):
def __init__(self, args):
super(DGCNN_lean, self).__init__()
self.args = args
self.k = args.k
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm2d(64)
self.bn5 = nn.BatchNorm2d(64)
self.bn6 = nn.BatchNorm1d(args.emb_dims)
self.bn7 = nn.BatchNorm1d(512)
self.bn8 = nn.BatchNorm1d(512)
self.conv1 = nn.Sequential(nn.Conv2d(6+64*2, 64, kernel_size=1, bias=False),
self.bn1,
nn.LeakyReLU(negative_slope=0.2))
self.conv2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=False),
self.bn2,
nn.LeakyReLU(negative_slope=0.2))
self.conv3 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
self.bn3,
nn.LeakyReLU(negative_slope=0.2))
self.conv4 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=False),
self.bn4,
nn.LeakyReLU(negative_slope=0.2))
self.conv5 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
self.bn5,
nn.LeakyReLU(negative_slope=0.2))
self.conv9 = nn.Sequential(nn.Conv1d(192, 128, kernel_size=1),nn.ReLU(),nn.Conv1d(128,16,1))
#self.out = nn.Sequential(nn.Conv1d(512,512,1),nn.GroupNorm(4,512),nn.ReLU(),nn.Conv1d(512,3*2048,1),nn.Tanh())
def forward(self, x):
batch_size = x.size(0)
num_points = x.size(2)
x,idx = get_graph_feature1(x, k=self.k, dim9=True) # (batch_size, 9, num_points) -> (batch_size, 9*2, num_points, k)
x = self.conv1(x) # (batch_size, 9*2, num_points, k) -> (batch_size, 64, num_points, k)
x = self.conv2(x) # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points, k)
x1 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
x,_ = get_graph_feature1(x1, k=self.k,idx=idx) # (batch_size, 64, num_points) -> (batch_size, 64*2, num_points, k)
x = self.conv3(x) # (batch_size, 64*2, num_points, k) -> (batch_size, 64, num_points, k)
x = self.conv4(x) # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points, k)
x2 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
x,_ = get_graph_feature1(x2, k=self.k,idx=idx) # (batch_size, 64, num_points) -> (batch_size, 64*2, num_points, k)
x = self.conv5(x) # (batch_size, 64*2, num_points, k) -> (batch_size, 64, num_points, k)
x3 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
x = torch.cat((x1, x2, x3), dim=1) # (batch_size, 64*3, num_points)
x = self.conv9(x) # (batch_size, 256, num_points) -> (batch_size, 13, num_points)
return x
class PointConvSceneFlowPWC8192(nn.Module):
def __init__(self):
super(PointConvSceneFlowPWC8192, self).__init__()
flow_nei = 32
feat_nei = 16
self.scale = scale
# l0: 8192
self.level0 = Conv1d(3, 32)
self.level0_1 = Conv1d(32, 32)
self.cost0 = PointConvFlow(flow_nei, 32 + 32 + 32 + 32 + 3, [32, 32])
self.flow0 = SceneFlowEstimatorPointConv(32 + 64, 32, use_bn=False)
self.level0_2 = Conv1d(32, 64)
# l1: 2048
self.level1 = PointConvD(2048, feat_nei, 64 + 3, 64)
self.cost1 = PointConvFlow(flow_nei, 64 + 32 + 64 + 32 + 3, [64, 64])
self.flow1 = SceneFlowEstimatorPointConv(64 + 64, 64, use_bn=False)
self.level1_0 = Conv1d(64, 64)
self.level1_1 = Conv1d(64, 128)
# l2: 512
self.level2 = PointConvD(512, feat_nei, 128 + 3, 128)
self.cost2 = PointConvFlow(flow_nei, 128 + 64 + 128 + 64 + 3, [128, 128])
self.flow2 = SceneFlowEstimatorPointConv(128 + 64, 128, use_bn=False)
self.level2_0 = Conv1d(128, 128)
self.level2_1 = Conv1d(128, 256)
# l3: 256
self.level3 = PointConvD(256, feat_nei, 256 + 3, 256)
self.cost3 = PointConvFlow(flow_nei, 256 + 64 + 256 + 64 + 3, [256, 256])
self.flow3 = SceneFlowEstimatorPointConv(256, 256, flow_ch=0, use_bn=False)
self.level3_0 = Conv1d(256, 256)
self.level3_1 = Conv1d(256, 512)
# l4: 64
self.level4 = PointConvD(64, feat_nei, 512 + 3, 256)
# deconv
self.deconv4_3 = Conv1d(256, 64)
self.deconv3_2 = Conv1d(256, 64)
self.deconv2_1 = Conv1d(128, 32)
self.deconv1_0 = Conv1d(64, 32)
# warping
self.warping = PointWarping()
# upsample
self.upsample = UpsampleFlow()
def forward(self, xyz1, xyz2, color1, color2):
# xyz1, xyz2: B, N, 3
# color1, color2: B, N, 3
# l0
pc1_l0 = xyz1.permute(0, 2, 1)
pc2_l0 = xyz2.permute(0, 2, 1)
color1 = color1.permute(0, 2, 1) # B 3 N
color2 = color2.permute(0, 2, 1) # B 3 N
feat1_l0 = self.level0(color1)
feat1_l0 = self.level0_1(feat1_l0)
feat1_l0_1 = self.level0_2(feat1_l0)
feat2_l0 = self.level0(color2)
feat2_l0 = self.level0_1(feat2_l0)
feat2_l0_1 = self.level0_2(feat2_l0)
# l1
pc1_l1, feat1_l1, fps_pc1_l1 = self.level1(pc1_l0, feat1_l0_1)
feat1_l1_2 = self.level1_0(feat1_l1)
feat1_l1_2 = self.level1_1(feat1_l1_2)
pc2_l1, feat2_l1, fps_pc2_l1 = self.level1(pc2_l0, feat2_l0_1)
feat2_l1_2 = self.level1_0(feat2_l1)
feat2_l1_2 = self.level1_1(feat2_l1_2)
# l2
pc1_l2, feat1_l2, fps_pc1_l2 = self.level2(pc1_l1, feat1_l1_2)
feat1_l2_3 = self.level2_0(feat1_l2)
feat1_l2_3 = self.level2_1(feat1_l2_3)
pc2_l2, feat2_l2, fps_pc2_l2 = self.level2(pc2_l1, feat2_l1_2)
feat2_l2_3 = self.level2_0(feat2_l2)
feat2_l2_3 = self.level2_1(feat2_l2_3)
# l3
pc1_l3, feat1_l3, fps_pc1_l3 = self.level3(pc1_l2, feat1_l2_3)
feat1_l3_4 = self.level3_0(feat1_l3)
feat1_l3_4 = self.level3_1(feat1_l3_4)
pc2_l3, feat2_l3, fps_pc2_l3 = self.level3(pc2_l2, feat2_l2_3)
feat2_l3_4 = self.level3_0(feat2_l3)
feat2_l3_4 = self.level3_1(feat2_l3_4)
# l4
pc1_l4, feat1_l4, _ = self.level4(pc1_l3, feat1_l3_4)
feat1_l4_3 = self.upsample(pc1_l3, pc1_l4, feat1_l4)
feat1_l4_3 = self.deconv4_3(feat1_l4_3)
pc2_l4, feat2_l4, _ = self.level4(pc2_l3, feat2_l3_4)
feat2_l4_3 = self.upsample(pc2_l3, pc2_l4, feat2_l4)
feat2_l4_3 = self.deconv4_3(feat2_l4_3)
# l3
c_feat1_l3 = torch.cat([feat1_l3, feat1_l4_3], dim=1)
c_feat2_l3 = torch.cat([feat2_l3, feat2_l4_3], dim=1)
cost3 = self.cost3(pc1_l3, pc2_l3, c_feat1_l3, c_feat2_l3)
feat3, flow3 = self.flow3(pc1_l3, feat1_l3, cost3)
feat1_l3_2 = self.upsample(pc1_l2, pc1_l3, feat1_l3)
feat1_l3_2 = self.deconv3_2(feat1_l3_2)
feat2_l3_2 = self.upsample(pc2_l2, pc2_l3, feat2_l3)
feat2_l3_2 = self.deconv3_2(feat2_l3_2)
c_feat1_l2 = torch.cat([feat1_l2, feat1_l3_2], dim=1)
c_feat2_l2 = torch.cat([feat2_l2, feat2_l3_2], dim=1)
feat1_l2_1 = self.upsample(pc1_l1, pc1_l2, feat1_l2)
feat1_l2_1 = self.deconv2_1(feat1_l2_1)
feat2_l2_1 = self.upsample(pc2_l1, pc2_l2, feat2_l2)
feat2_l2_1 = self.deconv2_1(feat2_l2_1)
c_feat1_l1 = torch.cat([feat1_l1, feat1_l2_1], dim=1)
c_feat2_l1 = torch.cat([feat2_l1, feat2_l2_1], dim=1)
feat1_l1_0 = self.upsample(pc1_l0, pc1_l1, feat1_l1)
feat1_l1_0 = self.deconv1_0(feat1_l1_0)
feat2_l1_0 = self.upsample(pc2_l0, pc2_l1, feat2_l1)
feat2_l1_0 = self.deconv1_0(feat2_l1_0)
c_feat1_l0 = torch.cat([feat1_l0, feat1_l1_0], dim=1)
c_feat2_l0 = torch.cat([feat2_l0, feat2_l1_0], dim=1)
# l2
up_flow2 = self.upsample(pc1_l2, pc1_l3, self.scale * flow3)
pc2_l2_warp = self.warping(pc1_l2, pc2_l2, up_flow2)
cost2 = self.cost2(pc1_l2, pc2_l2_warp, c_feat1_l2, c_feat2_l2)
feat3_up = self.upsample(pc1_l2, pc1_l3, feat3)
new_feat1_l2 = torch.cat([feat1_l2, feat3_up], dim=1)
feat2, flow2 = self.flow2(pc1_l2, new_feat1_l2, cost2, up_flow2)
# l1
up_flow1 = self.upsample(pc1_l1, pc1_l2, self.scale * flow2)
pc2_l1_warp = self.warping(pc1_l1, pc2_l1, up_flow1)
cost1 = self.cost1(pc1_l1, pc2_l1_warp, c_feat1_l1, c_feat2_l1)
feat2_up = self.upsample(pc1_l1, pc1_l2, feat2)
new_feat1_l1 = torch.cat([feat1_l1, feat2_up], dim=1)
feat1, flow1 = self.flow1(pc1_l1, new_feat1_l1, cost1, up_flow1)
# l0
up_flow0 = self.upsample(pc1_l0, pc1_l1, self.scale * flow1)
pc2_l0_warp = self.warping(pc1_l0, pc2_l0, up_flow0)
cost0 = self.cost0(pc1_l0, pc2_l0_warp, c_feat1_l0, c_feat2_l0)
feat1_up = self.upsample(pc1_l0, pc1_l1, feat1)
new_feat1_l0 = torch.cat([feat1_l0, feat1_up], dim=1)
_, flow0 = self.flow0(pc1_l0, new_feat1_l0, cost0, up_flow0)
flows = [flow0, flow1, flow2, flow3]
pc1 = [pc1_l0, pc1_l1, pc1_l2, pc1_l3]
pc2 = [pc2_l0, pc2_l1, pc2_l2, pc2_l3]
fps_pc1_idxs = [fps_pc1_l1, fps_pc1_l2, fps_pc1_l3]
fps_pc2_idxs = [fps_pc2_l1, fps_pc2_l2, fps_pc2_l3]
return flows, fps_pc1_idxs, fps_pc2_idxs, pc1, pc2
ppwc = PointConvSceneFlowPWC8192().to('cuda')