add pre-trained models for classification and partseg

Author: yanx27

README.md

@@ -3,9 +3,11 @@
 This repo is implementation for [PointNet](http://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf) and [PointNet++](http://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf) in pytorch.
 
 ## Update
-**2021/03/26:** 
+**2021/03/27:** 
 
-Release pre-trained models for semantic segmentation, where PointNet++ can achieve **53.5\%** mIoU.
+(1) Release pre-trained models for semantic segmentation, where PointNet++ can achieve **53.5\%** mIoU.
+
+(2) Release pre-trained models for classification and part segmentation in `log/`.
 
 **2021/03/20:** Update codes for classification, including:
 

log/classification/pointnet2_msg_normals/checkpoints/best_model.pth

@@ -0,0 +1,51 @@
+import torch.nn as nn
+import torch.nn.functional as F
+from pointnet2_utils import PointNetSetAbstractionMsg, PointNetSetAbstraction
+
+
+class get_model(nn.Module):
+    def __init__(self,num_class,normal_channel=True):
+        super(get_model, self).__init__()
+        in_channel = 3 if normal_channel else 0
+        self.normal_channel = normal_channel
+        self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [16, 32, 128], in_channel,[[32, 32, 64], [64, 64, 128], [64, 96, 128]])
+        self.sa2 = PointNetSetAbstractionMsg(128, [0.2, 0.4, 0.8], [32, 64, 128], 320,[[64, 64, 128], [128, 128, 256], [128, 128, 256]])
+        self.sa3 = PointNetSetAbstraction(None, None, None, 640 + 3, [256, 512, 1024], True)
+        self.fc1 = nn.Linear(1024, 512)
+        self.bn1 = nn.BatchNorm1d(512)
+        self.drop1 = nn.Dropout(0.4)
+        self.fc2 = nn.Linear(512, 256)
+        self.bn2 = nn.BatchNorm1d(256)
+        self.drop2 = nn.Dropout(0.5)
+        self.fc3 = nn.Linear(256, num_class)
+
+    def forward(self, xyz):
+        B, _, _ = xyz.shape
+        if self.normal_channel:
+            norm = xyz[:, 3:, :]
+            xyz = xyz[:, :3, :]
+        else:
+            norm = None
+        l1_xyz, l1_points = self.sa1(xyz, norm)
+        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
+        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)
+        x = l3_points.view(B, 1024)
+        x = self.drop1(F.relu(self.bn1(self.fc1(x)), inplace=True))
+        x = self.drop2(F.relu(self.bn2(self.fc2(x)), inplace=True))
+        x = self.fc3(x)
+        x = F.log_softmax(x, -1)
+
+
+        return x,l3_points
+
+
+class get_loss(nn.Module):
+    def __init__(self):
+        super(get_loss, self).__init__()
+
+    def forward(self, pred, target, trans_feat):
+        total_loss = F.nll_loss(pred, target)
+
+        return total_loss
+
+

log/classification/pointnet2_msg_normals/logs/pointnet2_cls_msg.txt

@@ -0,0 +1,316 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from time import time
+import numpy as np
+
+def timeit(tag, t):
+    print("{}: {}s".format(tag, time() - t))
+    return time()
+
+def pc_normalize(pc):
+    l = pc.shape[0]
+    centroid = np.mean(pc, axis=0)
+    pc = pc - centroid
+    m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
+    pc = pc / m
+    return pc
+
+def square_distance(src, dst):
+    """
+    Calculate Euclid distance between each two points.
+
+    src^T * dst = xn * xm + yn * ym + zn * zm；
+    sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
+    sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
+    dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
+         = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst
+
+    Input:
+        src: source points, [B, N, C]
+        dst: target points, [B, M, C]
+    Output:
+        dist: per-point square distance, [B, N, M]
+    """
+    B, N, _ = src.shape
+    _, M, _ = dst.shape
+    dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
+    dist += torch.sum(src ** 2, -1).view(B, N, 1)
+    dist += torch.sum(dst ** 2, -1).view(B, 1, M)
+    return dist
+
+
+def index_points(points, idx):
+    """
+
+    Input:
+        points: input points data, [B, N, C]
+        idx: sample index data, [B, S]
+    Return:
+        new_points:, indexed points data, [B, S, C]
+    """
+    device = points.device
+    B = points.shape[0]
+    view_shape = list(idx.shape)
+    view_shape[1:] = [1] * (len(view_shape) - 1)
+    repeat_shape = list(idx.shape)
+    repeat_shape[0] = 1
+    batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
+    new_points = points[batch_indices, idx, :]
+    return new_points
+
+
+def farthest_point_sample(xyz, npoint):
+    """
+    Input:
+        xyz: pointcloud data, [B, N, 3]
+        npoint: number of samples
+    Return:
+        centroids: sampled pointcloud index, [B, npoint]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
+    distance = torch.ones(B, N).to(device) * 1e10
+    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
+    batch_indices = torch.arange(B, dtype=torch.long).to(device)
+    for i in range(npoint):
+        centroids[:, i] = farthest
+        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)
+        dist = torch.sum((xyz - centroid) ** 2, -1)
+        mask = dist < distance
+        distance[mask] = dist[mask]
+        farthest = torch.max(distance, -1)[1]
+    return centroids
+
+
+def query_ball_point(radius, nsample, xyz, new_xyz):
+    """
+    Input:
+        radius: local region radius
+        nsample: max sample number in local region
+        xyz: all points, [B, N, 3]
+        new_xyz: query points, [B, S, 3]
+    Return:
+        group_idx: grouped points index, [B, S, nsample]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    _, S, _ = new_xyz.shape
+    group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
+    sqrdists = square_distance(new_xyz, xyz)
+    group_idx[sqrdists > radius ** 2] = N
+    group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]
+    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])
+    mask = group_idx == N
+    group_idx[mask] = group_first[mask]
+    return group_idx
+
+
+def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False):
+    """
+    Input:
+        npoint:
+        radius:
+        nsample:
+        xyz: input points position data, [B, N, 3]
+        points: input points data, [B, N, D]
+    Return:
+        new_xyz: sampled points position data, [B, npoint, nsample, 3]
+        new_points: sampled points data, [B, npoint, nsample, 3+D]
+    """
+    B, N, C = xyz.shape
+    S = npoint
+    fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint, C]
+    new_xyz = index_points(xyz, fps_idx)
+    idx = query_ball_point(radius, nsample, xyz, new_xyz)
+    grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]
+    grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)
+
+    if points is not None:
+        grouped_points = index_points(points, idx)
+        new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) # [B, npoint, nsample, C+D]
+    else:
+        new_points = grouped_xyz_norm
+    if returnfps:
+        return new_xyz, new_points, grouped_xyz, fps_idx
+    else:
+        return new_xyz, new_points
+
+
+def sample_and_group_all(xyz, points):
+    """
+    Input:
+        xyz: input points position data, [B, N, 3]
+        points: input points data, [B, N, D]
+    Return:
+        new_xyz: sampled points position data, [B, 1, 3]
+        new_points: sampled points data, [B, 1, N, 3+D]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    new_xyz = torch.zeros(B, 1, C).to(device)
+    grouped_xyz = xyz.view(B, 1, N, C)
+    if points is not None:
+        new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1)
+    else:
+        new_points = grouped_xyz
+    return new_xyz, new_points
+
+
+class PointNetSetAbstraction(nn.Module):
+    def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):
+        super(PointNetSetAbstraction, self).__init__()
+        self.npoint = npoint
+        self.radius = radius
+        self.nsample = nsample
+        self.mlp_convs = nn.ModuleList()
+        self.mlp_bns = nn.ModuleList()
+        last_channel = in_channel
+        for out_channel in mlp:
+            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))
+            self.mlp_bns.append(nn.BatchNorm2d(out_channel))
+            last_channel = out_channel
+        self.group_all = group_all
+
+    def forward(self, xyz, points):
+        """
+        Input:
+            xyz: input points position data, [B, C, N]
+            points: input points data, [B, D, N]
+        Return:
+            new_xyz: sampled points position data, [B, C, S]
+            new_points_concat: sample points feature data, [B, D', S]
+        """
+        xyz = xyz.permute(0, 2, 1)
+        if points is not None:
+            points = points.permute(0, 2, 1)
+
+        if self.group_all:
+            new_xyz, new_points = sample_and_group_all(xyz, points)
+        else:
+            new_xyz, new_points = sample_and_group(self.npoint, self.radius, self.nsample, xyz, points)
+        # new_xyz: sampled points position data, [B, npoint, C]
+        # new_points: sampled points data, [B, npoint, nsample, C+D]
+        new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]
+        for i, conv in enumerate(self.mlp_convs):
+            bn = self.mlp_bns[i]
+            new_points =  F.relu(bn(conv(new_points)), inplace=True)
+
+        new_points = torch.max(new_points, 2)[0]
+        new_xyz = new_xyz.permute(0, 2, 1)
+        return new_xyz, new_points
+
+
+class PointNetSetAbstractionMsg(nn.Module):
+    def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list):
+        super(PointNetSetAbstractionMsg, self).__init__()
+        self.npoint = npoint
+        self.radius_list = radius_list
+        self.nsample_list = nsample_list
+        self.conv_blocks = nn.ModuleList()
+        self.bn_blocks = nn.ModuleList()
+        for i in range(len(mlp_list)):
+            convs = nn.ModuleList()
+            bns = nn.ModuleList()
+            last_channel = in_channel + 3
+            for out_channel in mlp_list[i]:
+                convs.append(nn.Conv2d(last_channel, out_channel, 1))
+                bns.append(nn.BatchNorm2d(out_channel))
+                last_channel = out_channel
+            self.conv_blocks.append(convs)
+            self.bn_blocks.append(bns)
+
+    def forward(self, xyz, points):
+        """
+        Input:
+            xyz: input points position data, [B, C, N]
+            points: input points data, [B, D, N]
+        Return:
+            new_xyz: sampled points position data, [B, C, S]
+            new_points_concat: sample points feature data, [B, D', S]
+        """
+        xyz = xyz.permute(0, 2, 1)
+        if points is not None:
+            points = points.permute(0, 2, 1)
+
+        B, N, C = xyz.shape
+        S = self.npoint
+        new_xyz = index_points(xyz, farthest_point_sample(xyz, S))
+        new_points_list = []
+        for i, radius in enumerate(self.radius_list):
+            K = self.nsample_list[i]
+            group_idx = query_ball_point(radius, K, xyz, new_xyz)
+            grouped_xyz = index_points(xyz, group_idx)
+            grouped_xyz -= new_xyz.view(B, S, 1, C)
+            if points is not None:
+                grouped_points = index_points(points, group_idx)
+                grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1)
+            else:
+                grouped_points = grouped_xyz
+
+            grouped_points = grouped_points.permute(0, 3, 2, 1)  # [B, D, K, S]
+            for j in range(len(self.conv_blocks[i])):
+                conv = self.conv_blocks[i][j]
+                bn = self.bn_blocks[i][j]
+                grouped_points =  F.relu(bn(conv(grouped_points)), inplace=True)
+            new_points = torch.max(grouped_points, 2)[0]  # [B, D', S]
+            new_points_list.append(new_points)
+
+        new_xyz = new_xyz.permute(0, 2, 1)
+        new_points_concat = torch.cat(new_points_list, dim=1)
+        return new_xyz, new_points_concat
+
+
+class PointNetFeaturePropagation(nn.Module):
+    def __init__(self, in_channel, mlp):
+        super(PointNetFeaturePropagation, self).__init__()
+        self.mlp_convs = nn.ModuleList()
+        self.mlp_bns = nn.ModuleList()
+        last_channel = in_channel
+        for out_channel in mlp:
+            self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))
+            self.mlp_bns.append(nn.BatchNorm1d(out_channel))
+            last_channel = out_channel
+
+    def forward(self, xyz1, xyz2, points1, points2):
+        """
+        Input:
+            xyz1: input points position data, [B, C, N]
+            xyz2: sampled input points position data, [B, C, S]
+            points1: input points data, [B, D, N]
+            points2: input points data, [B, D, S]
+        Return:
+            new_points: upsampled points data, [B, D', N]
+        """
+        xyz1 = xyz1.permute(0, 2, 1)
+        xyz2 = xyz2.permute(0, 2, 1)
+
+        points2 = points2.permute(0, 2, 1)
+        B, N, C = xyz1.shape
+        _, S, _ = xyz2.shape
+
+        if S == 1:
+            interpolated_points = points2.repeat(1, N, 1)
+        else:
+            dists = square_distance(xyz1, xyz2)
+            dists, idx = dists.sort(dim=-1)
+            dists, idx = dists[:, :, :3], idx[:, :, :3]  # [B, N, 3]
+
+            dist_recip = 1.0 / (dists + 1e-8)
+            norm = torch.sum(dist_recip, dim=2, keepdim=True)
+            weight = dist_recip / norm
+            interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)
+
+        if points1 is not None:
+            points1 = points1.permute(0, 2, 1)
+            new_points = torch.cat([points1, interpolated_points], dim=-1)
+        else:
+            new_points = interpolated_points
+
+        new_points = new_points.permute(0, 2, 1)
+        for i, conv in enumerate(self.mlp_convs):
+            bn = self.mlp_bns[i]
+            new_points = F.relu(bn(conv(new_points)), inplace=True)
+        return new_points
+