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Nirav-Madhani/pointnet-3d_calassification.ipynb

Created February 3, 2024 20:39
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Pointnet-3D_Calassification.ipynb
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pointnet-3d_calassification.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"o0YEJYAaMkPG"
],
"gpuType": "T4",
"authorship_tag": "ABX9TyMLqxZYOFzfXzo3kgO0AGRA",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Nirav-Madhani/693d35710bb6e8500431097cf9ff9af4/pointnet-3d_calassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# 3D File and Mesh to Point Cloud"
],
"metadata": {
"id": "o0YEJYAaMkPG"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5gTD0bqlFCtu",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ea62c554-a8e6-451c-c655-a4a12af4375b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting path.py\n",
" Downloading path.py-12.5.0-py3-none-any.whl (2.3 kB)\n",
"Collecting path (from path.py)\n",
" Downloading path-16.6.0-py3-none-any.whl (26 kB)\n",
"Installing collected packages: path, path.py\n",
"Successfully installed path-16.6.0 path.py-12.5.0\n"
]
}
],
"source": [
"import numpy as np\n",
"import random\n",
"import math\n",
"!pip install path.py;\n",
"from path import Path\n",
"import plotly.graph_objects as go\n",
"import plotly.express as px\n",
"import os"
]
},
{
"cell_type": "code",
"source": [
"!wget http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip\n",
"!unzip -q ModelNet10.zip\n",
"\n",
"path = Path(\"ModelNet10\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-F76WMgiGG7O",
"outputId": "3afd05f3-cde4-4e66-fb78-e9c1576fb6e5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-06-21 14:36:44-- http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip\n",
"Resolving 3dvision.princeton.edu (3dvision.princeton.edu)... 128.112.136.74\n",
"Connecting to 3dvision.princeton.edu (3dvision.princeton.edu)|128.112.136.74|:80... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip [following]\n",
"--2023-06-21 14:36:44-- https://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip\n",
"Connecting to 3dvision.princeton.edu (3dvision.princeton.edu)|128.112.136.74|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 473402300 (451M) [application/zip]\n",
"Saving to: ‘ModelNet10.zip’\n",
"\n",
"ModelNet10.zip 100%[===================>] 451.47M 59.4MB/s in 18s \n",
"\n",
"2023-06-21 14:37:02 (24.7 MB/s) - ‘ModelNet10.zip’ saved [473402300/473402300]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!cat ModelNet10/bathtub/train/bathtub_0001.off"
],
"metadata": {
"id": "vpBdHZj6GnZE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def read_off(file):\n",
" if 'OFF' != file.readline().strip():\n",
" raise('Not a valid OFF header')\n",
" n_verts, n_faces, __ = tuple([int(s) for s in file.readline().strip().split(' ')])\n",
" verts = [[float(s) for s in file.readline().strip().split(' ')] for i_vert in range(n_verts)]\n",
" faces = [[int(s) for s in file.readline().strip().split(' ')][1:] for i_face in range(n_faces)]\n",
" return verts, faces\n",
"\n",
"with open(path/\"bed/train/bed_0001.off\", 'r') as f:\n",
" verts, faces = read_off(f)"
],
"metadata": {
"id": "0METs047Gmpc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#vertices are location in space\n",
"#faces are list of 3 vertex that form a plane\n",
"faces[100]"
],
"metadata": {
"id": "P6rpKZ3UKDF8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"i,j,k = np.array(faces).T\n",
"x,y,z = np.array(verts).T"
],
"metadata": {
"id": "Xz5T0AfGHSMo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Plot 3D object\n",
"go.Figure(data=[go.Mesh3d(x=x,y=y,z=z,i=i,j=j,k=k,color='grey',opacity=.5)],).show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "UvNGnQHdIJ95",
"outputId": "6ff74830-f256-4769-8d63-8493adbb4417"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
"<body>\n",
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},
{
"cell_type": "code",
"source": [
"#Point Cloud from Vertex\n",
"go.Figure(data=[go.Scatter3d(x=x,y=y,z=z,\n",
" mode='markers')],).show()"
],
"metadata": {
"colab": {
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"height": 542
},
"id": "i1TqpRTKJXMb",
"outputId": "b7f68cf3-a9a5-42fb-8210-6d3743bcb320"
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"outputs": [
{
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]
},
{
"cell_type": "code",
"source": [
"verts[faces[0][1]]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LIDXumK_LBXR",
"outputId": "bbe4958d-6a1c-4030-8877-2125974574dd"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[31.75, 40.972495, -21.0]"
]
},
"metadata": {},
"execution_count": 24
}
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{
"cell_type": "code",
"source": [
"areas = np.zeros(len(faces))\n",
"verts = np.array(verts)\n",
"def triangle_area(pt1, pt2, pt3):\n",
" side_a = np.linalg.norm(pt1 - pt2)\n",
" side_b = np.linalg.norm(pt2 - pt3)\n",
" side_c = np.linalg.norm(pt3 - pt1)\n",
" s = 0.5 * ( side_a + side_b + side_c)\n",
" return max(s * (s - side_a) * (s - side_b) * (s - side_c), 0)**0.5\n",
"\n",
"# we calculate areas of all faces in our mesh\n",
"for i in range(len(areas)):\n",
" areas[i] = (triangle_area(verts[faces[i][0]],\n",
" verts[faces[i][1]],\n",
" verts[faces[i][2]]))"
],
"metadata": {
"id": "sdmpLAQ3KUco"
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"execution_count": null,
"outputs": []
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"cell_type": "code",
"source": [
"num_points = 3000\n",
"sampled_faces = random.choices(faces,weights=areas,k=num_points)\n",
"def sample_point(pt1, pt2, pt3):\n",
" # barycentric coordinates on a triangle\n",
" # https://mathworld.wolfram.com/BarycentricCoordinates.html\n",
" s, t = sorted([random.random(), random.random()])\n",
" f = lambda i: s * pt1[i] + (t-s) * pt2[i] + (1-t) * pt3[i]\n",
" return (f(0), f(1), f(2))\n",
"sampled_points = np.zeros((num_points, 3))\n",
"\n",
"for i in range(len(sampled_faces)):\n",
" sampled_points[i] = (sample_point(verts[sampled_faces[i][0]],\n",
" verts[sampled_faces[i][1]],\n",
" verts[sampled_faces[i][2]]))"
],
"metadata": {
"id": "Nue0gObRLXUh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"len(point_cloud)\n",
"sampled_faces"
],
"metadata": {
"id": "PqiP8WxWPO-V"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"go.Figure(data=[go.Scatter3d(x=sampled_points[:,0],y=sampled_points[:,1],z=sampled_points[:,2],\n",
" mode='markers')],).show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "iCbbo925LjmH",
"outputId": "a55ea2b6-4c02-4c31-df24-e298dfc54578"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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},
{
"cell_type": "code",
"source": [
"#normalize\n",
"sampled_points = sampled_points - np.mean(sampled_points,axis=0)\n",
"sampled_points /= max(np.linalg.norm(sampled_points,axis=1))\n",
"\n",
"go.Figure(data=[go.Scatter3d(x=sampled_points[:,0],y=sampled_points[:,1],z=sampled_points[:,2],\n",
" mode='markers')],).show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "Nq_t6Ai8eayt",
"outputId": "fad35cbb-cde0-4a92-e22a-00a35bcdd49c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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"source": [
"def TransformMeshToNormalPoint(verts,faces,k):\n",
" verts = np.array(verts)\n",
" areas = np.zeros(len(faces))\n",
" for i in range(len(areas)):\n",
" areas[i] = (triangle_area(verts[faces[i][0]],\n",
" verts[faces[i][1]],\n",
" verts[faces[i][2]]))\n",
" sampled_faces = random.choices(faces,weights=areas,k=k)\n",
" sampled_points = np.zeros((k,3))\n",
" for i in range(len(sampled_faces)):\n",
" sampled_points[i] = (sample_point(verts[sampled_faces[i][0]],\n",
" verts[sampled_faces[i][1]],\n",
" verts[sampled_faces[i][2]]))\n",
" sampled_points = sampled_points - np.mean(sampled_points,axis=0)\n",
" sampled_points /= max(np.linalg.norm(sampled_points,axis=1))\n",
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"metadata": {
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"execution_count": null,
"outputs": []
},
{
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"source": [
"def RotateNoise(pointcloud,theta):\n",
" rot_matrix = np.array([[ math.cos(theta), -math.sin(theta), 0],\n",
" [ math.sin(theta), math.cos(theta), 0],\n",
" [0, 0, 1]])\n",
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" rot_pointcloud = rot_matrix.dot(pointcloud.T).T\n",
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" noise = np.random.normal(0, 0.02, (pointcloud.shape))\n",
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"id": "G5fBlJ2ciUlp"
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"execution_count": null,
"outputs": []
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{
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"source": [
"with open(path/\"bed/train/bed_0001.off\", 'r') as f:\n",
" mesh = read_off(f)\n",
"pc = RotateNoise(TransformMeshToNormalPoint(*mesh,3000),random.random() * 2. * math.pi)\n",
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" mode='markers')],).show()"
],
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"id": "U13aR0C5hHYn",
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}
]
},
{
"cell_type": "markdown",
"source": [
"# ML Part\n"
],
"metadata": {
"id": "zPFHy-hwLcDw"
}
},
{
"cell_type": "code",
"source": [
"#Dataset Transform Functions\n",
"class PointSampler(object):\n",
" def __init__(self, output_size):\n",
" assert isinstance(output_size, int)\n",
" self.output_size = output_size\n",
"\n",
" def triangle_area(self, pt1, pt2, pt3):\n",
" side_a = np.linalg.norm(pt1 - pt2)\n",
" side_b = np.linalg.norm(pt2 - pt3)\n",
" side_c = np.linalg.norm(pt3 - pt1)\n",
" s = 0.5 * ( side_a + side_b + side_c)\n",
" return max(s * (s - side_a) * (s - side_b) * (s - side_c), 0)**0.5\n",
"\n",
" def sample_point(self, pt1, pt2, pt3):\n",
" # barycentric coordinates on a triangle\n",
" # https://mathworld.wolfram.com/BarycentricCoordinates.html\n",
" s, t = sorted([random.random(), random.random()])\n",
" f = lambda i: s * pt1[i] + (t-s)*pt2[i] + (1-t)*pt3[i]\n",
" return (f(0), f(1), f(2))\n",
"\n",
"\n",
" def __call__(self, mesh):\n",
" verts, faces = mesh\n",
" verts = np.array(verts)\n",
" areas = np.zeros((len(faces)))\n",
"\n",
" for i in range(len(areas)):\n",
" areas[i] = (self.triangle_area(verts[faces[i][0]],\n",
" verts[faces[i][1]],\n",
" verts[faces[i][2]]))\n",
"\n",
" sampled_faces = (random.choices(faces,\n",
" weights=areas,\n",
" cum_weights=None,\n",
" k=self.output_size))\n",
"\n",
" sampled_points = np.zeros((self.output_size, 3))\n",
"\n",
" for i in range(len(sampled_faces)):\n",
" sampled_points[i] = (self.sample_point(verts[sampled_faces[i][0]],\n",
" verts[sampled_faces[i][1]],\n",
" verts[sampled_faces[i][2]]))\n",
"\n",
" return sampled_points\n",
"\n",
"class Normalize(object):\n",
" def __call__(self, pointcloud):\n",
" assert len(pointcloud.shape)==2\n",
"\n",
" norm_pointcloud = pointcloud - np.mean(pointcloud, axis=0)\n",
" norm_pointcloud /= np.max(np.linalg.norm(norm_pointcloud, axis=1))\n",
"\n",
" return norm_pointcloud\n",
"\n",
"class RandRotation_z(object):\n",
" def __call__(self, pointcloud):\n",
" assert len(pointcloud.shape)==2\n",
"\n",
" theta = random.random() * 2. * math.pi\n",
" rot_matrix = np.array([[ math.cos(theta), -math.sin(theta), 0],\n",
" [ math.sin(theta), math.cos(theta), 0],\n",
" [0, 0, 1]])\n",
"\n",
" rot_pointcloud = rot_matrix.dot(pointcloud.T).T\n",
" return rot_pointcloud\n",
"\n",
"class RandomNoise(object):\n",
" def __call__(self, pointcloud):\n",
" assert len(pointcloud.shape)==2\n",
"\n",
" noise = np.random.normal(0, 0.02, (pointcloud.shape))\n",
"\n",
" noisy_pointcloud = pointcloud + noise\n",
" return noisy_pointcloud\n",
"\n",
"class ToTensor(object):\n",
" def __call__(self, pointcloud):\n",
" assert len(pointcloud.shape)==2\n",
"\n",
" return torch.from_numpy(pointcloud)"
],
"metadata": {
"id": "T8-BIzgEnBRe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Creating Dataset\n",
"from torchvision import transforms, utils\n",
"def default_transforms():\n",
" return transforms.Compose([\n",
" PointSampler(1024),\n",
" Normalize(),\n",
" ToTensor()\n",
" ])\n",
"\n",
"from torch.utils.data import Dataset, DataLoader\n",
"class PointCloudData(Dataset):\n",
" def __init__(self,root_dir,valid=False,folder='train',transform=default_transforms()):\n",
" self.root_dir = root_dir\n",
" folders = [dir for dir in sorted(os.listdir(root_dir)) if os.path.isdir(root_dir/dir)]\n",
" self.classes = {folder: i for i, folder in enumerate(folders)}\n",
" self.transforms = transform if not valid else default_transforms()\n",
" self.valid = valid\n",
" self.files = []\n",
" for category in self.classes.keys():\n",
" new_dir = root_dir/Path(category)/folder\n",
" for file in os.listdir(new_dir):\n",
" if file.endswith('.off'):\n",
" sample = {}\n",
" sample['pcd_path'] = new_dir/file\n",
" sample['category'] = category\n",
" self.files.append(sample)\n",
"\n",
" def __len__(self):\n",
" return len(self.files)\n",
"\n",
" def __preproc__(self, file):\n",
" verts, faces = read_off(file)\n",
" if self.transforms:\n",
" pointcloud = self.transforms((verts, faces))\n",
" return pointcloud\n",
"\n",
" def __getitem__(self, idx):\n",
" pcd_path = self.files[idx]['pcd_path']\n",
" category = self.files[idx]['category']\n",
" with open(pcd_path, 'r') as f:\n",
" pointcloud = self.__preproc__(f)\n",
" return {'pointcloud': pointcloud,\n",
" 'category': self.classes[category]}\n",
"\n"
],
"metadata": {
"id": "PkgLtObSLelu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_transforms = transforms.Compose([\n",
" PointSampler(1024),\n",
" Normalize(),\n",
" RandRotation_z(),\n",
" RandomNoise(),\n",
" ToTensor()\n",
" ])"
],
"metadata": {
"id": "TZGtV_HXrXHn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_ds = PointCloudData(path, transform=train_transforms)\n",
"valid_ds = PointCloudData(path, valid=True, folder='test', transform=train_transforms)"
],
"metadata": {
"id": "fCWj3alarZkW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_loader = DataLoader(dataset=train_ds, batch_size=32, shuffle=True)\n",
"valid_loader = DataLoader(dataset=valid_ds, batch_size=64)"
],
"metadata": {
"id": "a0EaHS58ia4f"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import numpy as np\n",
"import torch.nn.functional as F\n",
"\n",
"class Tnet(nn.Module):\n",
" def __init__(self, k=3):\n",
" super().__init__()\n",
" self.k=k\n",
" self.conv1 = nn.Conv1d(k,64,1)\n",
" self.conv2 = nn.Conv1d(64,128,1)\n",
" self.conv3 = nn.Conv1d(128,1024,1)\n",
" self.fc1 = nn.Linear(1024,512)\n",
" self.fc2 = nn.Linear(512,256)\n",
" self.fc3 = nn.Linear(256,k*k)\n",
"\n",
" self.bn1 = nn.BatchNorm1d(64)\n",
" self.bn2 = nn.BatchNorm1d(128)\n",
" self.bn3 = nn.BatchNorm1d(1024)\n",
" self.bn4 = nn.BatchNorm1d(512)\n",
" self.bn5 = nn.BatchNorm1d(256)\n",
"\n",
"\n",
" def forward(self, input):\n",
" # input.shape == (bs,n,3)\n",
" bs = input.size(0)\n",
" xb = F.relu(self.bn1(self.conv1(input)))\n",
" xb = F.relu(self.bn2(self.conv2(xb)))\n",
" xb = F.relu(self.bn3(self.conv3(xb)))\n",
" pool = nn.MaxPool1d(xb.size(-1))(xb)\n",
" flat = nn.Flatten(1)(pool)\n",
" xb = F.relu(self.bn4(self.fc1(flat)))\n",
" xb = F.relu(self.bn5(self.fc2(xb)))\n",
"\n",
" #initialize as identity\n",
" init = torch.eye(self.k, requires_grad=True).repeat(bs,1,1)\n",
" if xb.is_cuda:\n",
" init=init.cuda()\n",
" matrix = self.fc3(xb).view(-1,self.k,self.k) + init\n",
" return matrix"
],
"metadata": {
"id": "_Yw6mvGDd6xS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class Transform(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.input_transform = Tnet(k=3)\n",
" self.feature_transform = Tnet(k=64)\n",
" self.conv1 = nn.Conv1d(3,64,1)\n",
"\n",
" self.conv2 = nn.Conv1d(64,128,1)\n",
" self.conv3 = nn.Conv1d(128,1024,1)\n",
"\n",
" self.bn1 = nn.BatchNorm1d(64)\n",
" self.bn2 = nn.BatchNorm1d(128)\n",
" self.bn3 = nn.BatchNorm1d(1024)\n",
"\n",
" def forward(self, input):\n",
" matrix3x3 = self.input_transform(input)\n",
" # batch matrix multiplication\n",
" xb = torch.bmm(torch.transpose(input,1,2), matrix3x3).transpose(1,2)\n",
" xb = F.relu(self.bn1(self.conv1(xb)))\n",
"\n",
" matrix64x64 = self.feature_transform(xb)\n",
" xb = torch.bmm(torch.transpose(xb,1,2), matrix64x64).transpose(1,2)\n",
"\n",
" xb = F.relu(self.bn2(self.conv2(xb)))\n",
" xb = self.bn3(self.conv3(xb))\n",
" xb = nn.MaxPool1d(xb.size(-1))(xb)\n",
" output = nn.Flatten(1)(xb)\n",
" return output, matrix3x3, matrix64x64"
],
"metadata": {
"id": "HaqmP4GChKr4"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class PointNet(nn.Module):\n",
" def __init__(self, classes = 10):\n",
" super().__init__()\n",
" self.transform = Transform()\n",
" self.fc1 = nn.Linear(1024, 512)\n",
" self.fc2 = nn.Linear(512, 256)\n",
" self.fc3 = nn.Linear(256, classes)\n",
"\n",
"\n",
" self.bn1 = nn.BatchNorm1d(512)\n",
" self.bn2 = nn.BatchNorm1d(256)\n",
" self.dropout = nn.Dropout(p=0.3)\n",
" self.logsoftmax = nn.LogSoftmax(dim=1)\n",
"\n",
" def forward(self, input):\n",
" xb, matrix3x3, matrix64x64 = self.transform(input)\n",
" xb = F.relu(self.bn1(self.fc1(xb)))\n",
" xb = F.relu(self.bn2(self.dropout(self.fc2(xb))))\n",
" output = self.fc3(xb)\n",
" return self.logsoftmax(output), matrix3x3, matrix64x64"
],
"metadata": {
"id": "w58eY6oShehg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def pointnetloss(outputs, labels, m3x3, m64x64, alpha = 0.0001):\n",
" criterion = torch.nn.NLLLoss()\n",
" bs=outputs.size(0)\n",
" id3x3 = torch.eye(3, requires_grad=True).repeat(bs,1,1)\n",
" id64x64 = torch.eye(64, requires_grad=True).repeat(bs,1,1)\n",
" if outputs.is_cuda:\n",
" id3x3=id3x3.cuda()\n",
" id64x64=id64x64.cuda()\n",
" diff3x3 = id3x3-torch.bmm(m3x3,m3x3.transpose(1,2))\n",
" diff64x64 = id64x64-torch.bmm(m64x64,m64x64.transpose(1,2))\n",
" return criterion(outputs, labels) + alpha * (torch.norm(diff3x3)+torch.norm(diff64x64)) / float(bs)"
],
"metadata": {
"id": "3ZNZmF1RhgrN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"print(device)\n",
"pointnet = PointNet()\n",
"pointnet.to(device)\n",
"optimizer = torch.optim.Adam(pointnet.parameters(), lr=0.001)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y2og0Je8h3V5",
"outputId": "7bc3a2ce-8663-487c-bf62-4d4cc880ea1c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"cuda:0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def train(model, train_loader, val_loader=None, epochs=15, save=True):\n",
" for epoch in range(epochs):\n",
" pointnet.train()\n",
" running_loss = 0.0\n",
" for i, data in enumerate(train_loader, 0):\n",
" inputs, labels = data['pointcloud'].to(device).float(), data['category'].to(device)\n",
" optimizer.zero_grad()\n",
" outputs, m3x3, m64x64 = pointnet(inputs.transpose(1,2))\n",
"\n",
" loss = pointnetloss(outputs, labels, m3x3, m64x64)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # print statistics\n",
" running_loss += loss.item()\n",
" if i % 10 == 9: # print every 10 mini-batches\n",
" print('[Epoch: %d, Batch: %4d / %4d], loss: %.3f' %\n",
" (epoch + 1, i + 1, len(train_loader), running_loss / 10))\n",
" running_loss = 0.0\n",
"\n",
" pointnet.eval()\n",
" correct = total = 0\n",
"\n",
" # validation\n",
" if val_loader:\n",
" with torch.no_grad():\n",
" for data in val_loader:\n",
" inputs, labels = data['pointcloud'].to(device).float(), data['category'].to(device)\n",
" outputs, __, __ = pointnet(inputs.transpose(1,2))\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += labels.size(0)\n",
" correct += (predicted == labels).sum().item()\n",
" val_acc = 100. * correct / total\n",
" print('Valid accuracy: %d %%' % val_acc)\n",
"\n",
" # save the model\n",
" if save:\n",
" torch.save(pointnet.state_dict(), \"save_\"+str(epoch)+\".pth\")"
],
"metadata": {
"id": "dRsKwlKeh--J"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train(pointnet, train_loader, valid_loader, save=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AOUx-gkWh4gV",
"outputId": "12e703bb-567c-4410-80d9-573d0ce98a28"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[Epoch: 1, Batch: 10 / 125], loss: 1.945\n",
"[Epoch: 1, Batch: 20 / 125], loss: 1.597\n",
"[Epoch: 1, Batch: 30 / 125], loss: 1.330\n",
"[Epoch: 1, Batch: 40 / 125], loss: 1.195\n",
"[Epoch: 1, Batch: 50 / 125], loss: 1.201\n",
"[Epoch: 1, Batch: 60 / 125], loss: 1.208\n",
"[Epoch: 1, Batch: 70 / 125], loss: 1.051\n",
"[Epoch: 1, Batch: 80 / 125], loss: 0.912\n",
"[Epoch: 1, Batch: 90 / 125], loss: 0.906\n",
"[Epoch: 1, Batch: 100 / 125], loss: 1.009\n",
"[Epoch: 1, Batch: 110 / 125], loss: 0.816\n",
"[Epoch: 1, Batch: 120 / 125], loss: 0.949\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Test"
],
"metadata": {
"id": "5wtsueb0iwZO"
}
},
{
"cell_type": "code",
"source": [
"!gdown 1FokXSAIIK-uj9QkGmSevIQJKE_-TvK8h"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XaOzZCVpYCeL",
"outputId": "356b8f4b-b234-40d5-fe12-64c072cad51c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1FokXSAIIK-uj9QkGmSevIQJKE_-TvK8h\n",
"To: /content/save.pth\n",
"100% 13.9M/13.9M [00:00<00:00, 49.4MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import confusion_matrix\n",
"pointnet = PointNet()\n",
"pointnet.load_state_dict(torch.load('save.pth'))\n",
"pointnet.eval()"
],
"metadata": {
"id": "-OetaYzliecN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "43fd167b-030e-45b2-abf2-8e9a85f44ecf"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PointNet(\n",
" (transform): Transform(\n",
" (input_transform): Tnet(\n",
" (conv1): Conv1d(3, 64, kernel_size=(1,), stride=(1,))\n",
" (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
" (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
" (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
" (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
" (fc3): Linear(in_features=256, out_features=9, bias=True)\n",
" (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (feature_transform): Tnet(\n",
" (conv1): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n",
" (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
" (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
" (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
" (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
" (fc3): Linear(in_features=256, out_features=4096, bias=True)\n",
" (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv1): Conv1d(3, 64, kernel_size=(1,), stride=(1,))\n",
" (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
" (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
" (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
" (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
" (fc3): Linear(in_features=256, out_features=10, bias=True)\n",
" (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (dropout): Dropout(p=0.3, inplace=False)\n",
" (logsoftmax): LogSoftmax(dim=1)\n",
")"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"all_preds = []\n",
"all_labels = []\n",
"with torch.no_grad():\n",
" for i, data in enumerate(valid_loader):\n",
" print('Batch [%4d / %4d]' % (i+1, len(valid_loader)))\n",
"\n",
" inputs, labels = data['pointcloud'].float(), data['category']\n",
" outputs, __, __ = pointnet(inputs.transpose(1,2))\n",
" _, preds = torch.max(outputs.data, 1)\n",
" all_preds += list(preds.numpy())\n",
" all_labels += list(labels.numpy())"
],
"metadata": {
"id": "CGTylHCaisBI",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6021b763-5c2a-41ed-9dd7-7dd31c86568a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Batch [ 1 / 15]\n",
"Batch [ 2 / 15]\n",
"Batch [ 3 / 15]\n",
"Batch [ 4 / 15]\n",
"Batch [ 5 / 15]\n",
"Batch [ 6 / 15]\n",
"Batch [ 7 / 15]\n",
"Batch [ 8 / 15]\n",
"Batch [ 9 / 15]\n",
"Batch [ 10 / 15]\n",
"Batch [ 11 / 15]\n",
"Batch [ 12 / 15]\n",
"Batch [ 13 / 15]\n",
"Batch [ 14 / 15]\n",
"Batch [ 15 / 15]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"cm = confusion_matrix(all_labels, all_preds);\n",
"cm"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qQeZXFgUekMU",
"outputId": "734369c3-032c-4a98-e029-4eb2868f85f6"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 40, 10, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 1, 96, 2, 0, 0, 0, 0, 1, 0, 0],\n",
" [ 0, 0, 100, 0, 0, 0, 0, 0, 0, 0],\n",
" [ 1, 1, 3, 32, 5, 0, 18, 5, 20, 1],\n",
" [ 0, 0, 0, 0, 42, 0, 43, 0, 1, 0],\n",
" [ 0, 0, 1, 0, 26, 70, 2, 1, 0, 0],\n",
" [ 0, 0, 1, 0, 6, 0, 78, 0, 1, 0],\n",
" [ 0, 1, 1, 0, 1, 0, 1, 96, 0, 0],\n",
" [ 0, 0, 0, 5, 0, 0, 1, 0, 94, 0],\n",
" [ 1, 0, 14, 0, 0, 0, 2, 1, 1, 81]])"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"folders = [dir for dir in sorted(os.listdir(path)) if os.path.isdir(path/dir)]\n",
"classes = {folder: i for i, folder in enumerate(folders)};\n",
"classes"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3WZx2FzrfEe3",
"outputId": "721bee12-f2c5-4c77-d7bc-cb647c69708a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'bathtub': 0,\n",
" 'bed': 1,\n",
" 'chair': 2,\n",
" 'desk': 3,\n",
" 'dresser': 4,\n",
" 'monitor': 5,\n",
" 'night_stand': 6,\n",
" 'sofa': 7,\n",
" 'table': 8,\n",
" 'toilet': 9}"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"import itertools\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# function from https://deeplizard.com/learn/video/0LhiS6yu2qQ\n",
"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
" if normalize:\n",
" cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
" print(\"Normalized confusion matrix\")\n",
" else:\n",
" print('Confusion matrix, without normalization')\n",
"\n",
" plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" plt.title(title)\n",
" plt.colorbar()\n",
" tick_marks = np.arange(len(classes))\n",
" plt.xticks(tick_marks, classes, rotation=45)\n",
" plt.yticks(tick_marks, classes)\n",
"\n",
" fmt = '.2f' if normalize else 'd'\n",
" thresh = cm.max() / 2.\n",
" for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
" plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
"\n",
" plt.tight_layout()\n",
" plt.ylabel('True label')\n",
" plt.xlabel('Predicted label')\n",
"plt.figure(figsize=(8,8))\n",
"plot_confusion_matrix(cm, list(classes.keys()), normalize=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 802
},
"id": "Z_-SrN15etuu",
"outputId": "f4d0b40e-3b16-414c-af48-e17c2e550f80"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Normalized confusion matrix\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
}
]
}
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