diff --git a/.gitignore b/.gitignore
index 181150a9068bd17d23aa75e0d346875ae43f996e..2bace18d74207d7f93466b1c31b253c6d731a79e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,6 +116,7 @@ pip_egg_info/
*.t7
*.las
*.png
+*.h5
# Deep learning
model.h5
@@ -125,4 +126,5 @@ logs/
tensorboard/
wandb/
data/
+shapenet_part_seg_hdf5_data/
```
diff --git a/wandb_vis/dgcnn_from_scratch.ipynb b/jupyters/dgcnn_from_scratch.ipynb
similarity index 100%
rename from wandb_vis/dgcnn_from_scratch.ipynb
rename to jupyters/dgcnn_from_scratch.ipynb
diff --git a/jupyters/shapenet_visualization.ipynb b/jupyters/shapenet_visualization.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..273dc2364e187348874dc6105f8da232c4536d03
--- /dev/null
+++ b/jupyters/shapenet_visualization.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/nibio/.local/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n",
+ "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
+ "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmaciej-wielgosz-nibio\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "wandb version 0.15.3 is available! To upgrade, please run:\n",
+ " $ pip install wandb --upgrade"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Tracking run with wandb version 0.14.2"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Run data is saved locally in <code>/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/wandb/run-20230525_133538-mpf5c532</code>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Syncing run <strong><a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn/runs/mpf5c532' target=\"_blank\">pretty-river-363</a></strong> to <a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " View project at <a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn' target=\"_blank\">https://wandb.ai/maciej-wielgosz-nibio/dgcnn</a>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " View run at <a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn/runs/mpf5c532' target=\"_blank\">https://wandb.ai/maciej-wielgosz-nibio/dgcnn/runs/mpf5c532</a>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Logging point cloud\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/wandb_vis/log_point_cloud.py:30: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)\n",
+ " label_gt = torch.tensor(label_gt)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "table saved\n"
+ ]
+ }
+ ],
+ "source": [
+ "# add the path to the src folder\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "\n",
+ "WORK_DIR = '/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg'\n",
+ "sys.path.append(os.path.join(WORK_DIR))\n",
+ "import wandb\n",
+ "from wandb_vis.log_point_cloud import LogPointCloud\n",
+ "from src.shapenet_data import ShapeNetPart\n",
+ "# from src.shapenet_data_new import ShapeNetPart\n",
+ "\n",
+ "use_wandb = True\n",
+ "\n",
+ "dataset = ShapeNetPart(num_points=2048, partition='train', class_choice='chair')\n",
+ "# take 10 data items and log them into an array\n",
+ "data_point = []\n",
+ "data_label = []\n",
+ "\n",
+ "data = [item for item in dataset]\n",
+ "\n",
+ "data_point = [item[0] for item in data]\n",
+ "data_label = [item[2] for item in data]\n",
+ "\n",
+ "\n",
+ "# create a wandb run\n",
+ "if use_wandb:\n",
+ " wandb_run = wandb.init(project=\"dgcnn\", entity=\"maciej-wielgosz-nibio\")\n",
+ " # log the point cloud\n",
+ " print(\"Logging point cloud\")\n",
+ " log_point_cloud = LogPointCloud(log_metrics=True)\n",
+ " log_point_cloud(data_point[:20], data_label[:20], data_label[:20])\n",
+ " log_point_cloud.save_table()\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/jupyters/test_shapenet_data.ipynb b/jupyters/test_shapenet_data.ipynb
deleted file mode 100644
index d07e8bbbc232267b1cd01e9daef6b27b569bbcb7..0000000000000000000000000000000000000000
--- a/jupyters/test_shapenet_data.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/nibio/.local/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n",
- "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
- "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmaciej-wielgosz-nibio\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "wandb version 0.15.0 is available! To upgrade, please run:\n",
- " $ pip install wandb --upgrade"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Tracking run with wandb version 0.14.2"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Run data is saved locally in <code>/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/wandb/run-20230428_100053-566cteoy</code>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Syncing run <strong><a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn/runs/566cteoy' target=\"_blank\">happy-plasma-356</a></strong> to <a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- " View project at <a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn' target=\"_blank\">https://wandb.ai/maciej-wielgosz-nibio/dgcnn</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- " View run at <a href='https://wandb.ai/maciej-wielgosz-nibio/dgcnn/runs/566cteoy' target=\"_blank\">https://wandb.ai/maciej-wielgosz-nibio/dgcnn/runs/566cteoy</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Logging point cloud\n"
- ]
- },
- {
- "ename": "IndexError",
- "evalue": "list index out of range",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/test_shapenet_data.ipynb Cell 1\u001b[0m in \u001b[0;36m3\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Boracle_docker/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/test_shapenet_data.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=28'>29</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mLogging point cloud\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Boracle_docker/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/test_shapenet_data.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=29'>30</a>\u001b[0m log_point_cloud \u001b[39m=\u001b[39m LogPointCloud(log_metrics\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Boracle_docker/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/test_shapenet_data.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=30'>31</a>\u001b[0m log_point_cloud(data_point[:\u001b[39m10\u001b[39;49m], data_label[:\u001b[39m20\u001b[39;49m], data_label[:\u001b[39m20\u001b[39;49m])\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Boracle_docker/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/jupyters/test_shapenet_data.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=31'>32</a>\u001b[0m log_point_cloud\u001b[39m.\u001b[39msave_table()\n",
- "File \u001b[0;32m~/mutable-outside-world/code/nibio_graph_sem_seg/wandb_vis/log_point_cloud.py:126\u001b[0m, in \u001b[0;36mLogPointCloud.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs: Any, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[0;32m--> 126\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlog(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
- "File \u001b[0;32m~/mutable-outside-world/code/nibio_graph_sem_seg/wandb_vis/log_point_cloud.py:112\u001b[0m, in \u001b[0;36mLogPointCloud.log\u001b[0;34m(self, points, label_gt, label_pred)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mlog\u001b[39m(\u001b[39mself\u001b[39m, points, label_gt, label_pred): \n\u001b[0;32m--> 112\u001b[0m data_point_clouds, pred_point_clouds, diff_point_clouds \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcreate_point_clouds(points, label_gt, label_pred)\n\u001b[1;32m 113\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlog_metrics:\n\u001b[1;32m 114\u001b[0m acc, prec, rec, iou \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcompute_metrics(label_gt, label_pred)\n",
- "File \u001b[0;32m~/mutable-outside-world/code/nibio_graph_sem_seg/wandb_vis/log_point_cloud.py:62\u001b[0m, in \u001b[0;36mLogPointCloud.create_point_clouds\u001b[0;34m(self, points, label_gt, label_pred)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(label_pred)):\n\u001b[1;32m 61\u001b[0m colors_per_point \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolors[label_pred[i][j]] \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(label_pred[i]))]\n\u001b[0;32m---> 62\u001b[0m points_rgb \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[p[\u001b[39m0\u001b[39m], p[\u001b[39m1\u001b[39m], p[\u001b[39m2\u001b[39m], c[\u001b[39m0\u001b[39m], c[\u001b[39m1\u001b[39m], c[\u001b[39m2\u001b[39m]] \u001b[39mfor\u001b[39;00m p, c \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(points[i], colors_per_point)])\n\u001b[1;32m 63\u001b[0m pred_point_clouds[i] \u001b[39m=\u001b[39m wandb\u001b[39m.\u001b[39mObject3D(\n\u001b[1;32m 64\u001b[0m {\n\u001b[1;32m 65\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtype\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mlidar/beta\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 66\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mpoints\u001b[39m\u001b[39m\"\u001b[39m: points_rgb\n\u001b[1;32m 67\u001b[0m }\n\u001b[1;32m 68\u001b[0m )\n\u001b[1;32m 70\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(points)):\n\u001b[1;32m 71\u001b[0m \u001b[39m# check if label_gt is equal to label_pred\u001b[39;00m\n",
- "\u001b[0;31mIndexError\u001b[0m: list index out of range"
- ]
- }
- ],
- "source": [
- "# add the path to the src folder\n",
- "import sys\n",
- "import os\n",
- "\n",
- "\n",
- "WORK_DIR = '/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg'\n",
- "sys.path.append(os.path.join(WORK_DIR))\n",
- "import wandb\n",
- "from wandb_vis.log_point_cloud import LogPointCloud\n",
- "from src.shapenet_data import ShapeNetPart\n",
- "\n",
- "use_wandb = True\n",
- "\n",
- "dataset = ShapeNetPart(num_points=2048, partition='train', class_choice='chair')\n",
- "# take 10 data items and log them into an array\n",
- "data_point = []\n",
- "data_label = []\n",
- "\n",
- "data = [item for item in dataset]\n",
- "\n",
- "data_point = [item[0] for item in data]\n",
- "data_label = [item[2] for item in data]\n",
- "\n",
- "\n",
- "# create a wandb run\n",
- "if use_wandb:\n",
- " wandb_run = wandb.init(project=\"dgcnn\", entity=\"maciej-wielgosz-nibio\")\n",
- " # log the point cloud\n",
- " print(\"Logging point cloud\")\n",
- " log_point_cloud = LogPointCloud(log_metrics=True)\n",
- " log_point_cloud(data_point[:20], data_label[:20], data_label[:20])\n",
- " log_point_cloud.save_table()\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "\n",
- "colors = json.load(open(\"/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/prepare_data/meta/partseg_colors.txt\", \"r\"))\n",
- "\n",
- "colors\n",
- "\n",
- "# color_array = []\n",
- "\n",
- "# for i in range(50):\n",
- "# color_array.append(colors[i]['color'])\n",
- "\n",
- "\n",
- "# color_array"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.10"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/model.py b/model.py
index 57582b483db9ef70888237ed93e4b088df8d65db..8cdf443161472f4368151e3f24a016526870a745 100644
--- a/model.py
+++ b/model.py
@@ -1,7 +1,3 @@
-import os
-import sys
-import copy
-import math
import numpy as np
import torch
import torch.nn as nn
diff --git a/src/shapenet_data.py b/src/shapenet_data.py
index 84742db244a934d14b34256911621db95a480c32..b1c408576b68da3337d87ef61cc19c73729ee828 100644
--- a/src/shapenet_data.py
+++ b/src/shapenet_data.py
@@ -1,23 +1,40 @@
import glob
import json
import os
+import subprocess
+import urllib.parse
+from pathlib import Path
+
import cv2
-from torch.utils.data import Dataset
-import numpy as np
import h5py
+import numpy as np
+from torch.utils.data import Dataset
-def download_shapenetpart():
- BASE_DIR = os.path.dirname(os.path.abspath(__file__))
- DATA_DIR = os.path.join(BASE_DIR, 'data')
- if not os.path.exists(DATA_DIR):
- os.mkdir(DATA_DIR)
- if not os.path.exists(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')):
- www = 'https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip'
- zipfile = os.path.basename(www)
- os.system('wget --no-check-certificate %s; unzip %s' % (www, zipfile))
- os.system('mv %s %s' % ('hdf5_data', os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')))
- os.system('rm %s' % (zipfile))
+
+
+def download_shapenetpart(download_location):
+ """Downloads the ShapeNet part dataset if it's not already in the download_location directory."""
+
+ DATASET_URL = 'https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip'
+ DATASET_NAME = 'shapenet_part_seg_hdf5_data'
+ HDF5_NAME = 'hdf5_data'
+
+ def download_dataset(url, dataset_name, hdf5_name):
+ """Downloads and unzips a file from a specified url."""
+ file_name = urllib.parse.urlsplit(url).path.split("/")[-1]
+ subprocess.run(['wget', '--no-check-certificate', url], check=True)
+ subprocess.run(['unzip', file_name], check=True)
+ subprocess.run(['mv', hdf5_name, dataset_name], check=True)
+ subprocess.run(['rm', file_name], check=True)
+
+ download_location = Path(download_location)
+ download_location.mkdir(parents=True, exist_ok=True)
+
+ if not (download_location / DATASET_NAME).exists():
+ print('Downloading ShapeNet part dataset...')
+ download_dataset(DATASET_URL, DATASET_NAME, HDF5_NAME)
+
def load_color_partseg():
colors = []
@@ -64,9 +81,9 @@ def load_color_partseg():
def load_data_partseg(partition):
- download_shapenetpart()
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
+ download_shapenetpart(DATA_DIR)
all_data = []
all_label = []
all_seg = []