Initial commit of PointCNN v1.0.

.gitignore

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+.idea
+arena/__pycache__/
+__pycache__/
+saver/
+sampling/
+data/

README.md

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+# PointCNN
+
+Created by <a href="http://yangyan.li" target="_blank">Yangyan Li</a>, Rui Bu, <a href="http://www.mcsun.cn" target="_blank">Mingchao Sun</a>, and <a href="http://www.cs.sdu.edu.cn/~baoquan/" target="_blank">Baoquan Chen</a> from Shandong University.
+
+### Introduction
+
+PointCNN is a simple and general framework for feature learning from point cloud, which refreshed five benchmark records in point cloud processing, including:
+
+* classification accuracy on ModelNet40 (91.7%)
+* classification accuracy on ScanNet (77.9%)
+* segmentation part averaged IoU on ShapeNet Parts (86.13%)
+* segmentation mean IoU on S3DIS (62.74%)
+* per voxel labelling accuracy on ScanNet (85.1%). 
+
+See our <a href="http://arxiv.org/abs/1801.07791" target="_blank">research paper on arXiv</a> for more details.
+
+### Code Organization
+The core X-Conv and PointCNN architecture are defined in ./pointcnn.py.
+
+The network/training/data augmentation hyperparameters for classification tasks are defined in ./pointcnn_cls/\*.py, for segmentation tasks are defined in ./pointcnn_cls/\*.py
+
+### Usage
+
+Commands for training and testing ModelNet40 classification:
+```
+cd data_conversions
+python3 ./download_datasets.py -d modelnet
+cd ../pointcnn_cls
+./train_val_modelnet.sh -g 0 -x modelnet_x2_l4
+```
+
+Commands for training and testing ShapeNet Parts segmentation:
+```
+cd data_conversions
+python3 ./download_datasets.py -d shapenet_partseg
+cd ../pointcnn_seg
+./train_val_shapenet.sh -g 0 -x shapenet_x8_2048_fps
+./test_shapenet.sh -g 0 -x shapenet_x8_2048_fps -l ../../models/seg/pointcnn_seg_shapenet_x8_2048_fps_xxxx/ckpts/iter-xxxxx -r 10
+cd ..
+python3 ./evaluate_seg.py -g ../data/shapenet_partseg/test_label -p ../data/shapenet_partseg/test_data_pred_10
+```
+
+Other datasets can be processed in a similar way.

_config.yml

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+theme: jekyll-theme-hacker

data_conversions/download_datasets.py

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+#!/usr/bin/python3
+'''Download datasets for this project.'''
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import gzip
+import html
+import shutil
+import tarfile
+import zipfile
+import requests
+import argparse
+from tqdm import tqdm
+
+
+# from https://gist.github.com/hrouault/1358474
+def query_yes_no(question, default="yes"):
+    """Ask a yes/no question via raw_input() and return their answer.
+    "question" is a string that is presented to the user.
+    "default" is the presumed answer if the user just hits <Enter>.
+        It must be "yes" (the default), "no" or None (meaning
+        an answer is required of the user).
+    The "answer" return value is one of "yes" or "no".
+    """
+    valid = {"yes": True, "y": True, "ye": True,
+             "no": False, "n": False}
+    if default == None:
+        prompt = " [y/n] "
+    elif default == "yes":
+        prompt = " [Y/n] "
+    elif default == "no":
+        prompt = " [y/N] "
+    else:
+        raise ValueError("invalid default answer: '%s'" % default)
+
+    while True:
+        sys.stdout.write(question + prompt)
+        choice = input().lower()
+        if default is not None and choice == '':
+            return valid[default]
+        elif choice in valid:
+            return valid[choice]
+        else:
+            sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n")
+
+
+def download_from_url(url, dst):
+    download = True
+    if os.path.exists(dst):
+        download = query_yes_no('Seems you have downloaded %s to %s, overwrite?' % (url, dst), default='no')
+        if download:
+            os.remove(dst)
+
+    if download:
+        response = requests.get(url, stream=True)
+        total_size = int(response.headers.get('content-length', 0))
+        chunk_size = 1024 * 1024
+        bars = total_size // chunk_size
+        with open(dst, "wb") as handle:
+            for data in tqdm(response.iter_content(chunk_size=chunk_size), total=bars, desc=url.split('/')[-1],
+                             unit='M'):
+                handle.write(data)
+
+
+def download_and_unzip(url, root, dataset):
+    folder = os.path.join(root, dataset)
+    folder_zips = os.path.join(folder, 'zips')
+    if not os.path.exists(folder_zips):
+        os.makedirs(folder_zips)
+    filename_zip = os.path.join(folder_zips, url.split('/')[-1])
+
+    download_from_url(url, filename_zip)
+
+    if filename_zip.endswith('.zip'):
+        zip_ref = zipfile.ZipFile(filename_zip, 'r')
+        zip_ref.extractall(folder)
+        zip_ref.close()
+    elif filename_zip.endswith(('.tar.gz', '.tgz')):
+        tarfile.open(name=filename_zip, mode="r:gz").extractall(folder)
+    elif filename_zip.endswith('.gz'):
+        filename_no_gz = filename_zip[:-3]
+        with gzip.open(filename_zip, 'rb') as f_in, open(filename_no_gz, 'wb') as f_out:
+            shutil.copyfileobj(f_in, f_out)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--folder', '-f', help='Path to data folder.')
+    parser.add_argument('--dataset', '-d', help='Dataset to download.')
+    args = parser.parse_args()
+    print(args)
+
+    root = args.folder if args.folder else '../../data'
+    if args.dataset == 'tu_berlin':
+        download_and_unzip('http://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/sketches_svg.zip', root,
+                           args.dataset)
+    elif args.dataset == 'modelnet':
+        download_and_unzip('https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip', root, args.dataset)
+        folder = os.path.join(root, args.dataset)
+        folder_h5 = os.path.join(folder, 'modelnet40_ply_hdf5_2048')
+        for filename in os.listdir(folder_h5):
+            shutil.move(os.path.join(folder_h5, filename), os.path.join(folder, filename))
+        shutil.rmtree(folder_h5)
+    elif args.dataset == 'shapenet_partseg':
+        download_and_unzip('https://shapenet.cs.stanford.edu/iccv17/partseg/train_data.zip', root, args.dataset)
+        download_and_unzip('https://shapenet.cs.stanford.edu/iccv17/partseg/train_label.zip', root, args.dataset)
+        download_and_unzip('https://shapenet.cs.stanford.edu/iccv17/partseg/val_data.zip', root, args.dataset)
+        download_and_unzip('https://shapenet.cs.stanford.edu/iccv17/partseg/val_label.zip', root, args.dataset)
+        download_and_unzip('https://shapenet.cs.stanford.edu/iccv17/partseg/test_data.zip', root, args.dataset)
+        download_and_unzip('https://shapenet.cs.stanford.edu/iccv17/partseg/test_label.zip', root, args.dataset)
+    elif args.dataset == 'mnist':
+        download_and_unzip('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', root, args.dataset)
+        download_and_unzip('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', root, args.dataset)
+        download_and_unzip('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', root, args.dataset)
+        download_and_unzip('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', root, args.dataset)
+    elif args.dataset == 'cifar10':
+        download_and_unzip('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz', root, args.dataset)
+    elif args.dataset == 'quick_draw':
+        url_categories = 'https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt'
+        folder = os.path.join(root, args.dataset)
+        folder_zips = os.path.join(folder, 'zips')
+        if not os.path.exists(folder_zips):
+            os.makedirs(folder_zips)
+        filename_categories = os.path.join(folder_zips, url_categories.split('/')[-1])
+        download_from_url(url_categories, filename_categories)
+
+        categories = [line.strip() for line in open(filename_categories, 'r')]
+        url_base = 'https://storage.googleapis.com/quickdraw_dataset/sketchrnn/'
+        for category in categories:
+            url = url_base + html.escape(category) + '.npz'
+            filename_category = os.path.join(folder_zips, category + '.npz')
+            download_from_url(url, filename_category)
+
+
+if __name__ == '__main__':
+    main()

data_conversions/prepare_cifar10_data.py

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+#!/usr/bin/python3
+'''Convert CIFAR-10 to points.'''
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import h5py
+import random
+import tarfile
+import argparse
+import numpy as np
+from datetime import datetime
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import data_utils
+
+
+def unpickle(file):
+    import pickle
+    with open(file, 'rb') as fo:
+        batch = pickle.load(fo, encoding='bytes')
+    return batch
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--folder', '-f', help='Path to data folder')
+    parser.add_argument('--save_ply', '-s', help='Convert .pts to .ply', action='store_true')
+    args = parser.parse_args()
+    print(args)
+
+    batch_size = 2048
+
+    folder_cifar10 = args.folder if args.folder else '../../data/cifar10/cifar-10-batches-py'
+    folder_pts = os.path.join(os.path.dirname(folder_cifar10), 'pts')
+
+    train_test_files = [('train', ['data_batch_%d' % (idx + 1) for idx in range(5)]),
+                        ('test', ['test_batch'])]
+
+    data = np.zeros((batch_size, 1024, 6))
+    label = np.zeros((batch_size), dtype=np.int32)
+    for tag, filelist in train_test_files:
+        data_list = []
+        labels_list = []
+        for filename in filelist:
+            batch = unpickle(os.path.join(folder_cifar10, filename))
+            data_list.append(np.reshape(batch[b'data'], (10000, 3, 32, 32)))
+            labels_list.append(batch[b'labels'])
+        images = np.concatenate(data_list, axis=0)
+        labels = np.concatenate(labels_list, axis=0)
+
+        idx_h5 = 0
+        filename_filelist_h5 = os.path.join(os.path.dirname(folder_cifar10), '%s_files.txt' % tag)
+        with open(filename_filelist_h5, 'w') as filelist_h5:
+            for idx_img, image in enumerate(images):
+                points = []
+                pixels = []
+                for x in range(32):
+                    for z in range(32):
+                        points.append((x, random.random() * 1e-6, z))
+                        pixels.append((image[0, x, z], image[1, x, z], image[2, x, z]))
+                points_array = np.array(points)
+                pixels_array = (np.array(pixels).astype(np.float32) / 255)-0.5
+
+                points_min = np.amin(points_array, axis=0)
+                points_max = np.amax(points_array, axis=0)
+                points_center = (points_min + points_max) / 2
+                scale = np.amax(points_max - points_min) / 2
+                points_array = (points_array - points_center) * (0.8 / scale)
+
+                if args.save_ply:
+                    filename_pts = os.path.join(folder_pts, tag, '{:06d}.ply'.format(idx_img))
+                    data_utils.save_ply(points_array, filename_pts, colors=pixels_array+0.5)
+
+                idx_in_batch = idx_img % batch_size
+                data[idx_in_batch, ...] = np.concatenate((points_array, pixels_array), axis=-1)
+                label[idx_in_batch] = labels[idx_img]
+                if ((idx_img + 1) % batch_size == 0) or idx_img == len(images) - 1:
+                    item_num = idx_in_batch + 1
+                    filename_h5 = os.path.join(os.path.dirname(folder_cifar10), '%s_%d.h5' % (tag, idx_h5))
+                    print('{}-Saving {}...'.format(datetime.now(), filename_h5))
+                    filelist_h5.write('./%s_%d.h5\n' % (tag, idx_h5))
+
+                    file = h5py.File(filename_h5, 'w')
+                    file.create_dataset('data', data=data[0:item_num, ...])
+                    file.create_dataset('label', data=label[0:item_num, ...])
+                    file.close()
+
+                    idx_h5 = idx_h5 + 1
+
+if __name__ == '__main__':
+    main()

data_conversions/prepare_mnist_data.py

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+#!/usr/bin/python3
+'''Convert MNIST to points.'''
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import h5py
+import random
+import argparse
+import numpy as np
+from mnist import MNIST
+from datetime import datetime
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import data_utils
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--folder', '-f', help='Path to data folder')
+    parser.add_argument('--point_num', '-p', help='Point number for each sample', type=int, default=256)
+    parser.add_argument('--save_ply', '-s', help='Convert .pts to .ply', action='store_true')
+    args = parser.parse_args()
+    print(args)
+
+    batch_size = 2048
+
+    folder_mnist = args.folder if args.folder else '../../data/mnist/zips'
+    folder_pts = os.path.join(os.path.dirname(folder_mnist), 'pts')
+
+    mnist_data = MNIST(folder_mnist)
+    mnist_train_test = [(mnist_data.load_training(), 'train'), (mnist_data.load_testing(), 'test')]
+
+    data = np.zeros((batch_size, args.point_num, 4))
+    label = np.zeros((batch_size), dtype=np.int32)
+    for ((images, labels), tag) in mnist_train_test:
+        idx_h5 = 0
+        filename_filelist_h5 = os.path.join(os.path.dirname(folder_mnist), '%s_files.txt' % tag)
+        point_num_total = 0
+        with open(filename_filelist_h5, 'w') as filelist_h5:
+            for idx_img, image in enumerate(images):
+                points = []
+                pixels = []
+                for idx_pixel, pixel in enumerate(image):
+                    if pixel == 0:
+                        continue
+                    x = idx_pixel // 28
+                    z = idx_pixel % 28
+                    points.append((x, random.random() * 1e-6, z))
+                    pixels.append(pixel)
+                point_num_total = point_num_total + len(points)
+                pixels_sum = sum(pixels)
+                probs = [pixel / pixels_sum for pixel in pixels]
+                indices = np.random.choice(list(range(len(points))), size=args.point_num,
+                                           replace=(len(points) < args.point_num), p=probs)
+                points_array = np.array(points)[indices]
+                pixels_array_1d = (np.array(pixels)[indices].astype(np.float32) / 255) - 0.5
+                pixels_array = np.expand_dims(pixels_array_1d, axis=-1)
+
+                points_min = np.amin(points_array, axis=0)
+                points_max = np.amax(points_array, axis=0)
+                points_center = (points_min + points_max) / 2
+                scale = np.amax(points_max - points_min) / 2
+                points_array = (points_array - points_center) * (0.8 / scale)
+
+                if args.save_ply:
+                    filename_pts = os.path.join(folder_pts, tag, '{:06d}.ply'.format(idx_img))
+                    data_utils.save_ply(points_array, filename_pts, colors=np.tile(pixels_array, (1, 3)) + 0.5)
+
+                idx_in_batch = idx_img % batch_size
+                data[idx_in_batch, ...] = np.concatenate((points_array, pixels_array), axis=-1)
+                label[idx_in_batch] = labels[idx_img]
+                if ((idx_img + 1) % batch_size == 0) or idx_img == len(images) - 1:
+                    item_num = idx_in_batch + 1
+                    filename_h5 = os.path.join(os.path.dirname(folder_mnist), '%s_%d.h5' % (tag, idx_h5))
+                    print('{}-Saving {}...'.format(datetime.now(), filename_h5))
+                    filelist_h5.write('./%s_%d.h5\n' % (tag, idx_h5))
+
+                    file = h5py.File(filename_h5, 'w')
+                    file.create_dataset('data', data=data[0:item_num, ...])
+                    file.create_dataset('label', data=label[0:item_num, ...])
+                    file.close()
+
+                    idx_h5 = idx_h5 + 1
+        print('Average point number in each sample is : %f!' % (point_num_total / len(images)))
+
+
+if __name__ == '__main__':
+    main()