From 6fdf44e24c0339fb6ee6df533eb71f0c014fbddf Mon Sep 17 00:00:00 2001 From: xiaoxiaoxh Date: Fri, 17 Dec 2021 18:21:13 +0800 Subject: [PATCH] init --- README.md | 75 ++- assets/OMAD.png | Bin 0 -> 362890 bytes assets/qualitative results.png | Bin 0 -> 2821013 bytes dataset/dataset_omad_priornet.py | 449 +++++++++++++ dataset/dataset_omadnet.py | 494 ++++++++++++++ libs/loss_omad_priornet.py | 254 +++++++ libs/loss_omadnet.py | 125 ++++ libs/utils.py | 96 +++ model/index_max_ext/index_max.cpp | 159 +++++ .../index_max_ext/index_max.egg-info/PKG-INFO | 10 + .../index_max.egg-info/SOURCES.txt | 7 + .../index_max.egg-info/dependency_links.txt | 1 + .../index_max.egg-info/top_level.txt | 1 + model/index_max_ext/index_max_cuda.cu | 99 +++ model/index_max_ext/setup.py | 18 + model/layers.py | 405 +++++++++++ model/omad_net.py | 239 +++++++ model/omad_priornet.py | 336 ++++++++++ model/pointnet_plus.py | 60 ++ model/pointnet_util.py | 329 +++++++++ requirements.txt | 110 +++ test_omad_net.py | 631 ++++++++++++++++++ test_omad_priornet.py | 176 +++++ train_omad_net.py | 188 ++++++ train_omad_priornet.py | 192 ++++++ 25 files changed, 4450 insertions(+), 4 deletions(-) create mode 100644 assets/OMAD.png create mode 100644 assets/qualitative results.png create mode 100644 dataset/dataset_omad_priornet.py create mode 100644 dataset/dataset_omadnet.py create mode 100644 libs/loss_omad_priornet.py create mode 100644 libs/loss_omadnet.py create mode 100644 libs/utils.py create mode 100644 model/index_max_ext/index_max.cpp create mode 100644 model/index_max_ext/index_max.egg-info/PKG-INFO create mode 100644 model/index_max_ext/index_max.egg-info/SOURCES.txt create mode 100644 model/index_max_ext/index_max.egg-info/dependency_links.txt create mode 100644 model/index_max_ext/index_max.egg-info/top_level.txt create mode 100644 model/index_max_ext/index_max_cuda.cu create mode 100644 model/index_max_ext/setup.py create mode 100644 model/layers.py create mode 100644 model/omad_net.py create mode 100644 model/omad_priornet.py create mode 100644 model/pointnet_plus.py create mode 100644 model/pointnet_util.py create mode 100644 requirements.txt create mode 100644 test_omad_net.py create mode 100644 test_omad_priornet.py create mode 100644 train_omad_net.py create mode 100644 train_omad_priornet.py diff --git a/README.md b/README.md index c523fd2..fd6b0fe 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,74 @@ # OMAD: Object Model with Articulated Deformations -This repository is the official implementation of the paper "OMAD: Object Model with Articulated -Deformations for Pose Estimation and -Retrieval" (BMVC2021). +This repository is the official implementation of the paper +[OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval](https://sites.google.com/view/omad-bmvc//). This paper has been accepted to BMVC 2021. -The code and the dataset will be released in a few days! +![Overview](assets/OMAD.png) +![Visulization](assets/qualitative%20results.png) + +## Datasets +[ArtImage](https://drive.google.com/file/d/1Gp3muPrSY7BPrePhbO1M4U0DQVd_4OqV/view?usp=sharing) dataset contains the synthetic images generated from Unity along with the following annotations: + +- RGB image +- depth map +- part mask +- part pose + +This dataset also contains **URDF** articulated object models of five categories from PartNet-Mobility, +which is re-annotated by us to align the rest state in the same category. + +## Usage +### Installation + +Environments: + +- Python >= 3.7 +- CUDA >= 10.0 + +```bash +git clone https://github.com/xiaoxiaoxh/OMAD.git +cd OMAD +``` + +Install the dependencies listed in ``requirements.txt`` + +``` +pip install -r requirements.txt +``` + +Then, compile CUDA module - index_max: + +```bash +cd models/index_max_ext +python setup.py install +``` + +Finally, download [ArtImage](https://drive.google.com/file/d/1Gp3muPrSY7BPrePhbO1M4U0DQVd_4OqV/view?usp=sharing) Dataset and put it in `OMAD/data` folder. + +Now you are ready to go! + +### Training of OMAD-PriorNet + +```bash +python train_omad_priornet.py --num_kp 24 --work_dir work_dir/omad_priornet_laptop --category 1 --num_parts 2 --use_relative_coverage --symtype shape +``` + +### Testing of OMAD-PriorNet + +```bash +python test_omad_priornet.py --num_kp 24 --checkpoint model_current_laptop.pth --work_dir work_dir/omad_priornet_laptop --bs 16 --workers 0 --use_gpu --symtype shape --out --mode train + +python test_omad_priornet.py --num_kp 24 --checkpoint model_current_laptop.pth --work_dir work_dir/omad_priornet_laptop --bs 16 --workers 0 --use_gpu --symtype shape --out --mode val +``` + +### Training of OMADNet + +```bash +python train_omad_net.py --num_kp 24 --work_dir work_dir/omad-net_laptop --params_dir work_dir/omad_priornet_laptop --num_basis 10 --symtype shape +``` + +### Testing of OMADNet + +```bash +python test_omad_net.py --num_kp 24 --checkpoint model_current_laptop.pth --work_dir work_dir/omad-net_laptop --params_dir work_dir/omad_priornet_laptop --category 1 --num_basis 10 --num_parts 2 --symtype shape --kp_thr 0.1 --reg_weight 0 --out raw_results.pkl --num_process 8 --use_gpu --data_postfix final_test --shuffle +``` diff --git a/assets/OMAD.png b/assets/OMAD.png new file mode 100644 index 0000000000000000000000000000000000000000..416650c711f5f49b6752ce23342366750d081c25 GIT binary patch literal 362890 zcmd421S)hop7SFhmWkP%g7P!PYcZC{Z*THwk&z?cn27eTJv7mmoq)lu zwaE>+dEe1-`xVvT3%*~i$smWsi+NY6b-G|vxX{>9fBbmx@JNDvvf`uE<+c7GvM+>K z_D1v_{Qo>UoCAAf-zboS{^x;G^=AFg%OHvjgzwcf{~yCGUVk`7`QNYfYJWra`QO(; zj(8OR|N4C8Xy)s`*12^t%`I7wuj&7mQp~W%Jeo-^`p%XuePk!)R}F#w8Pu^yzo_HC zg)*h(=_w0S8~AT9Rk??X@D+wCE5la`PYmfM)1maD>>RTEHB?M!ONLMlp>G#Yc#KTw zu?z!s>2LWogmSP=aL+eG1||Nc^Uamm*U^X5ziJ>WP7Wu&s21-y(s@4BQh>a{-)+*B 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b/dataset/dataset_omad_priornet.py @@ -0,0 +1,449 @@ +import torch.utils.data as data +import os +import os.path as osp +import torch +import numpy as np +import open3d as o3d +import copy +import xml.etree.ElementTree as ET +from scipy.spatial.transform import Rotation +import mmcv + + +class SapienDataset_OMADPriorNet(data.Dataset): + CLASSES = ('background', 'laptop', 'eyeglasses', 'dishwasher', 'drawer', 'scissors') + TEST_URDF_IDs = (10040, 10885, 11242, 11030, 11156) + (101300, 101859, 102586, 102590, 102596, 102612) + \ + (11700, 12530, 12559, 12580) + (46123, 45841, 46440) + \ + (10449, 10502, 10569, 10907) + + def __init__(self, mode, data_root, num_pts, num_cates, cate_id, num_parts, add_noise=True, + debug=False, device='cuda:0', data_tag='train', num_samples=10000, + node_num=16, use_scale_aug=True, use_rot_aug=False, scale_aug_max_range=0.2): + assert mode in ('train', 'val') + self.data_root = data_root + self.mode = mode + self.num_pts = num_pts + self.num_cates = num_cates + self.num_parts = num_parts + self.debug = debug + self.device = device + self.data_tag = data_tag + self.cate_id = cate_id + self.add_noise = add_noise + self.node_num = node_num + assert self.node_num % self.num_parts == 0 + self.use_scale_aug = use_scale_aug + self.use_rot_aug = use_rot_aug + self.scale_aug_max_range = scale_aug_max_range + + self.obj_list = {} + self.obj_name_list = {} + self.num_samples = num_samples + self.fathest_sampler = FarthestSampler() + + self.part_obj_pts_dict = dict() + self.part_obj_pts_dict[self.cate_id] = dict() + self.urdf_ids_dict = dict() + self.urdf_ids_dict[self.cate_id] = [] + self.urdf_dir = osp.join(self.data_root, self.CLASSES[cate_id], 'urdf') + self.rest_state_json = mmcv.load(osp.join(self.urdf_dir, 'rest_state.json')) + self.urdf_rest_transformation_dict = dict() + self.urdf_rest_transformation_dict[self.cate_id] = dict() + self.urdf_joint_loc_dict = dict() + self.urdf_joint_loc_dict[self.cate_id] = dict() + self.urdf_joint_axis_dict = dict() + self.urdf_joint_axis_dict[self.cate_id] = dict() + self.all_norm_obj_joint_loc_dict = dict() + self.all_norm_obj_joint_loc_dict[self.cate_id] = dict() # (joint anchor - center) -> normalized joint anchor + self.all_norm_obj_joint_axis_dict = dict() + self.all_norm_obj_joint_axis_dict[self.cate_id] = dict() # numpy array, the same as raw joint axis + self.all_obj_raw_scale = dict() + self.all_obj_raw_scale[self.cate_id] = dict() # raw mesh scale(rest state) + self.all_obj_raw_center = dict() # raw mesh center(rest state) + self.all_obj_raw_center[self.cate_id] = dict() # raw mesh center(rest state) + self.part_obj_raw_scale = dict() + self.part_obj_raw_scale[self.cate_id] = dict() # raw mesh scale(rest state), part-level + self.all_raw_obj_pts_dict = dict() # raw complete obj pts(rest state) + self.all_raw_obj_pts_dict[self.cate_id] = dict() + self.all_norm_obj_pts_dict = dict() # zero centered complete obj pts(rest state) + self.all_norm_obj_pts_dict[self.cate_id] = dict() + self.all_part_cls_dict = dict() # part classification label for complete pts(rest state) + self.all_part_cls_dict[self.cate_id] = dict() + for dir in sorted(os.listdir(self.urdf_dir)): + if osp.isdir(osp.join(self.urdf_dir, dir)): + urdf_id = int(dir) + if (self.mode == 'train' and urdf_id not in self.TEST_URDF_IDs) \ + or (self.mode == 'val' and urdf_id in self.TEST_URDF_IDs): + self.part_obj_pts_dict[self.cate_id][urdf_id] = [None for _ in range(self.num_parts)] + self.part_obj_raw_scale[self.cate_id][urdf_id] = [None for _ in range(self.num_parts)] + if urdf_id not in self.urdf_ids_dict[self.cate_id]: + self.urdf_ids_dict[self.cate_id].append(urdf_id) + new_urdf_file = osp.join(self.urdf_dir, dir, 'mobility_for_unity_align.urdf') + # TODO: more flexible + compute_relative = True if cate_id == 5 else False # only applied for scissors + self.urdf_rest_transformation_dict[self.cate_id][urdf_id],\ + self.urdf_joint_loc_dict[self.cate_id][urdf_id],\ + self.urdf_joint_axis_dict[self.cate_id][urdf_id] = \ + self.parse_joint_info(urdf_id, new_urdf_file, self.rest_state_json, compute_relative) + for file in sorted(os.listdir(osp.join(self.urdf_dir, dir, 'part_point_sample'))): + assert '.xyz' in file + part_idx = int(file.split('.xyz')[0]) + self.part_obj_pts_dict[self.cate_id][urdf_id][part_idx] = np.asarray(o3d.io.read_point_cloud( + osp.join(self.urdf_dir, dir, 'part_point_sample', file), print_progress=False, format='xyz').points) + num_pts = self.part_obj_pts_dict[self.cate_id][urdf_id][part_idx].shape[0] + # TODO: handle tree structure with depth > 2 + if part_idx in self.urdf_rest_transformation_dict[self.cate_id][urdf_id]: + homo_obj_pts = np.concatenate([self.part_obj_pts_dict[self.cate_id][urdf_id][part_idx], np.ones((num_pts, 1))], axis=1) + new_homo_obj_pts = (self.urdf_rest_transformation_dict[self.cate_id][urdf_id][part_idx] @ homo_obj_pts.T).T + self.part_obj_pts_dict[self.cate_id][urdf_id][part_idx] = new_homo_obj_pts[:, :3] + + for part_idx in range(self.num_parts): + _, part_scale = self.get_norm_factor(self.part_obj_pts_dict[self.cate_id][urdf_id][part_idx]) + self.part_obj_raw_scale[self.cate_id][urdf_id][part_idx] = part_scale + + self.all_raw_obj_pts_dict[self.cate_id][urdf_id] = np.concatenate( + self.part_obj_pts_dict[self.cate_id][urdf_id], axis=0) + + center, scale = self.get_norm_factor(self.all_raw_obj_pts_dict[self.cate_id][urdf_id]) + self.all_norm_obj_pts_dict[self.cate_id][urdf_id] = \ + (self.all_raw_obj_pts_dict[self.cate_id][urdf_id] - center[np.newaxis, :]) + self.all_obj_raw_center[self.cate_id][urdf_id] = center + self.all_obj_raw_scale[self.cate_id][urdf_id] = scale + + self.all_part_cls_dict[self.cate_id][urdf_id] = [] + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id] = [] + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id] = [] + for part_idx in range(self.num_parts): + part_pts_num = self.part_obj_pts_dict[self.cate_id][urdf_id][part_idx].shape[0] + self.all_part_cls_dict[self.cate_id][urdf_id].append(part_idx * np.ones((part_pts_num,))) + if part_idx in self.urdf_joint_loc_dict[self.cate_id][urdf_id]: + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id].append( + (self.urdf_joint_loc_dict[self.cate_id][urdf_id][part_idx] - center)) + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id].append( + self.urdf_joint_axis_dict[self.cate_id][urdf_id][part_idx] + ) + self.all_part_cls_dict[self.cate_id][urdf_id] = \ + np.concatenate(self.all_part_cls_dict[self.cate_id][urdf_id]).astype(np.int) + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id] = np.stack( + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id], axis=0) + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id] = np.stack( + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id], axis=0) + + self.num_objs = len(self.part_obj_pts_dict[self.cate_id]) + print('Finish loading {} objects!'.format(self.num_objs)) + + + def parse_joint_info(self, urdf_id, urdf_file, rest_state_json, compute_relative=False): + # TODO: support base joint + tree = ET.parse(urdf_file) + root_urdf = tree.getroot() + rest_transformation_dict = dict() + joint_loc_dict = dict() + joint_axis_dict = dict() + for i, joint in enumerate(root_urdf.iter('joint')): + if joint.attrib['type'] == 'fixed' or joint.attrib['type'] == '0': + continue + child_name = joint.attrib['name'].split('_')[-1] + for origin in joint.iter('origin'): + x, y, z = [float(x) for x in origin.attrib['xyz'].split()][::-1] + a, b, c = y, x, z + joint_loc_dict[int(child_name)] = np.array([a, b, c]) + for axis in joint.iter('axis'): + r, p, y = [float(x) for x in axis.attrib['xyz'].split()][::-1] + axis = np.array([p, r, y]) + axis /= np.linalg.norm(axis) + u, v, w = axis + joint_axis_dict[int(child_name)] = np.array([u, v, w]) + if joint.attrib['type'] == 'prismatic': + delta_state = rest_state_json[str(urdf_id)][child_name]['state'] + delta_transform = np.concatenate([np.concatenate([np.eye(3), np.array([[u*delta_state, v*delta_state, w*delta_state]]).T], + axis=1), np.array([[0., 0., 0., 1.]])], axis=0) + elif joint.attrib['type'] == 'revolute': + if str(urdf_id) in rest_state_json: + delta_state = -rest_state_json[str(urdf_id)][child_name]['state'] / 180 * np.pi + else: + delta_state = 0. + cos = np.cos(delta_state) + sin = np.sin(delta_state) + + delta_transform = np.concatenate( + [np.stack([u * u + (v * v + w * w) * cos, u * v * (1 - cos) - w * sin, u * w * (1 - cos) + v * sin, + (a * (v * v + w * w) - u * (b * v + c * w)) * (1 - cos) + (b * w - c * v) * sin, + u * v * (1 - cos) + w * sin, v * v + (u * u + w * w) * cos, v * w * (1 - cos) - u * sin, + (b * (u * u + w * w) - v * (a * u + c * w)) * (1 - cos) + (c * u - a * w) * sin, + u * w * (1 - cos) - v * sin, v * w * (1 - cos) + u * sin, w * w + (u * u + v * v) * cos, + (c * (u * u + v * v) - w * (a * u + b * v)) * (1 - cos) + (a * v - b * u) * sin]).reshape( + 3, 4), + np.array([[0., 0., 0., 1.]])], axis=0) + rest_transformation_dict[int(child_name)] = delta_transform + if not compute_relative: + return rest_transformation_dict, joint_loc_dict, joint_axis_dict + else: + # TODO: support structure with more than 1 depth + urdf_dir = os.path.dirname(urdf_file) + urdf_ins_old = self.get_urdf_mobility(urdf_dir, filename='mobility_for_unity.urdf') + urdf_ins_new = self.get_urdf_mobility(urdf_dir, filename='mobility_for_unity_align.urdf') + + joint_old_rpy_base = [-urdf_ins_old['joint']['rpy'][0][0], urdf_ins_old['joint']['rpy'][0][2], + -urdf_ins_old['joint']['rpy'][0][1]] + joint_old_xyz_base = [-urdf_ins_old['joint']['xyz'][0][0], urdf_ins_old['joint']['xyz'][0][2], + -urdf_ins_old['joint']['xyz'][0][1]] + joint_new_rpy_base = [-urdf_ins_new['joint']['rpy'][0][0], urdf_ins_new['joint']['rpy'][0][2], + -urdf_ins_new['joint']['rpy'][0][1]] + joint_new_xyz_base = [-urdf_ins_new['joint']['xyz'][0][0], urdf_ins_new['joint']['xyz'][0][2], + -urdf_ins_new['joint']['xyz'][0][1]] + + joint_rpy_relative = np.array(joint_new_rpy_base) - np.array(joint_old_rpy_base) + joint_xyz_relative = np.array(joint_new_xyz_base) - np.array(joint_old_xyz_base) + + transformation_base_relative = self.compose_rt( + Rotation.from_euler('ZXY', joint_rpy_relative.tolist()).as_matrix(), joint_xyz_relative) + + for child_name in rest_transformation_dict.keys(): + rest_transformation_dict[child_name] = transformation_base_relative @ rest_transformation_dict[ + child_name] + rest_transformation_dict[0] = transformation_base_relative + + for child_name in joint_loc_dict: + # TODO: support kinematic tree depth > 2 + homo_joint_loc = np.concatenate([joint_loc_dict[child_name], np.ones(1)], axis=-1) + joint_loc_dict[child_name] = (rest_transformation_dict[0] @ homo_joint_loc.T).T[:3] + joint_axis_dict[child_name] = (rest_transformation_dict[0][:3, :3] @ joint_axis_dict[child_name].T).T + + return rest_transformation_dict, joint_loc_dict, joint_axis_dict + + @staticmethod + def get_urdf_mobility(dir, filename='mobility_for_unity_align.urdf'): + urdf_ins = {} + tree_urdf = ET.parse(os.path.join(dir, filename)) + num_real_links = len(tree_urdf.findall('link')) + root_urdf = tree_urdf.getroot() + + rpy_xyz = {} + list_type = [None] * (num_real_links - 1) + list_parent = [None] * (num_real_links - 1) + list_child = [None] * (num_real_links - 1) + list_xyz = [None] * (num_real_links - 1) + list_rpy = [None] * (num_real_links - 1) + list_axis = [None] * (num_real_links - 1) + list_limit = [[0, 0]] * (num_real_links - 1) + # here we still have to read the URDF file + for joint in root_urdf.iter('joint'): + joint_index = int(joint.attrib['name'].split('_')[1]) + list_type[joint_index] = joint.attrib['type'] + + for parent in joint.iter('parent'): + link_name = parent.attrib['link'] + if link_name == 'base': + link_index = 0 + else: + # link_index = int(link_name.split('_')[1]) + 1 + link_index = int(link_name) + 1 + list_parent[joint_index] = link_index + for child in joint.iter('child'): + link_name = child.attrib['link'] + if link_name == 'base': + link_index = 0 + else: + # link_index = int(link_name.split('_')[1]) + 1 + link_index = int(link_name) + 1 + list_child[joint_index] = link_index + for origin in joint.iter('origin'): + if 'xyz' in origin.attrib: + list_xyz[joint_index] = [float(x) for x in origin.attrib['xyz'].split()] + else: + list_xyz[joint_index] = [0, 0, 0] + if 'rpy' in origin.attrib: + list_rpy[joint_index] = [float(x) for x in origin.attrib['rpy'].split()] + else: + list_rpy[joint_index] = [0, 0, 0] + for axis in joint.iter('axis'): # we must have + list_axis[joint_index] = [float(x) for x in axis.attrib['xyz'].split()] + for limit in joint.iter('limit'): + list_limit[joint_index] = [float(limit.attrib['lower']), float(limit.attrib['upper'])] + + rpy_xyz['type'] = list_type + rpy_xyz['parent'] = list_parent + rpy_xyz['child'] = list_child + rpy_xyz['xyz'] = list_xyz + rpy_xyz['rpy'] = list_rpy + rpy_xyz['axis'] = list_axis + rpy_xyz['limit'] = list_limit + + urdf_ins['joint'] = rpy_xyz + urdf_ins['num_links'] = num_real_links + + return urdf_ins + + @staticmethod + def compose_rt(rotation, translation): + aligned_RT = np.zeros((4, 4), dtype=np.float32) + aligned_RT[:3, :3] = rotation[:3, :3] + aligned_RT[:3, 3] = translation + aligned_RT[3, 3] = 1 + return aligned_RT + + @staticmethod + def get_norm_factor(obj_pts): + xmin, xmax = np.min(obj_pts[:, 0]), np.max(obj_pts[:, 0]) + ymin, ymax = np.min(obj_pts[:, 1]), np.max(obj_pts[:, 1]) + zmin, zmax = np.min(obj_pts[:, 2]), np.max(obj_pts[:, 2]) + + x_scale = xmax - xmin + y_scale = ymax - ymin + z_scale = zmax - zmin + + center = np.array([(xmin + xmax)/2., (ymin + ymax)/2., (zmin + zmax)/2.]) + scale = np.array([x_scale, y_scale, z_scale]) + return center, scale + + def get_frame(self, complete_all_mesh_pts, complete_all_part_cls, raw_joint_loc, raw_joint_axis): + if self.use_rot_aug: + angles = np.array([np.random.uniform(-80., 80.), + np.random.uniform(-180., 180.), + np.random.uniform(-80., 80.)]) + else: + angles = np.array([0., 0., 0.]) + part_target_r = Rotation.from_euler('yxz', angles, degrees=True).as_matrix() + part_target_inv_r = part_target_r.T + + num_all_pts = complete_all_mesh_pts.shape[0] + if num_all_pts >= self.num_pts: + choose = np.random.choice(np.arange(num_all_pts), self.num_pts, replace=False) + else: + choose = np.pad(np.arange(num_all_pts), (0, self.num_pts - num_all_pts), 'wrap') + all_mesh_pts = copy.deepcopy(complete_all_mesh_pts[choose, :]) + all_part_cls = copy.deepcopy(complete_all_part_cls[choose]) + + if self.use_scale_aug: + scale_factor = 2 * self.scale_aug_max_range * np.random.rand(1, 3) + (1-self.scale_aug_max_range) # [1-x, 1+x) + else: + scale_factor = np.ones((1, 3)) + cloud = (part_target_r @ (all_mesh_pts * scale_factor).T).T + joint_loc = (part_target_r @ (copy.deepcopy(raw_joint_loc) * scale_factor).T).T + joint_axis = (part_target_r @ (copy.deepcopy(raw_joint_axis) * scale_factor).T).T + + if self.mode == 'train' and self.add_noise: + # add random noise to point cloud + cloud = cloud + np.random.normal(loc=0.0, scale=0.003, size=cloud.shape) + + nodes_list = [] + for part_idx in range(self.num_parts): + part_cloud = cloud[all_part_cls == part_idx] + pts_num = part_cloud.shape[0] + part_nodes = self.fathest_sampler.sample( + part_cloud[np.random.choice(part_cloud.shape[0], pts_num, replace=False)], + self.node_num // self.num_parts, + ) + nodes_list.append(part_nodes) + nodes = np.concatenate(nodes_list, axis=0) + + return cloud, nodes, scale_factor, all_part_cls, part_target_r, part_target_inv_r, joint_loc, joint_axis + + def __getitem__(self, index): + choose_urdf_id = np.random.choice(self.urdf_ids_dict[self.cate_id], 1)[0] + # use normalized(zero-centered) all obj points + choose_obj_pts = self.all_norm_obj_pts_dict[self.cate_id][choose_urdf_id] + # classification(part-label) + choose_obj_cls = self.all_part_cls_dict[self.cate_id][choose_urdf_id] + # normazlied joint location and axis + choose_joint_loc = self.all_norm_obj_joint_loc_dict[self.cate_id][choose_urdf_id] + choose_joint_axis = self.all_norm_obj_joint_axis_dict[self.cate_id][choose_urdf_id] + + cloud, nodes, scale_factor, part_cls, part_r, part_inv_r, joint_loc, joint_axis = \ + self.get_frame(choose_obj_pts, choose_obj_cls, choose_joint_loc, choose_joint_axis) + + if self.debug and choose_urdf_id: + print(choose_urdf_id) + colors = [(1., 0., 0.), (0., 1., 0.), (0., 0., 1.), (1., 1., 0.), (1., 0., 1.)] + cloud_pcd = o3d.geometry.PointCloud() + cloud_pcd.points = o3d.utility.Vector3dVector(cloud) + cloud_pcd.paint_uniform_color([0., 0., 0.]) + pcd_color = np.zeros((self.num_pts, 3)) + for part_idx in range(self.num_parts): + part_num = np.sum(part_cls == part_idx) + pcd_color[part_cls == part_idx, :] = np.array(colors[part_idx])[np.newaxis, :].repeat(part_num, axis=0) + cloud_pcd.colors = o3d.utility.Vector3dVector(pcd_color) + + nodes_list = [ + o3d.geometry.TriangleMesh.create_sphere(radius=0.01, resolution=10).translate((x, y, z)) for x, y, z + in nodes] + sphere_pts_num = np.asarray(nodes_list[0].vertices).shape[0] + for idx, mesh in enumerate(nodes_list): + part_idx = idx // (self.node_num // self.num_parts) + mesh.vertex_colors = o3d.utility.Vector3dVector( + np.array(colors[part_idx])[np.newaxis, :].repeat(sphere_pts_num, axis=0)) + + line_pcd_list = [] + for joint_idx in range(self.num_parts - 1): + start_point = joint_loc[joint_idx] + end_point = start_point + joint_axis[joint_idx] + line_points = np.stack([start_point, end_point]) + lines = [[0, 1]] # Right leg + colors = [[0, 0, 1] for _ in range(len(lines))] + line_pcd = o3d.geometry.LineSet() + line_pcd.lines = o3d.utility.Vector2iVector(lines) + line_pcd.colors = o3d.utility.Vector3dVector(colors) + line_pcd.points = o3d.utility.Vector3dVector(line_points) + line_pcd_list.append(line_pcd) + + coord_mesh = o3d.geometry.TriangleMesh.create_coordinate_frame() + + o3d.visualization.draw_geometries([cloud_pcd, coord_mesh] + nodes_list + line_pcd_list) + + class_gt = np.array([self.cate_id-1]) + raw_part_scale = np.stack(self.part_obj_raw_scale[self.cate_id][choose_urdf_id], axis=0) + raw_center = self.all_obj_raw_center[self.cate_id][choose_urdf_id] + + return torch.from_numpy(cloud.astype(np.float32)).to(self.device), \ + torch.from_numpy(nodes.astype(np.float32)).to(self.device), \ + torch.from_numpy(part_cls.astype(np.int32)).to(torch.long).to(self.device), \ + torch.from_numpy(part_inv_r.astype(np.float32)).to(self.device), \ + torch.from_numpy(joint_loc.astype(np.float32)).to(self.device), \ + torch.from_numpy(joint_axis.astype(np.float32)).to(self.device), \ + torch.from_numpy(scale_factor.astype(np.float32)).to(self.device), \ + torch.from_numpy(raw_part_scale.astype(np.float32)).to(self.device), \ + torch.from_numpy(raw_center.astype(np.float32)).to(self.device), \ + torch.from_numpy(class_gt.astype(np.int32)).to(torch.long).to(self.device), \ + torch.tensor(choose_urdf_id, dtype=torch.long, device=self.device) + + def __len__(self): + return self.num_samples + + +class FarthestSampler: + def __init__(self): + pass + + def calc_distances(self, p0, points): + return ((p0 - points) ** 2).sum(axis=1) + + def sample(self, pts, k): + farthest_pts = np.zeros((k, 3)) + # use center as initial point + init_point = (np.max(pts, axis=0, keepdims=True) + np.min(pts, axis=0, keepdims=True)) / 2 + distances = self.calc_distances(init_point, pts) + for i in range(0, k): + farthest_pts[i] = pts[np.argmax(distances)] + distances = np.minimum(distances, self.calc_distances(farthest_pts[i], pts)) + return farthest_pts + + +if __name__ == '__main__': + dataset = SapienDataset_OMADPriorNet(mode='train', + data_root='data_v2', + num_pts=2048, + num_cates=5, + cate_id=1, + num_parts=2, + device='cpu', + debug=True, + node_num=128, + use_scale_aug=False, + use_rot_aug=False) + data_loader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0) + + for i, data in enumerate(data_loader): + cloud, nodes, part_cls, part_inv_r, joint_loc, joint_axis, \ + scale_factor, raw_part_scale, raw_center, cate, urdf_id = data + pass \ No newline at end of file diff --git a/dataset/dataset_omadnet.py b/dataset/dataset_omadnet.py new file mode 100644 index 0000000..adc496e --- /dev/null +++ b/dataset/dataset_omadnet.py @@ -0,0 +1,494 @@ +import torch.utils.data as data +import os +import os.path as osp +import torch +import numpy as np +import open3d as o3d +import copy +import xml.etree.ElementTree as ET +import mmcv +import cv2 +from scipy.spatial.transform import Rotation + + +class SapienDataset_OMADNet(data.Dataset): + CLASSES = ('background', 'laptop', 'eyeglasses', 'dishwasher', 'drawer', 'scissors') + TEST_URDF_IDs = (10040, 10885, 11242, 11030, 11156) + (101300, 101859, 102586, 102590, 102596, 102612) + \ + (11700, 12530, 12559, 12580) + (46123, 45841, 46440) + \ + (10449, 10502, 10569, 10907) + + def __init__(self, mode, data_root, + num_pts, num_cates, cate_id, num_parts, add_noise=False, + debug=False, device='cuda:0', data_tag='train', + kp_anno_path='unsup_train_keypoints.pkl'): + assert mode in ('train', 'val') + self.data_root = data_root + self.mode = mode + self.num_pts = num_pts + self.num_cates = num_cates + self.num_parts = num_parts + self.debug = debug + self.device = device + self.data_tag = data_tag + self.cate_id = cate_id + self.add_noise = add_noise + self.norm_part_kp_annotions = dict() + self.norm_part_kp_annotions[self.cate_id] = mmcv.load(kp_anno_path) + + valid_flag_path = osp.join(self.data_root, self.CLASSES[cate_id], 'train.txt' + if mode == 'train' else 'test.txt') + self.annotation_valid_flags = dict() + with open(valid_flag_path, 'r') as f: + self.annotation_valid_flags[self.cate_id] = f.readlines() + for idx in range(len(self.annotation_valid_flags[self.cate_id])): + self.annotation_valid_flags[self.cate_id][idx] = self.annotation_valid_flags[self.cate_id][idx].split('\n')[0] + + self.obj_list = {} + self.obj_name_list = {} + + intrinsics_path = osp.join(self.data_root, 'camera_intrinsic.json') + self.camera_intrinsic = o3d.io.read_pinhole_camera_intrinsic(intrinsics_path) + self.annotation_dir = osp.join(self.data_root, self.CLASSES[cate_id], self.data_tag, 'annotations') + self.obj_annotation_list = [] + self.obj_urdf_id_list = [] + self.num_samples = 0 + for file in sorted(os.listdir(self.annotation_dir)): + if '.json' in file and file in self.annotation_valid_flags[self.cate_id]: + annotation = mmcv.load(osp.join(self.annotation_dir, file)) + annotation['mask_path'] = osp.join(self.data_root, self.CLASSES[cate_id], self.data_tag, + annotation['depth_path'].replace('depth', 'category_mask')) + annotation['depth_path'] = osp.join(self.data_root, self.CLASSES[cate_id], self.data_tag, + annotation['depth_path']) + + instances = annotation['instances'] + assert len(instances) == 1, 'Only support one instance per image' + instance = instances[0] + urdf_id = instance['urdf_id'] + if (self.mode == 'train' and urdf_id not in self.TEST_URDF_IDs) \ + or (self.mode == 'val' and urdf_id in self.TEST_URDF_IDs): + self.obj_annotation_list.append(annotation) + self.obj_urdf_id_list.append(urdf_id) + self.num_samples += 1 + print('Finish loading {} annotations!'.format(self.num_samples)) + + self.raw_part_obj_pts_dict = dict() + self.raw_part_obj_pts_dict[self.cate_id] = dict() + self.urdf_ids_dict = dict() + self.urdf_ids_dict[self.cate_id] = [] + self.urdf_dir = osp.join(self.data_root, self.CLASSES[cate_id], 'urdf') + self.rest_state_json = mmcv.load(osp.join(self.urdf_dir, 'rest_state.json')) + self.urdf_rest_transformation_dict = dict() + self.urdf_rest_transformation_dict[self.cate_id] = dict() + self.raw_urdf_joint_loc_dict = dict() + self.raw_urdf_joint_loc_dict[self.cate_id] = dict() + self.raw_urdf_joint_axis_dict = dict() + self.raw_urdf_joint_axis_dict[self.cate_id] = dict() + self.all_norm_obj_joint_loc_dict = dict() + self.all_norm_obj_joint_loc_dict[self.cate_id] = dict() # (joint anchor - center) -> normalized joint anchor + self.all_norm_obj_joint_axis_dict = dict() + self.all_norm_obj_joint_axis_dict[self.cate_id] = dict() # numpy array, the same as raw joint axis + self.all_obj_raw_scale = dict() + self.all_obj_raw_scale[self.cate_id] = dict() # raw mesh scale(rest state) + self.all_obj_raw_center = dict() # raw mesh center(rest state) + self.all_obj_raw_center[self.cate_id] = dict() # raw mesh center(rest state) + self.norm_part_obj_corners = dict() + self.norm_part_obj_corners[self.cate_id] = dict() # raw mesh corners(rest state), part-level + self.all_raw_obj_pts_dict = dict() # raw complete obj pts(rest state) + self.all_raw_obj_pts_dict[self.cate_id] = dict() + self.norm_part_obj_pts_dict = dict() # zero centered complete obj pts(rest state) + self.norm_part_obj_pts_dict[self.cate_id] = dict() + for dir in sorted(os.listdir(self.urdf_dir)): + if osp.isdir(osp.join(self.urdf_dir, dir)): + urdf_id = int(dir) + if (self.mode == 'train' and urdf_id not in self.TEST_URDF_IDs) \ + or (self.mode == 'val' and urdf_id in self.TEST_URDF_IDs): + self.raw_part_obj_pts_dict[self.cate_id][urdf_id] = [None for _ in range(self.num_parts)] + if urdf_id not in self.urdf_ids_dict[self.cate_id]: + self.urdf_ids_dict[self.cate_id].append(urdf_id) + new_urdf_file = osp.join(self.urdf_dir, dir, 'mobility_for_unity_align.urdf') + # TODO: more flexible + compute_relative = True if cate_id == 5 else False # only applied for scissors + self.urdf_rest_transformation_dict[self.cate_id][urdf_id], \ + self.raw_urdf_joint_loc_dict[self.cate_id][urdf_id], \ + self.raw_urdf_joint_axis_dict[self.cate_id][urdf_id] = \ + self.parse_joint_info(urdf_id, new_urdf_file, self.rest_state_json, compute_relative) + for file in sorted(os.listdir(osp.join(self.urdf_dir, dir, 'part_point_sample'))): + assert '.xyz' in file + part_idx = int(file.split('.xyz')[0]) + self.raw_part_obj_pts_dict[self.cate_id][urdf_id][part_idx] = np.asarray(o3d.io.read_point_cloud( + osp.join(self.urdf_dir, dir, 'part_point_sample', file), print_progress=False, format='xyz').points) + num_pts = self.raw_part_obj_pts_dict[self.cate_id][urdf_id][part_idx].shape[0] + # TODO: handle tree structure with depth > 2 + if part_idx in self.urdf_rest_transformation_dict[self.cate_id][urdf_id]: + homo_obj_pts = np.concatenate([self.raw_part_obj_pts_dict[self.cate_id][urdf_id][part_idx], np.ones((num_pts, 1))], axis=1) + new_homo_obj_pts = (self.urdf_rest_transformation_dict[self.cate_id][urdf_id][part_idx] @ homo_obj_pts.T).T + self.raw_part_obj_pts_dict[self.cate_id][urdf_id][part_idx] = new_homo_obj_pts[:, :3] + + self.all_raw_obj_pts_dict[self.cate_id][urdf_id] = np.concatenate( + self.raw_part_obj_pts_dict[self.cate_id][urdf_id], axis=0) + + center, scale, _ = self.get_norm_factor(self.all_raw_obj_pts_dict[self.cate_id][urdf_id]) + self.norm_part_obj_pts_dict[self.cate_id][urdf_id] = [ + (self.raw_part_obj_pts_dict[self.cate_id][urdf_id][part_idx] - center[np.newaxis, :]) + for part_idx in range(self.num_parts)] + self.norm_part_obj_corners[self.cate_id][urdf_id] = [None for _ in range(self.num_parts)] + for part_idx in range(self.num_parts): + _, _, self.norm_part_obj_corners[self.cate_id][urdf_id][part_idx] = \ + self.get_norm_factor(self.norm_part_obj_pts_dict[self.cate_id][urdf_id][part_idx]) + self.norm_part_obj_corners[self.cate_id][urdf_id] = np.stack( + self.norm_part_obj_corners[self.cate_id][urdf_id], axis=0) + + self.all_obj_raw_center[self.cate_id][urdf_id] = center + self.all_obj_raw_scale[self.cate_id][urdf_id] = scale + + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id] = [] + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id] = [] + for part_idx in range(self.num_parts): + if part_idx in self.raw_urdf_joint_loc_dict[self.cate_id][urdf_id]: + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id].append( + (self.raw_urdf_joint_loc_dict[self.cate_id][urdf_id][part_idx] - center)) + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id].append( + self.raw_urdf_joint_axis_dict[self.cate_id][urdf_id][part_idx] + ) + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id] = np.stack( + self.all_norm_obj_joint_loc_dict[self.cate_id][urdf_id], axis=0) + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id] = np.stack( + self.all_norm_obj_joint_axis_dict[self.cate_id][urdf_id], axis=0) + + self.num_objs = len(self.raw_part_obj_pts_dict[self.cate_id]) + self.samples_per_obj = self.num_samples // self.num_objs + print('Finish loading {} objects!'.format(self.num_objs)) + + self.cam_cx, self.cam_cy = self.camera_intrinsic.get_principal_point() + self.cam_fx, self.cam_fy = self.camera_intrinsic.get_focal_length() + self.width = self.camera_intrinsic.width + self.height = self.camera_intrinsic.height + + self.xmap = np.array([[j for _ in range(self.width)] for j in range(self.height)]) + self.ymap = np.array([[i for i in range(self.width)] for _ in range(self.height)]) + + + @staticmethod + def load_depth(depth_path): + depth = cv2.imread(depth_path, -1) + + if len(depth.shape) == 3: + depth16 = np.uint16(depth[:, :, 1]*256) + np.uint16(depth[:, :, 2]) + depth16 = depth16.astype(np.uint16) + elif len(depth.shape) == 2 and depth.dtype == 'uint16': + depth16 = depth + else: + assert False, '[ Error ]: Unsupported depth type.' + + return depth16 + + def parse_joint_info(self, urdf_id, urdf_file, rest_state_json, compute_relative=False): + # TODO: support kinematic tree depth > 2 + tree = ET.parse(urdf_file) + root_urdf = tree.getroot() + rest_transformation_dict = dict() + joint_loc_dict = dict() + joint_axis_dict = dict() + for i, joint in enumerate(root_urdf.iter('joint')): + if joint.attrib['type'] == 'fixed' or joint.attrib['type'] == '0': + continue + child_name = joint.attrib['name'].split('_')[-1] + for origin in joint.iter('origin'): + x, y, z = [float(x) for x in origin.attrib['xyz'].split()][::-1] + a, b, c = y, x, z + joint_loc_dict[int(child_name)] = np.array([a, b, c]) + for axis in joint.iter('axis'): + r, p, y = [float(x) for x in axis.attrib['xyz'].split()][::-1] + axis = np.array([p, r, y]) + axis /= np.linalg.norm(axis) + u, v, w = axis + joint_axis_dict[int(child_name)] = np.array([u, v, w]) + if joint.attrib['type'] == 'prismatic': + delta_state = rest_state_json[str(urdf_id)][child_name]['state'] + delta_transform = np.concatenate([np.concatenate([np.eye(3), np.array([[u*delta_state, v*delta_state, w*delta_state]]).T], + axis=1), np.array([[0., 0., 0., 1.]])], axis=0) + elif joint.attrib['type'] == 'revolute': + if str(urdf_id) in rest_state_json: + delta_state = -rest_state_json[str(urdf_id)][child_name]['state'] / 180 * np.pi + else: + delta_state = 0. + cos = np.cos(delta_state) + sin = np.sin(delta_state) + + delta_transform = np.concatenate( + [np.stack([u * u + (v * v + w * w) * cos, u * v * (1 - cos) - w * sin, u * w * (1 - cos) + v * sin, + (a * (v * v + w * w) - u * (b * v + c * w)) * (1 - cos) + (b * w - c * v) * sin, + u * v * (1 - cos) + w * sin, v * v + (u * u + w * w) * cos, v * w * (1 - cos) - u * sin, + (b * (u * u + w * w) - v * (a * u + c * w)) * (1 - cos) + (c * u - a * w) * sin, + u * w * (1 - cos) - v * sin, v * w * (1 - cos) + u * sin, w * w + (u * u + v * v) * cos, + (c * (u * u + v * v) - w * (a * u + b * v)) * (1 - cos) + (a * v - b * u) * sin]).reshape( + 3, 4), + np.array([[0., 0., 0., 1.]])], axis=0) + rest_transformation_dict[int(child_name)] = delta_transform + if not compute_relative: + return rest_transformation_dict, joint_loc_dict, joint_axis_dict + else: + # TODO: support structure with more than 1 depth + urdf_dir = os.path.dirname(urdf_file) + urdf_ins_old = self.get_urdf_mobility(urdf_dir, filename='mobility_for_unity.urdf') + urdf_ins_new = self.get_urdf_mobility(urdf_dir, filename='mobility_for_unity_align.urdf') + + joint_old_rpy_base = [-urdf_ins_old['joint']['rpy'][0][0], urdf_ins_old['joint']['rpy'][0][2], + -urdf_ins_old['joint']['rpy'][0][1]] + joint_old_xyz_base = [-urdf_ins_old['joint']['xyz'][0][0], urdf_ins_old['joint']['xyz'][0][2], + -urdf_ins_old['joint']['xyz'][0][1]] + joint_new_rpy_base = [-urdf_ins_new['joint']['rpy'][0][0], urdf_ins_new['joint']['rpy'][0][2], + -urdf_ins_new['joint']['rpy'][0][1]] + joint_new_xyz_base = [-urdf_ins_new['joint']['xyz'][0][0], urdf_ins_new['joint']['xyz'][0][2], + -urdf_ins_new['joint']['xyz'][0][1]] + + joint_rpy_relative = np.array(joint_new_rpy_base) - np.array(joint_old_rpy_base) + joint_xyz_relative = np.array(joint_new_xyz_base) - np.array(joint_old_xyz_base) + + transformation_base_relative = self.compose_rt( + Rotation.from_euler('ZXY', joint_rpy_relative.tolist()).as_matrix(), joint_xyz_relative) + + for child_name in rest_transformation_dict.keys(): + rest_transformation_dict[child_name] = transformation_base_relative @ rest_transformation_dict[ + child_name] + rest_transformation_dict[0] = transformation_base_relative + + for child_name in joint_loc_dict: + # TODO: support kinematic tree depth > 2 + homo_joint_loc = np.concatenate([joint_loc_dict[child_name], np.ones(1)], axis=-1) + joint_loc_dict[child_name] = (rest_transformation_dict[0] @ homo_joint_loc.T).T[:3] + joint_axis_dict[child_name] = (rest_transformation_dict[0][:3, :3] @ joint_axis_dict[child_name].T).T + + return rest_transformation_dict, joint_loc_dict, joint_axis_dict + + @staticmethod + def get_urdf_mobility(dir, filename='mobility_for_unity_align.urdf'): + urdf_ins = {} + tree_urdf = ET.parse(os.path.join(dir, filename)) + num_real_links = len(tree_urdf.findall('link')) + root_urdf = tree_urdf.getroot() + + rpy_xyz = {} + list_type = [None] * (num_real_links - 1) + list_parent = [None] * (num_real_links - 1) + list_child = [None] * (num_real_links - 1) + list_xyz = [None] * (num_real_links - 1) + list_rpy = [None] * (num_real_links - 1) + list_axis = [None] * (num_real_links - 1) + list_limit = [[0, 0]] * (num_real_links - 1) + # here we still have to read the URDF file + for joint in root_urdf.iter('joint'): + joint_index = int(joint.attrib['name'].split('_')[1]) + list_type[joint_index] = joint.attrib['type'] + + for parent in joint.iter('parent'): + link_name = parent.attrib['link'] + if link_name == 'base': + link_index = 0 + else: + # link_index = int(link_name.split('_')[1]) + 1 + link_index = int(link_name) + 1 + list_parent[joint_index] = link_index + for child in joint.iter('child'): + link_name = child.attrib['link'] + if link_name == 'base': + link_index = 0 + else: + # link_index = int(link_name.split('_')[1]) + 1 + link_index = int(link_name) + 1 + list_child[joint_index] = link_index + for origin in joint.iter('origin'): + if 'xyz' in origin.attrib: + list_xyz[joint_index] = [float(x) for x in origin.attrib['xyz'].split()] + else: + list_xyz[joint_index] = [0, 0, 0] + if 'rpy' in origin.attrib: + list_rpy[joint_index] = [float(x) for x in origin.attrib['rpy'].split()] + else: + list_rpy[joint_index] = [0, 0, 0] + for axis in joint.iter('axis'): # we must have + list_axis[joint_index] = [float(x) for x in axis.attrib['xyz'].split()] + for limit in joint.iter('limit'): + list_limit[joint_index] = [float(limit.attrib['lower']), float(limit.attrib['upper'])] + + rpy_xyz['type'] = list_type + rpy_xyz['parent'] = list_parent + rpy_xyz['child'] = list_child + rpy_xyz['xyz'] = list_xyz + rpy_xyz['rpy'] = list_rpy + rpy_xyz['axis'] = list_axis + rpy_xyz['limit'] = list_limit + + urdf_ins['joint'] = rpy_xyz + urdf_ins['num_links'] = num_real_links + + return urdf_ins + + @staticmethod + def compose_rt(rotation, translation): + aligned_RT = np.zeros((4, 4), dtype=np.float32) + aligned_RT[:3, :3] = rotation[:3, :3] + aligned_RT[:3, 3] = translation + aligned_RT[3, 3] = 1 + return aligned_RT + + @staticmethod + def get_norm_factor(obj_pts): + xmin, xmax = np.min(obj_pts[:, 0]), np.max(obj_pts[:, 0]) + ymin, ymax = np.min(obj_pts[:, 1]), np.max(obj_pts[:, 1]) + zmin, zmax = np.min(obj_pts[:, 2]), np.max(obj_pts[:, 2]) + + x_scale = xmax - xmin + y_scale = ymax - ymin + z_scale = zmax - zmin + + center = np.array([(xmin + xmax)/2., (ymin + ymax)/2., (zmin + zmax)/2.]) + scale = np.array([x_scale, y_scale, z_scale]) + corners = np.array([xmin, xmax, ymin, ymax, zmin, zmax]) + return center, scale, corners + + def get_frame(self, choose_frame_annotation): + assert choose_frame_annotation['width'] == self.width + assert choose_frame_annotation['height'] == self.height + + link_category_to_idx_map = [_ for _ in range(self.num_parts)] + for idx in range(self.num_parts): + link_category_id = choose_frame_annotation['instances'][0]['links'][idx]['link_category_id'] + link_category_to_idx_map[link_category_id] = idx + raw_transform_matrix = [np.array(choose_frame_annotation['instances'][0]['links'][link_category_to_idx_map[i]]['transformation']) + for i in range(self.num_parts)] + rest_transform_matrix = [np.diag([1., 1., 1., 1.]) for _ in range(self.num_parts)] + joint_state = [0. for _ in range(self.num_parts)] + urdf_id = choose_frame_annotation['instances'][0]['urdf_id'] + # TODO: more flexible + joint_type = 'prismatic' if self.cate_id == 4 else 'revolute' + for link_idx, transform in self.urdf_rest_transformation_dict[self.cate_id][urdf_id].items(): + rest_transform_matrix[link_idx] = transform + if 'state' in choose_frame_annotation['instances'][0]['links'][link_category_to_idx_map[link_idx]]: + if joint_type == 'revolute': + joint_state[link_idx] = choose_frame_annotation['instances'][0]['links'][link_category_to_idx_map[link_idx]]['state'] / 180 * np.pi + else: + joint_state[link_idx] = \ + choose_frame_annotation['instances'][0]['links'][link_category_to_idx_map[link_idx]]['state'] + + rest_transform_matrix = np.array(rest_transform_matrix) + joint_state = np.array(joint_state) + all_center = copy.deepcopy(self.all_obj_raw_center[self.cate_id][urdf_id][np.newaxis, :]) + # new transform matrix for zero-centerd pts in rest state + part_transform_matrix = [_ for _ in range(self.num_parts)] + # X' = R_rest @ X_raw - Center(C) + for part_idx in range(self.num_parts): + # R' = R @ (R_rest)^-1 + transform_matrix = raw_transform_matrix[part_idx] @ np.linalg.inv(rest_transform_matrix[part_idx]) + # T' = T + R' @ C + transform_matrix[:3, -1] = transform_matrix[:3, -1] + (transform_matrix[:3, :3] @ all_center.T)[:, 0] + part_transform_matrix[part_idx] = transform_matrix + + part_target_r = np.stack([part_transform_matrix[i][:3, :3] for i in range(self.num_parts)], axis=0) + raw_part_target_quat = np.stack([Rotation.from_matrix(part_target_r[i]).as_quat() + for i in range(self.num_parts)], axis=0) # (x, y, z, w) + part_target_quat = np.concatenate([ + raw_part_target_quat[:, -1][:, np.newaxis], raw_part_target_quat[:, :3]], axis=-1) # (w, x, y, z) + part_target_t = np.stack([part_transform_matrix[i][:3, 3] + for i in range(self.num_parts)], axis=0)[:, np.newaxis, :] + + x1, y1, x2, y2 = choose_frame_annotation['instances'][0]['bbox'] + + depth = np.array(self.load_depth(choose_frame_annotation['depth_path'])) / 1000.0 + depth = depth[y1:y2, x1:x2] + + part_mask = mmcv.imread(choose_frame_annotation['mask_path'])[:, :, 0] + part_mask = part_mask[y1:y2, x1:x2] + + cam_scale = 1.0 + choose = (depth.flatten() != 0.0).nonzero()[0] + assert len(choose) > 0 + if len(choose) >= self.num_pts: + c_mask = np.random.choice(np.arange(len(choose)), self.num_pts, replace=False) + choose = choose[c_mask] + else: + choose = np.pad(choose, (0, self.num_pts - len(choose)), 'wrap') + + depth_masked = depth.flatten()[choose][:, np.newaxis].astype(np.float32) + xmap_masked = self.xmap[y1:y2, x1:x2].flatten()[choose][:, np.newaxis].astype(np.float32) + ymap_masked = self.ymap[y1:y2, x1:x2].flatten()[choose][:, np.newaxis].astype(np.float32) + pt2 = depth_masked / cam_scale + pt0 = (ymap_masked - self.cam_cx) * pt2 / self.cam_fx + pt1 = (xmap_masked - self.cam_cy) * pt2 / self.cam_fy + cloud = np.concatenate((pt0, pt1, pt2), axis=1) + + part_cls = part_mask.flatten()[choose] + + if self.add_noise: + # add random noise to point cloud + cloud = cloud + np.random.normal(loc=0.0, scale=0.003, size=cloud.shape) + + return part_cls, cloud, part_target_r, part_target_quat, part_target_t, joint_state + + def __getitem__(self, index): + choose_urdf_id = self.obj_urdf_id_list[index] + # annotations of zero-centerd rest-state keypoints in aligned model space + norm_part_kp = copy.deepcopy(self.norm_part_kp_annotions[self.cate_id][choose_urdf_id]) + # zero-centered joint location and axis in rest state + norm_joint_loc = copy.deepcopy(self.all_norm_obj_joint_loc_dict[self.cate_id][choose_urdf_id]) + norm_joint_axis = copy.deepcopy(self.all_norm_obj_joint_axis_dict[self.cate_id][choose_urdf_id]) + + choose_frame_annotation = self.obj_annotation_list[index] + + part_cls, cloud, part_r, part_quat, part_t, joint_state = self.get_frame(choose_frame_annotation) + + if self.debug: + print(choose_urdf_id) + colors = [(1, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 1)] + norm_part_obj_pts = self.norm_part_obj_pts_dict[self.cate_id][choose_urdf_id] + gt_part_obj_pts = [(part_r[i] @ norm_part_obj_pts[i].T).T + part_t[i] for i in range(self.num_parts)] + gt_part_pts_pcd_list = [o3d.geometry.PointCloud() for _ in range(self.num_parts)] + for part_idx in range(self.num_parts): + gt_part_pts_pcd_list[part_idx].points = o3d.utility.Vector3dVector(gt_part_obj_pts[part_idx]) + gt_part_pts_pcd_list[part_idx].paint_uniform_color(colors[part_idx]) + + cloud_pcd = o3d.geometry.PointCloud() + cloud_pcd.points = o3d.utility.Vector3dVector(cloud) + + o3d.visualization.draw_geometries([cloud_pcd] + gt_part_pts_pcd_list) + + class_gt = np.array([self.cate_id-1]) + raw_scale = self.all_obj_raw_scale[self.cate_id][choose_urdf_id] + raw_center = self.all_obj_raw_center[self.cate_id][choose_urdf_id] + norm_part_corners = self.norm_part_obj_corners[self.cate_id][choose_urdf_id] + + return torch.from_numpy(cloud.astype(np.float32)).to(self.device), \ + torch.from_numpy(part_cls).to(device=self.device, dtype=torch.long), \ + torch.from_numpy(part_r.astype(np.float32)).to(self.device), \ + torch.from_numpy(part_quat.astype(np.float32)).to(self.device), \ + torch.from_numpy(part_t.astype(np.float32)).to(self.device), \ + torch.from_numpy(joint_state.astype(np.float32)).to(self.device), \ + torch.from_numpy(norm_joint_loc.astype(np.float32)).to(self.device), \ + torch.from_numpy(norm_joint_axis.astype(np.float32)).to(self.device), \ + torch.from_numpy(norm_part_kp.astype(np.float32)).to(self.device), \ + torch.from_numpy(raw_scale.astype(np.float32)).to(self.device), \ + torch.from_numpy(raw_center.astype(np.float32)).to(self.device), \ + torch.from_numpy(norm_part_corners.astype(np.float32)).to(self.device), \ + torch.from_numpy(class_gt.astype(np.int32)).to(torch.long).to(self.device), \ + torch.tensor(choose_urdf_id, dtype=torch.long, device=self.device) + + def __len__(self): + return self.num_samples + + +if __name__ == '__main__': + dataset = SapienDataset_OMADNet(mode='train', + data_root='data_v2', + num_pts=1024, + num_cates=5, + cate_id=5, + num_parts=2, + kp_anno_path='work_dir/correct40_scissors_unsup_cos-sgd_new-pointnet_sym-none_sample50k_part-fps_bs32_pts2048_zero-center_scale-aug-0.5_no-rot-aug_basis10_part-chamfer1_nodes-maxmin-beta0.1-rel-coverage1_surf5_joint1_reg0.01_node-sep2-factor8_kp24/unsup_train_keypoints.pkl', + device='cpu', + debug=False) + data_loader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0) + + for i, data in enumerate(data_loader): + cloud, part_cls, part_r, part_quat, part_t, joint_state, norm_joint_loc, norm_joint_axis, \ + norm_part_kp, scale, center, norm_part_corners, cate, urdf_id = data + pass \ No newline at end of file diff --git a/libs/loss_omad_priornet.py b/libs/loss_omad_priornet.py new file mode 100644 index 0000000..626d9d7 --- /dev/null +++ b/libs/loss_omad_priornet.py @@ -0,0 +1,254 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PartChamferLoss_Brute(nn.Module): + def __init__(self, num_parts): + super(PartChamferLoss_Brute, self).__init__() + self.dimension = 3 + self.num_parts = num_parts + + def forward(self, part_pc_src_input, part_pc_dst_input): + ''' + :param part_pc_src_input: BxKX3x(M/K) Tensor in GPU + :param part_pc_dst_input: BxKX3x(N/k) Tensor in GPU + :return: + ''' + chamfer_pure_list = [] + for part_idx in range(self.num_parts): + pc_src_input = part_pc_src_input[:, part_idx, :, :] + pc_dst_input = part_pc_dst_input[:, part_idx, :, :] + B, M = pc_src_input.size()[0], pc_src_input.size()[2] + N = pc_dst_input.size()[2] + + pc_src_input_expanded = pc_src_input.unsqueeze(3).expand(B, 3, M, N) + pc_dst_input_expanded = pc_dst_input.unsqueeze(2).expand(B, 3, M, N) + + # the gradient of norm is set to 0 at zero-input. There is no need to use custom norm anymore. + diff = torch.norm(pc_src_input_expanded - pc_dst_input_expanded, dim=1, keepdim=False) # BxMxN + + # pc_src vs selected pc_dst, M + src_dst_min_dist, _ = torch.min(diff, dim=2, keepdim=False) # BxM + forward_loss = src_dst_min_dist.mean() + + # pc_dst vs selected pc_src, N + dst_src_min_dist, _ = torch.min(diff, dim=1, keepdim=False) # BxN + backward_loss = dst_src_min_dist.mean() + + chamfer_pure = forward_loss + backward_loss + chamfer_pure_list.append(chamfer_pure) + + chamfer_all = torch.mean(torch.stack(chamfer_pure_list)) + return chamfer_all + + +def smooth_l1_loss(pred, target, beta=1.0, reduction='mean'): + assert reduction in ('mean', 'none') + assert beta > 0 + assert pred.size() == target.size() + if target.numel() == 0: + return pred * 0. + diff = torch.abs(pred - target) + loss = torch.where(diff < beta, 0.5 * diff * diff / beta, + diff - 0.5 * beta) + if reduction == 'mean': + return loss.mean() + elif reduction == 'none': + return loss + + +class CoverageLoss(nn.Module): + """Directly calculate loss based on max and min, not scale""" + def __init__(self, beta=0.1, use_relative_coverage=False): + super(CoverageLoss, self).__init__() + self.beta = beta + self.use_relative_coverage = use_relative_coverage + + def forward(self, kp, pc): + if self.use_relative_coverage: + # volume + val_max_pc, _ = torch.max(pc, 2) + val_min_pc, _ = torch.min(pc, 2) + + val_max_kp, _ = torch.max(kp, 2) + val_min_kp, _ = torch.min(kp, 2) + + scale_pc = val_max_pc - val_min_pc + scale_kp = val_max_kp - val_min_kp + + cov_loss = (smooth_l1_loss(val_max_kp / scale_pc, val_max_pc / scale_pc, beta=self.beta) + + smooth_l1_loss(val_min_kp / scale_pc, val_min_pc / scale_pc, beta=self.beta) + + smooth_l1_loss(torch.log(scale_kp), torch.log(scale_pc), beta=self.beta) + ) / 3 + else: + # volume + val_max_pc, _ = torch.max(pc, 2) + val_min_pc, _ = torch.min(pc, 2) + + val_max_kp, _ = torch.max(kp, 2) + val_min_kp, _ = torch.min(kp, 2) + + cov_loss = (smooth_l1_loss(val_max_kp, val_max_pc, beta=self.beta) + + smooth_l1_loss(val_min_kp, val_min_pc, beta=self.beta))/2 + return cov_loss + + +class SurfaceLoss(nn.Module): + def __init__(self): + super(SurfaceLoss, self).__init__() + self.single_side_chamfer = SingleSideChamferLoss_Brute() + + def forward(self, keypoint, pc): + loss = self.single_side_chamfer(keypoint, pc) + + return torch.mean(loss) + + +class SingleSideChamferLoss_Brute(nn.Module): + def __init__(self): + super(SingleSideChamferLoss_Brute, self).__init__() + + def forward(self, pc_src_input, pc_dst_input): + ''' + :param pc_src_input: Bx3xM Variable in GPU + :param pc_dst_input: Bx3xN Variable in GPU + :return: + ''' + + B, M = pc_src_input.size()[0], pc_src_input.size()[2] + N = pc_dst_input.size()[2] + + pc_src_input_expanded = pc_src_input.unsqueeze(3).expand(B, 3, M, N) + pc_dst_input_expanded = pc_dst_input.unsqueeze(2).expand(B, 3, M, N) + + diff = torch.norm(pc_src_input_expanded - pc_dst_input_expanded, dim=1, keepdim=False) # BxMxN + + # pc_src vs selected pc_dst, M + src_dst_min_dist, _ = torch.min(diff, dim=2, keepdim=False) # BxM + + return src_dst_min_dist + + +class JointLoss(nn.Module): + def __init__(self, loc_weight=1.0, axis_weight=0.5): + super(JointLoss, self).__init__() + self.loc_weight = loc_weight + self.axis_weight = axis_weight + + def forward(self, pred_joint_loc, pred_joint_axis, gt_joint_loc, gt_joint_axis): + loss_axis = 1 - F.cosine_similarity(pred_joint_axis, gt_joint_axis, dim=-1).mean(dim=-1).mean(dim=-1) + + norm_gt_joint_axis = gt_joint_axis / torch.norm(gt_joint_axis, dim=-1, keepdim=True) + p = gt_joint_loc + q = gt_joint_loc + norm_gt_joint_axis + r = pred_joint_loc + x = p - q + loss_loc = torch.norm(((r-q)*x).sum(-1, keepdim=True)/(x*x).sum(-1, keepdim=True)*(p-q)+(q-r), dim=-1).mean(-1).mean(-1) + + loss_joint = loss_loc * self.loc_weight + loss_axis * self.axis_weight + return loss_joint + + +class Loss_OMAD_PriorNet(torch.nn.Module): + def __init__(self, + device, + num_kp, num_parts, num_cate, + loss_chamfer_weight=1.0, + loss_coverage_weight=1.0, + loss_surface_weight=5.0, + loss_joint_weight=1.0, + loss_reg_weight=0.01, + loss_sep_weight=2.0, + sep_factor=8, + beta=0.1, + joint_type='revolute', + use_relative_coverage=False, + ): + super(Loss_OMAD_PriorNet, self).__init__() + self.num_kp = num_kp + self.num_cate = num_cate + self.num_parts = num_parts + self.num_joints = num_parts - 1 + assert self.num_kp % self.num_parts == 0 + self.num_kp_per_part = self.num_kp // self.num_parts + self.joint_type = joint_type + self.device = device + assert joint_type in ('revolute', 'prismatic') + + self.loss_chamfer_weight = loss_chamfer_weight + self.loss_coverage_weight = loss_coverage_weight + self.loss_surface_weight = loss_surface_weight + self.loss_joint_weight = loss_joint_weight + self.loss_reg_weight = loss_reg_weight + self.loss_sep_weight = loss_sep_weight + self.sep_factor = sep_factor + self.use_relative_coverage = use_relative_coverage + + self.part_chamfer_criteria = PartChamferLoss_Brute(num_parts=num_parts) + self.surface_criteria = SurfaceLoss() + self.coverage_criteria = CoverageLoss(beta=beta, use_relative_coverage=use_relative_coverage) + self.joint_criteria = JointLoss(loc_weight=0. if self.joint_type == 'prismatic' else 1.0) + + self.zeros = torch.tensor( + [0.0 for _ in range(self.num_kp_per_part - 1) for _ in range(self.num_kp_per_part)]).to( + device) + self.select1 = torch.tensor( + [i for _ in range(self.num_kp_per_part - 1) for i in range(self.num_kp_per_part)]).to( + device) + self.select2 = torch.tensor([(i % self.num_kp_per_part) for j in range(1, self.num_kp_per_part) + for i in range(j, j + self.num_kp_per_part)]).to(device) + + def forward(self, coefs, part_pred_kps, part_nodes, cloud, cloud_cls, + pred_joint_loc, pred_joint_axis, gt_joint_loc, gt_joint_axis, gt_part_scale): + loss_dict = dict() + bs = part_pred_kps.size(0) + + part_pred_kps_trans = part_pred_kps.transpose(2, 3) # (B, K, 3, M/K) + part_nodes_trans = part_nodes.transpose(2, 3) # (B, K, 3, M/K) + nodes_trans = part_nodes.reshape(bs, -1, 3).transpose(1, 2) # (B, 3, M) + cloud_trans = cloud.transpose(1, 2) # (B, 3, N) + chf_loss = self.part_chamfer_criteria(part_pred_kps_trans, part_nodes_trans) + + cov_loss_list = [] + sep_loss_list = [] + for part_idx in range(self.num_parts): + for bs_idx in range(bs): + singe_part_nodes_trans = part_nodes_trans[bs_idx, part_idx, :, :].unsqueeze(0) # (1, 3, m') + cloud_inds = (cloud_cls[bs_idx] == part_idx) # (N) + single_part_cloud_trans = cloud_trans[bs_idx, :, cloud_inds].unsqueeze(0) # (1, 3, n') + part_cov_loss = self.coverage_criteria(singe_part_nodes_trans, single_part_cloud_trans) + cov_loss_list.append(part_cov_loss) + + """Separation Loss""" + max_rad = torch.norm(gt_part_scale[:, part_idx, :], dim=-1) + max_dist = max_rad / self.sep_factor + max_thr = max_dist.unsqueeze(1) # (bs, 1) + + pred_kp_select1 = torch.index_select(part_nodes[:, part_idx, :, :], 1, self.select1).contiguous() + pred_kp_select2 = torch.index_select(part_nodes[:, part_idx, :, :], 1, self.select2).contiguous() + dist_sep = torch.norm((pred_kp_select1 - pred_kp_select2), dim=2) + loss_sep = torch.max(self.zeros.reshape(1, -1).expand(dist_sep.shape), max_thr - dist_sep) + loss_sep = torch.mean(loss_sep) + + sep_loss_list.append(loss_sep) + + cov_loss = torch.mean(torch.stack(cov_loss_list)) + sep_loss = torch.mean(torch.stack(sep_loss_list)) + surf_loss = self.surface_criteria(nodes_trans, cloud_trans) + joint_loss = self.joint_criteria(pred_joint_loc, pred_joint_axis, gt_joint_loc, gt_joint_axis) + reg_loss = (coefs * coefs).mean() + + """SUM UP""" + loss = chf_loss * self.loss_chamfer_weight + cov_loss * self.loss_coverage_weight + \ + surf_loss * self.loss_surface_weight + joint_loss * self.loss_joint_weight + \ + reg_loss * self.loss_reg_weight + sep_loss * self.loss_sep_weight + score = loss.item() + loss_dict['loss_chamfer'] = chf_loss.item() + loss_dict['loss_coverage'] = cov_loss.item() + loss_dict['loss_surface'] = surf_loss.item() + loss_dict['loss_joint'] = joint_loss.item() + loss_dict['loss_reg'] = reg_loss.item() + loss_dict['loss_sep'] = sep_loss.item() + + return loss, score, loss_dict diff --git a/libs/loss_omadnet.py b/libs/loss_omadnet.py new file mode 100644 index 0000000..923d569 --- /dev/null +++ b/libs/loss_omadnet.py @@ -0,0 +1,125 @@ +import torch +import torch.nn.functional as F +import numpy as np + + +class Loss_OMAD_Net(torch.nn.Module): + def __init__(self, num_key_per_part, num_parts, num_cate, device, + loss_loc_weight=5.0, + loss_cls_weight=1.0, + loss_base_weight=0.2, + loss_joint_state_weight=5.0, + loss_shape_weight=3.0, + loss_joint_param_weight=3.0, + loss_reg_weight=0.01, + joint_type='revolute', + use_background=False + ): + super(Loss_OMAD_Net, self).__init__() + self.num_key_per_part = num_key_per_part + self.num_cate = num_cate + self.num_parts = num_parts + self.num_classes = self.num_parts + 1 if use_background else self.num_parts + self.num_joints = num_parts - 1 + self.device = device + self.joint_type = joint_type + assert joint_type in ('revolute', 'prismatic') + + self.loss_loc_weight = loss_loc_weight + self.loss_cls_weight = loss_cls_weight + self.loss_base_weight = loss_base_weight + self.loss_joint_state_weight = loss_joint_state_weight + self.loss_shape_weight = loss_shape_weight + self.loss_joint_param_weight = loss_joint_param_weight + self.loss_reg_weight = loss_reg_weight + + def forward(self, + pred_trans_part_kp, dense_part_cls_score, pred_base_quat, pred_base_t, pred_norm_part_kp, pred_joint_loc, pred_joint_axis, pred_joint_state, pred_beta, + gt_part_cls, gt_part_quat, gt_part_r, gt_part_t, gt_norm_part_kp, gt_joint_loc, gt_joint_axis, gt_joint_state): + losses = [] + loss_dict = dict(loss_loc=[], loss_shape=[]) + loss_base_list = [] + loss_joint_state_list = [] + loss_joint_param_list = [] + for part_idx in range(self.num_parts): + pred_trans_kp = pred_trans_part_kp[:, part_idx, :, :] + pred_norm_kp = pred_norm_part_kp[:, part_idx, :, :] + gt_norm_kp = gt_norm_part_kp[:, part_idx, :, :] + gt_r = gt_part_r[:, part_idx, :, :] + gt_inv_r = gt_r.transpose(1, 2) + gt_t = gt_part_t[:, part_idx, :, :] + + """Location Loss""" + inv_pred_kp = torch.bmm(gt_inv_r, (pred_trans_kp - gt_t).transpose(1, 2)).transpose(1, 2).contiguous() + loss_loc = torch.mean(torch.mean(torch.norm((inv_pred_kp - gt_norm_kp), dim=2), dim=1)) + + """Base Transformation Loss""" + if part_idx == 0: # Only count for base part + loss_base_r = torch.mean(1 - torch.sum(pred_base_quat * gt_part_quat[:, 0, :], dim=-1)) + loss_base_t = torch.mean(torch.norm(pred_base_t - gt_t[:, 0, :], dim=-1)) + loss_base = loss_base_r + 0.5*loss_base_t + loss_base_list.append(loss_base) + + """Joint State Loss""" + if part_idx != 0: # Only count for child part + diff_joint_state = (pred_joint_state[:, part_idx - 1] - gt_joint_state[:, part_idx]) / np.pi + loss_joint = torch.mean(diff_joint_state * diff_joint_state) + loss_joint_state_list.append(loss_joint) + + """Shape Loss""" + loss_shape = torch.mean(torch.mean(torch.norm(pred_norm_kp - gt_norm_kp, dim=2), dim=-1)) + + """Joint Params Loss""" + if part_idx != 0: # Only count for child part + loss_joint_axis = 1 - F.cosine_similarity(pred_joint_axis[:, part_idx - 1, :], + gt_joint_axis[:, part_idx - 1, :], dim=-1).mean(dim=-1).mean(dim=-1) + norm_gt_joint_axis = gt_joint_axis[:, part_idx - 1, :] / torch.norm(gt_joint_axis[:, part_idx - 1, :], + dim=-1, keepdim=True) + if self.joint_type == 'revolute': + p = gt_joint_loc[:, part_idx - 1, :] + q = gt_joint_loc[:, part_idx - 1, :] + norm_gt_joint_axis + r = pred_joint_loc[:, part_idx - 1, :] + x = p - q + loss_joint_loc = torch.norm( + ((r - q) * x).sum(-1, keepdim=True) / (x * x).sum(-1, keepdim=True) * (p - q) + (q - r), + dim=-1).mean(-1).mean(-1) + loss_joint_param = loss_joint_loc + loss_joint_axis + elif self.joint_type == 'prismatic': + loss_joint_param = loss_joint_axis + loss_joint_param_list.append(loss_joint_param) + + """SUM UP""" + loss = loss_loc * self.loss_loc_weight + loss_shape * self.loss_shape_weight + loss_dict['loss_loc'].append(loss_loc.item()) + loss_dict['loss_shape'].append(loss_shape.item()) + losses.append(loss) + + loss_all = torch.mean(torch.stack(losses, dim=0), dim=0) + + loss_base_all = torch.mean(torch.stack(loss_base_list)) + loss_all += self.loss_base_weight * loss_base_all + loss_dict['loss_base'] = [loss_base_all.item()] + + loss_joint_state_all = torch.mean(torch.stack(loss_joint_state_list)) + loss_all += self.loss_joint_state_weight * loss_joint_state_all + loss_dict['loss_joint_state'] = [loss_joint_state_all.item()] + + loss_joint_param_all = torch.mean(torch.stack(loss_joint_param_list)) + loss_all += self.loss_joint_param_weight * loss_joint_param_all + loss_dict['loss_joint_param'] = [loss_joint_param_all.item()] + + for key in loss_dict.keys(): + loss_dict[key] = np.mean(loss_dict[key]) + + """Classification(segmentation) Loss""" + loss_cls = torch.mean(F.cross_entropy(dense_part_cls_score.view(-1, self.num_classes), gt_part_cls.view(-1))) + loss_dict['loss_cls'] = loss_cls.item() + loss_all += self.loss_cls_weight * loss_cls + + """Regularization Loss""" + loss_reg = (pred_beta * pred_beta).mean() + loss_dict['loss_reg'] = loss_reg.item() + loss_all += self.loss_reg_weight * loss_reg + + scores_all = loss_all.item() + return loss_all, scores_all, loss_dict diff --git a/libs/utils.py b/libs/utils.py new file mode 100644 index 0000000..41c022b --- /dev/null +++ b/libs/utils.py @@ -0,0 +1,96 @@ +import numpy as np +import itertools +import torch +import torch.nn.functional as F + +def iou_3d(bbox1, bbox2, nres=50): + bmin = np.min(np.concatenate((bbox1, bbox2), 0), 0) + bmax = np.max(np.concatenate((bbox1, bbox2), 0), 0) + xs = np.linspace(bmin[0], bmax[0], nres) + ys = np.linspace(bmin[1], bmax[1], nres) + zs = np.linspace(bmin[2], bmax[2], nres) + pts = np.array([x for x in itertools.product(xs, ys, zs)]) + flag1 = pts_inside_box(pts, bbox1) + flag2 = pts_inside_box(pts, bbox2) + intersect = np.sum(np.logical_and(flag1, flag2)) + union = np.sum(np.logical_or(flag1, flag2)) + if union == 0: + return 1 + else: + return intersect/float(union) + + +def pts_inside_box(pts, bbox): + # pts: N x 3 + # bbox: 8 x 3 (-1, 1, 1), (1, 1, 1), (1, -1, 1), (-1, -1, 1), (-1, 1, -1), (1, 1, -1), (1, -1, -1), (-1, -1, -1) + u1 = bbox[5, :] - bbox[4, :] + u2 = bbox[7, :] - bbox[4, :] + u3 = bbox[0, :] - bbox[4, :] + + up = pts - np.reshape(bbox[4, :], (1, 3)) + p1 = np.matmul(up, u1.reshape((3, 1))) + p2 = np.matmul(up, u2.reshape((3, 1))) + p3 = np.matmul(up, u3.reshape((3, 1))) + p1 = np.logical_and(p1>0, p10, p20, p3 + +#include +#include +#include + +// cpu operations ------------------------------- +void max_forward_worker(torch::TensorAccessor* p_data_a, + torch::TensorAccessor* p_index_a, + torch::TensorAccessor* p_max_idx_a, + torch::TensorAccessor* p_max_val_a, + int c_begin, int c_end) { + int B = p_data_a->size(0); + // int C = p_data_a->size(1); + int N = p_data_a->size(2); + // int K = p_max_idx_a->size(2); + + // thread is on C channel + for (int b=0; b (*p_max_val_a)[b][c][k]) { + (*p_max_val_a)[b][c][k] = data_point; + (*p_max_idx_a)[b][c][k] = n; + } + } + } + } +} + +torch::Tensor index_max_forward_pthread_cpu(const torch::Tensor data, + const torch::Tensor index, + const int K, + const int thread_num) { + int B = data.size(0); + int C = data.size(1); + // int N = data.size(2); + + auto max_idx = torch::zeros({B, C, K}, torch::TensorOptions().dtype(torch::kInt32)); + auto max_val = torch::ones({B, C, K}, torch::TensorOptions().dtype(torch::kFloat32)) * -1000.0; + + // use accessor + auto data_a = data.accessor(); + auto index_a = index.accessor(); + auto max_idx_a = max_idx.accessor(); + auto max_val_a = max_val.accessor(); + + // multi thread for loop, divide on C channel + std::thread thread_pool[thread_num]; + int c_interval = int(C) / int(thread_num); + for (int t=0;t(); + auto index_a = index.accessor(); + auto max_idx_a = max_idx.accessor(); + auto max_val_a = max_val.accessor(); + + // single thread for loop + for (int b=0; b max_val_a[b][c][k]) { + max_val_a[b][c][k] = data_point; + max_idx_a[b][c][k] = n; + } + } + } + } + + return max_idx; +} +// cpu operations ------------------------------- + + + + +// cuda operations ------------------------------ +#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor/variable") +#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +// declare the functions in .cu file +torch::Tensor index_max_forward_cuda(const torch::Tensor data, + const torch::Tensor index, + const int K); + +torch::Tensor index_max_forward_cuda_shared_mem(const torch::Tensor data, + const torch::Tensor index, + const int K); + +torch::Tensor index_max_forward_cuda_wrapper(const torch::Tensor data, + const torch::Tensor index, + const int K){ + CHECK_INPUT(data); + CHECK_INPUT(index); + + return index_max_forward_cuda(data, index, K); +} + +torch::Tensor index_max_forward_cuda_wrapper_shared_mem(const torch::Tensor data, + const torch::Tensor index, + const int K){ + CHECK_INPUT(data); + CHECK_INPUT(index); + + return index_max_forward_cuda_shared_mem(data, index, K); +} +// cuda operations ------------------------------ + + + + +PYBIND11_MODULE(index_max, m) { + m.def("forward_cpu", &index_max_forward_cpu, "CPU single thread"); + m.def("forward_multi_thread_cpu", &index_max_forward_pthread_cpu, "CPU multi-thread"); + m.def("forward_cuda", &index_max_forward_cuda_wrapper, "CUDA code without shared memory"); + m.def("forward_cuda_shared_mem", &index_max_forward_cuda_wrapper_shared_mem, "CUDA code with shared memory"); +} diff --git a/model/index_max_ext/index_max.egg-info/PKG-INFO b/model/index_max_ext/index_max.egg-info/PKG-INFO new file mode 100644 index 0000000..a78206c --- /dev/null +++ b/model/index_max_ext/index_max.egg-info/PKG-INFO @@ -0,0 +1,10 @@ +Metadata-Version: 1.0 +Name: index-max +Version: 0.0.0 +Summary: UNKNOWN +Home-page: UNKNOWN +Author: UNKNOWN +Author-email: UNKNOWN +License: UNKNOWN +Description: UNKNOWN +Platform: UNKNOWN diff --git a/model/index_max_ext/index_max.egg-info/SOURCES.txt b/model/index_max_ext/index_max.egg-info/SOURCES.txt new file mode 100644 index 0000000..04ef629 --- /dev/null +++ b/model/index_max_ext/index_max.egg-info/SOURCES.txt @@ -0,0 +1,7 @@ +index_max.cpp +index_max_cuda.cu +setup.py +index_max.egg-info/PKG-INFO +index_max.egg-info/SOURCES.txt +index_max.egg-info/dependency_links.txt +index_max.egg-info/top_level.txt \ No newline at end of file diff --git a/model/index_max_ext/index_max.egg-info/dependency_links.txt b/model/index_max_ext/index_max.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/model/index_max_ext/index_max.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/model/index_max_ext/index_max.egg-info/top_level.txt b/model/index_max_ext/index_max.egg-info/top_level.txt new file mode 100644 index 0000000..24af537 --- /dev/null +++ b/model/index_max_ext/index_max.egg-info/top_level.txt @@ -0,0 +1 @@ +index_max diff --git a/model/index_max_ext/index_max_cuda.cu b/model/index_max_ext/index_max_cuda.cu new file mode 100644 index 0000000..b4ddb83 --- /dev/null +++ b/model/index_max_ext/index_max_cuda.cu @@ -0,0 +1,99 @@ +#include + +#include +#include + +#include + + +__global__ void index_max_forward_cuda_kernel(const float* __restrict__ data, + const int* __restrict__ index, + int* __restrict__ max_idx, + float* __restrict__ max_val, + const int B, const int C, const int N, const int K){ + int b = threadIdx.x; + int c = blockIdx.x; + + for(int n=0;n max_val[b*C*K+c*K+k]){ + max_val[b*C*K+c*K+k] = data_point; + max_idx[b*C*K+c*K+k] = n; + } + } +} + + + +__global__ void index_max_forward_cuda_kernel_shared_mem(const float* __restrict__ data, + const int* __restrict__ index, + int* __restrict__ max_idx, + float* __restrict__ max_val, + const int B, const int C, const int N, const int K){ + int b = threadIdx.x; + int c = blockIdx.x; + + extern __shared__ float max_val_shared[]; + for (int i=0;i max_val_shared[b*K+k]){ + max_val_shared[b*K+k] = data_point; + max_idx[b*C*K+c*K+k] = n; + } + } +// +// __syncthreads(); + +// for(int n=0;n max_val[b*C*K+c*K+k]){ +// max_val[b*C*K+c*K+k] = data_point; +// max_idx[b*C*K+c*K+k] = n; +// } +// } +} + + + +torch::Tensor index_max_forward_cuda(const torch::Tensor data, const torch::Tensor index, const int K){ + int B = data.size(0); + int C = data.size(1); + int N = data.size(2); + + auto device_idx = data.device().index(); + auto max_idx = torch::zeros({B, C, K}, torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA, device_idx)); + auto max_val = torch::ones({B, C, K}, torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_idx)) * -1000.0; + + index_max_forward_cuda_kernel<<>>(data.data(), + index.data(), + max_idx.data(), + max_val.data(), + B, C, N, K); + + return max_idx; +} + +torch::Tensor index_max_forward_cuda_shared_mem(const torch::Tensor data, const torch::Tensor index, const int K){ + int B = data.size(0); + int C = data.size(1); + int N = data.size(2); + + auto device_idx = data.device().index(); + auto max_idx = torch::zeros({B, C, K}, torch::TensorOptions({torch::kCUDA, device_idx}).dtype(torch::kInt32)); + auto max_val = torch::ones({B, C, K}, torch::TensorOptions({torch::kCUDA, device_idx}).dtype(torch::kFloat32)) * -1000.0; + + index_max_forward_cuda_kernel_shared_mem<<>>(data.data(), + index.data(), + max_idx.data(), + max_val.data(), + B, C, N, K); + + return max_idx; +} diff --git a/model/index_max_ext/setup.py b/model/index_max_ext/setup.py new file mode 100644 index 0000000..bac35c4 --- /dev/null +++ b/model/index_max_ext/setup.py @@ -0,0 +1,18 @@ +import setuptools +import torch +from setuptools import setup +from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension + +# setup(name='index_max', +# ext_modules=[CppExtension('index_max', ['index_max.cpp'])], +# cmdclass={'build_ext': BuildExtension}) + +# setuptools.Extension( +# name='index_max', +# sources=['index_max.cpp'], +# include_dirs=torch.utils.cpp_extension.include_paths(), +# language='c++') + +setup(name='index_max', + ext_modules=[CUDAExtension('index_max', ['index_max.cpp', 'index_max_cuda.cu'])], + cmdclass={'build_ext': BuildExtension}) diff --git a/model/layers.py b/model/layers.py new file mode 100644 index 0000000..83b5e80 --- /dev/null +++ b/model/layers.py @@ -0,0 +1,405 @@ +import torch +import torch.nn as nn +from torch.nn.modules.batchnorm import _BatchNorm +import math + + +## ---------- standard layers ---------- + + +class MyBatchNorm1d(_BatchNorm): + r"""Applies Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + .. math:: + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + During evaluation, this running mean/variance is used for normalization. + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + """ + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, momentum_decay_step=None, momentum_decay=1): + super(MyBatchNorm1d, self).__init__(num_features, eps, momentum, affine) + self.momentum_decay_step = momentum_decay_step + self.momentum_decay = momentum_decay + self.momentum_original = self.momentum + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(MyBatchNorm1d, self)._check_input_dim(input) + + def forward(self, input, epoch=None): + if (epoch is not None) and (epoch >= 1) and (self.momentum_decay_step is not None) and ( + self.momentum_decay_step > 0): + # perform momentum decay + self.momentum = self.momentum_original * (self.momentum_decay ** (epoch // self.momentum_decay_step)) + if self.momentum < 0.01: + self.momentum = 0.01 + + return nn.functional.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + +class MyBatchNorm2d(_BatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + .. math:: + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + During evaluation, this running mean/variance is used for normalization. + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + """ + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, momentum_decay_step=None, momentum_decay=1): + super(MyBatchNorm2d, self).__init__(num_features, eps, momentum, affine) + self.momentum_decay_step = momentum_decay_step + self.momentum_decay = momentum_decay + self.momentum_original = self.momentum + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(MyBatchNorm2d, self)._check_input_dim(input) + + def forward(self, input, epoch=None): + if (epoch is not None) and (epoch >= 1) and (self.momentum_decay_step is not None) and ( + self.momentum_decay_step > 0): + # perform momentum decay + self.momentum = self.momentum_original * (self.momentum_decay ** (epoch // self.momentum_decay_step)) + if self.momentum < 0.01: + self.momentum = 0.01 + + return nn.functional.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + +class MyConv2d(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, activation=None, + normalization=None, momentum=0.1, bn_momentum_decay_step=None, bn_momentum_decay=1): + super(MyConv2d, self).__init__() + self.activation = activation + self.normalization = normalization + + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias) + # self.conv = XConv(in_channels, out_channels, dim=3, kernel_size=kernel_size) + if self.normalization == 'batch': + self.norm = MyBatchNorm2d(out_channels, momentum=momentum, affine=True, + momentum_decay_step=bn_momentum_decay_step, momentum_decay=bn_momentum_decay) + elif self.normalization == 'instance': + self.norm = nn.InstanceNorm2d(out_channels, momentum=momentum, affine=True) + if self.activation == 'relu': + self.act = nn.ReLU() + elif self.activation == 'elu': + self.act = nn.ELU(alpha=1.0) + elif 'leakyrelu' == self.activation: + self.act = nn.LeakyReLU(0.01) + elif 'selu' == self.activation: + self.act = nn.SELU() + + self.weight_init() + + def weight_init(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d): + n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.fill_(0) + elif isinstance(m, MyBatchNorm2d) or isinstance(m, nn.InstanceNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def forward(self, x, epoch=None): + x = self.conv(x) + if self.normalization == 'batch': + x = self.norm(x, epoch) + elif self.normalization is not None: + x = self.norm(x) + + if self.activation is not None: + x = self.act(x) + return x + + +class EquivariantLayer(nn.Module): + def __init__(self, num_in_channels, num_out_channels, activation='relu', normalization=None, momentum=0.1, + bn_momentum_decay_step=None, bn_momentum_decay=1, num_groups=16): + super(EquivariantLayer, self).__init__() + + self.num_in_channels = num_in_channels + self.num_out_channels = num_out_channels + self.activation = activation + self.normalization = normalization + + self.conv = nn.Conv1d(self.num_in_channels, self.num_out_channels, kernel_size=1, stride=1, padding=0) + + if 'batch' == self.normalization: + self.norm = MyBatchNorm1d(self.num_out_channels, momentum=momentum, affine=True, + momentum_decay_step=bn_momentum_decay_step, momentum_decay=bn_momentum_decay) + elif 'instance' == self.normalization: + self.norm = nn.InstanceNorm1d(self.num_out_channels, momentum=momentum, affine=True) + elif 'group' == self.normalization: + self.norm = nn.GroupNorm(num_groups=num_groups, num_channels=self.num_out_channels) + + if 'relu' == self.activation: + self.act = nn.ReLU() + elif 'leakyrelu' == self.activation: + self.act = nn.LeakyReLU(0.01) + + self.weight_init() + + def weight_init(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + if self.activation == 'relu' or self.activation == 'leakyrelu': + nn.init.kaiming_normal_(m.weight, nonlinearity=self.activation) + else: + m.weight.data.normal_(0, std=0.01) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, MyBatchNorm1d) or isinstance(m, nn.InstanceNorm1d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def forward(self, x, epoch=None): + y = self.conv(x) + + if self.normalization == 'batch': + y = self.norm(y, epoch) + elif self.normalization is not None: + y = self.norm(y) + + if self.activation is not None: + y = self.act(y) + + return y + + +## ---------- nodes branch ---------- +class GeneralKNNFusionModule(nn.Module): + def __init__(self, in_channels, out_channels_list_before, out_channels_list_after, + activation, normalization, momentum=0.1, + bn_momentum_decay_step=None, bn_momentum_decay=1): + super(GeneralKNNFusionModule, self).__init__() + + self.layers_before = nn.ModuleList() + previous_out_channels = in_channels + for i, c_out in enumerate(out_channels_list_before): + self.layers_before.append( + MyConv2d(previous_out_channels, c_out, kernel_size=1, stride=1, padding=0, bias=True, + activation=activation, normalization=normalization, + momentum=momentum, bn_momentum_decay_step=bn_momentum_decay_step, + bn_momentum_decay=bn_momentum_decay)) + previous_out_channels = c_out + + self.layers_after = nn.ModuleList() + previous_out_channels = 2 * previous_out_channels + for i, c_out in enumerate(out_channels_list_after): + self.layers_after.append( + MyConv2d(previous_out_channels, c_out, kernel_size=1, stride=1, padding=0, bias=True, + activation=activation, normalization=normalization, + momentum=momentum, bn_momentum_decay_step=bn_momentum_decay_step, + bn_momentum_decay=bn_momentum_decay)) + previous_out_channels = c_out + + def forward(self, query, database, x, K, epoch=None): + ''' + + :param query: Bx3xM FloatTensor + :param database: Bx3xN FloatTensor + :param x: BxCxN FloatTensor + :param K: K neighbors + :return: + ''' + # 1. compute knn, query -> database + # 2. for each query, normalize neighbors with its coordinate + # 3. FC for these normalized points + # 4. maxpool for each query + + B, M, N, C = query.size()[0], query.size()[2], database.size()[2], x.size()[1] + + query_Mx1 = query.detach().unsqueeze(3) # Bx3xMx1 + database_1xN = database.detach().unsqueeze(2) # Bx3x1xN + + norm = torch.norm(query_Mx1 - database_1xN, dim=1, keepdim=False) # Bx3xMxN -> BxMxN + knn_D, knn_I = torch.topk(norm, k=K, dim=2, largest=False, sorted=True) # BxMxK, BxMxK + knn_I_3 = knn_I.unsqueeze(1).expand(B, 3, M, K).contiguous().view(B, 3, M * K) # Bx3xMxK -> Bx3xM*K + knn_I_C = knn_I.unsqueeze(1).expand(B, C, M, K).contiguous().view(B, C, M * K) # BxCxMxK -> BxCxM*K + + query_neighbor_coord = torch.gather(database, dim=2, index=knn_I_3).view(B, 3, M, K) # Bx3xMxK + query_neighbor_feature = torch.gather(x, dim=2, index=knn_I_C).view(B, C, M, K) # BxCxMxK + + query_neighbor_coord_decentered = (query_neighbor_coord - query_Mx1).detach() + query_neighbor = torch.cat((query_neighbor_coord_decentered, query_neighbor_feature), dim=1) # Bx(3+C)xMxK + + for layer in self.layers_before: + query_neighbor = layer(query_neighbor, epoch) + feature, _ = torch.max(query_neighbor, dim=3, keepdim=True) # BxCxMx1 + + y = torch.cat((feature.expand_as(query_neighbor), query_neighbor), dim=1) # Bx2CxMxK + for layer in self.layers_after: + y = layer(y, epoch) + feature, _ = torch.max(y, dim=3, keepdim=False) # BxCxM + + return feature + + +class PointNet(nn.Module): + def __init__(self, in_channels, out_channels_list, activation, normalization, momentum=0.1, + bn_momentum_decay_step=None, bn_momentum_decay=1, output_init_radius=None): + super(PointNet, self).__init__() + + self.layers = nn.ModuleList() + previous_out_channels = in_channels + for i, c_out in enumerate(out_channels_list): + if i != len(out_channels_list) - 1: + self.layers.append(EquivariantLayer(previous_out_channels, c_out, activation, normalization, + momentum, bn_momentum_decay_step, bn_momentum_decay)) + else: + self.layers.append(EquivariantLayer(previous_out_channels, c_out, None, None)) + previous_out_channels = c_out + + if output_init_radius is not None: + self.layers[len(out_channels_list) - 1].conv.bias.data.uniform_(-1 * output_init_radius, output_init_radius) + + def forward(self, x, epoch=None): + for layer in self.layers: + x = layer(x, epoch) + return x + + +## ---------- pose and coefficients branch ---------- +class InstanceBranch(nn.Module): + def __init__(self, in_channels, out_channels_list, num_basis, activation, normalization, momentum=0.1, + bn_momentum_decay_step=None, bn_momentum_decay=1, output_init_radius=None): + super(InstanceBranch, self).__init__() + # like the PointNet module but with fc + self.num_basis = num_basis + + self.layers = nn.ModuleList() + previous_out_channels = in_channels + for i, c_out in enumerate(out_channels_list): + if i != len(out_channels_list) - 1: + self.layers.append(EquivariantLayer(previous_out_channels, c_out, activation, normalization, + momentum, bn_momentum_decay_step, bn_momentum_decay)) + else: + self.layers.append(EquivariantLayer(previous_out_channels, c_out, None, None)) + previous_out_channels = c_out + + if output_init_radius is not None: + self.layers[len(out_channels_list) - 1].conv.bias.data.uniform_(-1 * output_init_radius, output_init_radius) + + def forward(self, x, epoch=None): + for layer in self.layers: + x = layer(x, epoch) # Bx64x300 / Bx128x300 / Bx10x300 + + x = torch.max(x, 2, keepdim=True)[0] # Bx10x1 + x = x.view(-1, x.shape[1]) # Bx10 + + return x + +## ---------- joint branch ---------- + +class JointBranch(nn.Module): + """PointNet-like structure""" + def __init__(self, in_channels, first_out_channels_list, second_out_channels_list, + activation, normalization, momentum=0.1, + bn_momentum_decay_step=None, bn_momentum_decay=1, output_init_radius=None): + super(JointBranch, self).__init__() + + self.first_layers = nn.ModuleList() + self.second_layers = nn.ModuleList() + previous_out_channels = in_channels + for i, c_out in enumerate(first_out_channels_list): + if i != len(first_out_channels_list) - 1: + self.first_layers.append(EquivariantLayer(previous_out_channels, c_out, activation, normalization, + momentum, bn_momentum_decay_step, bn_momentum_decay)) + else: + self.first_layers.append(EquivariantLayer(previous_out_channels, c_out, None, None)) + previous_out_channels = c_out + + if output_init_radius is not None: + self.first_layers[len(first_out_channels_list) - 1].conv.bias.data.uniform_(-1 * output_init_radius, output_init_radius) + + previous_out_channels = first_out_channels_list[-1] + for i, c_out in enumerate(second_out_channels_list): + if i != len(second_out_channels_list) - 1: + self.second_layers.append(EquivariantLayer(previous_out_channels, c_out, activation, normalization, + momentum, bn_momentum_decay_step, bn_momentum_decay)) + else: + self.second_layers.append(EquivariantLayer(previous_out_channels, c_out, None, None)) + previous_out_channels = c_out + + if output_init_radius is not None: + self.second_layers[len(second_out_channels_list) - 1].conv.bias.data.uniform_(-1 * output_init_radius, output_init_radius) + + def forward(self, x, epoch=None): + for layer in self.first_layers: + x = layer(x, epoch) + x = torch.max(x, 2, keepdim=True)[0] + for layer in self.second_layers: + x = layer(x, epoch) + return x + + +class JointBranch2(nn.Module): + """MLP-like structure""" + def __init__(self, in_channels, out_channels_list, activation, normalization, momentum=0.1, + bn_momentum_decay_step=None, bn_momentum_decay=1, output_init_radius=None): + super(JointBranch2, self).__init__() + + self.layers = nn.ModuleList() + previous_out_channels = in_channels + for i, c_out in enumerate(out_channels_list): + if i != len(out_channels_list) - 1: + self.layers.append(EquivariantLayer(previous_out_channels, c_out, activation, normalization, + momentum, bn_momentum_decay_step, bn_momentum_decay)) + else: + self.layers.append(EquivariantLayer(previous_out_channels, c_out, None, None)) + previous_out_channels = c_out + + if output_init_radius is not None: + self.layers[len(out_channels_list) - 1].conv.bias.data.uniform_(-1 * output_init_radius, output_init_radius) + + def forward(self, x, epoch=None): + for layer in self.layers: + x = layer(x, epoch) + return x \ No newline at end of file diff --git a/model/omad_net.py b/model/omad_net.py new file mode 100644 index 0000000..277e4a5 --- /dev/null +++ b/model/omad_net.py @@ -0,0 +1,239 @@ +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.utils.data +import torch.nn.functional as F +from model.pointnet_plus import PointNetPlus +from model.layers import EquivariantLayer, JointBranch2 + + +class OMAD_Net(nn.Module): + def __init__(self, device, + params_dict, + num_points, + num_kp, + num_parts=2, + init_dense_soft_factor=1.0, + num_basis=10, + symtype='shape', + use_attention=True, + use_normal=False, + use_background=False): + super(OMAD_Net, self).__init__() + self.device = device + assert 'basis' in params_dict and 'n_pl' in params_dict and 'joint_net' in params_dict + self.num_points = num_points + self.num_parts = num_parts + self.num_joints = num_parts - 1 + self.num_basis = num_basis + self.symtype = symtype + assert symtype in ('shape', 'none') + self.num_kp = num_kp + self.num_parts = num_parts + self.num_classes = self.num_parts + 1 if use_background else self.num_parts + self.use_background = use_background + self.num_kp_per_part = self.num_kp // self.num_parts + assert self.num_kp % self.num_parts == 0 + self.dense_soft_factor = init_dense_soft_factor + self.use_attention = use_attention + self.use_normal = use_normal + + if self.use_normal: + self.pointnet = PointNetPlus(in_channel=6) + else: + self.pointnet = PointNetPlus(in_channel=3) + + self.cls_1 = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.cls_2 = EquivariantLayer(64, self.num_classes, activation=None, normalization=None) + + self.kp_1 = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.kp_2 = EquivariantLayer(64, 3 * num_kp, activation=None, normalization=None) + + self.kp_att_1 = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.kp_att_2 = EquivariantLayer(64, num_kp, activation=None, normalization=None) + + self.conv1_r = EquivariantLayer(128, 128, activation='relu', normalization='batch') + self.conv2_r = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.conv3_r = EquivariantLayer(64, 4, activation=None, normalization=None) # quaternion (w, x, y, z) + + self.conv1_t = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.conv2_t = EquivariantLayer(64, 3, activation=None, normalization=None) # translation (x, y, z) + + self.joint_state_1 = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.joint_state_2 = EquivariantLayer(64, self.num_joints, activation=None, normalization=None) + + self.joint_att_1 = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.joint_att_2 = EquivariantLayer(64, self.num_joints, activation=None, normalization=None) + + self.beta_1 = EquivariantLayer(128, 128, activation='relu', normalization='batch') + self.beta_2 = EquivariantLayer(128, 64, activation='relu', normalization='batch') + self.beta_3 = EquivariantLayer(64, self.num_basis, activation=None, normalization=None) + + # Additional parameters(loaded) + self.basis = params_dict['basis'].clone().detach().to(self.device) + self.n_pl = params_dict['n_pl'].clone().detach().to(self.device) + self.joint_net = JointBranch2(self.num_basis, [64, 64, self.num_joints * 6], + activation='relu', normalization='batch') + for param in self.joint_net.parameters(): + param.requires_grad = False + self.joint_net.load_state_dict(params_dict['joint_net']) + + def set_device(self, device): + self.device = device + self.basis = self.basis.to(device) + self.n_pl = self.n_pl.to(device) + + def forward(self, pts_all, cls_label=None): + self.joint_net.eval() # freeze BN + bs, n, dim = pts_all.shape + if dim > 3 and self.use_normal: + pts = pts_all[:, :, :3] + feat = pts_all[:, :, 3:] + else: + pts = pts_all[:, :, :3] + feat = None + dense_pts_feat = self.pointnet(pts, feat).contiguous() # (bs, 128, n) + global_pts_feat = torch.max(dense_pts_feat, dim=-1, keepdim=True)[0] # (bs, 128, 1) + + dense_cls_score = self.cls_2(self.cls_1(dense_pts_feat)).transpose(1, 2).contiguous() # (bs, n, K) + + dense_kp_offset = self.kp_2(self.kp_1(dense_pts_feat)).transpose(1, 2).contiguous() # (bs, n, M*3) + dense_kp_offset = dense_kp_offset.reshape(bs, n, -1, 3) # (bs, n, M, 3) + + dense_kp_coords = pts.reshape(bs, n, 1, 3).expand(bs, n, self.num_kp, + 3) + dense_kp_offset # (bs, n, M, 3) + if self.use_attention: + dense_kp_score = self.kp_att_2(self.kp_att_1(dense_pts_feat))\ + .transpose(1, 2).contiguous().reshape(bs, n, -1, 1) # (bs, n, M, 1) + dense_kp_prob = F.softmax(dense_kp_score * self.dense_soft_factor, dim=1) # (bs, n, M, 1) + else: + dense_kp_prob = torch.ones_like(dense_kp_coords) / n + pred_kp = torch.sum(dense_kp_prob * dense_kp_coords, dim=1, keepdim=True) # (bs, 1, M, 3) + pred_part_kp = pred_kp.reshape(bs, self.num_parts, -1, 3) # (bs, K, M/k, 3) + + base_idx = 1 if self.use_background else 0 + if cls_label is None: + base_global_feat_list = [] + for idx in range(bs): + base_cls_idxs = torch.argmax(dense_cls_score[idx].detach(), dim=1) == base_idx # (n, ) + if torch.nonzero(base_cls_idxs).shape[0] == 0: + base_global_feat_list.append(torch.zeros(dense_pts_feat.shape[1], 1, device=dense_pts_feat.device)) # (128, 1) + else: + base_global_feat_list.append(torch.mean(dense_pts_feat[idx, :, base_cls_idxs], dim=-1, keepdim=True)) # (128, 1) + base_global_feat = torch.stack(base_global_feat_list, dim=0) # (bs, 128, 1) + else: + base_global_feat_list = [] + for idx in range(bs): + base_cls_idxs = cls_label[idx] == base_idx # (n, ) + if torch.nonzero(base_cls_idxs).shape[0] == 0: + base_global_feat_list.append(torch.zeros(dense_pts_feat.shape[1], 1, device=dense_pts_feat.device)) # (128, 1) + else: + base_global_feat_list.append( + torch.mean(dense_pts_feat[idx, :, base_cls_idxs], dim=-1, keepdim=True)) # (128, 1) + base_global_feat = torch.stack(base_global_feat_list, dim=0) # (bs, 128, 1) + + rx = self.conv3_r(self.conv2_r(self.conv1_r(base_global_feat))) # (bs, 4, 1) + + quat = (rx / torch.norm(rx, dim=1, keepdim=True)).squeeze(2) # (bs, 4) - (w, x, y, z) + base_r = torch.cat(((1.0 - 2.0 * (quat[:, 2] ** 2 + quat[:, 3] ** 2)).view(-1, 1), + (2.0 * quat[:, 1] * quat[:, 2] - 2.0 * quat[:, 0] * quat[:, 3]).view(-1, 1), + (2.0 * quat[:, 0] * quat[:, 2] + 2.0 * quat[:, 1] * quat[:, 3]).view(-1, 1), + (2.0 * quat[:, 1] * quat[:, 2] + 2.0 * quat[:, 3] * quat[:, 0]).view(-1, 1), + (1.0 - 2.0 * (quat[:, 1] ** 2 + quat[:, 3] ** 2)).view(-1, 1), + (-2.0 * quat[:, 0] * quat[:, 1] + 2.0 * quat[:, 2] * quat[:, 3]).view(-1, 1), + (-2.0 * quat[:, 0] * quat[:, 2] + 2.0 * quat[:, 1] * quat[:, 3]).view(-1, 1), + (2.0 * quat[:, 0] * quat[:, 1] + 2.0 * quat[:, 2] * quat[:, 3]).view(-1, 1), + (1.0 - 2.0 * (quat[:, 1] ** 2 + quat[:, 2] ** 2)).view(-1, 1)), + dim=-1).contiguous().reshape(bs, 3, 3) # (bs, 3, 3) + + tx = self.conv2_t(self.conv1_t(base_global_feat)) # (bs, 3, 1) + base_t = tx.squeeze(-1) # (bs, 3) + + joint_state_all = self.joint_state_2(self.joint_state_1(dense_pts_feat)).transpose(1, 2) # (bs, n, K-1) + + joint_conf = F.softmax(self.joint_att_2(self.joint_att_1(dense_pts_feat)), dim=2).transpose(1, 2) # (bs, n, K-1) + joint_state = torch.sum(joint_state_all * joint_conf, dim=1) # (bs, K-1) + + beta = self.beta_3(self.beta_2(self.beta_1(global_pts_feat))).squeeze(-1) # (bs, basis_num) + norm_part_kp = self.get_norm_keypoints(beta) # (bs, K, M/k, 3) + norm_joint_loc, norm_joint_axis = self.get_norm_joint_params(beta) # (bs, K-1, 3), (bs, K-1, 3) + + return dense_cls_score, pred_part_kp, quat, base_r, base_t, joint_state, \ + beta, norm_part_kp, norm_joint_loc, norm_joint_axis + + def get_norm_keypoints(self, beta): + """The category-specific symmetric 3D keypoints in normalized(zero-centered) space + are computed with the deformation function. + + Arguments: + beta {torch.Tensor} -- predicted def coefficients - Bxbais_num + + Returns: + torch.Tensor -- kpts: category-specific symmetric 3D keypoints - BXpart_numx(M/part_num)X3 + """ + refl_mat = self.get_reflection_operator(self.n_pl) + if self.symtype != "none": + basis_half = self.basis + else: + basis = self.basis + c = beta.unsqueeze(1).unsqueeze(1) # (B, 1, 1, basis_num) + bs = c.size(0) + + if self.symtype == "shape": + refl_batch = refl_mat.repeat(c.shape[0], 1, 1) # (B, 3, 3) + kpts_half = torch.sum(c * basis_half, 3) # (B, 3, M/2) + kpts_half_reflected = torch.matmul(refl_batch, kpts_half) # (B, 3, M/2) + + part_kpts_half = kpts_half.reshape(bs, 3, self.num_parts, -1) # (B, 3, part_num, M/2/part_num) + part_kpts_half_reflected = kpts_half_reflected.reshape(bs, 3, self.num_parts, -1) # (B, 3, part_num, M/2/part_num) + part_kpts = torch.cat((part_kpts_half, part_kpts_half_reflected), dim=-1) # (B, 3, part_num, M/part_num) + kpts = part_kpts.reshape(bs, 3, -1) # (B, 3, M) + elif self.symtype == "basis": + raise NotImplementedError + elif self.symtype == "none": + kpts = torch.sum(c * basis, 3) # (B, 3, M) + else: + raise NotImplementedError + + part_kpts = kpts.transpose(1, 2).contiguous().reshape(bs, self.num_parts, -1, 3) # (B, part_num, M/part_num, 3) + return part_kpts + + def get_norm_joint_params(self, beta): + """ + get joint params(joint location, joint axis) in normalized(zero-centerd) space in rest state + :param beta: {torch.Tensor} -- predicted def coefficients - Bxbais_num + :return: + norm_joint_loc: {torch.Tensor} -- predicted joint location - Bxjoint_numx3 + norm_joint_axis: {torch.Tensor} -- predicted joint axis(direction) - Bxjoint_numx3 + """ + norm_joint_params = self.joint_net(beta.unsqueeze(-1)).reshape(-1, self.num_joints, 6) + norm_joint_loc = norm_joint_params[:, :, :3] # (bs, K-1, 3) + norm_joint_axis = norm_joint_params[:, :, 3:] # (bs, K-1, 3) + return norm_joint_loc, norm_joint_axis + + @staticmethod + def get_reflection_operator(n_pl): + """ The reflection operator is parametrized by the normal vector + of the plane of symmetry passing through the origin. """ + norm_npl = torch.norm(n_pl, 2) + n_x = n_pl[0, 0] / norm_npl + n_y = torch.tensor(0.0, device=n_pl.device) + n_z = n_pl[0, 1] / norm_npl + refl_mat = torch.stack( + [ + 1 - 2 * n_x * n_x, + -2 * n_x * n_y, + -2 * n_x * n_z, + -2 * n_x * n_y, + 1 - 2 * n_y * n_y, + -2 * n_y * n_z, + -2 * n_x * n_z, + -2 * n_y * n_z, + 1 - 2 * n_z * n_z, + ], + dim=0, + ).reshape(1, 3, 3) + + return refl_mat + + diff --git a/model/omad_priornet.py b/model/omad_priornet.py new file mode 100644 index 0000000..0e3ac82 --- /dev/null +++ b/model/omad_priornet.py @@ -0,0 +1,336 @@ +import torch +import torch.nn as nn +import numpy as np +from model.layers import PointNet, GeneralKNNFusionModule, EquivariantLayer, InstanceBranch, JointBranch2 +import index_max + + +class OMAD_PriorNet(nn.Module): + def __init__(self, + surface_normal_len=0, + basis_num=10, + node_num=16, + part_num=2, + k=1, + node_knn_k_1=3, + symtype='shape', + device=torch.device('cuda:0'), + init_n_pl=None, + init_basis=None + ): + super(OMAD_PriorNet, self).__init__() + self.surface_normal_len = surface_normal_len + self.basis_num = basis_num + self.node_num = node_num + self.part_num = part_num + self.joint_num = part_num - 1 + self.k = k + self.node_knn_k_1 = node_knn_k_1 + self.symtype = symtype # defines the symmetric deformation space "shape" or "basis" (default: {"shape"}) + assert self.symtype in ('shape', 'basis', 'none') + self.device = device + assert self.node_num % self.part_num == 0, 'node number should be devided by part number' + if self.symtype == 'shape': + assert self.node_num % 2 == 0, 'node number must be an even number' + assert (self.node_num // 2) % self.part_num == 0 + + # ---- Nodes branch definition ---- + + self.C1 = 128 + self.C2 = 512 + input_channels = self.C1 + self.C2 + output_channels = 4 # 3 coordinates + sigma + assert self.node_knn_k_1 >= 2 + + self.first_pointnet = PointNet(3 + self.surface_normal_len, + [int(self.C1 / 2), int(self.C1 / 2), int(self.C1 / 2)], + activation='relu', + normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + self.second_pointnet = PointNet(self.C1, [self.C1, self.C1], + activation='relu', + normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + self.knnlayer_1 = GeneralKNNFusionModule(3 + self.C1, (int(self.C2 / 2), int(self.C2 / 2), int(self.C2 / 2)), + (self.C2, self.C2), + activation='relu', + normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + self.node_mlp1 = EquivariantLayer(input_channels, 512, + activation='relu', normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + self.node_mlp2 = EquivariantLayer(512, 256, + activation='relu', normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + self.node_mlp3 = EquivariantLayer(256, output_channels, activation=None, normalization=None) + + # ---- Joint branch defination --- + self.C0 = 64 + self.joint_net = JointBranch2(self.basis_num, [self.C0, self.C0, self.joint_num * 6], + activation='relu', normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + + # ---- Pose and coefficients branch definition ---- + + self.third_pointnet2 = InstanceBranch(3 + self.surface_normal_len, + [int(self.C1 / 2), self.C1, self.basis_num], + self.basis_num, + activation='relu', + normalization='batch', + momentum=0.1, + bn_momentum_decay_step=None, + bn_momentum_decay=1.0) + + # ---- Additional learnable parameters ---- + if self.symtype == 'shape': + self.basis = torch.nn.Parameter( + (torch.rand(1, 3, self.node_num // 2, self.basis_num) - 0.5).to(device), requires_grad=True) # 1x3xM/2xK + elif self.symtype == 'basis': + raise NotImplementedError + elif self.symtype == 'none': + if init_basis is not None: + self.basis = torch.nn.Parameter(init_basis.to(device)) # 1x3xMxK + else: + self.basis = torch.nn.Parameter((torch.rand(1, 3, self.node_num, self.basis_num) - 0.5).to(device)) # 1x3xMxK + if init_n_pl is None: + self.n_pl = torch.nn.Parameter(torch.rand(1, 2).to(device), requires_grad=True) + else: + self.n_pl = torch.nn.Parameter(torch.tensor(init_n_pl).to(device), requires_grad=True) + + def forward(self, x, sn, node, epoch=None): + ''' + :param x: BxNx3 Tensor + :param sn: BxNx3 Tensor + :param node: BxMx3 FloatTensor + :return: + ''' + bs = x.size(0) + x = x.transpose(1, 2) + if sn is not None: + sn = sn.transpose(1, 2) + node = node.transpose(1, 2) + # modify the x according to the nodes, minus the center + mask, mask_row_max, min_idx = self.query_topk(node, x, node.size()[2], + k=self.k) # BxkNxnode_num, Bxnode_num, BxkN + mask_row_sum = torch.sum(mask, dim=1) # Bxnode_num + mask = mask.unsqueeze(1) # Bx1xkNxnode_num + + # if necessary, stack the x + x_stack = x.repeat(1, 1, self.k) + if self.surface_normal_len >= 1: + sn_stack = sn.repeat(1, 1, self.k) + + x_stack_data_unsqueeze = x_stack.unsqueeze(3) # BxCxkNx1 + x_stack_data_masked = x_stack_data_unsqueeze * mask.float() # BxCxkNxnode_num + cluster_mean = torch.sum(x_stack_data_masked, dim=2) / ( + mask_row_sum.unsqueeze(1).float() + 1e-5).detach() # BxCxnode_num + som_node_cluster_mean = cluster_mean + + B, N, kN, M = x.size()[0], x.size()[2], x_stack.size()[2], som_node_cluster_mean.size()[2] + + # assign each point with a center + node_expanded = som_node_cluster_mean.unsqueeze(2) # BxCx1xnode_num, som.node is BxCxnode_num + centers = torch.sum(mask.float() * node_expanded, dim=3).detach() # BxCxkN + + x_decentered = (x_stack - centers).detach() # Bx3xkN + if self.surface_normal_len >= 1: + x_augmented = torch.cat((x_decentered, sn_stack), dim=1) # Bx6xkN + + # ---- Nodes branch ---- + + # First PointNet + if self.surface_normal_len >= 1: + first_pn_out = self.first_pointnet(x_augmented, epoch) + else: + first_pn_out = self.first_pointnet(x_decentered, epoch) + + with torch.cuda.device(first_pn_out.get_device()): + first_gather_index = index_max.forward_cuda_shared_mem(first_pn_out.detach(), min_idx.int(), + M).detach().long() + first_pn_out_masked_max = first_pn_out.gather(dim=2, index=first_gather_index) * mask_row_max.unsqueeze( + 1).float() # BxCxM + + # scatter the masked_max back to the kN points + scattered_first_masked_max = torch.gather(first_pn_out_masked_max, + dim=2, + index=min_idx.unsqueeze(1).expand(B, first_pn_out.size()[1], + kN)) # BxCxkN + first_pn_out_fusion = torch.cat((first_pn_out, scattered_first_masked_max), dim=1) # Bx2CxkN + + # Second PointNet + second_pn_out = self.second_pointnet(first_pn_out_fusion, epoch) + + with torch.cuda.device(second_pn_out.get_device()): + second_gather_index = index_max.forward_cuda_shared_mem(second_pn_out, min_idx.int(), M).detach().long() + second_pn_out_masked_max = second_pn_out.gather(dim=2, index=second_gather_index) * mask_row_max.unsqueeze( + 1).float() # BxCxM + + # knn search on nodes + knn_feature_1 = self.knnlayer_1(query=som_node_cluster_mean, + database=som_node_cluster_mean, + x=second_pn_out_masked_max, + K=self.node_knn_k_1, + epoch=epoch) + node_feature_aggregated = torch.cat((second_pn_out_masked_max, knn_feature_1), dim=1) # Bx(C1+C2)xM + + # mlp to calculate the per-node keypoint + y = self.node_mlp1(node_feature_aggregated) + point_descriptor = self.node_mlp2(y) + keypoint_sigma = self.node_mlp3(point_descriptor) # Bx(3+1)xkN + nodes = keypoint_sigma[:, 0:3, :] + som_node_cluster_mean # Bx3xM + nodes = nodes.transpose(1, 2).contiguous() # BxMx3 + part_nodes = nodes.reshape(bs, self.part_num, -1, 3) # (B, K, M/K, 3) + + # -- Pose and coefficients branch -- + if self.surface_normal_len >= 1: + x_init_augmented = torch.cat((x_stack, sn_stack), dim=1) + coefs = self.third_pointnet2(x_init_augmented, epoch) + else: + coefs = self.third_pointnet2(x_stack, epoch) + + coefs_expand = coefs.clone().unsqueeze(2) + joint_params = self.joint_net(coefs_expand).reshape(B, self.joint_num, 6) + joint_loc = joint_params[:, :, :3] + joint_axis = joint_params[:, :, 3:] + + return part_nodes, coefs, joint_loc, joint_axis + + def get_transformed_pred_keypoints(self, c, gt_r, gt_t): + """The category-specific symmetric 3D keypoints are computed with the deformation function. + (transformed based on gt_r and gt_t) + + Arguments: + c {torch.Tensor} -- predicted def coefficients - BxK + gt_r {torch.Tensor} -- ground truth rotation - Bx3x3 + gt_t {torch.Tensor} -- ground truth translation - Bx1x3 + + Returns: + torch.Tensor -- kpts: category-specific symmetric 3D keypoints - BXpart_numx(M/part_num)X3 + """ + refl_mat = self.get_reflection_operator(self.n_pl) + if self.symtype != "none": + basis_half = self.basis + else: + basis = self.basis + c = c.unsqueeze_(1).unsqueeze_(1) # (B, 1, 1, K) + bs = c.size(0) + + if self.symtype == "shape": + refl_batch = refl_mat.repeat(c.shape[0], 1, 1) # (B, 3, 3) + kpts_half = torch.sum(c * basis_half, 3) # (B, 3, M/2) + kpts_half_reflected = torch.matmul(refl_batch, kpts_half) # (B, 3, M/2) + + part_kpts_half = kpts_half.reshape(bs, 3, self.part_num, -1) # (B, 3, part_num, M/2/part_num) + part_kpts_half_reflected = kpts_half_reflected.reshape(bs, 3, self.part_num, -1) # (B, 3, part_num, M/2/part_num) + part_kpts = torch.cat((part_kpts_half, part_kpts_half_reflected), dim=-1) # (B, 3, part_num, M/part_num) + kpts = part_kpts.reshape(bs, 3, -1) # (B, 3, M) + elif self.symtype == "basis": + raise NotImplementedError + elif self.symtype == "none": + kpts = torch.sum(c * basis, 3) # (B, 3, M) + else: + raise NotImplementedError + + kpts = torch.bmm(gt_r, kpts).transpose(1, 2) + gt_t # (BxMx3) + part_kpts = kpts.reshape(bs, self.part_num, -1, 3) # (B, part_num, M/part_num, 3) + return part_kpts + + @staticmethod + def get_transformed_joint_params(pred_joint_loc, pred_joint_axis, gt_r, gt_t): + """ + transform predicted joint params based on gt_r and gt_t + :param pred_joint_loc: joint location, BxJx3 Tensor + :param pred_joint_axis: joint axis, BxJx3 Tensor + :param gt_r: ground truth rotation matrix, Bx3x3 Tensor + :param gt_t: ground truth translation, Bx1x3 Tensor + :return: + trans_joint_loc: transformed joint location + trans_joint_axis: transformed joint axis + """ + trans_joint_loc = torch.bmm(gt_r, pred_joint_loc.transpose(1, 2)).transpose(1, 2) + gt_t + trans_joint_axis = torch.bmm(gt_r, pred_joint_axis.transpose(1, 2)).transpose(1, 2) + gt_t + return trans_joint_loc, trans_joint_axis + + @staticmethod + def get_reflection_operator(n_pl): + """ The reflection operator is parametrized by the normal vector + of the plane of symmetry passing through the origin. """ + norm_npl = torch.norm(n_pl, 2) + n_x = n_pl[0, 0] / norm_npl # torch.tensor(1.0).cuda() + n_y = torch.tensor(0.0).cuda() + n_z = n_pl[0, 1] / norm_npl + refl_mat = torch.stack( + [ + 1 - 2 * n_x * n_x, + -2 * n_x * n_y, + -2 * n_x * n_z, + -2 * n_x * n_y, + 1 - 2 * n_y * n_y, + -2 * n_y * n_z, + -2 * n_x * n_z, + -2 * n_y * n_z, + 1 - 2 * n_z * n_z, + ], + dim=0, + ).reshape(1, 3, 3) + + return refl_mat + + @staticmethod + def query_topk(node, x, M, k): + ''' + :param node: SOM node of BxCxM tensor + :param x: input data BxCxN tensor + :param M: number of SOM nodes + :param k: topk + :return: mask: Nxnode_num + ''' + # ensure x, and other stored tensors are in the same device + device = x.device + node = node.to(x.device) + node_idx_list = torch.from_numpy(np.arange(M).astype(np.int64)).to(device) # node_num LongTensor + + # expand as BxCxNxnode_num + node = node.unsqueeze(2).expand(x.size(0), x.size(1), x.size(2), M) + x_expanded = x.unsqueeze(3).expand_as(node) + + # calcuate difference between x and each node + diff = x_expanded - node # BxCxNxnode_num + diff_norm = (diff ** 2).sum(dim=1) # BxNxnode_num + + # find the nearest neighbor + _, min_idx = torch.topk(diff_norm, k=k, dim=2, largest=False, sorted=False) # BxNxk + min_idx_expanded = min_idx.unsqueeze(2).expand(min_idx.size()[0], min_idx.size()[1], M, k) # BxNxnode_numxk + + node_idx_list = node_idx_list.unsqueeze(0).unsqueeze(0).unsqueeze(3).expand_as( + min_idx_expanded).long() # BxNxnode_numxk + mask = torch.eq(min_idx_expanded, node_idx_list).int() # BxNxnode_numxk + # mask = torch.sum(mask, dim=3) # BxNxnode_num + + # debug + B, N, M = mask.size()[0], mask.size()[1], mask.size()[2] + mask = mask.permute(0, 2, 3, 1).contiguous().view(B, M, k * N).permute(0, 2, + 1).contiguous() # BxMxkxN -> BxMxkN -> BxkNxM + min_idx = min_idx.permute(0, 2, 1).contiguous().view(B, k * N) + + mask_row_max, _ = torch.max(mask, dim=1) # Bxnode_num, this indicates whether the node has nearby x + + return mask, mask_row_max, min_idx \ No newline at end of file diff --git a/model/pointnet_plus.py b/model/pointnet_plus.py new file mode 100644 index 0000000..fcf43fc --- /dev/null +++ b/model/pointnet_plus.py @@ -0,0 +1,60 @@ +import torch +import torch.nn as nn + +from model.pointnet_util import PointNetSetAbstraction, PointNetFeaturePropagation + + +class PointNetPlus(nn.Module): + def __init__(self, in_channel=3): + super(PointNetPlus, self).__init__() + self.sa1 = PointNetSetAbstraction(npoint=512, radius=0.2, nsample=64, in_channel=in_channel, mlp=[64, 64, 128], + group_all=False) + self.sa2 = PointNetSetAbstraction(npoint=128, radius=0.4, nsample=64, in_channel=128 + 3, mlp=[128, 128, 256], + group_all=False) + self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=256 + 3, + mlp=[256, 512, 1024], group_all=True) + self.fp3 = PointNetFeaturePropagation(in_channel=1280, mlp=[256, 256]) + self.fp2 = PointNetFeaturePropagation(in_channel=384, mlp=[256, 128]) + self.fp1 = PointNetFeaturePropagation(in_channel=128 + in_channel, mlp=[128, 128, 128]) + + self.fc = nn.Sequential( + nn.Conv1d(128, 128, 1), + nn.BatchNorm1d(128), + nn.Dropout(p=0.5)) + + def forward(self, l0_xyz, l0_points=None): + l0_xyz = l0_xyz.permute(0, 2, 1).contiguous() # (B, 3, N) + if l0_points is not None: + l0_points = l0_points.permute(0, 2, 1).contiguous() # (B, C, N) + + # Set Abstraction layers + l1_xyz, l1_points = self.sa1(l0_xyz, l0_points) + + l2_xyz, l2_points = self.sa2(l1_xyz, l1_points) + + l3_xyz, l3_points = self.sa3(l2_xyz, l2_points) + + # Feature Propagation layers + l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points) + + l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points) + + if l0_points is not None: + l0_points = self.fp1(l0_xyz, l1_xyz, + torch.cat([l0_xyz, l0_points], 1), l1_points) + else: + l0_points = self.fp1(l0_xyz, l1_xyz, l0_xyz, l1_points) + + out = self.fc(l0_points) + + return out + + +if __name__ == '__main__': + net = PointNetPlus(3) + + batch = dict() + batch['P'] = torch.randn(16, 1024, 3) + out = net(batch['P']) + + print(out.shape) diff --git a/model/pointnet_util.py b/model/pointnet_util.py new file mode 100644 index 0000000..6b8cccb --- /dev/null +++ b/model/pointnet_util.py @@ -0,0 +1,329 @@ +import os, sys + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch.nn import init +from time import time +import numpy as np + + +this_dir = os.path.dirname(__file__) +lib_path = os.path.join(this_dir, '..') +sys.path.insert(0, lib_path) + + +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] + torch.cuda.empty_cache() + new_xyz = index_points(xyz, fps_idx) + torch.cuda.empty_cache() + idx = query_ball_point(radius, nsample, xyz, new_xyz) + torch.cuda.empty_cache() + grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C] + torch.cuda.empty_cache() + grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C) + torch.cuda.empty_cache() + + 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).contiguous() # [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))) + + new_points = torch.max(new_points, 2)[0] + new_xyz = new_xyz.permute(0, 2, 1).contiguous() + 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).contiguous() # [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))) + 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).contiguous() + 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).contiguous() + new_points = torch.cat([points1, interpolated_points], dim=-1) + else: + new_points = interpolated_points + + new_points = new_points.permute(0, 2, 1).contiguous() + for i, conv in enumerate(self.mlp_convs): + bn = self.mlp_bns[i] + new_points = F.relu(bn(conv(new_points))) + return new_points diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6d219b2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,110 @@ +absl-py==0.13.0 +addict==2.4.0 +anyio==3.3.0 +argcomplete==1.12.3 +argon2-cffi==21.1.0 +attrs==21.2.0 +Babel==2.9.1 +backcall==0.2.0 +bleach==4.1.0 +cachetools==4.2.2 +certifi==2021.5.30 +cffi==1.14.6 +charset-normalizer==2.0.4 +cycler==0.10.0 +debugpy==1.4.1 +decorator==5.0.9 +defusedxml==0.7.1 +deprecation==2.1.0 +entrypoints==0.3 +future==0.18.2 +google-auth==1.35.0 +google-auth-oauthlib==0.4.5 +grpcio==1.39.0 +idna==3.2 +importlib-metadata==4.8.1 +ipykernel==6.2.0 +ipython==7.27.0 +ipython-genutils==0.2.0 +ipywidgets==7.6.3 +jedi==0.18.0 +Jinja2==3.0.1 +joblib==1.0.1 +json5==0.9.6 +jsonschema==3.2.0 +jupyter-client==7.0.1 +jupyter-core==4.7.1 +jupyter-packaging==0.10.4 +jupyter-server==1.10.2 +jupyterlab==3.1.9 +jupyterlab-pygments==0.1.2 +jupyterlab-server==2.7.2 +jupyterlab-widgets==1.0.0 +kiwisolver==1.3.2 +Markdown==3.3.4 +MarkupSafe==2.0.1 +matplotlib==3.4.3 +matplotlib-inline==0.1.2 +mistune==0.8.4 +mmcv==1.3.12 +nbclassic==0.3.1 +nbclient==0.5.4 +nbconvert==6.1.0 +nbformat==5.1.3 +nest-asyncio==1.5.1 +notebook==6.4.3 +numpy==1.21.2 +oauthlib==3.1.1 +olefile==0.46 +open3d==0.13.0 +opencv-python==4.5.3.56 +packaging==21.0 +pandas==1.3.2 +pandocfilters==1.4.3 +parso==0.8.2 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow +prometheus-client==0.11.0 +prompt-toolkit==3.0.20 +protobuf==3.17.3 +ptyprocess==0.7.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.20 +Pygments==2.10.0 +pyparsing==2.4.7 +pyrsistent==0.18.0 +python-dateutil==2.8.2 +pytz==2021.1 +PyYAML==5.4.1 +pyzmq==22.2.1 +requests==2.26.0 +requests-oauthlib==1.3.0 +requests-unixsocket==0.2.0 +rsa==4.7.2 +scikit-learn==0.24.2 +scipy==1.7.1 +Send2Trash==1.8.0 +six==1.16.0 +sniffio==1.2.0 +tensorboard==2.6.0 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.0 +terminado==0.11.1 +testpath==0.5.0 +threadpoolctl==2.2.0 +tomlkit==0.7.2 +torch==1.9.0 +torchvision==0.10.0 +tornado==6.1 +tqdm==4.62.2 +traitlets==5.0.5 +urllib3==1.26.6 +wcwidth==0.2.5 +webencodings==0.5.1 +websocket-client==1.2.1 +Werkzeug==2.0.1 +widgetsnbextension==3.5.1 +yapf==0.31.0 +zipp==3.5.0 diff --git a/test_omad_net.py b/test_omad_net.py new file mode 100644 index 0000000..ecc50c1 --- /dev/null +++ b/test_omad_net.py @@ -0,0 +1,631 @@ +import argparse +import numpy as np +import os.path as osp +import open3d as o3d +import tqdm +import copy +import time +import os +import psutil +import mmcv +import torch.utils.data +from matplotlib import cm +import torch.multiprocessing as mp +from scipy.spatial.transform import Rotation as R +from dataset.dataset_omadnet import SapienDataset_OMADNet +from model.omad_net import OMAD_Net +from libs.utils import iou_3d, get_part_bbox_from_kp, get_part_bbox_from_corners, calc_joint_errors + +cate_list = ['laptop', 'eyeglasses', 'dishwasher', 'drawer', 'scissors'] + + +def rot_diff_rad(rot1, rot2): + if np.abs((np.trace(np.matmul(rot1, rot2.T)) - 1) / 2) > 1.: + print('Something wrong in rotation error!') + return np.arccos((np.trace(np.matmul(rot1, rot2.T)) - 1) / 2) % (2*np.pi) + + +def rot_diff_degree(rot1, rot2): + return rot_diff_rad(rot1, rot2) / np.pi * 180 + + +class PoseEstimator(torch.nn.Module): + def __init__(self, model, num_parts, num_kp, init_base_r, init_base_t, init_joint_state, + init_beta, part_kp_weight, device, joint_type='revolute', reg_weight=0.0): + super(PoseEstimator, self).__init__() + self.model = model.to(device) + self.model.eval() + self.num_parts = num_parts + self.num_kp = num_kp + self.num_joints = num_parts - 1 + self.device = device + self.part_kp_weight = part_kp_weight + self.joint_type = joint_type + assert joint_type in ('revolute', 'prismatic') + self.reg_weight = reg_weight + + x, y, z, w = R.from_matrix(init_base_r.cpu().numpy()).as_quat() + self.base_r_quat = torch.nn.Parameter(torch.tensor( + [w, x, y, z], device=device, dtype=torch.float), requires_grad=True) # q=a+bi+ci+di + self.base_t = torch.nn.Parameter(init_base_t.clone().detach().to(device), requires_grad=True) + self.joint_state = torch.nn.Parameter(init_joint_state.clone().detach().to(device), requires_grad=True) + self.beta = torch.nn.Parameter(init_beta.clone().detach().to(device), requires_grad=True) + + def forward(self, pred_kp, mode='base'): + assert mode in ('base', 'joint_single', 'all') + norm_kp = self.model.get_norm_keypoints(self.beta)[0] # bs=1 + homo_norm_kp = torch.cat([norm_kp, torch.ones(norm_kp.shape[0], norm_kp.shape[1], 1, device=norm_kp.device)], dim=-1) + homo_pred_kp = torch.cat([pred_kp, torch.ones(pred_kp.shape[0], pred_kp.shape[1], 1, device=pred_kp.device)], dim=-1) + + base_r_quat = self.base_r_quat / torch.norm(self.base_r_quat) + a, b, c, d = base_r_quat[0], base_r_quat[1], base_r_quat[2], base_r_quat[3] # q=a+bi+ci+di + base_rot_matrix = torch.stack([1 - 2 * c * c - 2 * d * d, 2 * b * c - 2 * a * d, 2 * a * c + 2 * b * d, + 2 * b * c + 2 * a * d, 1 - 2 * b * b - 2 * d * d, 2 * c * d - 2 * a * b, + 2 * b * d - 2 * a * c, 2 * a * b + 2 * c * d, + 1 - 2 * b * b - 2 * c * c]).reshape(3, 3) + base_transform = torch.cat([torch.cat([base_rot_matrix, self.base_t.transpose(0, 1)], dim=1), + torch.tensor([[0., 0., 0., 1.]], device=self.device)], dim=0) + base_objective = torch.mean( + torch.norm(base_transform.matmul(homo_norm_kp[0].T).T - homo_pred_kp[0], dim=-1) * self.part_kp_weight[0]) + all_objective = base_objective + + norm_joint_loc_all, norm_joint_axis_all = self.model.get_norm_joint_params(self.beta) + norm_joint_loc_all, norm_joint_axis_all = norm_joint_loc_all[0], norm_joint_axis_all[0] # bs=1 + norm_joint_axis_all = norm_joint_axis_all / torch.norm(norm_joint_axis_all, dim=-1, keepdim=True) + norm_joint_params_all = (norm_joint_loc_all.detach(), norm_joint_axis_all.detach()) + new_joint_anchor_list = [] + new_joint_axis_list = [] + relative_transform_list = [] + for joint_idx in range(self.num_joints): + part_idx = joint_idx + 1 + # TODO: support kinematic tree depth > 2 + norm_joint_loc, norm_joint_axis = norm_joint_loc_all[joint_idx], norm_joint_axis_all[joint_idx] # bs=1 + homo_joint_anchor = torch.cat([norm_joint_loc, torch.ones(1, device=self.device)]).unsqueeze(1) + new_joint_anchor = base_transform.matmul(homo_joint_anchor)[:3, 0] + new_joint_axis = base_rot_matrix.matmul(norm_joint_axis) + a, b, c = new_joint_anchor[0], new_joint_anchor[1], new_joint_anchor[2] + u, v, w = new_joint_axis[0], new_joint_axis[1], new_joint_axis[2] + if self.joint_type == 'revolute': + cos = torch.cos(-self.joint_state[joint_idx]) + sin = torch.sin(-self.joint_state[joint_idx]) + relative_transform = torch.cat([torch.stack([u*u+(v*v+w*w)*cos, u*v*(1-cos)-w*sin, u*w*(1-cos)+v*sin, + (a*(v*v+w*w)-u*(b*v+c*w))*(1-cos)+(b*w-c*v)*sin, + u*v*(1-cos)+w*sin, v*v+(u*u+w*w)*cos, v*w*(1-cos)-u*sin, + (b*(u*u+w*w)-v*(a*u+c*w))*(1-cos)+(c*u-a*w)*sin, + u*w*(1-cos)-v*sin, v*w*(1-cos)+u*sin, w*w+(u*u+v*v)*cos, + (c*(u*u+v*v)-w*(a*u+b*v))*(1-cos)+(a*v-b*u)*sin]).reshape(3, 4), + torch.tensor([[0., 0., 0., 1.]], device=self.device)], dim=0) + elif self.joint_type == 'prismatic': + relative_transform = torch.cat([torch.cat([torch.eye(3, device=self.device), + (new_joint_axis*self.joint_state[joint_idx]).unsqueeze(1)], dim=1), + torch.tensor([[0., 0., 0., 1.]], device=self.device)], dim=0) + relative_transform_list.append(relative_transform.detach()) + child_objective = torch.mean(torch.norm(relative_transform.matmul(base_transform).matmul(homo_norm_kp[part_idx].T).T - homo_pred_kp[part_idx], + dim=-1) * self.part_kp_weight[part_idx]) + all_objective += child_objective + + new_joint_anchor_list.append(new_joint_anchor.detach()) + new_joint_axis_list.append(new_joint_axis.detach()) + all_objective /= self.num_parts + all_objective += (self.beta * self.beta).mean() * self.reg_weight # regularization loss + new_joint_params_all = (torch.stack(new_joint_anchor_list, dim=0), torch.stack(new_joint_axis_list, dim=0)) + relative_transform_all = torch.stack(relative_transform_list, dim=0) + return all_objective, base_transform.detach(), relative_transform_all, \ + new_joint_params_all, norm_joint_params_all, norm_kp.detach() + + +def optimize_pose(estimator, pred_kp, rank=0, use_initial=False): + estimator.base_r_quat.requires_grad_(True) + estimator.base_t.requires_grad_(True) + estimator.joint_state.requires_grad_(True) + estimator.beta.requires_grad_(True) + if use_initial: + pass + else: + optimizer = torch.optim.Adam(estimator.parameters(), lr=1e-2) + last_loss = 0. + for iter in range(3000): # base transformation(r+t) + beta + joint state + loss, _, _, _, _, _ = estimator(pred_kp.detach(), mode='all') + if iter % 50 == 0: + if rank == 0: + print('base_r + base_t + joint state + beta: iter {}, loss={:05f}'.format(iter, loss.item())) + if abs(last_loss - loss.item()) < 0.5*1e-3: + break + last_loss = loss.item() + optimizer.zero_grad() + loss.backward() + optimizer.step() + _, base_transform, relative_transform_all, new_joint_params_all, norm_joint_params_all, norm_kp = estimator(pred_kp.detach()) + joint_state = estimator.joint_state.detach() + beta = estimator.beta.detach() + return base_transform, relative_transform_all, new_joint_params_all, norm_joint_params_all, norm_kp, joint_state, beta + + +def get_new_part_kp(cloud, pred_trans_part_kp, thr=0.1, use_raw_kp=False): + bs, n = cloud.size(0), cloud.size(1) + num_parts, num_kp_per_part = pred_trans_part_kp.size(1), pred_trans_part_kp.size(2) + num_kp = num_parts * num_kp_per_part + + pred_trans_kp = pred_trans_part_kp.detach().reshape(bs, 1, -1, 3) + cloud_expand = cloud.detach().reshape(bs, n, 1, 3).expand(-1, -1, num_kp, -1) # (bs, n, m, 3) + pred_trans_kp_expand = pred_trans_kp.expand(-1, n, -1, -1) # (bs, n, m, 3) + + dist_square = torch.sum((cloud_expand - pred_trans_kp_expand) ** 2, dim=-1) # (bs, n, m) + min_dist_square, cloud_idxs = torch.min(dist_square, dim=1) # (bs, m) + + kp_weight = torch.sign(thr ** 2 - min_dist_square) * 0.5 + 0.5 # (bs, m) + part_kp_weight = kp_weight.reshape(bs, num_parts, -1) # (bs, k, m/k) + + if use_raw_kp: + new_part_kp = pred_trans_part_kp + else: + cloud_idxs_expand = cloud_idxs.unsqueeze(-1).expand(-1, -1, 3) # (bs, m, 3) + new_kp = torch.gather(cloud.detach(), dim=1, index=cloud_idxs_expand) # (bs, m, 3) + new_part_kp = new_kp.reshape(bs, num_parts, -1, 3) # (bs, k, m/k, 3) + + return new_part_kp, part_kp_weight + + +def parallel_eval(pid, rank, results, model, opt, device): + print() + print('rank {} loading model sucessfully!'.format(rank)) + print() + + all_eval_results = dict() + + pps = psutil.Process(pid=pid) + if rank == 0: + results = tqdm.tqdm(results) + for result in results: + try: + if pps.status() in (psutil.STATUS_DEAD, psutil.STATUS_STOPPED): + print('Parent Process {} has stopped, rank {} quit now!!'.format(pid, rank)) + os._exit(0) + + sample_id = result['sample_id'] + pred_cls = torch.from_numpy(result['pred_cls']).to(device) + pred_trans_part_kp = torch.from_numpy(result['pred_trans_part_kp']).to(device) + pred_base_r = torch.from_numpy(result['pred_base_r']).to(device) + pred_base_t = torch.from_numpy(result['pred_base_t']).to(device) + pred_joint_state = torch.from_numpy(result['pred_joint_state']).to(device) + pred_beta = torch.from_numpy(result['pred_beta']).to(device) + pred_norm_part_kp = torch.from_numpy(result['pred_norm_part_kp']).to(device) + cloud = torch.from_numpy(result['cloud']).to(device) + gt_part_r = torch.from_numpy(result['gt_part_r']).to(device) + gt_part_t = torch.from_numpy(result['gt_part_t']).to(device) + gt_norm_joint_loc = torch.from_numpy(result['gt_norm_joint_loc']).to(device) + gt_norm_joint_axis = torch.from_numpy(result['gt_norm_joint_axis']).to(device) + gt_joint_state = torch.from_numpy(result['gt_joint_state']).to(device)[1:] # ignore base joint + gt_norm_part_kp = torch.from_numpy(result['gt_norm_part_kp']).to(device) + gt_norm_part_corners = torch.from_numpy(result['gt_norm_part_corners']).to(device) + cate = torch.from_numpy(result['cate']).to(device) + urdf_id = torch.from_numpy(result['urdf_id']).to(device) + + new_pred_trans_part_kp, part_kp_weight = get_new_part_kp(cloud.unsqueeze(0), pred_trans_part_kp.unsqueeze(0), + thr=opt.kp_thr, use_raw_kp=opt.use_raw_kp) + init_part_kp_weight = part_kp_weight[0] + new_pred_trans_part_kp = new_pred_trans_part_kp[0] + if rank == 0: + print() + print('URDF id={}'.format(urdf_id)) + print('{} valid keypoints!'.format(torch.sum(init_part_kp_weight).to(torch.int).item())) + + init_base_t = pred_base_t.unsqueeze(0) + init_base_r = pred_base_r + + gt_trans_joint_anchor = [] + gt_trans_joint_axis = [] + gt_base_transform = torch.cat([torch.cat([gt_part_r[0, :, :], gt_part_t[0, :, :].transpose(0, 1)], dim=1), + torch.tensor([[0., 0., 0., 1.]], device=device)], dim=0) + for joint_idx in range(opt.num_parts - 1): + homo_joint_anchor = torch.cat([gt_norm_joint_loc[joint_idx], + torch.ones(1, device=device)]).unsqueeze(1) + gt_trans_joint_anchor.append(gt_base_transform.matmul(homo_joint_anchor)[:3, 0]) + gt_trans_joint_axis.append(gt_base_transform[:3, :3].matmul(gt_norm_joint_axis[joint_idx])) + gt_trans_joint_anchor = torch.stack(gt_trans_joint_anchor, dim=0) + gt_trans_joint_axis = torch.stack(gt_trans_joint_axis, dim=0) + + gt_r_list = [] + gt_t_list = [] + for part_idx in range(opt.num_parts): + gt_r = gt_part_r[part_idx, :, :].cpu().numpy() + gt_r_list.append(gt_r) + gt_t = gt_part_t[part_idx, 0, :].cpu().numpy() + gt_t_list.append(gt_t) + gt_part_bbox = get_part_bbox_from_corners(gt_norm_part_corners) + + # TODO: support prismatic joints and kinematic tree depth > 2 + num_joints = opt.num_parts - 1 + init_joint_state = pred_joint_state + joint_type = 'revolute' if opt.category != 4 else 'prismatic' + if rank == 0: + if joint_type == 'revolute': + print('init_joint_state={} degree'.format((init_joint_state.cpu().numpy() / np.pi * 180).tolist())) + else: + print('init_joint_state={}'.format((init_joint_state.cpu().numpy()).tolist())) + init_beta = pred_beta.unsqueeze(0) + + pose_estimator = PoseEstimator(model, opt.num_parts, opt.num_kp, init_base_r, init_base_t, + init_joint_state, init_beta, init_part_kp_weight, device=device, + joint_type=joint_type, reg_weight=opt.reg_weight) + # ground-truth part-keypoint + single_gt_trans_part_kp = torch.stack([gt_part_r[i, :, :].matmul(gt_norm_part_kp[i, :, :].T).T + + gt_part_t[i, :, :] for i in range(opt.num_parts)], dim=0) + + if opt.use_gt_kp: + base_transform, relative_transform_all, pred_trans_joint_params_all, pred_norm_joint_params_all, \ + new_pred_norm_kp, new_pred_joint_state, new_pred_beta = optimize_pose(pose_estimator, + single_gt_trans_part_kp, rank=rank, + use_initial=opt.use_initial) + else: + base_transform, relative_transform_all, pred_trans_joint_params_all, pred_norm_joint_params_all, \ + new_pred_norm_kp, new_pred_joint_state, new_pred_beta = optimize_pose(pose_estimator, new_pred_trans_part_kp, + rank=rank, + use_initial=opt.use_initial) + + pred_r_list = [base_transform[:3, :3].cpu().numpy()] + [ + relative_transform_all[joint_idx].matmul(base_transform)[:3, :3].cpu().numpy() + for joint_idx in range(num_joints)] + pred_t_list = [base_transform[:3, -1].cpu().numpy()] + [ + relative_transform_all[joint_idx].matmul(base_transform)[:3, -1].cpu().numpy() + for joint_idx in range(num_joints)] + pred_part_bbox = get_part_bbox_from_kp(new_pred_norm_kp) + pred_norm_joint_loc, pred_norm_joint_axis = pred_norm_joint_params_all + pred_trans_joint_loc, pred_trans_joint_axis = pred_trans_joint_params_all + + r_errs = [] + t_errs = [] + ious = [] + + for part_idx in range(opt.num_parts): + r_err = rot_diff_degree(pred_r_list[part_idx], gt_r_list[part_idx]) + t_err = np.linalg.norm(pred_t_list[part_idx] - gt_t_list[part_idx], axis=-1) + iou = iou_3d(pred_part_bbox[part_idx], gt_part_bbox[part_idx]) + if rank == 0: + print('sample {}, urdf_id {}, part {}, r_error={:04f}, t_error={:04f}, iou_3d={:04f}'.format( + sample_id, urdf_id, part_idx, r_err, t_err, iou)) + r_errs.append(r_err) + t_errs.append(t_err) + ious.append(iou) + joint_loc_errs = [] + joint_axis_errs = [] + joint_state_errs = [] + for joint_idx in range(num_joints): + joint_loc_err, joint_axis_err, joint_state_err = calc_joint_errors( + pred_trans_joint_loc[joint_idx], pred_trans_joint_axis[joint_idx], + gt_trans_joint_anchor[joint_idx], gt_trans_joint_axis[joint_idx], + new_pred_joint_state[joint_idx], gt_joint_state[joint_idx], joint_type=joint_type) + if rank == 0: + print('sample {}, urdf_id {}, joint {}, (camera space) ' + 'joint_loc_err={:4f}, joint_axis_err={:4f} degree, ' + 'joint_state_err={:4f}'.format( + sample_id, urdf_id, joint_idx, joint_loc_err, joint_axis_err, joint_state_err)) + joint_loc_errs.append(joint_loc_err) + joint_axis_errs.append(joint_axis_err) + joint_state_errs.append(joint_state_err) + + if rank == 0 and opt.show: + base_colors = np.array([(207/255, 37/255, 38/255), (28/255, 108/255, 171/255), + (38/255, 148/255, 36/255), (254/255, 114/255, 16/255)] * 2) + cmap = cm.get_cmap("jet", opt.num_kp) + kp_colors = cmap(np.linspace(0, 1, opt.num_kp, endpoint=True))[:, :3] + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(cloud.cpu().numpy()) + pcd.colors = o3d.utility.Vector3dVector(base_colors[pred_cls.numpy(), :]) + + pred_trans_static_kp_pcd_list = [] + pred_kp_pcd_list = [] + gt_kp_pcd_list = [] + pred_trans_norm_kp_pcd_list = [] + gt_trans_norm_kp_pcd_list = [] + pred_trans_norm_kp_mesh_list = [] + + for part_idx in range(opt.num_parts): + pred_trans_static_kp = (pred_r_list[part_idx] @ gt_norm_part_kp[part_idx, :, :].cpu().numpy().T + + pred_t_list[part_idx][np.newaxis, :].T).T + pred_trans_static_kp_pcd = o3d.geometry.PointCloud() + pred_trans_static_kp_pcd.points = o3d.utility.Vector3dVector(pred_trans_static_kp) + pred_trans_static_kp_pcd.paint_uniform_color([0, 0, 1]) + pred_trans_static_kp_pcd_list.append(pred_trans_static_kp_pcd) + + pred_kp_pcd = o3d.geometry.PointCloud() + pred_kp_pcd.points = o3d.utility.Vector3dVector( + new_pred_trans_part_kp[part_idx, :, :].cpu().numpy()) + pred_kp_pcd.paint_uniform_color([0, 0, 0]) + pred_kp_pcd_list.append(pred_kp_pcd) + + gt_kp_pcd = o3d.geometry.PointCloud() + gt_kp_pcd.points = o3d.utility.Vector3dVector( + single_gt_trans_part_kp[part_idx, :, :].cpu().numpy()) + gt_kp_pcd.paint_uniform_color([0, 0, 0]) + gt_kp_pcd_list.append(gt_kp_pcd) + + # optimized beta + pred r,t + pred_trans_norm_kp_pcd = o3d.geometry.PointCloud() + pred_trans_norm_kp = (pred_r_list[part_idx] @ new_pred_norm_kp[part_idx, :, :].cpu().numpy().T + + pred_t_list[part_idx][np.newaxis, :].T).T + pred_trans_norm_kp_mesh_list += \ + [o3d.geometry.TriangleMesh.create_sphere(radius=0.02, resolution=5).translate((x, y, z)) for + x, y, z + in pred_trans_norm_kp] + pred_trans_norm_kp_pcd.points = o3d.utility.Vector3dVector(pred_trans_norm_kp) + pred_trans_norm_kp_pcd.paint_uniform_color([1, 0, 1]) + pred_trans_norm_kp_pcd_list.append(pred_trans_norm_kp_pcd) + + # raw predicted beta + gt_r,t + gt_trans_norm_kp_pcd = o3d.geometry.PointCloud() + gt_trans_norm_kp = (gt_r_list[part_idx] @ pred_norm_part_kp[part_idx, :, :].cpu().numpy().T + + gt_t_list[part_idx][np.newaxis, :].T).T + gt_trans_norm_kp_pcd.points = o3d.utility.Vector3dVector(gt_trans_norm_kp) + gt_trans_norm_kp_pcd.paint_uniform_color([1, 0, 1]) + gt_trans_norm_kp_pcd_list.append(gt_trans_norm_kp_pcd) + + sphere_pts_num = np.asarray(pred_trans_norm_kp_mesh_list[0].vertices).shape[0] + for idx, mesh in enumerate(pred_trans_norm_kp_mesh_list): + mesh.vertex_colors = o3d.utility.Vector3dVector( + kp_colors[np.newaxis, idx, :].repeat(sphere_pts_num, axis=0)) + + line_pcd_list = [] + for joint_idx in range(num_joints): + start_point = pred_trans_joint_params_all[0][joint_idx].cpu().numpy() + end_point = start_point + pred_trans_joint_params_all[1][joint_idx].cpu().numpy() + line_points = np.stack([start_point, end_point]) + lines = [[0, 1]] # Right leg + colors = [[0, 0, 1] for i in range(len(lines))] + line_pcd = o3d.geometry.LineSet() + line_pcd.lines = o3d.utility.Vector2iVector(lines) + line_pcd.colors = o3d.utility.Vector3dVector(colors) + line_pcd.points = o3d.utility.Vector3dVector(line_points) + line_pcd_list.append(line_pcd) + + # o3d.visualization.draw_geometries(pred_kp_pcd_list + [pcd] + line_pcd_list + # + pred_trans_norm_kp_pcd_list) + o3d.visualization.draw_geometries([pcd] + line_pcd_list + pred_trans_norm_kp_mesh_list) + result_dict = dict() + result_dict['r_err'] = r_errs + result_dict['t_err'] = t_errs + result_dict['iou_3d'] = ious + result_dict['joint_loc_err'] = joint_loc_errs + result_dict['joint_axis_err'] = joint_axis_errs + result_dict['joint_state_err'] = joint_state_errs + result_dict['beta'] = new_pred_beta.cpu().numpy()[0] + result_dict['base_transform'] = base_transform.cpu().numpy() + result_dict['relative_transform'] = relative_transform_all.cpu().numpy() + result_dict['pred_r'] = pred_r_list + result_dict['pred_t'] = pred_t_list + result_dict['joint_params'] = pred_trans_joint_params_all[0].cpu().numpy(), pred_trans_joint_params_all[1].cpu().numpy() + result_dict['joint_state'] = new_pred_joint_state.cpu().numpy() + result_dict['norm_kp'] = new_pred_norm_kp.cpu().numpy() + all_eval_results[sample_id] = result_dict + except psutil.NoSuchProcess: + print('Parent Process {} does not exist, rank {} quit now!!'.format(pid, rank)) + os._exit(0) + except Exception as e: + print(e) + raise e + if rank == 0: + print() + + file_dir = osp.join(opt.work_dir, 'tmp_results') + file_name = osp.join(opt.work_dir, 'tmp_results', '{}process_final_result_{}_{}.pkl'.format( + opt.num_process, opt.data_postfix, rank)) + if not osp.exists(file_dir): + os.mkdir(file_dir) + mmcv.dump(all_eval_results, file_name) + print('Rank {} finish writing {}!'.format(rank, file_name)) + + +def collect_results(opt): + final_results = dict() + print('Collecting results!') + for rank in range(opt.num_process): + tmp_filename = osp.join(opt.work_dir, 'tmp_results', '{}process_final_result_{}_{}.pkl'.format( + opt.num_process, opt.data_postfix, rank)) + while not osp.exists(tmp_filename): + print('Didn''t find {}, wait for 10 seconds.....'.format(tmp_filename)) + time.sleep(10) + tmp_results = mmcv.load(tmp_filename) + final_results.update(tmp_results) + del tmp_results + return final_results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_root', type=str, default='data', help='dataset root dir') + parser.add_argument('--params_dir', type=str, help='the dir for params and kp annotations') + parser.add_argument('--checkpoint', type=str, default=None, help='test checkpoint') + parser.add_argument('--category', type=int, default=1, help='category to train') + parser.add_argument('--num_points', type=int, default=1024, help='points') + parser.add_argument('--num_kp', type=int, default=12, help='number of all keypoints') + parser.add_argument('--num_cates', type=int, default=5, help='number of categories') + parser.add_argument('--num_parts', type=int, default=2, help='number of parts') + parser.add_argument('--num_basis', type=int, default=10, help='number of shape basis') + parser.add_argument('--symtype', type=str, default='shape', choices=['shape', 'none'], help='the symmetry type') + parser.add_argument('--workers', type=int, default=0, help='number of data loading workers') + parser.add_argument('--bs', type=int, default=16, help='batch size') + parser.add_argument('--dense_soft_factor', type=float, default=1.0, help='the factor of dense softmax') + parser.add_argument('--kp_thr', type=float, default=0.1, help='the threshold for kp weight') + parser.add_argument('--reg_weight', type=float, default=0, help='the weight of regularization loss') + parser.add_argument('--work_dir', type=str, default='work_dir/base', help='working dir') + parser.add_argument('--out', type=str, required=True, help='output raw_results filename') + parser.add_argument('--use_cache', action='store_true', help='whether to use cached tmp results') + parser.add_argument('--num_process', type=int, default=8, help='the number of parallel processes') + parser.add_argument('--data_postfix', type=str, default='v1', help='the postfix of .pkl') + parser.add_argument('--show', action='store_true', help='wheter to visualize keypoints') + parser.add_argument('--use_raw_kp', action='store_true', help='wheter to use raw keypoints') + parser.add_argument('--use_gpu', action='store_true', help='wheter to use GPU') + parser.add_argument('--use_gt_kp', action='store_true', help='wheter to use gt kp') + parser.add_argument('--use_initial', action='store_true', help='wheter to use initial prediction without refinement') + parser.add_argument('--shuffle', action='store_true', help='wheter to shuffle raw results') + opt = parser.parse_args() + + device = torch.device("cuda:0") + output_path = osp.join(opt.work_dir, opt.out) + if osp.exists(output_path): + results = mmcv.load(output_path) + else: + params_dict = torch.load(osp.join(opt.params_dir, 'params.pth')) + model = OMAD_Net(device=device, params_dict=params_dict, + num_points=opt.num_points, num_kp=opt.num_kp, num_parts=opt.num_parts, + init_dense_soft_factor=opt.dense_soft_factor, num_basis=opt.num_basis, symtype=opt.symtype) + model = model.to(device) + + assert opt.checkpoint is not None + model.load_state_dict(torch.load(osp.join(opt.work_dir, opt.checkpoint), map_location=device)) + + test_kp_anno_path = osp.join(opt.params_dir, 'unsup_test_keypoints.pkl') + test_dataset = SapienDataset_OMADNet('val', data_root=opt.dataset_root, add_noise=False, num_pts=opt.num_points, + num_parts=opt.num_parts, num_cates=opt.num_cates, cate_id=opt.category, + device=torch.device('cpu'), kp_anno_path=test_kp_anno_path) + test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.bs, shuffle=False, + num_workers=opt.workers) + + assert opt.num_kp % opt.num_parts == 0, 'number of keypoints must be divided by number of parts' + if not opt.show: + test_dataloader = tqdm.tqdm(test_dataloader) + + model.eval() + results = [] + sample_id = 0 + for j, data in enumerate(test_dataloader): + cloud, gt_part_cls, gt_part_r, gt_part_quat, gt_part_t, gt_joint_state, gt_norm_joint_loc, gt_norm_joint_axis, \ + gt_norm_part_kp, gt_scale, gt_center, gt_norm_part_corners, cate, urdf_id = data + + cloud, gt_part_cls, gt_part_r, gt_part_quat, gt_part_t, gt_joint_state, \ + gt_norm_joint_loc, gt_norm_joint_axis, gt_norm_part_kp, gt_center = \ + cloud.to(device), gt_part_cls.to(device), gt_part_r.to(device), gt_part_quat.to(device), \ + gt_part_t.to(device), gt_joint_state.to(device), gt_norm_joint_loc.to(device), \ + gt_norm_joint_axis.to(device), gt_norm_part_kp.to(device), gt_center.to(device) + + with torch.no_grad(): + dense_part_cls_score, pred_trans_part_kp, pred_base_quat, pred_base_r, pred_base_t, pred_joint_state, \ + pred_beta, pred_norm_part_kp, pred_joint_loc, pred_joint_axis = model(cloud, None) + pred_cls = torch.argmax(dense_part_cls_score, dim=-1) + + bs_now = cloud.size(0) + for i in range(bs_now): + result = dict( + sample_id=sample_id, + pred_cls=pred_cls[i].cpu().numpy(), + pred_trans_part_kp=pred_trans_part_kp[i].cpu().numpy(), + pred_base_quat=pred_base_quat[i].cpu().numpy(), + pred_base_r=pred_base_r[i].cpu().numpy(), + pred_base_t=pred_base_t[i].cpu().numpy(), + pred_joint_state=pred_joint_state[i].cpu().numpy(), + pred_beta=pred_beta[i].cpu().numpy(), + pred_norm_part_kp=pred_norm_part_kp[i].cpu().numpy(), + pred_joint_loc=pred_joint_loc[i].cpu().numpy(), + pred_joint_axis=pred_joint_axis[i].cpu().numpy(), + cloud=cloud[i].cpu().numpy(), + gt_part_r=gt_part_r[i].cpu().numpy(), + gt_part_quat=gt_part_quat[i].cpu().numpy(), + gt_part_t=gt_part_t[i].cpu().numpy(), + gt_joint_state=gt_joint_state[i].cpu().numpy(), + gt_norm_joint_loc=gt_norm_joint_loc[i].cpu().numpy(), + gt_norm_joint_axis=gt_norm_joint_axis[i].cpu().numpy(), + gt_norm_part_kp=gt_norm_part_kp[i].cpu().numpy(), + gt_scale=gt_scale[i].cpu().numpy(), + gt_center=gt_center[i].cpu().numpy(), + gt_norm_part_corners=gt_norm_part_corners[i].cpu().numpy(), + cate=cate[i].cpu().numpy(), + urdf_id=urdf_id[i].cpu().numpy()) + sample_id += 1 + results.append(result) + + print('\nwriting results to {}'.format(output_path)) + mmcv.dump(results, output_path) + + if opt.shuffle: + np.random.shuffle(results) + + # multi-process eval + num_process = opt.num_process + ctx = mp.get_context('spawn') + cpu_device = torch.device('cpu') + params_dict = torch.load(osp.join(opt.params_dir, 'params.pth')) + model = OMAD_Net(device=cpu_device, params_dict=params_dict, + num_points=opt.num_points, num_kp=opt.num_kp, num_parts=opt.num_parts, + init_dense_soft_factor=opt.dense_soft_factor, num_basis=opt.num_basis, symtype=opt.symtype) + + processes = [] + num_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(',')) + gpu_ids = [id for id in range(num_gpus)] + print('Using {} processes!'.format(num_process)) + chunk_size = int(len(results) / num_process) + 1 + split_results = [results[i:i + chunk_size] for i in range(0, len(results), chunk_size)] + if not opt.show and opt.use_gpu: + device_list = [gpu_ids[i % num_gpus] for i in range(num_process)] + model_list = dict() + for id in gpu_ids: + model_list[id] = copy.deepcopy(model).to(torch.device('cuda:{}'.format(id))) + model_list[id].set_device(torch.device('cuda:{}'.format(id))) + model_list[id].share_memory() + pid = os.getpid() + for rank in range(num_process): + file_name = osp.join(opt.work_dir, 'tmp_results', '{}process_final_result_{}_{}.pkl'.format( + opt.num_process, opt.data_postfix, rank)) + if opt.use_cache and osp.exists(file_name): + continue + print('Create process rank {}!'.format(rank)) + + if not opt.show and opt.use_gpu: + new_model = model_list[device_list[rank]] + new_device = torch.device('cuda:{}'.format(device_list[rank])) + assert new_model.device == new_device + else: + new_model = model.share_memory() + new_device = cpu_device + p = ctx.Process(target=parallel_eval, + args=(pid, rank, split_results[rank], new_model, opt, new_device)) + p.daemon = True + processes.append(p) + for p in processes: + p.start() + for p in processes: + p.join() + + final_eval_results = collect_results(opt) + + r_errs_all = [] + t_errs_all = [] + ious_all = [] + joint_loc_errs_all = [] + joint_axis_errs_all = [] + joint_state_errs_all = [] + for sample_id, result_dict in final_eval_results.items(): + r_errs_all.append(np.array(result_dict['r_err'])) + t_errs_all.append(np.array(result_dict['t_err'])) + ious_all.append(np.array(result_dict['iou_3d'])) + joint_loc_errs_all.append(np.array(result_dict['joint_loc_err'])) + joint_axis_errs_all.append(np.array(result_dict['joint_axis_err'])) + joint_state_errs_all.append(np.array(result_dict['joint_state_err'])) + r_errs_all = np.stack(r_errs_all, axis=0) + t_errs_all = np.stack(t_errs_all, axis=0) + ious_all = np.stack(ious_all, axis=0) + joint_loc_errs_all = np.stack(joint_loc_errs_all, axis=0) + joint_axis_errs_all = np.stack(joint_axis_errs_all, axis=0) + joint_state_errs_all = np.stack(joint_state_errs_all, axis=0) + + r_errs = [_ for _ in range(opt.num_parts)] + for part_idx in range(opt.num_parts): + idxs = ~ np.isnan(r_errs_all[:, part_idx]) + r_errs[part_idx] = np.mean(r_errs_all[idxs, part_idx]) + + t_errs = [np.mean(t_errs_all[:, part_idx], axis=0) for part_idx in range(opt.num_parts)] + ious = [np.mean(ious_all[:, part_idx], axis=0) for part_idx in range(opt.num_parts)] + num_joints = opt.num_parts - 1 + joint_loc_errs = [np.mean(joint_loc_errs_all[:, joint_idx], axis=0) for joint_idx in range(num_joints)] + + joint_axis_errs = [_ for _ in range(num_joints)] + for joint_idx in range(num_joints): + idxs = ~ np.isnan(joint_axis_errs_all[:, joint_idx]) + joint_axis_errs[joint_idx] = np.mean(joint_axis_errs_all[idxs, joint_idx]) + joint_state_errs = [np.mean(joint_state_errs_all[:, joint_idx], axis=0) for joint_idx in range(num_joints)] + print('mean r_errors:{}'.format(r_errs)) + print('mean t_errors:{}'.format(t_errs)) + print('mean iou_3d:{}'.format(ious)) + print('mean joint_loc_errors:{}'.format(joint_loc_errs)) + print('mean joint_axis_errors:{}'.format(joint_axis_errs)) + print('mean joint_state_errors:{}'.format(joint_state_errs)) + diff --git a/test_omad_priornet.py b/test_omad_priornet.py new file mode 100644 index 0000000..165c983 --- /dev/null +++ b/test_omad_priornet.py @@ -0,0 +1,176 @@ +import argparse +import numpy as np +import os.path as osp +import open3d as o3d +import tqdm +import mmcv +import torch.utils.data +from matplotlib import cm + +from dataset.dataset_omad_priornet import SapienDataset_OMADPriorNet +from model.omad_priornet import OMAD_PriorNet + +cate_list = ['laptop', 'eyeglasses', 'dishwasher', 'drawer', 'scissors'] + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_root', type=str, default='data', help='dataset root dir') + parser.add_argument('--checkpoint', type=str, default=None, help='test checkpoint') + parser.add_argument('--category', type=int, default=1, help='category to train') + parser.add_argument('--mode', type=str, default='train', choices=['train', 'val'], + help='the mode of training or validation') + parser.add_argument('--num_points', type=int, default=2048, help='points') + parser.add_argument('--num_cates', type=int, default=1, help='number of categories') + parser.add_argument('--num_parts', type=int, default=2, help='number of parts') + parser.add_argument('--num_basis', type=int, default=10, help='number of shape basis') + parser.add_argument('--workers', type=int, default=0, help='number of data loading workers') + parser.add_argument('--bs', type=int, default=1, help='batch size') + parser.add_argument('--num_kp', type=int, default=8, help='number of all keypoints') + parser.add_argument('--symtype', type=str, default='shape', choices=['shape', 'none'], help='the symmetry type') + parser.add_argument('--work_dir', type=str, default='work_dir/base', help='working dir') + parser.add_argument('--out', action='store_true', help='whether to save outputs') + parser.add_argument('--show', action='store_true', help='wheter to visualize keypoints') + parser.add_argument('--use_gpu', action='store_true', help='wheter to use GPU') + opt = parser.parse_args() + + device = torch.device("cuda:0") if opt.use_gpu else torch.device("cpu") + model = OMAD_PriorNet(node_num=opt.num_kp, basis_num=opt.num_basis, part_num=opt.num_parts, device=device, + symtype=opt.symtype) + model = model.to(device) + + assert opt.checkpoint is not None + model.load_state_dict(torch.load(osp.join(opt.work_dir, opt.checkpoint), map_location=device)) + + test_dataset = SapienDataset_OMADPriorNet(opt.mode, + data_root=opt.dataset_root, + add_noise=False, + num_pts=opt.num_points, + num_parts=opt.num_parts, + num_cates=opt.num_cates, cate_id=opt.category, + num_samples=1000 if opt.mode == 'train' else 1000, + node_num=opt.num_kp, + use_scale_aug=False, + use_rot_aug=False, + device=torch.device("cpu")) + test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.bs, shuffle=False, + num_workers=opt.workers) + test_dataloader = tqdm.tqdm(test_dataloader) + + assert opt.num_kp % opt.num_parts == 0, 'number of keypoints must be divided by number of parts' + + model.eval() + results = dict() + coefs_list = [] + for j, data in enumerate(test_dataloader): + cloud, init_nodes, cloud_cls, gt_inv_r, gt_joint_loc, gt_joint_axis, scale_factor, \ + raw_scale, raw_center, cate, urdf_id = data + cloud, init_nodes, gt_inv_r, cloud_cls, gt_joint_loc, gt_joint_axis = \ + cloud.to(device), init_nodes.to(device), gt_inv_r.to(device), cloud_cls.to(device), \ + gt_joint_loc.to(device), gt_joint_axis.to(device) + scale_factor, raw_scale, raw_center = scale_factor.to(device), raw_scale.to(device), raw_center.to(device) + + bs_now = cloud.shape[0] + gt_r = gt_inv_r.transpose(1, 2) + gt_t = torch.zeros(bs_now, 1, 3, device=device) + + with torch.no_grad(): + final_nodes, coefs, pred_joint_loc, pred_joint_axis = model(cloud, None, init_nodes) + part_pred_kps = model.get_transformed_pred_keypoints(coefs, gt_r, gt_t) + trans_joint_loc, trans_joint_axis = model.get_transformed_joint_params(pred_joint_loc, pred_joint_axis, + gt_r, gt_t) + + coefs_list.append(coefs.squeeze().cpu().numpy()) + pred_kps = part_pred_kps.reshape(bs_now, -1, 3) + trans_Kp = torch.bmm(gt_inv_r, (pred_kps - gt_t).transpose(1, 2)).transpose(1, 2).contiguous() + align_kp = trans_Kp / scale_factor # zero-centered keypoints + part_align_kp = align_kp.reshape(bs_now, opt.num_parts, -1, 3) + trans_cloud = torch.bmm(gt_inv_r, (cloud - gt_t).transpose(1, 2)).transpose(1, 2).contiguous() + + for idx, id in enumerate(urdf_id.cpu().numpy()): + if id not in results: + results[id] = [] + results[id].append(part_align_kp[idx].unsqueeze(0).cpu().numpy()) + + if opt.show and opt.bs == 1: + print('urdf id = {}'.format(urdf_id.cpu().numpy())) + base_colors = np.array([(1., 0., 0.), (0., 1., 0.), (0., 0., 1.), (0., 1., 1.)] * 2) + cls_colors = np.array([(1., 0., 0.), (0., 1., 0.), (0., 0., 1.)]) + cmap = cm.get_cmap("jet", opt.num_kp) + kp_colors = cmap(np.linspace(0, 1, opt.num_kp, endpoint=True))[:, :3] + + trans_pcd = o3d.geometry.PointCloud() + trans_pcd.points = o3d.utility.Vector3dVector(trans_cloud[0].cpu().numpy()) + trans_pcd.paint_uniform_color([0, 0, 0]) + + trans_pcd_kp_list = [ + o3d.geometry.TriangleMesh.create_sphere(radius=0.02, resolution=10).translate((x, y, z)) for + x, y, z + in trans_Kp[0].detach().cpu().numpy()] + + sphere_pts_num = np.asarray(trans_pcd_kp_list[0].vertices).shape[0] + for idx, mesh in enumerate(trans_pcd_kp_list): + mesh.vertex_colors = o3d.utility.Vector3dVector( + kp_colors[np.newaxis, idx, :].repeat(sphere_pts_num, axis=0)) + + # TODO: support prismatic joints + line_pcd_list = [] + for joint_idx in range(opt.num_parts - 1): + start_point = trans_joint_loc[0, joint_idx, :].cpu().numpy() + end_point = start_point + trans_joint_axis[0, joint_idx, :].cpu().numpy() + line_points = np.stack([start_point, end_point], axis=0) + lines = [[0, 1]] # Right leg + colors = [[1, 0, 0] for _ in range(len(lines))] + line_pcd = o3d.geometry.LineSet() + line_pcd.lines = o3d.utility.Vector2iVector(lines) + line_pcd.colors = o3d.utility.Vector3dVector(colors) + line_pcd.points = o3d.utility.Vector3dVector(line_points) + line_pcd_list.append(line_pcd) + + o3d.visualization.draw_geometries([trans_pcd] + line_pcd_list + trans_pcd_kp_list) + + for id in results.keys(): + print('urdf {} has {} valid results!'.format(id, len(results[id]))) + results[id] = np.mean(np.concatenate(results[id], axis=0), axis=0) + + coefs_all = np.concatenate(coefs_list, axis=0) + + if opt.out: + tag = 'train' if opt.mode == 'train' else 'test' + kp_path = osp.join(opt.work_dir, 'unsup_{}_keypoints.pkl'.format(tag)) + mmcv.dump(results, kp_path) + print('Saving keypoint results to {}!'.format(kp_path)) + coefs_path = osp.join(opt.work_dir, 'unsup_{}_coefs.pkl'.format(tag)) + mmcv.dump(coefs_all, coefs_path) + print('Saving coefs results to {}!'.format(coefs_path)) + + save_params_dict = dict() + save_params_dict['joint_net'] = model.joint_net.state_dict() + save_params_dict['basis'] = model.state_dict()['basis'] + save_params_dict['n_pl'] = model.state_dict()['n_pl'] + params_path = osp.join(opt.work_dir, 'params.pth') + torch.save(save_params_dict, params_path) + print('Saving params to {}!'.format(params_path)) + + if opt.show: + base_colors = np.array([(1., 0., 0.), (0., 1., 0.), (0., 0., 1.), (0., 1., 1.)] * 2) + for id in results.keys(): + print('urdf id={}'.format(id)) + all_align_pts = test_dataset.all_norm_obj_pts_dict[opt.category][id] + trans_pcd = o3d.geometry.PointCloud() + trans_pcd.points = o3d.utility.Vector3dVector(all_align_pts) + trans_pcd.paint_uniform_color([0, 0, 0]) + trans_pcd_kp_list = [] + for part_idx in range(opt.num_parts): + trans_pcd_kp_list += [ + o3d.geometry.TriangleMesh.create_sphere(radius=0.01, resolution=10).translate((x, y, z)) for + x, y, z + in results[id][part_idx]] + + sphere_pts_num = np.asarray(trans_pcd_kp_list[0].vertices).shape[0] + part_kp_num = opt.num_kp // opt.num_parts + for idx, mesh in enumerate(trans_pcd_kp_list[-part_kp_num:]): + mesh.vertex_colors = o3d.utility.Vector3dVector( + base_colors[np.newaxis, part_idx].repeat(sphere_pts_num, axis=0)) + + o3d.visualization.draw_geometries(trans_pcd_kp_list + [trans_pcd]) + diff --git a/train_omad_net.py b/train_omad_net.py new file mode 100644 index 0000000..ebeb5ae --- /dev/null +++ b/train_omad_net.py @@ -0,0 +1,188 @@ +import argparse +import numpy as np +import os.path as osp +from torch.utils.tensorboard import SummaryWriter + +import torch.optim as optim +import torch.utils.data +import tqdm +import time + +from dataset.dataset_omadnet import SapienDataset_OMADNet +from model.omad_net import OMAD_Net +from libs.loss_omadnet import Loss_OMAD_Net + +cate_list = ['laptop', 'eyeglasses', 'dishwasher', 'drawer', 'scissors'] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_root', type=str, default='data', help='dataset root dir') + parser.add_argument('--params_dir', type=str, help='the dir for params and kp annotations') + parser.add_argument('--resume', type=str, default=None, help='resume model') + parser.add_argument('--category', type=int, default=1, help='category to train') + parser.add_argument('--num_points', type=int, default=1024, help='points') + parser.add_argument('--num_cates', type=int, default=5, help='number of categories') + parser.add_argument('--num_parts', type=int, default=2, help='number of parts') + parser.add_argument('--workers', type=int, default=4, help='number of data loading workers') + parser.add_argument('--num_kp', type=int, default=12, help='number of all keypoints') + parser.add_argument('--num_basis', type=int, default=10, help='number of shape basis') + parser.add_argument('--bs', type=int, default=16, help='batch size') + parser.add_argument('--dense_soft_factor', type=float, default=1.0, help='the factor of dense softmax') + parser.add_argument('--loc_weight', type=float, default=5.0, help='the weight of pts loc weight') + parser.add_argument('--base_weight', type=float, default=0.2, help='the weight of base rotation loss') + parser.add_argument('--cls_weight', type=float, default=1.0, help='the weight of segmentation loss') + parser.add_argument('--joint_state_weight', type=float, default=5.0, help='the weight of joint state loss') + parser.add_argument('--shape_weight', type=float, default=3.0, help='the weight of shape loss') + parser.add_argument('--joint_param_weight', type=float, default=3.0, help='the weight of joint param loss') + parser.add_argument('--reg_weight', type=float, default=0.01, help='the weight of regularization loss') + parser.add_argument('--no_att', action='store_true', help='whether to not use attention map') + parser.add_argument('--symtype', type=str, default='shape', choices=['shape', 'none'], help='the symmetry type') + parser.add_argument('--work_dir', type=str, default='work_dir/base', help='save dir') + parser.add_argument('--lr', type=float, default=0.001, help='learning rate') + opt = parser.parse_args() + + device = torch.device("cuda:0") + params_dict = torch.load(osp.join(opt.params_dir, 'params.pth')) + model = OMAD_Net(device=device, params_dict=params_dict, + num_points=opt.num_points, num_kp=opt.num_kp, num_parts=opt.num_parts, + init_dense_soft_factor=opt.dense_soft_factor, num_basis=opt.num_basis, symtype=opt.symtype, + use_attention=not opt.no_att) + model = model.to(device) + + if opt.resume is not None: + model.load_state_dict(torch.load(osp.join(opt.work_dir, opt.resume))) + + train_kp_anno_path = osp.join(opt.params_dir, 'unsup_train_keypoints.pkl') + train_dataset = SapienDataset_OMADNet('train', data_root=opt.dataset_root, add_noise=True, num_pts=opt.num_points, + num_parts=opt.num_parts, num_cates=opt.num_cates, cate_id=opt.category, + device=torch.device("cpu"), + kp_anno_path=train_kp_anno_path) + train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs, shuffle=True, num_workers=opt.workers, + drop_last=True) + test_kp_anno_path = osp.join(opt.params_dir, 'unsup_test_keypoints.pkl') + test_dataset = SapienDataset_OMADNet('val', data_root=opt.dataset_root, add_noise=False, num_pts=opt.num_points, + num_parts=opt.num_parts, num_cates=opt.num_cates, cate_id=opt.category, + device=torch.device("cpu"), + kp_anno_path=test_kp_anno_path) + test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.bs, shuffle=False, num_workers=opt.workers, + drop_last=True) + + assert opt.num_kp % opt.num_parts == 0, 'number of keypoints must be divided by number of parts' + criterion = Loss_OMAD_Net(num_key_per_part=opt.num_kp // opt.num_parts, num_cate=opt.num_cates, num_parts=opt.num_parts, + loss_loc_weight=opt.loc_weight, + loss_cls_weight=opt.cls_weight, + loss_base_weight=opt.base_weight, + loss_joint_state_weight=opt.joint_state_weight, + loss_shape_weight=opt.shape_weight, + loss_joint_param_weight=opt.joint_param_weight, + loss_reg_weight=opt.reg_weight, + device=device) + + best_test = np.Inf + optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=0.9, weight_decay=0.0001) + scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0) + + writer = SummaryWriter(log_dir=opt.work_dir) + total_iter = 0 + for epoch in range(0, 100): + model.train() + train_count = 0 + + optimizer.zero_grad() + + writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch) + + for i, data in enumerate(train_dataloader): + cloud, gt_part_cls, gt_part_r, gt_part_quat, gt_part_t, gt_joint_state, gt_norm_joint_loc, gt_norm_joint_axis, \ + gt_norm_part_kp, gt_scale, gt_center, gt_norm_part_corners, cate, urdf_id = data + + cloud.requires_grad_() + + cloud, gt_part_cls, gt_part_r, gt_part_quat, gt_part_t, gt_joint_state, \ + gt_norm_joint_loc, gt_norm_joint_axis, gt_norm_part_kp = \ + cloud.to(device), gt_part_cls.to(device), gt_part_r.to(device), gt_part_quat.to(device), \ + gt_part_t.to(device), gt_joint_state.to(device), gt_norm_joint_loc.to(device), \ + gt_norm_joint_axis.to(device), gt_norm_part_kp.to(device) + + dense_part_cls_score, pred_trans_part_kp, pred_base_quat, pred_base_r, pred_base_t, pred_joint_state,\ + pred_beta, pred_norm_part_kp, pred_joint_loc, pred_joint_axis = model(cloud, gt_part_cls) + + loss, _, loss_dict = criterion(pred_trans_part_kp, dense_part_cls_score, pred_base_quat, pred_base_t, pred_norm_part_kp, + pred_joint_loc, pred_joint_axis, pred_joint_state, pred_beta, + gt_part_cls, gt_part_quat, + gt_part_r, gt_part_t, gt_norm_part_kp, + gt_norm_joint_loc, gt_norm_joint_axis, gt_joint_state) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=35) + + train_count += 1 + total_iter += 1 + optimizer.step() + optimizer.zero_grad() + if train_count % 10 == 0: + print('{}, Epoch: {}, iter: {}/{}, loss_all:{:05f}, loss_loc:{:05f}, loss_cls:{:05f}, ' + 'loss_base:{:05f}, loss_joint_state:{:05f}, loss_shape:{:05f}, loss_joint_param:{:05f}, ' + 'loss_reg:{:05f}'.format( + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())), + epoch, train_count, len(train_dataset) // opt.bs, float(loss.item()), + loss_dict['loss_loc'], + loss_dict['loss_cls'], + loss_dict['loss_base'], + loss_dict['loss_joint_state'], + loss_dict['loss_shape'], + loss_dict['loss_joint_param'], + loss_dict['loss_reg'] + )) + for key, value in loss_dict.items(): + writer.add_scalar('{}/train'.format(key), value, total_iter) + writer.add_scalar('loss_all/train', loss.item(), total_iter) + + torch.save(model.state_dict(), osp.join(opt.work_dir, 'model_current_{}.pth'.format(cate_list[opt.category - 1]))) + + # change lr + scheduler.step(epoch) + optimizer.zero_grad() + + if epoch % 10 == 0: + model.eval() + score = [] + print('>>>>>>>>>>>>Testing epoch {}>>>>>>>>>>>>>'.format(epoch)) + sum_test_loss_dict = dict(loss_loc=0., loss_cls=0., loss_base=0., loss_joint_state=0., + loss_shape=0., loss_joint_param=0., loss_reg=0.) + for j, data in enumerate(tqdm.tqdm(test_dataloader)): + cloud, gt_part_cls, gt_part_r, gt_part_quat, gt_part_t, gt_joint_state, gt_norm_joint_loc, gt_norm_joint_axis, \ + gt_norm_part_kp, gt_scale, gt_center, gt_norm_part_corners, cate, urdf_id = data + + cloud, gt_part_cls, gt_part_r, gt_part_quat, gt_part_t, gt_joint_state, \ + gt_norm_joint_loc, gt_norm_joint_axis, gt_norm_part_kp = \ + cloud.to(device), gt_part_cls.to(device), gt_part_r.to(device), gt_part_quat.to(device), \ + gt_part_t.to(device), gt_joint_state.to(device), gt_norm_joint_loc.to(device), \ + gt_norm_joint_axis.to(device), gt_norm_part_kp.to(device) + + with torch.no_grad(): + dense_part_cls_score, pred_trans_part_kp, pred_base_quat, pred_base_r, pred_base_t, pred_joint_state, \ + pred_beta, pred_norm_part_kp, pred_joint_loc, pred_joint_axis = model(cloud, None) + + _, item_score, loss_dict = criterion(pred_trans_part_kp, dense_part_cls_score, pred_base_quat, pred_base_t, + pred_norm_part_kp, + pred_joint_loc, pred_joint_axis, pred_joint_state, pred_beta, + gt_part_cls, gt_part_quat, + gt_part_r, gt_part_t, gt_norm_part_kp, + gt_norm_joint_loc, gt_norm_joint_axis, gt_joint_state) + for key, value in loss_dict.items(): + sum_test_loss_dict[key] += value + score.append(item_score) + + for key, value in sum_test_loss_dict.items(): + writer.add_scalar('{}/test'.format(key), value / len(score), total_iter) + + test_score = np.mean(np.array(score)) + writer.add_scalar('score/test', test_score, total_iter) + + if test_score < best_test: + best_test = test_score + torch.save(model.state_dict(), + '{0}/model_{1}_{2}_{3}.pth'.format(opt.work_dir, epoch, test_score, cate_list[opt.category - 1])) + print('epoch:', epoch, ' >>>>>>>>----------BEST TEST MODEL SAVED---------<<<<<<<<') + writer.close() diff --git a/train_omad_priornet.py b/train_omad_priornet.py new file mode 100644 index 0000000..4a31bb8 --- /dev/null +++ b/train_omad_priornet.py @@ -0,0 +1,192 @@ +import argparse +import numpy as np +import os.path as osp +from torch.utils.tensorboard import SummaryWriter + +import torch.optim as optim +import torch.utils.data +import tqdm +import time + +from dataset.dataset_omad_priornet import SapienDataset_OMADPriorNet +from model.omad_priornet import OMAD_PriorNet +from libs.loss_omad_priornet import Loss_OMAD_PriorNet + +cate_list = ['laptop', 'eyeglasses', 'dishwasher', 'drawer', 'scissors'] + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_root', type=str, default='data', help='dataset root dir') + parser.add_argument('--resume', type=str, default=None, help='resume model') + parser.add_argument('--category', type=int, default=1, help='category to train') + parser.add_argument('--num_samples', type=int, default=50000, help='number of samples of training dataset') + parser.add_argument('--num_points', type=int, default=2048, help='points') + parser.add_argument('--num_cates', type=int, default=1, help='number of categories') + parser.add_argument('--num_parts', type=int, default=2, help='number of parts') + parser.add_argument('--workers', type=int, default=4, help='number of data loading workers') + parser.add_argument('--num_kp', type=int, default=8, help='number of all keypoints') + parser.add_argument('--num_basis', type=int, default=10, help='number of shape basis') + parser.add_argument('--chamfer_weight', type=float, default=1.0, help='weight of chamfer loss') + parser.add_argument('--coverage_weight', type=float, default=1.0, help='weight of coverage loss') + parser.add_argument('--surface_weight', type=float, default=5.0, help='weight of surface loss') + parser.add_argument('--joint_weight', type=float, default=1.0, help='weight of joint loss') + parser.add_argument('--reg_weight', type=float, default=0.01, help='weight of regularization loss') + parser.add_argument('--sep_weight', type=float, default=2.0, help='weight of seperation loss') + parser.add_argument('--sep_factor', type=float, default=8.0, help='the sep factor') + parser.add_argument('--scale_aug_range', type=float, default=0.5, help='the range of scale augmentation') + parser.add_argument('--use_relative_coverage', action='store_true', default=False, + help='whether to use relative coverage loss') + parser.add_argument('--beta', type=float, default=0.1, help='the beta of smooth l1 loss for coverege loss') + parser.add_argument('--symtype', type=str, default='shape', choices=['shape', 'none'], help='the symmetry type') + parser.add_argument('--bs', type=int, default=32, help='batch size') + parser.add_argument('--work_dir', type=str, default='work_dir/base', help='save dir') + parser.add_argument('--lr', type=float, default=0.001, help='learning rate') + opt = parser.parse_args() + + device = torch.device("cuda:0") + model = OMAD_PriorNet(node_num=opt.num_kp, basis_num=opt.num_basis, part_num=opt.num_parts, device=device, + symtype=opt.symtype, init_n_pl=((1., 0.), )) + model = model.to(device) + + if opt.resume is not None: + model.load_state_dict(torch.load(osp.join(opt.work_dir, opt.resume))) + + train_dataset = SapienDataset_OMADPriorNet('train', + data_root=opt.dataset_root, add_noise=False, num_pts=opt.num_points, + num_parts=opt.num_parts, + num_cates=opt.num_cates, cate_id=opt.category, + num_samples=opt.num_samples, + node_num=opt.num_kp, + use_scale_aug=True, + use_rot_aug=False, + device=torch.device("cpu")) + train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs, shuffle=True, num_workers=opt.workers) + test_dataset = SapienDataset_OMADPriorNet('val', data_root=opt.dataset_root, add_noise=False, num_pts=opt.num_points, + num_parts=opt.num_parts, + num_cates=opt.num_cates, cate_id=opt.category, + num_samples=5000, + node_num=opt.num_kp, + use_scale_aug=False, + use_rot_aug=False, + scale_aug_max_range=opt.scale_aug_range, + device=torch.device("cpu")) + test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.bs, shuffle=False, num_workers=opt.workers) + + assert opt.num_kp % opt.num_parts == 0, 'number of keypoints must be divided by number of parts' + # TODO: more flexible + joint_type = 'revolute' if opt.category != 4 else 'prismatic' + criterion = Loss_OMAD_PriorNet(device=device, + num_kp=opt.num_kp, num_cate=opt.num_cates, num_parts=opt.num_parts, + loss_chamfer_weight=opt.chamfer_weight, + loss_coverage_weight=opt.coverage_weight, + loss_surface_weight=opt.surface_weight, + loss_joint_weight=opt.joint_weight, + loss_reg_weight=opt.reg_weight, + loss_sep_weight=opt.sep_weight, + sep_factor=opt.sep_factor, + beta=opt.beta, + joint_type=joint_type, + use_relative_coverage=opt.use_relative_coverage + ) + + best_test = np.Inf + optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=0.9, weight_decay=0.0001) + scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100, eta_min=0) + + writer = SummaryWriter(log_dir=opt.work_dir) + total_iter = 0 + for epoch in range(0, 100): + model.train() + train_count = 0 + + optimizer.zero_grad() + + writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch) + for i, data in enumerate(train_dataloader): + cloud, init_nodes, cloud_cls, gt_inv_r, gt_joint_loc, gt_joint_axis, scale_factor, \ + raw_part_scale, raw_center, cate, urdf_id = data + cloud, init_nodes, gt_inv_r, cloud_cls, gt_joint_loc, gt_joint_axis, raw_part_scale = \ + cloud.to(device), init_nodes.to(device), gt_inv_r.to(device), cloud_cls.to(device), \ + gt_joint_loc.to(device), gt_joint_axis.to(device), raw_part_scale.to(device) + + bs_now = cloud.shape[0] + gt_r = gt_inv_r.transpose(1, 2) + gt_t = torch.zeros(bs_now, 1, 3, device=device) + + cloud.requires_grad_() + init_nodes.requires_grad_() + + final_nodes, coefs, pred_joint_loc, pred_joint_axis = model(cloud, None, init_nodes) + part_pred_kps = model.get_transformed_pred_keypoints(coefs, gt_r, gt_t) + trans_joint_loc, trans_joint_axis = model.get_transformed_joint_params(pred_joint_loc, pred_joint_axis, gt_r, gt_t) + + loss, _, loss_dict = criterion(coefs, part_pred_kps, final_nodes, cloud, cloud_cls, + trans_joint_loc, trans_joint_axis, gt_joint_loc, gt_joint_axis, raw_part_scale) + loss.backward() + + train_count += 1 + total_iter += 1 + optimizer.step() + optimizer.zero_grad() + if train_count % 100 == 0: + print('{}, Epoch: {}, iter: {}/{}, loss_all:{:05f}, loss_chamfer:{:05f}, ' + 'loss_coverage:{:05f}, loss_surface:{:05f}, loss_joint:{:05f}, loss_reg:{:05f}, loss_sep:{:05f}'.format( + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())), + epoch, train_count, len(train_dataset) // opt.bs, float(loss.item()), + loss_dict['loss_chamfer'], + loss_dict['loss_coverage'], + loss_dict['loss_surface'], + loss_dict['loss_joint'], + loss_dict['loss_reg'], + loss_dict['loss_sep'] + )) + for key, value in loss_dict.items(): + writer.add_scalar('{}/train'.format(key), value, total_iter) + writer.add_scalar('loss_all/train', loss.item(), total_iter) + + torch.save(model.state_dict(), osp.join(opt.work_dir, 'model_current_{}.pth'.format(cate_list[opt.category - 1]))) + + # change lr + scheduler.step() + optimizer.zero_grad() + + if (epoch == 0) or ((epoch + 1) % 5 == 0): + model.eval() + score = [] + print('>>>>>>>>>>>>Testing epoch {}>>>>>>>>>>>>>'.format(epoch)) + sum_test_loss_dict = dict(loss_chamfer=0., loss_coverage=0., loss_surface=0., loss_joint=0., loss_reg=0., + loss_sep=0.) + for j, data in enumerate(tqdm.tqdm(test_dataloader)): + cloud, init_nodes, cloud_cls, gt_inv_r, gt_joint_loc, gt_joint_axis, scale_factor, \ + raw_part_scale, raw_center, cate, urdf_id = data + cloud, init_nodes, gt_inv_r, cloud_cls, gt_joint_loc, gt_joint_axis, raw_part_scale = \ + cloud.to(device), init_nodes.to(device), gt_inv_r.to(device), cloud_cls.to(device), \ + gt_joint_loc.to(device), gt_joint_axis.to(device), raw_part_scale.to(device) + + bs_now = cloud.shape[0] + gt_r = gt_inv_r.transpose(1, 2) + gt_t = torch.zeros(bs_now, 1, 3, device=device) + + with torch.no_grad(): + final_nodes, coefs, pred_joint_loc, pred_joint_axis = model(cloud, None, init_nodes) + part_pred_kps = model.get_transformed_pred_keypoints(coefs, gt_r, gt_t) + trans_joint_loc, trans_joint_axis = model.get_transformed_joint_params(pred_joint_loc, pred_joint_axis, gt_r, gt_t) + + _, item_score, loss_dict = criterion(coefs, part_pred_kps, final_nodes, cloud, cloud_cls, + trans_joint_loc, trans_joint_axis, gt_joint_loc, gt_joint_axis, raw_part_scale) + for key, value in loss_dict.items(): + sum_test_loss_dict[key] += value + score.append(item_score) + + for key, value in sum_test_loss_dict.items(): + writer.add_scalar('{}/test'.format(key), value / len(score), total_iter) + + test_score = np.mean(np.array(score)) + writer.add_scalar('score/test', test_score, total_iter) + + if test_score < best_test: + best_test = test_score + torch.save(model.state_dict(), + '{0}/model_{1}_{2}_{3}.pth'.format(opt.work_dir, epoch, test_score, cate_list[opt.category - 1])) + print('epoch:', epoch, ' >>>>>>>>----------BEST TEST MODEL SAVED---------<<<<<<<<') + writer.close()

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