RuntimeError:应为设备类型cuda的对象，但在调用_th_bmm_out时为参数#0'result'获取了设备类型cpu

import numpy as np from sklearn.neighbors import NearestNeighbors def best_fit_transform(A, B): ''' Calculates the least-squares best-fit transform that maps corresponding points A to B in m spatial dimensions Input: A: Nxm numpy array of corresponding points B: Nxm numpy array of corresponding points Returns: T: (m+1)x(m+1) homogeneous transformation matrix that maps A on to B R: mxm rotation matrix t: mx1 translation vector ''' assert A.shape == B.shape #条件为true正常执行，条件为false触发异常 # get number of dimensions m = A.shape[1] # translate points to their centroids #计算点云质心 centroid_A = np.mean(A, axis=0) centroid_B = np.mean(B, axis=0) #源、目标点云计算去质心 AA = A - centroid_A BB = B - centroid_B # rotation matrix H = np.dot(AA.T, BB) #SVD分解H协方差矩阵 U, S, Vt = np.linalg.svd(H) R = np.dot(Vt.T, U.T) # special reflection case if np.linalg.det(R) < 0: Vt[m-1,:] *= -1 R = np.dot(Vt.T, U.T) # translation #t:3X1 t = centroid_B.T - np.dot(R,centroid_A.T) # homogeneous transformation #T为m+1维的对角矩阵 T = np.identity(m+1) T[:m, :m] = R T[:m, m] = t return T, R, t def nearest_neighbor(src, dst): ''' Find the nearest (Euclidean) neighbor in dst for each point in src Input: src: Nxm array of points dst: Nxm array of points Output: distances: Euclidean distances of the nearest neighbor indices: dst indices of the nearest neighbor ''' assert src.shape == dst.shape neigh = NearestNeighbors(n_neighbors=1) neigh.fit(dst) distances, indices = neigh.kneighbors(src, return_distance=True) return distances.ravel(), indices.ravel() def icp(A, B, init_pose=None, max_iterations=20, tolerance=0.001): ''' The Iterative Closest Point method: finds best-fit transform that maps points A on to points B Input: A: Nxm numpy array of source mD points B: Nxm numpy array of destination mD point init_pose: (m+1)x(m+1) homogeneous transformation max_iterations: exit algorithm after max_iterations tolerance: convergence criteria Output: T: final homogeneous transformation that maps A on to B distances: Euclidean distances (errors) of the nearest neighbor i: number of iterations to converge ''' assert A.shape == B.shape # get number of dimensions m = A.shape[1] # make points homogeneous, copy them to maintain the originals src = np.ones((m+1,A.shape[0])) # print(type(src)) dst = np.ones((m+1,B.shape[0])) # print(dst.shape) src[:m,:] = np.copy(A.T) dst[:m,:] = np.copy(B.T) # apply the initial pose estimation if init_pose is not None: src = np.dot(init_pose, src) prev_error = 0 for i in range(max_iterations): # find the nearest neighbors between the current source and destination points distances, indices = nearest_neighbor(src[:m,:].T, dst[:m,:].T) # compute the transformation between the current source and nearest destination points T,_,_ = best_fit_transform(src[:m,:].T, dst[:m,indices].T) # update the current source src = np.dot(T, src) # check error mean_error = np.mean(distances) if np.abs(prev_error - mean_error) < tolerance: break prev_error = mean_error # calculate final transformation T,_,_ = best_fit_transform(A, src[:m,:].T) return T, distances, i

import numpy as np import time from ICP import icp from data import ModelNet40 from tqdm import tqdm from util import transform_point_cloud, npmat2euler from torch.utils.data import DataLoader from tensorboardX import SummaryWriter import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import MultiStepLR def test_one_epoch(test_loader): mse_ab = 0 mae_ab = 0 mse_ba = 0 mae_ba = 0 total_loss = 0 total_cycle_loss = 0 num_examples = 0 rotations_ab = [] translations_ab = [] rotations_ab_pred = [] translations_ab_pred = [] rotations_ba = [] translations_ba = [] rotations_ba_pred = [] translations_ba_pred = [] eulers_ab = [] eulers_ba = [] for src, target, rotation_ab, translation_ab, rotation_ba, translation_ba, euler_ab, euler_ba in tqdm(test_loader): src = src.cuda() src1 = src.cpu().numpy() src1 = np.reshape(src1,(-1,1024)) src1 = src1.T # print(src.shape) target = target.cuda() target1 = target.cpu().numpy() target1 = np.reshape(target1,(-1,1024)) target1 = target1.T rotation_ab = rotation_ab.cuda() translation_ab = translation_ab.cuda() # rotation_ba = rotation_ba.cuda() # translation_ba = translation_ba.cuda() batch_size = 1 num_examples += batch_size # rotation_ab_pred, translation_ab_pred, rotation_ba_pred, translation_ba_pred = net(src, target) T, distances, iterations= icp.icp(src1, target1, tolerance=0.000001) rotation_ab_pred = T[0:3,0:3] rotation_ab_pred = np.reshape(rotation_ab_pred,(1,3,3)) rotation_ab_pred = torch.from_numpy(rotation_ab_pred) rotation_ab_pred = rotation_ab_pred.cuda() rotation_ab_pred = torch.tensor(rotation_ab_pred, dtype=torch.float32) translation_ab_pred = T[0:3,3] translation_ab_pred = np.reshape(translation_ab_pred,(1,3)) translation_ab_pred = torch.from_numpy(translation_ab_pred) translation_ab_pred = translation_ab_pred.cuda() translation_ab_pred = torch.tensor(translation_ab_pred,dtype=torch.float32) ## save rotation and translation rotations_ab.append(rotation_ab.detach().cpu().numpy()) translations_ab.append(translation_ab.detach().cpu().numpy()) rotations_ab_pred.append(rotation_ab_pred.detach().cpu().numpy()) translations_ab_pred.append(translation_ab_pred.detach().cpu().numpy()) eulers_ab.append(euler_ab.numpy()) ## # rotations_ba.append(rotation_ba.detach().cpu().numpy()) # translations_ba.append(translation_ba.detach().cpu().numpy()) # rotations_ba_pred.append(rotation_ba_pred.detach().cpu().numpy()) # translations_ba_pred.append(translation_ba_pred.detach().cpu().numpy()) # eulers_ba.append(euler_ba.numpy()) transformed_src = transform_point_cloud(src, rotation_ab_pred, translation_ab_pred) # transformed_src = transformed_src.cuda() # transformed_target = transform_point_cloud(target, rotation_ba_pred, translation_ba_pred) ########################### identity = torch.eye(3).cuda().unsqueeze(0).repeat(batch_size, 1, 1) loss = F.mse_loss(torch.matmul(rotation_ab_pred.transpose(2, 1), rotation_ab), identity) \ + F.mse_loss(translation_ab_pred, translation_ab) # if args.cycle: # rotation_loss = F.mse_loss(torch.matmul(rotation_ba_pred, rotation_ab_pred), identity.clone()) # translation_loss = torch.mean((torch.matmul(rotation_ba_pred.transpose(2, 1), # translation_ab_pred.view(batch_size, 3, 1)).view(batch_size, 3) # + translation_ba_pred) ** 2, dim=[0, 1]) # cycle_loss = rotation_loss + translation_loss # # loss = loss + cycle_loss * 0.1 total_loss += loss.item() * batch_size # if args.cycle: # total_cycle_loss = total_cycle_loss + cycle_loss.item() * 0.1 * batch_size mse_ab += torch.mean((transformed_src - target) ** 2, dim=[0, 1, 2]).item() * batch_size mae_ab += torch.mean(torch.abs(transformed_src - target), dim=[0, 1, 2]).item() * batch_size # mse_ba += torch.mean((transformed_target - src) ** 2, dim=[0, 1, 2]).item() * batch_size # mae_ba += torch.mean(torch.abs(transformed_target - src), dim=[0, 1, 2]).item() * batch_size rotations_ab = np.concatenate(rotations_ab, axis=0) translations_ab = np.concatenate(translations_ab, axis=0) rotations_ab_pred = np.concatenate(rotations_ab_pred, axis=0) translations_ab_pred = np.concatenate(translations_ab_pred, axis=0) # rotations_ba = np.concatenate(rotations_ba, axis=0) # translations_ba = np.concatenate(translations_ba, axis=0) # rotations_ba_pred = np.concatenate(rotations_ba_pred, axis=0) # translations_ba_pred = np.concatenate(translations_ba_pred, axis=0) eulers_ab = np.concatenate(eulers_ab, axis=0) # eulers_ba = np.concatenate(eulers_ba, axis=0) return total_loss * 1.0 / num_examples, \ mse_ab * 1.0 / num_examples, mae_ab * 1.0 / num_examples, \ rotations_ab, \ translations_ab, rotations_ab_pred, translations_ab_pred, eulers_ab def test(test_loader): test_loss, \ test_mse_ab, test_mae_ab, test_rotations_ab, test_translations_ab, \ test_rotations_ab_pred, \ test_translations_ab_pred, \ test_eulers_ab = test_one_epoch(test_loader) test_rmse_ab = np.sqrt(test_mse_ab) test_rotations_ab_pred_euler = npmat2euler(test_rotations_ab_pred) test_r_mse_ab = np.mean((test_rotations_ab_pred_euler - np.degrees(test_eulers_ab)) ** 2) test_r_rmse_ab = np.sqrt(test_r_mse_ab) test_r_mae_ab = np.mean(np.abs(test_rotations_ab_pred_euler - np.degrees(test_eulers_ab))) test_t_mse_ab = np.mean((test_translations_ab - test_translations_ab_pred) ** 2) test_t_rmse_ab = np.sqrt(test_t_mse_ab) test_t_mae_ab = np.mean(np.abs(test_translations_ab - test_translations_ab_pred)) # test_rotations_ba_pred_euler = npmat2euler(test_rotations_ba_pred, 'xyz') # test_r_mse_ba = np.mean((test_rotations_ba_pred_euler - np.degrees(test_eulers_ba)) ** 2) # test_r_rmse_ba = np.sqrt(test_r_mse_ba) # test_r_mae_ba = np.mean(np.abs(test_rotations_ba_pred_euler - np.degrees(test_eulers_ba))) # test_t_mse_ba = np.mean((test_translations_ba - test_translations_ba_pred) ** 2) # test_t_rmse_ba = np.sqrt(test_t_mse_ba) # test_t_mae_ba = np.mean(np.abs(test_translations_ba - test_translations_ba_pred)) print('==FINAL TEST==') print('A--------->B') print('EPOCH:: %d, Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, ' 'rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f' % (-1, test_loss, test_mse_ab, test_rmse_ab, test_mae_ab,test_r_mse_ab, test_r_rmse_ab, test_r_mae_ab, test_t_mse_ab, test_t_rmse_ab, test_t_mae_ab)) # textio.cprint('B--------->A') # textio.cprint('EPOCH:: %d, Loss: %f, MSE: %f, RMSE: %f, MAE: %f, rot_MSE: %f, rot_RMSE: %f, ' # 'rot_MAE: %f, trans_MSE: %f, trans_RMSE: %f, trans_MAE: %f' # % (-1, test_loss, test_mse_ba, test_rmse_ba, test_mae_ba, test_r_mse_ba, test_r_rmse_ba, # test_r_mae_ba, test_t_mse_ba, test_t_rmse_ba, test_t_mae_ba)) def main(): test_loader = DataLoader( ModelNet40(num_points=1024, partition='test', gaussian_noise=False, unseen=False, factor=4), batch_size=1, shuffle=False, drop_last=False) test(test_loader) print('FINISH') if __name__ == '__main__': main()

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import sys import glob import h5py import numpy as np from scipy.spatial.transform import Rotation from torch.utils.data import Dataset # Part of the code is referred from: https://github.com/charlesq34/pointnet def download(): BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def load_data(partition): download() BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') all_data = [] all_label = [] for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5' % partition)): f = h5py.File(h5_name) data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2. / 3., high=3. / 2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.05): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1 * clip, clip) return pointcloud class ModelNet40(Dataset): def __init__(self, num_points, partition='train', gaussian_noise=False, unseen=False, factor=4): self.data, self.label = load_data(partition) self.num_points = num_points self.partition = partition self.gaussian_noise = gaussian_noise self.unseen = unseen self.label = self.label.squeeze() self.factor = factor if self.unseen: ######## simulate testing on first 20 categories while training on last 20 categories if self.partition == 'test': self.data = self.data[self.label>=20] self.label = self.label[self.label>=20] elif self.partition == 'train': self.data = self.data[self.label<20] self.label = self.label[self.label<20] def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] if self.gaussian_noise: pointcloud = jitter_pointcloud(pointcloud) if self.partition != 'train': np.random.seed(item) anglex = np.random.uniform() * np.pi / self.factor angley = np.random.uniform() * np.pi / self.factor anglez = np.random.uniform() * np.pi / self.factor cosx = np.cos(anglex) cosy = np.cos(angley) cosz = np.cos(anglez) sinx = np.sin(anglex) siny = np.sin(angley) sinz = np.sin(anglez) Rx = np.array([[1, 0, 0], [0, cosx, -sinx], [0, sinx, cosx]]) Ry = np.array([[cosy, 0, siny], [0, 1, 0], [-siny, 0, cosy]]) Rz = np.array([[cosz, -sinz, 0], [sinz, cosz, 0], [0, 0, 1]]) R_ab = Rx.dot(Ry).dot(Rz) R_ba = R_ab.T translation_ab = np.array([np.random.uniform(-0.5, 0.5), np.random.uniform(-0.5, 0.5), np.random.uniform(-0.5, 0.5)]) translation_ba = -R_ba.dot(translation_ab) pointcloud1 = pointcloud.T rotation_ab = Rotation.from_euler('zyx', [anglez, angley, anglex]) pointcloud2 = rotation_ab.apply(pointcloud1.T).T + np.expand_dims(translation_ab, axis=1) euler_ab = np.asarray([anglez, angley, anglex]) euler_ba = -euler_ab[::-1] pointcloud1 = np.random.permutation(pointcloud1.T).T pointcloud2 = np.random.permutation(pointcloud2.T).T return pointcloud1.astype('float32'), pointcloud2.astype('float32'), R_ab.astype('float32'), \ translation_ab.astype('float32'), R_ba.astype('float32'), translation_ba.astype('float32'), \ euler_ab.astype('float32'), euler_ba.astype('float32') def __len__(self): return self.data.shape[0] if __name__ == '__main__': train = ModelNet40(1024) test = ModelNet40(1024, 'test') for data in train: print(len(data)) break

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function import os import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import numpy as np from scipy.spatial.transform import Rotation # Part of the code is referred from: https://github.com/ClementPinard/SfmLearner-Pytorch/blob/master/inverse_warp.py def quat2mat(quat): x, y, z, w = quat[:, 0], quat[:, 1], quat[:, 2], quat[:, 3] B = quat.size(0) w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) wx, wy, wz = w*x, w*y, w*z xy, xz, yz = x*y, x*z, y*z rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz, 2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx, 2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).reshape(B, 3, 3) return rotMat def transform_point_cloud(point_cloud, rotation, translation): if len(rotation.size()) == 2: rot_mat = quat2mat(rotation) else: rot_mat = rotation return torch.matmul(rot_mat, point_cloud) + translation.unsqueeze(2) def npmat2euler(mats, seq='zyx'): eulers = [] for i in range(mats.shape[0]): r = Rotation.from_dcm(mats[i]) eulers.append(r.as_euler(seq, degrees=True)) return np.asarray(eulers, dtype='float32')

D:\Envs\DCP\Scripts\python.exe D:/pyproject/dcp-master/ICP/testICP.py D:\pyproject\dcp-master\data.py:51: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details. f = h5py.File(h5_name) 0%| | 0/2468 [00:00<?, ?it/s]D:/pyproject/dcp-master/ICP/testICP.py:62: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). rotation_ab_pred = torch.tensor(rotation_ab_pred, dtype=torch.float32) D:/pyproject/dcp-master/ICP/testICP.py:67: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). translation_ab_pred = torch.tensor(translation_ab_pred,dtype=torch.float32) 0%| | 0/2468 [00:02<?, ?it/s] Traceback (most recent call last): File "D:/pyproject/dcp-master/ICP/testICP.py", line 180, in <module> main() File "D:/pyproject/dcp-master/ICP/testICP.py", line 175, in main test(test_loader) File "D:/pyproject/dcp-master/ICP/testICP.py", line 138, in test test_eulers_ab = test_one_epoch(test_loader) File "D:/pyproject/dcp-master/ICP/testICP.py", line 83, in test_one_epoch transformed_src = transform_point_cloud(src, rotation_ab_pred, translation_ab_pred) File "D:\pyproject\dcp-master\util.py", line 38, in transform_point_cloud a = torch.matmul(rot_mat, point_cloud) + translation.unsqueeze(2) RuntimeError: Expected object of device type cuda but got device type cpu for argument #0 'result' in call to _th_bmm_out Process finished with exit code 1

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