| """ utils |
| """ |
| import os |
| import torch |
| import numpy as np |
|
|
|
|
| def load_checkpoint(fpath, model): |
| print('loading checkpoint... {}'.format(fpath)) |
|
|
| ckpt = torch.load(fpath, map_location='cpu')['model'] |
|
|
| load_dict = {} |
| for k, v in ckpt.items(): |
| if k.startswith('module.'): |
| k_ = k.replace('module.', '') |
| load_dict[k_] = v |
| else: |
| load_dict[k] = v |
|
|
| model.load_state_dict(load_dict) |
| print('loading checkpoint... / done') |
| return model |
|
|
|
|
| def compute_normal_error(pred_norm, gt_norm): |
| pred_error = torch.cosine_similarity(pred_norm, gt_norm, dim=1) |
| pred_error = torch.clamp(pred_error, min=-1.0, max=1.0) |
| pred_error = torch.acos(pred_error) * 180.0 / np.pi |
| pred_error = pred_error.unsqueeze(1) |
| return pred_error |
|
|
|
|
| def compute_normal_metrics(total_normal_errors): |
| total_normal_errors = total_normal_errors.detach().cpu().numpy() |
| num_pixels = total_normal_errors.shape[0] |
|
|
| metrics = { |
| 'mean': np.average(total_normal_errors), |
| 'median': np.median(total_normal_errors), |
| 'rmse': np.sqrt(np.sum(total_normal_errors * total_normal_errors) / num_pixels), |
| 'a1': 100.0 * (np.sum(total_normal_errors < 5) / num_pixels), |
| 'a2': 100.0 * (np.sum(total_normal_errors < 7.5) / num_pixels), |
| 'a3': 100.0 * (np.sum(total_normal_errors < 11.25) / num_pixels), |
| 'a4': 100.0 * (np.sum(total_normal_errors < 22.5) / num_pixels), |
| 'a5': 100.0 * (np.sum(total_normal_errors < 30) / num_pixels) |
| } |
|
|
| return metrics |
|
|
|
|
| def pad_input(orig_H, orig_W): |
| if orig_W % 32 == 0: |
| l = 0 |
| r = 0 |
| else: |
| new_W = 32 * ((orig_W // 32) + 1) |
| l = (new_W - orig_W) // 2 |
| r = (new_W - orig_W) - l |
|
|
| if orig_H % 32 == 0: |
| t = 0 |
| b = 0 |
| else: |
| new_H = 32 * ((orig_H // 32) + 1) |
| t = (new_H - orig_H) // 2 |
| b = (new_H - orig_H) - t |
| return l, r, t, b |
|
|
|
|
| def get_intrins_from_fov(new_fov, H, W, device): |
| |
| if W >= H: |
| new_fu = (W / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) |
| new_fv = (W / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) |
| else: |
| new_fu = (H / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) |
| new_fv = (H / 2.0) / np.tan(np.deg2rad(new_fov / 2.0)) |
|
|
| new_cu = (W / 2.0) - 0.5 |
| new_cv = (H / 2.0) - 0.5 |
|
|
| new_intrins = torch.tensor([ |
| [new_fu, 0, new_cu ], |
| [0, new_fv, new_cv ], |
| [0, 0, 1 ] |
| ], dtype=torch.float32, device=device) |
|
|
| return new_intrins |
|
|
|
|
| def get_intrins_from_txt(intrins_path, device): |
| |
| with open(intrins_path, 'r') as f: |
| intrins_ = f.readlines()[0].split()[0].split(',') |
| intrins_ = [float(i) for i in intrins_] |
| fx, fy, cx, cy = intrins_ |
|
|
| intrins = torch.tensor([ |
| [fx, 0,cx], |
| [ 0,fy,cy], |
| [ 0, 0, 1] |
| ], dtype=torch.float32, device=device) |
|
|
| return intrins |
