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| """ 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) # (B, 1, H, W) | |
| 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): | |
| # NOTE: top-left pixel should be (0,0) | |
| 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): | |
| # NOTE: top-left pixel should be (0,0) | |
| 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 | |