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| """ | |
| @Author: Haoxi Ran | |
| @Date: 01/03/2024 | |
| @Citation: Towards Realistic Scene Generation with LiDAR Diffusion Models | |
| """ | |
| import multiprocessing | |
| from functools import partial | |
| import numpy as np | |
| from scipy.spatial.distance import jensenshannon | |
| from tqdm import tqdm | |
| from . import OUTPUT_TEMPLATE | |
| from .metric_utils import compute_logits, compute_pairwise_cd, \ | |
| compute_pairwise_emd, pcd2bev_sum, compute_pairwise_cd_batch, pcd2bev_bin | |
| from .fid_score import calculate_frechet_distance | |
| def evaluate(reference, samples, metrics, data): | |
| # perceptual | |
| if 'frid' in metrics: | |
| compute_frid(reference, samples, data) | |
| if 'fsvd' in metrics: | |
| compute_fsvd(reference, samples, data) | |
| if 'fpvd' in metrics: | |
| compute_fpvd(reference, samples, data) | |
| # reconstruction | |
| if 'cd' in metrics: | |
| compute_cd(reference, samples) | |
| if 'emd' in metrics: | |
| compute_emd(reference, samples) | |
| # statistical | |
| if 'jsd' in metrics: | |
| compute_jsd(reference, samples, data) | |
| if 'mmd' in metrics: | |
| compute_mmd(reference, samples, data) | |
| def compute_cd(reference, samples): | |
| """ | |
| Calculate score of Chamfer Distance (CD) | |
| """ | |
| print('Evaluating (CD) ...') | |
| results = [] | |
| for x, y in zip(reference, samples): | |
| d = compute_pairwise_cd(x, y) | |
| results.append(d) | |
| score = sum(results) / len(results) | |
| print(OUTPUT_TEMPLATE.format('CD ', score)) | |
| def compute_emd(reference, samples): | |
| """ | |
| Calculate score of Earth Mover's Distance (EMD) | |
| """ | |
| print('Evaluating (EMD) ...') | |
| results = [] | |
| for x, y in zip(reference, samples): | |
| d = compute_pairwise_emd(x, y) | |
| results.append(d) | |
| score = sum(results) / len(results) | |
| print(OUTPUT_TEMPLATE.format('EMD ', score)) | |
| def compute_mmd(reference, samples, data, dist='cd', verbose=True): | |
| """ | |
| Calculate the score of Minimum Matching Distance (MMD) | |
| """ | |
| print('Evaluating (MMD) ...') | |
| assert dist in ['cd', 'emd'] | |
| reference, samples = pcd2bev_bin(data, reference, samples) | |
| compute_dist_func = compute_pairwise_cd_batch if dist == 'cd' else compute_pairwise_emd | |
| results = [] | |
| for r in tqdm(reference, disable=not verbose): | |
| dists = compute_dist_func(r, samples) | |
| results.append(min(dists)) | |
| score = sum(results) / len(results) | |
| print(OUTPUT_TEMPLATE.format('MMD ', score)) | |
| def compute_jsd(reference, samples, data): | |
| """ | |
| Calculate the score of Jensen-Shannon Divergence (JSD) | |
| """ | |
| print('Evaluating (JSD) ...') | |
| reference, samples = pcd2bev_sum(data, reference, samples) | |
| reference = (reference / np.sum(reference)).flatten() | |
| samples = (samples / np.sum(samples)).flatten() | |
| score = jensenshannon(reference, samples) | |
| print(OUTPUT_TEMPLATE.format('JSD ', score)) | |
| def compute_fd(reference, samples): | |
| mu1, mu2 = np.mean(reference, axis=0), np.mean(samples, axis=0) | |
| sigma1, sigma2 = np.cov(reference, rowvar=False), np.cov(samples, rowvar=False) | |
| distance = calculate_frechet_distance(mu1, sigma1, mu2, sigma2) | |
| return distance | |
| def compute_frid(reference, samples, data): | |
| """ | |
| Calculate the score of Fréchet Range Image Distance (FRID) | |
| """ | |
| print('Evaluating (FRID) ...') | |
| gt_logits, samples_logits = compute_logits(data, 'range', reference, samples) | |
| score = compute_fd(gt_logits, samples_logits) | |
| print(OUTPUT_TEMPLATE.format('FRID', score)) | |
| def compute_fsvd(reference, samples, data): | |
| """ | |
| Calculate the score of Fréchet Sparse Volume Distance (FSVD) | |
| """ | |
| print('Evaluating (FSVD) ...') | |
| gt_logits, samples_logits = compute_logits(data, 'voxel', reference, samples) | |
| score = compute_fd(gt_logits, samples_logits) | |
| print(OUTPUT_TEMPLATE.format('FSVD', score)) | |
| def compute_fpvd(reference, samples, data): | |
| """ | |
| Calculate the score of Fréchet Point-based Volume Distance (FPVD) | |
| """ | |
| print('Evaluating (FPVD) ...') | |
| gt_logits, samples_logits = compute_logits(data, 'point_voxel', reference, samples) | |
| score = compute_fd(gt_logits, samples_logits) | |
| print(OUTPUT_TEMPLATE.format('FPVD', score)) | |