from datetime import datetime import numpy as np import torch from datasets import get_dataset_motion_loader, get_motion_loader from models import MotionTransformer from utils.get_opt import get_opt from utils.metrics import * from datasets import EvaluatorModelWrapper from collections import OrderedDict from utils.plot_script import * from utils import paramUtil from utils.utils import * from trainers import DDPMTrainer from os.path import join as pjoin import sys def build_models(opt, dim_pose): encoder = MotionTransformer( input_feats=dim_pose, num_frames=opt.max_motion_length, num_layers=opt.num_layers, latent_dim=opt.latent_dim, no_clip=opt.no_clip, no_eff=opt.no_eff) return encoder torch.multiprocessing.set_sharing_strategy('file_system') def evaluate_matching_score(motion_loaders, file): match_score_dict = OrderedDict({}) R_precision_dict = OrderedDict({}) activation_dict = OrderedDict({}) # print(motion_loaders.keys()) print('========== Evaluating Matching Score ==========') for motion_loader_name, motion_loader in motion_loaders.items(): all_motion_embeddings = [] score_list = [] all_size = 0 matching_score_sum = 0 top_k_count = 0 # print(motion_loader_name) with torch.no_grad(): for idx, batch in enumerate(motion_loader): word_embeddings, pos_one_hots, _, sent_lens, motions, m_lens, _ = batch text_embeddings, motion_embeddings = eval_wrapper.get_co_embeddings( word_embs=word_embeddings, pos_ohot=pos_one_hots, cap_lens=sent_lens, motions=motions, m_lens=m_lens ) dist_mat = euclidean_distance_matrix(text_embeddings.cpu().numpy(), motion_embeddings.cpu().numpy()) matching_score_sum += dist_mat.trace() argsmax = np.argsort(dist_mat, axis=1) top_k_mat = calculate_top_k(argsmax, top_k=3) top_k_count += top_k_mat.sum(axis=0) all_size += text_embeddings.shape[0] all_motion_embeddings.append(motion_embeddings.cpu().numpy()) all_motion_embeddings = np.concatenate(all_motion_embeddings, axis=0) matching_score = matching_score_sum / all_size R_precision = top_k_count / all_size match_score_dict[motion_loader_name] = matching_score R_precision_dict[motion_loader_name] = R_precision activation_dict[motion_loader_name] = all_motion_embeddings print(f'---> [{motion_loader_name}] Matching Score: {matching_score:.4f}') print(f'---> [{motion_loader_name}] Matching Score: {matching_score:.4f}', file=file, flush=True) line = f'---> [{motion_loader_name}] R_precision: ' for i in range(len(R_precision)): line += '(top %d): %.4f ' % (i+1, R_precision[i]) print(line) print(line, file=file, flush=True) return match_score_dict, R_precision_dict, activation_dict def evaluate_fid(groundtruth_loader, activation_dict, file): eval_dict = OrderedDict({}) gt_motion_embeddings = [] print('========== Evaluating FID ==========') with torch.no_grad(): for idx, batch in enumerate(groundtruth_loader): _, _, _, sent_lens, motions, m_lens, _ = batch motion_embeddings = eval_wrapper.get_motion_embeddings( motions=motions, m_lens=m_lens ) gt_motion_embeddings.append(motion_embeddings.cpu().numpy()) gt_motion_embeddings = np.concatenate(gt_motion_embeddings, axis=0) gt_mu, gt_cov = calculate_activation_statistics(gt_motion_embeddings) # print(gt_mu) for model_name, motion_embeddings in activation_dict.items(): mu, cov = calculate_activation_statistics(motion_embeddings) # print(mu) fid = calculate_frechet_distance(gt_mu, gt_cov, mu, cov) print(f'---> [{model_name}] FID: {fid:.4f}') print(f'---> [{model_name}] FID: {fid:.4f}', file=file, flush=True) eval_dict[model_name] = fid return eval_dict def evaluate_diversity(activation_dict, file): eval_dict = OrderedDict({}) print('========== Evaluating Diversity ==========') for model_name, motion_embeddings in activation_dict.items(): diversity = calculate_diversity(motion_embeddings, diversity_times) eval_dict[model_name] = diversity print(f'---> [{model_name}] Diversity: {diversity:.4f}') print(f'---> [{model_name}] Diversity: {diversity:.4f}', file=file, flush=True) return eval_dict def evaluate_multimodality(mm_motion_loaders, file): eval_dict = OrderedDict({}) print('========== Evaluating MultiModality ==========') for model_name, mm_motion_loader in mm_motion_loaders.items(): mm_motion_embeddings = [] with torch.no_grad(): for idx, batch in enumerate(mm_motion_loader): # (1, mm_replications, dim_pos) motions, m_lens = batch motion_embedings = eval_wrapper.get_motion_embeddings(motions[0], m_lens[0]) mm_motion_embeddings.append(motion_embedings.unsqueeze(0)) if len(mm_motion_embeddings) == 0: multimodality = 0 else: mm_motion_embeddings = torch.cat(mm_motion_embeddings, dim=0).cpu().numpy() multimodality = calculate_multimodality(mm_motion_embeddings, mm_num_times) print(f'---> [{model_name}] Multimodality: {multimodality:.4f}') print(f'---> [{model_name}] Multimodality: {multimodality:.4f}', file=file, flush=True) eval_dict[model_name] = multimodality return eval_dict def get_metric_statistics(values): mean = np.mean(values, axis=0) std = np.std(values, axis=0) conf_interval = 1.96 * std / np.sqrt(replication_times) return mean, conf_interval def evaluation(log_file): with open(log_file, 'w') as f: all_metrics = OrderedDict({'Matching Score': OrderedDict({}), 'R_precision': OrderedDict({}), 'FID': OrderedDict({}), 'Diversity': OrderedDict({}), 'MultiModality': OrderedDict({})}) for replication in range(replication_times): motion_loaders = {} mm_motion_loaders = {} motion_loaders['ground truth'] = gt_loader for motion_loader_name, motion_loader_getter in eval_motion_loaders.items(): motion_loader, mm_motion_loader = motion_loader_getter() motion_loaders[motion_loader_name] = motion_loader mm_motion_loaders[motion_loader_name] = mm_motion_loader print(f'==================== Replication {replication} ====================') print(f'==================== Replication {replication} ====================', file=f, flush=True) print(f'Time: {datetime.now()}') print(f'Time: {datetime.now()}', file=f, flush=True) mat_score_dict, R_precision_dict, acti_dict = evaluate_matching_score(motion_loaders, f) print(f'Time: {datetime.now()}') print(f'Time: {datetime.now()}', file=f, flush=True) fid_score_dict = evaluate_fid(gt_loader, acti_dict, f) print(f'Time: {datetime.now()}') print(f'Time: {datetime.now()}', file=f, flush=True) div_score_dict = evaluate_diversity(acti_dict, f) print(f'Time: {datetime.now()}') print(f'Time: {datetime.now()}', file=f, flush=True) mm_score_dict = evaluate_multimodality(mm_motion_loaders, f) print(f'!!! DONE !!!') print(f'!!! DONE !!!', file=f, flush=True) for key, item in mat_score_dict.items(): if key not in all_metrics['Matching Score']: all_metrics['Matching Score'][key] = [item] else: all_metrics['Matching Score'][key] += [item] for key, item in R_precision_dict.items(): if key not in all_metrics['R_precision']: all_metrics['R_precision'][key] = [item] else: all_metrics['R_precision'][key] += [item] for key, item in fid_score_dict.items(): if key not in all_metrics['FID']: all_metrics['FID'][key] = [item] else: all_metrics['FID'][key] += [item] for key, item in div_score_dict.items(): if key not in all_metrics['Diversity']: all_metrics['Diversity'][key] = [item] else: all_metrics['Diversity'][key] += [item] for key, item in mm_score_dict.items(): if key not in all_metrics['MultiModality']: all_metrics['MultiModality'][key] = [item] else: all_metrics['MultiModality'][key] += [item] # print(all_metrics['Diversity']) for metric_name, metric_dict in all_metrics.items(): print('========== %s Summary ==========' % metric_name) print('========== %s Summary ==========' % metric_name, file=f, flush=True) for model_name, values in metric_dict.items(): # print(metric_name, model_name) mean, conf_interval = get_metric_statistics(np.array(values)) # print(mean, mean.dtype) if isinstance(mean, np.float64) or isinstance(mean, np.float32): print(f'---> [{model_name}] Mean: {mean:.4f} CInterval: {conf_interval:.4f}') print(f'---> [{model_name}] Mean: {mean:.4f} CInterval: {conf_interval:.4f}', file=f, flush=True) elif isinstance(mean, np.ndarray): line = f'---> [{model_name}]' for i in range(len(mean)): line += '(top %d) Mean: %.4f CInt: %.4f;' % (i+1, mean[i], conf_interval[i]) print(line) print(line, file=f, flush=True) if __name__ == '__main__': mm_num_samples = 100 mm_num_repeats = 30 mm_num_times = 10 diversity_times = 300 replication_times = 1 batch_size = 32 opt_path = sys.argv[1] dataset_opt_path = opt_path try: device_id = int(sys.argv[2]) except: device_id = 0 device = torch.device('cuda:%d' % device_id if torch.cuda.is_available() else 'cpu') torch.cuda.set_device(device_id) gt_loader, gt_dataset = get_dataset_motion_loader(dataset_opt_path, batch_size, device) wrapper_opt = get_opt(dataset_opt_path, device) eval_wrapper = EvaluatorModelWrapper(wrapper_opt) opt = get_opt(opt_path, device) encoder = build_models(opt, opt.dim_pose) trainer = DDPMTrainer(opt, encoder) eval_motion_loaders = { 'text2motion': lambda: get_motion_loader( opt, batch_size, trainer, gt_dataset, mm_num_samples, mm_num_repeats ) } log_file = './t2m_evaluation.log' evaluation(log_file)