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import os |
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import torch |
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import numpy as np |
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from torch.utils.tensorboard import SummaryWriter |
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import json |
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import models.tae as tae |
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import options.option_tae as option_tae |
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import utils.utils_model as utils_model |
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import utils.eval_trans as eval_trans |
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from humanml3d_272 import dataset_eval_tae |
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import sys |
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import warnings |
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warnings.filterwarnings('ignore') |
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os.chdir('Evaluator_272') |
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sys.path.insert(0, os.getcwd()) |
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comp_device = torch.device('cuda') |
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args = option_tae.get_args_parser() |
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torch.manual_seed(args.seed) |
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args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}') |
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os.makedirs(args.out_dir, exist_ok = True) |
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logger = utils_model.get_logger(args.out_dir) |
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writer = SummaryWriter(args.out_dir) |
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logger.info(json.dumps(vars(args), indent=4, sort_keys=True)) |
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val_loader = dataset_eval_tae.DATALoader(args.dataname, True, 32) |
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clip_range = [-30,20] |
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net = tae.Causal_HumanTAE( |
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hidden_size=args.hidden_size, |
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down_t=args.down_t, |
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stride_t=args.stride_t, |
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depth=args.depth, |
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dilation_growth_rate=args.dilation_growth_rate, |
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activation='relu', |
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latent_dim=args.latent_dim, |
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clip_range=clip_range |
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) |
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print ('loading checkpoint from {}'.format(args.resume_pth)) |
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ckpt = torch.load(args.resume_pth, map_location='cpu') |
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net.load_state_dict(ckpt['net'], strict=True) |
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net.eval() |
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net.to(comp_device) |
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import torch |
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from mld.models.architectures.temos.textencoder.distillbert_actor import DistilbertActorAgnosticEncoder |
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from mld.models.architectures.temos.motionencoder.actor import ActorAgnosticEncoder |
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modelpath = 'distilbert-base-uncased' |
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textencoder = DistilbertActorAgnosticEncoder(modelpath, num_layers=4, latent_dim=256) |
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motionencoder = ActorAgnosticEncoder(nfeats=272, vae = True, num_layers=4, latent_dim=256, max_len=300) |
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ckpt = torch.load('epoch=99.ckpt') |
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textencoder_ckpt = {} |
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for k, v in ckpt['state_dict'].items(): |
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if k.split(".")[0] == "textencoder": |
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name = k.replace("textencoder.", "") |
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textencoder_ckpt[name] = v |
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textencoder.load_state_dict(textencoder_ckpt, strict=True) |
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textencoder.eval() |
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textencoder.to(comp_device) |
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motionencoder_ckpt = {} |
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for k, v in ckpt['state_dict'].items(): |
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if k.split(".")[0] == "motionencoder": |
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name = k.replace("motionencoder.", "") |
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motionencoder_ckpt[name] = v |
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motionencoder.load_state_dict(motionencoder_ckpt, strict=True) |
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motionencoder.eval() |
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motionencoder.to(comp_device) |
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evaluator = [textencoder, motionencoder] |
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fid = [] |
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mpjpe = [] |
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best_fid, best_mpjpe, writer, logger = eval_trans.evaluation_tae_single(args.out_dir, val_loader, net, logger, writer, evaluator=evaluator, device=comp_device) |
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fid.append(best_fid) |
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mpjpe.append(best_mpjpe) |
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logger.info('final result:') |
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logger.info(f'fid: {fid}') |
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logger.info(f'mpjpe: {mpjpe} (mm)') |
