lib/eval/evaluate_emdb.py

import os
import time
import os.path as osp
from glob import glob
from collections import defaultdict

import torch
import pickle
import numpy as np
from smplx import SMPL
from loguru import logger
from progress.bar import Bar

from configs import constants as _C
from configs.config import parse_args
from lib.data.dataloader import setup_eval_dataloader
from lib.models import build_network, build_body_model
from lib.eval.eval_utils import (
    compute_jpe,
    compute_rte,
    compute_jitter,
    compute_error_accel,
    compute_foot_sliding,
    batch_align_by_pelvis,
    first_align_joints,
    global_align_joints,
    compute_rte,
    compute_jitter,
    compute_foot_sliding
    batch_compute_similarity_transform_torch,
)
from lib.utils import transforms
from lib.utils.utils import prepare_output_dir
from lib.utils.utils import prepare_batch
from lib.utils.imutils import avg_preds

"""

This is a tentative script to evaluate WHAM on EMDB dataset.

Current implementation requires EMDB dataset downloaded at ./datasets/EMDB/

"""

m2mm = 1e3
@torch.no_grad()
def main(cfg, args):
    torch.backends.cuda.matmul.allow_tf32 = False
    torch.backends.cudnn.allow_tf32 = False
    
    logger.info(f'GPU name -> {torch.cuda.get_device_name()}')
    logger.info(f'GPU feat -> {torch.cuda.get_device_properties("cuda")}')    
    
    # ========= Dataloaders ========= #
    eval_loader = setup_eval_dataloader(cfg, 'emdb', args.eval_split, cfg.MODEL.BACKBONE)
    logger.info(f'Dataset loaded')
    
    # ========= Load WHAM ========= #
    smpl_batch_size = cfg.TRAIN.BATCH_SIZE * cfg.DATASET.SEQLEN
    smpl = build_body_model(cfg.DEVICE, smpl_batch_size)
    network = build_network(cfg, smpl)
    network.eval()
    
    # Build SMPL models with each gender
    smpl = {k: SMPL(_C.BMODEL.FLDR, gender=k).to(cfg.DEVICE) for k in ['male', 'female', 'neutral']}
    
    # Load vertices -> joints regression matrix to evaluate
    pelvis_idxs = [1, 2]
    
    # WHAM uses Y-down coordinate system, while EMDB dataset uses Y-up one.
    yup2ydown = transforms.axis_angle_to_matrix(torch.tensor([[np.pi, 0, 0]])).float().to(cfg.DEVICE)
    
    # To torch tensor function
    tt = lambda x: torch.from_numpy(x).float().to(cfg.DEVICE)
    accumulator = defaultdict(list)
    bar = Bar('Inference', fill='#', max=len(eval_loader))
    with torch.no_grad():
        for i in range(len(eval_loader)):
            # Original batch
            batch = eval_loader.dataset.load_data(i, False)
            x, inits, features, kwargs, gt = prepare_batch(batch, cfg.DEVICE, cfg.TRAIN.STAGE == 'stage2')
            
            # Align with groundtruth data to the first frame
            cam2yup = batch['R'][0][:1].to(cfg.DEVICE)
            cam2ydown = cam2yup @ yup2ydown
            cam2root = transforms.rotation_6d_to_matrix(inits[1][:, 0, 0])
            ydown2root = cam2ydown.mT @ cam2root
            ydown2root = transforms.matrix_to_rotation_6d(ydown2root)
            kwargs['init_root'][:, 0] = ydown2root
            
            if cfg.FLIP_EVAL:
                flipped_batch = eval_loader.dataset.load_data(i, True)
                f_x, f_inits, f_features, f_kwargs, _ = prepare_batch(flipped_batch, cfg.DEVICE, cfg.TRAIN.STAGE == 'stage2')
            
                # Forward pass with flipped input
                flipped_pred = network(f_x, f_inits, f_features, **f_kwargs)
                
            # Forward pass with normal input
            pred = network(x, inits, features, **kwargs)
            
            if cfg.FLIP_EVAL:
                # Merge two predictions
                flipped_pose, flipped_shape = flipped_pred['pose'].squeeze(0), flipped_pred['betas'].squeeze(0)
                pose, shape = pred['pose'].squeeze(0), pred['betas'].squeeze(0)
                flipped_pose, pose = flipped_pose.reshape(-1, 24, 6), pose.reshape(-1, 24, 6)
                avg_pose, avg_shape = avg_preds(pose, shape, flipped_pose, flipped_shape)
                avg_pose = avg_pose.reshape(-1, 144)
                avg_contact = (flipped_pred['contact'][..., [2, 3, 0, 1]] + pred['contact']) / 2
                
                # Refine trajectory with merged prediction
                network.pred_pose = avg_pose.view_as(network.pred_pose)
                network.pred_shape = avg_shape.view_as(network.pred_shape)
                network.pred_contact = avg_contact.view_as(network.pred_contact)
                output = network.forward_smpl(**kwargs)
                pred = network.refine_trajectory(output, return_y_up=True, **kwargs)
            
            # <======= Prepare groundtruth data
            subj, seq = batch['vid'][:2], batch['vid'][3:]
            annot_pth = glob(osp.join(_C.PATHS.EMDB_PTH, subj, seq, '*_data.pkl'))[0]
            annot = pickle.load(open(annot_pth, 'rb'))
            
            masks = annot['good_frames_mask']
            gender = annot['gender']
            poses_body = annot["smpl"]["poses_body"]
            poses_root = annot["smpl"]["poses_root"]
            betas = np.repeat(annot["smpl"]["betas"].reshape((1, -1)), repeats=annot["n_frames"], axis=0)
            trans = annot["smpl"]["trans"]
            extrinsics = annot["camera"]["extrinsics"]
            
            # # Map to camear coordinate
            poses_root_cam = transforms.matrix_to_axis_angle(tt(extrinsics[:, :3, :3]) @ transforms.axis_angle_to_matrix(tt(poses_root)))
            
            # Groundtruth global motion
            target_glob = smpl[gender](body_pose=tt(poses_body), global_orient=tt(poses_root), betas=tt(betas), transl=tt(trans))
            target_j3d_glob = target_glob.joints[:, :24][masks]
            
            # Groundtruth local motion
            target_cam = smpl[gender](body_pose=tt(poses_body), global_orient=poses_root_cam, betas=tt(betas))
            target_verts_cam = target_cam.vertices[masks]
            target_j3d_cam = target_cam.joints[:, :24][masks]
            # =======>
            
            # Convert WHAM global orient to Y-up coordinate
            poses_root = pred['poses_root_world'].squeeze(0)
            pred_trans = pred['trans_world'].squeeze(0)
            poses_root = yup2ydown.mT @ poses_root
            pred_trans = (yup2ydown.mT @ pred_trans.unsqueeze(-1)).squeeze(-1)
            
            # <======= Build predicted motion
            # Predicted global motion
            pred_glob = smpl['neutral'](body_pose=pred['poses_body'], global_orient=poses_root.unsqueeze(1), betas=pred['betas'].squeeze(0), transl=pred_trans, pose2rot=False)
            pred_j3d_glob = pred_glob.joints[:, :24]
            
            # Predicted local motion
            pred_cam = smpl['neutral'](body_pose=pred['poses_body'], global_orient=pred['poses_root_cam'], betas=pred['betas'].squeeze(0), pose2rot=False)
            pred_verts_cam = pred_cam.vertices
            pred_j3d_cam = pred_cam.joints[:, :24]
            # =======>
            
            # <======= Evaluation on the local motion
            pred_j3d_cam, target_j3d_cam, pred_verts_cam, target_verts_cam = batch_align_by_pelvis(
                [pred_j3d_cam, target_j3d_cam, pred_verts_cam, target_verts_cam], pelvis_idxs
            )
            S1_hat = batch_compute_similarity_transform_torch(pred_j3d_cam, target_j3d_cam)
            pa_mpjpe = torch.sqrt(((S1_hat - target_j3d_cam) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() * m2mm
            mpjpe = torch.sqrt(((pred_j3d_cam - target_j3d_cam) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() * m2mm
            pve = torch.sqrt(((pred_verts_cam - target_verts_cam) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() * m2mm
            accel = compute_error_accel(joints_pred=pred_j3d_cam.cpu(), joints_gt=target_j3d_cam.cpu())[1:-1]
            accel = accel * (30 ** 2)       # per frame^s to per s^2
            
            summary_string = f'{batch["vid"]} | PA-MPJPE: {pa_mpjpe.mean():.1f}   MPJPE: {mpjpe.mean():.1f}   PVE: {pve.mean():.1f}'
            bar.suffix = summary_string
            bar.next()
            # =======>
            
            # <======= Evaluation on the global motion
            chunk_length = 100
            w_mpjpe, wa_mpjpe = [], []
            for start in range(0, masks.sum(), chunk_length):
                end = min(masks.sum(), start + chunk_length)

                target_j3d = target_j3d_glob[start:end].clone().cpu()
                pred_j3d = pred_j3d_glob[start:end].clone().cpu()
                
                w_j3d = first_align_joints(target_j3d, pred_j3d)
                wa_j3d = global_align_joints(target_j3d, pred_j3d)
                
                w_jpe = compute_jpe(target_j3d, w_j3d)
                wa_jpe = compute_jpe(target_j3d, wa_j3d)
                w_mpjpe.append(w_jpe)
                wa_mpjpe.append(wa_jpe)
            
            w_mpjpe = np.concatenate(w_mpjpe) * m2mm
            wa_mpjpe = np.concatenate(wa_mpjpe) * m2mm
            
            # Additional metrics
            rte = compute_rte(torch.from_numpy(trans[masks]), pred_trans.cpu()) * 1e2
            jitter = compute_jitter(pred_glob, fps=30)
            foot_sliding = compute_foot_sliding(target_glob, pred_glob, masks) * m2mm
            # =======>
            
            # Additional metrics
            rte = compute_rte(torch.from_numpy(trans[masks]), pred_trans.cpu()) * 1e2
            jitter = compute_jitter(pred_glob, fps=30)
            foot_sliding = compute_foot_sliding(target_glob, pred_glob, masks) * m2mm
            
            # <======= Accumulate the results over entire sequences
            accumulator['pa_mpjpe'].append(pa_mpjpe)
            accumulator['mpjpe'].append(mpjpe)
            accumulator['pve'].append(pve)
            accumulator['accel'].append(accel)
            accumulator['wa_mpjpe'].append(wa_mpjpe)
            accumulator['w_mpjpe'].append(w_mpjpe)
            accumulator['RTE'].append(rte)
            accumulator['jitter'].append(jitter)
            accumulator['FS'].append(foot_sliding)
            # =======>
            
    for k, v in accumulator.items():
        accumulator[k] = np.concatenate(v).mean()

    print('')
    log_str = f'Evaluation on EMDB {args.eval_split}, '
    log_str += ' '.join([f'{k.upper()}: {v:.4f},'for k,v in accumulator.items()])
    logger.info(log_str)
            
if __name__ == '__main__':
    cfg, cfg_file, args = parse_args(test=True)
    cfg = prepare_output_dir(cfg, cfg_file)
    
    main(cfg, args)