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import os
import time
import os.path as osp
from glob import glob
from collections import defaultdict
import torch
import imageio
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_error_accel,
batch_align_by_pelvis,
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
try:
from lib.vis.renderer import Renderer
_render = True
except:
print("PyTorch3D is not properly installed! Cannot render the SMPL mesh")
_render = False
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, '3dpw', 'test', 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
J_regressor_eval = torch.from_numpy(
np.load(_C.BMODEL.JOINTS_REGRESSOR_H36M)
)[_C.KEYPOINTS.H36M_TO_J14, :].unsqueeze(0).float().to(cfg.DEVICE)
pelvis_idxs = [2, 3]
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')
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)
# 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)
pred = network.forward_smpl(**kwargs)
# <======= Build predicted SMPL
pred_output = smpl['neutral'](body_pose=pred['poses_body'],
global_orient=pred['poses_root_cam'],
betas=pred['betas'].squeeze(0),
pose2rot=False)
pred_verts = pred_output.vertices.cpu()
pred_j3d = torch.matmul(J_regressor_eval, pred_output.vertices).cpu()
# =======>
# <======= Build groundtruth SMPL
target_output = smpl[batch['gender']](
body_pose=transforms.rotation_6d_to_matrix(gt['pose'][0, :, 1:]),
global_orient=transforms.rotation_6d_to_matrix(gt['pose'][0, :, :1]),
betas=gt['betas'][0],
pose2rot=False)
target_verts = target_output.vertices.cpu()
target_j3d = torch.matmul(J_regressor_eval, target_output.vertices).cpu()
# =======>
# <======= Compute performance of the current sequence
pred_j3d, target_j3d, pred_verts, target_verts = batch_align_by_pelvis(
[pred_j3d, target_j3d, pred_verts, target_verts], pelvis_idxs
)
S1_hat = batch_compute_similarity_transform_torch(pred_j3d, target_j3d)
pa_mpjpe = torch.sqrt(((S1_hat - target_j3d) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm
mpjpe = torch.sqrt(((pred_j3d - target_j3d) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm
pve = torch.sqrt(((pred_verts - target_verts) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm
accel = compute_error_accel(joints_pred=pred_j3d, joints_gt=target_j3d)[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()
# <======= Accumulate the results over entire sequences
accumulator['pa_mpjpe'].append(pa_mpjpe)
accumulator['mpjpe'].append(mpjpe)
accumulator['pve'].append(pve)
accumulator['accel'].append(accel)
# =======>
# <======= (Optional) Render the prediction
if not (_render and args.render):
# Skip if PyTorch3D is not installed or rendering argument is not parsed.
continue
# Save path
viz_pth = osp.join('output', 'visualization')
os.makedirs(viz_pth, exist_ok=True)
# Build Renderer
width, height = batch['cam_intrinsics'][0][0, :2, -1].numpy() * 2
focal_length = batch['cam_intrinsics'][0][0, 0, 0].item()
renderer = Renderer(width, height, focal_length, cfg.DEVICE, smpl['neutral'].faces)
# Get images and writer
frame_list = batch['frame_id'][0].numpy()
imname_list = sorted(glob(osp.join(_C.PATHS.THREEDPW_PTH, 'imageFiles', batch['vid'][:-2], '*.jpg')))
writer = imageio.get_writer(osp.join(viz_pth, batch['vid'] + '.mp4'),
mode='I', format='FFMPEG', fps=30, macro_block_size=1)
# Skip the invalid frames
for i, frame in enumerate(frame_list):
image = imageio.imread(imname_list[frame])
# NOTE: pred['verts'] is different from pred_verts as we substracted offset from SMPL mesh.
# Check line 121 in lib/models/smpl.py
vertices = pred['verts_cam'][i] + pred['trans_cam'][[i]]
image = renderer.render_mesh(vertices, image)
writer.append_data(image)
writer.close()
# =======>
for k, v in accumulator.items():
accumulator[k] = np.concatenate(v).mean()
print('')
log_str = 'Evaluation on 3DPW, '
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) |