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evaluate.py
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import os
import os.path as osp
import cv2
import torch
import joblib
import numpy as np
import shutil
from pathlib import Path
from tqdm import tqdm
from collections import defaultdict
import importlib
from lib.core.config import BASE_DATA_DIR, parse_args
from lib.data_utils._img_utils import split_into_chunks_test
from lib.data_utils._kp_utils import convert_kps
from lib.models.smpl import SMPL_MODEL_DIR, SMPL, H36M_TO_J14
from lib.utils.demo_utils import convert_crop_cam_to_orig_img, images_to_video
from lib.utils.eval_utils import compute_accel, compute_error_accel, batch_compute_similarity_transform_torch, compute_error_verts, compute_errors, plot_accel
from lib.utils.slerp_filter_utils import quaternion_from_matrix, quaternion_slerp, quaternion_matrix
from lib.utils.renderer import Renderer
def get_sequence(start_index, end_index, seqlen=16):
if end_index - start_index + 1 == seqlen:
return [i for i in range(start_index, end_index+1)]
else:
seq = []
if start_index == 0:
for i in range(seqlen - (end_index - start_index + 1)):
seq.append(start_index)
for i in range(start_index, end_index + 1):
seq.append(i)
else:
for i in range(start_index, end_index + 1):
seq.append(i)
for i in range(seqlen - (end_index - start_index + 1)):
seq.append(end_index)
return seq
""" Smoothing codes from MEVA (https://github.com/ZhengyiLuo/MEVA) """
def quat_correct(quat):
""" Converts quaternion to minimize Euclidean distance from previous quaternion (wxyz order) """
for q in range(1, quat.shape[0]):
if np.linalg.norm(quat[q-1] - quat[q], axis=0) > np.linalg.norm(quat[q-1] + quat[q], axis=0):
quat[q] = -quat[q]
return quat
def quat_smooth(quat, ratio = 0.3):
""" Converts quaternion to minimize Euclidean distance from previous quaternion (wxyz order) """
for q in range(1, quat.shape[0]):
quat[q] = quaternion_slerp(quat[q-1], quat[q], ratio)
return quat
def smooth_pose_mat(pose, ratio = 0.3):
quats_all = []
for j in range(pose.shape[1]):
quats = []
for i in range(pose.shape[0]):
R = pose[i,j,:,:]
quats.append(quaternion_from_matrix(R))
quats = quat_correct(np.array(quats))
quats = quat_smooth(quats, ratio = ratio)
quats_all.append(np.array([quaternion_matrix(i)[:3,:3] for i in quats]))
quats_all = np.stack(quats_all, axis=1)
return quats_all
if __name__ == "__main__":
cfg, cfg_file, args = parse_args()
SMPL_MAJOR_JOINTS = np.array([1, 2, 4, 5, 7, 8, 16, 17, 18, 19, 20, 21])
device = (
torch.device("cuda", index=0)
if torch.cuda.is_available()
else torch.device("cpu")
)
""" Evaluation Options """
target_dataset = args.dataset # 'mpii3d' '3dpw' 'h36m'
set = 'test'
target_action = args.seq
render = args.render or args.render_plain
render_plain = args.render_plain
only_img = False
render_frame_start = args.frame
plot = args.plot
avg_filter = args.filter
gender = 'neutral'
model_module = importlib.import_module('.%s' % cfg.MODEL.MODEL_NAME, 'lib.models')
model = model_module.GLoT(
seqlen=cfg.DATASET.SEQLEN,
batch_size=cfg.TRAIN.BATCH_SIZE,
n_layers=cfg.MODEL.n_layers,
d_model=cfg.MODEL.d_model,
num_head=cfg.MODEL.num_head,
dropout=cfg.MODEL.dropout,
drop_path_r=cfg.MODEL.drop_path_r,
atten_drop=cfg.MODEL.atten_drop,
mask_ratio=cfg.MODEL.mask_ratio,
short_n_layers = cfg.MODEL.short_n_layers,
short_d_model = cfg.MODEL.short_d_model,
short_num_head = cfg.MODEL.short_num_head,
short_dropout = cfg.MODEL.short_dropout,
short_drop_path_r = cfg.MODEL.short_drop_path_r,
short_atten_drop = cfg.MODEL.short_atten_drop,
stride_short=cfg.MODEL.stride_short,
drop_reg_short=cfg.MODEL.drop_reg_short,
pretrained=cfg.TRAIN.PRETRAINED_REGRESSOR
).to(cfg.DEVICE)
if cfg.TRAIN.PRETRAINED != '' and os.path.isfile(cfg.TRAIN.PRETRAINED):
checkpoint = torch.load(cfg.TRAIN.PRETRAINED)
# best_performance = checkpoint['performance']
model.load_state_dict(checkpoint['gen_state_dict'])
print(f"==> Loaded pretrained model from {cfg.TRAIN.PRETRAINED}...")
else:
print(f"{cfg.TRAIN.PRETRAINED} is not a pretrained model! Exiting...")
import sys; sys.exit()
model.regressor.smpl = SMPL(
SMPL_MODEL_DIR,
batch_size=64,
create_transl=False,
gender=gender
).cuda()
dtype = torch.float
J_regressor = torch.from_numpy(np.load(osp.join(BASE_DATA_DIR, 'J_regressor_h36m.npy'))).float()
""" Data """
seqlen = 16
stride = 1 # seqlen
out_dir = f'./output/{target_dataset}_test_output' # your path
Path(out_dir).mkdir(parents=True, exist_ok=True)
if target_dataset == '3dpw':
data_path = f'/home/shenxiaolong.sxl/remote_t1/data/preprocessed_data/{target_dataset}_{set}_db.pt' #
elif target_dataset == 'h36m':
if cfg.TITLE == 'repr_table4_h36m_mpii3d_model':
data_path = f'/home/shenxiaolong.sxl/remote_t1/data/preprocessed_data/{target_dataset}_{set}_25fps_db.pt' # Table 4
elif cfg.TITLE == 'repr_table6_h36m_model':
data_path = f'/home/shenxiaolong.sxl/remote_t1/data/preprocessed_data/{target_dataset}_{set}_front_25fps_tight_db.pt' # Table 6
elif target_dataset == 'mpii3d':
set = 'val'
data_path = f'/home/shenxiaolong.sxl/remote_t1/data/preprocessed_data/{target_dataset}_{set}_scale12_db.pt' #
else:
print("Wrong target dataset! Exiting...")
import sys; sys.exit()
print(f"Load data from {data_path}")
dataset_data = joblib.load(data_path)
full_res = defaultdict(list)
vid_name_list = dataset_data['vid_name']
unique_names = np.unique(vid_name_list)
data_keyed = {}
# make dictionary with video seqeunce names
for u_n in unique_names:
if (target_action != '') and (not target_action in u_n):
continue
indexes = vid_name_list == u_n
if 'valid' in dataset_data:
valids = dataset_data['valid'][indexes].astype(bool)
else:
valids = np.ones(dataset_data['features'][indexes].shape[0]).astype(bool)
# import pdb; pdb.set_trace()
# valids[:] = 1
data_keyed[u_n] = {
'features': dataset_data['features'][indexes][valids],
'joints3D': dataset_data['joints3D'][indexes][valids],
'vid_name': dataset_data['vid_name'][indexes][valids],
'imgname': dataset_data['img_name'][indexes][valids],
'bbox': dataset_data['bbox'][indexes][valids],
}
if 'mpii3d' in data_path:
data_keyed[u_n]['pose'] = np.zeros((len(valids), 72))
data_keyed[u_n]['shape'] = np.zeros((len(valids), 10))
data_keyed[u_n]['valid_i'] = dataset_data['valid_i'][indexes][valids]
J_regressor = None
else:
data_keyed[u_n]['pose'] = dataset_data['pose'][indexes][valids]
data_keyed[u_n]['shape'] = dataset_data['shape'][indexes][valids]
dataset_data = data_keyed
""" Run evaluation """
model.eval()
with torch.no_grad():
tot_num_pose = 0
pbar = tqdm(dataset_data.keys())
for seq_name in pbar:
curr_feats = dataset_data[seq_name]['features']
res_save = {}
curr_feat = torch.tensor(curr_feats).to(device)
num_frames = curr_feat.shape[0]
vid_names = dataset_data[seq_name]['vid_name']
chunk_idxes = split_into_chunks_test(vid_names, seqlen=seqlen, stride=stride, is_train=False, match_vibe=False) # match vibe eval number of poses
if chunk_idxes == []:
continue
pred_j3ds, pred_verts, pred_rotmats, pred_thetas, scores = [], [], [], [], []
for curr_idx in range(0, len(chunk_idxes), 8):
input_feat = []
if (curr_idx + 8) < len(chunk_idxes):
for ii in range(8):
seq_select = get_sequence(chunk_idxes[curr_idx+ii][0], chunk_idxes[curr_idx+ii][1])
input_feat.append(curr_feat[None, seq_select, :])
else:
for ii in range(curr_idx, len(chunk_idxes)):
seq_select = get_sequence(chunk_idxes[ii][0], chunk_idxes[ii][1])
input_feat.append(curr_feat[None, seq_select, :])
input_feat = torch.cat(input_feat, dim=0)
preds, mask_ids, pred_mae = model(input_feat, J_regressor=J_regressor, is_train=False)
n_kp = preds[-1]['kp_3d'].shape[-2]
pred_j3d = preds[-1]['kp_3d'].view(-1, n_kp, 3).cpu().numpy()
pred_vert = preds[-1]['verts'].view(-1, 6890, 3).cpu().numpy()
pred_rotmat = preds[-1]['rotmat'].view(-1,24,3,3).cpu().numpy()
pred_theta = preds[-1]['theta'].view(-1,85).cpu().numpy()
# score = score.cpu().numpy()
pred_j3ds.append(pred_j3d)
pred_verts.append(pred_vert)
pred_rotmats.append(pred_rotmat)
pred_thetas.append(pred_theta)
# scores.append(score)
# temporal smoothing post-processing following MEVA (https://github.com/ZhengyiLuo/MEVA)
if avg_filter:
# slerp avg filter
pred_thetas = np.vstack(pred_thetas).astype(np.float32)
pred_rotmats = np.vstack(pred_rotmats)
pred_rotmats = smooth_pose_mat(np.array(pred_rotmats), ratio=0.3).astype(np.float32)
smpl = SMPL(model_path=SMPL_MODEL_DIR)
smpl_output = smpl(
betas=torch.from_numpy(pred_thetas[:, 75:]),
body_pose=torch.from_numpy(pred_rotmats[:, 1:]),
global_orient=torch.from_numpy(pred_rotmats[:, 0:1]),
pose2rot=False,
)
filtered_pred_verts = smpl_output.vertices
# for render
pred_vertes = filtered_pred_verts
J_regressor_batch = J_regressor[None, :].expand(filtered_pred_verts.shape[0], -1, -1)
pred_joints = torch.matmul(J_regressor_batch, filtered_pred_verts)
pred_j3ds = pred_joints[:, H36M_TO_J14, :].detach().cpu().numpy()
else:
try:
pred_j3ds = np.vstack(pred_j3ds)
# scores = np.vstack(scores)
except:
import pdb; pdb.set_trace()
target_j3ds = dataset_data[seq_name]['joints3D']
pred_verts = np.vstack(pred_verts)
dummy_cam = np.repeat(np.array([[1., 0., 0.]]), len(target_j3ds), axis=0)
target_theta = np.concatenate([dummy_cam, dataset_data[seq_name]['pose'], dataset_data[seq_name]['shape']], axis=1).astype(np.float32)
target_j3ds, target_theta = target_j3ds[:len(pred_j3ds)], target_theta[:len(pred_j3ds)]
""" Rendering """
if render:
num_frames_to_render = 200
imgname = dataset_data[seq_name]['imgname']
bbox = dataset_data[seq_name]['bbox']
pred_cam = np.vstack(pred_thetas).astype(np.float32)[:, :3]
img = cv2.imread(imgname[0])
orig_height, orig_width = img.shape[:2]
renderer = Renderer(resolution=(orig_width, orig_height), orig_img=True, wireframe=False)
if target_dataset == 'h36m':
seq_name = seq_name.split('/')[-1]
if render_plain:
save_seq_name = f'{seq_name}_plain'
elif only_img:
save_seq_name = f'{seq_name}_input'
else:
save_seq_name = seq_name
save_seq_name = 'GLoT_' + save_seq_name + '_' + str(render_frame_start)
count = 0
for ii in tqdm(range(len(imgname))):
frame_i = int(imgname[ii].split('_')[-1][:-4])
if (frame_i < render_frame_start) or (frame_i > render_frame_start+num_frames_to_render):
continue
count += 1
Path(osp.join(out_dir, save_seq_name)).mkdir(parents=True, exist_ok=True)
bbox_ii = bbox[0:1].copy() if render_plain else bbox[ii:ii + 1]
bbox_ii[:, 2:] = bbox_ii[:, 2:] * 1.2
img_path = imgname[ii]
img = cv2.imread(img_path)
cam = np.array([[1, 0, 0]]) if render_plain else pred_cam[ii:ii + 1]
orig_cam = convert_crop_cam_to_orig_img(
cam=cam,
bbox=bbox_ii,
img_width=orig_width,
img_height=orig_height
)
if not only_img:
try:
if render_plain:
img[:] = 0
img = renderer.render(
img,
pred_verts[ii],
cam=orig_cam[0],
color=[1.0, 1.0, 0.9],
mesh_filename=None,
rotate=False
)
except:
print("Error on rendering! Exiting...")
import sys; sys.exit()
# resize image to save storage
h, w = img.shape[:2]
new_h, new_w = int(h/2), int(w/2)
new_h, new_w = new_h if new_h % 2 == 0 else new_h-1, new_w if new_w % 2 == 0 else new_w-1 # for ffmpeg
img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
new_height, new_width = img.shape[:2]
# plot attention weights
# cv2.putText(img, f'past: {str(scores[count-1][0].round(3))}', (new_width-110, 20), cv2.FONT_HERSHEY_PLAIN, 0.8, (255,255,255))
# cv2.putText(img, f'current: {str(scores[count-1][1].round(3))}', (new_width-110, 40), cv2.FONT_HERSHEY_PLAIN, 0.8, (255,255,255))
# cv2.putText(img, f'future: {str(scores[count-1][2].round(3))}', (new_width-110, 60), cv2.FONT_HERSHEY_PLAIN, 0.8, (255,255,255))
cv2.imwrite(osp.join(out_dir, save_seq_name, f'{count:06d}.jpg'), img)
save_path = osp.join(out_dir, 'video', save_seq_name + ".mp4")
Path(osp.join(out_dir, 'video')).mkdir(parents=True, exist_ok=True)
print(f"Saving result video to {osp.abspath(save_path)}")
images_to_video(img_folder=osp.join(out_dir, save_seq_name), output_vid_file=save_path)
shutil.rmtree(osp.join(out_dir, save_seq_name))
if 'mpii3d' in data_path:
target_j3ds = convert_kps(target_j3ds, src='spin', dst='mpii3d_test')
pred_j3ds = convert_kps(pred_j3ds, src='spin', dst='mpii3d_test')
valid_map = dataset_data[seq_name]['valid_i'][:,0].nonzero()[0]
if valid_map.size == 0:
print("No valid frames. Continue") # 'subj6_seg0'
continue
while True:
if valid_map[-1] >= len(pred_j3ds):
valid_map = valid_map[:-1]
else:
break
elif target_j3ds.shape[1] == 49:
target_j3ds = convert_kps(target_j3ds, src='spin', dst='common')
valid_map = np.arange(len(target_j3ds))
else:
valid_map = np.arange(len(target_j3ds))
pred_j3ds = torch.from_numpy(pred_j3ds).float()
target_j3ds = torch.from_numpy(target_j3ds).float()
num_eval_pose = len(valid_map)
print(f"Evaluating on {num_eval_pose} data (number of poses) in {seq_name}...")
tot_num_pose += num_eval_pose
if 'mpii3d' in data_path:
pred_pelvis = pred_j3ds[:, [-3], :]
target_pelvis = target_j3ds[:, [-3], :]
else:
pred_pelvis = (pred_j3ds[:, [2], :] + pred_j3ds[:, [3], :]) / 2.0
target_pelvis = (target_j3ds[:, [2], :] + target_j3ds[:, [3], :]) / 2.0
pred_j3ds -= pred_pelvis
target_j3ds -= target_pelvis
m2mm = 1000
# per-frame accuracy
mpvpe = compute_error_verts(target_theta=target_theta, pred_verts=pred_verts) * m2mm
mpjpe = torch.sqrt(((pred_j3ds - target_j3ds) ** 2).sum(dim=-1)).cpu().numpy()[valid_map]
mpjpe = mpjpe.mean(axis=-1) * m2mm
S1_hat = batch_compute_similarity_transform_torch(pred_j3ds, target_j3ds)
mpjpe_pa = torch.sqrt(((S1_hat - target_j3ds) ** 2).sum(dim=-1)).cpu().numpy()[valid_map]
mpjpe_pa = mpjpe_pa.mean(axis=-1) * m2mm
# acceleration error
if plot:
plot_accel(pred_j3ds, joints_gt=target_j3ds, out_dir=out_dir, name=target_action)
accel_err = np.zeros((len(pred_j3ds,)))
accel_err[1:-1] = compute_error_accel(joints_pred=pred_j3ds, joints_gt=target_j3ds) * m2mm
# exclude 0 from accel error calculation
if valid_map[0] == 0:
valid_map = valid_map[1:]
if valid_map[-1] == len(accel_err)-1:
valid_map = valid_map[:-1]
accel_err = accel_err[valid_map]
full_res['mpjpe'].append(mpjpe)
full_res['mpjpe_pa'].append(mpjpe_pa)
full_res['accel_err'].append(accel_err)
if target_dataset == '3dpw':
full_res['mpvpe'].append(mpvpe)
pbar.set_description(f"{np.mean(mpjpe_pa):.3f}")
print(f"\nEvaluated total {tot_num_pose} poses")
full_res.pop(0, None)
full_res = {k: np.mean(np.concatenate(v)) for k, v in full_res.items()}
print(full_res)