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eval_vae_act_stats_muti_seed_babel.py
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eval_vae_act_stats_muti_seed_babel.py
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import os
import sys
import math
import pickle
import argparse
import time
import numpy as np
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
import csv
from numba import cuda
sys.path.append(os.getcwd())
from utils import *
from motion_pred.utils.config import Config
from motion_pred.utils.dataset_ntu_act_transition import DatasetNTU
from motion_pred.utils.dataset_grab_action_transition import DatasetGrab
from motion_pred.utils.dataset_humanact12_act_transition import DatasetACT12
from motion_pred.utils.dataset_babel_action_transition import DatasetBabel
from models.motion_pred import *
from utils.fid import calculate_frechet_distance
from utils.dtw import batch_dtw_torch, batch_dtw_torch_parallel, accelerated_dtw, batch_dtw_cpu_parallel
from utils import eval_util
from utils import data_utils
def get_stop_sign_from_ss(ss):
tmp = ss >= 0.5
idx = torch.arange(tmp.shape[1], 0, -1, device=device)[None,:]
tmp2 = tmp * idx
tmp2[:,:dataset.min_len-t_his - 1] = 0
tmp2[:,-1] = 1
fn = tmp2 == tmp2.max(dim=1, keepdim=True)[0]
fn = fn.float().transpose(0,1)
fn = torch.cat([torch.zeros_like(fn[:t_his]),fn],dim=0)
return fn
def get_stop_sign(Y_r,args):
# get stop sign
if args.stop_fn > 0:
fn_tmp = Y_r.shape[0]
tmp1 = np.arange(fn_tmp)[:, None]
tmp2 = np.arange(args.stop_fn)[None, :]
idxs = tmp1 + tmp2
idxs[idxs > fn_tmp - 1] = fn_tmp - 1
yr_tmp = Y_r[idxs]
yr_mean = yr_tmp.mean(dim=1, keepdim=True)
dr = torch.mean(torch.norm(yr_tmp - yr_mean, dim=-1), dim=1)
else:
dr = torch.norm(Y_r[:-1] - Y_r[1:], dim=2)
dr = torch.cat([dr[:1, :], dr], dim=0)
threshold = args.threshold
tmp = dr < threshold
idx = torch.arange(tmp.shape[0], 0, -1, device=device)[:, None]
tmp2 = tmp * idx
tmp2[:dataset.min_len - 1] = 0
tmp2[-1, :] = 1
fn = tmp2 == tmp2.max(dim=0, keepdim=True)[0]
fn = fn.float()
return fn
def get_diversity_DTW(Y_r, fn, args, cfg,seq_l=None):
bs = args.bs
traj_dim = Y_r.shape[-1]
# convert to cpu tensor
Y_r = Y_r#.cpu()
# diversity after DTW
fn_mask_inv = torch.cumsum(fn, dim=0)
fn_mask_inv[fn == 1] = 0
# pad
fn_mask_inv = torch.cat([fn_mask_inv, torch.ones_like(fn_mask_inv[:1])], dim=0)
fn_mask_inv = fn_mask_inv[:, :, None].repeat([1, 1, Y_r.shape[-1]])
yr_tmp = Y_r.clone()
# pad
yr_tmp = torch.cat([yr_tmp, yr_tmp[-1:]], dim=0)
yr_tmp[fn_mask_inv == 1] = 1e10
yr = yr_tmp[cfg.t_his:].reshape([-1, bs, cfg.vae_specs['n_action'], args.nk, traj_dim]). \
permute(1, 2, 3, 0, 4).reshape([bs * cfg.vae_specs['n_action'], args.nk, -1, traj_dim])
# seq_len = tmp2.max(dim=0, keepdim=True)[1][0].reshape([-1, bs*cfg.vae_specs['n_action']]).\
# permute(1,0).cpu().data.numpy()-cfg.t_his
if seq_l is None:
seq_l = torch.where(fn[cfg.t_his:].transpose(0, 1) == 1)[1].cpu().data.numpy().reshape([-1, args.nk]) + 1
seq1 = []
seq2 = []
seq_l_1 = np.array([])
seq_l_2 = np.array([])
for ii in range(args.nk):
for jj in range(ii + 1, args.nk):
seq_l_1 = np.append(seq_l_1, seq_l[:, ii])
seq_l_2 = np.append(seq_l_2, seq_l[:, jj])
# ml_j = seq_l[:,jj].max()+1
seq1.append(yr[:, ii])
seq2.append(yr[:, jj])
seq1 = torch.cat(seq1, dim=0)#.data.numpy()
seq2 = torch.cat(seq2, dim=0)#.data.numpy()
cost, sl = batch_dtw_torch_parallel(seq1, seq2, seq_l_1, seq_l_2)
# cost, sl = batch_dtw_cpu_parallel(seq1, seq2, seq_l_1, seq_l_2)
return cost, sl
def val(epoch):
t_s = time.time()
train_losses = 0
total_num_sample = 0
loss_names = ['TOTAL', 'MSE', 'MSE_v', 'KLD']
feat = []
accuracy = 0
seq_len = []
smooth_dist = 0
smooth_dist_gt = 0
smooth_dist_rot = 0
smooth_dist_gt_rot = 0
accele_est = 0
accele_gt = 0
diversity = 0
diversity_perframe = np.zeros(dataset.max_len-cfg.t_his)
diversity_perframe_rot = np.zeros(dataset.max_len-cfg.t_his)
diversity_dtw = 0
diversity_dtw_rot = 0
if cfg.vae_specs['model_name'] == 'trans_vae_v3_3' or cfg.vae_specs['model_name'] == 'v3_6_1':
with_stop_sign = True
else:
with_stop_sign = False
with torch.no_grad():
for act in dataset.act_name:
st = time.time()
generator = dataset.sampling_generator(num_samples=args.num_samp, batch_size=args.bs,t_pre_extra=args.t_pre_extra,
act=act)
# traj_est = []
# label_est = []
# fn_est = []
# traj_gt = []
# label_gt = []
# fn_gt = []
acc_peract = 0
total_peract = 0
for traj_np, label, fn_gt, fn_mask_gt in generator:
# label_gt.append(label)
# traj_gt.append(traj_np.transpose([1,0,2]))
# fn_gt.append(fn.transpose([1,0]))
traj_tmp = tensor(traj_np, device=device, dtype=dtype).permute(1, 0, 2).contiguous()
seq_n, bs, dim = traj_tmp.shape
traj = traj_tmp[:, :, None, None, :].repeat([1, 1, cfg.vae_specs['n_action'], args.nk, 1]) \
.reshape([seq_n, -1, dim])
label = torch.eye(cfg.vae_specs['n_action'], device=device, dtype=dtype)
label = label[None, :, None, :].repeat([bs, 1, args.nk, 1]).reshape([-1, cfg.vae_specs['n_action']])
X = traj[:t_his]
if cfg.dataset == 'babel':
index_used = list(range(30)) + list(range(36, 66))
X = X[:, :, index_used]
# Y = traj[t_his:]
if with_stop_sign:
Y_r,ss = model.sample_prior(X, label)
else:
Y_r = model.sample_prior(X, label)
if 'is_6d' in cfg.vae_specs and cfg.vae_specs['is_6d']:
from utils.utils import compute_rotation_matrix_from_ortho6d
yr_proj = compute_rotation_matrix_from_ortho6d(Y_r.reshape([-1, 6]))
yr_proj = yr_proj[:, :, :2].transpose(1, 2).reshape([-1, 6]).reshape(Y_r.shape)
Y_r = yr_proj.clone()
smooth_dist += torch.sum(torch.norm(Y_r[0] - X[-1], dim=1)).item()
smooth_dist_gt += torch.sum(torch.norm(traj[:-1] - traj[1:], dim=2)).item() / (traj.shape[0] - 1)
Y_r = torch.cat([X, Y_r], dim=0)
if with_stop_sign:
fn = get_stop_sign_from_ss(ss)
else:
fn = get_stop_sign(Y_r,args)
seq_l = torch.where(fn[cfg.t_his:].transpose(0, 1) == 1)[1].cpu().data.numpy()+1
seq_len.append(seq_l)
seq_l = seq_l.reshape([-1, args.nk])
"""
get perceptual feature
"""
lest, h, hx = model_classifier(Y_r, fn.transpose(0, 1), is_feat=True)
# lgt = torch.where(label==1)[1]
lest = lest == torch.max(lest, dim=1, keepdim=True)[0]
accuracy += torch.sum(label * lest).item()
total_num_sample += label.shape[0]
acc_peract += torch.sum(label * lest).item()
total_peract += label.shape[0]
feat.append(hx.cpu().data.numpy())
"""
get diversity
"""
yr = Y_r[cfg.t_his:cfg.vae_specs['max_len']].reshape([-1, bs, cfg.vae_specs['n_action'], args.nk, 60]). \
reshape([-1, args.nk, 60])
mask = torch.tril(torch.ones([args.nk, args.nk], device=device)) == 0
div_tmp = torch.cdist(yr, yr, p=2)[:, mask].mean(dim=-1)\
.reshape([-1, bs, cfg.vae_specs['n_action']]).mean(dim=(1,2)).cpu().data.numpy()
diversity_perframe += div_tmp * label.shape[0]
# maxlen
# maxlen = seq_len[-1].max()
# st1 = time.time()
cost,sl = get_diversity_DTW(Y_r, fn, args, cfg,seq_l=seq_l)
# print(f"{time.time()-st1:.3f}")
diversity_dtw += (cost/sl).mean() * label.shape[0]
print(f">>>> action {act} time used {time.time()-st:.3f}")
logger.info(f">>>> action {act} accuracy {acc_peract/total_peract:.3f}")
smooth_dist_gt = smooth_dist_gt / total_num_sample
smooth_dist = smooth_dist / total_num_sample
smooth_dist_gt_rot = smooth_dist_gt_rot / total_num_sample
smooth_dist_rot = smooth_dist_rot / total_num_sample
diversity_perframe = diversity_perframe / total_num_sample
diversity_dtw = diversity_dtw / total_num_sample
diversity_perframe_rot = diversity_perframe_rot / total_num_sample
diversity_dtw_rot = diversity_dtw_rot / total_num_sample
accuracy = accuracy / total_num_sample
feat = np.concatenate(feat, axis=0)
mu2 = feat.mean(axis=0)
cov2 = np.matmul(feat.transpose(1, 0), feat) / feat.shape[0]
test_data = np.load(cfg_classifier.result_dir + f'/epo100_seed0_test.npz', allow_pickle=True)['data'].item()
mu1 = test_data['mu']
cov1 = test_data['cov']
fid = calculate_frechet_distance(mu1, cov1, mu2, cov2)
# logger.info(f' accuracy: {accuracy:.3f}, fid {fid:.3f}')
test_data = np.load(cfg_classifier.result_dir + f'/epo100_seed0_train.npz', allow_pickle=True)['data'].item()
mu1 = test_data['mu']
cov1 = test_data['cov']
fid2 = calculate_frechet_distance(mu1, cov1, mu2, cov2)
logger.info(
f'epo {args.iter} mode {args.mode} threshold {args.threshold} action classifier {args.cfg_classifier} accuracy: {accuracy:.3f}, test fid {fid:.3f}, train fid {fid2:.3f}, smoothness gt {smooth_dist_gt:.3f}, smoothness {smooth_dist:.3f}, div at 100frame {diversity:.3f}')
postfix = 'stopfn'
stats = {}
stats['accuracy'] = accuracy*100
stats['fid_train'] = fid2
stats['fid_test'] = fid
stats['div_dtw'] = diversity_dtw
stats['div_avg_per_frame'] = diversity_perframe.mean()
stats['lastframe_dist_est'] = smooth_dist
stats['frame_dist_gt'] = smooth_dist_gt
return stats
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', default='babel_rnn')
parser.add_argument('--cfg_classifier', default='babel_act_classifier_v2')
parser.add_argument('--mode', default='test')
parser.add_argument('--test', action='store_true', default=False)
parser.add_argument('--iter', type=int, default=480)
parser.add_argument('--nk', type=int, default=2)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--num_seed', type=int, default=1)
parser.add_argument('--gpu_index', type=int, default=1)
parser.add_argument('--threshold', type=float, default=0.010)
parser.add_argument('--stop_fn', type=int, default=5)
parser.add_argument('--bs', type=int, default=2)
parser.add_argument('--num_samp', type=int, default=10)
args = parser.parse_args()
"""setup"""
state = np.random.get_state()
np.random.seed(args.seed)
rand_seeds = np.random.choice(np.arange(100), replace=False, size=(args.num_seed))
np.random.set_state(state)
tmp = []
for seed in rand_seeds:
np.random.seed(seed)
torch.manual_seed(seed)
dtype = torch.float32
torch.set_default_dtype(dtype)
device = torch.device('cuda', index=args.gpu_index) if torch.cuda.is_available() else torch.device('cpu')
if torch.cuda.is_available():
torch.cuda.set_device(args.gpu_index)
cuda.select_device(args.gpu_index)
cfg = Config(args.cfg, test=args.test)
cfg_classifier = Config(args.cfg_classifier, test=args.test)
# tb_logger = SummaryWriter(cfg.tb_dir) if args.mode == 'train' else None
logger = create_logger(os.path.join(cfg.log_dir, 'log_eval.txt'))
"""parameter"""
mode = args.mode
nz = cfg.nz
t_his = cfg.t_his
t_pred = cfg.t_pred
if 't_pre_extra' in cfg.vae_specs:
args.t_pre_extra = cfg.vae_specs['t_pre_extra'] = 0
"""data"""
if cfg.dataset == 'grab':
dataset_cls = DatasetGrab
elif cfg.dataset == 'ntu':
dataset_cls = DatasetNTU
elif cfg.dataset == 'humanact12':
dataset_cls = DatasetACT12
elif cfg.dataset == 'babel':
dataset_cls = DatasetBabel
dataset = dataset_cls(args.mode, t_his, t_pred, actions='all', use_vel=cfg.use_vel,
acts=cfg.vae_specs['actions'] if 'actions' in cfg.vae_specs else None,
max_len=cfg.vae_specs['max_len'] if 'max_len' in cfg.vae_specs else None,
min_len=cfg.vae_specs['min_len'] if 'min_len' in cfg.vae_specs else None,
is_6d=cfg.vae_specs['is_6d'] if 'is_6d' in cfg.vae_specs else False,
data_file=cfg.vae_specs['data_file'] if 'data_file' in cfg.vae_specs else None)
logger.info(f'data sequences {dataset.data_len:d}.')
if cfg.normalize_data:
dataset.normalize_data()
"""model"""
model = get_action_vae_model(cfg, 60, max_len=dataset.max_len - cfg.t_his + cfg.vae_specs['t_pre_extra'])
print(">>> total params: {:.2f}M".format(sum(p.numel() for p in list(model.parameters())) / 1000000.0))
if args.iter > 0:
cp_path = cfg.vae_model_path % args.iter
print('loading model from checkpoint: %s' % cp_path)
model_cp = pickle.load(open(cp_path, "rb"))
model.load_state_dict(model_cp['model_dict'])
model.to(device)
model.eval()
"""action classifier model"""
# model_classifier = get_action_classifier(cfg_classifier, dataset.traj_dim, max_len=dataset.max_len)
model_classifier = get_action_classifier(cfg_classifier, 60, max_len=dataset.max_len)
print(">>> total params: {:.2f}M".format(sum(p.numel() for p in list(model_classifier.parameters())) / 1000000.0))
cp_path = cfg_classifier.vae_model_path % 100
print('loading model from checkpoint: %s' % cp_path)
model_cp = pickle.load(open(cp_path, "rb"))
model_classifier.load_state_dict(model_cp['model_dict'])
model_classifier.to(device)
model_classifier.eval()
stats = val(args.iter)
tmp2 = []
for key in stats.keys():
tmp2.append(stats[key])
tmp.append(tmp2)
postfix = f'epo{args.iter}_{args.mode}_{args.cfg_classifier}_nsamp{args.num_samp}_stfn{args.stop_fn:d}_th{args.threshold:.3f}_nk{args.nk}'
whead = False
if not os.path.exists('%s/stats_multirun_%s_%s.csv' % (cfg.result_dir, postfix, args.seed)):
whead = True
with open('%s/stats_multirun_%s_%s.csv' % (cfg.result_dir, postfix, args.seed), 'a') as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=list(stats.keys()))
if whead:
writer.writeheader()
writer.writerow(stats)
tmp = np.array(tmp)
std = np.std(tmp,axis=0)
mean = np.mean(tmp,axis=0)
stat_tmp = {}
for i, key in enumerate(stats.keys()):
stat_tmp[key] = mean[i]
whead = False
if not os.path.exists('%s/stats_multirun_%s_%s.csv' % (cfg.result_dir, postfix, args.seed)):
whead = True
with open('%s/stats_multirun_%s_%s.csv' % (cfg.result_dir, postfix, args.seed), 'a') as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=list(stat_tmp.keys()))
if whead:
writer.writeheader()
writer.writerow(stat_tmp)
stat_tmp = {}
for i, key in enumerate(stats.keys()):
stat_tmp[key] = std[i]
whead = False
if not os.path.exists('%s/stats_multirun_%s_%s.csv' % (cfg.result_dir, postfix, args.seed)):
whead = True
with open('%s/stats_multirun_%s_%s.csv' % (cfg.result_dir, postfix, args.seed), 'a') as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=list(stat_tmp.keys()))
if whead:
writer.writeheader()
writer.writerow(stat_tmp)