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trainer.py
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trainer.py
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# -*- coding: utf-8 -*-
#
# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
# acting on behalf of its Max Planck Institute for Intelligent Systems and the
# Max Planck Institute for Biological Cybernetics. All rights reserved.
#
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights
# on this computer program. You can only use this computer program if you have closed a license agreement
# with MPG or you get the right to use the computer program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and liable to prosecution.
# Contact: ps-license@tuebingen.mpg.de
#
import os
import shutil
import sys
sys.path.append('.')
sys.path.append('..')
import json
import numpy as np
import torch
import mano
from datetime import datetime
from grabnet.tools.utils import makepath, makelogger, to_cpu
from grabnet.tools.train_tools import EarlyStopping
from grabnet.models.models import CoarseNet, RefineNet
from grabnet.data.dataloader import LoadData
from grabnet.tools.train_tools import point2point_signed
from torch import nn, optim
from torch.utils.data import DataLoader
from pytorch3d.structures import Meshes
from tensorboardX import SummaryWriter
class Trainer:
def __init__(self,cfg, inference=False):
self.dtype = torch.float32
torch.manual_seed(cfg.seed)
starttime = datetime.now().replace(microsecond=0)
makepath(cfg.work_dir, isfile=False)
logger = makelogger(makepath(os.path.join(cfg.work_dir, '%s.log' % (cfg.expr_ID)), isfile=True)).info
self.logger = logger
summary_logdir = os.path.join(cfg.work_dir, 'summaries')
self.swriter = SummaryWriter(log_dir=summary_logdir)
logger('[%s] - Started training GrabNet, experiment code %s' % (cfg.expr_ID, starttime))
logger('tensorboard --logdir=%s' % summary_logdir)
logger('Torch Version: %s\n' % torch.__version__)
logger('Base dataset_dir is %s' % cfg.dataset_dir)
# shutil.copy2(os.path.basename(sys.argv[0]), cfg.work_dir)
use_cuda = torch.cuda.is_available()
if use_cuda:
torch.cuda.empty_cache()
self.device = torch.device("cuda:%d" % cfg.cuda_id if torch.cuda.is_available() else "cpu")
gpu_brand = torch.cuda.get_device_name(cfg.cuda_id) if use_cuda else None
gpu_count = torch.cuda.device_count() if cfg.use_multigpu else 1
if use_cuda:
logger('Using %d CUDA cores [%s] for training!' % (gpu_count, gpu_brand))
self.data_info = {}
self.load_data(cfg, inference)
with torch.no_grad():
self.rhm_train = mano.load(model_path=cfg.rhm_path,
model_type='mano',
num_pca_comps=45,
batch_size=cfg.batch_size // gpu_count,
flat_hand_mean=True).to(self.device)
self.coarse_net = CoarseNet().to(self.device)
self.refine_net = RefineNet().to(self.device)
self.LossL1 = torch.nn.L1Loss(reduction='mean')
self.LossL2 = torch.nn.MSELoss(reduction='mean')
if cfg.use_multigpu:
self.coarse_net = nn.DataParallel(self.coarse_net)
self.refine_net = nn.DataParallel(self.refine_net)
logger("Training on Multiple GPU's")
vars_cnet = [var[1] for var in self.coarse_net.named_parameters()]
vars_rnet = [var[1] for var in self.refine_net.named_parameters()]
cnet_n_params = sum(p.numel() for p in vars_cnet if p.requires_grad)
rnet_n_params = sum(p.numel() for p in vars_rnet if p.requires_grad)
logger('Total Trainable Parameters for CoarseNet is %2.2f M.' % ((cnet_n_params) * 1e-6))
logger('Total Trainable Parameters for RefineNet is %2.2f M.' % ((rnet_n_params) * 1e-6))
self.optimizer_cnet = optim.Adam(vars_cnet, lr=cfg.base_lr, weight_decay=cfg.reg_coef)
self.optimizer_rnet = optim.Adam(vars_rnet, lr=cfg.base_lr, weight_decay=cfg.reg_coef)
self.best_loss_cnet = np.inf
self.best_loss_rnet = np.inf
self.try_num = cfg.try_num
self.epochs_completed = 0
self.cfg = cfg
self.coarse_net.cfg = cfg
if cfg.best_cnet is not None:
self._get_cnet_model().load_state_dict(torch.load(cfg.best_cnet, map_location=self.device), strict=False)
logger('Restored CoarseNet model from %s' % cfg.best_cnet)
if cfg.best_rnet is not None:
self._get_rnet_model().load_state_dict(torch.load(cfg.best_rnet, map_location=self.device), strict=False)
logger('Restored RefineNet model from %s' % cfg.best_rnet)
# weights for contact, penetration and distance losses
self.vpe = torch.from_numpy(np.load(cfg.vpe_path)).to(self.device).to(torch.long)
rh_f = torch.from_numpy(self.rhm_train.faces.astype(np.int32)).view(1, -1, 3)
self.rh_f = rh_f.repeat(self.cfg.batch_size,1,1).to(self.device).to(torch.long)
v_weights = torch.from_numpy(np.load(cfg.c_weights_path)).to(torch.float32).to(self.device)
v_weights2 = torch.pow(v_weights, 1.0 / 2.5)
self.refine_net.v_weights = v_weights
self.refine_net.v_weights2 = v_weights2
self.refine_net.rhm_train = self.rhm_train
self.v_weights = v_weights
self.v_weights2 = v_weights2
self.w_dist = torch.ones([self.cfg.batch_size,self.n_obj_verts]).to(self.device)
self.contact_v = v_weights > 0.8
def load_data(self,cfg, inference):
kwargs = {'num_workers': cfg.n_workers,
'batch_size':cfg.batch_size,
'shuffle':True,
'drop_last':True
}
ds_name = 'test'
self.data_info[ds_name] = {}
ds_test = LoadData(dataset_dir=cfg.dataset_dir, ds_name=ds_name)
self.data_info[ds_name]['frame_names'] = ds_test.frame_names
self.data_info[ds_name]['frame_sbjs'] = ds_test.frame_sbjs
self.ds_test = DataLoader(ds_test, batch_size=cfg.batch_size, shuffle=True, drop_last=True)
if not inference:
ds_name = 'train'
self.data_info[ds_name] = {}
ds_train = LoadData(dataset_dir=cfg.dataset_dir, ds_name=ds_name, load_on_ram=cfg.load_on_ram)
self.data_info[ds_name]['frame_names'] = ds_train.frame_names
self.data_info[ds_name]['frame_sbjs'] = ds_train.frame_sbjs
self.data_info['hand_vtmp'] = ds_train.sbj_vtemp
self.data_info['hand_betas'] = ds_train.sbj_betas
self.ds_train = DataLoader(ds_train, **kwargs)
ds_name = 'val'
self.data_info[ds_name] = {}
ds_val = LoadData(dataset_dir=cfg.dataset_dir, ds_name=ds_name, load_on_ram=cfg.load_on_ram)
self.data_info[ds_name]['frame_names'] = ds_val.frame_names
self.data_info[ds_name]['frame_sbjs'] = ds_val.frame_sbjs
self.ds_val = DataLoader(ds_val, **kwargs)
self.logger('Dataset Train, Vald, Test size respectively: %.2f M, %.2f K, %.2f K' %
(len(self.ds_train.dataset) * 1e-6, len(self.ds_val.dataset) * 1e-3, len(self.ds_test.dataset) * 1e-3))
self.bps = ds_test.bps
self.n_obj_verts = ds_test[0]['verts_object'].shape[0]
def edges_for(self, x, vpe):
return (x[:, vpe[:, 0]] - x[:, vpe[:, 1]])
def _get_cnet_model(self):
return self.coarse_net.module if isinstance(self.coarse_net, torch.nn.DataParallel) else self.coarse_net
def save_cnet(self):
torch.save(self.coarse_net.module.state_dict()
if isinstance(self.coarse_net, torch.nn.DataParallel)
else self.coarse_net.state_dict(), self.cfg.best_cnet)
def _get_rnet_model(self):
return self.refine_net.module if isinstance(self.refine_net, torch.nn.DataParallel) else self.refine_net
def save_rnet(self):
torch.save(self.refine_net.module.state_dict()
if isinstance(self.refine_net, torch.nn.DataParallel)
else self.refine_net.state_dict(), self.cfg.best_rnet)
def train(self):
self.coarse_net.train()
self.refine_net.train()
save_every_it = len(self.ds_train) / self.cfg.log_every_epoch
train_loss_dict_cnet = {}
train_loss_dict_rnet = {}
torch.autograd.set_detect_anomaly(True)
for it, dorig in enumerate(self.ds_train):
dorig = {k: dorig[k].to(self.device) for k in dorig.keys()}
self.optimizer_cnet.zero_grad()
self.optimizer_rnet.zero_grad()
if self.fit_cnet:
drec_cnet = self.coarse_net(**dorig)
loss_total_cnet, cur_loss_dict_cnet = self.loss_cnet(dorig, drec_cnet)
loss_total_cnet.backward()
self.optimizer_cnet.step()
train_loss_dict_cnet = {k: train_loss_dict_cnet.get(k, 0.0) + v.item() for k, v in cur_loss_dict_cnet.items()}
if it % (save_every_it + 1) == 0:
cur_train_loss_dict_cnet = {k: v / (it + 1) for k, v in train_loss_dict_cnet.items()}
train_msg = self.create_loss_message(cur_train_loss_dict_cnet,
expr_ID=self.cfg.expr_ID,
epoch_num=self.epochs_completed,
model_name='CoarseNet',
it=it,
try_num=self.try_num,
mode='train')
self.logger(train_msg)
if self.fit_rnet:
params_rnet = self.params_rnet(dorig)
dorig.update(params_rnet)
drec_rnet = self.refine_net(**dorig)
loss_total_rnet, cur_loss_dict_rnet = self.loss_rnet(dorig, drec_rnet)
loss_total_rnet.backward()
self.optimizer_rnet.step()
train_loss_dict_rnet = {k: train_loss_dict_rnet.get(k, 0.0) + v.item() for k, v in cur_loss_dict_rnet.items()}
if it % (save_every_it + 1) == 0:
cur_train_loss_dict_rnet = {k: v / (it + 1) for k, v in train_loss_dict_rnet.items()}
train_msg = self.create_loss_message(cur_train_loss_dict_rnet,
expr_ID=self.cfg.expr_ID,
epoch_num=self.epochs_completed,
model_name='RefineNet',
it=it,
try_num=self.try_num,
mode='train')
self.logger(train_msg)
train_loss_dict_cnet = {k: v / len(self.ds_train) for k, v in train_loss_dict_cnet.items()}
train_loss_dict_rnet = {k: v / len(self.ds_train) for k, v in train_loss_dict_rnet.items()}
return train_loss_dict_cnet, train_loss_dict_rnet
def evaluate(self, ds_name='val'):
self.coarse_net.eval()
self.refine_net.eval()
eval_loss_dict_cnet = {}
eval_loss_dict_rnet = {}
data = self.ds_val if ds_name == 'val' else self.ds_test
with torch.no_grad():
for dorig in data:
dorig = {k: dorig[k].to(self.device) for k in dorig.keys()}
if self.fit_cnet:
drec_cnet = self.coarse_net(**dorig)
loss_total_cnet, cur_loss_dict_cnet = self.loss_cnet(dorig, drec_cnet)
eval_loss_dict_cnet = {k: eval_loss_dict_cnet.get(k, 0.0) + v.item() for k, v in cur_loss_dict_cnet.items()}
if self.fit_rnet:
params_rnet = self.params_rnet(dorig)
dorig.update(params_rnet)
drec_rnet = self.refine_net(**dorig)
loss_total_rnet, cur_loss_dict_rnet = self.loss_rnet(dorig, drec_rnet)
eval_loss_dict_rnet = {k: eval_loss_dict_rnet.get(k, 0.0) + v.item() for k, v in cur_loss_dict_rnet.items()}
eval_loss_dict_cnet = {k: v / len(data) for k, v in eval_loss_dict_cnet.items()}
eval_loss_dict_rnet = {k: v / len(data) for k, v in eval_loss_dict_rnet.items()}
return eval_loss_dict_cnet, eval_loss_dict_rnet
def params_rnet(self,dorig):
rh_mesh = Meshes(verts=dorig['verts_rhand_f'], faces=self.rh_f).to(self.device).verts_normals_packed().view(-1, 778, 3)
rh_mesh_gt = Meshes(verts=dorig['verts_rhand'], faces=self.rh_f).to(self.device).verts_normals_packed().view(-1, 778, 3)
o2h_signed, h2o, _ = point2point_signed(dorig['verts_rhand_f'], dorig['verts_object'], rh_mesh)
o2h_signed_gt, h2o_gt, _ = point2point_signed(dorig['verts_rhand'], dorig['verts_object'], rh_mesh_gt)
h2o = h2o.abs()
h2o_gt = h2o_gt.abs()
return {'h2o_dist': h2o, 'h2o_gt': h2o_gt, 'o2h_gt': o2h_signed_gt}
def loss_rnet(self, dorig, drec, ds_name='train'):
out_put = self.rhm_train(**drec)
verts_rhand = out_put.vertices
rh_mesh = Meshes(verts=verts_rhand, faces=self.rh_f).to(self.device).verts_normals_packed().view(-1, 778, 3)
h2o_gt = dorig['h2o_gt']
o2h_signed, h2o, _ = point2point_signed(verts_rhand, dorig['verts_object'], rh_mesh)
######### dist loss
loss_dist_h = 35 * (1. - self.cfg.kl_coef) * torch.mean(torch.einsum('ij,j->ij', torch.abs(h2o.abs() - h2o_gt.abs()), self.v_weights2))
########## verts loss
loss_mesh_rec_w = 20 * (1. - self.cfg.kl_coef) * torch.mean(torch.einsum('ijk,j->ijk', torch.abs((dorig['verts_rhand'] - verts_rhand)), self.v_weights2))
########## edge loss
loss_edge = 10 * (1. - self.cfg.kl_coef) * self.LossL1(self.edges_for(verts_rhand, self.vpe), self.edges_for(dorig['verts_rhand'], self.vpe))
##########
loss_dict = {
'loss_edge_r': loss_edge,
'loss_mesh_rec_r': loss_mesh_rec_w,
'loss_dist_h_r': loss_dist_h,
}
loss_total = torch.stack(list(loss_dict.values())).sum()
loss_dict['loss_total'] = loss_total
return loss_total, loss_dict
def loss_cnet(self, dorig, drec, ds_name='train'):
device = dorig['verts_rhand'].device
dtype = dorig['verts_rhand'].dtype
q_z = torch.distributions.normal.Normal(drec['mean'], drec['std'])
out_put = self.rhm_train(**drec)
verts_rhand = out_put.vertices
rh_mesh = Meshes(verts=verts_rhand, faces=self.rh_f).to(self.device).verts_normals_packed().view(-1, 778, 3)
rh_mesh_gt = Meshes(verts=dorig['verts_rhand'], faces=self.rh_f).to(self.device).verts_normals_packed().view(-1, 778, 3)
o2h_signed, h2o, _ = point2point_signed(verts_rhand, dorig['verts_object'], rh_mesh)
o2h_signed_gt, h2o_gt, o2h_idx = point2point_signed(dorig['verts_rhand'], dorig['verts_object'], rh_mesh_gt)
# addaptive weight for penetration and contact verts
w_dist = (o2h_signed_gt < 0.01) * (o2h_signed_gt > -0.005)
w_dist_neg = o2h_signed < 0.
w = self.w_dist.clone()
w[~w_dist] = .1 # less weight for far away vertices
w[w_dist_neg] = 1.5 # more weight for penetration
######### dist loss
loss_dist_h = 35 * (1. - self.cfg.kl_coef) * torch.mean(torch.einsum('ij,j->ij', torch.abs(h2o.abs() - h2o_gt.abs()), self.v_weights2))
loss_dist_o = 30 * (1. - self.cfg.kl_coef) * torch.mean(torch.einsum('ij,ij->ij', torch.abs(o2h_signed - o2h_signed_gt), w))
########## verts loss
loss_mesh_rec_w = 35 * (1. - self.cfg.kl_coef) * torch.mean(torch.einsum('ijk,j->ijk', torch.abs((dorig['verts_rhand'] - verts_rhand)), self.v_weights))
########## edge loss
loss_edge = 30 * (1. - self.cfg.kl_coef) * self.LossL1(self.edges_for(verts_rhand, self.vpe), self.edges_for(dorig['verts_rhand'], self.vpe))
########## KL loss
p_z = torch.distributions.normal.Normal(
loc=torch.tensor(np.zeros([self.cfg.batch_size, self.cfg.latentD]), requires_grad=False).to(device).type(dtype),
scale=torch.tensor(np.ones([self.cfg.batch_size, self.cfg.latentD]), requires_grad=False).to(device).type(dtype))
loss_kl = self.cfg.kl_coef * torch.mean(torch.sum(torch.distributions.kl.kl_divergence(q_z, p_z), dim=[1]))
##########
loss_dict = {'loss_kl': loss_kl,
'loss_edge': loss_edge,
'loss_mesh_rec': loss_mesh_rec_w,
'loss_dist_h': loss_dist_h,
'loss_dist_o': loss_dist_o,
}
loss_total = torch.stack(list(loss_dict.values())).sum()
loss_dict['loss_total'] = loss_total
return loss_total, loss_dict
def fit(self, n_epochs=None, message=None):
starttime = datetime.now().replace(microsecond=0)
if n_epochs is None:
n_epochs = self.cfg.n_epochs
self.logger('Started Training at %s for %d epochs' % (datetime.strftime(starttime, '%Y-%m-%d_%H:%M:%S'), n_epochs))
if message is not None:
self.logger(message)
prev_lr_cnet = np.inf
prev_lr_rnet = np.inf
self.fit_cnet = True
self.fit_rnet = True
lr_scheduler_cnet = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer_cnet, 'min')
lr_scheduler_rnet = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer_rnet, 'min')
early_stopping_cnet = EarlyStopping(patience=8, trace_func=self.logger)
early_stopping_rnet = EarlyStopping(patience=8, trace_func=self.logger)
for epoch_num in range(1, n_epochs + 1):
self.logger('--- starting Epoch # %03d' % epoch_num)
train_loss_dict_cnet, train_loss_dict_rnet = self.train()
eval_loss_dict_cnet , eval_loss_dict_rnet = self.evaluate()
if self.fit_cnet:
lr_scheduler_cnet.step(eval_loss_dict_cnet['loss_total'])
cur_lr_cnet = self.optimizer_cnet.param_groups[0]['lr']
if cur_lr_cnet != prev_lr_cnet:
self.logger('--- CoarseNet learning rate changed from %.2e to %.2e ---' % (prev_lr_cnet, cur_lr_cnet))
prev_lr_cnet = cur_lr_cnet
with torch.no_grad():
eval_msg = Trainer.create_loss_message(eval_loss_dict_cnet, expr_ID=self.cfg.expr_ID,
epoch_num=self.epochs_completed, it=len(self.ds_val),
model_name='CoarseNet',
try_num=self.try_num, mode='evald')
if eval_loss_dict_cnet['loss_total'] < self.best_loss_cnet:
self.cfg.best_cnet = makepath(os.path.join(self.cfg.work_dir, 'snapshots', 'TR%02d_E%03d_cnet.pt' % (self.try_num, self.epochs_completed)), isfile=True)
self.save_cnet()
self.logger(eval_msg + ' ** ')
self.best_loss_cnet = eval_loss_dict_cnet['loss_total']
else:
self.logger(eval_msg)
self.swriter.add_scalars('total_loss_cnet/scalars',
{'train_loss_total': train_loss_dict_cnet['loss_total'],
'evald_loss_total': eval_loss_dict_cnet['loss_total'], },
self.epochs_completed)
if early_stopping_cnet(eval_loss_dict_cnet['loss_total']):
self.fit_cnet = False
self.logger('Early stopping CoarseNet training!')
if self.fit_rnet:
lr_scheduler_rnet.step(eval_loss_dict_rnet['loss_total'])
cur_lr_rnet = self.optimizer_rnet.param_groups[0]['lr']
if cur_lr_rnet != prev_lr_rnet:
self.logger('--- RefineNet learning rate changed from %.2e to %.2e ---' % (prev_lr_rnet, cur_lr_rnet))
prev_lr_rnet = cur_lr_rnet
with torch.no_grad():
eval_msg = Trainer.create_loss_message(eval_loss_dict_rnet, expr_ID=self.cfg.expr_ID,
epoch_num=self.epochs_completed, it=len(self.ds_val),
model_name='RefineNet',
try_num=self.try_num, mode='evald')
if eval_loss_dict_rnet['loss_total'] < self.best_loss_rnet:
self.cfg.best_rnet = makepath(os.path.join(self.cfg.work_dir, 'snapshots', 'TR%02d_E%03d_rnet.pt' % (self.try_num, self.epochs_completed)), isfile=True)
self.save_rnet()
self.logger(eval_msg + ' ** ')
self.best_loss_rnet = eval_loss_dict_rnet['loss_total']
else:
self.logger(eval_msg)
self.swriter.add_scalars('total_loss_rnet/scalars',
{'train_loss_total': train_loss_dict_rnet['loss_total'],
'evald_loss_total': eval_loss_dict_rnet['loss_total'], },
self.epochs_completed)
if early_stopping_rnet(eval_loss_dict_rnet['loss_total']):
self.fit_rnet = False
self.logger('Early stopping RefineNet training!')
self.epochs_completed += 1
if not self.fit_cnet and not self.fit_rnet:
self.logger('Stopping the training!')
break
endtime = datetime.now().replace(microsecond=0)
self.logger('Finished Training at %s\n' % (datetime.strftime(endtime, '%Y-%m-%d_%H:%M:%S')))
self.logger(
'Training done in %s!\n' % (endtime - starttime))
self.logger('Best CoarseNet val total loss achieved: %.2e\n' % (self.best_loss_cnet))
self.logger('Best CoarseNet model path: %s\n' % self.cfg.best_cnet)
self.logger(
'Best RefineNet val total loss achieved: %.2e\n' % (self.best_loss_rnet))
self.logger('Best RefineNet model path: %s\n' % self.cfg.best_rnet)
def eval(self):
self.coarse_net.eval()
self.refine_net.eval()
ds_name = self.cfg.dataset_dir.split('/')[-1]
total_error_cnet = {}
total_error_rnet = {}
for split, ds in [('val', self.ds_val), ('test', self.ds_test), ('train', self.ds_train)]:
mean_error_cnet = []
mean_error_rnet = []
with torch.no_grad():
for dorig in ds:
dorig = {k: dorig[k].to(self.device) for k in dorig.keys()}
MESH_SCALER = 1000
drec_cnet = self.coarse_net(**dorig)
verts_hand_cnet = self.rhm_train(**drec_cnet).vertices
mean_error_cnet.append(torch.mean(torch.abs(dorig['verts_rhand'] - verts_hand_cnet) * MESH_SCALER))
########## refine net
params_rnet = self.params_rnet(dorig)
dorig.update(params_rnet)
drec_rnet = self.refine_net(**dorig)
verts_hand_mano = self.rhm_train(**drec_rnet).vertices
mean_error_rnet.append(torch.mean(torch.abs(dorig['verts_rhand'] - verts_hand_mano) * MESH_SCALER))
total_error_cnet[split] = {'v2v_mae': float(to_cpu(torch.stack(mean_error_cnet).mean()))}
total_error_rnet[split] = {'v2v_mae': float(to_cpu(torch.stack(mean_error_rnet).mean()))}
outpath = makepath(os.path.join(self.cfg.work_dir, 'evaluations', 'ds_%s' %
ds_name, os.path.basename(self.cfg.best_cnet).
replace('.pt', '_CoarseNet.json')), isfile=True)
with open(outpath, 'w') as f:
json.dump(total_error_cnet, f)
with open(outpath.replace('.json', '_RefineNet.json'), 'w') as f:
json.dump(total_error_rnet, f)
return total_error_cnet, total_error_rnet
@staticmethod
def create_loss_message(loss_dict, expr_ID='XX', epoch_num=0,model_name='CoarseNet', it=0, try_num=0, mode='evald'):
ext_msg = ' | '.join(['%s = %.2e' % (k, v) for k, v in loss_dict.items() if k != 'loss_total'])
return '[%s]_TR%02d_E%03d - It %05d - %s - %s: [T:%.2e] - [%s]' % (
expr_ID, try_num, epoch_num, it,model_name, mode, loss_dict['loss_total'], ext_msg)