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train_stage1.py
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train_stage1.py
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from __future__ import print_function, division
import logging
import numpy as np
import cv2
import os
from pathlib import Path
from tqdm import tqdm
from datetime import datetime
from lib.human_loader import StereoHumanDataset
from lib.network import RtStereoHumanModel
from config.stereo_human_config import ConfigStereoHuman as config
from lib.train_recoder import Logger, file_backup
from lib.utils import get_novel_calib_for_show as get_novel_calib
from lib.TaichiRender import TaichiRenderBatch
import torch
import torch.optim as optim
from torch.cuda.amp import GradScaler
from torch.utils.data import DataLoader
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
class Trainer:
def __init__(self, cfg_file):
self.cfg = cfg_file
self.model = RtStereoHumanModel(self.cfg, with_gs_render=False)
self.train_set = StereoHumanDataset(self.cfg.dataset, phase='train')
self.train_loader = DataLoader(self.train_set, batch_size=self.cfg.batch_size, shuffle=True,
num_workers=self.cfg.batch_size*2, pin_memory=True)
self.train_iterator = iter(self.train_loader)
self.val_set = StereoHumanDataset(self.cfg.dataset, phase='val')
self.val_loader = DataLoader(self.val_set, batch_size=2, shuffle=False, num_workers=4, pin_memory=True)
self.len_val = int(len(self.val_loader) / self.val_set.val_boost) # real length of val set
self.val_iterator = iter(self.val_loader)
self.optimizer = optim.AdamW(self.model.parameters(), lr=self.cfg.lr, weight_decay=self.cfg.wdecay, eps=1e-8)
self.scheduler = optim.lr_scheduler.OneCycleLR(self.optimizer, self.cfg.lr, 100100, pct_start=0.01,
cycle_momentum=False, anneal_strategy='linear')
self.logger = Logger(self.scheduler, cfg.record)
self.total_steps = 0
self.model.cuda()
if self.cfg.restore_ckpt:
self.load_ckpt(self.cfg.restore_ckpt)
self.model.train()
self.model.raft_stereo.freeze_bn()
self.scaler = GradScaler(enabled=self.cfg.raft.mixed_precision)
self.render = TaichiRenderBatch(bs=1, res=self.cfg.dataset.src_res)
def train(self):
for _ in tqdm(range(self.total_steps, self.cfg.num_steps)):
self.optimizer.zero_grad()
data = self.fetch_data(phase='train')
# Raft Stereo
_, flow_loss, metrics = self.model(data)
loss = flow_loss
if self.total_steps and self.total_steps % self.cfg.record.loss_freq == 0:
self.logger.writer.add_scalar(f'lr', self.optimizer.param_groups[0]['lr'], self.total_steps)
self.save_ckpt(save_path=Path('%s/%s_latest.pth' % (cfg.record.ckpt_path, cfg.name)), show_log=False)
self.logger.push(metrics)
self.scaler.scale(loss).backward()
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
self.scaler.step(self.optimizer)
self.scheduler.step()
self.scaler.update()
if self.total_steps and self.total_steps % self.cfg.record.eval_freq == 0:
self.model.eval()
self.run_eval()
self.model.train()
self.model.raft_stereo.freeze_bn()
self.total_steps += 1
print("FINISHED TRAINING")
self.logger.close()
self.save_ckpt(save_path=Path('%s/%s_final.pth' % (cfg.record.ckpt_path, cfg.name)))
def run_eval(self):
logging.info(f"Doing validation ...")
torch.cuda.empty_cache()
epe_list, one_pix_list = [], []
show_idx = np.random.choice(list(range(self.len_val)), 1)
for idx in range(self.len_val):
data = self.fetch_data(phase='val')
with torch.no_grad():
data, _, _ = self.model(data, is_train=False)
if idx == show_idx:
data = get_novel_calib(data, ratio=0.5)
data = self.render.flow2render(data)
tmp_novel = data['novel_view']['img_pred'][0].detach()
tmp_novel = (tmp_novel / 2.0 + 0.5) * 255
tmp_novel = tmp_novel.permute(1, 2, 0).cpu().numpy()
tmp_img_name = '%s/%s.jpg' % (cfg.record.show_path, self.total_steps)
cv2.imwrite(tmp_img_name, tmp_novel[:, :, ::-1].astype(np.uint8))
for view in ['lmain', 'rmain']:
valid = (data[view]['valid'] >= 0.5)
epe = torch.sum((data[view]['flow'] - data[view]['flow_pred']) ** 2, dim=1).sqrt()
epe = epe.view(-1)[valid.view(-1)]
one_pix = (epe < 1)
epe_list.append(epe.mean().item())
one_pix_list.append(one_pix.float().mean().item())
val_epe = np.round(np.mean(np.array(epe_list)), 4)
val_one_pix = np.round(np.mean(np.array(one_pix_list)), 4)
logging.info(f"Validation Metrics ({self.total_steps}): epe {val_epe}, 1pix {val_one_pix}")
self.logger.write_dict({'val_epe': val_epe, 'val_1pix': val_one_pix}, write_step=self.total_steps)
torch.cuda.empty_cache()
def fetch_data(self, phase):
if phase == 'train':
try:
data = next(self.train_iterator)
except:
self.train_iterator = iter(self.train_loader)
data = next(self.train_iterator)
elif phase == 'val':
try:
data = next(self.val_iterator)
except:
self.val_iterator = iter(self.val_loader)
data = next(self.val_iterator)
for view in ['lmain', 'rmain']:
for item in data[view].keys():
data[view][item] = data[view][item].cuda()
return data
def load_ckpt(self, load_path, load_optimizer=True, strict=True):
assert os.path.exists(load_path)
logging.info(f"Loading checkpoint from {load_path} ...")
ckpt = torch.load(load_path, map_location='cuda')
self.model.load_state_dict(ckpt['network'], strict=strict)
logging.info(f"Parameter loading done")
if load_optimizer:
self.total_steps = ckpt['total_steps'] + 1
self.logger.total_steps = self.total_steps
self.optimizer.load_state_dict(ckpt['optimizer'])
self.scheduler.load_state_dict(ckpt['scheduler'])
logging.info(f"Optimizer loading done")
def save_ckpt(self, save_path, show_log=True):
if show_log:
logging.info(f"Save checkpoint to {save_path} ...")
torch.save({
'total_steps': self.total_steps,
'network': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict()
}, save_path)
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s')
cfg = config()
cfg.load("config/stage1.yaml")
cfg = cfg.get_cfg()
cfg.defrost()
dt = datetime.today()
cfg.exp_name = '%s_%s%s' % (cfg.name, str(dt.month).zfill(2), str(dt.day).zfill(2))
cfg.record.ckpt_path = "experiments/%s/ckpt" % cfg.exp_name
cfg.record.show_path = "experiments/%s/show" % cfg.exp_name
cfg.record.logs_path = "experiments/%s/logs" % cfg.exp_name
cfg.record.file_path = "experiments/%s/file" % cfg.exp_name
cfg.freeze()
for path in [cfg.record.ckpt_path, cfg.record.show_path, cfg.record.logs_path, cfg.record.file_path]:
Path(path).mkdir(exist_ok=True, parents=True)
file_backup(cfg.record.file_path, cfg, train_script=os.path.basename(__file__))
torch.manual_seed(1314)
np.random.seed(1314)
trainer = Trainer(cfg)
trainer.train()