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intra_train.py
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intra_train.py
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# --------------------------------------------------------
# AdvEnt training
# Copyright (c) 2019 valeo.ai
#
# Written by Tuan-Hung Vu
# --------------------------------------------------------
import argparse
import os
import os.path as osp
import pprint
import random
import warnings
import numpy as np
import yaml
import torch
from torch.utils import data
from advent.model.deeplabv2 import get_deeplab_v2
from advent.model.psp import PSPNet
from advent.model.group_modules import AggregateFuse
from advent.dataset.gta5 import GTA5DataSet
from advent.dataset import voc
from advent.dataset.cityscapes import CityscapesDataSet
from advent.domain_adaptation.config import cfg, cfg_from_file
from advent.domain_adaptation.train_UDA import train_domain_adaptation
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore")
cfg_sup_gp = {
"data_dir": "/disk1/datasets/personal/id2",
"list_file": "easy_split.txt",
"batch_size": 8,
"crop_size": 320,
"shuffle": True,
"base_size": 400,
"scale": True,
"augment": True,
"flip": True,
"rotate": False,
"blur": False,
"split": "train_supervised",
"num_workers": 0
}
cfg_unsup = {
"data_dir": "/disk1/datasets/personal/id2",
"list_file": "hard_split.txt",
"weak_labels_output": "pseudo_labels/result/pseudo_labels",
"batch_size": 8,
"crop_size": 320,
"shuffle": True,
"base_size": 400,
"scale": True,
"augment": True,
"flip": True,
"rotate": False,
"blur": False,
"split": "train_unsupervised",
"num_workers": 0
}
cfg_val = {
"data_dir": "/disk1/datasets/personal/id2",
"list_file": "train.txt",
"batch_size": 8,
"val": True,
"split": "val",
"num_workers": 0,
"base_size": 320,
"crop_size": 320,
"shuffle": True,
"shuffle_seed": 1
}
def get_arguments():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description="Code for domain adaptation (DA) training")
parser.add_argument('--cfg', type=str, default=None,
help='optional config file', )
parser.add_argument("--random-train", action="store_true",
help="not fixing random seed.")
parser.add_argument("--tensorboard", action="store_true",
help="visualize training loss with tensorboardX.")
parser.add_argument("--viz-every-iter", type=int, default=None,
help="visualize results.")
parser.add_argument("--exp-suffix", type=str, default=None,
help="optional experiment suffix")
return parser.parse_args()
def main():
# LOAD ARGS
args = get_arguments()
print('Called with args:')
print(args)
assert args.cfg is not None, 'Missing cfg file'
cfg_from_file(args.cfg)
# auto-generate exp name if not specified
if cfg.EXP_NAME == '':
cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}'
if args.exp_suffix:
cfg.EXP_NAME += f'_{args.exp_suffix}'
# auto-generate snapshot path if not specified
if cfg.TRAIN.SNAPSHOT_DIR == '':
cfg.TRAIN.SNAPSHOT_DIR = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.SNAPSHOT_DIR, exist_ok=True)
# tensorboard
if args.tensorboard:
if cfg.TRAIN.TENSORBOARD_LOGDIR == '':
cfg.TRAIN.TENSORBOARD_LOGDIR = osp.join(cfg.EXP_ROOT_LOGS, 'tensorboard', cfg.EXP_NAME)
os.makedirs(cfg.TRAIN.TENSORBOARD_LOGDIR, exist_ok=True)
if args.viz_every_iter is not None:
cfg.TRAIN.TENSORBOARD_VIZRATE = args.viz_every_iter
else:
cfg.TRAIN.TENSORBOARD_LOGDIR = ''
print('Using config:')
pprint.pprint(cfg)
# INIT
_init_fn = None
if not args.random_train:
torch.manual_seed(cfg.TRAIN.RANDOM_SEED)
torch.cuda.manual_seed(cfg.TRAIN.RANDOM_SEED)
np.random.seed(cfg.TRAIN.RANDOM_SEED)
random.seed(cfg.TRAIN.RANDOM_SEED)
def _init_fn(worker_id):
np.random.seed(cfg.TRAIN.RANDOM_SEED + worker_id)
if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
return
# LOAD SEGMENTATION NET
group = None
assert osp.exists(cfg.TRAIN.RESTORE_FROM), f'Missing init model {cfg.TRAIN.RESTORE_FROM}'
if cfg.TRAIN.MODEL == 'DeepLabv2':
model = get_deeplab_v2(num_classes=cfg.NUM_CLASSES, multi_level=cfg.TRAIN.MULTI_LEVEL)
saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)
if 'DeepLab_resnet_pretrained_imagenet' in cfg.TRAIN.RESTORE_FROM:
new_params = model.state_dict().copy()
for i in saved_state_dict:
i_parts = i.split('.')
if not i_parts[1] == 'layer5':
new_params['.'.join(i_parts[1:])] = saved_state_dict[i]
model.load_state_dict(new_params)
else:
model.load_state_dict(saved_state_dict)
elif cfg.TRAIN.MODEL == 'psp':
model = PSPNet(pretrained=True)
# group = AggregateFuse(channel=512)
else:
raise NotImplementedError(f"Not yet supported {cfg.TRAIN.MODEL}")
print('Model loaded')
# source_loader = voc.VOC(cfg_sup)
source_loader = voc.GroupLoader(cfg_sup_gp)
target_loader = voc.GroupLoader(cfg_unsup)
val_loader = voc.GroupLoader(cfg_val)
with open(osp.join(cfg.TRAIN.SNAPSHOT_DIR, 'train_cfg.yml'), 'w') as yaml_file:
yaml.dump(cfg, yaml_file, default_flow_style=False)
# UDA TRAINING
group = True
train_domain_adaptation(model, source_loader, target_loader, val_loader, cfg, group)
if __name__ == '__main__':
main()