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add src only exp

master
wogong 5 years ago
parent
commit
fc297d9fe4
  1. 81
      experiments/synsigns_gtsrb_src_only.py

81
experiments/synsigns_gtsrb_src_only.py

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import os
import sys
import datetime
from tensorboardX import SummaryWriter
import torch
sys.path.append('../')
from models.model import GTSRBmodel
from core.train import train_dann
from utils.utils import get_data_loader, init_model, init_random_seed
class Config(object):
# params for path
model_name = "synsigns-gtsrb"
model_base = '/home/wogong/models/pytorch-dann'
note = 'src-only'
model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
os.makedirs(model_root)
config = os.path.join(model_root, 'config.txt')
finetune_flag = False
lr_adjust_flag = 'simple'
src_only_flag = True
# params for datasets and data loader
batch_size = 128
# params for source dataset
src_dataset = "synsigns"
src_image_root = os.path.join('/home/wogong/datasets', 'synsigns')
src_model_trained = True
src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
# params for target dataset
tgt_dataset = "gtsrb"
tgt_image_root = os.path.join('/home/wogong/datasets', 'gtsrb')
tgt_model_trained = True
dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
# params for training dann
gpu_id = '0'
## for digit
num_epochs = 200
log_step = 50
save_step = 100
eval_step = 5
manual_seed = None
alpha = 0
# params for optimizing models
lr = 0.01
momentum = 0.9
def __init__(self):
"""save config to model root"""
public_props = (name for name in dir(self) if not name.startswith('_'))
with open(self.config, 'w') as f:
for name in public_props:
f.write(name + ': ' + str(getattr(self, name)) + '\n')
params = Config()
logger = SummaryWriter(params.model_root)
device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
# init random seed
init_random_seed(params.manual_seed)
# load dataset
src_data_loader = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=True)
src_data_loader_eval = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=True)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=False)
# load dann model
dann = init_model(net=GTSRBmodel(), restore=None)
# train dann model
print("Training dann model")
if not (dann.restored and params.dann_restore):
dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger)
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