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train.py
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train.py
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import numpy as np
import torch
import torch.optim
import os
from methods.backbone import model_dict
from data.datamgr import SetDataManager
from methods.matchingnet import MatchingNet
from methods.relationnet import RelationNet, RelationNetLRP
from methods.protonet import ProtoNet
from methods.gnnnet import GnnNet, GnnNetLRP
from methods.tpn import TPN
from options import parse_args
def train(base_loader, val_loader, model, start_epoch, stop_epoch, params):
optimizer = torch.optim.Adam(model.parameters())
max_acc = 0.
total_it = 0
for epoch in range(start_epoch, stop_epoch):
model.train()
total_it = model.train_loop(epoch, base_loader, optimizer, total_it)
model.eval()
with torch.no_grad():
acc = model.test_loop(val_loader)
if acc > max_acc:
print("Best model! save...")
max_acc = acc
outfile = os.path.join(params.checkpoint_dir, 'best_model.tar')
torch.save({'epoch':epoch, 'state':model.state_dict()}, outfile)
else:
print("GG! Best accuracy {:f}".format(max_acc))
if ((epoch+1) % params.save_freq == 0) or (epoch == stop_epoch-1):
outfile = os.path.join(params.checkpoint_dir, '{:d}.tar'.format(epoch))
torch.save({'epoch': epoch, 'state': model.state_dict()}, outfile)
return model
# --- main function ---
if __name__=='__main__':
# set numpy random seed
np.random.seed(10)
# parser argument
params = parse_args()
print('--- Training ---\n')
print(params)
# output and tensorboard dir
params.checkpoint_dir = '%s/checkpoints/%s'%(params.save_dir, params.name)
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
# dataloader
print('\n--- Prepare dataloader ---')
print('\ttrain with seen domain {}'.format(params.dataset))
print('\tval with seen domain {}'.format(params.testset))
base_file = os.path.join(params.data_dir, params.dataset, 'base.json')
val_file = os.path.join(params.data_dir, params.testset, 'val.json')
# model
image_size = 224
n_query = max(1, int(16*params.test_n_way/params.train_n_way))
base_datamgr = SetDataManager(image_size, n_query=n_query, n_way=params.train_n_way, n_support=params.n_shot)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
val_datamgr = SetDataManager(image_size, n_query=n_query, n_way=params.test_n_way, n_support=params.n_shot)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if params.method == 'MatchingNet':
model = MatchingNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'RelationNet':
model = RelationNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'RelationNetLRP':
model = RelationNetLRP(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'ProtoNet':
model = ProtoNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'GNN':
model = GnnNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'GNNLRP':
model = GnnNetLRP(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'TPN':
model = TPN(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
else:
print("Please specify the method!")
assert(False)
# load model
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
if params.resume_epoch > 0:
resume_file = os.path.join(params.checkpoint_dir, '{:d}.tar'.format(params.resume_epoch))
tmp = torch.load(resume_file)
start_epoch = tmp['epoch']+1
model.load_state_dict(tmp['state'])
print('\tResume the training weight at {} epoch.'.format(start_epoch))
else:
path = '%s/checkpoints/%s/399.tar' % (params.save_dir, params.resume_dir)
state = torch.load(path)['state']
model_params = model.state_dict()
pretrained_dict = {k: v for k, v in state.items() if k in model_params}
print(pretrained_dict.keys())
model_params.update(pretrained_dict)
model.load_state_dict(model_params)
# training
print('\n--- start the training ---')
model = train(base_loader, val_loader, model, start_epoch, stop_epoch, params)