def main():
torch.manual_seed(args.seed)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
args.save_dir = osp.join(args.save_dir, str(args.nExemplars) + '-shot')
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print('Initializing image data manager')
dm = DataManager(args, use_gpu)
trainloader, testloader = dm.return_dataloaders()
model = Model(args)
criterion = CrossEntropyLoss()
optimizer = init_optimizer(args.optim, model.parameters(), args.lr,
args.weight_decay)
if use_gpu:
model = model.cuda()
start_time = time.time()
train_time = 0
best_acc = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.max_epoch):
learning_rate = adjust_learning_rate(optimizer, epoch, args.LUT_lr)
start_train_time = time.time()
train(epoch, model, criterion, optimizer, trainloader, learning_rate,
use_gpu)
train_time += round(time.time() - start_train_time)
if epoch == 0 or epoch > (args.stepsize[0] -
1) or (epoch + 1) % 10 == 0:
acc = test(model, testloader, use_gpu)
is_best = acc > best_acc
if is_best:
best_acc = acc
best_epoch = epoch + 1
save_checkpoint(
{
'state_dict': model.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".
format(best_acc, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
print("==========\nArgs:{}\n==========".format(args))
def main():
os.system('cp ./train_with_inpaint_read_from_data_fixed.py ' +args.save_dir + 'train_with_inpaint_read_from_data_fixed.py')
#exit()
loss_fn = losses.GenericLoss('batchsgm', 0.02, 64)
torch.manual_seed(args.seed)
#os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
#print(use_gpu)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
#torch.cuda.manual_seed(args.seed)
#torch.manual_seed(config.SEED)
#torch.cuda.manual_seed_all(config.SEED)
np.random.seed(args.seed)
random.seed(args.seed)
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = False
torch.cuda.manual_seed_all(args.seed)
torch.cuda.manual_seed(args.seed)
#exit(0)
else:
print("Currently using CPU (GPU is highly recommended)")
print('Initializing image data manager')
dm = DataManager(args, use_gpu)
trainloader, testloader = dm.return_dataloaders()
#model_edge = EdgeConnect(config)
#model_edge.load()
print('\nstart testing...\n')
#model_edge.test()
#print(args.scale_cls,args.num_classes)
#exit(0)
#GNN_model=create_models(args,512)
#print(args.use_similarity)
#exit(0)
if args.use_similarity:
#GNN_model=create_models(args,512)
#model = Model(args,GNN_model,scale_cls=args.scale_cls, num_classes=args.num_classes)
print('similarity remove')
exit()
else:
model = Model(scale_cls=args.scale_cls,only_CSEI=only_CSEI, num_classes=args.num_classes)
#model_tradclass = Model_tradi(scale_cls=args.scale_cls, num_classes=args.num_classes)
#params_tradclass = torch.load('result/%s/CAM/1-shot-seed112_classic_classifier_avg_nouse_CAN/%s' % (args.dataset, 'best_model.pth.tar'))
#model_tradclass.load_state_dict(params_tradclass['state_dict'])
#params = torch.load('result/%s/CAM/5-shot-seed112_inpaint_batchsgmregular_begin_70epoch/%s' % (args.dataset, 'best_model.pth.tar'))
#model.load_state_dict(params['state_dict'])
#print('enter model_tradclass')
#exit(0)
if not only_CSEI:
#params = torch.load('result/%s/CAM/5-shot-seed112_inpaint_batchsgmregular_begin_70epoch/%s' % (args.dataset, 'best_model.pth.tar'))
#params = torch.load('../fewshot-CAN-master/result/%s/CAM/1-shot-seed112_inpaint_use_CAM_argumenttest_nouse_similarity_read_from_data1/%s' % (args.dataset, 'best_model.pth.tar'))
params = torch.load('/home/yfchen/ljj_code/trained_model/mini/only_inpaint/1-shot/%s' % ('best_model.pth.tar'))
#params = torch.load("/home/yfchen/ljj_code/spatial_test/result/miniImageNet/CAM/1-shot_only_augment_test_fixed_GPU0_2333/best_model.pth.tar")
#params = torch.load('./result/%s/CAM/1-shot-seed112_inpaint_support_fuse_Cam_surport_from_65.3_spatial_tanh_8argument_traingloballabelargurement_contrast_normal/%s' % (args.dataset, 'best_model.pth.tar'))
#params = torch.load('result/%s/CAM/1-shot-seed112_inpaint_support_fuse_Cam_surport_from_65.3_debug/%s' % (args.dataset, 'best_model.pth.tar'))
#params = torch.load('result/%s/CAM/1-shot-seed112_inpaint_support_fuse_Cam_surport/%s' % (args.dataset, 'best_model.pth.tar'))
#params_tradclass = torch.load('result/%s/CAM/1-shot-seed112_classic_classifier_global_avg/%s' % (args.dataset, 'checkpoint_inpaint67.pth.tar'))
print(type(params))
model.load_state_dict(params['state_dict'], strict=False)
#model_tradclass.load_state_dict(params_tradclass['state_dict'])
criterion = CrossEntropyLoss()
optimizer = init_optimizer(args.optim, model.parameters(), args.lr, args.weight_decay)
if use_gpu:
model = model.cuda()
#model_tradclass = model_tradclass.cuda()
start_time = time.time()
train_time = 0
best_acc = -np.inf
best_epoch = 0
print("==> Start training")
for i in range(1):
#print(i+14)
acc = test_ori(model, testloader, use_gpu,28)
#acc_5 = test_ori_5(model, testloader, use_gpu)
#print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".format( acc, 0))
#exit()
#print(args.save_dir)
#exit()
best_path=args.save_dir+'best_model.pth.tar'
if only_CSEI:
args.max_epoch=70
for epoch in range(args.max_epoch):
if not args.Classic:
learning_rate = adjust_learning_rate(optimizer, epoch, args.LUT_lr)
else:
optimizer_tradclass = init_optimizer(args.optim, model_tradclass.parameters(), args.lr, args.weight_decay)
learning_rate = adjust_learning_rate(optimizer_tradclass, epoch, args.LUT_lr)
start_train_time = time.time()
#model.base.eval()
#exit(0)
#print(not True)
#exit(0)
if not only_test:
#print(';;;;;;;;;;;')
#exit(0)
if not args.Classic:
print('enter train code')
train(epoch, model, criterion,loss_fn, optimizer, trainloader, learning_rate, use_gpu)
#print('oooo')
else:
acc=train(epoch,model_edge, model_tradclass, criterion, optimizer_tradclass, trainloader, learning_rate, use_gpu)
train_time += round(time.time() - start_train_time)
if epoch == 0 or epoch > (args.stepsize[0]-1) or (epoch + 1) % 10 == 0:
print('enter test code')
#exit(0)
if not args.Classic:
#acc = test(model_edge, model, model_tradclass,weight_softmax, testloader, use_gpu)
acc = test_ori(model, testloader, use_gpu)
#acc_5 = test_ori_5(model, testloader, use_gpu)
is_best = acc > best_acc
#else:
#print(acc)
#exit(0)
if is_best:
best_acc = acc
best_epoch = epoch + 1
if not only_test:
if not args.Classic:
if save_best:
save_checkpoint({
'state_dict': model.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best, osp.join(args.save_dir, 'checkpoint_inpaint' + str(epoch + 1) + '.pth.tar'))
else:
print('not save')
if args.Classic:
save_checkpoint({
'state_dict': model_tradclass.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best, osp.join(args.save_dir, 'checkpoint_classic' + str(epoch + 1) + '.pth.tar'))
print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".format(best_acc, best_epoch))
acc_5 = test_ori_5(model,best_path, testloader, use_gpu)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
print("==========\nArgs:{}\n==========".format(args))
def main():
torch.manual_seed(args.seed)
#os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
config.DEVICE = torch.device("cuda")
torch.backends.cudnn.benchmark = True
#torch.manual_seed(config.SEED)
#torch.cuda.manual_seed_all(config.SEED)
np.random.seed(args.seed)
random.seed(args.seed)
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print('Initializing image data manager')
dm = DataManager(args, use_gpu)
trainloader, testloader = dm.return_dataloaders()
model_edge = EdgeConnect(config)
model_edge.load()
print('\nstart testing...\n')
#model_edge.test()
#print(args.scale_cls,args.num_classes)
#exit(0)
#GNN_model=create_models(args,512)
#print(args.use_similarity)
#exit(0)
if args.use_similarity:
GNN_model=create_models(args,512)
model = Model(args,GNN_model,scale_cls=args.scale_cls, num_classes=args.num_classes)
else:
model = Model_mltizhixin(scale_cls=args.scale_cls, num_classes=args.num_classes)
model_tradclass = Model_tradi(scale_cls=args.scale_cls, num_classes=args.num_classes)
params_tradclass = torch.load('result/%s/CAM/tired_images_ave_class/%s' % (args.dataset, 'best_model.pth.tar'))
model_tradclass.load_state_dict(params_tradclass['state_dict'])
#params = torch.load('result/%s/CAM/1-shot-seed112_inpaint_use_CAM/%s' % (args.dataset, 'checkpoint_inpaint67.pth.tar'))
#model.load_state_dict(params['state_dict'])
#print('enter model_tradclass')
#exit(0)
if False:
params = torch.load('result/%s/CAM/1-shot-seed112/%s' % (args.dataset, 'best_model.pth.tar'))
params_tradclass = torch.load('result/%s/CAM/1-shot-seed112_classic_classifier_global_avg/%s' % (args.dataset, 'checkpoint_inpaint67.pth.tar'))
print(type(params))
#exit(0)
#for key in params.keys():
#print(type(key))
#exit(0)
#model.load_state_dict(params['state_dict'])
model_tradclass.load_state_dict(params_tradclass['state_dict'])
#exit(0)
#for ind,i in model.state_dict().items():
#print (ind,i.shape)
#exit(0)
params = list(model_tradclass.parameters())
#fc_params=params[-2]
weight_softmax = np.squeeze(params[-2].data.numpy())
#print(weight_softmax.shape,type(params[-2]),params[-2].shape,params[-2].data.shape)
#exit(0)
criterion = CrossEntropyLoss()
optimizer = init_optimizer(args.optim, model.parameters(), args.lr, args.weight_decay)
#optimizer_tradclass = init_optimizer(args.optim, model_tradclass.parameters(), args.lr, args.weight_decay)
#model_tradclass
if use_gpu:
model = model.cuda()
model_tradclass = model_tradclass.cuda()
start_time = time.time()
train_time = 0
best_acc = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.max_epoch):
if not args.Classic:
learning_rate = adjust_learning_rate(optimizer, epoch, args.LUT_lr)
else:
optimizer_tradclass = init_optimizer(args.optim, model_tradclass.parameters(), args.lr, args.weight_decay)
learning_rate = adjust_learning_rate(optimizer_tradclass, epoch, args.LUT_lr)
#print('enter optimizer_tradclass')
#exit(0)
start_train_time = time.time()
#exit(0)
#print(not True)
#exit(0)
args.Classic=0
if not only_test:
#print(';;;;;;;;;;;')
#exit(0)
if not args.Classic:
print('enter train code')
train(epoch,model_edge, model, model_tradclass,weight_softmax, criterion, optimizer, trainloader, learning_rate, use_gpu)
#print('oooo')
else:
acc=train(epoch,model_edge, model_tradclass, criterion, optimizer_tradclass, trainloader, learning_rate, use_gpu)
train_time += round(time.time() - start_train_time)
if epoch == 0 or epoch > (args.stepsize[0]-1) or (epoch + 1) % 10 == 0:
print('enter test code')
#exit(0)
if not args.Classic:
#acc = test(model_edge, model, model_tradclass,weight_softmax, testloader, use_gpu)
acc = test_ori(model, testloader, use_gpu)
is_best = acc > best_acc
#else:
#print(acc)
#exit(0)
if is_best:
best_acc = acc
best_epoch = epoch + 1
if not only_test:
if not args.Classic:
save_checkpoint({
'state_dict': model.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best, osp.join(args.save_dir, 'checkpoint_inpaint' + str(epoch + 1) + '.pth.tar'))
if args.Classic:
save_checkpoint({
'state_dict': model_tradclass.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best, osp.join(args.save_dir, 'checkpoint_classic' + str(epoch + 1) + '.pth.tar'))
print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".format(best_acc, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
print("==========\nArgs:{}\n==========".format(args))
def main():
torch.manual_seed(args.seed)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print('Initializing image data manager')
dm = DataManager(args, use_gpu)
trainloader, testloader = dm.return_dataloaders()
#print(args.scale_cls,args.num_classes)
#exit(0)
GNN_model = create_models(args, 512)
if args.use_similarity:
model = Model(args,
GNN_model,
scale_cls=args.scale_cls,
num_classes=args.num_classes)
else:
model = Model(scale_cls=args.scale_cls, num_classes=args.num_classes)
if only_test:
params = torch.load('result/%s/CAM/1-shot-seed112/%s' %
(args.dataset, 'best_model.pth.tar'))
print(type(params))
#exit(0)
#for key in params.keys():
#print(type(key))
#exit(0)
model.load_state_dict(params['state_dict'])
#exit(0)
criterion = CrossEntropyLoss()
optimizer = init_optimizer(args.optim, model.parameters(), args.lr,
args.weight_decay)
if use_gpu:
model = model.cuda()
start_time = time.time()
train_time = 0
best_acc = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.max_epoch):
learning_rate = adjust_learning_rate(optimizer, epoch, args.LUT_lr)
start_train_time = time.time()
#exit(0)
#print(not True)
#exit(0)
if not only_test:
#print(';;;;;;;;;;;')
#exit(0)
train(epoch, model, criterion, optimizer, trainloader,
learning_rate, use_gpu)
train_time += round(time.time() - start_train_time)
if epoch == 0 or epoch > (args.stepsize[0] -
1) or (epoch + 1) % 10 == 0:
print('enter test code')
#exit(0)
acc = test(model, testloader, use_gpu)
is_best = acc > best_acc
#print(acc)
#exit(0)
if is_best:
best_acc = acc
best_epoch = epoch + 1
if not only_test:
save_checkpoint(
{
'state_dict': model.state_dict(),
'acc': acc,
'epoch': epoch,
}, is_best,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print("==> Test 5-way Best accuracy {:.2%}, achieved at epoch {}".
format(best_acc, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
print("==========\nArgs:{}\n==========".format(args))