aug=False)
test_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
val_datamgr = SetDataManager(image_size,
n_query=15,
**test_few_shot_params)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if params.method == 'baseline++':
model = BaselineTrain(model_dict[params.model],
params.num_classes,
loss_type='dist')
elif params.method == 'manifold_mixup':
if params.model == 'WideResNet28_10':
model = wrn_mixup_model.wrn28_10(params.num_classes)
elif params.model == 'ResNet18':
model = res_mixup_model.resnet18(
num_classes=params.num_classes)
elif params.method == 'S2M2_R' or 'rotation':
if params.model == 'WideResNet28_10':
model = wrn_mixup_model.wrn28_10(
num_classes=params.num_classes,
dct_status=params.dct_status)
elif params.model == 'ResNet18':
model = res_mixup_model.resnet18(
num_classes=params.num_classes)
if params.method == 'baseline++':
if use_gpu:
image_size = 32
base_file = configs.data_dir[params.dataset] + 'base.json'
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
base_datamgr = SimpleDataManager(image_size, batch_size=params.batch_size)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
val_datamgr = SimpleDataManager(image_size,
batch_size=params.test_batch_size)
val_loader = base_datamgr.get_data_loader(base_file, aug=False)
if params.method == 'manifold_mixup':
model = wrn_mixup_model.wrn28_10(64)
elif params.method == 'S2M2_R':
model = wrn_mixup_model.wrn28_10(200)
elif params.method == 'rotation':
model = BaselineTrain(model_dict[params.model], 64, loss_type='dist')
if params.method == 'S2M2_R':
if use_gpu:
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
model.cuda()
if params.resume:
if __name__ == '__main__':
params = parse_args('test')
loadfile = configs.data_dir[params.dataset] + 'novel.json'
print('loading data file from: ', loadfile)
datamgr = SimpleDataManager(84, batch_size=256)
novel_loader = datamgr.get_data_loader(loadfile, aug=False)
checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
modelfile = get_resume_file(checkpoint_dir)
print('loading model file from: ', checkpoint_dir)
if params.model == 'WideResNet28_10':
model = wrn_mixup_model.wrn28_10(num_classes=params.num_classes)
elif params.model == 'ResNet18':
model = res_mixup_model.resnet18(num_classes=params.num_classes)
model = model.cuda()
cudnn.benchmark = True
checkpoint = torch.load(modelfile)
state = checkpoint['state']
state_keys = list(state.keys())
callwrap = False
if 'module' in state_keys[0]:
callwrap = True
if callwrap:
model = WrappedModel(model)
modelfile = get_resume_file(checkpoint_dir)
if params.save_iter != -1:
outfile = os.path.join(
checkpoint_dir.replace("checkpoints", "features"),
split + "_" + str(params.save_iter) + ".hdf5")
else:
outfile = os.path.join(
checkpoint_dir.replace("checkpoints", "features"), split + ".hdf5")
datamgr = SimpleDataManager(image_size, batch_size=3)
data_loader = datamgr.get_data_loader(loadfile, aug=False)
if params.method == 'manifold_mixup':
if params.dataset == 'cifar':
model = wrn_mixup_model.wrn28_10(64)
else:
model = wrn_mixup_model.wrn28_10(200)
elif params.method == 'S2M2_R':
if params.dataset == 'cifar':
model = wrn_mixup_model.wrn28_10(64, loss_type='softmax')
else:
model = wrn_mixup_model.wrn28_10(200)
else:
model = model_dict[params.model]()
print(checkpoint_dir, modelfile)
if params.method == 'manifold_mixup' or params.method == 'S2M2_R':
if modelfile is not None:
if torch.cuda.is_available():
image_size = 32
base_file = configs.data_dir[params.dataset] + 'base.json'
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
base_datamgr = SimpleDataManager(image_size, batch_size=params.batch_size)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
val_datamgr = SimpleDataManager(image_size,
batch_size=params.test_batch_size)
val_loader = base_datamgr.get_data_loader(base_file, aug=False)
if params.method == 'manifold_mixup':
model = wrn_mixup_model.wrn28_10(64, 0.9)
elif params.method == 'S2M2_R':
model = wrn_mixup_model.wrn28_10(64, 0.9)
elif params.method == 'rotation':
model = BaselineTrain(model_dict[params.model],
64,
dropRate=0.9,
loss_type='dist')
if params.method == 'S2M2_R':
if use_gpu:
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
if params.save_iter != -1:
outfile = os.path.join( checkpoint_dir_plain.replace("checkpoints","features"), split + "_" + str(params.save_iter)+ "_both.hdf5")
else:
outfile = os.path.join( checkpoint_dir_plain.replace("checkpoints","features"), split + "_both.hdf5")
datamgr = SimpleDataManager_both(image_size, batch_size = 1)
print("out file: ", outfile)
data_loader_dct = datamgr.get_data_loader_dct(loadfile, aug = False)
data_loader_plain = datamgr.get_data_loader(loadfile, aug = False)
if params.method == 'manifold_mixup':
if params.dataset == 'cifar':
model = wrn_mixup_model.wrn28_10(64)
else:
model = wrn_mixup_model.wrn28_10(200)
elif params.method == 'S2M2_R':
if params.dataset == 'cifar':
model_plain = wrn_mixup_model.wrn28_10(64 , dct_status = False)
model_dct = wrn_mixup_model.wrn28_10(64, dct_status = True)
else:
model_plain = wrn_mixup_model.wrn28_10(200)
model_dct = wrn_mixup_model.wrn28_10(200, dct_status = True)
else:
model_plain = model_dict[params.model]()
model_dct = model_dict[params.model + 'dct']()
# print(checkpoint_dir , modelfile)
if params.method == 'manifold_mixup' or params.method == 'S2M2_R' :
novel_loader_dct = datamgr.get_data_loader_dct(loadfile, aug=False)
novel_loader_plain = datamgr.get_data_loader(loadfile, aug=False)
checkpoint_dir_plain = '%s/checkpoints/%s/%s_%s_%sway_%sshot_aug' % (
configs.save_dir, params.dataset, params.model, params.method,
params.test_n_way, params.n_shot)
checkpoint_dir_dct = '%s/checkpoints/%s/%s_%s_%sway_%sshot_aug_dct' % (
configs.save_dir, params.dataset, params.model, params.method,
params.test_n_way, params.n_shot)
modelfile_plain = get_best_file(checkpoint_dir_plain)
modelfile_dct = get_best_file(checkpoint_dir_dct)
print(checkpoint_dir_plain, checkpoint_dir_dct)
if params.model == 'WideResNet28_10':
model_plain = wrn_mixup_model.wrn28_10(num_classes=params.num_classes,
dct_status=False)
model_dct = wrn_mixup_model.wrn28_10(num_classes=params.num_classes,
dct_status=True)
elif params.model == 'ResNet18':
model = res_mixup_model.resnet18(num_classes=params.num_classes)
model_plain = model_plain.cuda()
model_dct = model_dct.cuda()
cudnn.benchmark = True
checkpoint_plain = torch.load(modelfile_plain)
checkpoint_dct = torch.load(modelfile_dct)
state_plain = checkpoint_plain['state']
state_dct = checkpoint_dct['state']
state_keys_plain = list(state_plain.keys())
state_keys_dct = list(state_dct.keys())
image_size = 32
base_file = configs.data_dir[params.dataset] + 'base.json'
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
base_datamgr = SimpleDataManager(image_size, batch_size=params.batch_size)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
base_datamgr_test = SimpleDataManager(image_size,
batch_size=params.test_batch_size)
base_loader_test = base_datamgr_test.get_data_loader(base_file, aug=False)
if params.model == 'WideResNet28_10':
model = wrn_mixup_model.wrn28_10(num_classes=64)
elif params.model == 'ResNet18':
model = res_mixup_model.resnet18(num_classes=64)
if params.method == 'S2M2_R':
if use_gpu:
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
model.cuda()
if params.resume:
resume_file = get_resume_file(params.checkpoint_dir)
print("resume_file", resume_file)