def __init__(self):
self.best_accuracy = 0.0
self.adjust_learning_rate = Config.adjust_learning_rate
# all data
self.data_train = MiniImageNetDataset.get_data_all(Config.data_root)
self.task_train = MiniImageNetDataset(self.data_train, Config.num_way, Config.num_shot)
self.task_train_loader = DataLoader(self.task_train, Config.batch_size, True, num_workers=Config.num_workers)
# model
self.matching_net = RunnerTool.to_cuda(Config.matching_net)
RunnerTool.to_cuda(self.matching_net.apply(RunnerTool.weights_init))
self.norm = Normalize(2)
# loss
self.loss = RunnerTool.to_cuda(nn.MSELoss())
# optim
self.matching_net_optim = torch.optim.SGD(
self.matching_net.parameters(), lr=Config.learning_rate, momentum=0.9, weight_decay=5e-4)
self.test_tool = TestTool(self.matching_test, data_root=Config.data_root,
num_way=Config.num_way_test, num_shot=Config.num_shot,
episode_size=Config.episode_size, test_episode=Config.test_episode,
transform=self.task_train.transform_test)
pass
def __init__(self, resnet, low_dim=512, modify_head=False):
super().__init__()
self.resnet = resnet(num_classes=low_dim)
self.l2norm = Normalize(2)
if modify_head:
self.resnet.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
pass
pass
def __init__(self):
self.adjust_learning_rate = Config.adjust_learning_rate
# all data
self.data_train = MiniImageNetDataset.get_data_all(Config.data_root)
self.task_train = MiniImageNetDataset(self.data_train, Config.num_way,
Config.num_shot)
self.task_train_loader = DataLoader(self.task_train,
Config.batch_size,
True,
num_workers=Config.num_workers)
# IC
self.produce_class = ProduceClass(len(self.data_train),
Config.ic_out_dim, Config.ic_ratio)
self.produce_class.init()
self.task_train.set_samples_class(self.produce_class.classes)
# model
self.matching_net = RunnerTool.to_cuda(Config.matching_net)
self.norm = Normalize(2)
if Config.multi_gpu:
self.matching_net = RunnerTool.to_cuda(
nn.DataParallel(self.matching_net))
cudnn.benchmark = True
pass
RunnerTool.to_cuda(self.matching_net.apply(RunnerTool.weights_init))
# optim
self.matching_net_optim = torch.optim.SGD(
self.matching_net.parameters(),
lr=Config.learning_rate,
momentum=0.9,
weight_decay=5e-4)
# loss
self.ic_loss = RunnerTool.to_cuda(nn.CrossEntropyLoss())
self.fsl_loss = RunnerTool.to_cuda(nn.MSELoss())
# Eval
self.test_tool_fsl = TestTool(self.matching_test,
data_root=Config.data_root,
num_way=Config.num_way,
num_shot=Config.num_shot,
episode_size=Config.episode_size,
test_episode=Config.test_episode,
transform=self.task_train.transform_test)
self.test_tool_ic = ICTestTool(feature_encoder=None,
ic_model=self.matching_net,
data_root=Config.data_root,
batch_size=Config.batch_size,
num_workers=Config.num_workers,
ic_out_dim=Config.ic_out_dim)
pass
def __init__(self):
self.best_accuracy = 0.0
# all data
self.data_train = MiniImageNetDataset.get_data_all(Config.data_root)
self.task_train = MiniImageNetDataset(self.data_train, Config.num_way,
Config.num_shot)
self.task_train_loader = DataLoader(self.task_train,
Config.batch_size,
True,
num_workers=Config.num_workers)
# model
self.matching_net = RunnerTool.to_cuda(Config.matching_net)
RunnerTool.to_cuda(self.matching_net.apply(RunnerTool.weights_init))
self.norm = Normalize(2)
self.has_norm = Config.has_norm
self.has_softmax = Config.has_softmax
# loss
self.loss = RunnerTool.to_cuda(nn.MSELoss())
# optim
self.matching_net_optim = torch.optim.Adam(
self.matching_net.parameters(), lr=Config.learning_rate)
self.matching_net_scheduler = StepLR(self.matching_net_optim,
Config.train_epoch // 3,
gamma=0.5)
self.test_tool = TestTool(self.matching_test,
data_root=Config.data_root,
num_way=Config.num_way,
num_shot=Config.num_shot,
episode_size=Config.episode_size,
test_episode=Config.test_episode,
transform=self.task_train.transform_test)
pass
def __init__(self, encoder, low_dim=512):
super().__init__()
self.encoder = encoder
self.fc = nn.Linear(self.encoder.out_dim, low_dim)
self.l2norm = Normalize(2)
pass