def __init__(self, input_size, hidden_size, loss_type='mse'):
super(RelationModule, self).__init__()
self.loss_type = loss_type
padding = 1 if (input_size[1] < 10) and (
input_size[2] < 10
) else 0 # when using Resnet, conv map without avgpooling is 7x7, need padding in block to do pooling
self.layer1 = RelationConvBlock(input_size[0] * 2,
input_size[0],
padding=padding)
self.layer2 = RelationConvBlock(input_size[0],
input_size[0],
padding=padding)
shrink_s = lambda s: int((int(
(s - 2 + 2 * padding) / 2) - 2 + 2 * padding) / 2)
if self.maml:
self.fc1 = backbone.Linear_fw(
input_size[0] * shrink_s(input_size[1]) *
shrink_s(input_size[2]), hidden_size)
self.fc2 = backbone.Linear_fw(hidden_size, 1)
else:
self.fc1 = nn.Linear(
input_size[0] * shrink_s(input_size[1]) *
shrink_s(input_size[2]), hidden_size)
self.fc2 = nn.Linear(hidden_size, 1)
def __init__(self,
model_func,
n_way,
n_support,
tf_path=None,
approx=False,
dropout_method='none',
dropout_rate=0.,
dropout_schedule='constant'):
super(MAML, self).__init__(model_func,
n_way,
n_support,
tf_path=tf_path,
change_way=False)
self.loss_fn = nn.CrossEntropyLoss()
self.classifier = backbone.Linear_fw(self.feat_dim, n_way)
self.classifier.bias.data.fill_(0)
self.batch_size = 4
self.task_update_num = 5
self.train_lr = 0.01
self.approx = approx #first order approx.
self.dropout = DropGrad(dropout_method, dropout_rate, dropout_schedule)
self.optimizer = torch.optim.Adam(self.parameters())
def __init__(self, model_func, n_way, n_support, tf_path=None):
super(GnnNet, self).__init__(model_func,
n_way,
n_support,
tf_path=tf_path)
# loss function
self.loss_fn = nn.CrossEntropyLoss()
# metric function
self.fc = nn.Sequential(
nn.Linear(self.feat_dim, 128),
nn.BatchNorm1d(128, track_running_stats=False)) if not (
self.maml or self.maml_adain) else nn.Sequential(
backbone.Linear_fw(self.feat_dim, 128),
backbone.BatchNorm1d_fw(128, track_running_stats=False))
self.gnn = GNN_nl(128 + self.n_way, 96, self.n_way)
self.method = 'GnnNet'
# fix label for training the metric function 1*nw(1 + ns)*nw
support_label = torch.from_numpy(
np.repeat(range(self.n_way), self.n_support)).unsqueeze(1)
support_label = torch.zeros(
self.n_way * self.n_support,
self.n_way).scatter(1, support_label,
1).view(self.n_way, self.n_support, self.n_way)
support_label = torch.cat(
[support_label,
torch.zeros(self.n_way, 1, self.n_way)], dim=1)
self.support_label = support_label.view(1, -1, self.n_way)
def __init__(self, input_size, output_size):
super(ClassifierModule, self).__init__()
if self.maml:
self.classifier = backbone.Linear_fw(input_size, output_size)
self.classifier.bias.data.fill_(0)
else:
self.classifier = nn.Linear(input_size, output_size)
self.classifier.bias.data.fill_(0)
def __init__(self, model_func, n_way, n_support):
super(GnnNet, self).__init__(model_func, n_way, n_support)
# loss function
self.loss_fn = nn.CrossEntropyLoss()
# metric function
self.fc = nn.Sequential(nn.Linear(
self.feat_dim, 128), nn.BatchNorm1d(
128,
track_running_stats=False)) if not self.FWT else nn.Sequential(
backbone.Linear_fw(self.feat_dim, 128),
backbone.BatchNorm1d_fw(128, track_running_stats=False))
self.gnn = GNN_nl(128 + self.n_way, 96, self.n_way)
self.method = 'GnnNet'
