def model():
model = MetaSequential(
MetaLinear(2, 3, bias=True),
nn.ReLU(),
MetaLinear(3, 1, bias=False))
return model
def __init__(self):
super(MIL, self).__init__()
h1, w1 = 200, 200
h2, w2 = calcHW(h1, w1, kernel_size=8, stride=2)
h3, w3 = calcHW(h2, w2, kernel_size=4, stride=2)
h4, w4 = calcHW(h3, w3, kernel_size=4, stride=2)
self.features = MetaSequential(
MetaConv2d(in_channels=3, out_channels=32, kernel_size=8,
stride=2), MetaLayerNorm([32, h2, w2]), nn.ReLU(),
MetaConv2d(in_channels=32,
out_channels=64,
kernel_size=4,
stride=2), MetaLayerNorm([64, h3, w3]), nn.ReLU(),
MetaConv2d(in_channels=64,
out_channels=64,
kernel_size=4,
stride=2), MetaLayerNorm([64, h4, w4]), nn.ReLU(),
SpatialSoftmax(h4, w4))
self.policy = MetaSequential(
MetaLinear(2 * 64 + 3, 128),
nn.ReLU(),
MetaLinear(128, 128),
nn.ReLU(),
MetaLinear(128, 128),
nn.ReLU(),
MetaLinear(128, 4),
)
def __init__(self,
out_features,
in_channels=1,
hidden_size=32,
mid_feats=256,
feature_size=25088):
super(MetaMNISTConvModel, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.hidden_size = hidden_size
self.feature_size = feature_size
self.mid_feats = mid_feats
self.features = MetaSequential(
OrderedDict([
('layer1',
conv_block(in_channels,
hidden_size,
kernel_size=3,
stride=1,
padding=1,
bias=True)),
('layer2',
conv_block(hidden_size,
hidden_size,
kernel_size=3,
stride=1,
padding=1,
bias=True)),
]))
self.classifier_first = MetaLinear(feature_size, mid_feats, bias=True)
self.classifier = MetaLinear(mid_feats, out_features, bias=True)
def __init__(self, l_obs, n_action, l1=64, l2=64):
super(MetaNet_PG, self).__init__()
self.l_obs = l_obs
self.n_action = n_action
self.l1 = l1
self.l2 = l2
self.actor_net = MetaSequential(
MetaLinear(self.l_obs, self.l1), nn.ReLU(),
MetaLinear(self.l1, self.l2), nn.ReLU(),
MetaLinear(self.l2, self.n_action), nn.Sigmoid(),
MetaLinear(self.n_action, self.n_action))
def __init__(self, in_channels, hidden1_size=40, hidden2_size=80):
super(RegressionNeuralNetwork, self).__init__()
self.in_channels = in_channels
self.hidden1_size = hidden1_size
self.hidden2_size = hidden2_size
self.regressor = MetaSequential(MetaLinear(in_channels, hidden1_size),
nn.ReLU(),
MetaLinear(hidden1_size, hidden2_size),
nn.ReLU(),
MetaLinear(hidden2_size, hidden1_size),
nn.ReLU(), MetaLinear(hidden1_size, 1))
def __init__(self, in_features, out_features, hidden_sizes):
super(MetaMLPModel, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.hidden_sizes = hidden_sizes
layer_sizes = [in_features] + hidden_sizes
self.features = MetaSequential(OrderedDict([('layer{0}'.format(i + 1),
MetaSequential(OrderedDict([
('linear', MetaLinear(hidden_size, layer_sizes[i + 1], bias=True)),
('relu', nn.ReLU())
]))) for (i, hidden_size) in enumerate(layer_sizes[:-1])]))
self.classifier = MetaLinear(hidden_sizes[-1], out_features, bias=True)
def test_ridge_regression_requires_grad(reg_lambda, use_woodbury, scale, bias):
# Numpy
num_classes = 3
embeddings_np = np.random.randn(5, 7).astype(np.float32)
targets_np = np.random.randint(0, num_classes, size=(5, ))
# PyTorch
embeddings_th = torch.as_tensor(embeddings_np).requires_grad_()
targets_th = torch.as_tensor(targets_np)
model = MetaLinear(7, 3, bias=bias)
solution = ridge_regression(embeddings_th,
targets_th,
reg_lambda,
num_classes,
use_woodbury=use_woodbury,
scale=scale,
bias=bias)
params = OrderedDict([('weight', solution.weight)])
if bias:
params['bias'] = solution.bias
# Compute loss on test/query samples
test_embeddings = torch.randn(11, 7)
test_logits = model(test_embeddings, params=params)
test_targets = torch.randint(num_classes, size=(11, ))
loss = F.cross_entropy(test_logits, test_targets)
# Backpropagation
loss.backward()
assert embeddings_th.grad is not None
def __init__(self,
in_dim,
out_dim,
num_layers=3,
hidden_size=64,
nonlinearity="relu"):
super(MultiLayerPerceptron, self).__init__()
self.in_dim = in_dim
self.hidden_size = hidden_size
self.out_dim = out_dim
if nonlinearity == "relu":
self.activation = nn.ReLU
elif nonlinearity == "swish":
self.activation = swish
elif nonlinearity == "sigmoid":
self.activation = nn.sigmoid
else:
raise ()
self.layer_list = [
nn.Flatten(),
nn.Linear(in_dim, hidden_size),
self.activation()
]
for _ in range(num_layers):
self.layer_list.extend(
[nn.Linear(hidden_size, hidden_size),
self.activation()])
# Should be able to add variable layers
self.features = MetaSequential(*self.layer_list)
self.classifier = MetaLinear(hidden_size, out_dim)
def __init__(self,
out_features,
in_channels=1,
hidden_size=64,
feature_size=64):
super(MetaToyConvModel, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.hidden_size = hidden_size
self.feature_size = feature_size
self.features = MetaSequential(
OrderedDict([
('layer1',
conv_block(in_channels,
hidden_size,
kernel_size=3,
stride=1,
padding=1,
bias=True)),
('layer2',
conv_block(hidden_size,
hidden_size,
kernel_size=3,
stride=1,
padding=1,
bias=True)),
# ('layer3', conv_block(hidden_size, hidden_size, kernel_size=3,
# stride=1, padding=1, bias=True)),
# ('layer4', conv_block(hidden_size, hidden_size, kernel_size=3,
# stride=1, padding=1, bias=True))
]))
self.classifier = MetaLinear(feature_size, out_features, bias=True)
def test_metasequential_params():
meta_model = MetaSequential(
nn.Linear(2, 3, bias=True),
nn.ReLU(),
MetaLinear(3, 5, bias=True))
model = nn.Sequential(
nn.Linear(2, 3, bias=True),
nn.ReLU(),
nn.Linear(3, 5, bias=True))
# Set same weights for both models (first layer)
weight0 = torch.randn(3, 2)
meta_model[0].weight.data.copy_(weight0)
model[0].weight.data.copy_(weight0)
bias0 = torch.randn(3)
meta_model[0].bias.data.copy_(bias0)
model[0].bias.data.copy_(bias0)
params = OrderedDict()
params['2.weight'] = torch.randn(5, 3)
model[2].weight.data.copy_(params['2.weight'])
params['2.bias'] = torch.randn(5)
model[2].bias.data.copy_(params['2.bias'])
inputs = torch.randn(5, 2)
outputs_torchmeta = meta_model(inputs, params=params)
outputs_nn = model(inputs)
np.testing.assert_equal(outputs_torchmeta.detach().numpy(),
outputs_nn.detach().numpy())
def __init__(self,
in_channels,
out_features,
hidden_size=64,
fc_in_size=None,
conv_kernel=[3, 3, 3, 3],
strides=None):
super(ConvolutionalNeuralNetwork, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.hidden_size = hidden_size
if strides is None:
strides = [None] * len(conv_kernel)
else:
assert (len(strides) == len(conv_kernel))
self.features = MetaSequential(
conv3x3(in_channels, hidden_size, conv_kernel[0], strides[0]), )
for k in range(1, len(conv_kernel)):
self.features.add_module(
'block_' + str(k),
conv3x3(hidden_size, hidden_size, conv_kernel[k], strides[k]))
if fc_in_size is None:
fc_in_size = hidden_size
self.classifier = MetaLinear(fc_in_size, out_features)
def __init__(self,
out_features=10,
input_size=100,
hidden_size1=300,
hidden_size2=200):
super(MLP, self).__init__()
self.out_features = out_features
self.features = MetaSequential(nn.Linear(input_size, hidden_size1),
nn.ReLU(),
nn.Linear(hidden_size1, hidden_size2),
nn.ReLU(),
nn.Linear(hidden_size2, hidden_size1),
nn.ReLU(),
nn.Linear(hidden_size1, hidden_size2),
nn.ReLU(),
nn.Linear(hidden_size2, hidden_size1),
nn.ReLU(),
nn.Linear(hidden_size1, hidden_size2),
nn.ReLU(),
nn.Linear(hidden_size2, hidden_size1),
nn.ReLU())
self.classifier = MetaLinear(hidden_size1, out_features)
def __init__(self, backbone, in_features, num_ways):
super(ConvolutionalNeuralNetwork, self).__init__()
self.in_features = in_features
self.num_ways = num_ways
self.encoder = get_meta_backbone(backbone)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = MetaLinear(in_features, num_ways) #1600
def __init__(self, nc, num_classes, block, num_blocks):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = MetaConv2d(nc, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = MetaBatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = MetaLinear(512 * block.expansion, num_classes)
def __init__(self, model_dict, dataset, device):
super().__init__()
self.dataset = dataset
self.model_dict = model_dict
if self.model_dict['name'] == 'resnet18':
if self.model_dict['pretrained']:
self.net = models.resnet18(pretrained=True)
self.net.fc = nn.Linear(512, self.dataset.n_classes)
else:
self.net = models.resnet18(num_classes=self.dataset.n_classes)
elif self.model_dict['name'] == 'resnet18_meta':
if self.model_dict.get('pretrained', True):
self.net = resnet_meta.resnet18(pretrained=True)
self.net.fc = MetaLinear(512, self.dataset.n_classes)
else:
self.net = resnet_meta.resnet18(
num_classes=self.dataset.n_classes)
elif self.model_dict['name'] == 'resnet18_meta_2':
self.net = resnet_meta_2.ResNet18(nc=3,
nclasses=self.dataset.n_classes)
elif self.model_dict['name'] == 'resnet18_meta_old':
self.net = resnet_meta_old.ResNet18(
nc=3, nclasses=self.dataset.n_classes)
else:
raise ValueError('network %s does not exist' % model_dict['name'])
if (device.type == 'cuda'):
self.net = DataParallel(self.net)
self.net.to(device)
# set optimizer
self.opt_dict = model_dict['opt']
self.lr_init = self.opt_dict['lr']
if self.model_dict['opt']['name'] == 'sps':
n_batches_per_epoch = 120
self.opt = sps.Sps(self.net.parameters(),
n_batches_per_epoch=n_batches_per_epoch,
c=0.5,
adapt_flag='smooth_iter',
eps=0,
eta_max=None)
else:
self.opt = optim.SGD(self.net.parameters(),
lr=self.opt_dict['lr'],
momentum=self.opt_dict['momentum'],
weight_decay=self.opt_dict['weight_decay'])
# variables
self.device = device
def __init__(self, in_channels, out_features, hidden_size=64):
super(ConvolutionalNeuralNetwork, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.hidden_size = hidden_size
self.features = MetaSequential(conv3x3(in_channels, hidden_size),
conv3x3(hidden_size, hidden_size),
conv3x3(hidden_size, hidden_size),
conv3x3(hidden_size, hidden_size))
self.classifier = MetaLinear(hidden_size, out_features)
def __init__(self,
blocks,
keep_prob=1.0,
avg_pool=False,
drop_rate=0.0,
dropblock_size=5,
out_features=5,
wh_size=None):
self.inplanes = 3
super(ResNet, self).__init__()
self.layer1 = self._make_layer(blocks[0],
64,
stride=2,
drop_rate=drop_rate,
drop_block=True,
block_size=dropblock_size)
self.layer2 = self._make_layer(blocks[1],
128,
stride=2,
drop_rate=drop_rate,
drop_block=True,
block_size=dropblock_size)
self.layer3 = self._make_layer(blocks[2],
256,
stride=2,
drop_rate=drop_rate,
drop_block=True,
block_size=dropblock_size)
self.layer4 = self._make_layer(blocks[3],
512,
stride=2,
drop_rate=drop_rate,
drop_block=True,
block_size=dropblock_size)
if avg_pool:
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.keep_prob = keep_prob
self.keep_avg_pool = avg_pool
self.dropout = nn.Dropout(p=1 - self.keep_prob, inplace=False)
self.drop_rate = drop_rate
self.classifier = MetaLinear(512 * wh_size * wh_size, out_features)
for m in self.modules():
if isinstance(m, MetaConv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='leaky_relu')
elif isinstance(m, MetaBatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def __init__(self, in_channels, out_features, hidden_size=64):
super(ConvolutionalNeuralNetwork, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.hidden_size = hidden_size
self.features = nn.Sequential(conv3x3(in_channels, hidden_size),
conv3x3(hidden_size, hidden_size),
conv3x3(hidden_size, hidden_size),
conv3x3(hidden_size, hidden_size))
# Only the last (linear) layer is used for adaptation in ANIL
self.classifier = MetaLinear(hidden_size, out_features)
def test_ridge_regression(reg_lambda, use_woodbury, scale, bias):
# Numpy
num_classes = 3
embeddings_np = np.random.randn(5, 7).astype(np.float32)
targets_np = np.random.randint(0, num_classes, size=(5, ))
# PyTorch
embeddings_th = torch.as_tensor(embeddings_np)
targets_th = torch.as_tensor(targets_np)
model = MetaLinear(7, 3, bias=bias)
solution = ridge_regression(embeddings_th,
targets_th,
reg_lambda,
num_classes,
use_woodbury=use_woodbury,
scale=scale,
bias=bias)
assert solution.weight.shape == (3, 7)
if bias:
assert solution.bias is not None
assert solution.bias.shape == (3, )
else:
assert solution.bias is None
# Optimality criterion
# Check if the gradient of the L2-regularized MSE at the solution
# is close to 0
params = OrderedDict([('weight', solution.weight.requires_grad_())])
if bias:
params['bias'] = solution.bias.requires_grad_()
logits = model(embeddings_th, params=params)
targets_binary = F.one_hot(targets_th, num_classes=num_classes).float()
# Least-square
loss = F.mse_loss(logits, targets_binary, reduction='sum')
if scale:
loss /= embeddings_th.size(0)
# L2-regularization
loss += reg_lambda * torch.sum(solution.weight**2)
if bias:
loss += reg_lambda * torch.sum(solution.bias**2)
loss.backward()
np.testing.assert_allclose(solution.weight.grad.numpy(), 0., atol=1e-4)
if bias:
np.testing.assert_allclose(solution.bias.grad.numpy(), 0., atol=1e-4)
def __init__(self,
channels,
init_block_channels,
bottleneck,
conv1_stride,
in_channels=3,
in_size=(224, 224),
num_classes=1000,
mode='',
linear=True):
super(ResNet, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.mode = mode
self.linear = linear
self.features = MetaSequential()
self.features.add_module(
"init_block",
ResInitBlock(in_channels=in_channels,
out_channels=init_block_channels,
mode=self.mode))
in_channels = init_block_channels
for i, channels_per_stage in enumerate(channels):
stage = MetaSequential()
for j, out_channels in enumerate(channels_per_stage):
stride = 2 if (j == 0) and (i != 0) else 1
stage.add_module(
"unit{}".format(j + 1),
ResUnit(in_channels=in_channels,
out_channels=out_channels,
stride=stride,
bottleneck=bottleneck,
conv1_stride=conv1_stride,
mode=self.mode))
in_channels = out_channels
self.features.add_module("stage{}".format(i + 1), stage)
self.features.add_module("final_pool", nn.AdaptiveAvgPool2d(1))
# self.features.add_module("final_pool", nn.AvgPool2d(kernel_size=7, stride=1))
if self.mode == 'maml':
self.output = MetaLinear(in_features=in_channels,
out_features=num_classes)
else:
self.output = nn.Linear(in_features=in_channels,
out_features=num_classes)
self._init_params()
def __init__(self, in_channels, input_dim, out_features, hidden_size=64):
super(ConvolutionalNeuralNetwork, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.features = MetaSequential(conv3x3(in_channels, hidden_size),
conv3x3(hidden_size, hidden_size),
conv3x3(hidden_size, hidden_size),
conv3x3(hidden_size, hidden_size))
i = 4
idim = input_dim
for x in range(i):
idim = math.floor(idim / 2)
self.linear_input = hidden_size * idim * idim
self.classifier = MetaLinear(self.linear_input, out_features)
def __init__(self,
in_channels,
out_features,
hidden_size=64,
feature_size=64,
embedding=False):
super(MetaConvModel, self).__init__()
self.in_channels = in_channels
self.out_features = out_features
self.hidden_size = hidden_size
self.feature_size = feature_size
self.embedding = embedding
self.features = MetaSequential(
OrderedDict([('layer1', conv_block(in_channels, hidden_size)),
('layer2', conv_block(hidden_size, hidden_size)),
('layer3', conv_block(hidden_size, hidden_size)),
('layer4', conv_block(hidden_size, hidden_size))]))
self.classifier = MetaLinear(feature_size, out_features)
def test_metasequential():
meta_model = MetaSequential(
nn.Linear(2, 3, bias=True),
nn.ReLU(),
MetaLinear(3, 5, bias=True))
model = nn.Sequential(
nn.Linear(2, 3, bias=True),
nn.ReLU(),
nn.Linear(3, 5, bias=True))
assert isinstance(meta_model, MetaModule)
assert isinstance(meta_model, nn.Sequential)
params = OrderedDict(meta_model.meta_named_parameters())
assert set(params.keys()) == set(['2.weight', '2.bias'])
# Set same weights for both models
weight0 = torch.randn(3, 2)
meta_model[0].weight.data.copy_(weight0)
model[0].weight.data.copy_(weight0)
bias0 = torch.randn(3)
meta_model[0].bias.data.copy_(bias0)
model[0].bias.data.copy_(bias0)
weight2 = torch.randn(5, 3)
meta_model[2].weight.data.copy_(weight2)
model[2].weight.data.copy_(weight2)
bias2 = torch.randn(5)
meta_model[2].bias.data.copy_(bias2)
model[2].bias.data.copy_(bias2)
inputs = torch.randn(5, 2)
outputs_torchmeta = meta_model(inputs, params=None)
outputs_nn = model(inputs)
np.testing.assert_equal(outputs_torchmeta.detach().numpy(),
outputs_nn.detach().numpy())
def __init__(self, in_dims, out_dims, hidden_size=100):
super(PolicyNetwork, self).__init__()
self.in_dims = in_dims
self.out_dims = out_dims * 2 # diag guassian distribution
self.hidden_size = hidden_size
fc1 = MetaLinear(in_dims, hidden_size)
fc2 = MetaLinear(hidden_size, hidden_size)
fc3 = MetaLinear(hidden_size, self.out_dims)
self.features = MetaSequential(
fc1,
nn.Tanh(),
fc2,
nn.Tanh(),
fc3,
nn.Tanh(),
)
self.activation = {}
def get_activation(name):
def hook(model, input, output):
self.activation[name] = output.detach()
return hook
fc1.register_forward_hook(get_activation('fc1'))
fc2.register_forward_hook(get_activation('fc2'))
fc3.register_forward_hook(get_activation('fc3'))
def model():
model = MetaLinear(3, 1, bias=False)
model.weight.data = torch.tensor([[2., 3., 5.]])
return model
def __init__(self, node_embedding_dim, hidden_dim, num_classes):
super(GraphClassificationOutputModule, self).__init__()
self.linear1 = MetaLinear(node_embedding_dim, hidden_dim)
self.linear2 = MetaLinear(hidden_dim, num_classes)
def __init__(self, node_embedding_dim):
super(LinkPredictionOutputModule, self).__init__()
self.linear_a = MetaLinear(node_embedding_dim, node_embedding_dim)
self.linear = MetaLinear(2 * node_embedding_dim, 1)
def __init__(self, node_embedding_dim, num_classes):
super(NodeClassificationOutputModule, self).__init__()
self.linear = MetaLinear(node_embedding_dim, num_classes)
def linear_model():
return MetaLinear(2, 1)
def __init__(self,
block,
layers,
num_classes=1000,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = MetaBatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(
replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = MetaConv2d(3,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False)
# self.conv1 = MetaConv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
# bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = MetaLinear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, MetaConv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, (MetaBatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
