def create(input_width, input_height, input_channels=1): """ Vel factory function """ def instantiate(**_): return DoubleNatureCnn(input_width=input_width, input_height=input_height, input_channels=input_channels) return ModelFactory.generic(instantiate)
def create(input_length, hidden_layers, activation='tanh', normalization=None): """ Vel factory function """ def instantiate(**_): return MLP(input_length=input_length, hidden_layers=hidden_layers, activation=activation, normalization=normalization) return ModelFactory.generic(instantiate)
def create(input_width, input_height, input_channels=1, output_dim=512): """ Vel factory function """ def instantiate(**_): return NatureCnn(input_width=input_width, input_height=input_height, input_channels=input_channels, output_dim=output_dim) return ModelFactory.generic(instantiate)
def create(blocks, mode='basic', inplanes=16, divisor=4, num_classes=1000): """ Vel factory function """ block_dict = { 'basic': BasicBlock, 'bottleneck': Bottleneck } def instantiate(**_): return ResNetV2(block_dict[mode], blocks, inplanes=inplanes, divisor=divisor, num_classes=num_classes) return ModelFactory.generic(instantiate)
def create(input_shape): """ Vel factory function """ if isinstance(input_shape, numbers.Number): input_shape = (input_shape, ) elif not isinstance(input_shape, tuple): input_shape = tuple(input_shape) def instantiate(**_): return NormalizeObservations(input_shape) return ModelFactory.generic(instantiate)
def create(alphabet_size: int, output_dim: int, pretrained: bool = False, frozen: bool = False, source: TextData = None): """ Vel factory function """ def instantiate(**_): return EmbeddingInput(alphabet_size, output_dim, pretrained=pretrained, frozen=frozen, source=source) return ModelFactory.generic(instantiate)
def create(input_block: ModelFactory, rnn_type: str, hidden_layers: typing.List[int], output_dim: int, dropout=0.0): """ Vel factory function """ def instantiate(**_): return MultilayerRnnSequenceModel(input_block.instantiate(), rnn_type=rnn_type, hidden_layers=hidden_layers, output_dim=output_dim, dropout=dropout) return ModelFactory.generic(instantiate)
def create(input_width, input_height, input_channels=1, output_dim=512, initial_std_dev=0.4, factorized_noise=True): """ Vel factory function """ def instantiate(**_): return NoisyNatureCnn(input_width=input_width, input_height=input_height, input_channels=input_channels, output_dim=output_dim, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise) return ModelFactory.generic(instantiate)
def create(input_width, input_height, input_channels=1, rnn_type='lstm', cnn_output_dim=512, hidden_units=128): """ Vel factory function """ def instantiate(**_): return NatureCnnRnnBackbone(input_width=input_width, input_height=input_height, input_channels=input_channels, rnn_type=rnn_type, cnn_output_dim=cnn_output_dim, hidden_units=hidden_units) return ModelFactory.generic(instantiate)
def create(blocks, mode='basic', inplanes=64, cardinality=4, image_features=64, divisor=4, num_classes=1000): """ Vel factory function """ block_dict = { # 'basic': BasicBlock, 'bottleneck': ResNeXtBottleneck } def instantiate(**_): return ResNeXt(block_dict[mode], blocks, inplanes=inplanes, image_features=image_features, cardinality=cardinality, divisor=divisor, num_classes=num_classes) return ModelFactory.generic(instantiate)
def create(input_block: ModelFactory, rnn_type: str, output_dim: int, rnn_layers: typing.List[int], rnn_dropout: float = 0.0, bidirectional: bool = False, linear_layers: typing.List[int] = None, linear_dropout: float = 0.0): """ Vel factory function """ if linear_layers is None: linear_layers = [] def instantiate(**_): return MultilayerRnnSequenceClassification( input_block=input_block.instantiate(), rnn_type=rnn_type, output_dim=output_dim, rnn_layers=rnn_layers, rnn_dropout=rnn_dropout, bidirectional=bidirectional, linear_layers=linear_layers, linear_dropout=linear_dropout) return ModelFactory.generic(instantiate)
def create(fc_layers=None, dropout=None, pretrained=True): """ Vel factory function """ def instantiate(**_): return Resnet34(fc_layers, dropout, pretrained) return ModelFactory.generic(instantiate)
def create(alphabet_size: int): """ Vel factory function """ def instantiate(**_): return OneHotEncodingInput(alphabet_size) return ModelFactory.generic(instantiate)
def create(img_rows, img_cols, img_channels, num_classes): """ Vel factory function """ def instantiate(**_): return Net(img_rows, img_cols, img_channels, num_classes) return ModelFactory.generic(instantiate)
def create(): """ Vel factory function """ return ModelFactory.generic(ImageToTensor)
def create(): """ Vel factory function """ def instantiate(**_): return Identity() return ModelFactory.generic(instantiate)