RegNet#
eqxvision.models.RegNet
#
A simple port of torchvision.models.regnet
__init__(self, block_params: BlockParams, num_classes: int = 1000, stem_width: int = 32, stem_type: Optional[equinox.module.Module] = None, block_type: Optional[equinox.module.Module] = None, norm_layer: Optional[equinox.module.Module] = None, activation: Optional[Callable] = None, *, key: jax.random.PRNGKey = None)
#
Arguments:
block_params
: Configuration for the building blocks of the networknum_classes
: Number of classes in the classification task. Also controls the final output shape(num_classes,)
. Defaults to1000
stem_width
: Width of stems in the modelstem_type
: Block type for the stemsblock_type
: Type of block to be used in building the modelnorm_layer
: Normalisation to be applied on the inputs. Defaults toBatchNorm
activation
: Activation to be applied to the intermediate outputs. Defaults tojax.nn.relu
key
: -key
: Ajax.random.PRNGKey
used to provide randomness for parameter initialisation. (Keyword only argument.)
__call__(self, x: Array, *, key: jax.random.PRNGKey) -> Array
#
Arguments:
x
: The input. Should be a JAX array with3
channelskey
: Required parameter. Utilised by few layers such asDropout
orDropPath
eqxvision.models.regnet_x_400mf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_400MF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_x_800mf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_800MF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_x_8gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_8GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_x_16gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_16GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_x_32gf(torch_weights: bool = False, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_32GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_x_1_6gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_1.6GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_x_3_2gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetX_3.2GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_400mf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_400MF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_800mf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_800MF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_8gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_8GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_16gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_16GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_32gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_32GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_1_6gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_1.6GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.regnet_y_3_2gf(torch_weights: str = None, **kwargs: Any) -> RegNet
#
Constructs a RegNetY_3.2GF architecture from Designing Network Design Spaces.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone