dacapo.experiments.architectures.cnnectome_unet_config
Classes
This class configures the CNNectomeUNet based on |
Module Contents
- class dacapo.experiments.architectures.cnnectome_unet_config.CNNectomeUNetConfig
This class configures the CNNectomeUNet based on https://github.com/saalfeldlab/CNNectome/blob/master/CNNectome/networks/unet_class.py
Includes support for super resolution via the upsampling factors.
- input_shape
Coordinate The shape of the data passed into the network during training.
- fmaps_out
int The number of channels produced by your architecture.
- fmaps_in
int The number of channels expected from the raw data.
- num_fmaps
int The number of feature maps in the top level of the UNet.
- fmap_inc_factor
int The multiplication factor for the number of feature maps for each level of the UNet.
- downsample_factors
List[Coordinate] The factors to downsample the feature maps along each axis per layer.
- kernel_size_down
Optional[List[Coordinate]] The size of the convolutional kernels used before downsampling in each layer.
- kernel_size_up
Optional[List[Coordinate]] The size of the convolutional kernels used before upsampling in each layer.
- _eval_shape_increase
Optional[Coordinate] The amount by which to increase the input size when just prediction rather than training. It is generally possible to significantly increase the input size since we don’t have the memory constraints of the gradients, the optimizer and the batch size.
- upsample_factors
Optional[List[Coordinate]] The amount by which to upsample the output of the UNet.
- constant_upsample
bool Whether to use a transpose convolution or simply copy voxels to upsample.
- padding
str The padding to use in convolution operations.
- use_attention
bool Whether to use attention blocks in the UNet. This is supported for 2D and 3D.
- architecture_type()
Returns the architecture type.
Note
The architecture_type attribute is set to CNNectomeUNet.
References
Saalfeld, S., Fetter, R., Cardona, A., & Tomancak, P. (2012).
- architecture_type
- input_shape: funlib.geometry.Coordinate
- fmaps_out: int
- fmaps_in: int
- num_fmaps: int
- fmap_inc_factor: int
- downsample_factors: List[funlib.geometry.Coordinate]
- constant_upsample: bool
- padding: str
- use_attention: bool
- batch_norm: bool