sleap.nn.architectures.leap

This module provides a generalized implementation of the LEAP CNN.

See the LeapCNN class docstring for more information.

class sleap.nn.architectures.leap.LeapCNN(stacks: int = 1, filters: int = 64, filters_rate: float = 2, down_blocks: int = 3, down_convs_per_block: int = 3, up_blocks: int = 3, up_interpolate: bool = False, up_convs_per_block: int = 2)

LEAP CNN from “Fast animal pose estimation using deep neural networks” (2019).

This is a simple encoder-decoder style architecture without skip connections.

This implementation is generalized from original paper (Pereira et al., 2019) and code.

Using the defaults will create a network with ~10.8M parameters.

filters

Base number of filters in the first encoder block. More filters will increase the representational capacity of the network at the cost of memory and runtime.

Type:

int

filters_rate

Factor to increase the number of filters by in each block.

Type:

float

down_blocks

Number of blocks with pooling in the encoder. More down blocks will increase the effective maximum receptive field, but may incur loss of spatial precision.

Type:

int

down_convs_per_block

Number of convolutions in each encoder block. More convolutions per block will increase the representational capacity of the network at the cost of memory and runtime.

Type:

int

up_blocks

Number of blocks with upsampling in the decoder. If this is equal to down_blocks, the output of this network will be at the same stride (scale) as the input.

Type:

int

up_interpolate

If True, use bilinear interpolation instead of transposed convolutions for upsampling. Interpolation is faster but transposed convolutions may be able to learn richer or more complex upsampling to recover details from higher scales. If using transposed convolutions, the number of filters are determined by filters and filters_rate to progressively decrease the number of filters at each step.

Type:

bool

up_convs_per_block

Number of convolution layers after each upsampling operation. These will use the filters and filters_rate to progressively decrease the number of filters at each step.

Type:

int

property decoder_stack: List[SimpleUpsamplingBlock]

Return the decoder block configuration.

property encoder_stack: List[SimpleConvBlock]

Return the encoder block configuration.

classmethod from_config(config: LEAPConfig) → LeapCNN

Create a model from a set of configuration parameters.

Parameters:

config – An LEAPConfig instance with the desired parameters.

Returns:

An instance of this class with the specified configuration.