Skip to content

Latest commit

 

History

History
83 lines (67 loc) · 3.27 KB

README.md

File metadata and controls

83 lines (67 loc) · 3.27 KB
Raw

Models Definition module

The basic network (supernet) is defined by the unified framework with the structure info list. CnnNet is the overall backbone network, which is composed of differet downsample stages with structure info list. A stage is composed of several blocks, such as Resnet bottleneck block, MobileV2 Depthwise block. Basic block is composed of 2D convolutions.

SuperModel

  • cnnnet.py: Define the backbone network for CnnNet, such as classification and detection.

Typical Structure Info List for CnnNet

[{'class': 'ConvKXBNRELU', 'in': 3, 'out': 32, 's': 2, 'k': 3},
{'class': 'SuperResK1KXK1', 'in': 32, 'out': 256, 's': 2, 'k': 3, 'L': 1, 'btn': 64},
{'class': 'SuperResK1KXK1', 'in': 256, 'out': 512, 's': 2, 'k': 3, 'L': 1, 'btn': 128},
{'class': 'SuperResK1KXK1', 'in': 512, 'out': 1024, 's': 2, 'k': 3, 'L': 1, 'btn': 256},
{'class': 'SuperResK1KXK1', 'in': 1024, 'out': 2048, 's': 2, 'k': 3, 'L': 1, 'btn': 512}, ]

Supported Blocks

Supported 2D CNN blocks:

__all_blocks__ = {
    'ConvKXBN': ConvKXBN,
    'ConvKXBNRELU': ConvKXBNRELU,
    'BaseSuperBlock': BaseSuperBlock,
    'ResK1KXK1': ResK1KXK1,
    'ResK1KX': ResK1KX,
    'ResKXKX': ResKXKX,
    'ResK1DWK1': ResK1DWK1,
    'ResK1DWSEK1': ResK1DWSEK1,
    'SuperResK1KXK1': SuperResK1KXK1,
    'SuperResK1KX': SuperResK1KX,
    'SuperResKXKX': SuperResKXKX,
    'SuperResK1DWK1': SuperResK1DWK1,
    'SuperResK1DWSEK1': SuperResK1DWSEK1,
    'SuperQuantResK1DWK1': SuperQuantResK1DWK1,
}

Supported 3D CNN blocks:

__all_blocks_3D__ = {
    'Conv3DKXBN': Conv3DKXBN,
    'Conv3DKXBNRELU': Conv3DKXBNRELU,
    'BaseSuperBlock3D': BaseSuperBlock3D,
    'Res3DK1DWK1': Res3DK1DWK1,
    'SuperRes3DK1DWK1': SuperRes3DK1DWK1,
}

Note:

  • BaseSuperBlock is the basic class for super block.
  • SuperResK1KXK1 is the derived class from BaseSuperBlock to unit L class ResK1KXK1.
  • SuperResK1DWK1 is the derived class from BaseSuperBlock to unit L class ResK1DWK1.
  • SuperResK1DWSEK1 is the derived class from BaseSuperBlock to unit L class ResK1DWSEK1.
  • SuperResK1KX is the derived class from BaseSuperBlock to unit L class ResK1KX.
  • SuperResKXKX is the derived class from BaseSuperBlock to unit L class ResKXKX.
  • SuperQuantResK1DWK1 is the derived class from SuperResK1DWK1.
  • SuperRes3DK1DWK1 is the derived class from BaseSuperBlock to unit L class Res3DK1DWK1.

Useful functions for masternet

get_model_size: Get the number of parameters of the network

get_flops: Get the FLOPs of the network

get_layers: Get the Conv layers of the network

get_latency: Get the latency from predictor or benchmark

get_params_for_trt: Get the paramters of the network for latency prediction.

get_max_feature: Get the number of max feature map for MCU.

get_efficient_score: Get efficient_score of the network.

madnas_forward_pre_GAP: Get the madnas score of the network, which does not need forward on GPU and has very fast speed .

deepmad_forward_pre_GAP: Get the deepmad score of the network, which does not need forward on GPU and has very fast speed.

stentr_forward_pre_GAP: Get the spatio-temporal entropy score of the network, which does not need forward on GPU and has very fast speed.