84 lines
1.7 KiB
Python
84 lines
1.7 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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TensorLayer provides rich layer implementations trailed for
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various benchmarks and domain-specific problems. In addition, we also
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support transparent access to native TensorFlow parameters.
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For example, we provide not only layers for local response normalization, but also
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layers that allow user to apply ``tf.ops.lrn`` on ``network.outputs``.
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More functions can be found in `TensorFlow API <https://www.tensorflow.org/versions/master/api_docs/index.html>`__.
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"""
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from .binary_conv import *
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from .deformable_conv import *
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from .depthwise_conv import *
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from .dorefa_conv import *
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# from .expert_conv import *
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# from .expert_deconv import *
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from .group_conv import *
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from .quan_conv import *
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from .quan_conv_bn import *
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from .separable_conv import *
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from .simplified_conv import *
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# from .simplified_deconv import *
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from .super_resolution import *
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from .ternary_conv import *
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__all__ = [
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# simplified conv
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'Conv1d',
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'Conv2d',
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'Conv3d',
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# simplified deconv
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'DeConv1d',
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'DeConv2d',
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'DeConv3d',
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# expert conv
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# 'Conv1dLayer',
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# 'Conv2dLayer',
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# 'Conv3dLayer',
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# expert conv
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# 'DeConv1dLayer',
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# 'DeConv2dLayer',
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# 'DeConv3dLayer',
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# atrous
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# 'AtrousConv1dLayer',
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# 'AtrousConv2dLayer',
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# 'AtrousDeConv2d',
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# binary
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'BinaryConv2d',
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# deformable
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'DeformableConv2d',
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# depthwise
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'DepthwiseConv2d',
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# dorefa
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'DorefaConv2d',
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# group
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'GroupConv2d',
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# separable
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'SeparableConv1d',
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'SeparableConv2d',
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# subpixel
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'SubpixelConv1d',
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'SubpixelConv2d',
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# ternary
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'TernaryConv2d',
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#quan_conv
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'QuanConv2d',
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'QuanConv2dWithBN',
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]
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