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from .activations import *
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from .adaptive_avgmax_pool import \
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adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
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from .anti_aliasing import AntiAliasDownsampleLayer
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from .blur_pool import BlurPool2d
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from .classifier import ClassifierHead, create_classifier
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Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
5 years ago
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from .cond_conv2d import CondConv2d, get_condconv_initializer
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from .config import is_exportable, is_scriptable, is_no_jit, set_exportable, set_scriptable, set_no_jit,\
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set_layer_config
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Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
5 years ago
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from .conv2d_same import Conv2dSame
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from .conv_bn_act import ConvBnAct
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from .create_act import create_act_layer, get_act_layer, get_act_fn
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from .create_attn import create_attn
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from .create_conv2d import create_conv2d
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from .create_norm_act import create_norm_act, get_norm_act_layer
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from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
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from .eca import EcaModule, CecaModule
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from .evo_norm import EvoNormBatch2d, EvoNormSample2d
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from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple
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Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
5 years ago
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from .inplace_abn import InplaceAbn
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from .linear import Linear
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Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
5 years ago
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from .mixed_conv2d import MixedConv2d
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from .norm_act import BatchNormAct2d
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from .padding import get_padding
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from .pool2d_same import AvgPool2dSame, create_pool2d
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from .se import SEModule
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from .selective_kernel import SelectiveKernelConv
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from .separable_conv import SeparableConv2d, SeparableConvBnAct
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from .space_to_depth import SpaceToDepthModule
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from .split_attn import SplitAttnConv2d
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Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
5 years ago
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from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
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from .test_time_pool import TestTimePoolHead, apply_test_time_pool
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from .weight_init import trunc_normal_
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