def get_primitives(cls, **primitive_kwargs) -> [CNNPrimitive]: act = dict(act_fun='relu', order='act_w_bn') df = dict(act_inplace=False, bn_affine=True, use_bn=True) return [ CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=3, dilation=1, **act, **df), stacked=2), CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=5, dilation=1, **act, **df), stacked=2), CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=3, dilation=2, **act, **df)), CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=5, dilation=2, **act, **df)), CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='max', act_fun=None, order='w_bn', **df)), CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='avg', act_fun=None, order='w_bn', **df)), StrideChoiceCNNPrimitive([ CNNPrimitive(cls=SkipLayer, kwargs=dict()), CNNPrimitive(cls=FactorizedReductionLayer, kwargs=dict(**act, **df)) ]), CNNPrimitive(cls=ZeroLayer, kwargs=dict()), ]
def get_primitives(cls, **primitive_kwargs) -> [CNNPrimitive]: df = dict(dilation=1, bn_affine=True, act_fun='relu', act_inplace=False, expansion=1.0, att_dict=None) return [ CNNPrimitive(cls=ShuffleNetV2Layer, kwargs=dict(k_size=3, **df)), CNNPrimitive(cls=ShuffleNetV2Layer, kwargs=dict(k_size=5, **df)), CNNPrimitive(cls=ShuffleNetV2Layer, kwargs=dict(k_size=7, **df)), CNNPrimitive(cls=ShuffleNetV2XceptionLayer, kwargs=dict(k_size=3, **df)), ]
def get_primitives(cls, stride=1, **primitive_kwargs) -> [CNNPrimitive]: primitives = MobileNetV2Primitives.get_primitives(stride=stride, **primitive_kwargs) if stride == 1: primitives.append( CNNPrimitive(cls=LinearTransformerLayer, kwargs=dict())), return primitives
def get_primitives(cls, stride=1, **primitive_kwargs) -> [CNNPrimitive]: df = dict(dilation=1, act_inplace=True, bn_affine=True, act_fun='swish') df['att_dict'] = dict(att_cls='EfficientChannelAttentionModule', use_c_substitute=True) primitives = [ CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=3, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=5, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=7, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=3, expansion=6.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=5, expansion=6.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=7, expansion=6.0, **df)), ] if stride == 1: primitives.append( CNNPrimitive(cls=LinearTransformerLayer, kwargs=dict())) return primitives
def get_primitives(cls, stride=1, **primitive_kwargs) -> [CNNPrimitive]: df = dict(dilation=1, act_inplace=True, bn_affine=True, act_fun='swish') df['att_dict'] = dict(att_cls='SqueezeExcitationChannelModule', use_c_substitute=True, c_mul=0.25, squeeze_act='swish', excite_act='sigmoid', squeeze_bias=True, excite_bias=True, squeeze_bn=False) primitives = [ CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=3, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=5, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=7, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=3, expansion=6.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=5, expansion=6.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=7, expansion=6.0, **df)), ] if stride == 1: primitives.append( CNNPrimitive(cls=LinearTransformerLayer, kwargs=dict())) return primitives
def get_primitives(cls, **primitive_kwargs) -> [CNNPrimitive]: act = dict(act_fun='relu', order='w_bn_act', act_inplace=False, bn_affine=False, use_bn=True) return [ CNNPrimitive(cls=ConvLayer, kwargs=dict(k_size=3, dilation=1, **act)), CNNPrimitive(cls=ConvLayer, kwargs=dict(k_size=1, dilation=1, **act)), CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='max', act_fun=None, order='w', use_bn=False)), ]
def get_primitives(cls, **primitive_kwargs) -> [CNNPrimitive]: act = dict(act_fun='relu', order='act_w_bn') df = dict(act_inplace=False, bn_affine=True, use_bn=True) dfnb = df.copy() dfnb['use_bn'] = False return [ CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=3, dilation=1, **act, **df), stacked=2), CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=5, dilation=1, **act, **df), stacked=2), CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=3, dilation=2, **act, **df)), CNNPrimitive(cls=SepConvLayer, kwargs=dict(k_size=5, dilation=2, **act, **df)), DifferentConfigPrimitive( CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='max', act_fun=None, order='w_bn', **df)), CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='max', act_fun=None, order='w', **dfnb))), DifferentConfigPrimitive( CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='avg', act_fun=None, order='w_bn', **df)), CNNPrimitive(PoolingLayer, kwargs=dict(k_size=3, pool_type='avg', act_fun=None, order='w', **dfnb))), StrideChoiceCNNPrimitive([ CNNPrimitive(cls=LinearTransformerLayer, kwargs=dict()), CNNPrimitive(cls=FactorizedReductionLayer, kwargs=dict(**act, **df)) ]), ]
def get_primitives(cls, **primitive_kwargs) -> [CNNPrimitive]: act = dict(act_fun='relu', order='act_w_bn', act_inplace=False, bn_affine=False, use_bn=True) return [ CNNPrimitive(cls=ZeroLayer, kwargs=dict()), StrideChoiceCNNPrimitive([ CNNPrimitive(cls=LinearTransformerLayer, kwargs=dict()), CNNPrimitive(cls=FactorizedReductionLayer, kwargs=dict(**act)) ]), CNNPrimitive(cls=ConvLayer, kwargs=dict(k_size=1, dilation=1, **act)), CNNPrimitive(cls=ConvLayer, kwargs=dict(k_size=3, dilation=1, **act)), CNNPrimitive(PoolingConvLayer, kwargs=dict(k_size=3, pool_type='avg', act_fun=None, order='w', bn_affine=False, use_bn=False, bias=False)), ]
def get_primitives(cls, stride=1, **primitive_kwargs) -> [CNNPrimitive]: df = dict(dilation=1, act_inplace=True, bn_affine=True, act_fun='hswish') att_dict = dict(att_cls='EfficientChannelAttentionModule', use_c_substitute=False, k_size=-1, gamma=2, b=1, excite_act='sigmoid') primitives = [ CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(expansion=3, att_dict=att_dict, k_size=(3, ), **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(expansion=3, att_dict=att_dict, k_size=(3, 5), **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(expansion=3, att_dict=att_dict, k_size=(3, 5, 7), **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(expansion=6, att_dict=att_dict, k_size=(3, ), **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(expansion=6, att_dict=att_dict, k_size=(3, 5), **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(expansion=6, att_dict=att_dict, k_size=(3, 5, 7), **df)), ] if stride == 1: primitives.append( CNNPrimitive(cls=LinearTransformerLayer, kwargs=dict())) return primitives
def get_primitives(cls, stride=1, **primitive_kwargs) -> [CNNPrimitive]: df = dict(dilation=1, act_inplace=True, bn_affine=True, act_fun='relu6', att_dict=None, stride=stride) return [ CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=3, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=5, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=7, expansion=3.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=3, expansion=6.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=5, expansion=6.0, **df)), CNNPrimitive(cls=MobileInvertedConvLayer, kwargs=dict(k_size=7, expansion=6.0, **df)), ]