SYMBOL INDEX (242 symbols across 13 files)

FILE: backbones/efficientnet.py
  class EfficientNet (line 10) | class EfficientNet(nn.Module):
    method __init__ (line 39) | def __init__(self,
    method _freeze_stages (line 55) | def _freeze_stages(self):
    method init_weights (line 61) | def init_weights(self, pretrained=None):
    method forward (line 64) | def forward(self, x):
    method train (line 71) | def train(self, mode=True): #need modify

FILE: backbones/geffnet/activations/__init__.py
  function add_override_act_fn (line 57) | def add_override_act_fn(name, fn):
  function update_override_act_fn (line 62) | def update_override_act_fn(overrides):
  function clear_override_act_fn (line 68) | def clear_override_act_fn():
  function add_override_act_layer (line 73) | def add_override_act_layer(name, fn):
  function update_override_act_layer (line 77) | def update_override_act_layer(overrides):
  function clear_override_act_layer (line 83) | def clear_override_act_layer():
  function get_act_fn (line 88) | def get_act_fn(name='relu'):
  function get_act_layer (line 106) | def get_act_layer(name='relu'):

FILE: backbones/geffnet/activations/activations.py
  function swish (line 5) | def swish(x, inplace: bool = False):
  class Swish (line 11) | class Swish(nn.Module):
    method __init__ (line 12) | def __init__(self, inplace: bool = False):
    method forward (line 16) | def forward(self, x):
  function mish (line 20) | def mish(x, inplace: bool = False):
  class Mish (line 26) | class Mish(nn.Module):
    method __init__ (line 27) | def __init__(self, inplace: bool = False):
    method forward (line 31) | def forward(self, x):
  function sigmoid (line 35) | def sigmoid(x, inplace: bool = False):
  class Sigmoid (line 40) | class Sigmoid(nn.Module):
    method __init__ (line 41) | def __init__(self, inplace: bool = False):
    method forward (line 45) | def forward(self, x):
  function tanh (line 49) | def tanh(x, inplace: bool = False):
  class Tanh (line 54) | class Tanh(nn.Module):
    method __init__ (line 55) | def __init__(self, inplace: bool = False):
    method forward (line 59) | def forward(self, x):
  function hard_swish (line 63) | def hard_swish(x, inplace: bool = False):
  class HardSwish (line 68) | class HardSwish(nn.Module):
    method __init__ (line 69) | def __init__(self, inplace: bool = False):
    method forward (line 73) | def forward(self, x):
  function hard_sigmoid (line 77) | def hard_sigmoid(x, inplace: bool = False):
  class HardSigmoid (line 84) | class HardSigmoid(nn.Module):
    method __init__ (line 85) | def __init__(self, inplace: bool = False):
    method forward (line 89) | def forward(self, x):

FILE: backbones/geffnet/activations/activations_autofn.py
  class SwishAutoFn (line 9) | class SwishAutoFn(torch.autograd.Function):
    method forward (line 15) | def forward(ctx, x):
    method backward (line 21) | def backward(ctx, grad_output):
  function swish_auto (line 27) | def swish_auto(x, inplace=False):
  class SwishAuto (line 32) | class SwishAuto(nn.Module):
    method __init__ (line 33) | def __init__(self, inplace: bool = False):
    method forward (line 37) | def forward(self, x):
  class MishAutoFn (line 41) | class MishAutoFn(torch.autograd.Function):
    method forward (line 47) | def forward(ctx, x):
    method backward (line 53) | def backward(ctx, grad_output):
  function mish_auto (line 60) | def mish_auto(x, inplace=False):
  class MishAuto (line 65) | class MishAuto(nn.Module):
    method __init__ (line 66) | def __init__(self, inplace: bool = False):
    method forward (line 70) | def forward(self, x):

FILE: backbones/geffnet/activations/activations_jit.py
  function swish_jit_fwd (line 11) | def swish_jit_fwd(x):
  function swish_jit_bwd (line 16) | def swish_jit_bwd(x, grad_output):
  class SwishJitAutoFn (line 21) | class SwishJitAutoFn(torch.autograd.Function):
    method forward (line 27) | def forward(ctx, x):
    method backward (line 32) | def backward(ctx, grad_output):
  function swish_jit (line 37) | def swish_jit(x, inplace=False):
  class SwishJit (line 42) | class SwishJit(nn.Module):
    method __init__ (line 43) | def __init__(self, inplace: bool = False):
    method forward (line 47) | def forward(self, x):
  function mish_jit_fwd (line 52) | def mish_jit_fwd(x):
  function mish_jit_bwd (line 57) | def mish_jit_bwd(x, grad_output):
  class MishJitAutoFn (line 63) | class MishJitAutoFn(torch.autograd.Function):
    method forward (line 65) | def forward(ctx, x):
    method backward (line 70) | def backward(ctx, grad_output):
  function mish_jit (line 75) | def mish_jit(x, inplace=False):
  class MishJit (line 80) | class MishJit(nn.Module):
    method __init__ (line 81) | def __init__(self, inplace: bool = False):
    method forward (line 85) | def forward(self, x):

FILE: backbones/geffnet/config.py
  function is_exportable (line 13) | def is_exportable():
  function set_exportable (line 17) | def set_exportable(value):
  function is_scriptable (line 22) | def is_scriptable():
  function set_scriptable (line 26) | def set_scriptable(value):

FILE: backbones/geffnet/conv2d_layers.py
  function _ntuple (line 15) | def _ntuple(n):
  function _is_static_pad (line 29) | def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
  function _get_padding (line 33) | def _get_padding(kernel_size, stride=1, dilation=1, **_):
  function _calc_same_pad (line 38) | def _calc_same_pad(i: int, k: int, s: int, d: int):
  function _same_pad_arg (line 42) | def _same_pad_arg(input_size, kernel_size, stride, dilation):
  function _split_channels (line 50) | def _split_channels(num_chan, num_groups):
  function conv2d_same (line 56) | def conv2d_same(
  class Conv2dSame (line 68) | class Conv2dSame(nn.Conv2d):
    method __init__ (line 73) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 78) | def forward(self, x):
  class Conv2dSameExport (line 82) | class Conv2dSameExport(nn.Conv2d):
    method __init__ (line 89) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 96) | def forward(self, x):
  function get_padding_value (line 110) | def get_padding_value(padding, kernel_size, **kwargs):
  function create_conv2d_pad (line 133) | def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
  class MixedConv2d (line 147) | class MixedConv2d(nn.Module):
    method __init__ (line 155) | def __init__(self, in_channels, out_channels, kernel_size=3,
    method forward (line 174) | def forward(self, x):
  function get_condconv_initializer (line 181) | def get_condconv_initializer(initializer, num_experts, expert_shape):
  class CondConv2d (line 194) | class CondConv2d(nn.Module):
    method __init__ (line 203) | def __init__(self, in_channels, out_channels, kernel_size=3,
    method reset_parameters (line 233) | def reset_parameters(self):
    method forward (line 244) | def forward(self, x, routing_weights):
  function select_conv2d (line 285) | def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):

FILE: backbones/geffnet/efficientnet_builder.py
  function get_bn_args_tf (line 21) | def get_bn_args_tf():
  function resolve_bn_args (line 25) | def resolve_bn_args(kwargs):
  function resolve_se_args (line 43) | def resolve_se_args(kwargs, in_chs, act_layer=None):
  function resolve_act_layer (line 58) | def resolve_act_layer(kwargs, default='relu'):
  function make_divisible (line 65) | def make_divisible(v: int, divisor: int = 8, min_value: int = None):
  function round_channels (line 73) | def round_channels(channels, multiplier=1.0, divisor=8, channel_min=None):
  function drop_connect (line 81) | def drop_connect(inputs, training: bool = False, drop_connect_rate: floa...
  class SqueezeExcite (line 94) | class SqueezeExcite(nn.Module):
    method __init__ (line 97) | def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None, act_l...
    method forward (line 106) | def forward(self, x):
  class ConvBnAct (line 116) | class ConvBnAct(nn.Module):
    method __init__ (line 117) | def __init__(self, in_chs, out_chs, kernel_size,
    method forward (line 126) | def forward(self, x):
  class DepthwiseSeparableConv (line 133) | class DepthwiseSeparableConv(nn.Module):
    method __init__ (line 138) | def __init__(self, in_chs, out_chs, dw_kernel_size=3,
    method forward (line 165) | def forward(self, x):
  class InvertedResidual (line 185) | class InvertedResidual(nn.Module):
    method __init__ (line 188) | def __init__(self, in_chs, out_chs, dw_kernel_size=3,
    method forward (line 223) | def forward(self, x):
  class CondConvResidual (line 250) | class CondConvResidual(InvertedResidual):
    method __init__ (line 253) | def __init__(self, in_chs, out_chs, dw_kernel_size=3,
    method forward (line 271) | def forward(self, x):
  class EdgeResidual (line 302) | class EdgeResidual(nn.Module):
    method __init__ (line 305) | def __init__(self, in_chs, out_chs, exp_kernel_size=3, exp_ratio=1.0, ...
    method forward (line 331) | def forward(self, x):
  class EfficientNetBuilder (line 354) | class EfficientNetBuilder:
    method __init__ (line 364) | def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_...
    method _round_channels (line 382) | def _round_channels(self, chs):
    method _make_block (line 385) | def _make_block(self, ba):
    method _make_stack (line 420) | def _make_stack(self, stack_args):
    method __call__ (line 432) | def __call__(self, in_chs, block_args):
  function _parse_ksize (line 453) | def _parse_ksize(ss):
  function _decode_block_str (line 460) | def _decode_block_str(block_str):
  function _scale_stage_depth (line 579) | def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_...
  function decode_arch_def (line 617) | def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', ...
  function initialize_weight_goog (line 634) | def initialize_weight_goog(m, n=''):
  function initialize_weight_default (line 662) | def initialize_weight_default(m, n=''):

FILE: backbones/geffnet/gen_efficientnet.py
  class GenEfficientNet (line 140) | class GenEfficientNet(nn.Module):
    method __init__ (line 151) | def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size...
    method features (line 187) | def features(self, x):
    method as_sequential (line 201) | def as_sequential(self):
    method forward (line 209) | def forward(self, x):
  function _create_model (line 217) | def _create_model(model_kwargs, variant, pretrained=False):
  function _gen_mnasnet_a1 (line 227) | def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, *...
  function _gen_mnasnet_b1 (line 264) | def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, *...
  function _gen_mnasnet_small (line 301) | def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False...
  function _gen_fbnetc (line 331) | def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwa...
  function _gen_spnasnet (line 362) | def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **k...
  function _gen_efficientnet (line 398) | def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=...
  function _gen_efficientnet_edge (line 444) | def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multip...
  function _gen_efficientnet_condconv (line 468) | def _gen_efficientnet_condconv(
  function _gen_mixnet_s (line 493) | def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **k...
  function _gen_mixnet_m (line 527) | def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,...
  function mnasnet_050 (line 561) | def mnasnet_050(pretrained=False, **kwargs):
  function mnasnet_075 (line 567) | def mnasnet_075(pretrained=False, **kwargs):
  function mnasnet_100 (line 573) | def mnasnet_100(pretrained=False, **kwargs):
  function mnasnet_b1 (line 579) | def mnasnet_b1(pretrained=False, **kwargs):
  function mnasnet_140 (line 584) | def mnasnet_140(pretrained=False, **kwargs):
  function semnasnet_050 (line 590) | def semnasnet_050(pretrained=False, **kwargs):
  function semnasnet_075 (line 596) | def semnasnet_075(pretrained=False, **kwargs):
  function semnasnet_100 (line 602) | def semnasnet_100(pretrained=False, **kwargs):
  function mnasnet_a1 (line 608) | def mnasnet_a1(pretrained=False, **kwargs):
  function semnasnet_140 (line 613) | def semnasnet_140(pretrained=False, **kwargs):
  function mnasnet_small (line 619) | def mnasnet_small(pretrained=False, **kwargs):
  function fbnetc_100 (line 625) | def fbnetc_100(pretrained=False, **kwargs):
  function spnasnet_100 (line 634) | def spnasnet_100(pretrained=False, **kwargs):
  function efficientnet_b0 (line 640) | def efficientnet_b0(pretrained=False, **kwargs):
  function efficientnet_b1 (line 648) | def efficientnet_b1(pretrained=False, **kwargs):
  function efficientnet_b2 (line 656) | def efficientnet_b2(pretrained=False, **kwargs):
  function efficientnet_b3 (line 664) | def efficientnet_b3(pretrained=False, **kwargs):
  function efficientnet_b4 (line 672) | def efficientnet_b4(pretrained=False, **kwargs):
  function efficientnet_b5 (line 680) | def efficientnet_b5(pretrained=False, **kwargs):
  function efficientnet_b6 (line 688) | def efficientnet_b6(pretrained=False, **kwargs):
  function efficientnet_b7 (line 696) | def efficientnet_b7(pretrained=False, **kwargs):
  function efficientnet_b8 (line 704) | def efficientnet_b8(pretrained=False, **kwargs):
  function efficientnet_es (line 712) | def efficientnet_es(pretrained=False, **kwargs):
  function efficientnet_em (line 719) | def efficientnet_em(pretrained=False, **kwargs):
  function efficientnet_el (line 726) | def efficientnet_el(pretrained=False, **kwargs):
  function efficientnet_cc_b0_4e (line 733) | def efficientnet_cc_b0_4e(pretrained=False, **kwargs):
  function efficientnet_cc_b0_8e (line 741) | def efficientnet_cc_b0_8e(pretrained=False, **kwargs):
  function efficientnet_cc_b1_8e (line 750) | def efficientnet_cc_b1_8e(pretrained=False, **kwargs):
  function tf_efficientnet_b0 (line 759) | def tf_efficientnet_b0(pretrained=False, **kwargs):
  function tf_efficientnet_b1 (line 768) | def tf_efficientnet_b1(pretrained=False, **kwargs):
  function tf_efficientnet_b2 (line 777) | def tf_efficientnet_b2(pretrained=False, **kwargs):
  function tf_efficientnet_b3 (line 786) | def tf_efficientnet_b3(pretrained=False, num_classes=1000, in_chans=3, *...
  function tf_efficientnet_b4 (line 795) | def tf_efficientnet_b4(pretrained=False, **kwargs):
  function tf_efficientnet_b5 (line 804) | def tf_efficientnet_b5(pretrained=False, **kwargs):
  function tf_efficientnet_b6 (line 813) | def tf_efficientnet_b6(pretrained=False, **kwargs):
  function tf_efficientnet_b7 (line 822) | def tf_efficientnet_b7(pretrained=False, **kwargs):
  function tf_efficientnet_b0_ap (line 831) | def tf_efficientnet_b0_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b1_ap (line 840) | def tf_efficientnet_b1_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b2_ap (line 849) | def tf_efficientnet_b2_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b3_ap (line 858) | def tf_efficientnet_b3_ap(pretrained=False, num_classes=1000, in_chans=3...
  function tf_efficientnet_b4_ap (line 867) | def tf_efficientnet_b4_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b5_ap (line 876) | def tf_efficientnet_b5_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b6_ap (line 885) | def tf_efficientnet_b6_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b7_ap (line 895) | def tf_efficientnet_b7_ap(pretrained=False, **kwargs):
  function tf_efficientnet_b8_ap (line 905) | def tf_efficientnet_b8_ap(pretrained=False, **kwargs):
  function tf_efficientnet_es (line 915) | def tf_efficientnet_es(pretrained=False, **kwargs):
  function tf_efficientnet_em (line 924) | def tf_efficientnet_em(pretrained=False, **kwargs):
  function tf_efficientnet_el (line 933) | def tf_efficientnet_el(pretrained=False, **kwargs):
  function tf_efficientnet_cc_b0_4e (line 942) | def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs):
  function tf_efficientnet_cc_b0_8e (line 952) | def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs):
  function tf_efficientnet_cc_b1_8e (line 962) | def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs):
  function mixnet_s (line 972) | def mixnet_s(pretrained=False, **kwargs):
  function mixnet_m (line 981) | def mixnet_m(pretrained=False, **kwargs):
  function mixnet_l (line 990) | def mixnet_l(pretrained=False, **kwargs):
  function mixnet_xl (line 999) | def mixnet_xl(pretrained=False, **kwargs):
  function mixnet_xxl (line 1009) | def mixnet_xxl(pretrained=False, **kwargs):
  function tf_mixnet_s (line 1019) | def tf_mixnet_s(pretrained=False, **kwargs):
  function tf_mixnet_m (line 1029) | def tf_mixnet_m(pretrained=False, **kwargs):
  function tf_mixnet_l (line 1039) | def tf_mixnet_l(pretrained=False, **kwargs):

FILE: backbones/geffnet/helpers.py
  function load_checkpoint (line 10) | def load_checkpoint(model, checkpoint_path):
  function load_pretrained (line 31) | def load_pretrained(model, url, filter_fn=None, strict=True):

FILE: backbones/geffnet/mobilenetv3.py
  class MobileNetV3 (line 44) | class MobileNetV3(nn.Module):
    method __init__ (line 53) | def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size...
    method as_sequential (line 82) | def as_sequential(self):
    method features (line 90) | def features(self, x):
    method forward (line 100) | def forward(self, x):
  function _create_model (line 108) | def _create_model(model_kwargs, variant, pretrained=False):
  function _gen_mobilenet_v3_rw (line 118) | def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=Fal...
  function _gen_mobilenet_v3 (line 166) | def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False,...
  function mobilenetv3_rw (line 262) | def mobilenetv3_rw(pretrained=False, **kwargs):
  function mobilenetv3_large_075 (line 274) | def mobilenetv3_large_075(pretrained=False, **kwargs):
  function mobilenetv3_large_100 (line 281) | def mobilenetv3_large_100(pretrained=False, **kwargs):
  function mobilenetv3_large_minimal_100 (line 288) | def mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
  function mobilenetv3_small_075 (line 295) | def mobilenetv3_small_075(pretrained=False, **kwargs):
  function mobilenetv3_small_100 (line 301) | def mobilenetv3_small_100(pretrained=False, **kwargs):
  function mobilenetv3_small_minimal_100 (line 307) | def mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
  function tf_mobilenetv3_large_075 (line 313) | def tf_mobilenetv3_large_075(pretrained=False, **kwargs):
  function tf_mobilenetv3_large_100 (line 321) | def tf_mobilenetv3_large_100(pretrained=False, **kwargs):
  function tf_mobilenetv3_large_minimal_100 (line 329) | def tf_mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
  function tf_mobilenetv3_small_075 (line 337) | def tf_mobilenetv3_small_075(pretrained=False, **kwargs):
  function tf_mobilenetv3_small_100 (line 345) | def tf_mobilenetv3_small_100(pretrained=False, **kwargs):
  function tf_mobilenetv3_small_minimal_100 (line 353) | def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs):

FILE: backbones/geffnet/model_factory.py
  function create_model (line 6) | def create_model(

FILE: necks/bifpn.py
  class BIFPN (line 12) | class BIFPN(nn.Module):
    method __init__ (line 14) | def __init__(self,
    method init_weights (line 95) | def init_weights(self):
    method forward (line 101) | def forward(self, inputs):
  class BiFPNModule (line 136) | class BiFPNModule(nn.Module):
    method __init__ (line 137) | def __init__(self,
    method init_weights (line 177) | def init_weights(self):
    method forward (line 183) | def forward(self, inputs):