SYMBOL INDEX (222 symbols across 11 files)

FILE: detection/models/overlock.py
  function get_conv2d (line 22) | def get_conv2d(in_channels,
  function get_bn (line 61) | def get_bn(dim, use_sync_bn=False):
  function fuse_bn (line 68) | def fuse_bn(conv, bn):
  function convert_dilated_to_nondilated (line 73) | def convert_dilated_to_nondilated(kernel, dilate_rate):
  function merge_dilated_into_large_kernel (line 87) | def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilate...
  function stem (line 97) | def stem(in_chans=3, embed_dim=96):
  function downsample (line 113) | def downsample(in_dim, out_dim):
  class SEModule (line 120) | class SEModule(nn.Module):
    method __init__ (line 121) | def __init__(self, dim, red=8, inner_act=nn.GELU, out_act=nn.Sigmoid):
    method forward (line 132) | def forward(self, x):
  class LayerScale (line 138) | class LayerScale(nn.Module):
    method __init__ (line 139) | def __init__(self, dim, init_value=1e-5):
    method forward (line 145) | def forward(self, x):
  class LayerNorm2d (line 150) | class LayerNorm2d(nn.LayerNorm):
    method __init__ (line 151) | def __init__(self, dim):
    method forward (line 154) | def forward(self, x):
  class GRN (line 161) | class GRN(nn.Module):
    method __init__ (line 167) | def __init__(self, dim, use_bias=True):
    method forward (line 174) | def forward(self, x):
  class DilatedReparamBlock (line 184) | class DilatedReparamBlock(nn.Module):
    method __init__ (line 189) | def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, a...
    method forward (line 233) | def forward(self, x):
    method merge_dilated_branches (line 243) | def merge_dilated_branches(self):
  class CTXDownsample (line 264) | class CTXDownsample(nn.Module):
    method __init__ (line 265) | def __init__(self, dim, h_dim):
    method forward (line 277) | def forward(self, x, ctx):
  class ResDWConv (line 283) | class ResDWConv(nn.Conv2d):
    method __init__ (line 287) | def __init__(self, dim, kernel_size=3):
    method forward (line 290) | def forward(self, x):
  class RepConvBlock (line 295) | class RepConvBlock(nn.Module):
    method __init__ (line 297) | def __init__(self,
    method forward_features (line 332) | def forward_features(self, x):
    method forward (line 344) | def forward(self, x):
  class DynamicConvBlock (line 354) | class DynamicConvBlock(nn.Module):
    method __init__ (line 355) | def __init__(self,
    method get_rpb (line 458) | def get_rpb(self):
    method generate_idx (line 468) | def generate_idx(self, kernel_size):
    method apply_rpb (line 476) | def apply_rpb(self, attn, rpb, height, width, kernel_size, idx_h, idx_...
    method _forward_inner (line 493) | def _forward_inner(self, x, h_x, h_r):
    method forward (line 575) | def forward(self, x, h_x, h_r):
  class OverLoCK (line 583) | class OverLoCK(nn.Module):
    method __init__ (line 588) | def __init__(self,
    method _init_weights (line 775) | def _init_weights(self, m):
    method _convert_sync_batchnorm (line 784) | def _convert_sync_batchnorm(self):
    method forward_pre_features (line 788) | def forward_pre_features(self, x):
    method forward_base_features (line 807) | def forward_base_features(self, x):
    method forward_sub_features (line 820) | def forward_sub_features(self, x, ctx):
    method forward_features (line 846) | def forward_features(self, x):
    method forward (line 854) | def forward(self, x):
  function overlock_xt (line 862) | def overlock_xt(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_t (line 884) | def overlock_t(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_s (line 906) | def overlock_s(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_b (line 928) | def overlock_b(pretrained=None, pretrained_cfg=None, **kwargs):

FILE: detection/test.py
  function parse_args (line 22) | def parse_args():
  function main (line 109) | def main():

FILE: detection/train.py
  function parse_args (line 26) | def parse_args():
  function main (line 120) | def main():

FILE: models/contmix.py
  function get_conv2d (line 22) | def get_conv2d(in_channels,
  function get_bn (line 61) | def get_bn(dim, use_sync_bn=False):
  function fuse_bn (line 68) | def fuse_bn(conv, bn):
  function convert_dilated_to_nondilated (line 73) | def convert_dilated_to_nondilated(kernel, dilate_rate):
  function merge_dilated_into_large_kernel (line 87) | def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilate...
  class SEModule (line 97) | class SEModule(nn.Module):
    method __init__ (line 98) | def __init__(self, dim, red=8, inner_act=nn.GELU, out_act=nn.Sigmoid):
    method forward (line 109) | def forward(self, x):
  class LayerScale (line 114) | class LayerScale(nn.Module):
    method __init__ (line 115) | def __init__(self, dim, init_value=1e-5):
    method forward (line 121) | def forward(self, x):
  class LayerNorm2d (line 128) | class LayerNorm2d(nn.LayerNorm):
    method __init__ (line 129) | def __init__(self, dim):
    method forward (line 132) | def forward(self, x):
  class GRN (line 140) | class GRN(nn.Module):
    method __init__ (line 146) | def __init__(self, dim, use_bias=True):
    method forward (line 154) | def forward(self, x):
  class DilatedReparamBlock (line 164) | class DilatedReparamBlock(nn.Module):
    method __init__ (line 169) | def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, a...
    method forward (line 213) | def forward(self, x):
    method merge_dilated_branches (line 223) | def merge_dilated_branches(self):
  class ResDWConv (line 244) | class ResDWConv(nn.Conv2d):
    method __init__ (line 248) | def __init__(self, dim, kernel_size=3):
    method forward (line 251) | def forward(self, x):
  class ContMixBlock (line 256) | class ContMixBlock(nn.Module):
    method __init__ (line 261) | def __init__(self,
    method get_rpb (line 357) | def get_rpb(self):
    method generate_idx (line 366) | def generate_idx(self, kernel_size):
    method apply_rpb (line 373) | def apply_rpb(self, attn, rpb, height, width, kernel_size, idx_h, idx_...
    method reparm (line 389) | def reparm(self):
    method _forward_inner (line 394) | def _forward_inner(self, x):
    method forward (line 488) | def forward(self, x):

FILE: models/overlock.py
  function get_conv2d (line 18) | def get_conv2d(in_channels,
  function get_bn (line 57) | def get_bn(dim, use_sync_bn=False):
  function fuse_bn (line 64) | def fuse_bn(conv, bn):
  function convert_dilated_to_nondilated (line 69) | def convert_dilated_to_nondilated(kernel, dilate_rate):
  function merge_dilated_into_large_kernel (line 83) | def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilate...
  function stem (line 93) | def stem(in_chans=3, embed_dim=96):
  function downsample (line 109) | def downsample(in_dim, out_dim):
  class SEModule (line 116) | class SEModule(nn.Module):
    method __init__ (line 117) | def __init__(self, dim, red=8, inner_act=nn.GELU, out_act=nn.Sigmoid):
    method forward (line 128) | def forward(self, x):
  class LayerScale (line 134) | class LayerScale(nn.Module):
    method __init__ (line 135) | def __init__(self, dim, init_value=1e-5):
    method forward (line 141) | def forward(self, x):
  class LayerNorm2d (line 146) | class LayerNorm2d(nn.LayerNorm):
    method __init__ (line 147) | def __init__(self, dim):
    method forward (line 150) | def forward(self, x):
  class GRN (line 157) | class GRN(nn.Module):
    method __init__ (line 163) | def __init__(self, dim, use_bias=True):
    method forward (line 170) | def forward(self, x):
  class DilatedReparamBlock (line 180) | class DilatedReparamBlock(nn.Module):
    method __init__ (line 185) | def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, a...
    method forward (line 229) | def forward(self, x):
    method merge_dilated_branches (line 239) | def merge_dilated_branches(self):
  class CTXDownsample (line 260) | class CTXDownsample(nn.Module):
    method __init__ (line 261) | def __init__(self, dim, h_dim):
    method forward (line 273) | def forward(self, x, ctx):
  class ResDWConv (line 279) | class ResDWConv(nn.Conv2d):
    method __init__ (line 283) | def __init__(self, dim, kernel_size=3):
    method forward (line 286) | def forward(self, x):
  class RepConvBlock (line 291) | class RepConvBlock(nn.Module):
    method __init__ (line 293) | def __init__(self,
    method forward_features (line 328) | def forward_features(self, x):
    method forward (line 340) | def forward(self, x):
  class DynamicConvBlock (line 350) | class DynamicConvBlock(nn.Module):
    method __init__ (line 351) | def __init__(self,
    method get_rpb (line 454) | def get_rpb(self):
    method generate_idx (line 464) | def generate_idx(self, kernel_size):
    method apply_rpb (line 472) | def apply_rpb(self, attn, rpb, height, width, kernel_size, idx_h, idx_...
    method _forward_inner (line 489) | def _forward_inner(self, x, h_x, h_r):
    method forward (line 570) | def forward(self, x, h_x, h_r):
  class OverLoCK (line 578) | class OverLoCK(nn.Module):
    method __init__ (line 583) | def __init__(self,
    method _init_weights (line 757) | def _init_weights(self, m):
    method reparam (line 766) | def reparam(self):
    method forward_pre_features (line 771) | def forward_pre_features(self, x):
    method forward_base_features (line 784) | def forward_base_features(self, x):
    method forward_sub_features (line 797) | def forward_sub_features(self, x, ctx):
    method forward_features (line 814) | def forward_features(self, x):
    method forward (line 822) | def forward(self, x):
  function _cfg (line 834) | def _cfg(url=None, **kwargs):
  function overlock_xt (line 849) | def overlock_xt(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_t (line 872) | def overlock_t(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_s (line 895) | def overlock_s(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_b (line 918) | def overlock_b(pretrained=None, pretrained_cfg=None, **kwargs):
  function overlock_xt_reparam (line 951) | def overlock_xt_reparam(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_t_reparam (line 964) | def overlock_t_reparam(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_s_reparam (line 977) | def overlock_s_reparam(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_b_reparam (line 989) | def overlock_b_reparam(pretrained=False, pretrained_cfg=None, **kwargs):

FILE: segmentation/mmseg_custom/align_resize.py
  class AlignResize (line 10) | class AlignResize(object):
    method __init__ (line 13) | def __init__(self,
    method random_select (line 44) | def random_select(img_scales):
    method random_sample (line 62) | def random_sample(img_scales):
    method random_sample_ratio (line 89) | def random_sample_ratio(img_scale, ratio_range):
    method _random_scale (line 115) | def _random_scale(self, results):
    method _align (line 153) | def _align(self, img, size_divisor, interpolation=None):
    method _resize_img (line 162) | def _resize_img(self, results):
    method _resize_seg (line 191) | def _resize_seg(self, results):
    method __call__ (line 207) | def __call__(self, results):
    method __repr__ (line 225) | def __repr__(self):

FILE: segmentation/models/overlock.py
  function get_conv2d (line 19) | def get_conv2d(in_channels,
  function get_bn (line 58) | def get_bn(dim, use_sync_bn=False):
  function fuse_bn (line 65) | def fuse_bn(conv, bn):
  function convert_dilated_to_nondilated (line 70) | def convert_dilated_to_nondilated(kernel, dilate_rate):
  function merge_dilated_into_large_kernel (line 84) | def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilate...
  function stem (line 94) | def stem(in_chans=3, embed_dim=96):
  function downsample (line 110) | def downsample(in_dim, out_dim):
  class SEModule (line 117) | class SEModule(nn.Module):
    method __init__ (line 118) | def __init__(self, dim, red=8, inner_act=nn.GELU, out_act=nn.Sigmoid):
    method forward (line 129) | def forward(self, x):
  class LayerScale (line 135) | class LayerScale(nn.Module):
    method __init__ (line 136) | def __init__(self, dim, init_value=1e-5):
    method forward (line 142) | def forward(self, x):
  class LayerNorm2d (line 147) | class LayerNorm2d(nn.LayerNorm):
    method __init__ (line 148) | def __init__(self, dim):
    method forward (line 151) | def forward(self, x):
  class GRN (line 158) | class GRN(nn.Module):
    method __init__ (line 164) | def __init__(self, dim, use_bias=True):
    method forward (line 171) | def forward(self, x):
  class DilatedReparamBlock (line 181) | class DilatedReparamBlock(nn.Module):
    method __init__ (line 186) | def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, a...
    method forward (line 230) | def forward(self, x):
    method merge_dilated_branches (line 240) | def merge_dilated_branches(self):
  class CTXDownsample (line 261) | class CTXDownsample(nn.Module):
    method __init__ (line 262) | def __init__(self, dim, h_dim):
    method forward (line 274) | def forward(self, x, ctx):
  class ResDWConv (line 280) | class ResDWConv(nn.Conv2d):
    method __init__ (line 284) | def __init__(self, dim, kernel_size=3):
    method forward (line 287) | def forward(self, x):
  class RepConvBlock (line 292) | class RepConvBlock(nn.Module):
    method __init__ (line 294) | def __init__(self,
    method forward_features (line 329) | def forward_features(self, x):
    method forward (line 341) | def forward(self, x):
  class DynamicConvBlock (line 351) | class DynamicConvBlock(nn.Module):
    method __init__ (line 352) | def __init__(self,
    method get_rpb (line 455) | def get_rpb(self):
    method generate_idx (line 465) | def generate_idx(self, kernel_size):
    method apply_rpb (line 473) | def apply_rpb(self, attn, rpb, height, width, kernel_size, idx_h, idx_...
    method _forward_inner (line 490) | def _forward_inner(self, x, h_x, h_r):
    method forward (line 572) | def forward(self, x, h_x, h_r):
  class OverLoCK (line 580) | class OverLoCK(nn.Module):
    method __init__ (line 585) | def __init__(self,
    method _init_weights (line 766) | def _init_weights(self, m):
    method _convert_sync_batchnorm (line 776) | def _convert_sync_batchnorm(self):
    method forward_pre_features (line 780) | def forward_pre_features(self, x):
    method forward_base_features (line 799) | def forward_base_features(self, x):
    method forward_sub_features (line 812) | def forward_sub_features(self, x, ctx):
    method forward_features (line 835) | def forward_features(self, x):
    method forward (line 843) | def forward(self, x):
  function overlock_xt (line 851) | def overlock_xt(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_t (line 873) | def overlock_t(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_s (line 895) | def overlock_s(pretrained=False, pretrained_cfg=None, **kwargs):
  function overlock_b (line 917) | def overlock_b(pretrained=None, pretrained_cfg=None, **kwargs):

FILE: segmentation/test.py
  function parse_args (line 20) | def parse_args():
  function main (line 75) | def main():

FILE: segmentation/train.py
  function parse_args (line 22) | def parse_args():
  function main (line 81) | def main():

FILE: train.py
  function get_args_parser (line 73) | def get_args_parser():
  function main (line 333) | def main(args):
  function train_one_epoch (line 813) | def train_one_epoch(epoch, model, loader, optimizer, loss_fn, args,
  function validate (line 1006) | def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_su...

FILE: validate.py
  function validate (line 117) | def validate(args):
  function main (line 276) | def main():
  function write_results (line 339) | def write_results(results_file, results):