SYMBOL INDEX (1441 symbols across 83 files) FILE: model/__init__.py function test (line 2) | def test(): FILE: model/attention/A2Atttention.py class DoubleAttention (line 9) | class DoubleAttention(nn.Module): method __init__ (line 11) | def __init__(self, in_channels,c_m,c_n,reconstruct = True): method init_weights (line 25) | def init_weights(self): method forward (line 39) | def forward(self, x): FILE: model/attention/ACmixAttention.py function position (line 5) | def position(H, W, is_cuda=True): function stride (line 16) | def stride(x, stride): function init_rate_half (line 20) | def init_rate_half(tensor): function init_rate_0 (line 24) | def init_rate_0(tensor): class ACmix (line 29) | class ACmix(nn.Module): method __init__ (line 30) | def __init__(self, in_planes, out_planes, kernel_att=7, head=4, kernel... method reset_parameters (line 58) | def reset_parameters(self): method forward (line 68) | def forward(self, x): FILE: model/attention/AFT.py class AFT_FULL (line 8) | class AFT_FULL(nn.Module): method __init__ (line 10) | def __init__(self, d_model,n=49,simple=False): method init_weights (line 27) | def init_weights(self): method forward (line 41) | def forward(self, input): FILE: model/attention/Axial_attention.py class Deterministic (line 9) | class Deterministic(nn.Module): method __init__ (line 10) | def __init__(self, net): method record_rng (line 18) | def record_rng(self, *args): method forward (line 24) | def forward(self, *args, record_rng = False, set_rng = False, **kwargs): class ReversibleBlock (line 43) | class ReversibleBlock(nn.Module): method __init__ (line 44) | def __init__(self, f, g): method forward (line 49) | def forward(self, x, f_args = {}, g_args = {}): method backward_pass (line 59) | def backward_pass(self, y, dy, f_args = {}, g_args = {}): class IrreversibleBlock (line 97) | class IrreversibleBlock(nn.Module): method __init__ (line 98) | def __init__(self, f, g): method forward (line 103) | def forward(self, x, f_args, g_args): class _ReversibleFunction (line 109) | class _ReversibleFunction(Function): method forward (line 111) | def forward(ctx, x, blocks, kwargs): method backward (line 120) | def backward(ctx, dy): class ReversibleSequence (line 127) | class ReversibleSequence(nn.Module): method __init__ (line 128) | def __init__(self, blocks, ): method forward (line 132) | def forward(self, x, arg_route = (True, True), **kwargs): function exists (line 142) | def exists(val): function map_el_ind (line 145) | def map_el_ind(arr, ind): function sort_and_return_indices (line 148) | def sort_and_return_indices(arr): function calculate_permutations (line 157) | def calculate_permutations(num_dimensions, emb_dim): class ChanLayerNorm (line 174) | class ChanLayerNorm(nn.Module): method __init__ (line 175) | def __init__(self, dim, eps = 1e-5): method forward (line 181) | def forward(self, x): class PreNorm (line 186) | class PreNorm(nn.Module): method __init__ (line 187) | def __init__(self, dim, fn): method forward (line 192) | def forward(self, x): class Sequential (line 196) | class Sequential(nn.Module): method __init__ (line 197) | def __init__(self, blocks): method forward (line 201) | def forward(self, x): class PermuteToFrom (line 207) | class PermuteToFrom(nn.Module): method __init__ (line 208) | def __init__(self, permutation, fn): method forward (line 215) | def forward(self, x, **kwargs): class AxialPositionalEmbedding (line 234) | class AxialPositionalEmbedding(nn.Module): method __init__ (line 235) | def __init__(self, dim, shape, emb_dim_index = 1): method forward (line 250) | def forward(self, x): class SelfAttention (line 257) | class SelfAttention(nn.Module): method __init__ (line 258) | def __init__(self, dim, heads, dim_heads = None): method forward (line 268) | def forward(self, x, kv = None): class AxialAttention (line 287) | class AxialAttention(nn.Module): method __init__ (line 288) | def __init__(self, dim, num_dimensions = 2, heads = 8, dim_heads = Non... method forward (line 302) | def forward(self, x): class AxialImageTransformer (line 316) | class AxialImageTransformer(nn.Module): method __init__ (line 317) | def __init__(self, dim, depth, heads = 8, dim_heads = None, dim_index ... method forward (line 340) | def forward(self, x): FILE: model/attention/BAM.py class Flatten (line 6) | class Flatten(nn.Module): method forward (line 7) | def forward(self,x): class ChannelAttention (line 10) | class ChannelAttention(nn.Module): method __init__ (line 11) | def __init__(self,channel,reduction=16,num_layers=3): method forward (line 28) | def forward(self, x) : class SpatialAttention (line 34) | class SpatialAttention(nn.Module): method __init__ (line 35) | def __init__(self,channel,reduction=16,num_layers=3,dia_val=2): method forward (line 47) | def forward(self, x) : class BAMBlock (line 55) | class BAMBlock(nn.Module): method __init__ (line 57) | def __init__(self, channel=512,reduction=16,dia_val=2): method init_weights (line 64) | def init_weights(self): method forward (line 78) | def forward(self, x): FILE: model/attention/CBAM.py class ChannelAttention (line 8) | class ChannelAttention(nn.Module): method __init__ (line 9) | def __init__(self,channel,reduction=16): method forward (line 20) | def forward(self, x) : class SpatialAttention (line 28) | class SpatialAttention(nn.Module): method __init__ (line 29) | def __init__(self,kernel_size=7): method forward (line 34) | def forward(self, x) : class CBAMBlock (line 44) | class CBAMBlock(nn.Module): method __init__ (line 46) | def __init__(self, channel=512,reduction=16,kernel_size=49): method init_weights (line 52) | def init_weights(self): method forward (line 66) | def forward(self, x): FILE: model/attention/CoAtNet.py class CoAtNet (line 9) | class CoAtNet(nn.Module): method __init__ (line 10) | def __init__(self,in_ch,image_size,out_chs=[64,96,192,384,768]): method forward (line 56) | def forward(self, x) : FILE: model/attention/CoTAttention.py class CoTAttention (line 11) | class CoTAttention(nn.Module): method __init__ (line 13) | def __init__(self, dim=512,kernel_size=3): method forward (line 37) | def forward(self, x): FILE: model/attention/CoordAttention.py class h_sigmoid (line 5) | class h_sigmoid(nn.Module): method __init__ (line 6) | def __init__(self, inplace=True): method forward (line 10) | def forward(self, x): class h_swish (line 13) | class h_swish(nn.Module): method __init__ (line 14) | def __init__(self, inplace=True): method forward (line 18) | def forward(self, x): class CoordAtt (line 21) | class CoordAtt(nn.Module): method __init__ (line 22) | def __init__(self, inp, oup, reduction=32): method forward (line 37) | def forward(self, x): FILE: model/attention/CrissCrossAttention.py function INF (line 11) | def INF(B,H,W): class CrissCrossAttention (line 15) | class CrissCrossAttention(nn.Module): method __init__ (line 17) | def __init__(self, in_dim): method forward (line 27) | def forward(self, x): FILE: model/attention/Crossformer.py class Mlp (line 7) | class Mlp(nn.Module): method __init__ (line 8) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 17) | def forward(self, x): class DynamicPosBias (line 25) | class DynamicPosBias(nn.Module): method __init__ (line 26) | def __init__(self, dim, num_heads, residual): method forward (line 47) | def forward(self, biases): method flops (line 57) | def flops(self, N): class Attention (line 64) | class Attention(nn.Module): method __init__ (line 77) | def __init__(self, dim, group_size, num_heads, qkv_bias=True, qk_scale... method forward (line 118) | def forward(self, x, mask=None): method extra_repr (line 154) | def extra_repr(self) -> str: method flops (line 157) | def flops(self, N): class CrossFormerBlock (line 173) | class CrossFormerBlock(nn.Module): method __init__ (line 192) | def __init__(self, dim, input_resolution, num_heads, group_size=7, lsd... method forward (line 223) | def forward(self, x): method extra_repr (line 257) | def extra_repr(self) -> str: method flops (line 261) | def flops(self): class PatchMerging (line 275) | class PatchMerging(nn.Module): method __init__ (line 284) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm, pat... method forward (line 302) | def forward(self, x): method extra_repr (line 321) | def extra_repr(self) -> str: method flops (line 324) | def flops(self): class Stage (line 336) | class Stage(nn.Module): method __init__ (line 356) | def __init__(self, dim, input_resolution, depth, num_heads, group_size, method forward (line 388) | def forward(self, x): method extra_repr (line 398) | def extra_repr(self) -> str: method flops (line 401) | def flops(self): class PatchEmbed (line 410) | class PatchEmbed(nn.Module): method __init__ (line 421) | def __init__(self, img_size=224, patch_size=[4], in_chans=3, embed_dim... method forward (line 448) | def forward(self, x): method flops (line 462) | def flops(self): class CrossFormer (line 476) | class CrossFormer(nn.Module): method __init__ (line 501) | def __init__(self, img_size=224, patch_size=[4], in_chans=3, num_class... method _init_weights (line 565) | def _init_weights(self, m): method no_weight_decay (line 575) | def no_weight_decay(self): method no_weight_decay_keywords (line 579) | def no_weight_decay_keywords(self): method forward_features (line 582) | def forward_features(self, x): method forward (line 596) | def forward(self, x): method flops (line 601) | def flops(self): FILE: model/attention/DANet.py class PositionAttentionModule (line 8) | class PositionAttentionModule(nn.Module): method __init__ (line 10) | def __init__(self,d_model=512,kernel_size=3,H=7,W=7): method forward (line 15) | def forward(self,x): class ChannelAttentionModule (line 23) | class ChannelAttentionModule(nn.Module): method __init__ (line 25) | def __init__(self,d_model=512,kernel_size=3,H=7,W=7): method forward (line 30) | def forward(self,x): class DAModule (line 40) | class DAModule(nn.Module): method __init__ (line 42) | def __init__(self,d_model=512,kernel_size=3,H=7,W=7): method forward (line 47) | def forward(self,input): FILE: model/attention/DAT.py class LocalAttention (line 19) | class LocalAttention(nn.Module): method __init__ (line 21) | def __init__(self, dim, heads, window_size, attn_drop, proj_drop): method forward (line 55) | def forward(self, x, mask=None): class ShiftWindowAttention (line 92) | class ShiftWindowAttention(LocalAttention): method __init__ (line 94) | def __init__(self, dim, heads, window_size, attn_drop, proj_drop, shif... method forward (line 120) | def forward(self, x): class DAttentionBaseline (line 129) | class DAttentionBaseline(nn.Module): method __init__ (line 131) | def __init__( method _get_ref_points (line 205) | def _get_ref_points(self, H_key, W_key, B, dtype, device): method forward (line 218) | def forward(self, x): class TransformerMLP (line 297) | class TransformerMLP(nn.Module): method __init__ (line 299) | def __init__(self, channels, expansion, drop): method forward (line 312) | def forward(self, x): class LayerNormProxy (line 320) | class LayerNormProxy(nn.Module): method __init__ (line 322) | def __init__(self, dim): method forward (line 327) | def forward(self, x): class TransformerMLPWithConv (line 333) | class TransformerMLPWithConv(nn.Module): method __init__ (line 335) | def __init__(self, channels, expansion, drop): method forward (line 348) | def forward(self, x): class TransformerStage (line 355) | class TransformerStage(nn.Module): method __init__ (line 357) | def __init__(self, fmap_size, window_size, ns_per_pt, method forward (line 405) | def forward(self, x): class DAT (line 424) | class DAT(nn.Module): method __init__ (line 426) | def __init__(self, img_size=224, patch_size=4, num_classes=1000, expan... method reset_parameters (line 489) | def reset_parameters(self): method load_pretrained (line 497) | def load_pretrained(self, state_dict): method no_weight_decay (line 537) | def no_weight_decay(self): method no_weight_decay_keywords (line 541) | def no_weight_decay_keywords(self): method forward (line 544) | def forward(self, x): FILE: model/attention/ECAAttention.py class ECAAttention (line 9) | class ECAAttention(nn.Module): method __init__ (line 11) | def __init__(self, kernel_size=3): method init_weights (line 17) | def init_weights(self): method forward (line 31) | def forward(self, x): FILE: model/attention/EMSA.py class EMSA (line 8) | class EMSA(nn.Module): method __init__ (line 10) | def __init__(self, d_model, d_k, d_v, h,dropout=.1,H=7,W=7,ratio=3,app... method init_weights (line 42) | def init_weights(self): method forward (line 56) | def forward(self, queries, keys, values, attention_mask=None, attentio... FILE: model/attention/ExternalAttention.py class ExternalAttention (line 8) | class ExternalAttention(nn.Module): method __init__ (line 10) | def __init__(self, d_model,S=64): method init_weights (line 18) | def init_weights(self): method forward (line 32) | def forward(self, queries): FILE: model/attention/HaloAttention.py function to (line 9) | def to(x): function pair (line 12) | def pair(x): function expand_dim (line 15) | def expand_dim(t, dim, k): function rel_to_abs (line 21) | def rel_to_abs(x): function relative_logits_1d (line 34) | def relative_logits_1d(q, rel_k): class RelPosEmb (line 46) | class RelPosEmb(nn.Module): method __init__ (line 47) | def __init__( method forward (line 61) | def forward(self, q): class HaloAttention (line 75) | class HaloAttention(nn.Module): method __init__ (line 76) | def __init__( method forward (line 107) | def forward(self, x): FILE: model/attention/MOATransformer.py class Mlp (line 13) | class Mlp(nn.Module): method __init__ (line 14) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 23) | def forward(self, x): function window_partition (line 32) | def window_partition(x, window_size): function window_reverse (line 50) | def window_reverse(windows, window_size, H, W): class WindowAttention (line 67) | class WindowAttention(nn.Module): method __init__ (line 81) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal... method forward (line 120) | def forward(self, x): method extra_repr (line 149) | def extra_repr(self) -> str: method flops (line 152) | def flops(self, N): class GlobalAttention (line 165) | class GlobalAttention(nn.Module): method __init__ (line 178) | def __init__(self, dim, window_size, input_resolution,num_heads, qkv_b... method forward (line 238) | def forward(self, x, H, W): method extra_repr (line 284) | def extra_repr(self) -> str: method flops (line 287) | def flops(self, N): class LocalTransformerBlock (line 301) | class LocalTransformerBlock(nn.Module): method __init__ (line 320) | def __init__(self, dim, input_resolution, num_heads, window_size=7, method forward (line 348) | def forward(self, x): method extra_repr (line 377) | def extra_repr(self) -> str: method flops (line 381) | def flops(self): class PatchMerging (line 396) | class PatchMerging(nn.Module): method __init__ (line 405) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): method forward (line 412) | def forward(self, x): method extra_repr (line 435) | def extra_repr(self) -> str: method flops (line 438) | def flops(self): class BasicLayer (line 445) | class BasicLayer(nn.Module): method __init__ (line 465) | def __init__(self, dim, input_resolution, depth, num_heads, window_size, method forward (line 508) | def forward(self, x): method extra_repr (line 539) | def extra_repr(self) -> str: method flops (line 542) | def flops(self): class PatchEmbed (line 551) | class PatchEmbed(nn.Module): method __init__ (line 562) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9... method forward (line 581) | def forward(self, x): method flops (line 591) | def flops(self): class MOATransformer (line 599) | class MOATransformer(nn.Module): method __init__ (line 625) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes... method _init_weights (line 684) | def _init_weights(self, m): method no_weight_decay (line 694) | def no_weight_decay(self): method no_weight_decay_keywords (line 698) | def no_weight_decay_keywords(self): method forward_features (line 701) | def forward_features(self, x): method forward (line 715) | def forward(self, x): method flops (line 720) | def flops(self): FILE: model/attention/MUSEAttention.py class Depth_Pointwise_Conv1d (line 8) | class Depth_Pointwise_Conv1d(nn.Module): method __init__ (line 9) | def __init__(self,in_ch,out_ch,k): method forward (line 27) | def forward(self,x): class MUSEAttention (line 33) | class MUSEAttention(nn.Module): method __init__ (line 35) | def __init__(self, d_model, d_k, d_v, h,dropout=.1): method init_weights (line 59) | def init_weights(self): method forward (line 73) | def forward(self, queries, keys, values, attention_mask=None, attentio... FILE: model/attention/MobileViTAttention.py class PreNorm (line 6) | class PreNorm(nn.Module): method __init__ (line 7) | def __init__(self,dim,fn): method forward (line 11) | def forward(self,x,**kwargs): class FeedForward (line 14) | class FeedForward(nn.Module): method __init__ (line 15) | def __init__(self,dim,mlp_dim,dropout) : method forward (line 24) | def forward(self,x): class Attention (line 27) | class Attention(nn.Module): method __init__ (line 28) | def __init__(self,dim,heads,head_dim,dropout): method forward (line 44) | def forward(self,x): class Transformer (line 57) | class Transformer(nn.Module): method __init__ (line 58) | def __init__(self,dim,depth,heads,head_dim,mlp_dim,dropout=0.): method forward (line 68) | def forward(self,x): class MobileViTAttention (line 75) | class MobileViTAttention(nn.Module): method __init__ (line 76) | def __init__(self,in_channel=3,dim=512,kernel_size=3,patch_size=7): method forward (line 87) | def forward(self,x): FILE: model/attention/MobileViTv2Attention.py class MobileViTv2Attention (line 8) | class MobileViTv2Attention(nn.Module): method __init__ (line 13) | def __init__(self, d_model): method init_weights (line 30) | def init_weights(self): method forward (line 44) | def forward(self, input): FILE: model/attention/OutlookAttention.py class OutlookAttention (line 8) | class OutlookAttention(nn.Module): method __init__ (line 10) | def __init__(self,dim,num_heads=1,kernel_size=3,padding=1,stride=1,qkv... method forward (line 31) | def forward(self, x) : FILE: model/attention/PSA.py class PSA (line 8) | class PSA(nn.Module): method __init__ (line 10) | def __init__(self, channel=512,reduction=4,S=4): method init_weights (line 31) | def init_weights(self): method forward (line 45) | def forward(self, x): FILE: model/attention/ParNetAttention.py class ParNetAttention (line 8) | class ParNetAttention(nn.Module): method __init__ (line 10) | def __init__(self, channel=512): method forward (line 29) | def forward(self, x): FILE: model/attention/PolarizedSelfAttention.py class ParallelPolarizedSelfAttention (line 8) | class ParallelPolarizedSelfAttention(nn.Module): method __init__ (line 10) | def __init__(self, channel=512): method forward (line 23) | def forward(self, x): class SequentialPolarizedSelfAttention (line 54) | class SequentialPolarizedSelfAttention(nn.Module): method __init__ (line 56) | def __init__(self, channel=512): method forward (line 69) | def forward(self, x): FILE: model/attention/ResidualAttention.py class ResidualAttention (line 8) | class ResidualAttention(nn.Module): method __init__ (line 10) | def __init__(self, channel=512 , num_class=1000,la=0.2): method forward (line 15) | def forward(self, x): FILE: model/attention/S2Attention.py function spatial_shift1 (line 7) | def spatial_shift1(x): function spatial_shift2 (line 16) | def spatial_shift2(x): class SplitAttention (line 25) | class SplitAttention(nn.Module): method __init__ (line 26) | def __init__(self,channel=512,k=3): method forward (line 35) | def forward(self,x_all): class S2Attention (line 48) | class S2Attention(nn.Module): method __init__ (line 50) | def __init__(self, channels=512 ): method forward (line 56) | def forward(self, x): FILE: model/attention/SEAttention.py class SEAttention (line 8) | class SEAttention(nn.Module): method __init__ (line 10) | def __init__(self, channel=512,reduction=16): method init_weights (line 21) | def init_weights(self): method forward (line 35) | def forward(self, x): FILE: model/attention/SGE.py class SpatialGroupEnhance (line 8) | class SpatialGroupEnhance(nn.Module): method __init__ (line 10) | def __init__(self, groups): method init_weights (line 20) | def init_weights(self): method forward (line 34) | def forward(self, x): FILE: model/attention/SKAttention.py class SKAttention (line 9) | class SKAttention(nn.Module): method __init__ (line 11) | def __init__(self, channel=512,kernels=[1,3,5,7],reduction=16,group=1,... method forward (line 31) | def forward(self, x): FILE: model/attention/SelfAttention.py class ScaledDotProductAttention (line 8) | class ScaledDotProductAttention(nn.Module): method __init__ (line 13) | def __init__(self, d_model, d_k, d_v, h,dropout=.1): method init_weights (line 35) | def init_weights(self): method forward (line 49) | def forward(self, queries, keys, values, attention_mask=None, attentio... FILE: model/attention/ShuffleAttention.py class ShuffleAttention (line 8) | class ShuffleAttention(nn.Module): method __init__ (line 10) | def __init__(self, channel=512,reduction=16,G=8): method init_weights (line 23) | def init_weights(self): method channel_shuffle (line 39) | def channel_shuffle(x, groups): method forward (line 49) | def forward(self, x): FILE: model/attention/SimAM.py class SimAM (line 5) | class SimAM(torch.nn.Module): method __init__ (line 6) | def __init__(self, channels = None, e_lambda = 1e-4): method __repr__ (line 12) | def __repr__(self): method get_module_name (line 18) | def get_module_name(): method forward (line 21) | def forward(self, x): FILE: model/attention/SimplifiedSelfAttention.py class SimplifiedScaledDotProductAttention (line 8) | class SimplifiedScaledDotProductAttention(nn.Module): method __init__ (line 13) | def __init__(self, d_model, h,dropout=.1): method init_weights (line 35) | def init_weights(self): method forward (line 49) | def forward(self, queries, keys, values, attention_mask=None, attentio... FILE: model/attention/TripletAttention.py class BasicConv (line 4) | class BasicConv(nn.Module): method __init__ (line 5) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi... method forward (line 12) | def forward(self, x): class ZPool (line 20) | class ZPool(nn.Module): method forward (line 21) | def forward(self, x): class AttentionGate (line 24) | class AttentionGate(nn.Module): method __init__ (line 25) | def __init__(self): method forward (line 30) | def forward(self, x): class TripletAttention (line 36) | class TripletAttention(nn.Module): method __init__ (line 37) | def __init__(self, no_spatial=False): method forward (line 44) | def forward(self, x): FILE: model/attention/UFOAttention.py function XNorm (line 8) | def XNorm(x,gamma): class UFOAttention (line 13) | class UFOAttention(nn.Module): method __init__ (line 18) | def __init__(self, d_model, d_k, d_v, h,dropout=.1): method init_weights (line 41) | def init_weights(self): method forward (line 55) | def forward(self, queries, keys, values): FILE: model/attention/ViP.py class MLP (line 5) | class MLP(nn.Module): method __init__ (line 6) | def __init__(self,in_features,hidden_features,out_features,act_layer=n... method forward (line 13) | def forward(self, x) : class WeightedPermuteMLP (line 16) | class WeightedPermuteMLP(nn.Module): method __init__ (line 17) | def __init__(self,dim,seg_dim=8, qkv_bias=False, proj_drop=0.): method forward (line 30) | def forward(self,x) : FILE: model/attention/gfnet.py class PatchEmbed (line 6) | class PatchEmbed(nn.Module): method __init__ (line 9) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 19) | def forward(self, x): class GlobalFilter (line 27) | class GlobalFilter(nn.Module): method __init__ (line 28) | def __init__(self, dim, h=14, w=8): method forward (line 34) | def forward(self, x, spatial_size=None): class Mlp (line 53) | class Mlp(nn.Module): method __init__ (line 54) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 63) | def forward(self, x): class Block (line 71) | class Block(nn.Module): method __init__ (line 72) | def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0., act_layer... method forward (line 81) | def forward(self, x): class GFNet (line 86) | class GFNet(nn.Module): method __init__ (line 87) | def __init__(self, embed_dim=384, img_size=224, patch_size=16, mlp_rat... method forward (line 104) | def forward(self, x): FILE: model/backbone/CMT.py function _cfg (line 21) | def _cfg(url='', **kwargs): class SwishImplementation (line 33) | class SwishImplementation(torch.autograd.Function): method forward (line 35) | def forward(ctx, i): method backward (line 41) | def backward(ctx, grad_output): class MemoryEfficientSwish (line 47) | class MemoryEfficientSwish(nn.Module): method forward (line 48) | def forward(self, x): class Mlp (line 52) | class Mlp(nn.Module): method __init__ (line 53) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 71) | def forward(self, x, H, W): class Attention (line 85) | class Attention(nn.Module): method __init__ (line 86) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, method forward (line 109) | def forward(self, x, H, W, relative_pos): class Block (line 131) | class Block(nn.Module): method __init__ (line 132) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 146) | def forward(self, x, H, W, relative_pos): class PatchEmbed (line 156) | class PatchEmbed(nn.Module): method __init__ (line 159) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 175) | def forward(self, x): class CMT (line 187) | class CMT(nn.Module): method __init__ (line 188) | def __init__(self, img_size=224, in_chans=3, num_classes=1000, embed_d... method _init_weights (line 276) | def _init_weights(self, m): method update_temperature (line 292) | def update_temperature(self): method no_weight_decay (line 298) | def no_weight_decay(self): method get_classifier (line 301) | def get_classifier(self): method reset_classifier (line 304) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 308) | def forward_features(self, x): method forward (line 350) | def forward(self, x): function resize_pos_embed (line 356) | def resize_pos_embed(posemb, posemb_new): function checkpoint_filter_fn (line 376) | def checkpoint_filter_fn(state_dict, model): function _create_cmt_model (line 394) | def _create_cmt_model(pretrained=False, distilled=False, **kwargs): function cmt_ti (line 419) | def cmt_ti(pretrained=False, **kwargs): function cmt_xs (line 428) | def cmt_xs(pretrained=False, **kwargs): function cmt_s (line 439) | def cmt_s(pretrained=False, **kwargs): function cmt_b (line 450) | def cmt_b(pretrained=False, **kwargs): function CMT_Tiny (line 461) | def CMT_Tiny(pretrained=False, **kwargs): FILE: model/backbone/CPVT.py class Mlp (line 13) | class Mlp(nn.Module): method __init__ (line 14) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 23) | def forward(self, x): class GroupAttention (line 32) | class GroupAttention(nn.Module): method __init__ (line 36) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 52) | def forward(self, x, H, W): class Attention (line 74) | class Attention(nn.Module): method __init__ (line 78) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 98) | def forward(self, x, H, W): class Block (line 122) | class Block(nn.Module): method __init__ (line 124) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 137) | def forward(self, x, H, W): class SBlock (line 144) | class SBlock(TimmBlock): method __init__ (line 145) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 150) | def forward(self, x, H, W): class GroupBlock (line 154) | class GroupBlock(TimmBlock): method __init__ (line 155) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 165) | def forward(self, x, H, W): class PatchEmbed (line 171) | class PatchEmbed(nn.Module): method __init__ (line 175) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 189) | def forward(self, x): class PyramidVisionTransformer (line 200) | class PyramidVisionTransformer(nn.Module): method __init__ (line 201) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method reset_drop_path (line 251) | def reset_drop_path(self, drop_path_rate): method _init_weights (line 259) | def _init_weights(self, m): method no_weight_decay (line 269) | def no_weight_decay(self): method get_classifier (line 272) | def get_classifier(self): method reset_classifier (line 275) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 279) | def forward_features(self, x): method forward (line 297) | def forward(self, x): class PosCNN (line 305) | class PosCNN(nn.Module): method __init__ (line 306) | def __init__(self, in_chans, embed_dim=768, s=1): method forward (line 311) | def forward(self, x, H, W): method no_weight_decay (line 322) | def no_weight_decay(self): class CPVTV2 (line 326) | class CPVTV2(PyramidVisionTransformer): method __init__ (line 333) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes... method _init_weights (line 347) | def _init_weights(self, m): method no_weight_decay (line 366) | def no_weight_decay(self): method forward_features (line 369) | def forward_features(self, x): class PCPVT (line 387) | class PCPVT(CPVTV2): method __init__ (line 388) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes... class ALTGVT (line 397) | class ALTGVT(PCPVT): method __init__ (line 401) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes... function _conv_filter (line 425) | def _conv_filter(state_dict, patch_size=16): function pcpvt_small_v0 (line 437) | def pcpvt_small_v0(pretrained=False, **kwargs): function pcpvt_base_v0 (line 447) | def pcpvt_base_v0(pretrained=False, **kwargs): function pcpvt_large_v0 (line 457) | def pcpvt_large_v0(pretrained=False, **kwargs): FILE: model/backbone/CaiT.py class Class_Attention (line 20) | class Class_Attention(nn.Module): method __init__ (line 23) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 37) | def forward(self, x ): class LayerScale_Block_CA (line 56) | class LayerScale_Block_CA(nn.Module): method __init__ (line 59) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 74) | def forward(self, x, x_cls): class Attention_talking_head (line 86) | class Attention_talking_head(nn.Module): method __init__ (line 89) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 110) | def forward(self, x): class LayerScale_Block (line 129) | class LayerScale_Block(nn.Module): method __init__ (line 132) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 146) | def forward(self, x): class CaiT (line 154) | class CaiT(nn.Module): method __init__ (line 157) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 212) | def _init_weights(self, m): method no_weight_decay (line 222) | def no_weight_decay(self): method forward_features (line 226) | def forward_features(self, x): method forward (line 247) | def forward(self, x): function cait_XXS24_224 (line 255) | def cait_XXS24_224(pretrained=False, **kwargs): function cait_XXS24 (line 277) | def cait_XXS24(pretrained=False, **kwargs): function cait_XXS36_224 (line 298) | def cait_XXS36_224(pretrained=False, **kwargs): function cait_XXS36 (line 320) | def cait_XXS36(pretrained=False, **kwargs): function cait_XS24 (line 342) | def cait_XS24(pretrained=False, **kwargs): function cait_S24_224 (line 367) | def cait_S24_224(pretrained=False, **kwargs): function cait_S24 (line 389) | def cait_S24(pretrained=False, **kwargs): function cait_S36 (line 411) | def cait_S36(pretrained=False, **kwargs): function cait_M36 (line 433) | def cait_M36(pretrained=False, **kwargs): function cait_M48 (line 456) | def cait_M48(pretrained=False, **kwargs): FILE: model/backbone/CeiT.py class Image2Tokens (line 18) | class Image2Tokens(nn.Module): method __init__ (line 19) | def __init__(self, in_chans=3, out_chans=64, kernel_size=7, stride=2): method forward (line 26) | def forward(self, x): class Mlp (line 33) | class Mlp(nn.Module): method __init__ (line 34) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 43) | def forward(self, x): class LocallyEnhancedFeedForward (line 52) | class LocallyEnhancedFeedForward(nn.Module): method __init__ (line 53) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 76) | def forward(self, x): class Attention (line 101) | class Attention(nn.Module): method __init__ (line 102) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 115) | def forward(self, x): class AttentionLCA (line 131) | class AttentionLCA(Attention): method __init__ (line 132) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 137) | def forward(self, x): class Block (line 164) | class Block(nn.Module): method __init__ (line 166) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 191) | def forward(self, x): class HybridEmbed (line 203) | class HybridEmbed(nn.Module): method __init__ (line 207) | def __init__(self, backbone, img_size=224, patch_size=16, feature_size... method forward (line 236) | def forward(self, x): class CeIT (line 244) | class CeIT(nn.Module): method __init__ (line 245) | def __init__(self, method _init_weights (line 325) | def _init_weights(self, m): method no_weight_decay (line 335) | def no_weight_decay(self): method get_classifier (line 338) | def get_classifier(self): method reset_classifier (line 341) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 345) | def forward_features(self, x): method forward (line 367) | def forward(self, x): function ceit_tiny_patch16_224 (line 374) | def ceit_tiny_patch16_224(pretrained=False, **kwargs): function ceit_small_patch16_224 (line 390) | def ceit_small_patch16_224(pretrained=False, **kwargs): function ceit_base_patch16_224 (line 406) | def ceit_base_patch16_224(pretrained=False, **kwargs): function ceit_tiny_patch16_384 (line 422) | def ceit_tiny_patch16_384(pretrained=False, **kwargs): function ceit_small_patch16_384 (line 438) | def ceit_small_patch16_384(pretrained=False, **kwargs): FILE: model/backbone/CoaT.py function _cfg_coat (line 29) | def _cfg_coat(url='', **kwargs): class Mlp (line 40) | class Mlp(nn.Module): method __init__ (line 42) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 51) | def forward(self, x): class ConvRelPosEnc (line 60) | class ConvRelPosEnc(nn.Module): method __init__ (line 62) | def __init__(self, Ch, h, window): method forward (line 97) | def forward(self, q, v, size): class FactorAtt_ConvRelPosEnc (line 119) | class FactorAtt_ConvRelPosEnc(nn.Module): method __init__ (line 121) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 135) | def forward(self, x, size): class ConvPosEnc (line 161) | class ConvPosEnc(nn.Module): method __init__ (line 165) | def __init__(self, dim, k=3): method forward (line 169) | def forward(self, x, size): class SerialBlock (line 188) | class SerialBlock(nn.Module): method __init__ (line 191) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 210) | def forward(self, x, size): class ParallelBlock (line 225) | class ParallelBlock(nn.Module): method __init__ (line 227) | def __init__(self, dims, num_heads, mlp_ratios=[], qkv_bias=False, qk_... method upsample (line 261) | def upsample(self, x, output_size, size): method downsample (line 265) | def downsample(self, x, output_size, size): method interpolate (line 269) | def interpolate(self, x, output_size, size): method forward (line 286) | def forward(self, x1, x2, x3, x4, sizes): class PatchEmbed (line 327) | class PatchEmbed(nn.Module): method __init__ (line 329) | def __init__(self, patch_size=16, in_chans=3, embed_dim=768): method forward (line 337) | def forward(self, x): class CoaT (line 347) | class CoaT(nn.Module): method __init__ (line 349) | def __init__(self, patch_size=16, in_chans=3, num_classes=1000, embed_... method _init_weights (line 461) | def _init_weights(self, m): method no_weight_decay (line 471) | def no_weight_decay(self): method get_classifier (line 474) | def get_classifier(self): method reset_classifier (line 477) | def reset_classifier(self, num_classes, global_pool=''): method insert_cls (line 481) | def insert_cls(self, x, cls_token): method remove_cls (line 487) | def remove_cls(self, x): method forward_features (line 491) | def forward_features(self, x0): method forward (line 578) | def forward(self, x): function coat_tiny (line 589) | def coat_tiny(**kwargs): function coat_mini (line 595) | def coat_mini(**kwargs): function coat_small (line 601) | def coat_small(**kwargs): function coat_lite_tiny (line 608) | def coat_lite_tiny(**kwargs): function coat_lite_mini (line 614) | def coat_lite_mini(**kwargs): function coat_lite_small (line 620) | def coat_lite_small(**kwargs): function coat_lite_medium (line 626) | def coat_lite_medium(**kwargs): FILE: model/backbone/ConTNet.py function _no_grad_trunc_normal_ (line 20) | def _no_grad_trunc_normal_(tensor, mean, std, a, b): function trunc_normal_ (line 56) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): function fixed_padding (line 76) | def fixed_padding(inputs, kernel_size, dilation): class ConvBN (line 84) | class ConvBN(nn.Sequential): method __init__ (line 85) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, group... class MHSA (line 99) | class MHSA(nn.Module): method __init__ (line 104) | def __init__(self, method forward (line 139) | def forward(self, x): class MLP (line 163) | class MLP(nn.Module): method __init__ (line 168) | def __init__(self, method forward (line 179) | def forward(self, x): class STE (line 189) | class STE(nn.Module): method __init__ (line 195) | def __init__(self, method forward (line 222) | def forward(self, x): class ConTBlock (line 250) | class ConTBlock(nn.Module): method __init__ (line 254) | def __init__(self, method forward (line 283) | def forward(self, x): class ConTNet (line 297) | class ConTNet(nn.Module): method __init__ (line 301) | def __init__(self, method _make_layer (line 382) | def _make_layer(self, method _initialize_weights (line 415) | def _initialize_weights(self): method forward (line 428) | def forward(self, x): function create_ConTNet_Ti (line 440) | def create_ConTNet_Ti(kwargs): function create_ConTNet_S (line 450) | def create_ConTNet_S(kwargs): function create_ConTNet_M (line 460) | def create_ConTNet_M(kwargs): function create_ConTNet_B (line 470) | def create_ConTNet_B(kwargs): function build_model (line 480) | def build_model(use_avgdown, relative, qkv_bias, pre_norm): FILE: model/backbone/ConViT.py class Mlp (line 26) | class Mlp(nn.Module): method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non... method _init_weights (line 37) | def _init_weights(self, m): method forward (line 46) | def forward(self, x): class GPSA (line 55) | class GPSA(nn.Module): method __init__ (line 56) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method _init_weights (line 77) | def _init_weights(self, m): method forward (line 86) | def forward(self, x): method get_attention (line 98) | def get_attention(self, x): method get_attention_map (line 114) | def get_attention_map(self, x, return_map = False): method local_init (line 125) | def local_init(self, locality_strength=1.): method get_rel_indices (line 140) | def get_rel_indices(self, num_patches): class MHSA (line 154) | class MHSA(nn.Module): method __init__ (line 155) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method _init_weights (line 167) | def _init_weights(self, m): method get_attention_map (line 176) | def get_attention_map(self, x, return_map = False): method forward (line 200) | def forward(self, x): class Block (line 214) | class Block(nn.Module): method __init__ (line 216) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_s... method forward (line 230) | def forward(self, x): class PatchEmbed (line 236) | class PatchEmbed(nn.Module): method __init__ (line 239) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 250) | def forward(self, x): method _init_weights (line 256) | def _init_weights(self, m): class HybridEmbed (line 266) | class HybridEmbed(nn.Module): method __init__ (line 269) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans... method forward (line 291) | def forward(self, x): class VisionTransformer (line 298) | class VisionTransformer(nn.Module): method __init__ (line 301) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 350) | def _init_weights(self, m): method no_weight_decay (line 360) | def no_weight_decay(self): method get_classifier (line 363) | def get_classifier(self): method reset_classifier (line 366) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 370) | def forward_features(self, x): method forward (line 388) | def forward(self, x): function convit_tiny (line 395) | def convit_tiny(pretrained=False, **kwargs): function convit_small (line 411) | def convit_small(pretrained=False, **kwargs): function convit_base (line 427) | def convit_base(pretrained=False, **kwargs): FILE: model/backbone/Container.py class Mlp (line 17) | class Mlp(nn.Module): method __init__ (line 19) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 28) | def forward(self, x): class CMlp (line 36) | class CMlp(nn.Module): method __init__ (line 38) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 47) | def forward(self, x): class Attention (line 57) | class Attention(nn.Module): method __init__ (line 59) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 71) | def forward(self, x): class Attention_pure (line 86) | class Attention_pure(nn.Module): method __init__ (line 88) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 98) | def forward(self, x): class MixBlock (line 112) | class MixBlock(nn.Module): method __init__ (line 114) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 135) | def forward(self, x): class CBlock (line 154) | class CBlock(nn.Module): method __init__ (line 156) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 170) | def forward(self, x): class Block (line 176) | class Block(nn.Module): method __init__ (line 178) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 192) | def forward(self, x): class PatchEmbed (line 199) | class PatchEmbed(nn.Module): method __init__ (line 203) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 214) | def forward(self, x): class HybridEmbed (line 226) | class HybridEmbed(nn.Module): method __init__ (line 228) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans... method forward (line 257) | def forward(self, x): class VisionTransformer (line 265) | class VisionTransformer(nn.Module): method __init__ (line 271) | def __init__(self, img_size=[224, 56, 28, 14], patch_size=[4, 2, 2, 2]... method _init_weights (line 356) | def _init_weights(self, m): method no_weight_decay (line 366) | def no_weight_decay(self): method get_classifier (line 369) | def get_classifier(self): method reset_classifier (line 372) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 376) | def forward_features(self, x): method forward (line 395) | def forward(self, x): function container_v1_light (line 404) | def container_v1_light(pretrained=False, **kwargs): FILE: model/backbone/ConvMixer.py class Residual (line 6) | class Residual(nn.Module): method __init__ (line 7) | def __init__(self,fn): method forward (line 10) | def forward(self,x): function ConvMixer (line 13) | def ConvMixer(dim,depth,kernel_size=9,patch_size=7,num_classes=1000): FILE: model/backbone/CrossViT.py class PatchEmbed (line 36) | class PatchEmbed(nn.Module): method __init__ (line 39) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 67) | def forward(self, x): class CrossAttention (line 76) | class CrossAttention(nn.Module): method __init__ (line 77) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 91) | def forward(self, x): class CrossAttentionBlock (line 108) | class CrossAttentionBlock(nn.Module): method __init__ (line 110) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 124) | def forward(self, x): class MultiScaleBlock (line 132) | class MultiScaleBlock(nn.Module): method __init__ (line 134) | def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias... method forward (line 186) | def forward(self, x): function _compute_num_patches (line 201) | def _compute_num_patches(img_size, patches): class VisionTransformer (line 205) | class VisionTransformer(nn.Module): method __init__ (line 208) | def __init__(self, img_size=(224, 224), patch_size=(8, 16), in_chans=3... method _init_weights (line 263) | def _init_weights(self, m): method no_weight_decay (line 273) | def no_weight_decay(self): method get_classifier (line 279) | def get_classifier(self): method reset_classifier (line 282) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 286) | def forward_features(self, x): method forward (line 307) | def forward(self, x): function crossvit_tiny_224 (line 317) | def crossvit_tiny_224(pretrained=False, **kwargs): function crossvit_small_224 (line 330) | def crossvit_small_224(pretrained=False, **kwargs): function crossvit_base_224 (line 343) | def crossvit_base_224(pretrained=False, **kwargs): function crossvit_9_224 (line 356) | def crossvit_9_224(pretrained=False, **kwargs): function crossvit_15_224 (line 369) | def crossvit_15_224(pretrained=False, **kwargs): function crossvit_18_224 (line 382) | def crossvit_18_224(pretrained=False, **kwargs): function crossvit_9_dagger_224 (line 395) | def crossvit_9_dagger_224(pretrained=False, **kwargs): function crossvit_15_dagger_224 (line 407) | def crossvit_15_dagger_224(pretrained=False, **kwargs): function crossvit_15_dagger_384 (line 419) | def crossvit_15_dagger_384(pretrained=False, **kwargs): function crossvit_18_dagger_224 (line 431) | def crossvit_18_dagger_224(pretrained=False, **kwargs): function crossvit_18_dagger_384 (line 443) | def crossvit_18_dagger_384(pretrained=False, **kwargs): FILE: model/backbone/DViT.py class Mlp (line 23) | class Mlp(nn.Module): method __init__ (line 24) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 34) | def forward(self, x): class Attention (line 42) | class Attention(nn.Module): method __init__ (line 43) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 56) | def forward(self, x, atten=None): class ReAttention (line 70) | class ReAttention(nn.Module): method __init__ (line 75) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 94) | def forward(self, x, atten=None): class Block (line 110) | class Block(nn.Module): method __init__ (line 112) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 135) | def forward(self, x, atten=None): class PatchEmbed_CNN (line 147) | class PatchEmbed_CNN(nn.Module): method __init__ (line 151) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 170) | def forward(self, x): class PatchEmbed (line 182) | class PatchEmbed(nn.Module): method __init__ (line 186) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 197) | def forward(self, x): class HybridEmbed (line 206) | class HybridEmbed(nn.Module): method __init__ (line 210) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans... method forward (line 231) | def forward(self, x): function _cfg (line 237) | def _cfg(url='', **kwargs): class DeepVisionTransformer (line 269) | class DeepVisionTransformer(nn.Module): method __init__ (line 272) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 314) | def _init_weights(self, m): method no_weight_decay (line 324) | def no_weight_decay(self): method get_classifier (line 327) | def get_classifier(self): method reset_classifier (line 330) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 334) | def forward_features(self, x): method forward (line 356) | def forward(self, x): function deepvit_patch16_224_re_attn_16b (line 368) | def deepvit_patch16_224_re_attn_16b(pretrained=False, **kwargs): function deepvit_patch16_224_re_attn_24b (line 381) | def deepvit_patch16_224_re_attn_24b(pretrained=False, **kwargs): function deepvit_patch16_224_re_attn_32b (line 394) | def deepvit_patch16_224_re_attn_32b(pretrained=False, **kwargs): function deepvit_S (line 406) | def deepvit_S(pretrained=False, **kwargs): function deepvit_L (line 418) | def deepvit_L(pretrained=False, **kwargs): function deepvit_L_384 (line 431) | def deepvit_L_384(pretrained=False, **kwargs): FILE: model/backbone/DeiT.py class DistilledVisionTransformer (line 21) | class DistilledVisionTransformer(VisionTransformer): method __init__ (line 22) | def __init__(self, *args, **kwargs): method forward_features (line 33) | def forward_features(self, x): method forward (line 52) | def forward(self, x): function deit_tiny_patch16_224 (line 64) | def deit_tiny_patch16_224(pretrained=False, **kwargs): function deit_small_patch16_224 (line 79) | def deit_small_patch16_224(pretrained=False, **kwargs): function deit_base_patch16_224 (line 94) | def deit_base_patch16_224(pretrained=False, **kwargs): function deit_tiny_distilled_patch16_224 (line 109) | def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs): function deit_small_distilled_patch16_224 (line 124) | def deit_small_distilled_patch16_224(pretrained=False, **kwargs): function deit_base_distilled_patch16_224 (line 139) | def deit_base_distilled_patch16_224(pretrained=False, **kwargs): function deit_base_patch16_384 (line 154) | def deit_base_patch16_384(pretrained=False, **kwargs): function deit_base_distilled_patch16_384 (line 169) | def deit_base_distilled_patch16_384(pretrained=False, **kwargs): FILE: model/backbone/EfficientFormer.py class Attention (line 30) | class Attention(torch.nn.Module): method __init__ (line 31) | def __init__(self, dim=384, key_dim=32, num_heads=8, method train (line 62) | def train(self, mode=True): method forward (line 69) | def forward(self, x): # x (B,N,C) function stem (line 89) | def stem(in_chs, out_chs): class Embedding (line 99) | class Embedding(nn.Module): method __init__ (line 106) | def __init__(self, patch_size=16, stride=16, padding=0, method forward (line 116) | def forward(self, x): class Flat (line 122) | class Flat(nn.Module): method __init__ (line 124) | def __init__(self, ): method forward (line 127) | def forward(self, x): class Pooling (line 132) | class Pooling(nn.Module): method __init__ (line 138) | def __init__(self, pool_size=3): method forward (line 143) | def forward(self, x): class LinearMlp (line 147) | class LinearMlp(nn.Module): method __init__ (line 151) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 162) | def forward(self, x): class Mlp (line 171) | class Mlp(nn.Module): method __init__ (line 177) | def __init__(self, in_features, hidden_features=None, method _init_weights (line 191) | def _init_weights(self, m): method forward (line 197) | def forward(self, x): class Meta3D (line 212) | class Meta3D(nn.Module): method __init__ (line 214) | def __init__(self, dim, mlp_ratio=4., method forward (line 237) | def forward(self, x): class Meta4D (line 252) | class Meta4D(nn.Module): method __init__ (line 254) | def __init__(self, dim, pool_size=3, mlp_ratio=4., method forward (line 274) | def forward(self, x): function meta_blocks (line 289) | def meta_blocks(dim, index, layers, class EfficientFormer (line 323) | class EfficientFormer(nn.Module): method __init__ (line 325) | def __init__(self, layers, embed_dims=None, method cls_init_weights (line 403) | def cls_init_weights(self, m): method init_weights (line 411) | def init_weights(self, pretrained=None): method forward_tokens (line 441) | def forward_tokens(self, x): method forward (line 453) | def forward(self, x): function _cfg (line 470) | def _cfg(url='', **kwargs): function efficientformer_l1 (line 482) | def efficientformer_l1(pretrained=False, **kwargs): function efficientformer_l3 (line 494) | def efficientformer_l3(pretrained=False, **kwargs): function efficientformer_l7 (line 506) | def efficientformer_l7(pretrained=False, **kwargs): FILE: model/backbone/HATNet.py class InvertedResidual (line 11) | class InvertedResidual(nn.Module): method __init__ (line 12) | def __init__(self, in_dim, hidden_dim=None, out_dim=None, kernel_size=3, method forward (line 33) | def forward(self, x): class Attention (line 43) | class Attention(nn.Module): method __init__ (line 44) | def __init__(self, dim, head_dim, grid_size=1, ds_ratio=1, drop=0.): method forward (line 65) | def forward(self, x): class Block (line 105) | class Block(nn.Module): method __init__ (line 106) | def __init__(self, dim, head_dim, grid_size=1, ds_ratio=1, expansion=4, method forward (line 114) | def forward(self, x): class Downsample (line 120) | class Downsample(nn.Module): method __init__ (line 121) | def __init__(self, in_dim, out_dim, kernel_size=3): method forward (line 126) | def forward(self, x): class HATNet (line 131) | class HATNet(nn.Module): method __init__ (line 132) | def __init__(self, img_size=224, in_chans=3, num_classes=1000, dims=[6... method reset_drop_path (line 166) | def reset_drop_path(self, drop_path_rate): method _init_weights (line 174) | def _init_weights(self, m): method forward (line 183) | def forward(self, x): FILE: model/backbone/LeViT.py function replace_batchnorm (line 15) | def replace_batchnorm(net): function LeViT_128S (line 48) | def LeViT_128S(num_classes=1000, distillation=True, function LeViT_128 (line 55) | def LeViT_128(num_classes=1000, distillation=True, function LeViT_192 (line 62) | def LeViT_192(num_classes=1000, distillation=True, function LeViT_256 (line 69) | def LeViT_256(num_classes=1000, distillation=True, function LeViT_384 (line 76) | def LeViT_384(num_classes=1000, distillation=True, class Conv2d_BN (line 85) | class Conv2d_BN(torch.nn.Sequential): method __init__ (line 86) | def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, method fuse (line 102) | def fuse(self): class Linear_BN (line 115) | class Linear_BN(torch.nn.Sequential): method __init__ (line 116) | def __init__(self, a, b, bn_weight_init=1, resolution=-100000): method fuse (line 129) | def fuse(self): method forward (line 140) | def forward(self, x): class BN_Linear (line 146) | class BN_Linear(torch.nn.Sequential): method __init__ (line 147) | def __init__(self, a, b, bias=True, std=0.02): method fuse (line 159) | def fuse(self): function b16 (line 175) | def b16(n, activation, resolution=224): class Residual (line 186) | class Residual(torch.nn.Module): method __init__ (line 187) | def __init__(self, m, drop): method forward (line 192) | def forward(self, x): class Attention (line 200) | class Attention(torch.nn.Module): method __init__ (line 201) | def __init__(self, dim, key_dim, num_heads=8, method train (line 242) | def train(self, mode=True): method forward (line 249) | def forward(self, x): # x (B,N,C) class Subsample (line 270) | class Subsample(torch.nn.Module): method __init__ (line 271) | def __init__(self, stride, resolution): method forward (line 276) | def forward(self, x): class AttentionSubsample (line 283) | class AttentionSubsample(torch.nn.Module): method __init__ (line 284) | def __init__(self, in_dim, out_dim, key_dim, num_heads=8, method train (line 342) | def train(self, mode=True): method forward (line 349) | def forward(self, x): class LeViT (line 368) | class LeViT(torch.nn.Module): method __init__ (line 372) | def __init__(self, img_size=224, method no_weight_decay (line 455) | def no_weight_decay(self): method forward (line 458) | def forward(self, x): function model_factory (line 472) | def model_factory(C, D, X, N, drop_path, weights, FILE: model/backbone/MobileNetV3.py function _cfg (line 26) | def _cfg(url='', **kwargs): class MobileNetV3 (line 107) | class MobileNetV3(nn.Module): method __init__ (line 119) | def __init__( method as_sequential (line 157) | def as_sequential(self): method group_matcher (line 165) | def group_matcher(self, coarse=False): method set_grad_checkpointing (line 172) | def set_grad_checkpointing(self, enable=True): method get_classifier (line 176) | def get_classifier(self): method reset_classifier (line 179) | def reset_classifier(self, num_classes, global_pool='avg'): method forward_features (line 186) | def forward_features(self, x): method forward_head (line 195) | def forward_head(self, x, pre_logits: bool = False): method forward (line 207) | def forward(self, x): class MobileNetV3Features (line 212) | class MobileNetV3Features(nn.Module): method __init__ (line 218) | def __init__( method forward (line 252) | def forward(self, x) -> List[torch.Tensor]: function _create_mnv3 (line 270) | def _create_mnv3(variant, pretrained=False, **kwargs): function _gen_mobilenet_v3_rw (line 287) | def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=Fal... function _gen_mobilenet_v3 (line 322) | def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False,... function _gen_fbnetv3 (line 416) | def _gen_fbnetv3(variant, channel_multiplier=1.0, pretrained=False, **kw... function _gen_lcnet (line 476) | def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwar... function _gen_lcnet (line 511) | def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwar... function mobilenetv3_large_075 (line 548) | def mobilenetv3_large_075(pretrained=False, **kwargs): function mobilenetv3_large_100 (line 555) | def mobilenetv3_large_100(pretrained=False, **kwargs): function mobilenetv3_large_100_miil (line 562) | def mobilenetv3_large_100_miil(pretrained=False, **kwargs): function mobilenetv3_large_100_miil_in21k (line 571) | def mobilenetv3_large_100_miil_in21k(pretrained=False, **kwargs): function mobilenetv3_small_050 (line 580) | def mobilenetv3_small_050(pretrained=False, **kwargs): function mobilenetv3_small_075 (line 587) | def mobilenetv3_small_075(pretrained=False, **kwargs): function mobilenetv3_small_100 (line 594) | def mobilenetv3_small_100(pretrained=False, **kwargs): function mobilenetv3_rw (line 601) | def mobilenetv3_rw(pretrained=False, **kwargs): function tf_mobilenetv3_large_075 (line 611) | def tf_mobilenetv3_large_075(pretrained=False, **kwargs): function tf_mobilenetv3_large_100 (line 620) | def tf_mobilenetv3_large_100(pretrained=False, **kwargs): function tf_mobilenetv3_large_minimal_100 (line 629) | def tf_mobilenetv3_large_minimal_100(pretrained=False, **kwargs): function tf_mobilenetv3_small_075 (line 638) | def tf_mobilenetv3_small_075(pretrained=False, **kwargs): function tf_mobilenetv3_small_100 (line 647) | def tf_mobilenetv3_small_100(pretrained=False, **kwargs): function tf_mobilenetv3_small_minimal_100 (line 656) | def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs): function fbnetv3_b (line 665) | def fbnetv3_b(pretrained=False, **kwargs): function fbnetv3_d (line 672) | def fbnetv3_d(pretrained=False, **kwargs): function fbnetv3_g (line 679) | def fbnetv3_g(pretrained=False, **kwargs): function lcnet_035 (line 686) | def lcnet_035(pretrained=False, **kwargs): function lcnet_050 (line 693) | def lcnet_050(pretrained=False, **kwargs): function lcnet_075 (line 700) | def lcnet_075(pretrained=False, **kwargs): function lcnet_100 (line 707) | def lcnet_100(pretrained=False, **kwargs): function lcnet_150 (line 714) | def lcnet_150(pretrained=False, **kwargs): FILE: model/backbone/MobileViT.py function conv_bn (line 9) | def conv_bn(inp,oup,kernel_size=3,stride=1): class PreNorm (line 16) | class PreNorm(nn.Module): method __init__ (line 17) | def __init__(self,dim,fn): method forward (line 21) | def forward(self,x,**kwargs): class FeedForward (line 24) | class FeedForward(nn.Module): method __init__ (line 25) | def __init__(self,dim,mlp_dim,dropout) : method forward (line 34) | def forward(self,x): class Attention (line 37) | class Attention(nn.Module): method __init__ (line 38) | def __init__(self,dim,heads,head_dim,dropout): method forward (line 54) | def forward(self,x): class Transformer (line 67) | class Transformer(nn.Module): method __init__ (line 68) | def __init__(self,dim,depth,heads,head_dim,mlp_dim,dropout=0.): method forward (line 78) | def forward(self,x): class MobileViTAttention (line 85) | class MobileViTAttention(nn.Module): method __init__ (line 86) | def __init__(self,in_channel=3,dim=512,kernel_size=3,patch_size=7,dept... method forward (line 97) | def forward(self,x): class MV2Block (line 117) | class MV2Block(nn.Module): method __init__ (line 118) | def __init__(self,inp,out,stride=1,expansion=4): method forward (line 144) | def forward(self,x): class MobileViT (line 151) | class MobileViT(nn.Module): method __init__ (line 152) | def __init__(self,image_size,dims,channels,num_classes,depths=[2,4,3],... method forward (line 179) | def forward(self,x): function mobilevit_xxs (line 199) | def mobilevit_xxs(): function mobilevit_xs (line 204) | def mobilevit_xs(): function mobilevit_s (line 209) | def mobilevit_s(): function count_paratermeters (line 215) | def count_paratermeters(model): FILE: model/backbone/PIT.py class Transformer (line 15) | class Transformer(nn.Module): method __init__ (line 16) | def __init__(self, base_dim, depth, heads, mlp_ratio, method forward (line 38) | def forward(self, x, cls_tokens): class conv_head_pooling (line 54) | class conv_head_pooling(nn.Module): method __init__ (line 55) | def __init__(self, in_feature, out_feature, stride, method forward (line 64) | def forward(self, x, cls_token): class conv_embedding (line 72) | class conv_embedding(nn.Module): method __init__ (line 73) | def __init__(self, in_channels, out_channels, patch_size, method forward (line 79) | def forward(self, x): class PoolingTransformer (line 84) | class PoolingTransformer(nn.Module): method __init__ (line 85) | def __init__(self, image_size, patch_size, stride, base_dims, depth, h... method _init_weights (line 149) | def _init_weights(self, m): method no_weight_decay (line 155) | def no_weight_decay(self): method get_classifier (line 158) | def get_classifier(self): method reset_classifier (line 161) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 168) | def forward_features(self, x): method forward (line 184) | def forward(self, x): class DistilledPoolingTransformer (line 190) | class DistilledPoolingTransformer(PoolingTransformer): method __init__ (line 191) | def __init__(self, *args, **kwargs): method forward (line 205) | def forward(self, x): function pit_b (line 215) | def pit_b(pretrained, **kwargs): function pit_s (line 233) | def pit_s(pretrained, **kwargs): function pit_xs (line 252) | def pit_xs(pretrained, **kwargs): function pit_ti (line 270) | def pit_ti(pretrained, **kwargs): function pit_b_distilled (line 289) | def pit_b_distilled(pretrained, **kwargs): function pit_s_distilled (line 308) | def pit_s_distilled(pretrained, **kwargs): function pit_xs_distilled (line 327) | def pit_xs_distilled(pretrained, **kwargs): function pit_ti_distilled (line 346) | def pit_ti_distilled(pretrained, **kwargs): FILE: model/backbone/PVT.py class Mlp (line 15) | class Mlp(nn.Module): method __init__ (line 16) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 25) | def forward(self, x): class Attention (line 34) | class Attention(nn.Module): method __init__ (line 35) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 55) | def forward(self, x, H, W): class Block (line 79) | class Block(nn.Module): method __init__ (line 81) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 95) | def forward(self, x, H, W): class PatchEmbed (line 102) | class PatchEmbed(nn.Module): method __init__ (line 106) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 120) | def forward(self, x): class PyramidVisionTransformer (line 130) | class PyramidVisionTransformer(nn.Module): method __init__ (line 131) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 180) | def _init_weights(self, m): method no_weight_decay (line 190) | def no_weight_decay(self): method get_classifier (line 194) | def get_classifier(self): method reset_classifier (line 197) | def reset_classifier(self, num_classes, global_pool=''): method _get_pos_embed (line 201) | def _get_pos_embed(self, pos_embed, patch_embed, H, W): method forward_features (line 209) | def forward_features(self, x): method forward (line 237) | def forward(self, x): function _conv_filter (line 244) | def _conv_filter(state_dict, patch_size=16): function pvt_tiny (line 256) | def pvt_tiny(pretrained=False, **kwargs): function pvt_small (line 267) | def pvt_small(pretrained=False, **kwargs): function pvt_medium (line 277) | def pvt_medium(pretrained=False, **kwargs): function pvt_large (line 288) | def pvt_large(pretrained=False, **kwargs): function pvt_huge_v2 (line 299) | def pvt_huge_v2(pretrained=False, **kwargs): FILE: model/backbone/PatchConvnet.py class Mlp (line 18) | class Mlp(nn.Module): method __init__ (line 19) | def __init__( method forward (line 35) | def forward(self, x: torch.Tensor) -> torch.Tensor: class Learned_Aggregation_Layer (line 44) | class Learned_Aggregation_Layer(nn.Module): method __init__ (line 45) | def __init__( method forward (line 68) | def forward(self, x: torch.Tensor) -> torch.Tensor: class Learned_Aggregation_Layer_multi (line 88) | class Learned_Aggregation_Layer_multi(nn.Module): method __init__ (line 89) | def __init__( method forward (line 112) | def forward(self, x: torch.Tensor) -> torch.Tensor: class Layer_scale_init_Block_only_token (line 143) | class Layer_scale_init_Block_only_token(nn.Module): method __init__ (line 144) | def __init__( method forward (line 173) | def forward(self, x: torch.Tensor, x_cls: torch.Tensor) -> torch.Tensor: class Conv_blocks_se (line 180) | class Conv_blocks_se(nn.Module): method __init__ (line 181) | def __init__(self, dim: int): method forward (line 193) | def forward(self, x: torch.Tensor) -> torch.Tensor: class Layer_scale_init_Block (line 204) | class Layer_scale_init_Block(nn.Module): method __init__ (line 205) | def __init__( method forward (line 220) | def forward(self, x: torch.Tensor) -> torch.Tensor: function conv3x3 (line 224) | def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Sequ... class ConvStem (line 231) | class ConvStem(nn.Module): method __init__ (line 234) | def __init__(self, img_size: int = 224, patch_size: int = 16, in_chans... method forward (line 253) | def forward(self, x: torch.Tensor, padding_size: Optional[int] = None)... class PatchConvnet (line 259) | class PatchConvnet(nn.Module): method __init__ (line 260) | def __init__( method _init_weights (line 360) | def _init_weights(self, m): method no_weight_decay (line 370) | def no_weight_decay(self): method get_classifier (line 373) | def get_classifier(self): method get_num_layers (line 376) | def get_num_layers(self): method reset_classifier (line 379) | def reset_classifier(self, num_classes: int, global_pool: str = ''): method forward_features (line 383) | def forward_features(self, x: torch.Tensor) -> torch.Tensor: method forward (line 402) | def forward(self, x: torch.Tensor) -> torch.Tensor: function S60 (line 416) | def S60(pretrained: bool = False, **kwargs): function S120 (line 435) | def S120(pretrained: bool = False, **kwargs): function B60 (line 454) | def B60(pretrained: bool = False, **kwargs): function B120 (line 471) | def B120(pretrained: bool = False, **kwargs): function L60 (line 489) | def L60(pretrained: bool = False, **kwargs): function L120 (line 508) | def L120(pretrained: bool = False, **kwargs): function S60_multi (line 527) | def S60_multi(pretrained: bool = False, **kwargs): FILE: model/backbone/ShuffleTransformer.py class Mlp (line 8) | class Mlp(nn.Module): method __init__ (line 9) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 19) | def forward(self, x): class Attention (line 27) | class Attention(nn.Module): method __init__ (line 28) | def __init__(self, dim, num_heads, window_size=1, shuffle=False, qkv_b... method forward (line 65) | def forward(self, x): class Block (line 95) | class Block(nn.Module): method __init__ (line 96) | def __init__(self, dim, out_dim, num_heads, window_size=1, shuffle=Fal... method forward (line 111) | def forward(self, x): class PatchMerging (line 118) | class PatchMerging(nn.Module): method __init__ (line 119) | def __init__(self, dim, out_dim, norm_layer=nn.BatchNorm2d): method forward (line 126) | def forward(self, x): method extra_repr (line 131) | def extra_repr(self) -> str: class StageModule (line 135) | class StageModule(nn.Module): method __init__ (line 136) | def __init__(self, layers, dim, out_dim, num_heads, window_size=1, shu... method forward (line 159) | def forward(self, x): class PatchEmbedding (line 169) | class PatchEmbedding(nn.Module): method __init__ (line 170) | def __init__(self, inter_channel=32, out_channels=48): method forward (line 184) | def forward(self, x): class ShuffleTransformer (line 189) | class ShuffleTransformer(nn.Module): method __init__ (line 190) | def __init__(self, img_size=224, in_chans=3, num_classes=1000, token_d... method _init_weights (line 227) | def _init_weights(self, m): method no_weight_decay (line 237) | def no_weight_decay(self): method no_weight_decay_keywords (line 241) | def no_weight_decay_keywords(self): method get_classifier (line 244) | def get_classifier(self): method reset_classifier (line 247) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 251) | def forward_features(self, x): method forward (line 268) | def forward(self, x): FILE: model/backbone/TnT.py function _cfg (line 15) | def _cfg(url='', **kwargs): function make_divisible (line 36) | def make_divisible(v, divisor=8, min_value=None): class Mlp (line 45) | class Mlp(nn.Module): method __init__ (line 46) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 55) | def forward(self, x): class SE (line 64) | class SE(nn.Module): method __init__ (line 65) | def __init__(self, dim, hidden_ratio=None): method forward (line 78) | def forward(self, x): class Attention (line 85) | class Attention(nn.Module): method __init__ (line 86) | def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, qk_sc... method forward (line 101) | def forward(self, x): class Block (line 117) | class Block(nn.Module): method __init__ (line 120) | def __init__(self, outer_dim, inner_dim, outer_num_heads, inner_num_he... method forward (line 153) | def forward(self, inner_tokens, outer_tokens): class PatchEmbed (line 169) | class PatchEmbed(nn.Module): method __init__ (line 172) | def __init__(self, img_size=224, patch_size=16, in_chans=3, outer_dim=... method forward (line 186) | def forward(self, x): class TNT (line 198) | class TNT(nn.Module): method __init__ (line 201) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 253) | def _init_weights(self, m): method no_weight_decay (line 263) | def no_weight_decay(self): method get_classifier (line 266) | def get_classifier(self): method reset_classifier (line 269) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 273) | def forward_features(self, x): method forward (line 289) | def forward(self, x): function _conv_filter (line 295) | def _conv_filter(state_dict, patch_size=16): function tnt_s_patch16_224 (line 306) | def tnt_s_patch16_224(pretrained=False, **kwargs): function tnt_b_patch16_224 (line 326) | def tnt_b_patch16_224(pretrained=False, **kwargs): FILE: model/backbone/VOLO.py function _cfg (line 28) | def _cfg(url='', **kwargs): class OutlookAttention (line 45) | class OutlookAttention(nn.Module): method __init__ (line 54) | def __init__(self, dim, num_heads, kernel_size=3, padding=1, stride=1, method forward (line 74) | def forward(self, x): class Outlooker (line 103) | class Outlooker(nn.Module): method __init__ (line 113) | def __init__(self, dim, kernel_size, padding, stride=1, method forward (line 134) | def forward(self, x): class Mlp (line 140) | class Mlp(nn.Module): method __init__ (line 143) | def __init__(self, in_features, hidden_features=None, method forward (line 154) | def forward(self, x): class Attention (line 163) | class Attention(nn.Module): method __init__ (line 166) | def __init__(self, dim, num_heads=8, qkv_bias=False, method forward (line 178) | def forward(self, x): class Transformer (line 197) | class Transformer(nn.Module): method __init__ (line 202) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, method forward (line 220) | def forward(self, x): class ClassAttention (line 226) | class ClassAttention(nn.Module): method __init__ (line 231) | def __init__(self, dim, num_heads=8, head_dim=None, qkv_bias=False, method forward (line 250) | def forward(self, x): class ClassBlock (line 269) | class ClassBlock(nn.Module): method __init__ (line 275) | def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., method forward (line 293) | def forward(self, x): function get_block (line 300) | def get_block(block_type, **kargs): function rand_bbox (line 308) | def rand_bbox(size, lam, scale=1): class PatchEmbed (line 331) | class PatchEmbed(nn.Module): method __init__ (line 337) | def __init__(self, img_size=224, stem_conv=False, stem_stride=1, method forward (line 365) | def forward(self, x): class Downsample (line 372) | class Downsample(nn.Module): method __init__ (line 376) | def __init__(self, in_embed_dim, out_embed_dim, patch_size): method forward (line 381) | def forward(self, x): function outlooker_blocks (line 388) | def outlooker_blocks(block_fn, index, dim, layers, num_heads=1, kernel_s... function transformer_blocks (line 409) | def transformer_blocks(block_fn, index, dim, layers, num_heads, mlp_rati... class VOLO (line 433) | class VOLO(nn.Module): method __init__ (line 458) | def __init__(self, layers, img_size=224, in_chans=3, num_classes=1000,... method _init_weights (line 548) | def _init_weights(self, m): method no_weight_decay (line 558) | def no_weight_decay(self): method get_classifier (line 561) | def get_classifier(self): method reset_classifier (line 564) | def reset_classifier(self, num_classes): method forward_embeddings (line 569) | def forward_embeddings(self, x): method forward_tokens (line 576) | def forward_tokens(self, x): method forward_cls (line 587) | def forward_cls(self, x): method forward (line 595) | def forward(self, x): function volo_d1 (line 649) | def volo_d1(pretrained=False, **kwargs): function volo_d2 (line 682) | def volo_d2(pretrained=False, **kwargs): function volo_d3 (line 705) | def volo_d3(pretrained=False, **kwargs): function volo_d4 (line 728) | def volo_d4(pretrained=False, **kwargs): function volo_d5 (line 751) | def volo_d5(pretrained=False, **kwargs): FILE: model/backbone/convnextv2.py class Block (line 13) | class Block(nn.Module): method __init__ (line 20) | def __init__(self, dim, drop_path=0.): method forward (line 30) | def forward(self, x): class LayerNorm (line 44) | class LayerNorm(nn.Module): method __init__ (line 50) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l... method forward (line 60) | def forward(self, x): class GRN (line 70) | class GRN(nn.Module): method __init__ (line 73) | def __init__(self, dim): method forward (line 78) | def forward(self, x): class ConvNeXtV2 (line 84) | class ConvNeXtV2(nn.Module): method __init__ (line 95) | def __init__(self, in_chans=3, num_classes=1000, method _init_weights (line 131) | def _init_weights(self, m): method forward_features (line 136) | def forward_features(self, x): method forward (line 142) | def forward(self, x): function convnextv2_atto (line 147) | def convnextv2_atto(**kwargs): function convnextv2_femto (line 151) | def convnextv2_femto(**kwargs): function convnext_pico (line 155) | def convnext_pico(**kwargs): function convnextv2_nano (line 159) | def convnextv2_nano(**kwargs): function convnextv2_tiny (line 163) | def convnextv2_tiny(**kwargs): function convnextv2_base (line 167) | def convnextv2_base(**kwargs): function convnextv2_large (line 171) | def convnextv2_large(**kwargs): function convnextv2_huge (line 175) | def convnextv2_huge(**kwargs): FILE: model/backbone/resnet.py class BottleNeck (line 10) | class BottleNeck(nn.Module): method __init__ (line 12) | def __init__(self,in_channel,channel,stride=1,downsample=None): method forward (line 29) | def forward(self,x): class ResNet (line 43) | class ResNet(nn.Module): method __init__ (line 44) | def __init__(self,block,layers,num_classes=1000): method forward (line 65) | def forward(self,x): method _make_layer (line 86) | def _make_layer(self,block,channel,blocks,stride=1): function ResNet50 (line 101) | def ResNet50(num_classes=1000): function ResNet101 (line 105) | def ResNet101(num_classes=1000): function ResNet152 (line 109) | def ResNet152(num_classes=1000): FILE: model/backbone/resnext.py class BottleNeck (line 10) | class BottleNeck(nn.Module): method __init__ (line 12) | def __init__(self,in_channel,channel,stride=1,C=32,downsample=None): method forward (line 29) | def forward(self,x): class ResNeXt (line 43) | class ResNeXt(nn.Module): method __init__ (line 44) | def __init__(self,block,layers,num_classes=1000): method forward (line 65) | def forward(self,x): method _make_layer (line 86) | def _make_layer(self,block,channel,blocks,stride=1): function ResNeXt50 (line 101) | def ResNeXt50(num_classes=1000): function ResNeXt101 (line 105) | def ResNeXt101(num_classes=1000): function ResNeXt152 (line 109) | def ResNeXt152(num_classes=1000): FILE: model/backbone/swin_transformer.py function _cfg (line 35) | def _cfg(url='', **kwargs): function window_partition (line 99) | def window_partition(x, window_size: int): function window_reverse (line 114) | def window_reverse(windows, window_size: int, H: int, W: int): function get_relative_position_index (line 130) | def get_relative_position_index(win_h, win_w): class WindowAttention (line 142) | class WindowAttention(nn.Module): method __init__ (line 155) | def __init__(self, dim, num_heads, head_dim=None, window_size=7, qkv_b... method _get_rel_pos_bias (line 181) | def _get_rel_pos_bias(self) -> torch.Tensor: method forward (line 187) | def forward(self, x, mask: Optional[torch.Tensor] = None): class SwinTransformerBlock (line 217) | class SwinTransformerBlock(nn.Module): method __init__ (line 235) | def __init__( method forward (line 284) | def forward(self, x): class PatchMerging (line 324) | class PatchMerging(nn.Module): method __init__ (line 332) | def __init__(self, input_resolution, dim, out_dim=None, norm_layer=nn.... method forward (line 340) | def forward(self, x): class BasicLayer (line 364) | class BasicLayer(nn.Module): method __init__ (line 382) | def __init__( method forward (line 408) | def forward(self, x): class SwinTransformer (line 418) | class SwinTransformer(nn.Module): method __init__ (line 442) | def __init__( method init_weights (line 502) | def init_weights(self, mode=''): method no_weight_decay (line 510) | def no_weight_decay(self): method group_matcher (line 518) | def group_matcher(self, coarse=False): method set_grad_checkpointing (line 529) | def set_grad_checkpointing(self, enable=True): method get_classifier (line 534) | def get_classifier(self): method reset_classifier (line 537) | def reset_classifier(self, num_classes, global_pool=None): method forward_features (line 544) | def forward_features(self, x): method forward_head (line 553) | def forward_head(self, x, pre_logits: bool = False): method forward (line 558) | def forward(self, x): function _create_swin_transformer (line 564) | def _create_swin_transformer(variant, pretrained=False, **kwargs): function swin_base_patch4_window12_384 (line 574) | def swin_base_patch4_window12_384(pretrained=False, **kwargs): function swin_base_patch4_window7_224 (line 583) | def swin_base_patch4_window7_224(pretrained=False, **kwargs): function swin_large_patch4_window12_384 (line 592) | def swin_large_patch4_window12_384(pretrained=False, **kwargs): function swin_large_patch4_window7_224 (line 601) | def swin_large_patch4_window7_224(pretrained=False, **kwargs): function swin_small_patch4_window7_224 (line 610) | def swin_small_patch4_window7_224(pretrained=False, **kwargs): function swin_tiny_patch4_window7_224 (line 619) | def swin_tiny_patch4_window7_224(pretrained=False, **kwargs): function swin_base_patch4_window12_384_in22k (line 628) | def swin_base_patch4_window12_384_in22k(pretrained=False, **kwargs): function swin_base_patch4_window7_224_in22k (line 637) | def swin_base_patch4_window7_224_in22k(pretrained=False, **kwargs): function swin_large_patch4_window12_384_in22k (line 646) | def swin_large_patch4_window12_384_in22k(pretrained=False, **kwargs): function swin_large_patch4_window7_224_in22k (line 655) | def swin_large_patch4_window7_224_in22k(pretrained=False, **kwargs): function swin_s3_tiny_224 (line 664) | def swin_s3_tiny_224(pretrained=False, **kwargs): function swin_s3_small_224 (line 674) | def swin_s3_small_224(pretrained=False, **kwargs): function swin_s3_base_224 (line 684) | def swin_s3_base_224(pretrained=False, **kwargs): FILE: model/backbone/swin_transformer_v2.py function _cfg (line 30) | def _cfg(url='', **kwargs): function window_partition (line 94) | def window_partition(x, window_size: Tuple[int, int]): function window_reverse (line 109) | def window_reverse(windows, window_size: Tuple[int, int], img_size: Tupl... class WindowAttention (line 125) | class WindowAttention(nn.Module): method __init__ (line 138) | def __init__( method forward (line 202) | def forward(self, x, mask: Optional[torch.Tensor] = None): class SwinTransformerBlock (line 244) | class SwinTransformerBlock(nn.Module): method __init__ (line 262) | def __init__( method _calc_window_shift (line 311) | def _calc_window_shift(self, target_window_size, target_shift_size) ->... method _attn (line 318) | def _attn(self, x): method forward (line 350) | def forward(self, x): class PatchMerging (line 356) | class PatchMerging(nn.Module): method __init__ (line 364) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): method forward (line 371) | def forward(self, x): class BasicLayer (line 396) | class BasicLayer(nn.Module): method __init__ (line 414) | def __init__( method forward (line 445) | def forward(self, x): method _init_respostnorm (line 454) | def _init_respostnorm(self): class SwinTransformerV2 (line 462) | class SwinTransformerV2(nn.Module): method __init__ (line 487) | def __init__( method _init_weights (line 550) | def _init_weights(self, m): method no_weight_decay (line 557) | def no_weight_decay(self): method group_matcher (line 565) | def group_matcher(self, coarse=False): method set_grad_checkpointing (line 576) | def set_grad_checkpointing(self, enable=True): method get_classifier (line 581) | def get_classifier(self): method reset_classifier (line 584) | def reset_classifier(self, num_classes, global_pool=None): method forward_features (line 591) | def forward_features(self, x): method forward_head (line 603) | def forward_head(self, x, pre_logits: bool = False): method forward (line 608) | def forward(self, x): function checkpoint_filter_fn (line 614) | def checkpoint_filter_fn(state_dict, model): function _create_swin_transformer_v2 (line 626) | def _create_swin_transformer_v2(variant, pretrained=False, **kwargs): function swinv2_tiny_window16_256 (line 635) | def swinv2_tiny_window16_256(pretrained=False, **kwargs): function swinv2_tiny_window8_256 (line 644) | def swinv2_tiny_window8_256(pretrained=False, **kwargs): function swinv2_small_window16_256 (line 653) | def swinv2_small_window16_256(pretrained=False, **kwargs): function swinv2_small_window8_256 (line 662) | def swinv2_small_window8_256(pretrained=False, **kwargs): function swinv2_base_window16_256 (line 671) | def swinv2_base_window16_256(pretrained=False, **kwargs): function swinv2_base_window8_256 (line 680) | def swinv2_base_window8_256(pretrained=False, **kwargs): function swinv2_base_window12_192_22k (line 689) | def swinv2_base_window12_192_22k(pretrained=False, **kwargs): function swinv2_base_window12to16_192to256_22kft1k (line 698) | def swinv2_base_window12to16_192to256_22kft1k(pretrained=False, **kwargs): function swinv2_base_window12to24_192to384_22kft1k (line 709) | def swinv2_base_window12to24_192to384_22kft1k(pretrained=False, **kwargs): function swinv2_large_window12_192_22k (line 720) | def swinv2_large_window12_192_22k(pretrained=False, **kwargs): function swinv2_large_window12to16_192to256_22kft1k (line 729) | def swinv2_large_window12to16_192to256_22kft1k(pretrained=False, **kwargs): function swinv2_large_window12to24_192to384_22kft1k (line 740) | def swinv2_large_window12to24_192to384_22kft1k(pretrained=False, **kwargs): FILE: model/backbone/swin_transformer_v2_cr.py function _cfg (line 46) | def _cfg(url='', **kwargs): function bchw_to_bhwc (line 100) | def bchw_to_bhwc(x: torch.Tensor) -> torch.Tensor: function bhwc_to_bchw (line 105) | def bhwc_to_bchw(x: torch.Tensor) -> torch.Tensor: function window_partition (line 110) | def window_partition(x, window_size: Tuple[int, int]): function window_reverse (line 125) | def window_reverse(windows, window_size: Tuple[int, int], img_size: Tupl... class WindowMultiHeadAttention (line 141) | class WindowMultiHeadAttention(nn.Module): method __init__ (line 153) | def __init__( method _make_pair_wise_relative_positions (line 187) | def _make_pair_wise_relative_positions(self) -> None: method update_input_size (line 199) | def update_input_size(self, new_window_size: int, **kwargs: Any) -> None: method _relative_positional_encodings (line 209) | def _relative_positional_encodings(self) -> torch.Tensor: method _forward_sequential (line 223) | def _forward_sequential( method _forward_batch (line 233) | def _forward_batch( method forward (line 265) | def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None... class SwinTransformerBlock (line 279) | class SwinTransformerBlock(nn.Module): method __init__ (line 296) | def __init__( method _calc_window_shift (line 349) | def _calc_window_shift(self, target_window_size): method _make_attention_mask (line 354) | def _make_attention_mask(self) -> None: method init_weights (line 380) | def init_weights(self): method update_input_size (line 386) | def update_input_size(self, new_window_size: Tuple[int, int], new_feat... method _shifted_window_attn (line 399) | def _shifted_window_attn(self, x): method forward (line 434) | def forward(self, x: torch.Tensor) -> torch.Tensor: class PatchMerging (line 448) | class PatchMerging(nn.Module): method __init__ (line 455) | def __init__(self, dim: int, norm_layer: Type[nn.Module] = nn.LayerNor... method forward (line 460) | def forward(self, x: torch.Tensor) -> torch.Tensor: class PatchEmbed (line 476) | class PatchEmbed(nn.Module): method __init__ (line 478) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 490) | def forward(self, x): class SwinTransformerStage (line 499) | class SwinTransformerStage(nn.Module): method __init__ (line 518) | def __init__( method update_input_size (line 569) | def update_input_size(self, new_window_size: int, new_feat_size: Tuple... method forward (line 581) | def forward(self, x: torch.Tensor) -> torch.Tensor: class SwinTransformerV2Cr (line 603) | class SwinTransformerV2Cr(nn.Module): method __init__ (line 627) | def __init__( method update_input_size (line 700) | def update_input_size( method group_matcher (line 729) | def group_matcher(self, coarse=False): method set_grad_checkpointing (line 739) | def set_grad_checkpointing(self, enable=True): method get_classifier (line 744) | def get_classifier(self) -> nn.Module: method reset_classifier (line 751) | def reset_classifier(self, num_classes: int, global_pool: Optional[str... method forward_features (line 762) | def forward_features(self, x: torch.Tensor) -> torch.Tensor: method forward_head (line 767) | def forward_head(self, x, pre_logits: bool = False): method forward (line 772) | def forward(self, x: torch.Tensor) -> torch.Tensor: function init_weights (line 778) | def init_weights(module: nn.Module, name: str = ''): function checkpoint_filter_fn (line 795) | def checkpoint_filter_fn(state_dict, model): function _create_swin_transformer_v2_cr (line 810) | def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs): function swinv2_cr_tiny_384 (line 822) | def swinv2_cr_tiny_384(pretrained=False, **kwargs): function swinv2_cr_tiny_224 (line 834) | def swinv2_cr_tiny_224(pretrained=False, **kwargs): function swinv2_cr_tiny_ns_224 (line 846) | def swinv2_cr_tiny_ns_224(pretrained=False, **kwargs): function swinv2_cr_small_384 (line 861) | def swinv2_cr_small_384(pretrained=False, **kwargs): function swinv2_cr_small_224 (line 874) | def swinv2_cr_small_224(pretrained=False, **kwargs): function swinv2_cr_small_ns_224 (line 886) | def swinv2_cr_small_ns_224(pretrained=False, **kwargs): function swinv2_cr_base_384 (line 899) | def swinv2_cr_base_384(pretrained=False, **kwargs): function swinv2_cr_base_224 (line 911) | def swinv2_cr_base_224(pretrained=False, **kwargs): function swinv2_cr_base_ns_224 (line 923) | def swinv2_cr_base_ns_224(pretrained=False, **kwargs): function swinv2_cr_large_384 (line 936) | def swinv2_cr_large_384(pretrained=False, **kwargs): function swinv2_cr_large_224 (line 949) | def swinv2_cr_large_224(pretrained=False, **kwargs): function swinv2_cr_huge_384 (line 961) | def swinv2_cr_huge_384(pretrained=False, **kwargs): function swinv2_cr_huge_224 (line 974) | def swinv2_cr_huge_224(pretrained=False, **kwargs): function swinv2_cr_giant_384 (line 987) | def swinv2_cr_giant_384(pretrained=False, **kwargs): function swinv2_cr_giant_224 (line 1001) | def swinv2_cr_giant_224(pretrained=False, **kwargs): FILE: model/conv/CondConv.py class Attention (line 5) | class Attention(nn.Module): method __init__ (line 6) | def __init__(self,in_planes,K,init_weight=True): method _initialize_weights (line 16) | def _initialize_weights(self): method forward (line 26) | def forward(self,x): class CondConv (line 31) | class CondConv(nn.Module): method __init__ (line 32) | def __init__(self,in_planes,out_planes,kernel_size,stride,padding=0,di... method _initialize_weights (line 56) | def _initialize_weights(self): method forward (line 60) | def forward(self,x): FILE: model/conv/DepthwiseSeparableConvolution.py class DepthwiseSeparableConvolution (line 4) | class DepthwiseSeparableConvolution(nn.Module): method __init__ (line 5) | def __init__(self,in_ch,out_ch,kernel_size=3,stride=1,padding=1): method forward (line 24) | def forward(self, x): FILE: model/conv/DynamicConv.py class Attention (line 5) | class Attention(nn.Module): method __init__ (line 6) | def __init__(self,in_planes,ratio,K,temprature=30,init_weight=True): method update_temprature (line 21) | def update_temprature(self): method _initialize_weights (line 25) | def _initialize_weights(self): method forward (line 35) | def forward(self,x): class DynamicConv (line 40) | class DynamicConv(nn.Module): method __init__ (line 41) | def __init__(self,in_planes,out_planes,kernel_size,stride,padding=0,di... method _initialize_weights (line 65) | def _initialize_weights(self): method forward (line 69) | def forward(self,x): FILE: model/conv/HorNet.py function get_dwconv (line 9) | def get_dwconv(dim, kernel, bias): class GlobalLocalFilter (line 12) | class GlobalLocalFilter(nn.Module): method __init__ (line 14) | def __init__(self, dim, h=14, w=8): method forward (line 22) | def forward(self, x): class gnconv (line 45) | class gnconv(nn.Module): method __init__ (line 46) | def __init__(self, dim, order=5, gflayer=None, h=14, w=8, s=1.0): method forward (line 66) | def forward(self, x, mask=None, dummy=False): class Block (line 84) | class Block(nn.Module): method __init__ (line 87) | def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6, gnc... method forward (line 104) | def forward(self, x): class HorNet (line 126) | class HorNet(nn.Module): method __init__ (line 127) | def __init__(self, in_chans=3, num_classes=1000, method _init_weights (line 176) | def _init_weights(self, m): method forward_features (line 188) | def forward_features(self, x): method forward (line 195) | def forward(self, x): class LayerNorm (line 200) | class LayerNorm(nn.Module): method __init__ (line 206) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l... method forward (line 216) | def forward(self, x): function hornet_tiny_7x7 (line 227) | def hornet_tiny_7x7(pretrained=False,in_22k=False, **kwargs): function hornet_tiny_gf (line 241) | def hornet_tiny_gf(pretrained=False,in_22k=False, **kwargs): function hornet_small_7x7 (line 255) | def hornet_small_7x7(pretrained=False,in_22k=False, **kwargs): function hornet_small_gf (line 269) | def hornet_small_gf(pretrained=False,in_22k=False, **kwargs): function hornet_base_7x7 (line 283) | def hornet_base_7x7(pretrained=False,in_22k=False, **kwargs): function hornet_base_gf (line 297) | def hornet_base_gf(pretrained=False,in_22k=False, **kwargs): function hornet_base_gf_img384 (line 311) | def hornet_base_gf_img384(pretrained=False,in_22k=False, **kwargs): function hornet_large_7x7 (line 325) | def hornet_large_7x7(pretrained=False,in_22k=False, **kwargs): function hornet_large_gf (line 339) | def hornet_large_gf(pretrained=False,in_22k=False, **kwargs): function hornet_large_gf_img384 (line 353) | def hornet_large_gf_img384(pretrained=False,in_22k=False, **kwargs): FILE: model/conv/Involution.py class Involution (line 9) | class Involution(nn.Module): method __init__ (line 10) | def __init__(self, kernel_size, in_channel=4, stride=1, group=1,ratio=4): method forward (line 34) | def forward(self, inputs): FILE: model/conv/MBConv.py class SwishImplementation (line 8) | class SwishImplementation(torch.autograd.Function): method forward (line 10) | def forward(ctx, i): method backward (line 16) | def backward(ctx, grad_output): class MemoryEfficientSwish (line 21) | class MemoryEfficientSwish(nn.Module): method forward (line 22) | def forward(self, x): function drop_connect (line 26) | def drop_connect(inputs, p, training): function get_same_padding_conv2d (line 38) | def get_same_padding_conv2d(image_size=None): function get_width_and_height_from_size (line 41) | def get_width_and_height_from_size(x): function calculate_output_image_size (line 47) | def calculate_output_image_size(input_image_size, stride): class Conv2dStaticSamePadding (line 60) | class Conv2dStaticSamePadding(nn.Conv2d): method __init__ (line 63) | def __init__(self, in_channels, out_channels, kernel_size, image_size=... method forward (line 80) | def forward(self, x): class Identity (line 85) | class Identity(nn.Module): method __init__ (line 86) | def __init__(self, ): method forward (line 89) | def forward(self, input): class MBConvBlock (line 94) | class MBConvBlock(nn.Module): method __init__ (line 98) | def __init__(self, ksize, input_filters, output_filters, expand_ratio=... method forward (line 140) | def forward(self, inputs, drop_connect_rate=None): FILE: model/mlp/g_mlp.py function exist (line 6) | def exist(x): class Residual (line 9) | class Residual(nn.Module): method __init__ (line 10) | def __init__(self,fn): method forward (line 14) | def forward(self,x): class SpatialGatingUnit (line 17) | class SpatialGatingUnit(nn.Module): method __init__ (line 18) | def __init__(self,dim,len_sen): method forward (line 26) | def forward(self,x): class gMLP (line 35) | class gMLP(nn.Module): method __init__ (line 36) | def __init__(self,num_tokens=None,len_sen=49,dim=512,d_ff=1024,num_lay... method forward (line 58) | def forward(self,x): FILE: model/mlp/mlp_mixer.py class MlpBlock (line 4) | class MlpBlock(nn.Module): method __init__ (line 5) | def __init__(self,input_dim,mlp_dim=512) : method forward (line 11) | def forward(self,x): class MixerBlock (line 17) | class MixerBlock(nn.Module): method __init__ (line 18) | def __init__(self,tokens_mlp_dim=16,channels_mlp_dim=1024,tokens_hidde... method forward (line 24) | def forward(self,x): class MlpMixer (line 39) | class MlpMixer(nn.Module): method __init__ (line 40) | def __init__(self,num_classes,num_blocks,patch_size,tokens_hidden_dim,... method forward (line 54) | def forward(self,x): FILE: model/mlp/repmlp.py function setup_seed (line 9) | def setup_seed(seed): class RepMLP (line 16) | class RepMLP(nn.Module): method __init__ (line 17) | def __init__(self,C,O,H,W,h,w,fc1_fc2_reduction=1,fc3_groups=8,repconv... method switch_to_deploy (line 69) | def switch_to_deploy(self): method get_equivalent_fc1_fc3_params (line 95) | def get_equivalent_fc1_fc3_params(self): method _conv_to_fc (line 148) | def _conv_to_fc(self,conv_kernel, conv_bias): method _fuse_bn (line 156) | def _fuse_bn(self, conv_or_fc, bn): method forward (line 166) | def forward(self,x) : FILE: model/mlp/resmlp.py class Rearange (line 5) | class Rearange(nn.Module): method __init__ (line 6) | def __init__(self,image_size=14,patch_size=7) : method forward (line 15) | def forward(self,x): class Affine (line 24) | class Affine(nn.Module): method __init__ (line 25) | def __init__(self, channel): method forward (line 30) | def forward(self, x): class PreAffinePostLayerScale (line 33) | class PreAffinePostLayerScale(nn.Module): # https://arxiv.org/abs/2103.1... method __init__ (line 34) | def __init__(self, dim, depth, fn): method forward (line 48) | def forward(self, x): class ResMLP (line 52) | class ResMLP(nn.Module): method __init__ (line 53) | def __init__(self,dim=128,image_size=14,patch_size=7,expansion_factor=... method forward (line 74) | def forward(self, x) : FILE: model/mlp/sMLP_block.py class sMLPBlock (line 10) | class sMLPBlock(nn.Module): method __init__ (line 11) | def __init__(self,h=224,w=224,c=3): method forward (line 17) | def forward(self,x): FILE: model/mlp/vip-mlp.py function _cfg (line 8) | def _cfg(url='', **kwargs): class Mlp (line 24) | class Mlp(nn.Module): method __init__ (line 25) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 34) | def forward(self, x): class WeightedPermuteMLP (line 42) | class WeightedPermuteMLP(nn.Module): method __init__ (line 43) | def __init__(self, dim, segment_dim=8, qkv_bias=False, qk_scale=None, ... method forward (line 58) | def forward(self, x): class PermutatorBlock (line 81) | class PermutatorBlock(nn.Module): method __init__ (line 83) | def __init__(self, dim, segment_dim, mlp_ratio=4., qkv_bias=False, qk_... method forward (line 97) | def forward(self, x): class PatchEmbed (line 102) | class PatchEmbed(nn.Module): method __init__ (line 105) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 109) | def forward(self, x): class Downsample (line 114) | class Downsample(nn.Module): method __init__ (line 117) | def __init__(self, in_embed_dim, out_embed_dim, patch_size): method forward (line 121) | def forward(self, x): function basic_blocks (line 127) | def basic_blocks(dim, index, layers, segment_dim, mlp_ratio=3., qkv_bias... class VisionPermutator (line 140) | class VisionPermutator(nn.Module): method __init__ (line 143) | def __init__(self, layers, img_size=224, patch_size=4, in_chans=3, num... method _init_weights (line 174) | def _init_weights(self, m): method get_classifier (line 183) | def get_classifier(self): method reset_classifier (line 186) | def reset_classifier(self, num_classes, global_pool=''): method forward_embeddings (line 190) | def forward_embeddings(self, x): method forward_tokens (line 196) | def forward_tokens(self,x): method forward (line 203) | def forward(self, x): function vip_s14 (line 214) | def vip_s14(pretrained=False, **kwargs): function vip_s7 (line 226) | def vip_s7(pretrained=False, **kwargs): function vip_m7 (line 238) | def vip_m7(pretrained=False, **kwargs): function vip_l7 (line 252) | def vip_l7(pretrained=False, **kwargs): FILE: model/rep/acnet.py function setup_seed (line 8) | def setup_seed(seed): function _conv_bn (line 15) | def _conv_bn(input_channel,output_channel,kernel_size=3,padding=1,stride... class ACNet (line 21) | class ACNet(nn.Module): method __init__ (line 22) | def __init__(self,input_channel,output_channel,kernel_size=3,groups=1,... method forward (line 43) | def forward(self, inputs): method _switch_to_deploy (line 52) | def _switch_to_deploy(self): method _pad_1x3_kernel (line 72) | def _pad_1x3_kernel(self,kernel): method _pad_3x1_kernel (line 79) | def _pad_3x1_kernel(self,kernel): method _get_equivalent_kernel_bias (line 87) | def _get_equivalent_kernel_bias(self): method _fuse_conv_bn (line 95) | def _fuse_conv_bn(self,branch): FILE: model/rep/ddb.py function transI_conv_bn (line 5) | def transI_conv_bn(conv, bn): function transII_conv_branch (line 17) | def transII_conv_branch(conv1, conv2): function transIII_conv_sequential (line 23) | def transIII_conv_sequential(conv1, conv2): function transIV_conv_concat (line 28) | def transIV_conv_concat(conv1, conv2): function transV_avg (line 35) | def transV_avg(channel,kernel): function transVI_conv_scale (line 42) | def transVI_conv_scale(conv1, conv2, conv3): FILE: model/rep/repvgg.py function setup_seed (line 8) | def setup_seed(seed): function _conv_bn (line 15) | def _conv_bn(input_channel,output_channel,kernel_size=3,padding=1,stride... class RepBlock (line 21) | class RepBlock(nn.Module): method __init__ (line 22) | def __init__(self,input_channel,output_channel,kernel_size=3,groups=1,... method forward (line 47) | def forward(self, inputs): method _switch_to_deploy (line 61) | def _switch_to_deploy(self): method _pad_1x1_kernel (line 80) | def _pad_1x1_kernel(self,kernel): method _get_equivalent_kernel_bias (line 88) | def _get_equivalent_kernel_bias(self): method _fuse_conv_bn (line 96) | def _fuse_conv_bn(self,branch):