SYMBOL INDEX (2361 symbols across 178 files) FILE: cv-attention/A2Attention.py class DoubleAttention (line 9) | class DoubleAttention(nn.Module): method __init__ (line 11) | def __init__(self, in_channels,c_m=128,c_n=128,reconstruct = True): method init_weights (line 25) | def init_weights(self): method forward (line 39) | def forward(self, x): FILE: cv-attention/BAM.py function autopad (line 6) | def autopad(k, p=None, d=1): # kernel, padding, dilation class Flatten (line 14) | class Flatten(nn.Module): method forward (line 15) | def forward(self, x): class ChannelAttention (line 19) | class ChannelAttention(nn.Module): method __init__ (line 20) | def __init__(self, channel, reduction=16, num_layers=3): method forward (line 35) | def forward(self, x): class SpatialAttention (line 42) | class SpatialAttention(nn.Module): method __init__ (line 43) | def __init__(self, channel, reduction=16, num_layers=3, dia_val=2): method forward (line 57) | def forward(self, x): class BAMBlock (line 63) | class BAMBlock(nn.Module): method __init__ (line 64) | def __init__(self, channel=512, reduction=16, dia_val=2): method init_weights (line 70) | def init_weights(self): method forward (line 84) | def forward(self, x): FILE: cv-attention/Biformer.py class TopkRouting (line 22) | class TopkRouting(nn.Module): method __init__ (line 33) | def __init__(self, qk_dim, topk=4, qk_scale=None, param_routing=False,... method forward (line 44) | def forward(self, query:Tensor, key:Tensor)->Tuple[Tensor]: class KVGather (line 61) | class KVGather(nn.Module): method __init__ (line 62) | def __init__(self, mul_weight='none'): method forward (line 67) | def forward(self, r_idx:Tensor, r_weight:Tensor, kv:Tensor): class QKVLinear (line 95) | class QKVLinear(nn.Module): method __init__ (line 96) | def __init__(self, dim, qk_dim, bias=True): method forward (line 102) | def forward(self, x): class BiLevelRoutingAttention (line 108) | class BiLevelRoutingAttention(nn.Module): method __init__ (line 118) | def __init__(self, dim, n_win=7, num_heads=8, qk_dim=None, qk_scale=None, method forward (line 203) | def forward(self, x, ret_attn_mask=False): class Attention (line 287) | class Attention(nn.Module): method __init__ (line 291) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 303) | def forward(self, x): class AttentionLePE (line 330) | class AttentionLePE(nn.Module): method __init__ (line 334) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 348) | def forward(self, x): function _grid2seq (line 380) | def _grid2seq(x:Tensor, region_size:Tuple[int], num_heads:int): function _seq2grid (line 397) | def _seq2grid(x:Tensor, region_h:int, region_w:int, region_size:Tuple[in... function regional_routing_attention_torch (line 411) | def regional_routing_attention_torch( class BiLevelRoutingAttention_nchw (line 484) | class BiLevelRoutingAttention_nchw(nn.Module): method __init__ (line 498) | def __init__(self, dim, num_heads=8, n_win=7, qk_scale=None, topk=4, ... method forward (line 525) | def forward(self, x:Tensor, ret_attn_mask=False): FILE: cv-attention/CAA.py function autopad (line 3) | def autopad(k, p=None, d=1): # kernel, padding, dilation class Conv (line 12) | class Conv(nn.Module): method __init__ (line 17) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): method forward (line 24) | def forward(self, x): method forward_fuse (line 28) | def forward_fuse(self, x): class CAA (line 32) | class CAA(nn.Module): method __init__ (line 33) | def __init__(self, ch, h_kernel_size = 11, v_kernel_size = 11) -> None: method forward (line 43) | def forward(self, x): FILE: cv-attention/CBAM.py class ChannelAttention (line 7) | class ChannelAttention(nn.Module): method __init__ (line 8) | def __init__(self, channel, reduction=16): method forward (line 19) | 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 43) | class CBAMBlock(nn.Module): method __init__ (line 45) | def __init__(self, channel=512, reduction=16, kernel_size=7): method init_weights (line 50) | def init_weights(self): method forward (line 64) | def forward(self, x): FILE: cv-attention/CPCA.py class CPCA_ChannelAttention (line 5) | class CPCA_ChannelAttention(nn.Module): method __init__ (line 7) | def __init__(self, input_channels, internal_neurons): method forward (line 13) | def forward(self, inputs): class CPCA (line 28) | class CPCA(nn.Module): method __init__ (line 29) | def __init__(self, channels, channelAttention_reduce=4): method forward (line 43) | def forward(self, inputs): FILE: cv-attention/CloAttention.py class AttnMap (line 5) | class AttnMap(nn.Module): method __init__ (line 6) | def __init__(self, dim): method forward (line 13) | def forward(self, x): class EfficientAttention (line 16) | class EfficientAttention(nn.Module): method __init__ (line 17) | def __init__(self, dim, num_heads=8, group_split=[4, 4], kernel_sizes=... method high_fre_attntion (line 56) | def high_fre_attntion(self, x: torch.Tensor, to_qkv: nn.Module, mixer:... method low_fre_attention (line 69) | def low_fre_attention(self, x : torch.Tensor, to_q: nn.Module, to_kv: ... method forward (line 85) | def forward(self, x: torch.Tensor): FILE: cv-attention/CoTAttention.py class CoTAttention (line 10) | class CoTAttention(nn.Module): method __init__ (line 12) | def __init__(self, dim=512, kernel_size=3): method forward (line 35) | def forward(self, x): FILE: cv-attention/CoordAttention.py class h_sigmoid (line 6) | class h_sigmoid(nn.Module): method __init__ (line 7) | def __init__(self, inplace=True): method forward (line 11) | def forward(self, x): class h_swish (line 15) | class h_swish(nn.Module): method __init__ (line 16) | def __init__(self, inplace=True): method forward (line 20) | def forward(self, x): class CoordAtt (line 24) | class CoordAtt(nn.Module): method __init__ (line 25) | def __init__(self, inp, reduction=32): method forward (line 39) | def forward(self, x): FILE: cv-attention/DAttention.py class LayerNormProxy (line 7) | class LayerNormProxy(nn.Module): method __init__ (line 8) | def __init__(self, dim): method forward (line 12) | def forward(self, x): class DAttention (line 17) | class DAttention(nn.Module): method __init__ (line 20) | def __init__( method _get_ref_points (line 107) | def _get_ref_points(self, H_key, W_key, B, dtype, device): method _get_q_grid (line 122) | def _get_q_grid(self, H, W, B, dtype, device): method forward (line 136) | def forward(self, x): FILE: cv-attention/ECA.py class EfficientChannelAttention (line 4) | class EfficientChannelAttention(nn.Module): # Efficient Channe... method __init__ (line 5) | def __init__(self, c, b=1, gamma=2): method forward (line 14) | def forward(self, x): FILE: cv-attention/ELA.py class ELA (line 3) | class ELA(nn.Module): method __init__ (line 4) | def __init__(self, channels) -> None: method forward (line 14) | def forward(self, x): FILE: cv-attention/EMA.py class EMA (line 4) | class EMA(nn.Module): method __init__ (line 5) | def __init__(self, channels, factor=8): method forward (line 17) | def forward(self, x): FILE: cv-attention/EffectiveSE.py class EffectiveSEModule (line 6) | class EffectiveSEModule(nn.Module): method __init__ (line 7) | def __init__(self, channels, add_maxpool=False, gate_layer='hard_sigmo... method forward (line 13) | def forward(self, x): FILE: cv-attention/GAM.py class GAM_Attention (line 4) | class GAM_Attention(nn.Module): method __init__ (line 5) | def __init__(self, in_channels, rate=4): method forward (line 22) | def forward(self, x): FILE: cv-attention/GC.py class GlobalContext (line 10) | class GlobalContext(nn.Module): method __init__ (line 12) | def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale... method reset_parameters (line 34) | def reset_parameters(self): method forward (line 40) | def forward(self, x): FILE: cv-attention/GE.py class GatherExcite (line 11) | class GatherExcite(nn.Module): method __init__ (line 12) | def __init__( method forward (line 54) | def forward(self, x): FILE: cv-attention/LSKA.py class LSKA (line 3) | class LSKA(nn.Module): method __init__ (line 6) | def __init__(self, dim, k_size=7): method forward (line 44) | def forward(self, x): FILE: cv-attention/LSKBlock.py class LSKblock (line 4) | class LSKblock(nn.Module): method __init__ (line 5) | def __init__(self, dim): method forward (line 14) | def forward(self, x): FILE: cv-attention/MHSA.py class MHSA (line 4) | class MHSA(nn.Module): method __init__ (line 5) | def __init__(self, n_dims, width=14, height=14, heads=4, pos_emb=False): method forward (line 20) | def forward(self, x): FILE: cv-attention/MLCA.py class MLCA (line 5) | class MLCA(nn.Module): method __init__ (line 6) | def __init__(self, in_size, local_size=5, gamma = 2, b = 1,local_weigh... method forward (line 24) | def forward(self, x): FILE: cv-attention/MobileViTAttention.py class PreNorm (line 6) | class PreNorm(nn.Module): method __init__ (line 7) | def __init__(self, dim, fn): method forward (line 12) | def forward(self, x, **kwargs): class FeedForward (line 16) | class FeedForward(nn.Module): method __init__ (line 17) | def __init__(self, dim, mlp_dim, dropout): method forward (line 27) | def forward(self, x): class Attention (line 31) | class Attention(nn.Module): method __init__ (line 32) | def __init__(self, dim, heads, head_dim, dropout): method forward (line 48) | def forward(self, x): class Transformer (line 58) | class Transformer(nn.Module): method __init__ (line 59) | def __init__(self, dim, depth, heads, head_dim, mlp_dim, dropout=0.): method forward (line 68) | def forward(self, x): class MobileViTAttention (line 76) | class MobileViTAttention(nn.Module): method __init__ (line 77) | def __init__(self, in_channel=3, dim=512, kernel_size=3, patch_size=7): method forward (line 88) | def forward(self, x): FILE: cv-attention/ParNetAttention.py class ParNetAttention (line 7) | class ParNetAttention(nn.Module): method __init__ (line 9) | def __init__(self, channel=512): method forward (line 27) | def forward(self, x): FILE: cv-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): FILE: cv-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: cv-attention/SE.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: cv-attention/SGE.py class SpatialGroupEnhance (line 6) | class SpatialGroupEnhance(nn.Module): method __init__ (line 7) | def __init__(self, groups=8): method init_weights (line 16) | def init_weights(self): method forward (line 30) | def forward(self, x): FILE: cv-attention/SK.py class SKAttention (line 8) | class SKAttention(nn.Module): method __init__ (line 10) | def __init__(self, channel=512, kernels=[1, 3, 5, 7], reduction=16, gr... method forward (line 28) | def forward(self, x): FILE: cv-attention/SequentialSelfAttention.py class SequentialPolarizedSelfAttention (line 6) | class SequentialPolarizedSelfAttention(nn.Module): method __init__ (line 8) | def __init__(self, channel=512): method forward (line 21) | def forward(self, x): FILE: cv-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 22) | def init_weights(self): method channel_shuffle (line 37) | def channel_shuffle(x, groups): method forward (line 47) | def forward(self, x): FILE: cv-attention/SimAM.py class SimAM (line 5) | class SimAM(torch.nn.Module): method __init__ (line 6) | def __init__(self, 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: cv-attention/TripletAttention.py class BasicConv (line 5) | class BasicConv(nn.Module): method __init__ (line 6) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi... method forward (line 15) | def forward(self, x): class ZPool (line 24) | class ZPool(nn.Module): method forward (line 25) | def forward(self, x): class AttentionGate (line 29) | class AttentionGate(nn.Module): method __init__ (line 30) | def __init__(self): method forward (line 36) | def forward(self, x): class TripletAttention (line 43) | class TripletAttention(nn.Module): method __init__ (line 44) | def __init__(self, no_spatial=False): method forward (line 52) | def forward(self, x): FILE: damo-yolo/voc2coco.py function find_classes (line 10) | def find_classes(path): function get (line 29) | def get(root, name): function get_and_check (line 33) | def get_and_check(root, name, length): function convert (line 44) | def convert(xml_list, json_file): FILE: data-offline-aug/object_detection_data_aug.py function parallelise (line 59) | def parallelise(function: Callable, data: List, chunksize=100, verbose=T... function draw_detections (line 69) | def draw_detections(box, name, img): function show_labels (line 84) | def show_labels(images_base_path, labels_base_path): function data_aug_single (line 112) | def data_aug_single(images_name): function data_aug (line 143) | def data_aug(): FILE: data-offline-aug/segment_data_aug.py function generate_color_map (line 13) | def generate_color_map(num_classes): function draw_segments (line 64) | def draw_segments(image, mask): function show_labels (line 68) | def show_labels(images_base_path, labels_base_path): function data_aug_single (line 89) | def data_aug_single(images_name): function data_aug (line 116) | def data_aug(): FILE: mmdet-course/mmdet2yolo.py function clip_boxes (line 5) | def clip_boxes(boxes, shape): function scale_boxes (line 16) | def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): function box_iou (line 31) | def box_iou(box1, box2, eps=1e-7): function process_batch (line 53) | def process_batch(detections, labels, iouv): function smooth (line 77) | def smooth(y, f=0.05): function ap_per_class (line 85) | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.... function compute_ap (line 145) | def compute_ap(recall, precision): function parse_opt (line 172) | def parse_opt(): FILE: mmdet-course/yolo2coco.py function train_test_val_split_random (line 20) | def train_test_val_split_random(img_paths,ratio_train=0.8,ratio_test=0.1... function train_test_val_split_by_files (line 29) | def train_test_val_split_by_files(img_paths, root_dir): function yolo2coco (line 44) | def yolo2coco(arg): FILE: objectdetection-tricks/tricks_1.py function xywh2xyxy (line 4) | def xywh2xyxy(box): function iou (line 11) | def iou(box1, box2): function draw_box (line 29) | def draw_box(img, box, color): FILE: objectdetection-tricks/tricks_12.py function deal_yolov7_result (line 5) | def deal_yolov7_result(data_path): FILE: objectdetection-tricks/tricks_14.py function get_weight_size (line 12) | def get_weight_size(path): FILE: objectdetection-tricks/tricks_15.py function get_color_by_class (line 38) | def get_color_by_class(class_id): function draw_detections (line 42) | def draw_detections(box, name, color, img): function get_images_and_labels_path (line 57) | def get_images_and_labels_path(images_folder_path, labels_folder_path): function show_dataset_info (line 84) | def show_dataset_info(image_label_dict, visual_box=False, save_path='vis... function remap_yolo_dataset_class (line 158) | def remap_yolo_dataset_class(labels_path_list, delete_label=[0, 1, 3, 5]): FILE: objectdetection-tricks/tricks_16.py function get_color_by_class (line 35) | def get_color_by_class(class_id): function draw_detections (line 39) | def draw_detections(box, name, color, img): FILE: objectdetection-tricks/tricks_2.py function time_synchronized (line 9) | def time_synchronized(): function autopad (line 15) | def autopad(k, p=None, d=1): # kernel, padding, dilation class Conv2D (line 23) | class Conv2D(nn.Module): method __init__ (line 24) | def __init__(self, inc, ouc, kernel_size, g=1): method forward (line 31) | def forward(self, x): method __str__ (line 34) | def __str__(self): class DConv2D (line 37) | class DConv2D(nn.Module): method __init__ (line 38) | def __init__(self, inc, ouc, kernel_size): method forward (line 44) | def forward(self, x): method __str__ (line 47) | def __str__(self): class GhostConv2D (line 50) | class GhostConv2D(nn.Module): method __init__ (line 51) | def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3): method forward (line 60) | def forward(self, x): method __str__ (line 66) | def __str__(self): class GSConv (line 69) | class GSConv(nn.Module): method __init__ (line 71) | def __init__(self, c1, c2, k=1, s=1, g=1): method forward (line 77) | def forward(self, x): method __str__ (line 93) | def __str__(self): class DSConv (line 96) | class DSConv(_ConvNd): method __init__ (line 97) | def __init__(self, in_channels, out_channels, kernel_size, block_size=... method get_weight_res (line 125) | def get_weight_res(self): method forward (line 165) | def forward(self, input): class DSConv2D (line 174) | class DSConv2D(Conv2D): method __init__ (line 175) | def __init__(self, inc, ouc, kernel_size, g=1): method __str__ (line 179) | def __str__(self): class Partial_conv3 (line 182) | class Partial_conv3(nn.Module): method __init__ (line 183) | def __init__(self, dim, kernel_size, n_div=4, forward='split_cat'): method forward_slicing (line 196) | def forward_slicing(self, x): method forward_split_cat (line 202) | def forward_split_cat(self, x): class PConv (line 209) | class PConv(Conv2D): method __init__ (line 210) | def __init__(self, inc, ouc, kernel_size, g=1): method __str__ (line 214) | def __str__(self): class DCNV2 (line 217) | class DCNV2(nn.Module): method __init__ (line 218) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method forward (line 250) | def forward(self, x): method reset_parameters (line 272) | def reset_parameters(self): method __str__ (line 282) | def __str__(self): class DCNV3 (line 286) | class DCNV3(Conv2D): method __init__ (line 287) | def __init__(self, inc, ouc, k=1, s=1, p=None, g=1, d=1, act=True): method __str__ (line 291) | def __str__(self): method forward (line 294) | def forward(self, x): FILE: objectdetection-tricks/tricks_3.py function feature_visualization (line 1) | def feature_visualization(x, module_type, stage, n=32, save_dir=Path('ru... FILE: objectdetection-tricks/tricks_4.py function yolo2coco (line 14) | def yolo2coco(arg): FILE: objectdetection-tricks/tricks_5.py function show_mask (line 6) | def show_mask(mask, ax, random_color=False): function show_points (line 15) | def show_points(coords, labels, ax, marker_size=375): function show_box (line 21) | def show_box(box, ax): class Select_RoI (line 26) | class Select_RoI: method __init__ (line 27) | def __init__(self, img) -> None: method on_mouse (line 45) | def on_mouse(self, event, x, y, flags, param): method clear (line 59) | def clear(self): method confirm (line 79) | def confirm(self): method get_result (line 87) | def get_result(self): FILE: objectdetection-tricks/tricks_6.py function check_version (line 2) | def check_version(current='0.0.0', minimum='0.0.0', name='version ', pin... function set_seeds (line 9) | def set_seeds(seed=0, deterministic=False): FILE: objectdetection-tricks/tricks_7.py function get_weight_size (line 29) | def get_weight_size(path): FILE: objectdetection-tricks/tricks_8.py function get_weight_size (line 31) | def get_weight_size(path): FILE: objectdetection-tricks/tricks_9.py function time_synchronized (line 7) | def time_synchronized(): function fuse_conv_and_bn (line 13) | def fuse_conv_and_bn(conv, bn): function autopad (line 42) | def autopad(k, p=None, d=1): # kernel, padding, dilation class Conv (line 50) | class Conv(nn.Module): method __init__ (line 55) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): method forward (line 62) | def forward(self, x): method forward_fuse (line 66) | def forward_fuse(self, x): class Bottleneck (line 70) | class Bottleneck(nn.Module): method __init__ (line 73) | def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): method forward (line 83) | def forward(self, x): class ELAN (line 89) | class ELAN(nn.Module): method __init__ (line 90) | def __init__(self, inc, ouc, hidc, act=True): method forward (line 99) | def forward(self, x): method __str__ (line 107) | def __str__(self): class C2f (line 112) | class C2f(nn.Module): method __init__ (line 115) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): method forward (line 125) | def forward(self, x): method forward_split (line 131) | def forward_split(self, x): method __str__ (line 137) | def __str__(self): class C3 (line 142) | class C3(nn.Module): method __init__ (line 145) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): method forward (line 154) | def forward(self, x): method __str__ (line 158) | def __str__(self): class RepConvN (line 163) | class RepConvN(nn.Module): method __init__ (line 169) | def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False... method forward_fuse (line 181) | def forward_fuse(self, x): method forward (line 185) | def forward(self, x): method get_equivalent_kernel_bias (line 190) | def get_equivalent_kernel_bias(self): method _avg_to_3x3_tensor (line 196) | def _avg_to_3x3_tensor(self, avgp): method _pad_1x1_to_3x3_tensor (line 205) | def _pad_1x1_to_3x3_tensor(self, kernel1x1): method _fuse_bn_tensor (line 211) | def _fuse_bn_tensor(self, branch): method fuse_convs (line 238) | def fuse_convs(self): class RepNBottleneck (line 263) | class RepNBottleneck(nn.Module): method __init__ (line 265) | def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5, act=Tr... method forward (line 272) | def forward(self, x): class RepNCSP (line 275) | class RepNCSP(nn.Module): method __init__ (line 277) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, act=True): ... method forward (line 285) | def forward(self, x): class RepNCSPELAN4 (line 288) | class RepNCSPELAN4(nn.Module): method __init__ (line 290) | def __init__(self, c1, c2, c3, c4, c5=1, act=True): # ch_in, ch_out, ... method forward (line 298) | def forward(self, x): method forward_split (line 303) | def forward_split(self, x): method __str__ (line 308) | def __str__(self): class RepNCSPELAN4_Att (line 311) | class RepNCSPELAN4_Att(nn.Module): method __init__ (line 313) | def __init__(self, c1, c2, c3, c4, c5=1, act=True): # ch_in, ch_out, ... method forward (line 321) | def forward(self, x): method forward_split (line 326) | def forward_split(self, x): method __str__ (line 331) | def __str__(self): FILE: yolo-gradcam/yolov11_heatmap.py function letterbox (line 18) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True... class ActivationsAndGradients (line 50) | class ActivationsAndGradients: method __init__ (line 54) | def __init__(self, model, target_layers, reshape_transform): method save_activation (line 68) | def save_activation(self, module, input, output): method save_gradient (line 75) | def save_gradient(self, module, input, output): method post_process (line 88) | def post_process(self, result): method __call__ (line 122) | def __call__(self, x): method release (line 142) | def release(self): class yolo_detect_target (line 146) | class yolo_detect_target(torch.nn.Module): method __init__ (line 147) | def __init__(self, ouput_type, conf, ratio, end2end) -> None: method forward (line 154) | def forward(self, data): class yolo_segment_target (line 170) | class yolo_segment_target(yolo_detect_target): method __init__ (line 171) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 174) | def forward(self, data): class yolo_pose_target (line 189) | class yolo_pose_target(yolo_detect_target): method __init__ (line 190) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 193) | def forward(self, data): class yolo_obb_target (line 208) | class yolo_obb_target(yolo_detect_target): method __init__ (line 209) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 212) | def forward(self, data): class yolo_classify_target (line 227) | class yolo_classify_target(yolo_detect_target): method __init__ (line 228) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 231) | def forward(self, data): class yolo_heatmap (line 234) | class yolo_heatmap: method __init__ (line 235) | def __init__(self, weight, device, method, layer, backward_type, conf_... method post_process (line 271) | def post_process(self, result): method draw_detections (line 275) | def draw_detections(self, box, color, name, img): method renormalize_cam_in_bounding_boxes (line 281) | def renormalize_cam_in_bounding_boxes(self, boxes, image_float_np, gra... method process (line 293) | def process(self, img_path, save_path): method __call__ (line 331) | def __call__(self, img_path, save_path): function get_params (line 344) | def get_params(): FILE: yolo-gradcam/yolov5_heatmap.py class ActivationsAndGradients (line 19) | class ActivationsAndGradients: method __init__ (line 23) | def __init__(self, model, target_layers, reshape_transform): method save_activation (line 37) | def save_activation(self, module, input, output): method save_gradient (line 44) | def save_gradient(self, module, input, output): method post_process (line 57) | def post_process(self, result): method __call__ (line 63) | def __call__(self, x): method release (line 70) | def release(self): class yolov5_target (line 74) | class yolov5_target(torch.nn.Module): method __init__ (line 75) | def __init__(self, ouput_type, conf, ratio) -> None: method forward (line 81) | def forward(self, data): class yolov5_heatmap (line 94) | class yolov5_heatmap: method __init__ (line 95) | def __init__(self, weight, device, method, layer, backward_type, conf_... method post_process (line 112) | def post_process(self, result): method draw_detections (line 116) | def draw_detections(self, box, color, name, img): method renormalize_cam_in_bounding_boxes (line 122) | def renormalize_cam_in_bounding_boxes(self, boxes, image_float_np, gra... method process (line 134) | def process(self, img_path, save_path): method __call__ (line 163) | def __call__(self, img_path, save_path): function get_params (line 176) | def get_params(): FILE: yolo-gradcam/yolov7_heatmap.py class ActivationsAndGradients (line 18) | class ActivationsAndGradients: method __init__ (line 22) | def __init__(self, model, target_layers, reshape_transform): method save_activation (line 36) | def save_activation(self, module, input, output): method save_gradient (line 43) | def save_gradient(self, module, input, output): method post_process (line 56) | def post_process(self, result): method __call__ (line 68) | def __call__(self, x): method release (line 75) | def release(self): class yolov7_target (line 79) | class yolov7_target(torch.nn.Module): method __init__ (line 80) | def __init__(self, ouput_type, conf, ratio) -> None: method forward (line 86) | def forward(self, data): class yolov7_heatmap (line 99) | class yolov7_heatmap: method __init__ (line 100) | def __init__(self, weight, device, method, layer, backward_type, conf_... method post_process (line 117) | def post_process(self, result): method draw_detections (line 121) | def draw_detections(self, box, color, name, img): method renormalize_cam_in_bounding_boxes (line 127) | def renormalize_cam_in_bounding_boxes(self, boxes, image_float_np, gra... method process (line 139) | def process(self, img_path, save_path): method __call__ (line 168) | def __call__(self, img_path, save_path): function get_params (line 181) | def get_params(): FILE: yolo-gradcam/yolov8_heatmap.py function letterbox (line 18) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True... class ActivationsAndGradients (line 50) | class ActivationsAndGradients: method __init__ (line 54) | def __init__(self, model, target_layers, reshape_transform): method save_activation (line 68) | def save_activation(self, module, input, output): method save_gradient (line 75) | def save_gradient(self, module, input, output): method post_process (line 88) | def post_process(self, result): method __call__ (line 122) | def __call__(self, x): method release (line 142) | def release(self): class yolo_detect_target (line 146) | class yolo_detect_target(torch.nn.Module): method __init__ (line 147) | def __init__(self, ouput_type, conf, ratio, end2end) -> None: method forward (line 154) | def forward(self, data): class yolo_segment_target (line 170) | class yolo_segment_target(yolo_detect_target): method __init__ (line 171) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 174) | def forward(self, data): class yolo_pose_target (line 189) | class yolo_pose_target(yolo_detect_target): method __init__ (line 190) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 193) | def forward(self, data): class yolo_obb_target (line 208) | class yolo_obb_target(yolo_detect_target): method __init__ (line 209) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 212) | def forward(self, data): class yolo_classify_target (line 227) | class yolo_classify_target(yolo_detect_target): method __init__ (line 228) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 231) | def forward(self, data): class yolo_heatmap (line 234) | class yolo_heatmap: method __init__ (line 235) | def __init__(self, weight, device, method, layer, backward_type, conf_... method post_process (line 271) | def post_process(self, result): method draw_detections (line 275) | def draw_detections(self, box, color, name, img): method renormalize_cam_in_bounding_boxes (line 281) | def renormalize_cam_in_bounding_boxes(self, boxes, image_float_np, gra... method process (line 293) | def process(self, img_path, save_path): method __call__ (line 331) | def __call__(self, img_path, save_path): function get_params (line 344) | def get_params(): FILE: yolo-gradcam/yolov9_heatmap.py class ActivationsAndGradients (line 18) | class ActivationsAndGradients: method __init__ (line 22) | def __init__(self, model, target_layers, reshape_transform): method save_activation (line 36) | def save_activation(self, module, input, output): method save_gradient (line 43) | def save_gradient(self, module, input, output): method post_process (line 56) | def post_process(self, result): method __call__ (line 63) | def __call__(self, x): method release (line 70) | def release(self): class yolov9_target (line 74) | class yolov9_target(torch.nn.Module): method __init__ (line 75) | def __init__(self, ouput_type, conf, ratio) -> None: method forward (line 81) | def forward(self, data): class yolov9_heatmap (line 94) | class yolov9_heatmap: method __init__ (line 95) | def __init__(self, weight, device, method, layer, backward_type, conf_... method post_process (line 112) | def post_process(self, result): method draw_detections (line 116) | def draw_detections(self, box, color, name, img): method renormalize_cam_in_bounding_boxes (line 122) | def renormalize_cam_in_bounding_boxes(self, boxes, image_float_np, gra... method process (line 134) | def process(self, img_path, save_path): method __call__ (line 163) | def __call__(self, img_path, save_path): function get_params (line 176) | def get_params(): FILE: yolo-improve/CAM.py class CAM (line 1) | class CAM(nn.Module): method __init__ (line 2) | def __init__(self, inc, fusion='weight'): method forward (line 19) | def forward(self, x): FILE: yolo-improve/iou.py class WIoU_Scale (line 4) | class WIoU_Scale: method __init__ (line 17) | def __init__(self, iou): method _update (line 22) | def _update(cls, self): method _scaled_loss (line 27) | def _scaled_loss(cls, self, gamma=1.9, delta=3): function bbox_iou (line 38) | def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, ... FILE: yolo-improve/ultralytics-yolo/get_COCO_metrice.py function parse_opt (line 11) | def parse_opt(): FILE: yolo-improve/ultralytics-yolo/heatmap.py function patch_pose_classes_for_gradcam (line 21) | def patch_pose_classes_for_gradcam(): function letterbox (line 71) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True... class ActivationsAndGradients (line 103) | class ActivationsAndGradients: method __init__ (line 107) | def __init__(self, model, target_layers, reshape_transform): method save_activation (line 121) | def save_activation(self, module, input, output): method save_gradient (line 128) | def save_gradient(self, module, input, output): method post_process (line 141) | def post_process(self, result): method __call__ (line 175) | def __call__(self, x): method release (line 195) | def release(self): class yolo_detect_target (line 199) | class yolo_detect_target(torch.nn.Module): method __init__ (line 200) | def __init__(self, ouput_type, conf, ratio, end2end) -> None: method _accumulate (line 208) | def _accumulate(acc, value): method _zero_scalar_like (line 212) | def _zero_scalar_like(tensor): method forward (line 216) | def forward(self, data): class yolo_segment_target (line 230) | class yolo_segment_target(yolo_detect_target): method __init__ (line 231) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 234) | def forward(self, data): class yolo_pose_target (line 250) | class yolo_pose_target(yolo_detect_target): method __init__ (line 251) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 254) | def forward(self, data): class yolo_obb_target (line 270) | class yolo_obb_target(yolo_detect_target): method __init__ (line 271) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 274) | def forward(self, data): class yolo_classify_target (line 290) | class yolo_classify_target(yolo_detect_target): method __init__ (line 291) | def __init__(self, ouput_type, conf, ratio, end2end): method forward (line 294) | def forward(self, data): class yolo_heatmap (line 297) | class yolo_heatmap: method __init__ (line 298) | def __init__(self, weight, device, method, layer, backward_type, conf_... method post_process (line 350) | def post_process(self, result): method draw_detections (line 354) | def draw_detections(self, box, color, name, img): method renormalize_cam_in_bounding_boxes (line 360) | def renormalize_cam_in_bounding_boxes(self, boxes, image_float_np, gra... method process (line 372) | def process(self, img_path, save_path): method __call__ (line 414) | def __call__(self, img_path, save_path): function get_params (line 435) | def get_params(): FILE: yolo-improve/ultralytics-yolo/val.py function get_weight_size (line 14) | def get_weight_size(path): FILE: yolo-improve/ultralytics-yolo/yolo2coco.py class YOLOtoCOCO (line 7) | class YOLOtoCOCO: method __init__ (line 8) | def __init__(self, yolo_dir, image_dir, class_names, output_json='coco... method create_categories (line 32) | def create_categories(self): method yolo_to_coco_bbox (line 42) | def yolo_to_coco_bbox(self, yolo_bbox, img_width, img_height): method bbox_to_segmentation (line 63) | def bbox_to_segmentation(self, bbox): method process_image (line 91) | def process_image(self, image_path, label_path): method convert (line 153) | def convert(self): FILE: yolo-improve/yolov5-AIFI.py class TransformerEncoderLayer (line 4) | class TransformerEncoderLayer(nn.Module): method __init__ (line 7) | def __init__(self, c1, cm=2048, num_heads=8, dropout=0.0, act=nn.GELU(... method with_pos_embed (line 25) | def with_pos_embed(tensor, pos=None): method forward_post (line 29) | def forward_post(self, src, src_mask=None, src_key_padding_mask=None, ... method forward_pre (line 39) | def forward_pre(self, src, src_mask=None, src_key_padding_mask=None, p... method forward (line 49) | def forward(self, src, src_mask=None, src_key_padding_mask=None, pos=N... class AIFI (line 56) | class AIFI(TransformerEncoderLayer): method __init__ (line 59) | def __init__(self, c1, cm=2048, num_heads=8, dropout=0, act=nn.GELU(),... method forward (line 63) | def forward(self, x): method build_2d_sincos_position_embedding (line 72) | def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperatur... FILE: yolo-improve/yolov5-AUX/benchmarks.py function run (line 52) | def run( function test (line 114) | def test( function parse_opt (line 146) | def parse_opt(): function main (line 163) | def main(opt): FILE: yolo-improve/yolov5-AUX/detect.py function run (line 54) | def run( function parse_opt (line 219) | def parse_opt(): function main (line 254) | def main(opt): FILE: yolo-improve/yolov5-AUX/export.py function export_formats (line 80) | def export_formats(): function try_export (line 98) | def try_export(inner_func): function export_torchscript (line 117) | def export_torchscript(model, im, file, optimize, prefix=colorstr('Torch... function export_onnx (line 133) | def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colors... function export_openvino (line 189) | def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')): function export_paddle (line 211) | def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePadd... function export_coreml (line 226) | def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')): function export_engine (line 249) | def export_engine(model, im, file, half, dynamic, simplify, workspace=4,... function export_saved_model (line 311) | def export_saved_model(model, function export_pb (line 363) | def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')): function export_tflite (line 380) | def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, ... function export_edgetpu (line 411) | def export_edgetpu(file, prefix=colorstr('Edge TPU:')): function export_tfjs (line 443) | def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')): function add_tflite_metadata (line 476) | def add_tflite_metadata(file, metadata, num_outputs): function run (line 510) | def run( function parse_opt (line 632) | def parse_opt(known=False): function main (line 665) | def main(opt): FILE: yolo-improve/yolov5-AUX/hubconf.py function _create (line 16) | def _create(name, pretrained=True, channels=3, classes=80, autoshape=Tru... function custom (line 81) | def custom(path='path/to/model.pt', autoshape=True, _verbose=True, devic... function yolov5n (line 86) | def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _ve... function yolov5s (line 91) | def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _ve... function yolov5m (line 96) | def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _ve... function yolov5l (line 101) | def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _ve... function yolov5x (line 106) | def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _ve... function yolov5n6 (line 111) | def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _v... function yolov5s6 (line 116) | def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _v... function yolov5m6 (line 121) | def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _v... function yolov5l6 (line 126) | def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _v... function yolov5x6 (line 131) | def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _v... FILE: yolo-improve/yolov5-AUX/models/common.py function autopad (line 37) | def autopad(k, p=None, d=1): # kernel, padding, dilation class Conv (line 46) | class Conv(nn.Module): method __init__ (line 50) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): method forward (line 56) | def forward(self, x): method forward_fuse (line 59) | def forward_fuse(self, x): class DWConv (line 63) | class DWConv(Conv): method __init__ (line 65) | def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out,... class DWConvTranspose2d (line 69) | class DWConvTranspose2d(nn.ConvTranspose2d): method __init__ (line 71) | def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, ke... class TransformerLayer (line 75) | class TransformerLayer(nn.Module): method __init__ (line 77) | def __init__(self, c, num_heads): method forward (line 86) | def forward(self, x): class TransformerBlock (line 92) | class TransformerBlock(nn.Module): method __init__ (line 94) | def __init__(self, c1, c2, num_heads, num_layers): method forward (line 103) | def forward(self, x): class Bottleneck (line 111) | class Bottleneck(nn.Module): method __init__ (line 113) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 120) | def forward(self, x): class BottleneckCSP (line 124) | class BottleneckCSP(nn.Module): method __init__ (line 126) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ... method forward (line 137) | def forward(self, x): class CrossConv (line 143) | class CrossConv(nn.Module): method __init__ (line 145) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): method forward (line 153) | def forward(self, x): class C3 (line 157) | class C3(nn.Module): method __init__ (line 159) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ... method forward (line 167) | def forward(self, x): class C3x (line 171) | class C3x(C3): method __init__ (line 173) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): class C3TR (line 179) | class C3TR(C3): method __init__ (line 181) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): class C3SPP (line 187) | class C3SPP(C3): method __init__ (line 189) | def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): class C3Ghost (line 195) | class C3Ghost(C3): method __init__ (line 197) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): class SPP (line 203) | class SPP(nn.Module): method __init__ (line 205) | def __init__(self, c1, c2, k=(5, 9, 13)): method forward (line 212) | def forward(self, x): class SPPF (line 219) | class SPPF(nn.Module): method __init__ (line 221) | def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) method forward (line 228) | def forward(self, x): class Focus (line 237) | class Focus(nn.Module): method __init__ (line 239) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,... method forward (line 244) | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) class GhostConv (line 249) | class GhostConv(nn.Module): method __init__ (line 251) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out,... method forward (line 257) | def forward(self, x): class GhostBottleneck (line 262) | class GhostBottleneck(nn.Module): method __init__ (line 264) | def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride method forward (line 274) | def forward(self, x): class Contract (line 278) | class Contract(nn.Module): method __init__ (line 280) | def __init__(self, gain=2): method forward (line 284) | def forward(self, x): class Expand (line 292) | class Expand(nn.Module): method __init__ (line 294) | def __init__(self, gain=2): method forward (line 298) | def forward(self, x): class Concat (line 306) | class Concat(nn.Module): method __init__ (line 308) | def __init__(self, dimension=1): method forward (line 312) | def forward(self, x): class DetectMultiBackend (line 316) | class DetectMultiBackend(nn.Module): method __init__ (line 318) | def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), d... method forward (line 506) | def forward(self, im, augment=False, visualize=False): method from_numpy (line 588) | def from_numpy(self, x): method warmup (line 591) | def warmup(self, imgsz=(1, 3, 640, 640)): method _model_type (line 600) | def _model_type(p='path/to/model.pt'): method _load_metadata (line 615) | def _load_metadata(f=Path('path/to/meta.yaml')): class AutoShape (line 623) | class AutoShape(nn.Module): method __init__ (line 633) | def __init__(self, model, verbose=True): method _apply (line 646) | def _apply(self, fn): method forward (line 658) | def forward(self, ims, size=640, augment=False, profile=False): class Detections (line 722) | class Detections: method __init__ (line 724) | def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shap... method _run (line 741) | def _run(self, pprint=False, show=False, save=False, crop=False, rende... method show (line 787) | def show(self, labels=True): method save (line 790) | def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): method crop (line 794) | def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): method render (line 798) | def render(self, labels=True): method pandas (line 802) | def pandas(self): method tolist (line 812) | def tolist(self): method print (line 821) | def print(self): method __len__ (line 824) | def __len__(self): # override len(results) method __str__ (line 827) | def __str__(self): # override print(results) method __repr__ (line 830) | def __repr__(self): class Proto (line 834) | class Proto(nn.Module): method __init__ (line 836) | def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, num... method forward (line 843) | def forward(self, x): class Classify (line 847) | class Classify(nn.Module): method __init__ (line 849) | def __init__(self, method forward (line 864) | def forward(self, x): FILE: yolo-improve/yolov5-AUX/models/experimental.py class Sum (line 14) | class Sum(nn.Module): method __init__ (line 16) | def __init__(self, n, weight=False): # n: number of inputs method forward (line 23) | def forward(self, x): class MixConv2d (line 35) | class MixConv2d(nn.Module): method __init__ (line 37) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch... method forward (line 56) | def forward(self, x): class Ensemble (line 60) | class Ensemble(nn.ModuleList): method __init__ (line 62) | def __init__(self): method forward (line 65) | def forward(self, x, augment=False, profile=False, visualize=False): function attempt_load (line 73) | def attempt_load(weights, device=None, inplace=True, fuse=True): FILE: yolo-improve/yolov5-AUX/models/tf.py class TFBN (line 38) | class TFBN(keras.layers.Layer): method __init__ (line 40) | def __init__(self, w=None): method call (line 49) | def call(self, inputs): class TFPad (line 53) | class TFPad(keras.layers.Layer): method __init__ (line 55) | def __init__(self, pad): method call (line 62) | def call(self, inputs): class TFConv (line 66) | class TFConv(keras.layers.Layer): method __init__ (line 68) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): method call (line 86) | def call(self, inputs): class TFDWConv (line 90) | class TFDWConv(keras.layers.Layer): method __init__ (line 92) | def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): method call (line 108) | def call(self, inputs): class TFDWConvTranspose2d (line 112) | class TFDWConvTranspose2d(keras.layers.Layer): method __init__ (line 114) | def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): method call (line 131) | def call(self, inputs): class TFFocus (line 135) | class TFFocus(keras.layers.Layer): method __init__ (line 137) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): method call (line 142) | def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) class TFBottleneck (line 148) | class TFBottleneck(keras.layers.Layer): method __init__ (line 150) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_i... method call (line 157) | def call(self, inputs): class TFCrossConv (line 161) | class TFCrossConv(keras.layers.Layer): method __init__ (line 163) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): method call (line 170) | def call(self, inputs): class TFConv2d (line 174) | class TFConv2d(keras.layers.Layer): method __init__ (line 176) | def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): method call (line 188) | def call(self, inputs): class TFBottleneckCSP (line 192) | class TFBottleneckCSP(keras.layers.Layer): method __init__ (line 194) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): method call (line 206) | def call(self, inputs): class TFC3 (line 212) | class TFC3(keras.layers.Layer): method __init__ (line 214) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): method call (line 223) | def call(self, inputs): class TFC3x (line 227) | class TFC3x(keras.layers.Layer): method __init__ (line 229) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): method call (line 239) | def call(self, inputs): class TFSPP (line 243) | class TFSPP(keras.layers.Layer): method __init__ (line 245) | def __init__(self, c1, c2, k=(5, 9, 13), w=None): method call (line 252) | def call(self, inputs): class TFSPPF (line 257) | class TFSPPF(keras.layers.Layer): method __init__ (line 259) | def __init__(self, c1, c2, k=5, w=None): method call (line 266) | def call(self, inputs): class TFDetect (line 273) | class TFDetect(keras.layers.Layer): method __init__ (line 275) | def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None)... method call (line 292) | def call(self, inputs): method _make_grid (line 316) | def _make_grid(nx=20, ny=20): class TFSegment (line 323) | class TFSegment(TFDetect): method __init__ (line 325) | def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(64... method call (line 334) | def call(self, x): class TFProto (line 342) | class TFProto(keras.layers.Layer): method __init__ (line 344) | def __init__(self, c1, c_=256, c2=32, w=None): method call (line 351) | def call(self, inputs): class TFUpsample (line 355) | class TFUpsample(keras.layers.Layer): method __init__ (line 357) | def __init__(self, size, scale_factor, mode, w=None): # warning: all ... method call (line 366) | def call(self, inputs): class TFConcat (line 370) | class TFConcat(keras.layers.Layer): method __init__ (line 372) | def __init__(self, dimension=1, w=None): method call (line 377) | def call(self, inputs): function parse_model (line 381) | def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) class TFModel (line 437) | class TFModel: method __init__ (line 439) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgs... method predict (line 455) | def predict(self, method _xywh2xyxy (line 498) | def _xywh2xyxy(xywh): class AgnosticNMS (line 504) | class AgnosticNMS(keras.layers.Layer): method call (line 506) | def call(self, input, topk_all, iou_thres, conf_thres): method _nms (line 514) | def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnosti... function activations (line 542) | def activations(act=nn.SiLU): function representative_dataset_gen (line 554) | def representative_dataset_gen(dataset, ncalib=100): function run (line 565) | def run( function parse_opt (line 590) | def parse_opt(): function main (line 602) | def main(opt): FILE: yolo-improve/yolov5-AUX/models/yolo.py class Detect (line 38) | class Detect(nn.Module): method __init__ (line 44) | def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detecti... method forward (line 57) | def forward(self, x): method _make_grid (line 86) | def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch... class Segment (line 97) | class Segment(Detect): method __init__ (line 99) | def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=T... method forward (line 108) | def forward(self, x): class BaseModel (line 114) | class BaseModel(nn.Module): method forward (line 116) | def forward(self, x, profile=False, visualize=False): method _forward_once (line 119) | def _forward_once(self, x, profile=False, visualize=False): method _profile_one_layer (line 132) | def _profile_one_layer(self, m, x, dt): method fuse (line 145) | def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers method info (line 155) | def info(self, verbose=False, img_size=640): # print model information method _apply (line 158) | def _apply(self, fn): class DetectionModel (line 170) | class DetectionModel(BaseModel): method __init__ (line 172) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): ... method forward (line 211) | def forward(self, x, augment=False, profile=False, visualize=False): method _forward_augment (line 216) | def _forward_augment(self, x): method _descale_pred (line 230) | def _descale_pred(self, p, flips, scale, img_size): method _clip_augmented (line 247) | def _clip_augmented(self, y): method _initialize_biases (line 258) | def _initialize_biases(self, cf=None): # initialize biases into Detec... class SegmentationModel (line 272) | class SegmentationModel(DetectionModel): method __init__ (line 274) | def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None): class ClassificationModel (line 278) | class ClassificationModel(BaseModel): method __init__ (line 280) | def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml,... method _from_detection_model (line 284) | def _from_detection_model(self, model, nc=1000, cutoff=10): method _from_yaml (line 299) | def _from_yaml(self, cfg): function parse_model (line 304) | def parse_model(d, ch): # model_dict, input_channels(3) FILE: yolo-improve/yolov5-AUX/train.py function train (line 70) | def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hy... function parse_opt (line 436) | def parse_opt(known=False): function main (line 482) | def main(opt, callbacks=Callbacks()): function run (line 630) | def run(**kwargs): FILE: yolo-improve/yolov5-AUX/utils/__init__.py function emojis (line 11) | def emojis(str=''): class TryExcept (line 16) | class TryExcept(contextlib.ContextDecorator): method __init__ (line 18) | def __init__(self, msg=''): method __enter__ (line 21) | def __enter__(self): method __exit__ (line 24) | def __exit__(self, exc_type, value, traceback): function threaded (line 30) | def threaded(func): function join_threads (line 40) | def join_threads(verbose=False): function notebook_init (line 50) | def notebook_init(verbose=True): FILE: yolo-improve/yolov5-AUX/utils/activations.py class SiLU (line 11) | class SiLU(nn.Module): method forward (line 14) | def forward(x): class Hardswish (line 18) | class Hardswish(nn.Module): method forward (line 21) | def forward(x): class Mish (line 26) | class Mish(nn.Module): method forward (line 29) | def forward(x): class MemoryEfficientMish (line 33) | class MemoryEfficientMish(nn.Module): class F (line 35) | class F(torch.autograd.Function): method forward (line 38) | def forward(ctx, x): method backward (line 43) | def backward(ctx, grad_output): method forward (line 49) | def forward(self, x): class FReLU (line 53) | class FReLU(nn.Module): method __init__ (line 55) | def __init__(self, c1, k=3): # ch_in, kernel method forward (line 60) | def forward(self, x): class AconC (line 64) | class AconC(nn.Module): method __init__ (line 70) | def __init__(self, c1): method forward (line 76) | def forward(self, x): class MetaAconC (line 81) | class MetaAconC(nn.Module): method __init__ (line 87) | def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r method forward (line 97) | def forward(self, x): FILE: yolo-improve/yolov5-AUX/utils/augmentations.py class Albumentations (line 22) | class Albumentations: method __init__ (line 24) | def __init__(self, size=640): method __call__ (line 48) | def __call__(self, im, labels, p=1.0): function normalize (line 55) | def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): function denormalize (line 60) | def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): function augment_hsv (line 67) | def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): function hist_equalize (line 83) | def hist_equalize(im, clahe=True, bgr=False): function replicate (line 94) | def replicate(im, labels): function letterbox (line 111) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True... function random_perspective (line 144) | def random_perspective(im, function copy_paste (line 240) | def copy_paste(im, labels, segments, p=0.5): function cutout (line 262) | def cutout(im, labels, p=0.5): function mixup (line 289) | def mixup(im, labels, im2, labels2): function box_candidates (line 297) | def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1... function classify_albumentations (line 305) | def classify_albumentations( function classify_transforms (line 347) | def classify_transforms(size=224): class LetterBox (line 354) | class LetterBox: method __init__ (line 356) | def __init__(self, size=(640, 640), auto=False, stride=32): method __call__ (line 362) | def __call__(self, im): # im = np.array HWC class CenterCrop (line 373) | class CenterCrop: method __init__ (line 375) | def __init__(self, size=640): method __call__ (line 379) | def __call__(self, im): # im = np.array HWC class ToTensor (line 386) | class ToTensor: method __init__ (line 388) | def __init__(self, half=False): method __call__ (line 392) | def __call__(self, im): # im = np.array HWC in BGR order FILE: yolo-improve/yolov5-AUX/utils/autoanchor.py function check_anchor_order (line 19) | def check_anchor_order(m): function check_anchors (line 30) | def check_anchors(dataset, model, thr=4.0, imgsz=640): function kmean_anchors (line 67) | def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=... FILE: yolo-improve/yolov5-AUX/utils/autobatch.py function check_train_batch_size (line 15) | def check_train_batch_size(model, imgsz=640, amp=True): function autobatch (line 21) | def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): FILE: yolo-improve/yolov5-AUX/utils/callbacks.py class Callbacks (line 9) | class Callbacks: method __init__ (line 14) | def __init__(self): method register_action (line 38) | def register_action(self, hook, name='', callback=None): method get_registered_actions (line 51) | def get_registered_actions(self, hook=None): method run (line 60) | def run(self, hook, *args, thread=False, **kwargs): FILE: yolo-improve/yolov5-AUX/utils/dataloaders.py function get_hash (line 52) | def get_hash(paths): function exif_size (line 60) | def exif_size(img): function exif_transpose (line 70) | def exif_transpose(image): function seed_worker (line 96) | def seed_worker(worker_id): function create_dataloader (line 103) | def create_dataloader(path, class InfiniteDataLoader (line 156) | class InfiniteDataLoader(dataloader.DataLoader): method __init__ (line 162) | def __init__(self, *args, **kwargs): method __len__ (line 167) | def __len__(self): method __iter__ (line 170) | def __iter__(self): class _RepeatSampler (line 175) | class _RepeatSampler: method __init__ (line 182) | def __init__(self, sampler): method __iter__ (line 185) | def __iter__(self): class LoadScreenshots (line 190) | class LoadScreenshots: method __init__ (line 192) | def __init__(self, source, img_size=640, stride=32, auto=True, transfo... method __iter__ (line 221) | def __iter__(self): method __next__ (line 224) | def __next__(self): class LoadImages (line 239) | class LoadImages: method __init__ (line 241) | def __init__(self, path, img_size=640, stride=32, auto=True, transform... method __iter__ (line 276) | def __iter__(self): method __next__ (line 280) | def __next__(self): method _new_video (line 320) | def _new_video(self, path): method _cv2_rotate (line 328) | def _cv2_rotate(self, im): method __len__ (line 338) | def __len__(self): class LoadStreams (line 342) | class LoadStreams: method __init__ (line 344) | def __init__(self, sources='file.streams', img_size=640, stride=32, au... method update (line 388) | def update(self, i, cap, stream): method __iter__ (line 404) | def __iter__(self): method __next__ (line 408) | def __next__(self): method __len__ (line 424) | def __len__(self): function img2label_paths (line 428) | def img2label_paths(img_paths): class LoadImagesAndLabels (line 434) | class LoadImagesAndLabels(Dataset): method __init__ (line 439) | def __init__(self, method check_cache_ram (line 589) | def check_cache_ram(self, safety_margin=0.1, prefix=''): method cache_labels (line 606) | def cache_labels(self, path=Path('./labels.cache'), prefix=''): method __len__ (line 644) | def __len__(self): method __getitem__ (line 653) | def __getitem__(self, index): method load_image (line 727) | def load_image(self, i): method cache_images_to_disk (line 744) | def cache_images_to_disk(self, i): method load_mosaic (line 750) | def load_mosaic(self, index): method load_mosaic9 (line 808) | def load_mosaic9(self, index): method collate_fn (line 886) | def collate_fn(batch): method collate_fn4 (line 893) | def collate_fn4(batch): function flatten_recursive (line 920) | def flatten_recursive(path=DATASETS_DIR / 'coco128'): function extract_boxes (line 930) | def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataload... function autosplit (line 964) | def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0... function verify_image_label (line 990) | def verify_image_label(args): class HUBDatasetStats (line 1042) | class HUBDatasetStats(): method __init__ (line 1057) | def __init__(self, path='coco128.yaml', autodownload=False): method _find_yaml (line 1076) | def _find_yaml(dir): method _unzip (line 1086) | def _unzip(self, path): method _hub_ops (line 1096) | def _hub_ops(self, f, max_dim=1920): method get_json (line 1114) | def get_json(self, save=False, verbose=False): method process_images (line 1149) | def process_images(self): class ClassificationDataset (line 1163) | class ClassificationDataset(torchvision.datasets.ImageFolder): method __init__ (line 1172) | def __init__(self, root, augment, imgsz, cache=False): method __getitem__ (line 1180) | def __getitem__(self, i): function create_classification_dataloader (line 1197) | def create_classification_dataloader(path, FILE: yolo-improve/yolov5-AUX/utils/downloads.py function is_url (line 16) | def is_url(url, check=True): function gsutil_getsize (line 27) | def gsutil_getsize(url=''): function url_getsize (line 35) | def url_getsize(url='https://ultralytics.com/images/bus.jpg'): function curl_download (line 41) | def curl_download(url, filename, *, silent: bool = False) -> bool: function safe_download (line 60) | def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): function attempt_download (line 84) | def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'): FILE: yolo-improve/yolov5-AUX/utils/flask_rest_api/restapi.py function predict (line 20) | def predict(model): FILE: yolo-improve/yolov5-AUX/utils/general.py function is_ascii (line 64) | def is_ascii(s=''): function is_chinese (line 70) | def is_chinese(s='人工智能'): function is_colab (line 75) | def is_colab(): function is_notebook (line 80) | def is_notebook(): function is_kaggle (line 86) | def is_kaggle(): function is_docker (line 91) | def is_docker() -> bool: function is_writeable (line 102) | def is_writeable(dir, test=False): function set_logging (line 119) | def set_logging(name=LOGGING_NAME, verbose=True): function user_config_dir (line 148) | def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): class Profile (line 164) | class Profile(contextlib.ContextDecorator): method __init__ (line 166) | def __init__(self, t=0.0): method __enter__ (line 170) | def __enter__(self): method __exit__ (line 174) | def __exit__(self, type, value, traceback): method time (line 178) | def time(self): class Timeout (line 184) | class Timeout(contextlib.ContextDecorator): method __init__ (line 186) | def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors... method _timeout_handler (line 191) | def _timeout_handler(self, signum, frame): method __enter__ (line 194) | def __enter__(self): method __exit__ (line 199) | def __exit__(self, exc_type, exc_val, exc_tb): class WorkingDirectory (line 206) | class WorkingDirectory(contextlib.ContextDecorator): method __init__ (line 208) | def __init__(self, new_dir): method __enter__ (line 212) | def __enter__(self): method __exit__ (line 215) | def __exit__(self, exc_type, exc_val, exc_tb): function methods (line 219) | def methods(instance): function print_args (line 224) | def print_args(args: Optional[dict] = None, show_file=True, show_func=Fa... function init_seeds (line 239) | def init_seeds(seed=0, deterministic=False): function intersect_dicts (line 254) | def intersect_dicts(da, db, exclude=()): function get_default_args (line 259) | def get_default_args(func): function get_latest_run (line 265) | def get_latest_run(search_dir='.'): function file_age (line 271) | def file_age(path=__file__): function file_date (line 277) | def file_date(path=__file__): function file_size (line 283) | def file_size(path): function check_online (line 295) | def check_online(): function git_describe (line 310) | def git_describe(path=ROOT): # path must be a directory function check_git_status (line 321) | def check_git_status(repo='ultralytics/yolov5', branch='master'): function check_git_info (line 348) | def check_git_info(path='.'): function check_python (line 365) | def check_python(minimum='3.7.0'): function check_version (line 370) | def check_version(current='0.0.0', minimum='0.0.0', name='version ', pin... function check_requirements (line 383) | def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(... function check_img_size (line 417) | def check_img_size(imgsz, s=32, floor=0): function check_imshow (line 429) | def check_imshow(warn=False): function check_suffix (line 445) | def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''): function check_yaml (line 456) | def check_yaml(file, suffix=('.yaml', '.yml')): function check_file (line 461) | def check_file(file, suffix=''): function check_font (line 489) | def check_font(font=FONT, progress=False): function check_dataset (line 499) | def check_dataset(data, autodownload=True): function check_amp (line 565) | def check_amp(model): function yaml_load (line 593) | def yaml_load(file='data.yaml'): function yaml_save (line 599) | def yaml_save(file='data.yaml', data={}): function unzip_file (line 605) | def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): function url2file (line 615) | def url2file(url): function download (line 621) | def download(url, dir='.', unzip=True, delete=True, curl=False, threads=... function make_divisible (line 667) | def make_divisible(x, divisor): function clean_str (line 674) | def clean_str(s): function one_cycle (line 679) | def one_cycle(y1=0.0, y2=1.0, steps=100): function colorstr (line 684) | def colorstr(*input): function labels_to_class_weights (line 710) | def labels_to_class_weights(labels, nc=80): function labels_to_image_weights (line 729) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): function coco80_to_coco91_class (line 736) | def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index... function xyxy2xywh (line 748) | def xyxy2xywh(x): function xywh2xyxy (line 758) | def xywh2xyxy(x): function xywhn2xyxy (line 768) | def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): function xyxy2xywhn (line 778) | def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): function xyn2xy (line 790) | def xyn2xy(x, w=640, h=640, padw=0, padh=0): function segment2box (line 798) | def segment2box(segment, width=640, height=640): function segments2boxes (line 806) | def segments2boxes(segments): function resample_segments (line 815) | def resample_segments(segments, n=1000): function scale_boxes (line 825) | def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): function scale_segments (line 841) | def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, nor... function clip_boxes (line 860) | def clip_boxes(boxes, shape): function clip_segments (line 872) | def clip_segments(segments, shape): function non_max_suppression (line 882) | def non_max_suppression( function strip_optimizer (line 995) | def strip_optimizer(f='best.pt', s=''): # from utils.general import *; ... function print_mutation (line 1011) | def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr... function apply_classifier (line 1050) | def apply_classifier(x, model, img, im0): function increment_path (line 1085) | def increment_path(path, exist_ok=False, sep='', mkdir=False): function imread (line 1115) | def imread(path, flags=cv2.IMREAD_COLOR): function imwrite (line 1119) | def imwrite(path, im): function imshow (line 1127) | def imshow(path, im): FILE: yolo-improve/yolov5-AUX/utils/loggers/__init__.py class Loggers (line 57) | class Loggers(): method __init__ (line 59) | def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, lo... method remote_dataset (line 135) | def remote_dataset(self): method on_train_start (line 147) | def on_train_start(self): method on_pretrain_routine_start (line 151) | def on_pretrain_routine_start(self): method on_pretrain_routine_end (line 155) | def on_pretrain_routine_end(self, labels, names): method on_train_batch_end (line 167) | def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): method on_train_epoch_end (line 187) | def on_train_epoch_end(self, epoch): method on_val_start (line 195) | def on_val_start(self): method on_val_image_end (line 199) | def on_val_image_end(self, pred, predn, path, names, im): method on_val_batch_end (line 206) | def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): method on_val_end (line 210) | def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusi... method on_fit_epoch_end (line 222) | def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): method on_model_save (line 255) | def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): method on_train_end (line 268) | def on_train_end(self, last, best, epoch, results): method on_params_update (line 300) | def on_params_update(self, params: dict): class GenericLogger (line 308) | class GenericLogger: method __init__ (line 318) | def __init__(self, opt, console_logger, include=('tb', 'wandb')): method log_metrics (line 337) | def log_metrics(self, metrics, epoch): method log_images (line 353) | def log_images(self, files, name='Images', epoch=0): method log_graph (line 365) | def log_graph(self, model, imgsz=(640, 640)): method log_model (line 370) | def log_model(self, model_path, epoch=0, metadata={}): method update_params (line 377) | def update_params(self, params): function log_tensorboard_graph (line 383) | def log_tensorboard_graph(tb, model, imgsz=(640, 640)): function web_project_name (line 396) | def web_project_name(project): FILE: yolo-improve/yolov5-AUX/utils/loggers/clearml/clearml_utils.py function construct_dataset (line 20) | def construct_dataset(clearml_info_string): class ClearmlLogger (line 55) | class ClearmlLogger: method __init__ (line 66) | def __init__(self, opt, hyp): method log_debug_samples (line 116) | def log_debug_samples(self, files, title='Debug Samples'): method log_image_with_boxes (line 133) | def log_image_with_boxes(self, image_path, boxes, class_names, image, ... FILE: yolo-improve/yolov5-AUX/utils/loggers/comet/__init__.py class CometLogger (line 64) | class CometLogger: method __init__ (line 69) | def __init__(self, opt, hyp, run_id=None, job_type='Training', **exper... method _get_experiment (line 164) | def _get_experiment(self, mode, experiment_id=None): method log_metrics (line 193) | def log_metrics(self, log_dict, **kwargs): method log_parameters (line 196) | def log_parameters(self, log_dict, **kwargs): method log_asset (line 199) | def log_asset(self, asset_path, **kwargs): method log_asset_data (line 202) | def log_asset_data(self, asset, **kwargs): method log_image (line 205) | def log_image(self, img, **kwargs): method log_model (line 208) | def log_model(self, path, opt, epoch, fitness_score, best_model=False): method check_dataset (line 230) | def check_dataset(self, data_file): method log_predictions (line 244) | def log_predictions(self, image, labelsn, path, shape, predn): method preprocess_prediction (line 288) | def preprocess_prediction(self, image, labels, shape, pred): method add_assets_to_artifact (line 307) | def add_assets_to_artifact(self, artifact, path, asset_path, split): method upload_dataset_artifact (line 324) | def upload_dataset_artifact(self): method download_dataset_artifact (line 348) | def download_dataset_artifact(self, artifact_path): method update_data_paths (line 368) | def update_data_paths(self, data_dict): method on_pretrain_routine_end (line 379) | def on_pretrain_routine_end(self, paths): method on_train_start (line 392) | def on_train_start(self): method on_train_epoch_start (line 395) | def on_train_epoch_start(self): method on_train_epoch_end (line 398) | def on_train_epoch_end(self, epoch): method on_train_batch_start (line 403) | def on_train_batch_start(self): method on_train_batch_end (line 406) | def on_train_batch_end(self, log_dict, step): method on_train_end (line 413) | def on_train_end(self, files, save_dir, last, best, epoch, results): method on_val_start (line 440) | def on_val_start(self): method on_val_batch_start (line 443) | def on_val_batch_start(self): method on_val_batch_end (line 446) | def on_val_batch_end(self, batch_i, images, targets, paths, shapes, ou... method on_val_end (line 464) | def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusi... method on_fit_epoch_end (line 497) | def on_fit_epoch_end(self, result, epoch): method on_model_save (line 500) | def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): method on_params_update (line 504) | def on_params_update(self, params): method finish_run (line 507) | def finish_run(self): FILE: yolo-improve/yolov5-AUX/utils/loggers/comet/comet_utils.py function download_model_checkpoint (line 19) | def download_model_checkpoint(opt, experiment): function set_opt_parameters (line 66) | def set_opt_parameters(opt, experiment): function check_comet_weights (line 97) | def check_comet_weights(opt): function check_comet_resume (line 124) | def check_comet_resume(opt): FILE: yolo-improve/yolov5-AUX/utils/loggers/comet/hpo.py function get_args (line 27) | def get_args(known=False): function run (line 83) | def run(parameters, opt): FILE: yolo-improve/yolov5-AUX/utils/loss.py function smooth_BCE (line 15) | def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues... class BCEBlurWithLogitsLoss (line 20) | class BCEBlurWithLogitsLoss(nn.Module): method __init__ (line 22) | def __init__(self, alpha=0.05): method forward (line 27) | def forward(self, pred, true): class FocalLoss (line 37) | class FocalLoss(nn.Module): method __init__ (line 39) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): method forward (line 47) | def forward(self, pred, true): class QFocalLoss (line 67) | class QFocalLoss(nn.Module): method __init__ (line 69) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): method forward (line 77) | def forward(self, pred, true): class ComputeLoss (line 93) | class ComputeLoss: method __init__ (line 97) | def __init__(self, model, autobalance=False): method __call__ (line 123) | def __call__(self, p, targets): # predictions, targets method build_targets (line 179) | def build_targets(self, p, targets): class ComputeLossAuxOTA (line 238) | class ComputeLossAuxOTA: method __init__ (line 240) | def __init__(self, model, autobalance=False): method __call__ (line 264) | def __call__(self, p, targets, imgs): # predictions, targets, model method build_targets (line 349) | def build_targets(self, p, targets, imgs): method build_targets2 (line 502) | def build_targets2(self, p, targets, imgs): method find_5_positive (line 655) | def find_5_positive(self, p, targets): method find_3_positive (line 708) | def find_3_positive(self, p, targets): FILE: yolo-improve/yolov5-AUX/utils/metrics.py function fitness (line 17) | def fitness(x): function smooth (line 23) | def smooth(y, f=0.05): function ap_per_class (line 31) | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.... function compute_ap (line 98) | def compute_ap(recall, precision): class ConfusionMatrix (line 126) | class ConfusionMatrix: method __init__ (line 128) | def __init__(self, nc, conf=0.25, iou_thres=0.45): method process_batch (line 134) | def process_batch(self, detections, labels): method tp_fp (line 180) | def tp_fp(self): method plot (line 187) | def plot(self, normalize=True, save_dir='', names=()): method print (line 217) | def print(self): function bbox_iou (line 222) | def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, ... function box_iou (line 263) | def box_iou(box1, box2, eps=1e-7): function bbox_ioa (line 284) | def bbox_ioa(box1, box2, eps=1e-7): function wh_iou (line 306) | def wh_iou(wh1, wh2, eps=1e-7): function plot_pr_curve (line 318) | def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()): function plot_mc_curve (line 341) | def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabe... FILE: yolo-improve/yolov5-AUX/utils/plots.py class Colors (line 34) | class Colors: method __init__ (line 36) | def __init__(self): method __call__ (line 43) | def __call__(self, i, bgr=False): method hex2rgb (line 48) | def hex2rgb(h): # rgb order (PIL) function check_pil_font (line 55) | def check_pil_font(font=FONT, size=10): class Annotator (line 71) | class Annotator: method __init__ (line 73) | def __init__(self, im, line_width=None, font_size=None, font='Arial.tt... method box_label (line 86) | def box_label(self, box, label='', color=(128, 128, 128), txt_color=(2... method masks (line 118) | def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False): method rectangle (line 148) | def rectangle(self, xy, fill=None, outline=None, width=1): method text (line 152) | def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'): method fromarray (line 159) | def fromarray(self, im): method result (line 164) | def result(self): function feature_visualization (line 169) | def feature_visualization(x, module_type, stage, n=32, save_dir=Path('ru... function hist2d (line 197) | def hist2d(x, y, n=100): function butter_lowpass_filtfilt (line 206) | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): function output_to_target (line 219) | def output_to_target(output, max_det=300): function plot_images (line 230) | def plot_images(images, targets, paths=None, fname='images.jpg', names=N... function plot_lr_scheduler (line 294) | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): function plot_val_txt (line 311) | def plot_val_txt(): # from utils.plots import *; plot_val() function plot_targets_txt (line 328) | def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() function plot_val_study (line 341) | def plot_val_study(file='', dir='', x=None): # from utils.plots import ... function plot_labels (line 387) | def plot_labels(labels, names=(), save_dir=Path('')): function imshow_cls (line 432) | def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=... function plot_evolve (line 461) | def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots im... function plot_results (line 488) | def plot_results(file='path/to/results.csv', dir=''): function profile_idetection (line 514) | def profile_idetection(start=0, stop=0, labels=(), save_dir=''): function save_one_box (line 545) | def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, squar... FILE: yolo-improve/yolov5-AUX/utils/segment/augmentations.py function mixup (line 16) | def mixup(im, labels, segments, im2, labels2, segments2): function random_perspective (line 25) | def random_perspective(im, FILE: yolo-improve/yolov5-AUX/utils/segment/dataloaders.py function create_dataloader (line 23) | def create_dataloader(path, class LoadImagesAndLabelsAndMasks (line 82) | class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/... method __init__ (line 84) | def __init__( method __getitem__ (line 107) | def __getitem__(self, index): method load_mosaic (line 208) | def load_mosaic(self, index): method collate_fn (line 267) | def collate_fn(batch): function polygon2mask (line 275) | def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): function polygons2masks (line 295) | def polygons2masks(img_size, polygons, color, downsample_ratio=1): function polygons2masks_overlap (line 310) | def polygons2masks_overlap(img_size, segments, downsample_ratio=1): FILE: yolo-improve/yolov5-AUX/utils/segment/general.py function crop_mask (line 7) | def crop_mask(masks, boxes): function process_mask_upsample (line 25) | def process_mask_upsample(protos, masks_in, bboxes, shape): function process_mask (line 43) | def process_mask(protos, masks_in, bboxes, shape, upsample=False): function process_mask_native (line 70) | def process_mask_native(protos, masks_in, bboxes, shape): function scale_image (line 93) | def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): function mask_iou (line 121) | def mask_iou(mask1, mask2, eps=1e-7): function masks_iou (line 134) | def masks_iou(mask1, mask2, eps=1e-7): function masks2segments (line 147) | def masks2segments(masks, strategy='largest'): FILE: yolo-improve/yolov5-AUX/utils/segment/loss.py class ComputeLoss (line 12) | class ComputeLoss: method __init__ (line 14) | def __init__(self, model, autobalance=False, overlap=False): method __call__ (line 44) | def __call__(self, preds, targets, masks): # predictions, targets, model method single_mask_loss (line 112) | def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): method build_targets (line 118) | def build_targets(self, p, targets): FILE: yolo-improve/yolov5-AUX/utils/segment/metrics.py function fitness (line 11) | def fitness(x): function ap_per_class_box_and_mask (line 17) | def ap_per_class_box_and_mask( class Metric (line 66) | class Metric: method __init__ (line 68) | def __init__(self) -> None: method ap50 (line 76) | def ap50(self): method ap (line 84) | def ap(self): method mp (line 92) | def mp(self): method mr (line 100) | def mr(self): method map50 (line 108) | def map50(self): method map (line 116) | def map(self): method mean_results (line 123) | def mean_results(self): method class_result (line 127) | def class_result(self, i): method get_maps (line 131) | def get_maps(self, nc): method update (line 137) | def update(self, results): class Metrics (line 150) | class Metrics: method __init__ (line 153) | def __init__(self) -> None: method update (line 157) | def update(self, results): method mean_results (line 165) | def mean_results(self): method class_result (line 168) | def class_result(self, i): method get_maps (line 171) | def get_maps(self, nc): method ap_class_index (line 175) | def ap_class_index(self): FILE: yolo-improve/yolov5-AUX/utils/segment/plots.py function plot_images_and_masks (line 17) | def plot_images_and_masks(images, targets, masks, paths=None, fname='ima... function plot_results_with_masks (line 111) | def plot_results_with_masks(file='path/to/results.csv', dir='', best=True): FILE: yolo-improve/yolov5-AUX/utils/torch_utils.py function smart_inference_mode (line 38) | def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9... function smartCrossEntropyLoss (line 46) | def smartCrossEntropyLoss(label_smoothing=0.0): function smart_DDP (line 55) | def smart_DDP(model): function reshape_classifier_output (line 66) | def reshape_classifier_output(model, n=1000): function torch_distributed_zero_first (line 89) | def torch_distributed_zero_first(local_rank: int): function device_count (line 98) | def device_count(): function select_device (line 108) | def select_device(device='', batch_size=0, newline=True): function time_sync (line 144) | def time_sync(): function profile (line 151) | def profile(input, ops, n=10, device=None): function is_parallel (line 202) | def is_parallel(model): function de_parallel (line 207) | def de_parallel(model): function initialize_weights (line 212) | def initialize_weights(model): function find_modules (line 224) | def find_modules(model, mclass=nn.Conv2d): function sparsity (line 229) | def sparsity(model): function prune (line 238) | def prune(model, amount=0.3): function fuse_conv_and_bn (line 248) | def fuse_conv_and_bn(conv, bn): function model_info (line 272) | def model_info(model, verbose=False, imgsz=640): function scale_img (line 297) | def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,... function copy_attr (line 309) | def copy_attr(a, b, include=(), exclude=()): function smart_optimizer (line 318) | def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e... function smart_hub_load (line 349) | def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): function smart_resume (line 361) | def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs... class EarlyStopping (line 381) | class EarlyStopping: method __init__ (line 383) | def __init__(self, patience=30): method __call__ (line 389) | def __call__(self, epoch, fitness): class ModelEMA (line 404) | class ModelEMA: method __init__ (line 410) | def __init__(self, model, decay=0.9999, tau=2000, updates=0): method update (line 418) | def update(self, model): method update_attr (line 430) | def update_attr(self, model, include=(), exclude=('process_group', 're... FILE: yolo-improve/yolov5-AUX/utils/triton.py class TritonRemoteModel (line 11) | class TritonRemoteModel: method __init__ (line 17) | def __init__(self, url: str): method runtime (line 51) | def runtime(self): method __call__ (line 55) | def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typi... method _create_inputs (line 68) | def _create_inputs(self, *args, **kwargs): FILE: yolo-improve/yolov5-AUX/val.py function save_one_txt (line 50) | def save_one_txt(predn, save_conf, shape, file): function save_one_json (line 60) | def save_one_json(predn, jdict, path, class_map): function process_batch (line 73) | def process_batch(detections, labels, iouv): function run (line 99) | def run( function parse_opt (line 340) | def parse_opt(): function main (line 372) | def main(opt): FILE: yolo-improve/yolov5-C3RFEM.py class TridentBlock (line 1) | class TridentBlock(nn.Module): method __init__ (line 2) | def __init__(self, c1, c2, stride=1, c=False, e=0.5, padding=[1, 2, 3]... method forward_for_small (line 28) | def forward_for_small(self, x): method forward_for_middle (line 42) | def forward_for_middle(self, x): method forward_for_big (line 56) | def forward_for_big(self, x): method forward (line 70) | def forward(self, x): class RFEM (line 88) | class RFEM(nn.Module): method __init__ (line 89) | def __init__(self, c1, c2, n=1, e=0.5, stride=1): method forward (line 101) | def forward(self, x): class C3RFEM (line 107) | class C3RFEM(C3): method __init__ (line 109) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): FILE: yolo-improve/yolov5-CARAFE.py class CARAFE (line 1) | class CARAFE(nn.Module): method __init__ (line 2) | def __init__(self, c, k_enc=3, k_up=5, c_mid=64, scale=2): method forward (line 25) | def forward(self, X): FILE: yolo-improve/yolov5-CCFM.py class RepConv (line 1) | class RepConv(nn.Module): method __init__ (line 10) | def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False... method forward_fuse (line 23) | def forward_fuse(self, x): method forward (line 27) | def forward(self, x): method get_equivalent_kernel_bias (line 32) | def get_equivalent_kernel_bias(self): method _pad_1x1_to_3x3_tensor (line 39) | def _pad_1x1_to_3x3_tensor(self, kernel1x1): method _fuse_bn_tensor (line 46) | def _fuse_bn_tensor(self, branch): method fuse_convs (line 74) | def fuse_convs(self): class RepC3 (line 100) | class RepC3(nn.Module): method __init__ (line 103) | def __init__(self, c1, c2, n=3, e=1.0): method forward (line 112) | def forward(self, x): FILE: yolo-improve/yolov5-ContextAggregation.py class ContextAggregation (line 3) | class ContextAggregation(nn.Module): method __init__ (line 14) | def __init__(self, in_channels, reduction=1): method init_weights (line 29) | def init_weights(self): method forward (line 34) | def forward(self, x): FILE: yolo-improve/yolov5-CoordConv.py class AddCoords (line 1) | class AddCoords(nn.Module): method __init__ (line 3) | def __init__(self, with_r=False): method forward (line 7) | def forward(self, input_tensor): class CoordConv (line 38) | class CoordConv(nn.Module): method __init__ (line 40) | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,... method forward (line 48) | def forward(self, x): FILE: yolo-improve/yolov5-DBB.py function transI_fusebn (line 2) | def transI_fusebn(kernel, bn): function transII_addbranch (line 7) | def transII_addbranch(kernels, biases): function transIII_1x1_kxk (line 10) | def transIII_1x1_kxk(k1, b1, k2, b2, groups): function transIV_depthconcat (line 28) | def transIV_depthconcat(kernels, biases): function transV_avg (line 31) | def transV_avg(channels, kernel_size, groups): function transVI_multiscale (line 38) | def transVI_multiscale(kernel, target_kernel_size): function conv_bn (line 43) | def conv_bn(in_channels, out_channels, kernel_size, stride=1, padding=0,... class IdentityBasedConv1x1 (line 55) | class IdentityBasedConv1x1(nn.Conv2d): method __init__ (line 56) | def __init__(self, channels, groups=1): method forward (line 67) | def forward(self, input): method get_actual_kernel (line 72) | def get_actual_kernel(self): class BNAndPadLayer (line 76) | class BNAndPadLayer(nn.Module): method __init__ (line 77) | def __init__(self, method forward (line 88) | def forward(self, input): method weight (line 104) | def weight(self): method bias (line 108) | def bias(self): method running_mean (line 112) | def running_mean(self): method running_var (line 116) | def running_var(self): method eps (line 120) | def eps(self): class DiverseBranchBlock (line 124) | class DiverseBranchBlock(nn.Module): method __init__ (line 125) | def __init__(self, in_channels, out_channels, kernel_size, method get_equivalent_kernel_bias (line 184) | def get_equivalent_kernel_bias(self): method switch_to_deploy (line 211) | def switch_to_deploy(self): method forward (line 228) | def forward(self, inputs): method init_gamma (line 239) | def init_gamma(self, gamma_value): method single_init (line 249) | def single_init(self): class Bottleneck_DBB (line 254) | class Bottleneck_DBB(nn.Module): method __init__ (line 256) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 263) | def forward(self, x): class C3_DBB (line 266) | class C3_DBB(C3): method __init__ (line 268) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): FILE: yolo-improve/yolov5-DCN.py class DCNv2 (line 1) | class DCNv2(nn.Module): method __init__ (line 2) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method forward (line 34) | def forward(self, x): method reset_parameters (line 56) | def reset_parameters(self): class Bottleneck_DCN (line 66) | class Bottleneck_DCN(nn.Module): method __init__ (line 68) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 75) | def forward(self, x): class C3_DCN (line 78) | class C3_DCN(C3): method __init__ (line 80) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): FILE: yolo-improve/yolov5-DCNV3/commod.py class DCNV3_YoLo (line 2) | class DCNV3_YoLo(nn.Module): method __init__ (line 3) | def __init__(self, inc, ouc, k=1, s=1, p=None, g=1, d=1, act=True): method forward (line 11) | def forward(self, x): class Bottleneck_DCNV3 (line 19) | class Bottleneck_DCNV3(nn.Module): method __init__ (line 21) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 28) | def forward(self, x): class C3_DCNV3 (line 31) | class C3_DCNV3(nn.Module): method __init__ (line 33) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ... method forward (line 41) | def forward(self, x): FILE: yolo-improve/yolov5-DCNV3/ops_dcnv3/functions/dcnv3_func.py class DCNv3Function (line 19) | class DCNv3Function(Function): method forward (line 22) | def forward( method backward (line 51) | def backward(ctx, grad_output): method symbolic (line 64) | def symbolic(g, input, offset, mask, kernel_h, kernel_w, stride_h, function _get_reference_points (line 92) | def _get_reference_points(spatial_shapes, device, kernel_h, kernel_w, di... function _generate_dilation_grids (line 123) | def _generate_dilation_grids(spatial_shapes, kernel_h, kernel_w, dilatio... function dcnv3_core_pytorch (line 148) | def dcnv3_core_pytorch( FILE: yolo-improve/yolov5-DCNV3/ops_dcnv3/modules/dcnv3.py function autopad (line 17) | def autopad(k, p=None, d=1): # kernel, padding, dilation class Conv (line 26) | class Conv(nn.Module): method __init__ (line 30) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): method forward (line 36) | def forward(self, x): method forward_fuse (line 39) | def forward_fuse(self, x): function _is_power_of_2 (line 42) | def _is_power_of_2(n): class DCNv3 (line 50) | class DCNv3(nn.Module): method __init__ (line 51) | def __init__( method _reset_parameters (line 99) | def _reset_parameters(self): method forward (line 109) | def forward(self, input): FILE: yolo-improve/yolov5-DCNV3/ops_dcnv3/setup.py function get_extensions (line 22) | def get_extensions(): FILE: yolo-improve/yolov5-DCNV3/ops_dcnv3/src/cpu/dcnv3_cpu.cpp function dcnv3_cpu_forward (line 17) | at::Tensor dcnv3_cpu_forward(const at::Tensor &input, const at::Tensor &... function dcnv3_cpu_backward (line 28) | std::vector FILE: yolo-improve/yolov5-DCNV3/ops_dcnv3/src/vision.cpp function PYBIND11_MODULE (line 14) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { FILE: yolo-improve/yolov5-DCNV3/ops_dcnv3/test.py function check_forward_equal_with_pytorch_double (line 34) | def check_forward_equal_with_pytorch_double(): function check_forward_equal_with_pytorch_float (line 64) | def check_forward_equal_with_pytorch_float(): function check_backward_equal_with_pytorch_double (line 93) | def check_backward_equal_with_pytorch_double(channels=4, grad_input=True... function check_backward_equal_with_pytorch_float (line 156) | def check_backward_equal_with_pytorch_float(channels=4, grad_input=True,... function check_time_cost (line 220) | def check_time_cost(im2col_step=128): FILE: yolo-improve/yolov5-DSConv.py class DSConv (line 5) | class DSConv(_ConvNd): method __init__ (line 6) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method get_weight_res (line 34) | def get_weight_res(self): method forward (line 74) | def forward(self, input): class DSConv2D (line 83) | class DSConv2D(Conv): method __init__ (line 84) | def __init__(self, inc, ouc, k=1, s=1, p=None, g=1, d=1, act=True): class Bottleneck_DSConv (line 88) | class Bottleneck_DSConv(nn.Module): method __init__ (line 90) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 97) | def forward(self, x): class C3_DSConv (line 100) | class C3_DSConv(C3): method __init__ (line 102) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): FILE: yolo-improve/yolov5-DecoupledHead.py class Decoupled_Detect (line 1) | class Decoupled_Detect(nn.Module): method __init__ (line 7) | def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detecti... method forward (line 25) | def forward(self, x): method _make_grid (line 55) | def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch... function _initialize_biases (line 65) | def _initialize_biases(self, cf=None): # initialize biases into Detect(... FILE: yolo-improve/yolov5-DySnakeConv.py class DySnakeConv (line 4) | class DySnakeConv(nn.Module): method __init__ (line 5) | def __init__(self, inc, ouc, k=3, act=True) -> None: method forward (line 13) | def forward(self, x): class DSConv (line 16) | class DSConv(nn.Module): method __init__ (line 17) | def __init__(self, in_ch, out_ch, morph, kernel_size=3, if_offset=True... method forward (line 57) | def forward(self, f): class DSC (line 78) | class DSC(object): method __init__ (line 79) | def __init__(self, input_shape, kernel_size, extend_scope, morph): method _coordinate_map_3D (line 99) | def _coordinate_map_3D(self, offset, if_offset): method _bilinear_interpolate_3D (line 244) | def _bilinear_interpolate_3D(self, input_feature, y, x): method deform_conv (line 344) | def deform_conv(self, input, offset, if_offset): class Bottleneck_DySnake (line 351) | class Bottleneck_DySnake(nn.Module): method __init__ (line 353) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 360) | def forward(self, x): class C3_DySnake (line 363) | class C3_DySnake(C3): method __init__ (line 365) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): FILE: yolo-improve/yolov5-EVC.py class Encoding (line 5) | class Encoding(nn.Module): method __init__ (line 6) | def __init__(self, in_channels, num_codes): method scaled_l2 (line 19) | def scaled_l2(x, codewords, scale): method aggregate (line 35) | def aggregate(assignment_weights, x, codewords): method forward (line 52) | def forward(self, x): class Mlp (line 67) | class Mlp(nn.Module): method __init__ (line 71) | def __init__(self, in_features, hidden_features=None, method _init_weights (line 82) | def _init_weights(self, m): method forward (line 88) | def forward(self, x): class ConvBlock (line 97) | class ConvBlock(nn.Module): method __init__ (line 98) | def __init__(self, in_channels, out_channels, stride=1, res_conv=False... method zero_init_last_bn (line 116) | def zero_init_last_bn(self): method forward (line 119) | def forward(self, x, return_x_2=True): class Mean (line 140) | class Mean(nn.Module): method __init__ (line 141) | def __init__(self, dim, keep_dim=False): method forward (line 146) | def forward(self, input): class LVCBlock (line 149) | class LVCBlock(nn.Module): method __init__ (line 150) | def __init__(self, in_channels, out_channels, num_codes, channel_ratio... method forward (line 166) | def forward(self, x): class GroupNorm (line 175) | class GroupNorm(nn.GroupNorm): method __init__ (line 180) | def __init__(self, num_channels, **kwargs): class DWConv_LMLP (line 183) | class DWConv_LMLP(nn.Module): method __init__ (line 185) | def __init__(self, in_channels, out_channels, ksize, stride=1, act="si... method forward (line 198) | def forward(self, x): class LightMLPBlock (line 203) | class LightMLPBlock(nn.Module): method __init__ (line 204) | def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="... method forward (line 229) | def forward(self, x): class EVCBlock (line 240) | class EVCBlock(nn.Module): method __init__ (line 241) | def __init__(self, in_channels, out_channels, channel_ratio=4, base_ch... method forward (line 256) | def forward(self, x): FILE: yolo-improve/yolov5-FasterBlock.py class Partial_conv3 (line 2) | class Partial_conv3(nn.Module): method __init__ (line 3) | def __init__(self, dim, n_div, forward): method forward_slicing (line 16) | def forward_slicing(self, x): method forward_split_cat (line 22) | def forward_split_cat(self, x): class Faster_Block (line 29) | class Faster_Block(nn.Module): method __init__ (line 30) | def __init__(self, method forward (line 70) | def forward(self, x): method forward_layer_scale (line 78) | def forward_layer_scale(self, x): class C3_Faster (line 85) | class C3_Faster(C3): method __init__ (line 87) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): FILE: yolo-improve/yolov5-GFPN/extra_modules.py function conv_bn (line 5) | def conv_bn(in_channels, out_channels, kernel_size, stride, padding, gro... class RepConv (line 20) | class RepConv(nn.Module): method __init__ (line 24) | def __init__(self, method forward (line 78) | def forward(self, inputs): method get_equivalent_kernel_bias (line 91) | def get_equivalent_kernel_bias(self): method _pad_1x1_to_3x3_tensor (line 98) | def _pad_1x1_to_3x3_tensor(self, kernel1x1): method _fuse_bn_tensor (line 104) | def _fuse_bn_tensor(self, branch): method switch_to_deploy (line 134) | def switch_to_deploy(self): class Swish (line 159) | class Swish(nn.Module): method __init__ (line 160) | def __init__(self, inplace=True): method forward (line 164) | def forward(self, x): function get_activation (line 171) | def get_activation(name='silu', inplace=True): function get_norm (line 198) | def get_norm(name, out_channels, inplace=True): class ConvBNAct (line 205) | class ConvBNAct(nn.Module): method __init__ (line 207) | def __init__( method forward (line 238) | def forward(self, x): method fuseforward (line 246) | def fuseforward(self, x): class BasicBlock_3x3_Reverse (line 249) | class BasicBlock_3x3_Reverse(nn.Module): method __init__ (line 250) | def __init__(self, method forward (line 263) | def forward(self, x): class SPP (line 271) | class SPP(nn.Module): method __init__ (line 272) | def __init__( method forward (line 291) | def forward(self, x): class CSPStage (line 301) | class CSPStage(nn.Module): method __init__ (line 302) | def __init__(self, method forward (line 337) | def forward(self, x): FILE: yolo-improve/yolov5-GOLDYOLO/common.py function conv_bn (line 3) | def conv_bn(in_channels, out_channels, kernel_size, stride, padding, gro... class RepVGGBlock (line 12) | class RepVGGBlock(nn.Module): method __init__ (line 17) | def __init__(self, in_channels, out_channels, kernel_size=3, method forward (line 66) | def forward(self, inputs): method get_equivalent_kernel_bias (line 78) | def get_equivalent_kernel_bias(self): method _pad_1x1_to_3x3_tensor (line 84) | def _pad_1x1_to_3x3_tensor(self, kernel1x1): method _fuse_bn_tensor (line 90) | def _fuse_bn_tensor(self, branch): method switch_to_deploy (line 118) | def switch_to_deploy(self): function onnx_AdaptiveAvgPool2d (line 139) | def onnx_AdaptiveAvgPool2d(x, output_size): function get_avg_pool (line 146) | def get_avg_pool(): class SimFusion_3in (line 153) | class SimFusion_3in(nn.Module): method __init__ (line 154) | def __init__(self, in_channel_list, out_channels): method forward (line 162) | def forward(self, x): class SimFusion_4in (line 175) | class SimFusion_4in(nn.Module): method __init__ (line 176) | def __init__(self): method forward (line 180) | def forward(self, x): class IFM (line 195) | class IFM(nn.Module): method __init__ (line 196) | def __init__(self, inc, ouc, embed_dim_p=96, fuse_block_num=3) -> None: method forward (line 205) | def forward(self, x): class h_sigmoid (line 208) | class h_sigmoid(nn.Module): method __init__ (line 209) | def __init__(self, inplace=True): method forward (line 213) | def forward(self, x): class InjectionMultiSum_Auto_pool (line 216) | class InjectionMultiSum_Auto_pool(nn.Module): method __init__ (line 217) | def __init__( method forward (line 232) | def forward(self, x): function get_shape (line 263) | def get_shape(tensor): class PyramidPoolAgg (line 269) | class PyramidPoolAgg(nn.Module): method __init__ (line 270) | def __init__(self, inc, ouc, stride, pool_mode='torch'): method forward (line 279) | def forward(self, inputs): function drop_path (line 296) | def drop_path(x, drop_prob: float = 0., training: bool = False): class Mlp (line 313) | class Mlp(nn.Module): method __init__ (line 314) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 324) | def forward(self, x): class DropPath (line 333) | class DropPath(nn.Module): method __init__ (line 337) | def __init__(self, drop_prob=None): method forward (line 341) | def forward(self, x): class Attention (line 344) | class Attention(torch.nn.Module): method __init__ (line 345) | def __init__(self, dim, key_dim, num_heads, attn_ratio=4): method forward (line 361) | def forward(self, x): # x (B,N,C) class top_Block (line 377) | class top_Block(nn.Module): method __init__ (line 379) | def __init__(self, dim, key_dim, num_heads, mlp_ratio=4., attn_ratio=2... method forward (line 393) | def forward(self, x1): class TopBasicLayer (line 398) | class TopBasicLayer(nn.Module): method __init__ (line 399) | def __init__(self, embedding_dim, ouc_list, block_num=2, key_dim=8, nu... method forward (line 412) | def forward(self, x): class AdvPoolFusion (line 418) | class AdvPoolFusion(nn.Module): method forward (line 419) | def forward(self, x): FILE: yolo-improve/yolov5-NWD.py function wasserstein_loss (line 1) | def wasserstein_loss(pred, target, eps=1e-7, constant=12.8): FILE: yolo-improve/yolov5-OTA/loss.py class ComputeLossOTA (line 6) | class ComputeLossOTA: method __init__ (line 8) | def __init__(self, model, autobalance=False): method __call__ (line 32) | def __call__(self, p, targets, imgs): # predictions, targets, model method build_targets (line 92) | def build_targets(self, p, targets, imgs): method find_3_positive (line 244) | def find_3_positive(self, p, targets): FILE: yolo-improve/yolov5-RepNCSPELAN.py class RepConvN (line 1) | class RepConvN(nn.Module): method __init__ (line 7) | def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False... method forward_fuse (line 19) | def forward_fuse(self, x): method forward (line 23) | def forward(self, x): method get_equivalent_kernel_bias (line 28) | def get_equivalent_kernel_bias(self): method _avg_to_3x3_tensor (line 34) | def _avg_to_3x3_tensor(self, avgp): method _pad_1x1_to_3x3_tensor (line 43) | def _pad_1x1_to_3x3_tensor(self, kernel1x1): method _fuse_bn_tensor (line 49) | def _fuse_bn_tensor(self, branch): method fuse_convs (line 76) | def fuse_convs(self): class RepNBottleneck (line 101) | class RepNBottleneck(nn.Module): method __init__ (line 103) | def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch... method forward (line 110) | def forward(self, x): class RepNCSP (line 113) | class RepNCSP(nn.Module): method __init__ (line 115) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ... method forward (line 123) | def forward(self, x): class RepNCSPELAN4 (line 126) | class RepNCSPELAN4(nn.Module): method __init__ (line 128) | def __init__(self, c1, c2, c3, c4, c5=1): # ch_in, ch_out, number, sh... method forward (line 136) | def forward(self, x): method forward_split (line 141) | def forward_split(self, x): FILE: yolo-improve/yolov5-SAConv.py class ConvAWS2d (line 1) | class ConvAWS2d(nn.Conv2d): method __init__ (line 2) | def __init__(self, method _get_weight (line 23) | def _get_weight(self, weight): method forward (line 32) | def forward(self, x): method _load_from_state_dict (line 36) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st... class SAConv2d (line 50) | class SAConv2d(ConvAWS2d): method __init__ (line 51) | def __init__(self, method forward (line 98) | def forward(self, x): class Bottleneck_SAC (line 127) | class Bottleneck_SAC(nn.Module): method __init__ (line 129) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou... method forward (line 136) | def forward(self, x): class C3_SAC (line 139) | class C3_SAC(C3): method __init__ (line 141) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ... FILE: yolo-improve/yolov5-TSCODE.py class TSCODE_Detect (line 3) | class TSCODE_Detect(nn.Module): method __init__ (line 9) | def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detecti... method forward (line 29) | def forward(self, x_): method _make_grid (line 61) | def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch... class Decoupled_Detect (line 71) | class Decoupled_Detect(nn.Module): method __init__ (line 77) | def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detecti... method forward (line 95) | def forward(self, x): method _make_grid (line 125) | def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch... function _initialize_biases (line 135) | def _initialize_biases(self, cf=None): # initialize biases into Detect(... class SCE (line 159) | class SCE(nn.Module): method __init__ (line 160) | def __init__(self, c1): method forward (line 164) | def forward(self, x): class DPE (line 169) | class DPE(nn.Module): method __init__ (line 170) | def __init__(self, c1, c2): method forward (line 186) | def forward(self, x): FILE: yolo-improve/yolov5-aLRPLoss.py class aLRPLoss (line 1) | class aLRPLoss(torch.autograd.Function): method forward (line 3) | def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): method backward (line 66) | def backward(ctx, out_grad1, out_grad2, out_grad3): function __call__ (line 79) | def __call__(self, p, targets): # predictions, targets FILE: yolo-improve/yolov5-asf.py class Zoom_cat (line 3) | class Zoom_cat(nn.Module): method __init__ (line 4) | def __init__(self): method forward (line 7) | def forward(self, x): class ScalSeq (line 16) | class ScalSeq(nn.Module): method __init__ (line 17) | def __init__(self, inc, channel): method forward (line 26) | def forward(self, x): class Add (line 43) | class Add(nn.Module): method __init__ (line 45) | def __init__(self): method forward (line 48) | def forward(self, x): class channel_att (line 53) | class channel_att(nn.Module): method __init__ (line 54) | def __init__(self, channel, b=1, gamma=2): method forward (line 63) | def forward(self, x): class local_att (line 71) | class local_att(nn.Module): method __init__ (line 72) | def __init__(self, channel, reduction=16): method forward (line 86) | def forward(self, x): class attention_model (line 102) | class attention_model(nn.Module): method __init__ (line 104) | def __init__(self, ch = 256): method forward (line 108) | def forward(self, x): FILE: yolo-improve/yolov5-backbone/CVPR2023-EfficientViT/EfficientViT.py class Conv2d_BN (line 19) | class Conv2d_BN(torch.nn.Sequential): method __init__ (line 20) | def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, method fuse (line 30) | def fuse(self): function replace_batchnorm (line 42) | def replace_batchnorm(net): class PatchMerging (line 52) | class PatchMerging(torch.nn.Module): method __init__ (line 53) | def __init__(self, dim, out_dim, input_resolution): method forward (line 62) | def forward(self, x): class Residual (line 67) | class Residual(torch.nn.Module): method __init__ (line 68) | def __init__(self, m, drop=0.): method forward (line 73) | def forward(self, x): class FFN (line 81) | class FFN(torch.nn.Module): method __init__ (line 82) | def __init__(self, ed, h, resolution): method forward (line 88) | def forward(self, x): class CascadedGroupAttention (line 93) | class CascadedGroupAttention(torch.nn.Module): method __init__ (line 104) | def __init__(self, dim, key_dim, num_heads=8, method train (line 141) | def train(self, mode=True): method forward (line 148) | def forward(self, x): # x (B,C,H,W) class LocalWindowAttention (line 173) | class LocalWindowAttention(torch.nn.Module): method __init__ (line 185) | def __init__(self, dim, key_dim, num_heads=8, method forward (line 202) | def forward(self, x): class EfficientViTBlock (line 238) | class EfficientViTBlock(torch.nn.Module): method __init__ (line 251) | def __init__(self, type, method forward (line 269) | def forward(self, x): class EfficientViT (line 273) | class EfficientViT(torch.nn.Module): method __init__ (line 274) | def __init__(self, img_size=400, method forward (line 321) | def forward(self, x): function EfficientViT_M0 (line 392) | def EfficientViT_M0(pretrained='', frozen_stages=0, distillation=False, ... function EfficientViT_M1 (line 400) | def EfficientViT_M1(pretrained='', frozen_stages=0, distillation=False, ... function EfficientViT_M2 (line 408) | def EfficientViT_M2(pretrained='', frozen_stages=0, distillation=False, ... function EfficientViT_M3 (line 416) | def EfficientViT_M3(pretrained='', frozen_stages=0, distillation=False, ... function EfficientViT_M4 (line 424) | def EfficientViT_M4(pretrained='', frozen_stages=0, distillation=False, ... function EfficientViT_M5 (line 432) | def EfficientViT_M5(pretrained='', frozen_stages=0, distillation=False, ... function update_weight (line 440) | def update_weight(model_dict, weight_dict): FILE: yolo-improve/yolov5-backbone/CVPR2024-StarNet/starnet.py class ConvBN (line 26) | class ConvBN(torch.nn.Sequential): method __init__ (line 27) | def __init__(self, in_planes, out_planes, kernel_size=1, stride=1, pad... class Block (line 36) | class Block(nn.Module): method __init__ (line 37) | def __init__(self, dim, mlp_ratio=3, drop_path=0.): method forward (line 47) | def forward(self, x): class StarNet (line 57) | class StarNet(nn.Module): method __init__ (line 58) | def __init__(self, base_dim=32, depths=[3, 3, 12, 5], mlp_ratio=4, dro... method _init_weights (line 79) | def _init_weights(self, m): method forward (line 88) | def forward(self, x): function starnet_s1 (line 99) | def starnet_s1(pretrained=False, **kwargs): function starnet_s2 (line 109) | def starnet_s2(pretrained=False, **kwargs): function starnet_s3 (line 119) | def starnet_s3(pretrained=False, **kwargs): function starnet_s4 (line 129) | def starnet_s4(pretrained=False, **kwargs): function starnet_s050 (line 140) | def starnet_s050(pretrained=False, **kwargs): function starnet_s100 (line 145) | def starnet_s100(pretrained=False, **kwargs): function starnet_s150 (line 150) | def starnet_s150(pretrained=False, **kwargs): FILE: yolo-improve/yolov5-backbone/ConvNextV2/convnextv2.py class LayerNorm (line 16) | class LayerNorm(nn.Module): method __init__ (line 22) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l... method forward (line 32) | def forward(self, x): class GRN (line 42) | class GRN(nn.Module): method __init__ (line 45) | def __init__(self, dim): method forward (line 50) | def forward(self, x): class Block (line 55) | class Block(nn.Module): method __init__ (line 62) | def __init__(self, dim, drop_path=0.): method forward (line 72) | def forward(self, x): class ConvNeXtV2 (line 86) | class ConvNeXtV2(nn.Module): method __init__ (line 97) | def __init__(self, in_chans=3, num_classes=1000, method _init_weights (line 132) | def _init_weights(self, m): method forward (line 137) | def forward(self, x): function update_weight (line 145) | def update_weight(model_dict, weight_dict): function convnextv2_atto (line 155) | def convnextv2_atto(weights='', **kwargs): function convnextv2_femto (line 161) | def convnextv2_femto(weights='', **kwargs): function convnextv2_pico (line 167) | def convnextv2_pico(weights='', **kwargs): function convnextv2_nano (line 173) | def convnextv2_nano(weights='', **kwargs): function convnextv2_tiny (line 179) | def convnextv2_tiny(weights='', **kwargs): function convnextv2_base (line 185) | def convnextv2_base(weights='', **kwargs): function convnextv2_large (line 191) | def convnextv2_large(weights='', **kwargs): function convnextv2_huge (line 197) | def convnextv2_huge(weights='', **kwargs): FILE: yolo-improve/yolov5-backbone/EMO/emo.py function get_act (line 14) | def get_act(act_layer='relu'): class LayerNorm2d (line 32) | class LayerNorm2d(nn.Module): method __init__ (line 34) | def __init__(self, normalized_shape, eps=1e-6, elementwise_affine=True): method forward (line 38) | def forward(self, x): function get_norm (line 44) | def get_norm(norm_layer='in_1d'): class ConvNormAct (line 60) | class ConvNormAct(nn.Module): method __init__ (line 62) | def __init__(self, dim_in, dim_out, kernel_size, stride=1, dilation=1,... method forward (line 72) | def forward(self, x): class MSPatchEmb (line 84) | class MSPatchEmb(nn.Module): method __init__ (line 86) | def __init__(self, dim_in, emb_dim, kernel_size=2, c_group=-1, stride=... method forward (line 99) | def forward(self, x): class iRMB (line 108) | class iRMB(nn.Module): method __init__ (line 109) | def __init__(self, dim_in, dim_out, norm_in=True, has_skip=True, exp_r... method forward (line 139) | def forward(self, x): class EMO (line 189) | class EMO(nn.Module): method __init__ (line 190) | def __init__(self, dim_in=3, num_classes=1000, img_size=224, method _init_weights (line 237) | def _init_weights(self, m): method no_weight_decay (line 249) | def no_weight_decay(self): method no_weight_decay_keywords (line 253) | def no_weight_decay_keywords(self): method no_ft_keywords (line 257) | def no_ft_keywords(self): method ft_head_keywords (line 262) | def ft_head_keywords(self): method get_classifier (line 265) | def get_classifier(self): method reset_classifier (line 268) | def reset_classifier(self, num_classes): method check_bn (line 272) | def check_bn(self): method forward_features (line 278) | def forward_features(self, x): method forward (line 296) | def forward(self, x): function update_weight (line 301) | def update_weight(model_dict, weight_dict): function EMO_1M (line 311) | def EMO_1M(weights='', **kwargs): function EMO_2M (line 324) | def EMO_2M(weights='', **kwargs): function EMO_5M (line 337) | def EMO_5M(weights='', **kwargs): function EMO_6M (line 350) | def EMO_6M(weights='', **kwargs): FILE: yolo-improve/yolov5-backbone/EfficientFormerV2/EfficientFormerV2.py class Attention4D (line 64) | class Attention4D(torch.nn.Module): method __init__ (line 65) | def __init__(self, dim=384, key_dim=32, num_heads=8, method train (line 125) | def train(self, mode=True): method forward (line 132) | def forward(self, x): # x (B,N,C) function stem (line 164) | def stem(in_chs, out_chs, act_layer=nn.ReLU): class LGQuery (line 175) | class LGQuery(torch.nn.Module): method __init__ (line 176) | def __init__(self, in_dim, out_dim, resolution1, resolution2): method forward (line 186) | def forward(self, x): class Attention4DDownsample (line 194) | class Attention4DDownsample(torch.nn.Module): method __init__ (line 195) | def __init__(self, dim=384, key_dim=16, num_heads=8, method train (line 261) | def train(self, mode=True): method forward (line 268) | def forward(self, x): # x (B,N,C) class Embedding (line 293) | class Embedding(nn.Module): method __init__ (line 294) | def __init__(self, patch_size=3, stride=2, padding=1, method forward (line 330) | def forward(self, x): class Mlp (line 343) | class Mlp(nn.Module): method __init__ (line 349) | def __init__(self, in_features, hidden_features=None, method _init_weights (line 369) | def _init_weights(self, m): method forward (line 375) | def forward(self, x): class AttnFFN (line 393) | class AttnFFN(nn.Module): method __init__ (line 394) | def __init__(self, dim, mlp_ratio=4., method forward (line 416) | def forward(self, x): class FFN (line 427) | class FFN(nn.Module): method __init__ (line 428) | def __init__(self, dim, pool_size=3, mlp_ratio=4., method forward (line 445) | def forward(self, x): function eformer_block (line 453) | def eformer_block(dim, index, layers, class EfficientFormerV2 (line 489) | class EfficientFormerV2(nn.Module): method __init__ (line 490) | def __init__(self, layers, embed_dims=None, method forward_tokens (line 557) | def forward_tokens(self, x): method forward (line 567) | def forward(self, x): function update_weight (line 572) | def update_weight(model_dict, weight_dict): function efficientformerv2_s0 (line 582) | def efficientformerv2_s0(weights='', **kwargs): function efficientformerv2_s1 (line 596) | def efficientformerv2_s1(weights='', **kwargs): function efficientformerv2_s2 (line 610) | def efficientformerv2_s2(weights='', **kwargs): function efficientformerv2_l (line 624) | def efficientformerv2_l(weights='', **kwargs): FILE: yolo-improve/yolov5-backbone/EfficientViT/efficientViT.py function build_kwargs_from_config (line 19) | def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dic... function build_norm (line 32) | def build_norm(name="bn2d", num_features=None, **kwargs) -> Optional[nn.... function build_act (line 50) | def build_act(name: str, **kwargs) -> Optional[nn.Module]: function get_same_padding (line 58) | def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[... function list_sum (line 65) | def list_sum(x: List) -> Any: function merge_tensor (line 68) | def merge_tensor(x: List[torch.Tensor], mode="cat", dim=1) -> torch.Tensor: function resize (line 76) | def resize( function val2list (line 96) | def val2list(x: Union[List, Tuple, Any], repeat_time=1) -> List: function val2tuple (line 101) | def val2tuple(x: Union[List, Tuple, Any], min_len: int = 1, idx_repeat: ... class ConvLayer (line 111) | class ConvLayer(nn.Module): method __init__ (line 112) | def __init__( method forward (line 144) | def forward(self, x: torch.Tensor) -> torch.Tensor: class UpSampleLayer (line 155) | class UpSampleLayer(nn.Module): method __init__ (line 156) | def __init__( method forward (line 169) | def forward(self, x: torch.Tensor) -> torch.Tensor: class LinearLayer (line 173) | class LinearLayer(nn.Module): method __init__ (line 174) | def __init__( method _try_squeeze (line 190) | def _try_squeeze(self, x: torch.Tensor) -> torch.Tensor: method forward (line 195) | def forward(self, x: torch.Tensor) -> torch.Tensor: class IdentityLayer (line 207) | class IdentityLayer(nn.Module): method forward (line 208) | def forward(self, x: torch.Tensor) -> torch.Tensor: class DSConv (line 217) | class DSConv(nn.Module): method __init__ (line 218) | def __init__( method forward (line 253) | def forward(self, x: torch.Tensor) -> torch.Tensor: class MBConv (line 259) | class MBConv(nn.Module): method __init__ (line 260) | def __init__( method forward (line 307) | def forward(self, x: torch.Tensor) -> torch.Tensor: class LiteMSA (line 314) | class LiteMSA(nn.Module): method __init__ (line 316) | def __init__( method forward (line 369) | def forward(self, x: torch.Tensor) -> torch.Tensor: class EfficientViTBlock (line 414) | class EfficientViTBlock(nn.Module): method __init__ (line 415) | def __init__(self, in_channels: int, heads_ratio: float = 1.0, dim=32,... method forward (line 437) | def forward(self, x: torch.Tensor) -> torch.Tensor: class ResidualBlock (line 448) | class ResidualBlock(nn.Module): method __init__ (line 449) | def __init__( method forward_main (line 463) | def forward_main(self, x: torch.Tensor) -> torch.Tensor: method forward (line 469) | def forward(self, x: torch.Tensor) -> torch.Tensor: class DAGBlock (line 481) | class DAGBlock(nn.Module): method __init__ (line 482) | def __init__( method forward (line 502) | def forward(self, feature_dict: Dict[str, torch.Tensor]) -> Dict[str, ... class OpSequential (line 513) | class OpSequential(nn.Module): method __init__ (line 514) | def __init__(self, op_list: List[Optional[nn.Module]]): method forward (line 522) | def forward(self, x: torch.Tensor) -> torch.Tensor: class EfficientViTBackbone (line 527) | class EfficientViTBackbone(nn.Module): method __init__ (line 528) | def __init__(self, width_list: List[int], depth_list: List[int], in_ch... method build_local_block (line 605) | def build_local_block(in_channels: int, out_channels: int, stride: int... method forward (line 627) | def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: function update_weight (line 636) | def update_weight(model_dict, weight_dict): function efficientvit_b0 (line 647) | def efficientvit_b0(weights='', **kwargs) -> EfficientViTBackbone: function efficientvit_b1 (line 659) | def efficientvit_b1(weights='', **kwargs) -> EfficientViTBackbone: function efficientvit_b2 (line 671) | def efficientvit_b2(weights='', **kwargs) -> EfficientViTBackbone: function efficientvit_b3 (line 683) | def efficientvit_b3(weights='', **kwargs) -> EfficientViTBackbone: FILE: yolo-improve/yolov5-backbone/FocalNet/FocalNet.py function update_weight (line 17) | def update_weight(model_dict, weight_dict): class Mlp (line 27) | class Mlp(nn.Module): method __init__ (line 28) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 37) | def forward(self, x): class FocalModulation (line 45) | class FocalModulation(nn.Module): method __init__ (line 46) | def __init__(self, dim, focal_window, focal_level, focal_factor=2, bia... method forward (line 78) | def forward(self, x): method extra_repr (line 113) | def extra_repr(self) -> str: method flops (line 116) | def flops(self, N): class FocalNetBlock (line 136) | class FocalNetBlock(nn.Module): method __init__ (line 154) | def __init__(self, dim, input_resolution, mlp_ratio=4., drop=0., drop_... method forward (line 189) | def forward(self, x): method extra_repr (line 206) | def extra_repr(self) -> str: method flops (line 210) | def flops(self): class BasicLayer (line 225) | class BasicLayer(nn.Module): method __init__ (line 248) | def __init__(self, dim, out_dim, input_resolution, depth, method forward (line 296) | def forward(self, x, H, W): method extra_repr (line 311) | def extra_repr(self) -> str: method flops (line 314) | def flops(self): class PatchEmbed (line 322) | class PatchEmbed(nn.Module): method __init__ (line 333) | def __init__(self, img_size=(224, 224), patch_size=4, in_chans=3, embe... method forward (line 360) | def forward(self, x): method flops (line 370) | def flops(self): class FocalNet (line 377) | class FocalNet(nn.Module): method __init__ (line 400) | def __init__(self, method _init_weights (line 481) | def _init_weights(self, m): method no_weight_decay (line 491) | def no_weight_decay(self): method no_weight_decay_keywords (line 495) | def no_weight_decay_keywords(self): method forward (line 498) | def forward(self, x): method flops (line 516) | def flops(self): function focalnet_tiny_srf (line 540) | def focalnet_tiny_srf(pretrained=False, **kwargs): function focalnet_small_srf (line 548) | def focalnet_small_srf(pretrained=False, **kwargs): function focalnet_base_srf (line 556) | def focalnet_base_srf(pretrained=False, **kwargs): function focalnet_tiny_lrf (line 564) | def focalnet_tiny_lrf(pretrained=False, **kwargs): function focalnet_small_lrf (line 572) | def focalnet_small_lrf(pretrained=False, **kwargs): function focalnet_base_lrf (line 580) | def focalnet_base_lrf(pretrained=False, **kwargs): function focalnet_tiny_iso (line 588) | def focalnet_tiny_iso(pretrained=False, **kwargs): function focalnet_small_iso (line 596) | def focalnet_small_iso(pretrained=False, **kwargs): function focalnet_base_iso (line 604) | def focalnet_base_iso(pretrained=False, **kwargs): function focalnet_large_fl3 (line 613) | def focalnet_large_fl3(pretrained=False, **kwargs): function focalnet_large_fl4 (line 621) | def focalnet_large_fl4(pretrained=False, **kwargs): function focalnet_xlarge_fl3 (line 629) | def focalnet_xlarge_fl3(pretrained=False, **kwargs): function focalnet_xlarge_fl4 (line 637) | def focalnet_xlarge_fl4(pretrained=False, **kwargs): function focalnet_huge_fl3 (line 645) | def focalnet_huge_fl3(pretrained=False, **kwargs): function focalnet_huge_fl4 (line 653) | def focalnet_huge_fl4(pretrained=False, **kwargs): FILE: yolo-improve/yolov5-backbone/LSKNet/lsknet.py class Mlp (line 10) | class Mlp(nn.Module): method __init__ (line 11) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 21) | def forward(self, x): class LSKblock (line 31) | class LSKblock(nn.Module): method __init__ (line 32) | def __init__(self, dim): method forward (line 41) | def forward(self, x): class Attention (line 59) | class Attention(nn.Module): method __init__ (line 60) | def __init__(self, d_model): method forward (line 68) | def forward(self, x): class Block (line 78) | class Block(nn.Module): method __init__ (line 79) | def __init__(self, dim, mlp_ratio=4., drop=0.,drop_path=0., act_layer=... method forward (line 93) | def forward(self, x): class OverlapPatchEmbed (line 99) | class OverlapPatchEmbed(nn.Module): method __init__ (line 103) | def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, e... method forward (line 111) | def forward(self, x): class LSKNet (line 117) | class LSKNet(nn.Module): method __init__ (line 118) | def __init__(self, img_size=224, in_chans=3, embed_dims=[64, 128, 256,... method forward (line 149) | def forward(self, x): class DWConv (line 166) | class DWConv(nn.Module): method __init__ (line 167) | def __init__(self, dim=768): method forward (line 171) | def forward(self, x): function update_weight (line 175) | def update_weight(model_dict, weight_dict): function lsknet_t (line 185) | def lsknet_t(weights=''): function lsknet_s (line 191) | def lsknet_s(weights=''): FILE: yolo-improve/yolov5-backbone/MobileNetV4/mobilenetv4.py function make_divisible (line 215) | def make_divisible( function conv_2d (line 245) | def conv_2d(inp, oup, kernel_size=3, stride=1, groups=1, bias=False, nor... class InvertedResidual (line 255) | class InvertedResidual(nn.Module): method __init__ (line 256) | def __init__(self, inp, oup, stride, expand_ratio, act=False): method forward (line 268) | def forward(self, x): class UniversalInvertedBottleneckBlock (line 274) | class UniversalInvertedBottleneckBlock(nn.Module): method __init__ (line 275) | def __init__(self, method forward (line 306) | def forward(self, x): function build_blocks (line 319) | def build_blocks(layer_spec): class MobileNetV4 (line 347) | class MobileNetV4(nn.Module): method __init__ (line 348) | def __init__(self, model): method forward (line 376) | def forward(self, x): function MobileNetV4ConvSmall (line 386) | def MobileNetV4ConvSmall(): function MobileNetV4ConvMedium (line 390) | def MobileNetV4ConvMedium(): function MobileNetV4ConvLarge (line 394) | def MobileNetV4ConvLarge(): function MobileNetV4HybridMedium (line 398) | def MobileNetV4HybridMedium(): function MobileNetV4HybridLarge (line 402) | def MobileNetV4HybridLarge(): FILE: yolo-improve/yolov5-backbone/NextViT/NextViT.py class ConvBNReLU (line 14) | class ConvBNReLU(nn.Module): method __init__ (line 15) | def __init__( method forward (line 28) | def forward(self, x): function _make_divisible (line 35) | def _make_divisible(v, divisor, min_value=None): class PatchEmbed (line 45) | class PatchEmbed(nn.Module): method __init__ (line 46) | def __init__(self, method forward (line 65) | def forward(self, x): class MHCA (line 69) | class MHCA(nn.Module): method __init__ (line 73) | def __init__(self, out_channels, head_dim): method forward (line 82) | def forward(self, x): class Mlp (line 90) | class Mlp(nn.Module): method __init__ (line 91) | def __init__(self, in_features, out_features=None, mlp_ratio=None, dro... method forward (line 100) | def forward(self, x): class NCB (line 109) | class NCB(nn.Module): method __init__ (line 113) | def __init__(self, in_channels, out_channels, stride=1, path_dropout=0, method forward (line 130) | def forward(self, x): class E_MHSA (line 141) | class E_MHSA(nn.Module): method __init__ (line 145) | def __init__(self, dim, out_dim=None, head_dim=32, qkv_bias=True, qk_s... method forward (line 166) | def forward(self, x): class NTB (line 197) | class NTB(nn.Module): method __init__ (line 201) | def __init__( method forward (line 230) | def forward(self, x): class NextViT (line 253) | class NextViT(nn.Module): method __init__ (line 254) | def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0... method _initialize_weights (line 310) | def _initialize_weights(self): method forward (line 324) | def forward(self, x): function update_weight (line 337) | def update_weight(model_dict, weight_dict): function nextvit_small (line 347) | def nextvit_small(weights=''): function nextvit_base (line 355) | def nextvit_base(weights=''): function nextvit_large (line 363) | def nextvit_large(weights=''): FILE: yolo-improve/yolov5-backbone/ODConv/od_mobilenetv2.py function _make_divisible (line 10) | def _make_divisible(v, divisor, min_value=None): class ConvBNReLU (line 30) | class ConvBNReLU(nn.Sequential): method __init__ (line 31) | def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, gro... class ODConvBNReLU (line 40) | class ODConvBNReLU(nn.Sequential): method __init__ (line 41) | def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, gro... class InvertedResidual (line 52) | class InvertedResidual(nn.Module): method __init__ (line 53) | def __init__(self, inp, oup, stride, expand_ratio, norm_layer=nn.Batch... method forward (line 75) | def forward(self, x): class OD_MobileNetV2 (line 82) | class OD_MobileNetV2(nn.Module): method __init__ (line 83) | def __init__(self, method net_update_temperature (line 160) | def net_update_temperature(self, temperature): method forward (line 165) | def forward(self, x): function update_weight (line 175) | def update_weight(model_dict, weight_dict): function od_mobilenetv2_050 (line 185) | def od_mobilenetv2_050(weights=None, kernel_num=1): function od_mobilenetv2_075 (line 192) | def od_mobilenetv2_075(weights=None, kernel_num=1): function od_mobilenetv2_100 (line 199) | def od_mobilenetv2_100(weights=None, kernel_num=1): FILE: yolo-improve/yolov5-backbone/ODConv/od_resnet.py function odconv3x3 (line 9) | def odconv3x3(in_planes, out_planes, stride=1, reduction=0.0625, kernel_... function odconv1x1 (line 14) | def odconv1x1(in_planes, out_planes, stride=1, reduction=0.0625, kernel_... class BasicBlock (line 19) | class BasicBlock(nn.Module): method __init__ (line 22) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct... method forward (line 32) | def forward(self, x): class Bottleneck (line 50) | class Bottleneck(nn.Module): method __init__ (line 53) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct... method forward (line 65) | def forward(self, x): class OD_ResNet (line 87) | class OD_ResNet(nn.Module): method __init__ (line 88) | def __init__(self, block, layers, num_classes=1000, dropout=0.1, reduc... method net_update_temperature (line 113) | def net_update_temperature(self, temperature): method _make_layer (line 118) | def _make_layer(self, block, planes, blocks, stride=1, reduction=0.625... method forward (line 134) | def forward(self, x): function update_weight (line 147) | def update_weight(model_dict, weight_dict): function od_resnet18 (line 157) | def od_resnet18(weights=None, kernel_num=1): function od_resnet34 (line 164) | def od_resnet34(weights=None, kernel_num=1): function od_resnet50 (line 171) | def od_resnet50(weights=None, kernel_num=1): function od_resnet101 (line 178) | def od_resnet101(weights=None, kernel_num=1): FILE: yolo-improve/yolov5-backbone/ODConv/odconv.py class Attention (line 7) | class Attention(nn.Module): method __init__ (line 8) | def __init__(self, in_planes, out_planes, kernel_size, groups=1, reduc... method _initialize_weights (line 43) | def _initialize_weights(self): method update_temperature (line 53) | def update_temperature(self, temperature): method skip (line 57) | def skip(_): method get_channel_attention (line 60) | def get_channel_attention(self, x): method get_filter_attention (line 64) | def get_filter_attention(self, x): method get_spatial_attention (line 68) | def get_spatial_attention(self, x): method get_kernel_attention (line 73) | def get_kernel_attention(self, x): method forward (line 78) | def forward(self, x): class ODConv2d (line 86) | class ODConv2d(nn.Module): method __init__ (line 87) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi... method _initialize_weights (line 109) | def _initialize_weights(self): method update_temperature (line 113) | def update_temperature(self, temperature): method _forward_impl_common (line 116) | def _forward_impl_common(self, x): method _forward_impl_pw1x (line 132) | def _forward_impl_pw1x(self, x): method forward (line 140) | def forward(self, x): FILE: yolo-improve/yolov5-backbone/ODConvFuse/od_mobilenetv2.py function fuse_conv_bn (line 9) | def fuse_conv_bn(conv, bn): function _make_divisible (line 42) | def _make_divisible(v, divisor, min_value=None): class ConvBNReLU (line 62) | class ConvBNReLU(nn.Sequential): method __init__ (line 63) | def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, gro... method fuse (line 71) | def fuse(self): class ODConvBNReLU (line 77) | class ODConvBNReLU(nn.Sequential): method __init__ (line 78) | def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, gro... class InvertedResidual (line 89) | class InvertedResidual(nn.Module): method __init__ (line 90) | def __init__(self, inp, oup, stride, expand_ratio, norm_layer=nn.Batch... method forward (line 112) | def forward(self, x): class OD_MobileNetV2 (line 119) | class OD_MobileNetV2(nn.Module): method __init__ (line 120) | def __init__(self, method net_update_temperature (line 197) | def net_update_temperature(self, temperature): method forward (line 202) | def forward(self, x): function update_weight (line 212) | def update_weight(model_dict, weight_dict): function od_mobilenetv2_050 (line 222) | def od_mobilenetv2_050(weights=None, kernel_num=1): function od_mobilenetv2_075 (line 229) | def od_mobilenetv2_075(weights=None, kernel_num=1): function od_mobilenetv2_100 (line 236) | def od_mobilenetv2_100(weights=None, kernel_num=1): FILE: yolo-improve/yolov5-backbone/ODConvFuse/od_resnet.py function odconv3x3 (line 9) | def odconv3x3(in_planes, out_planes, stride=1, reduction=0.0625, kernel_... function odconv1x1 (line 14) | def odconv1x1(in_planes, out_planes, stride=1, reduction=0.0625, kernel_... class BasicBlock (line 19) | class BasicBlock(nn.Module): method __init__ (line 22) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct... method forward (line 32) | def forward(self, x): class Bottleneck (line 50) | class Bottleneck(nn.Module): method __init__ (line 53) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct... method forward (line 65) | def forward(self, x): class OD_ResNet (line 87) | class OD_ResNet(nn.Module): method __init__ (line 88) | def __init__(self, block, layers, num_classes=1000, dropout=0.1, reduc... method net_update_temperature (line 113) | def net_update_temperature(self, temperature): method _make_layer (line 118) | def _make_layer(self, block, planes, blocks, stride=1, reduction=0.625... method forward (line 134) | def forward(self, x): function update_weight (line 147) | def update_weight(model_dict, weight_dict): function od_resnet18 (line 157) | def od_resnet18(weights=None, kernel_num=1): function od_resnet34 (line 164) | def od_resnet34(weights=None, kernel_num=1): function od_resnet50 (line 171) | def od_resnet50(weights=None, kernel_num=1): function od_resnet101 (line 178) | def od_resnet101(weights=None, kernel_num=1): FILE: yolo-improve/yolov5-backbone/ODConvFuse/odconv.py function fuse_conv_bn (line 6) | def fuse_conv_bn(conv, bn): class Attention (line 39) | class Attention(nn.Module): method __init__ (line 40) | def __init__(self, in_planes, out_planes, kernel_size, groups=1, reduc... method _initialize_weights (line 75) | def _initialize_weights(self): method update_temperature (line 85) | def update_temperature(self, temperature): method skip (line 89) | def skip(_): method get_channel_attention (line 92) | def get_channel_attention(self, x): method get_filter_attention (line 96) | def get_filter_attention(self, x): method get_spatial_attention (line 100) | def get_spatial_attention(self, x): method get_kernel_attention (line 105) | def get_kernel_attention(self, x): method forward (line 110) | def forward(self, x): method fuse (line 118) | def fuse(self): class ODConv2d (line 123) | class ODConv2d(nn.Module): method __init__ (line 124) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi... method _initialize_weights (line 146) | def _initialize_weights(self): method update_temperature (line 150) | def update_temperature(self, temperature): method _forward_impl_common (line 153) | def _forward_impl_common(self, x): method _forward_impl_pw1x (line 169) | def _forward_impl_pw1x(self, x): method forward (line 177) | def forward(self, x): FILE: yolo-improve/yolov5-backbone/PoolFormer/poolformer.py function _cfg (line 29) | def _cfg(url='', **kwargs): class PatchEmbed (line 46) | class PatchEmbed(nn.Module): method __init__ (line 52) | def __init__(self, patch_size=16, stride=16, padding=0, method forward (line 62) | def forward(self, x): class LayerNormChannel (line 68) | class LayerNormChannel(nn.Module): method __init__ (line 73) | def __init__(self, num_channels, eps=1e-05): method forward (line 79) | def forward(self, x): class GroupNorm (line 88) | class GroupNorm(nn.GroupNorm): method __init__ (line 93) | def __init__(self, num_channels, **kwargs): class Pooling (line 97) | class Pooling(nn.Module): method __init__ (line 102) | def __init__(self, pool_size=3): method forward (line 107) | def forward(self, x): class Mlp (line 111) | class Mlp(nn.Module): method __init__ (line 116) | def __init__(self, in_features, hidden_features=None, method _init_weights (line 127) | def _init_weights(self, m): method forward (line 133) | def forward(self, x): class PoolFormerBlock (line 142) | class PoolFormerBlock(nn.Module): method __init__ (line 156) | def __init__(self, dim, pool_size=3, mlp_ratio=4., method forward (line 180) | def forward(self, x): function basic_blocks (line 194) | def basic_blocks(dim, index, layers, class PoolFormer (line 219) | class PoolFormer(nn.Module): method __init__ (line 236) | def __init__(self, layers, embed_dims=None, method reset_classifier (line 308) | def reset_classifier(self, num_classes): method forward_embeddings (line 313) | def forward_embeddings(self, x): method forward_tokens (line 317) | def forward_tokens(self, x): method forward (line 327) | def forward(self, x): function update_weight (line 343) | def update_weight(model_dict, weight_dict): function poolformer_s12 (line 353) | def poolformer_s12(pretrained=False, **kwargs): function poolformer_s24 (line 376) | def poolformer_s24(pretrained=False, **kwargs): function poolformer_s36 (line 395) | def poolformer_s36(pretrained=False, **kwargs): function poolformer_m36 (line 415) | def poolformer_m36(pretrained=False, **kwargs): function poolformer_m48 (line 437) | def poolformer_m48(pretrained=False, **kwargs): FILE: yolo-improve/yolov5-backbone/RIFormer/RIFormer.py class Mlp (line 11) | class Mlp(nn.Module): method __init__ (line 25) | def __init__(self, method forward (line 39) | def forward(self, x): class PatchEmbed (line 47) | class PatchEmbed(nn.Module): method __init__ (line 62) | def __init__(self, method forward (line 78) | def forward(self, x): class Affine (line 84) | class Affine(nn.Module): method __init__ (line 91) | def __init__(self, in_features): method forward (line 102) | def forward(self, x): class RIFormerBlock (line 106) | class RIFormerBlock(BaseModule): method __init__ (line 124) | def __init__(self, method forward (line 160) | def forward(self, x): method fuse_affine (line 177) | def fuse_affine(self, norm, token_mixer): method get_equivalent_scale_bias (line 185) | def get_equivalent_scale_bias(self): method switch_to_deploy (line 189) | def switch_to_deploy(self): function basic_blocks (line 202) | def basic_blocks(dim, function update_weight (line 232) | def update_weight(model_dict, weight_dict): class RIFormer (line 243) | class RIFormer(nn.Module): method __init__ (line 327) | def __init__(self, method forward_embeddings (line 421) | def forward_embeddings(self, x): method forward_tokens (line 425) | def forward_tokens(self, x): method forward (line 435) | def forward(self, x): FILE: yolo-improve/yolov5-backbone/RepViT/repvit.py function replace_batchnorm (line 8) | def replace_batchnorm(net): function _make_divisible (line 19) | def _make_divisible(v, divisor, min_value=None): class Conv2d_BN (line 38) | class Conv2d_BN(torch.nn.Sequential): method __init__ (line 39) | def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1, method fuse_self (line 49) | def fuse_self(self): class Residual (line 62) | class Residual(torch.nn.Module): method __init__ (line 63) | def __init__(self, m, drop=0.): method forward (line 68) | def forward(self, x): method fuse_self (line 76) | def fuse_self(self): class RepVGGDW (line 94) | class RepVGGDW(torch.nn.Module): method __init__ (line 95) | def __init__(self, ed) -> None: method forward (line 102) | def forward(self, x): method fuse_self (line 106) | def fuse_self(self): class RepViTBlock (line 134) | class RepViTBlock(nn.Module): method __init__ (line 135) | def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, ... method forward (line 169) | def forward(self, x): class RepViT (line 172) | class RepViT(nn.Module): method __init__ (line 173) | def __init__(self, cfgs): method forward (line 193) | def forward(self, x): method switch_to_deploy (line 203) | def switch_to_deploy(self): function update_weight (line 206) | def update_weight(model_dict, weight_dict): function repvit_m0_9 (line 217) | def repvit_m0_9(weights=''): function repvit_m1_0 (line 255) | def repvit_m1_0(weights=''): function repvit_m1_1 (line 293) | def repvit_m1_1(weights=''): function repvit_m1_5 (line 329) | def repvit_m1_5(weights=''): function repvit_m2_3 (line 383) | def repvit_m2_3(weights=''): FILE: yolo-improve/yolov5-backbone/SwinTransformer/SwinTransformer.py class Mlp (line 17) | class Mlp(nn.Module): method __init__ (line 20) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 29) | def forward(self, x): function window_partition (line 38) | def window_partition(x, window_size): function window_reverse (line 53) | def window_reverse(windows, window_size, H, W): class WindowAttention (line 70) | class WindowAttention(nn.Module): method __init__ (line 84) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal... method forward (line 118) | def forward(self, x, mask=None): class SwinTransformerBlock (line 153) | class SwinTransformerBlock(nn.Module): method __init__ (line 171) | def __init__(self, dim, num_heads, window_size=7, shift_size=0, method forward (line 195) | def forward(self, x, mask_matrix): class PatchMerging (line 255) | class PatchMerging(nn.Module): method __init__ (line 262) | def __init__(self, dim, norm_layer=nn.LayerNorm): method forward (line 268) | def forward(self, x, H, W): class BasicLayer (line 298) | class BasicLayer(nn.Module): method __init__ (line 317) | def __init__(self, method forward (line 359) | def forward(self, x, H, W): class PatchEmbed (line 402) | class PatchEmbed(nn.Module): method __init__ (line 412) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=... method forward (line 426) | def forward(self, x): class SwinTransformer (line 444) | class SwinTransformer(nn.Module): method __init__ (line 473) | def __init__(self, method forward (line 551) | def forward(self, x): function update_weight (line 578) | def update_weight(model_dict, weight_dict): function SwinTransformer_Tiny (line 588) | def SwinTransformer_Tiny(weights=''): FILE: yolo-improve/yolov5-backbone/UniRepLKNet/unireplknet.py class GRNwithNHWC (line 21) | class GRNwithNHWC(nn.Module): method __init__ (line 27) | def __init__(self, dim, use_bias=True): method forward (line 34) | def forward(self, x): class NCHWtoNHWC (line 43) | class NCHWtoNHWC(nn.Module): method __init__ (line 44) | def __init__(self): method forward (line 47) | def forward(self, x): class NHWCtoNCHW (line 51) | class NHWCtoNCHW(nn.Module): method __init__ (line 52) | def __init__(self): method forward (line 55) | def forward(self, x): function get_conv2d (line 63) | def get_conv2d(in_channels, out_channels, kernel_size, stride, padding, ... function get_bn (line 88) | def get_bn(dim, use_sync_bn=False): class SEBlock (line 94) | class SEBlock(nn.Module): method __init__ (line 99) | def __init__(self, input_channels, internal_neurons): method forward (line 108) | def forward(self, inputs): function fuse_bn (line 116) | def fuse_bn(conv, bn): function convert_dilated_to_nondilated (line 121) | def convert_dilated_to_nondilated(kernel, dilate_rate): function merge_dilated_into_large_kernel (line 135) | def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilate... class DilatedReparamBlock (line 145) | class DilatedReparamBlock(nn.Module): method __init__ (line 150) | def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, a... method forward (line 191) | def forward(self, x): method merge_dilated_branches (line 201) | def merge_dilated_branches(self): class UniRepLKNetBlock (line 222) | class UniRepLKNetBlock(nn.Module): method __init__ (line 224) | def __init__(self, method forward (line 290) | def forward(self, inputs): method reparameterize (line 306) | def reparameterize(self): class UniRepLKNet (line 364) | class UniRepLKNet(nn.Module): method __init__ (line 383) | def __init__(self, method _init_weights (line 454) | def _init_weights(self, m): method forward (line 460) | def forward(self, x): method switch_to_deploy (line 478) | def switch_to_deploy(self): class LayerNorm (line 485) | class LayerNorm(nn.Module): method __init__ (line 493) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l... method forward (line 504) | def forward(self, x): function update_weight (line 514) | def update_weight(model_dict, weight_dict): function unireplknet_a (line 524) | def unireplknet_a(weights='', **kwargs): function unireplknet_f (line 530) | def unireplknet_f(weights='', **kwargs): function unireplknet_p (line 536) | def unireplknet_p(weights='', **kwargs): function unireplknet_n (line 542) | def unireplknet_n(weights='', **kwargs): function unireplknet_t (line 548) | def unireplknet_t(weights='', **kwargs): function unireplknet_s (line 554) | def unireplknet_s(weights='', **kwargs): function unireplknet_b (line 560) | def unireplknet_b(weights='', **kwargs): function unireplknet_l (line 566) | def unireplknet_l(weights='', **kwargs): function unireplknet_xl (line 572) | def unireplknet_xl(weights='', **kwargs): FILE: yolo-improve/yolov5-backbone/VanillaNet/VanillaNet.py class activation (line 15) | class activation(nn.ReLU): method __init__ (line 16) | def __init__(self, dim, act_num=3, deploy=False): method forward (line 26) | def forward(self, x): method _fuse_bn_tensor (line 36) | def _fuse_bn_tensor(self, weight, bn): method switch_to_deploy (line 47) | def switch_to_deploy(self): class Block (line 57) | class Block(nn.Module): method __init__ (line 58) | def __init__(self, dim, dim_out, act_num=3, stride=2, deploy=False, ad... method forward (line 81) | def forward(self, x): method _fuse_bn_tensor (line 93) | def _fuse_bn_tensor(self, conv, bn): method switch_to_deploy (line 105) | def switch_to_deploy(self): class VanillaNet (line 121) | class VanillaNet(nn.Module): method __init__ (line 122) | def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 7... method _init_weights (line 156) | def _init_weights(self, m): method change_act (line 161) | def change_act(self, m): method forward (line 166) | def forward(self, x): method _fuse_bn_tensor (line 184) | def _fuse_bn_tensor(self, conv, bn): method switch_to_deploy (line 196) | def switch_to_deploy(self): function update_weight (line 214) | def update_weight(model_dict, weight_dict): function vanillanet_5 (line 224) | def vanillanet_5(pretrained='',in_22k=False, **kwargs): function vanillanet_6 (line 231) | def vanillanet_6(pretrained='',in_22k=False, **kwargs): function vanillanet_7 (line 238) | def vanillanet_7(pretrained='',in_22k=False, **kwargs): function vanillanet_8 (line 245) | def vanillanet_8(pretrained='', in_22k=False, **kwargs): function vanillanet_9 (line 252) | def vanillanet_9(pretrained='', in_22k=False, **kwargs): function vanillanet_10 (line 259) | def vanillanet_10(pretrained='', in_22k=False, **kwargs): function vanillanet_11 (line 269) | def vanillanet_11(pretrained='', in_22k=False, **kwargs): function vanillanet_12 (line 279) | def vanillanet_12(pretrained='', in_22k=False, **kwargs): function vanillanet_13 (line 289) | def vanillanet_13(pretrained='', in_22k=False, **kwargs): function vanillanet_13_x1_5 (line 299) | def vanillanet_13_x1_5(pretrained='', in_22k=False, **kwargs): function vanillanet_13_x1_5_ada_pool (line 309) | def vanillanet_13_x1_5_ada_pool(pretrained='', in_22k=False, **kwargs): FILE: yolo-improve/yolov5-backbone/fasternet/fasternet.py class Partial_conv3 (line 15) | class Partial_conv3(nn.Module): method __init__ (line 17) | def __init__(self, dim, n_div, forward): method forward_slicing (line 30) | def forward_slicing(self, x: Tensor) -> Tensor: method forward_split_cat (line 37) | def forward_split_cat(self, x: Tensor) -> Tensor: class MLPBlock (line 46) | class MLPBlock(nn.Module): method __init__ (line 48) | def __init__(self, method forward (line 88) | def forward(self, x: Tensor) -> Tensor: method forward_layer_scale (line 94) | def forward_layer_scale(self, x: Tensor) -> Tensor: class BasicStage (line 102) | class BasicStage(nn.Module): method __init__ (line 104) | def __init__(self, method forward (line 134) | def forward(self, x: Tensor) -> Tensor: class PatchEmbed (line 139) | class PatchEmbed(nn.Module): method __init__ (line 141) | def __init__(self, patch_size, patch_stride, in_chans, embed_dim, norm... method forward (line 149) | def forward(self, x: Tensor) -> Tensor: class PatchMerging (line 154) | class PatchMerging(nn.Module): method __init__ (line 156) | def __init__(self, patch_size2, patch_stride2, dim, norm_layer): method forward (line 164) | def forward(self, x: Tensor) -> Tensor: class FasterNet (line 169) | class FasterNet(nn.Module): method __init__ (line 170) | def __init__(self, method forward (line 262) | def forward(self, x: Tensor) -> Tensor: function update_weight (line 274) | def update_weight(model_dict, weight_dict): function fasternet_t0 (line 284) | def fasternet_t0(weights=None, cfg='models/faster_cfg/fasternet_t0.yaml'): function fasternet_t1 (line 293) | def fasternet_t1(weights=None, cfg='models/faster_cfg/fasternet_t1.yaml'): function fasternet_t2 (line 302) | def fasternet_t2(weights=None, cfg='models/faster_cfg/fasternet_t2.yaml'): function fasternet_s (line 311) | def fasternet_s(weights=None, cfg='models/faster_cfg/fasternet_s.yaml'): function fasternet_m (line 320) | def fasternet_m(weights=None, cfg='models/faster_cfg/fasternet_m.yaml'): function fasternet_l (line 329) | def fasternet_l(weights=None, cfg='models/faster_cfg/fasternet_l.yaml'): FILE: yolo-improve/yolov5-backbone/inceptionnext/inceptionnext.py class InceptionDWConv2d (line 19) | class InceptionDWConv2d(nn.Module): method __init__ (line 22) | def __init__(self, in_channels, square_kernel_size=3, band_kernel_size... method forward (line 31) | def forward(self, x): class ConvMlp (line 39) | class ConvMlp(nn.Module): method __init__ (line 43) | def __init__( method forward (line 57) | def forward(self, x): class MlpHead (line 66) | class MlpHead(nn.Module): method __init__ (line 69) | def __init__(self, dim, num_classes=1000, mlp_ratio=3, act_layer=nn.GELU, method forward (line 79) | def forward(self, x): class MetaNeXtBlock (line 89) | class MetaNeXtBlock(nn.Module): method __init__ (line 97) | def __init__( method forward (line 116) | def forward(self, x): class MetaNeXtStage (line 127) | class MetaNeXtStage(nn.Module): method __init__ (line 128) | def __init__( method forward (line 164) | def forward(self, x): class MetaNeXt (line 173) | class MetaNeXt(nn.Module): method __init__ (line 191) | def __init__( method set_grad_checkpointing (line 248) | def set_grad_checkpointing(self, enable=True): method no_weight_decay (line 253) | def no_weight_decay(self): method forward (line 256) | def forward(self, x): method _init_weights (line 268) | def _init_weights(self, m): function _cfg (line 274) | def _cfg(url='', **kwargs): function update_weight (line 284) | def update_weight(model_dict, weight_dict): function inceptionnext_tiny (line 310) | def inceptionnext_tiny(pretrained=False, **kwargs): function inceptionnext_small (line 321) | def inceptionnext_small(pretrained=False, **kwargs): function inceptionnext_base (line 332) | def inceptionnext_base(pretrained=False, **kwargs): function inceptionnext_base_384 (line 343) | def inceptionnext_base_384(pretrained=False, **kwargs): FILE: yolo-improve/yolov5-backbone/yolo.py function parse_model (line 1) | def parse_model(d, ch): # model_dict, input_channels(3) function _forward_once (line 84) | def _forward_once(self, x, profile=False, visualize=False): FILE: yolo-improve/yolov5-dyhead.py function _make_divisible (line 9) | def _make_divisible(v, divisor, min_value=None): class swish (line 19) | class swish(nn.Module): method forward (line 20) | def forward(self, x): class h_swish (line 24) | class h_swish(nn.Module): method __init__ (line 25) | def __init__(self, inplace=False): method forward (line 29) | def forward(self, x): class h_sigmoid (line 33) | class h_sigmoid(nn.Module): method __init__ (line 34) | def __init__(self, inplace=True, h_max=1): method forward (line 39) | def forward(self, x): class DyReLU (line 43) | class DyReLU(nn.Module): method __init__ (line 44) | def __init__(self, inp, reduction=4, lambda_a=1.0, K2=True, use_bias=T... method forward (line 82) | def forward(self, x): class DyDCNv2 (line 126) | class DyDCNv2(nn.Module): method __init__ (line 139) | def __init__(self, method forward (line 152) | def forward(self, x, offset, mask): class DyHeadBlock (line 160) | class DyHeadBlock(nn.Module): method __init__ (line 166) | def __init__(self, method _init_weights (line 193) | def _init_weights(self): method forward (line 200) | def forward(self, x): FILE: yolo-improve/yolov5-res2block.py class Bottle2neck (line 1) | class Bottle2neck(nn.Module): method __init__ (line 4) | def __init__(self, inplanes, planes, shortcut, baseWidth=26, scale = 4): method forward (line 33) | def forward(self, x): class C3_Res2Block (line 58) | class C3_Res2Block(C3): method __init__ (line 60) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ... FILE: yolo-improve/yolov5-softnms.py function box_iou_for_nms (line 1) | def box_iou_for_nms(box1, box2, GIoU=False, DIoU=False, CIoU=False, SIoU... function soft_nms (line 58) | def soft_nms(bboxes, scores, iou_thresh=0.5,sigma=0.5,score_threshold=0.... FILE: yolo-improve/yolov7-CoordConv.py class AddCoords (line 1) | class AddCoords(nn.Module): method __init__ (line 2) | def __init__(self, with_r=False): method forward (line 6) | def forward(self, input_tensor): class CoordConv (line 36) | class CoordConv(nn.Module): method __init__ (line 37) | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,... method forward (line 45) | def forward(self, x): FILE: yolo-improve/yolov7-DBB.py function transI_fusebn (line 2) | def transI_fusebn(kernel, bn): function transII_addbranch (line 7) | def transII_addbranch(kernels, biases): function transIII_1x1_kxk (line 10) | def transIII_1x1_kxk(k1, b1, k2, b2, groups): function transIV_depthconcat (line 28) | def transIV_depthconcat(kernels, biases): function transV_avg (line 31) | def transV_avg(channels, kernel_size, groups): function transVI_multiscale (line 38) | def transVI_multiscale(kernel, target_kernel_size): function conv_bn (line 43) | def conv_bn(in_channels, out_channels, kernel_size, stride=1, padding=0,... class IdentityBasedConv1x1 (line 55) | class IdentityBasedConv1x1(nn.Conv2d): method __init__ (line 56) | def __init__(self, channels, groups=1): method forward (line 67) | def forward(self, input): method get_actual_kernel (line 72) | def get_actual_kernel(self): class BNAndPadLayer (line 76) | class BNAndPadLayer(nn.Module): method __init__ (line 77) | def __init__(self, method forward (line 88) | def forward(self, input): method weight (line 104) | def weight(self): method bias (line 108) | def bias(self): method running_mean (line 112) | def running_mean(self): method running_var (line 116) | def running_var(self): method eps (line 120) | def eps(self): class DiverseBranchBlock (line 124) | class DiverseBranchBlock(nn.Module): method __init__ (line 125) | def __init__(self, in_channels, out_channels, k, method get_equivalent_kernel_bias (line 178) | def get_equivalent_kernel_bias(self): method switch_to_deploy (line 205) | def switch_to_deploy(self): method forward (line 222) | def forward(self, inputs): method init_gamma (line 233) | def init_gamma(self, gamma_value): method single_init (line 243) | def single_init(self): FILE: yolo-improve/yolov7-DCN.py class DCNv2 (line 1) | class DCNv2(nn.Module): method __init__ (line 2) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method forward (line 34) | def forward(self, x): method reset_parameters (line 56) | def reset_parameters(self): FILE: yolo-improve/yolov7-DCNV3.py class DCNV3_YoLo (line 2) | class DCNV3_YoLo(nn.Module): method __init__ (line 3) | def __init__(self, inc, ouc, k=1, s=1, p=None, g=1, act=True): method forward (line 11) | def forward(self, x): FILE: yolo-improve/yolov7-DSConv.py class DSConv (line 5) | class DSConv(_ConvNd): method __init__ (line 6) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method get_weight_res (line 34) | def get_weight_res(self): method forward (line 74) | def forward(self, input): class DSConv2D (line 83) | class DSConv2D(Conv): method __init__ (line 84) | def __init__(self, inc, ouc, k=1, s=1, p=None, g=1, act=True): FILE: yolo-improve/yolov7-DecoupledHead.py class IDetect_Decoupled (line 1) | class IDetect_Decoupled(nn.Module): method __init__ (line 8) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer method forward (line 33) | def forward(self, x): method fuseforward (line 58) | def fuseforward(self, x): method fuse (line 101) | def fuse(self): method _make_grid (line 136) | def _make_grid(nx=20, ny=20): method convert (line 140) | def convert(self, z): function _initialize_biases (line 152) | def _initialize_biases(self, cf=None): # initialize biases into Detect(... FILE: yolo-improve/yolov7-DySnakeConv.py class DySnakeConv (line 1) | class DySnakeConv(nn.Module): method __init__ (line 2) | def __init__(self, inc, ouc, k=3, act=True) -> None: method forward (line 10) | def forward(self, x): class DSConv (line 13) | class DSConv(nn.Module): method __init__ (line 14) | def __init__(self, in_ch, out_ch, morph, kernel_size=3, if_offset=True... method forward (line 54) | def forward(self, f): class DSC (line 75) | class DSC(object): method __init__ (line 76) | def __init__(self, input_shape, kernel_size, extend_scope, morph): method _coordinate_map_3D (line 96) | def _coordinate_map_3D(self, offset, if_offset): method _bilinear_interpolate_3D (line 241) | def _bilinear_interpolate_3D(self, input_feature, y, x): method deform_conv (line 341) | def deform_conv(self, input, offset, if_offset): FILE: yolo-improve/yolov7-MPDiou.py function bbox_mpdiou (line 1) | def bbox_mpdiou(box1, box2, x1y1x2y2=True, mpdiou_hw=None, grid=None, ep... FILE: yolo-improve/yolov7-NWD.py function wasserstein_loss (line 1) | def wasserstein_loss(pred, target, eps=1e-7, constant=12.8): FILE: yolo-improve/yolov7-PConv.py class PConv (line 1) | class PConv(nn.Module): method __init__ (line 2) | def __init__(self, dim, ouc, n_div=4, forward='split_cat'): method forward_slicing (line 16) | def forward_slicing(self, x): method forward_split_cat (line 23) | def forward_split_cat(self, x): FILE: yolo-improve/yolov7-RFEM.py class TridentBlock (line 1) | class TridentBlock(nn.Module): method __init__ (line 2) | def __init__(self, c1, c2, stride=1, c=False, e=0.5, padding=[1, 2, 3]... method forward_for_small (line 28) | def forward_for_small(self, x): method forward_for_middle (line 42) | def forward_for_middle(self, x): method forward_for_big (line 56) | def forward_for_big(self, x): method forward (line 70) | def forward(self, x): class RFEM (line 88) | class RFEM(nn.Module): method __init__ (line 89) | def __init__(self, c1, c2, n=1, e=0.5, stride=1): method forward (line 101) | def forward(self, x): FILE: yolo-improve/yolov7-RepNCSPELAN.py class RepConvN (line 1) | class RepConvN(nn.Module): method __init__ (line 7) | def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False... method forward_fuse (line 19) | def forward_fuse(self, x): method forward (line 23) | def forward(self, x): method get_equivalent_kernel_bias (line 28) | def get_equivalent_kernel_bias(self): method _avg_to_3x3_tensor (line 34) | def _avg_to_3x3_tensor(self, avgp): method _pad_1x1_to_3x3_tensor (line 43) | def _pad_1x1_to_3x3_tensor(self, kernel1x1): method _fuse_bn_tensor (line 49) | def _fuse_bn_tensor(self, branch): method fuse_convs (line 76) | def fuse_convs(self): class RepNBottleneck (line 101) | class RepNBottleneck(nn.Module): method __init__ (line 103) | def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5, act=Tr... method forward (line 110) | def forward(self, x): class RepNCSP (line 113) | class RepNCSP(nn.Module): method __init__ (line 115) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, act=True): ... method forward (line 123) | def forward(self, x): class RepNCSPELAN4 (line 126) | class RepNCSPELAN4(nn.Module): method __init__ (line 128) | def __init__(self, c1, c2, c3, c4, c5=1, act=True): # ch_in, ch_out, ... method forward (line 136) | def forward(self, x): method forward_split (line 141) | def forward_split(self, x): FILE: yolo-improve/yolov7-SAConv.py class ConvAWS2d (line 1) | class ConvAWS2d(nn.Conv2d): method __init__ (line 2) | def __init__(self, method _get_weight (line 23) | def _get_weight(self, weight): method forward (line 32) | def forward(self, x): method _load_from_state_dict (line 36) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st... class SAConv2d (line 50) | class SAConv2d(ConvAWS2d): method __init__ (line 51) | def __init__(self, method forward (line 98) | def forward(self, x): FILE: yolo-improve/yolov7-asf.py class Zoom_cat (line 2) | class Zoom_cat(nn.Module): method __init__ (line 3) | def __init__(self): method forward (line 6) | def forward(self, x): class ScalSeq (line 15) | class ScalSeq(nn.Module): method __init__ (line 16) | def __init__(self, inc, channel): method forward (line 26) | def forward(self, x): class Add (line 44) | class Add(nn.Module): method __init__ (line 46) | def __init__(self): method forward (line 49) | def forward(self, x): class channel_att (line 54) | class channel_att(nn.Module): method __init__ (line 55) | def __init__(self, channel, b=1, gamma=2): method forward (line 64) | def forward(self, x): class local_att (line 72) | class local_att(nn.Module): method __init__ (line 73) | def __init__(self, channel, reduction=16): method forward (line 87) | def forward(self, x): class attention_model (line 103) | class attention_model(nn.Module): method __init__ (line 105) | def __init__(self, ch = 256): method forward (line 109) | def forward(self, x): FILE: yolo-improve/yolov7-iou.py class WIoU_Scale (line 4) | class WIoU_Scale: method __init__ (line 17) | def __init__(self, iou): method _update (line 22) | def _update(cls, self): method _scaled_loss (line 27) | def _scaled_loss(cls, self, gamma=1.9, delta=3): function bbox_iou (line 37) | def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=Fal... FILE: yolo-improve/yolov7-odconv.py class Attention (line 7) | class Attention(nn.Module): method __init__ (line 8) | def __init__(self, in_planes, out_planes, kernel_size, groups=1, reduc... method _initialize_weights (line 41) | def _initialize_weights(self): method update_temperature (line 51) | def update_temperature(self, temperature): method skip (line 55) | def skip(_): method get_channel_attention (line 58) | def get_channel_attention(self, x): method get_filter_attention (line 62) | def get_filter_attention(self, x): method get_spatial_attention (line 66) | def get_spatial_attention(self, x): method get_kernel_attention (line 71) | def get_kernel_attention(self, x): method forward (line 76) | def forward(self, x): class ODConv2d (line 82) | class ODConv2d(nn.Module): method __init__ (line 83) | def __init__(self, in_planes, out_planes, k, s=1, p=None, g=1, act=Tru... method _initialize_weights (line 107) | def _initialize_weights(self): method update_temperature (line 111) | def update_temperature(self, temperature): method _forward_impl_common (line 114) | def _forward_impl_common(self, x): method _forward_impl_pw1x (line 130) | def _forward_impl_pw1x(self, x): method forward (line 138) | def forward(self, x): FILE: yolo-improve/yolov7-slimneck.py class GSConv (line 1) | class GSConv(nn.Module): method __init__ (line 4) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): method forward (line 10) | def forward(self, x): class GSBottleneck (line 26) | class GSBottleneck(nn.Module): method __init__ (line 28) | def __init__(self, c1, c2, k=3, s=1, e=0.5): method forward (line 37) | def forward(self, x): class GSBottleneckC (line 40) | class GSBottleneckC(GSBottleneck): method __init__ (line 42) | def __init__(self, c1, c2, k=3, s=1): class VoVGSCSP (line 46) | class VoVGSCSP(nn.Module): method __init__ (line 48) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): method forward (line 58) | def forward(self, x): class VoVGSCSPC (line 63) | class VoVGSCSPC(VoVGSCSP): method __init__ (line 65) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): FILE: yolo-improve/yolov7-softnms.py function box_iou_for_nms (line 1) | def box_iou_for_nms(box1, box2, GIoU=False, DIoU=False, CIoU=False, SIoU... function soft_nms (line 58) | def soft_nms(bboxes, scores, iou_thresh=0.5,sigma=0.5,score_threshold=0.... FILE: yolo-improve/yolov8-DCN.py class DCNv2 (line 1) | class DCNv2(nn.Module): method __init__ (line 2) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method forward (line 34) | def forward(self, x): method reset_parameters (line 56) | def reset_parameters(self): class Bottleneck_DCN (line 66) | class Bottleneck_DCN(nn.Module): method __init__ (line 68) | def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch... method forward (line 81) | def forward(self, x): class C2f_DCN (line 84) | class C2f_DCN(nn.Module): method __init__ (line 86) | def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in,... method forward (line 93) | def forward(self, x): FILE: yolo-improve/yolov8-erf.py function get_activation (line 16) | def get_activation(feat, backbone_idx=-1): function letterbox (line 25) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True... function get_rectangle (line 57) | def get_rectangle(data, thresh): function heatmap (line 67) | def heatmap(data, camp='RdYlGn', figsize=(10, 10.75), ax=None, save_path... class yolov8_erf (line 76) | class yolov8_erf: method __init__ (line 79) | def __init__(self, weight, device, layer, dataset, num_images, save_pa... method get_input_grad (line 99) | def get_input_grad(self, samples): method process (line 112) | def process(self): function get_params (line 160) | def get_params(): FILE: yolo-improve/yolov8-objectcount.py function get_video_cfg (line 7) | def get_video_cfg(path): function plot_and_counting (line 13) | def plot_and_counting(result): FILE: yolo-improve/yolov8-track.py function get_video_cfg (line 9) | def get_video_cfg(path): function counting (line 15) | def counting(image_plot, result): function transform_mot (line 20) | def transform_mot(result): FILE: yolo-improve/yolov9-backbone/yolo.py function _forward_once (line 1) | def _forward_once(self, x, profile=False, visualize=False): function parse_model (line 30) | def parse_model(d, ch): # model_dict, input_channels(3) FILE: yolo/dataset/xml2txt.py function convert (line 9) | def convert(size, box): function convert_annotation (line 23) | def convert_annotation(xmlpath, xmlname):