SYMBOL INDEX (601 symbols across 85 files)

FILE: demo/MegEngine/cpp/yolox.cpp
  function static_resize (line 26) | cv::Mat static_resize(cv::Mat &img) {
  function blobFromImage (line 37) | void blobFromImage(cv::Mat &img, float *blob_data) {
  type Object (line 51) | struct Object {
  type GridAndStride (line 57) | struct GridAndStride {
  function generate_grids_and_stride (line 63) | static void
  function generate_yolox_proposals (line 76) | static void generate_yolox_proposals(std::vector<GridAndStride> grid_str...
  function intersection_area (line 118) | static inline float intersection_area(const Object &a, const Object &b) {
  function qsort_descent_inplace (line 123) | static void qsort_descent_inplace(std::vector<Object> &faceobjects, int ...
  function qsort_descent_inplace (line 160) | static void qsort_descent_inplace(std::vector<Object> &objects) {
  function nms_sorted_bboxes (line 167) | static void nms_sorted_bboxes(const std::vector<Object> &faceobjects,
  function decode_outputs (line 198) | static void decode_outputs(const float *prob, std::vector<Object> &objects,
  function draw_objects (line 264) | static void draw_objects(const cv::Mat &bgr,
  function make_callback_copy (line 347) | cg::ComputingGraph::OutputSpecItem make_callback_copy(SymbolVar dev,
  function main (line 353) | int main(int argc, char *argv[]) {

FILE: demo/MegEngine/python/build.py
  function build_yolox (line 13) | def build_yolox(name="yolox-s"):
  function build_and_load (line 49) | def build_and_load(weight_file, name="yolox-s"):

FILE: demo/MegEngine/python/convert_weights.py
  function make_parser (line 10) | def make_parser():
  function numpy_weights (line 23) | def numpy_weights(weight_file):
  function map_weights (line 33) | def map_weights(weight_file, output_file):
  function main (line 57) | def main():

FILE: demo/MegEngine/python/demo.py
  function make_parser (line 23) | def make_parser():
  function get_image_list (line 44) | def get_image_list(path):
  function postprocess (line 55) | def postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45):
  class Predictor (line 92) | class Predictor(object):
    method __init__ (line 93) | def __init__(
    method inference (line 111) | def inference(self, img):
    method visual (line 136) | def visual(self, output, img_info, cls_conf=0.35):
  function image_demo (line 153) | def image_demo(predictor, vis_folder, path, current_time, save_result):
  function imageflow_demo (line 175) | def imageflow_demo(predictor, vis_folder, current_time, args):
  function main (line 206) | def main(args):

FILE: demo/MegEngine/python/dump.py
  function make_parser (line 14) | def make_parser():
  function dump_static_graph (line 24) | def dump_static_graph(model, graph_name="model.mge"):
  function main (line 44) | def main(args):

FILE: demo/MegEngine/python/models/darknet.py
  class Darknet (line 10) | class Darknet(M.Module):
    method __init__ (line 14) | def __init__(
    method make_group_layer (line 49) | def make_group_layer(self, in_channels: int, num_blocks: int, stride: ...
    method make_spp_block (line 56) | def make_spp_block(self, filters_list, in_filters):
    method forward (line 72) | def forward(self, x):
  class CSPDarknet (line 87) | class CSPDarknet(M.Module):
    method __init__ (line 89) | def __init__(
    method forward (line 142) | def forward(self, x):

FILE: demo/MegEngine/python/models/network_blocks.py
  class UpSample (line 9) | class UpSample(M.Module):
    method __init__ (line 11) | def __init__(self, scale_factor=2, mode="bilinear"):
    method forward (line 16) | def forward(self, x):
  class SiLU (line 20) | class SiLU(M.Module):
    method forward (line 24) | def forward(x):
  function get_activation (line 28) | def get_activation(name="silu"):
  class BaseConv (line 40) | class BaseConv(M.Module):
    method __init__ (line 43) | def __init__(self, in_channels, out_channels, ksize, stride, groups=1,...
    method forward (line 59) | def forward(self, x):
    method fuseforward (line 62) | def fuseforward(self, x):
  class DWConv (line 66) | class DWConv(M.Module):
    method __init__ (line 68) | def __init__(self, in_channels, out_channels, ksize, stride=1, act="si...
    method forward (line 79) | def forward(self, x):
  class Bottleneck (line 84) | class Bottleneck(M.Module):
    method __init__ (line 86) | def __init__(
    method forward (line 97) | def forward(self, x):
  class ResLayer (line 104) | class ResLayer(M.Module):
    method __init__ (line 106) | def __init__(self, in_channels: int):
    method forward (line 112) | def forward(self, x):
  class SPPBottleneck (line 117) | class SPPBottleneck(M.Module):
    method __init__ (line 119) | def __init__(self, in_channels, out_channels, kernel_sizes=(5, 9, 13),...
    method forward (line 127) | def forward(self, x):
  class CSPLayer (line 134) | class CSPLayer(M.Module):
    method __init__ (line 137) | def __init__(
    method forward (line 159) | def forward(self, x):
  class Focus (line 167) | class Focus(M.Module):
    method __init__ (line 170) | def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="...
    method forward (line 174) | def forward(self, x):

FILE: demo/MegEngine/python/models/yolo_fpn.py
  class YOLOFPN (line 12) | class YOLOFPN(M.Module):
    method __init__ (line 17) | def __init__(
    method _make_cbl (line 36) | def _make_cbl(self, _in, _out, ks):
    method _make_embedding (line 39) | def _make_embedding(self, filters_list, in_filters):
    method forward (line 53) | def forward(self, inputs):

FILE: demo/MegEngine/python/models/yolo_head.py
  function meshgrid (line 11) | def meshgrid(x, y):
  class YOLOXHead (line 21) | class YOLOXHead(M.Module):
    method __init__ (line 22) | def __init__(
    method forward (line 127) | def forward(self, xin, labels=None, imgs=None):
    method get_output_and_grid (line 156) | def get_output_and_grid(self, output, k, stride, dtype):
    method decode_outputs (line 177) | def decode_outputs(self, outputs):

FILE: demo/MegEngine/python/models/yolo_pafpn.py
  class YOLOPAFPN (line 12) | class YOLOPAFPN(M.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 78) | def forward(self, input):

FILE: demo/MegEngine/python/models/yolox.py
  class YOLOX (line 11) | class YOLOX(M.Module):
    method __init__ (line 18) | def __init__(self, backbone=None, head=None):
    method forward (line 28) | def forward(self, x):

FILE: demo/ONNXRuntime/onnx_inference.py
  function make_parser (line 17) | def make_parser():

FILE: demo/OpenVINO/cpp/yolox_openvino.cpp
  function static_resize (line 28) | cv::Mat static_resize(cv::Mat& img) {
  function blobFromImage (line 41) | void blobFromImage(cv::Mat& img, Blob::Ptr& blob){
  type Object (line 70) | struct Object
  type GridAndStride (line 77) | struct GridAndStride
  function generate_grids_and_stride (line 84) | static void generate_grids_and_stride(const int target_w, const int targ...
  function generate_yolox_proposals (line 101) | static void generate_yolox_proposals(std::vector<GridAndStride> grid_str...
  function intersection_area (line 147) | static inline float intersection_area(const Object& a, const Object& b)
  function qsort_descent_inplace (line 153) | static void qsort_descent_inplace(std::vector<Object>& faceobjects, int ...
  function qsort_descent_inplace (line 191) | static void qsort_descent_inplace(std::vector<Object>& objects)
  function nms_sorted_bboxes (line 199) | static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, st...
  function decode_outputs (line 234) | static void decode_outputs(const float* prob, std::vector<Object>& objec...
  function draw_objects (line 355) | static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& ...
  function main (line 419) | int main(int argc, char* argv[]) {

FILE: demo/OpenVINO/python/openvino_inference.py
  function parse_args (line 22) | def parse_args() -> argparse.Namespace:
  function main (line 78) | def main():

FILE: demo/TensorRT/cpp/logging.h
  function class (line 31) | class LogStreamConsumerBuffer : public std::stringbuf
  function class (line 106) | class LogStreamConsumerBase
  function std (line 160) | static std::string severityPrefix(Severity severity)
  function TestResult (line 213) | enum class TestResult
  function LogStreamConsumer (line 447) | inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
  function LogStreamConsumer (line 459) | inline LogStreamConsumer LOG_INFO(const Logger& logger)
  function LogStreamConsumer (line 471) | inline LogStreamConsumer LOG_WARN(const Logger& logger)
  function LogStreamConsumer (line 483) | inline LogStreamConsumer LOG_ERROR(const Logger& logger)
  function LogStreamConsumer (line 496) | inline LogStreamConsumer LOG_FATAL(const Logger& logger)

FILE: demo/TensorRT/cpp/yolox.cpp
  function static_resize (line 38) | cv::Mat static_resize(cv::Mat& img) {
  type Object (line 50) | struct Object
  type GridAndStride (line 57) | struct GridAndStride
  function generate_grids_and_stride (line 64) | static void generate_grids_and_stride(std::vector<int>& strides, std::ve...
  function intersection_area (line 80) | static inline float intersection_area(const Object& a, const Object& b)
  function qsort_descent_inplace (line 86) | static void qsort_descent_inplace(std::vector<Object>& faceobjects, int ...
  function qsort_descent_inplace (line 123) | static void qsort_descent_inplace(std::vector<Object>& objects)
  function nms_sorted_bboxes (line 131) | static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, st...
  function generate_yolox_proposals (line 166) | static void generate_yolox_proposals(std::vector<GridAndStride> grid_str...
  function decode_outputs (line 230) | static void decode_outputs(float* prob, std::vector<Object>& objects, fl...
  function draw_objects (line 356) | static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& ...
  function doInference (line 420) | void doInference(IExecutionContext& context, float* input, float* output...
  function main (line 457) | int main(int argc, char** argv) {

FILE: demo/ncnn/android/app/src/main/java/com/megvii/yoloXncnn/MainActivity.java
  class MainActivity (line 28) | public class MainActivity extends Activity
    method onCreate (line 39) | @Override
    method showObjects (line 89) | private void showObjects(YOLOXncnn.Obj[] objects)
    method onActivityResult (line 166) | @Override
    method decodeUri (line 192) | private Bitmap decodeUri(Uri selectedImage) throws FileNotFoundException

FILE: demo/ncnn/android/app/src/main/java/com/megvii/yoloXncnn/YOLOXncnn.java
  class YOLOXncnn (line 8) | public class YOLOXncnn
    method Init (line 10) | public native boolean Init(AssetManager mgr);
    class Obj (line 12) | public class Obj
    method Detect (line 22) | public native Obj[] Detect(Bitmap bitmap, boolean use_gpu);

FILE: demo/ncnn/android/app/src/main/java/com/megvii/yoloXncnn/yoloXncnn.java
  class YOLOXncnn (line 8) | public class YOLOXncnn
    method Init (line 10) | public native boolean Init(AssetManager mgr);
    class Obj (line 12) | public class Obj
    method Detect (line 22) | public native Obj[] Detect(Bitmap bitmap, boolean use_gpu);

FILE: demo/ncnn/android/app/src/main/jni/yoloXncnn_jni.cpp
  class YoloV5Focus (line 25) | class YoloV5Focus : public ncnn::Layer
    method YoloV5Focus (line 28) | YoloV5Focus()
    method forward (line 33) | virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob,...
  type Object (line 73) | struct Object
  type GridAndStride (line 83) | struct GridAndStride
  function intersection_area (line 90) | static inline float intersection_area(const Object& a, const Object& b)
  function qsort_descent_inplace (line 104) | static void qsort_descent_inplace(std::vector<Object>& faceobjects, int ...
  function qsort_descent_inplace (line 141) | static void qsort_descent_inplace(std::vector<Object>& faceobjects)
  function nms_sorted_bboxes (line 149) | static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, st...
  function generate_grids_and_stride (line 183) | static void generate_grids_and_stride(const int target_size, std::vector...
  function generate_yolox_proposals (line 198) | static void generate_yolox_proposals(std::vector<GridAndStride> grid_str...
  function JNIEXPORT (line 261) | JNIEXPORT jint JNI_OnLoad(JavaVM* vm, void* reserved)
  function JNIEXPORT (line 270) | JNIEXPORT void JNI_OnUnload(JavaVM* vm, void* reserved)
  function JNIEXPORT (line 278) | JNIEXPORT jboolean JNICALL Java_com_megvii_yoloXncnn_YOLOXncnn_Init(JNIE...
  function JNIEXPORT (line 334) | JNIEXPORT jobjectArray JNICALL Java_com_megvii_yoloXncnn_YOLOXncnn_Detec...

FILE: demo/ncnn/cpp/yolox.cpp
  class YoloV5Focus (line 35) | class YoloV5Focus : public ncnn::Layer
    method YoloV5Focus (line 38) | YoloV5Focus()
    method forward (line 43) | virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob,...
  type Object (line 83) | struct Object
  type GridAndStride (line 90) | struct GridAndStride
  function intersection_area (line 97) | static inline float intersection_area(const Object& a, const Object& b)
  function qsort_descent_inplace (line 103) | static void qsort_descent_inplace(std::vector<Object>& faceobjects, int ...
  function qsort_descent_inplace (line 140) | static void qsort_descent_inplace(std::vector<Object>& objects)
  function nms_sorted_bboxes (line 148) | static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, st...
  function generate_grids_and_stride (line 182) | static void generate_grids_and_stride(const int target_size, std::vector...
  function generate_yolox_proposals (line 202) | static void generate_yolox_proposals(std::vector<GridAndStride> grid_str...
  function detect_yolox (line 249) | static int detect_yolox(const cv::Mat& bgr, std::vector<Object>& objects)
  function draw_objects (line 344) | static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& ...
  function main (line 393) | int main(int argc, char** argv)

FILE: docs/conf.py
  class GithubURLDomain (line 33) | class GithubURLDomain(Domain):
    method resolve_any_xref (line 43) | def resolve_any_xref(self, env, fromdocname, builder, target, node, co...
  function autodoc_skip_member (line 280) | def autodoc_skip_member(app, what, name, obj, skip, options):
  function setup (line 373) | def setup(app):

FILE: exps/default/yolov3.py
  class Exp (line 12) | class Exp(MyExp):
    method __init__ (line 13) | def __init__(self):
    method get_model (line 19) | def get_model(self, sublinear=False):

FILE: exps/default/yolox_l.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_m.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_nano.py
  class Exp (line 12) | class Exp(MyExp):
    method __init__ (line 13) | def __init__(self):
    method get_model (line 25) | def get_model(self, sublinear=False):

FILE: exps/default/yolox_s.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_tiny.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_x.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/example/custom/nano.py
  class Exp (line 12) | class Exp(MyExp):
    method __init__ (line 13) | def __init__(self):
    method get_model (line 31) | def get_model(self, sublinear=False):

FILE: exps/example/custom/yolox_s.py
  class Exp (line 9) | class Exp(MyExp):
    method __init__ (line 10) | def __init__(self):

FILE: exps/example/yolox_voc/yolox_voc_s.py
  class Exp (line 8) | class Exp(MyExp):
    method __init__ (line 9) | def __init__(self):
    method get_dataset (line 24) | def get_dataset(self, cache: bool, cache_type: str = "ram"):
    method get_eval_dataset (line 39) | def get_eval_dataset(self, **kwargs):
    method get_evaluator (line 50) | def get_evaluator(self, batch_size, is_distributed, testdev=False, leg...

FILE: setup.py
  function get_package_dir (line 17) | def get_package_dir():
  function get_install_requirements (line 25) | def get_install_requirements():
  function get_yolox_version (line 32) | def get_yolox_version():
  function get_long_description (line 41) | def get_long_description():
  function get_ext_modules (line 47) | def get_ext_modules():
  function get_cmd_class (line 57) | def get_cmd_class():

FILE: tests/utils/test_model_utils.py
  class TestModelUtils (line 14) | class TestModelUtils(unittest.TestCase):
    method setUp (line 16) | def setUp(self):
    method test_model_state_adjust_status (line 19) | def test_model_state_adjust_status(self):
    method test_model_effect_adjust_status (line 50) | def test_model_effect_adjust_status(self):
    method test_freeze_module (line 72) | def test_freeze_module(self):

FILE: tools/demo.py
  function make_parser (line 22) | def make_parser():
  function get_image_list (line 89) | def get_image_list(path):
  class Predictor (line 100) | class Predictor(object):
    method __init__ (line 101) | def __init__(
    method inference (line 132) | def inference(self, img):
    method visual (line 168) | def visual(self, output, img_info, cls_conf=0.35):
  function image_demo (line 187) | def image_demo(predictor, vis_folder, path, current_time, save_result):
  function imageflow_demo (line 209) | def imageflow_demo(predictor, vis_folder, current_time, args):
  function main (line 244) | def main(exp, args):

FILE: tools/eval.py
  function make_parser (line 27) | def make_parser():
  function main (line 116) | def main(exp, args, num_gpu):

FILE: tools/export_onnx.py
  function make_parser (line 17) | def make_parser():
  function main (line 62) | def main():

FILE: tools/export_torchscript.py
  function make_parser (line 14) | def make_parser():
  function main (line 46) | def main():

FILE: tools/train.py
  function make_parser (line 18) | def make_parser():
  function main (line 101) | def main(exp: Exp, args):

FILE: tools/trt.py
  function make_parser (line 17) | def make_parser():
  function main (line 39) | def main():

FILE: tools/visualize_assign.py
  class AssignVisualizer (line 20) | class AssignVisualizer(Trainer):
    method __init__ (line 22) | def __init__(self, exp: Exp, args):
    method train_one_iter (line 28) | def train_one_iter(self):
    method after_train (line 54) | def after_train(self):
  function assign_vis_parser (line 58) | def assign_vis_parser():
  function main (line 65) | def main(exp: Exp, args):

FILE: yolox/core/launch.py
  function _find_free_port (line 24) | def _find_free_port():
  function launch (line 39) | def launch(
  function _distributed_worker (line 101) | def _distributed_worker(

FILE: yolox/core/trainer.py
  class Trainer (line 37) | class Trainer:
    method __init__ (line 38) | def __init__(self, exp: Exp, args):
    method train (line 74) | def train(self):
    method train_in_epoch (line 84) | def train_in_epoch(self):
    method train_in_iter (line 90) | def train_in_iter(self):
    method train_one_iter (line 96) | def train_one_iter(self):
    method before_train (line 131) | def before_train(self):
    method after_train (line 199) | def after_train(self):
    method before_epoch (line 217) | def before_epoch(self):
    method after_epoch (line 232) | def after_epoch(self):
    method before_iter (line 239) | def before_iter(self):
    method after_iter (line 242) | def after_iter(self):
    method progress_in_iter (line 308) | def progress_in_iter(self):
    method resume_train (line 311) | def resume_train(self, model):
    method evaluate_and_save_model (line 346) | def evaluate_and_save_model(self):
    method save_ckpt (line 399) | def save_ckpt(self, ckpt_name, update_best_ckpt=False, ap=None):

FILE: yolox/data/data_augment.py
  function augment_hsv (line 21) | def augment_hsv(img, hgain=5, sgain=30, vgain=30):
  function get_aug_params (line 34) | def get_aug_params(value, center=0):
  function get_affine_matrix (line 46) | def get_affine_matrix(
  function apply_affine_to_bboxes (line 82) | def apply_affine_to_bboxes(targets, target_size, M, scale):
  function random_affine (line 114) | def random_affine(
  function _mirror (line 134) | def _mirror(image, boxes, prob=0.5):
  function preproc (line 142) | def preproc(img, input_size, swap=(2, 0, 1)):
  class TrainTransform (line 161) | class TrainTransform:
    method __init__ (line 162) | def __init__(self, max_labels=50, flip_prob=0.5, hsv_prob=1.0):
    method __call__ (line 167) | def __call__(self, image, targets, input_dim):
  class ValTransform (line 213) | class ValTransform:
    method __init__ (line 231) | def __init__(self, swap=(2, 0, 1), legacy=False):
    method __call__ (line 236) | def __call__(self, img, res, input_size):

FILE: yolox/data/data_prefetcher.py
  class DataPrefetcher (line 8) | class DataPrefetcher:
    method __init__ (line 16) | def __init__(self, loader):
    method preload (line 23) | def preload(self):
    method next (line 35) | def next(self):
    method _input_cuda_for_image (line 46) | def _input_cuda_for_image(self):
    method _record_stream_for_image (line 50) | def _record_stream_for_image(input):

FILE: yolox/data/dataloading.py
  function get_yolox_datadir (line 18) | def get_yolox_datadir():
  class DataLoader (line 32) | class DataLoader(torchDataLoader):
    method __init__ (line 40) | def __init__(self, *args, **kwargs):
    method close_mosaic (line 88) | def close_mosaic(self):
  function list_collate (line 92) | def list_collate(batch):
  function worker_init_reset_seed (line 109) | def worker_init_reset_seed(worker_id):

FILE: yolox/data/datasets/coco.py
  function remove_useless_info (line 15) | def remove_useless_info(coco):
  class COCODataset (line 34) | class COCODataset(CacheDataset):
    method __init__ (line 39) | def __init__(
    method __len__ (line 86) | def __len__(self):
    method _load_coco_annotations (line 89) | def _load_coco_annotations(self):
    method load_anno_from_ids (line 92) | def load_anno_from_ids(self, id_):
    method load_anno (line 130) | def load_anno(self, index):
    method load_resized_img (line 133) | def load_resized_img(self, index):
    method load_image (line 143) | def load_image(self, index):
    method read_img (line 154) | def read_img(self, index):
    method pull_item (line 157) | def pull_item(self, index):
    method __getitem__ (line 165) | def __getitem__(self, index):

FILE: yolox/data/datasets/datasets_wrapper.py
  class ConcatDataset (line 22) | class ConcatDataset(torchConcatDataset):
    method __init__ (line 23) | def __init__(self, datasets):
    method pull_item (line 29) | def pull_item(self, idx):
  class MixConcatDataset (line 44) | class MixConcatDataset(torchConcatDataset):
    method __init__ (line 45) | def __init__(self, datasets):
    method __getitem__ (line 51) | def __getitem__(self, index):
  class Dataset (line 72) | class Dataset(torchDataset):
    method __init__ (line 80) | def __init__(self, input_dimension, mosaic=True):
    method input_dim (line 86) | def input_dim(self):
    method mosaic_getitem (line 100) | def mosaic_getitem(getitem_fn):
  class CacheDataset (line 127) | class CacheDataset(Dataset, metaclass=ABCMeta):
    method __init__ (line 147) | def __init__(
    method __del__ (line 176) | def __del__(self):
    method read_img (line 181) | def read_img(self, index):
    method cache_images (line 190) | def cache_images(
    method cal_cache_occupy (line 262) | def cal_cache_occupy(self, num_imgs):
  function cache_read_img (line 272) | def cache_read_img(use_cache=True):

FILE: yolox/data/datasets/mosaicdetection.py
  function get_mosaic_coordinate (line 16) | def get_mosaic_coordinate(mosaic_image, mosaic_index, xc, yc, w, h, inpu...
  class MosaicDetection (line 37) | class MosaicDetection(Dataset):
    method __init__ (line 40) | def __init__(
    method __len__ (line 75) | def __len__(self):
    method __getitem__ (line 79) | def __getitem__(self, idx):
    method mixup (line 162) | def mixup(self, origin_img, origin_labels, input_dim):

FILE: yolox/data/datasets/voc.py
  class AnnotationTransform (line 23) | class AnnotationTransform(object):
    method __init__ (line 37) | def __init__(self, class_to_ind=None, keep_difficult=True):
    method __call__ (line 43) | def __call__(self, target):
  class VOCDetection (line 82) | class VOCDetection(CacheDataset):
    method __init__ (line 101) | def __init__(
    method __len__ (line 151) | def __len__(self):
    method _load_coco_annotations (line 154) | def _load_coco_annotations(self):
    method load_anno_from_ids (line 157) | def load_anno_from_ids(self, index):
    method load_anno (line 171) | def load_anno(self, index):
    method load_resized_img (line 174) | def load_resized_img(self, index):
    method load_image (line 185) | def load_image(self, index):
    method read_img (line 193) | def read_img(self, index):
    method pull_item (line 196) | def pull_item(self, index):
    method __getitem__ (line 213) | def __getitem__(self, index):
    method evaluate_detections (line 221) | def evaluate_detections(self, all_boxes, output_dir=None):
    method _get_voc_results_file_template (line 245) | def _get_voc_results_file_template(self):
    method _write_voc_results_file (line 253) | def _write_voc_results_file(self, all_boxes):
    method _do_python_eval (line 278) | def _do_python_eval(self, output_dir="output", iou=0.5):

FILE: yolox/data/samplers.py
  class YoloBatchSampler (line 14) | class YoloBatchSampler(torchBatchSampler):
    method __init__ (line 21) | def __init__(self, *args, mosaic=True, **kwargs):
    method __iter__ (line 25) | def __iter__(self):
  class InfiniteSampler (line 30) | class InfiniteSampler(Sampler):
    method __init__ (line 41) | def __init__(
    method __iter__ (line 69) | def __iter__(self):
    method _infinite_indices (line 75) | def _infinite_indices(self):
    method __len__ (line 84) | def __len__(self):

FILE: yolox/evaluators/coco_evaluator.py
  function per_class_AR_table (line 31) | def per_class_AR_table(coco_eval, class_names=COCO_CLASSES, headers=["cl...
  function per_class_AP_table (line 54) | def per_class_AP_table(coco_eval, class_names=COCO_CLASSES, headers=["cl...
  class COCOEvaluator (line 79) | class COCOEvaluator:
    method __init__ (line 85) | def __init__(
    method evaluate (line 116) | def evaluate(
    method convert_to_coco_format (line 207) | def convert_to_coco_format(self, outputs, info_imgs, ids, return_outpu...
    method evaluate_prediction (line 255) | def evaluate_prediction(self, data_dict, statistics):

FILE: yolox/evaluators/voc_eval.py
  function parse_rec (line 14) | def parse_rec(filename):
  function voc_ap (line 36) | def voc_ap(rec, prec, use_07_metric=False):
  function voc_eval (line 70) | def voc_eval(

FILE: yolox/evaluators/voc_evaluator.py
  class VOCEvaluator (line 19) | class VOCEvaluator:
    method __init__ (line 24) | def __init__(self, dataloader, img_size, confthre, nmsthre, num_classes):
    method evaluate (line 41) | def evaluate(
    method convert_to_voc_format (line 120) | def convert_to_voc_format(self, outputs, info_imgs, ids):
    method evaluate_prediction (line 140) | def evaluate_prediction(self, data_dict, statistics):

FILE: yolox/exp/base_exp.py
  class BaseExp (line 16) | class BaseExp(metaclass=ABCMeta):
    method __init__ (line 19) | def __init__(self):
    method get_model (line 27) | def get_model(self) -> Module:
    method get_dataset (line 31) | def get_dataset(self, cache: bool = False, cache_type: str = "ram"):
    method get_data_loader (line 35) | def get_data_loader(
    method get_optimizer (line 41) | def get_optimizer(self, batch_size: int) -> torch.optim.Optimizer:
    method get_lr_scheduler (line 45) | def get_lr_scheduler(
    method get_evaluator (line 51) | def get_evaluator(self):
    method eval (line 55) | def eval(self, model, evaluator, weights):
    method __repr__ (line 58) | def __repr__(self):
    method merge (line 67) | def merge(self, cfg_list):

FILE: yolox/exp/build.py
  function get_exp_by_file (line 10) | def get_exp_by_file(exp_file):
  function get_exp_by_name (line 20) | def get_exp_by_name(exp_name):
  function get_exp (line 27) | def get_exp(exp_file=None, exp_name=None):

FILE: yolox/exp/default/__init__.py
  class _ExpFinder (line 17) | class _ExpFinder(abc.MetaPathFinder):
    method find_spec (line 19) | def find_spec(self, name, path, target=None):

FILE: yolox/exp/yolox_base.py
  class Exp (line 16) | class Exp(BaseExp):
    method __init__ (line 17) | def __init__(self):
    method get_model (line 111) | def get_model(self):
    method get_dataset (line 131) | def get_dataset(self, cache: bool = False, cache_type: str = "ram"):
    method get_data_loader (line 155) | def get_data_loader(self, batch_size, is_distributed, no_aug=False, ca...
    method random_resize (line 224) | def random_resize(self, data_loader, epoch, rank, is_distributed):
    method preprocess (line 245) | def preprocess(self, inputs, targets, tsize):
    method get_optimizer (line 256) | def get_optimizer(self, batch_size):
    method get_lr_scheduler (line 284) | def get_lr_scheduler(self, lr, iters_per_epoch):
    method get_eval_dataset (line 299) | def get_eval_dataset(self, **kwargs):
    method get_eval_loader (line 312) | def get_eval_loader(self, batch_size, is_distributed, **kwargs):
    method get_evaluator (line 333) | def get_evaluator(self, batch_size, is_distributed, testdev=False, leg...
    method get_trainer (line 346) | def get_trainer(self, args):
    method eval (line 352) | def eval(self, model, evaluator, is_distributed, half=False, return_ou...
  function check_exp_value (line 356) | def check_exp_value(exp: Exp):

FILE: yolox/layers/cocoeval/cocoeval.cpp
  type COCOeval (line 10) | namespace COCOeval {
    function SortInstancesByDetectionScore (line 16) | void SortInstancesByDetectionScore(
    function SortInstancesByIgnore (line 32) | void SortInstancesByIgnore(
    function MatchDetectionsToGroundTruth (line 59) | void MatchDetectionsToGroundTruth(
    function EvaluateImages (line 140) | std::vector<ImageEvaluation> EvaluateImages(
    function list_to_vec (line 201) | std::vector<T> list_to_vec(const py::list& l) {
    function BuildSortedDetectionList (line 221) | int BuildSortedDetectionList(
    function ComputePrecisionRecallCurve (line 282) | void ComputePrecisionRecallCurve(
    function Accumulate (line 370) | py::dict Accumulate(

FILE: yolox/layers/cocoeval/cocoeval.h
  function namespace (line 12) | namespace COCOeval {

FILE: yolox/layers/fast_coco_eval_api.py
  class COCOeval_opt (line 17) | class COCOeval_opt(COCOeval):
    method __init__ (line 22) | def __init__(self, *args, **kwargs):
    method evaluate (line 26) | def evaluate(self):
    method accumulate (line 125) | def accumulate(self):

FILE: yolox/layers/jit_ops.py
  class JitOp (line 14) | class JitOp:
    method __init__ (line 23) | def __init__(self, name):
    method absolute_name (line 26) | def absolute_name(self) -> str:
    method sources (line 30) | def sources(self) -> List:
    method include_dirs (line 39) | def include_dirs(self) -> List:
    method define_macros (line 48) | def define_macros(self) -> List:
    method cxx_args (line 52) | def cxx_args(self) -> List:
    method nvcc_args (line 57) | def nvcc_args(self) -> List:
    method build_op (line 68) | def build_op(self):
    method load (line 80) | def load(self, verbose=True):
    method jit_load (line 89) | def jit_load(self, verbose=True):
    method clear_dynamic_library (line 114) | def clear_dynamic_library(self):
  class FastCOCOEvalOp (line 120) | class FastCOCOEvalOp(JitOp):
    method __init__ (line 122) | def __init__(self, name="fast_cocoeval"):
    method absolute_name (line 125) | def absolute_name(self):
    method sources (line 128) | def sources(self):
    method include_dirs (line 137) | def include_dirs(self):

FILE: yolox/models/build.py
  function create_yolox_model (line 32) | def create_yolox_model(name: str, pretrained: bool = True, num_classes: ...
  function yolox_nano (line 82) | def yolox_nano(pretrained: bool = True, num_classes: int = 80, device: s...
  function yolox_tiny (line 86) | def yolox_tiny(pretrained: bool = True, num_classes: int = 80, device: s...
  function yolox_s (line 90) | def yolox_s(pretrained: bool = True, num_classes: int = 80, device: str ...
  function yolox_m (line 94) | def yolox_m(pretrained: bool = True, num_classes: int = 80, device: str ...
  function yolox_l (line 98) | def yolox_l(pretrained: bool = True, num_classes: int = 80, device: str ...
  function yolox_x (line 102) | def yolox_x(pretrained: bool = True, num_classes: int = 80, device: str ...
  function yolov3 (line 106) | def yolov3(pretrained: bool = True, num_classes: int = 80, device: str =...
  function yolox_custom (line 110) | def yolox_custom(ckpt_path: str = None, exp_path: str = None, device: st...

FILE: yolox/models/darknet.py
  class Darknet (line 10) | class Darknet(nn.Module):
    method __init__ (line 14) | def __init__(
    method make_group_layer (line 59) | def make_group_layer(self, in_channels: int, num_blocks: int, stride: ...
    method make_spp_block (line 66) | def make_spp_block(self, filters_list, in_filters):
    method forward (line 82) | def forward(self, x):
  class CSPDarknet (line 97) | class CSPDarknet(nn.Module):
    method __init__ (line 98) | def __init__(
    method forward (line 167) | def forward(self, x):

FILE: yolox/models/losses.py
  class IOUloss (line 9) | class IOUloss(nn.Module):
    method __init__ (line 10) | def __init__(self, reduction="none", loss_type="iou"):
    method forward (line 15) | def forward(self, pred, target):

FILE: yolox/models/network_blocks.py
  class SiLU (line 9) | class SiLU(nn.Module):
    method forward (line 13) | def forward(x):
  function get_activation (line 17) | def get_activation(name="silu", inplace=True):
  class BaseConv (line 29) | class BaseConv(nn.Module):
    method __init__ (line 32) | def __init__(
    method forward (line 50) | def forward(self, x):
    method fuseforward (line 53) | def fuseforward(self, x):
  class DWConv (line 57) | class DWConv(nn.Module):
    method __init__ (line 60) | def __init__(self, in_channels, out_channels, ksize, stride=1, act="si...
    method forward (line 74) | def forward(self, x):
  class Bottleneck (line 79) | class Bottleneck(nn.Module):
    method __init__ (line 81) | def __init__(
    method forward (line 97) | def forward(self, x):
  class ResLayer (line 104) | class ResLayer(nn.Module):
    method __init__ (line 107) | def __init__(self, in_channels: int):
    method forward (line 117) | def forward(self, x):
  class SPPBottleneck (line 122) | class SPPBottleneck(nn.Module):
    method __init__ (line 125) | def __init__(
    method forward (line 140) | def forward(self, x):
  class CSPLayer (line 147) | class CSPLayer(nn.Module):
    method __init__ (line 150) | def __init__(
    method forward (line 180) | def forward(self, x):
  class Focus (line 188) | class Focus(nn.Module):
    method __init__ (line 191) | def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="...
    method forward (line 195) | def forward(self, x):

FILE: yolox/models/yolo_fpn.py
  class YOLOFPN (line 12) | class YOLOFPN(nn.Module):
    method __init__ (line 17) | def __init__(
    method _make_cbl (line 38) | def _make_cbl(self, _in, _out, ks):
    method _make_embedding (line 41) | def _make_embedding(self, filters_list, in_filters):
    method load_pretrained_model (line 53) | def load_pretrained_model(self, filename="./weights/darknet53.mix.pth"):
    method forward (line 59) | def forward(self, inputs):

FILE: yolox/models/yolo_head.py
  class YOLOXHead (line 18) | class YOLOXHead(nn.Module):
    method __init__ (line 19) | def __init__(
    method initialize_biases (line 131) | def initialize_biases(self, prior_prob):
    method forward (line 142) | def forward(self, xin, labels=None, imgs=None):
    method get_output_and_grid (line 215) | def get_output_and_grid(self, output, k, stride, dtype):
    method decode_outputs (line 235) | def decode_outputs(self, outputs, dtype):
    method get_losses (line 255) | def get_losses(
    method get_l1_target (line 415) | def get_l1_target(self, l1_target, gt, stride, x_shifts, y_shifts, eps...
    method get_assignments (line 423) | def get_assignments(
    method get_geometry_constraint (line 513) | def get_geometry_constraint(
    method simota_matching (line 544) | def simota_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg...
    method visualize_assign_result (line 578) | def visualize_assign_result(self, xin, labels=None, imgs=None, save_pr...

FILE: yolox/models/yolo_pafpn.py
  class YOLOPAFPN (line 12) | class YOLOPAFPN(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 83) | def forward(self, input):

FILE: yolox/models/yolox.py
  class YOLOX (line 11) | class YOLOX(nn.Module):
    method __init__ (line 18) | def __init__(self, backbone=None, head=None):
    method forward (line 28) | def forward(self, x, targets=None):
    method visualize (line 50) | def visualize(self, x, targets, save_prefix="assign_vis_"):

FILE: yolox/tools/__init__.py
  class _PathFinder (line 16) | class _PathFinder(abc.MetaPathFinder):
    method find_spec (line 18) | def find_spec(self, name, path, target=None):

FILE: yolox/utils/allreduce_norm.py
  function get_async_norm_states (line 32) | def get_async_norm_states(module):
  function pyobj2tensor (line 41) | def pyobj2tensor(pyobj, device="cuda"):
  function tensor2pyobj (line 47) | def tensor2pyobj(tensor):
  function _get_reduce_op (line 52) | def _get_reduce_op(op_name):
  function all_reduce (line 59) | def all_reduce(py_dict, op="sum", group=None):
  function all_reduce_norm (line 97) | def all_reduce_norm(module):

FILE: yolox/utils/boxes.py
  function filter_box (line 21) | def filter_box(output, scale_range):
  function postprocess (line 32) | def postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45, c...
  function bboxes_iou (line 79) | def bboxes_iou(bboxes_a, bboxes_b, xyxy=True):
  function matrix_iou (line 105) | def matrix_iou(a, b):
  function adjust_box_anns (line 118) | def adjust_box_anns(bbox, scale_ratio, padw, padh, w_max, h_max):
  function xyxy2xywh (line 124) | def xyxy2xywh(bboxes):
  function xyxy2cxcywh (line 130) | def xyxy2cxcywh(bboxes):
  function cxcywh2xyxy (line 138) | def cxcywh2xyxy(bboxes):

FILE: yolox/utils/checkpoint.py
  function load_ckpt (line 11) | def load_ckpt(model, ckpt):
  function save_checkpoint (line 36) | def save_checkpoint(state, is_best, save_dir, model_name=""):

FILE: yolox/utils/compat.py
  function meshgrid (line 11) | def meshgrid(*tensors):

FILE: yolox/utils/demo_utils.py
  function random_color (line 15) | def random_color():
  function visualize_assign (line 19) | def visualize_assign(img, boxes, coords, match_results, save_name=None) ...
  function mkdir (line 52) | def mkdir(path):
  function nms (line 57) | def nms(boxes, scores, nms_thr):
  function multiclass_nms (line 87) | def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True):
  function multiclass_nms_class_aware (line 96) | def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr):
  function multiclass_nms_class_agnostic (line 120) | def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr):
  function demo_postprocess (line 139) | def demo_postprocess(outputs, img_size, p6=False):

FILE: yolox/utils/dist.py
  function get_num_devices (line 41) | def get_num_devices():
  function wait_for_the_master (line 52) | def wait_for_the_master(local_rank: int = None):
  function synchronize (line 75) | def synchronize():
  function get_world_size (line 89) | def get_world_size() -> int:
  function get_rank (line 97) | def get_rank() -> int:
  function get_local_rank (line 105) | def get_local_rank() -> int:
  function get_local_size (line 120) | def get_local_size() -> int:
  function is_main_process (line 132) | def is_main_process() -> bool:
  function _get_global_gloo_group (line 137) | def _get_global_gloo_group():
  function _serialize_to_tensor (line 148) | def _serialize_to_tensor(data, group):
  function _pad_to_largest_tensor (line 165) | def _pad_to_largest_tensor(tensor, group):
  function all_gather (line 195) | def all_gather(data, group=None):
  function gather (line 233) | def gather(data, dst=0, group=None):
  function shared_random_seed (line 277) | def shared_random_seed():
  function time_synchronized (line 290) | def time_synchronized():

FILE: yolox/utils/ema.py
  function is_parallel (line 13) | def is_parallel(model):
  class ModelEMA (line 22) | class ModelEMA:
    method __init__ (line 33) | def __init__(self, model, decay=0.9999, updates=0):
    method update (line 48) | def update(self, model):

FILE: yolox/utils/logger.py
  function get_caller_name (line 16) | def get_caller_name(depth=0):
  class StreamToLoguru (line 33) | class StreamToLoguru:
    method __init__ (line 38) | def __init__(self, level="INFO", caller_names=("apex", "pycocotools")):
    method write (line 49) | def write(self, buf):
    method flush (line 59) | def flush(self):
    method isatty (line 63) | def isatty(self):
    method fileno (line 71) | def fileno(self):
  function redirect_sys_output (line 76) | def redirect_sys_output(log_level="INFO"):
  function setup_logger (line 82) | def setup_logger(save_dir, distributed_rank=0, filename="log.txt", mode=...
  class WandbLogger (line 117) | class WandbLogger(object):
    method __init__ (line 129) | def __init__(self,
    method run (line 222) | def run(self):
    method _log_validation_set (line 236) | def _log_validation_set(self, val_dataset):
    method _convert_prediction_format (line 266) | def _convert_prediction_format(self, predictions):
    method log_metrics (line 303) | def log_metrics(self, metrics, step=None):
    method log_images (line 320) | def log_images(self, predictions):
    method save_checkpoint (line 391) | def save_checkpoint(self, save_dir, model_name, is_best, metadata=None):
    method finish (line 426) | def finish(self):
    method initialize_wandb_logger (line 430) | def initialize_wandb_logger(cls, args, exp, val_dataset):

FILE: yolox/utils/lr_scheduler.py
  class LRScheduler (line 9) | class LRScheduler:
    method __init__ (line 10) | def __init__(self, name, lr, iters_per_epoch, total_epochs, **kwargs):
    method update_lr (line 33) | def update_lr(self, iters):
    method _get_lr_func (line 36) | def _get_lr_func(self, name):
  function cos_lr (line 97) | def cos_lr(lr, total_iters, iters):
  function warm_cos_lr (line 103) | def warm_cos_lr(lr, total_iters, warmup_total_iters, warmup_lr_start, it...
  function yolox_warm_cos_lr (line 121) | def yolox_warm_cos_lr(
  function yolox_semi_warm_cos_lr (line 151) | def yolox_semi_warm_cos_lr(
  function multistep_lr (line 201) | def multistep_lr(lr, milestones, gamma, iters):

FILE: yolox/utils/metric.py
  function get_total_and_free_memory_in_Mb (line 24) | def get_total_and_free_memory_in_Mb(cuda_device):
  function occupy_mem (line 36) | def occupy_mem(cuda_device, mem_ratio=0.9):
  function gpu_mem_usage (line 48) | def gpu_mem_usage():
  function mem_usage (line 56) | def mem_usage():
  class AverageMeter (line 65) | class AverageMeter:
    method __init__ (line 70) | def __init__(self, window_size=50):
    method update (line 75) | def update(self, value):
    method median (line 81) | def median(self):
    method avg (line 86) | def avg(self):
    method global_avg (line 92) | def global_avg(self):
    method latest (line 96) | def latest(self):
    method total (line 100) | def total(self):
    method reset (line 103) | def reset(self):
    method clear (line 108) | def clear(self):
  class MeterBuffer (line 112) | class MeterBuffer(defaultdict):
    method __init__ (line 115) | def __init__(self, window_size=20):
    method reset (line 119) | def reset(self):
    method get_filtered_meter (line 123) | def get_filtered_meter(self, filter_key="time"):
    method update (line 126) | def update(self, values=None, **kwargs):
    method clear_meters (line 135) | def clear_meters(self):

FILE: yolox/utils/mlflow_logger.py
  class MlflowLogger (line 29) | class MlflowLogger:
    method __init__ (line 33) | def __init__(self):
    method is_required_library_available (line 60) | def is_required_library_available(self):
    method flatten_dict (line 73) | def flatten_dict(self, d: MutableMapping, parent_key: str = "", delimi...
    method setup (line 97) | def setup(self, args, exp):
    method log_params_mlflow (line 217) | def log_params_mlflow(self, params_dict):
    method convert_exp_todict (line 256) | def convert_exp_todict(self, exp):
    method on_log (line 274) | def on_log(self, args, exp, step, logs):
    method on_train_end (line 309) | def on_train_end(self, args, file_name, metadata):
    method save_log_file (line 334) | def save_log_file(self, args, file_name):
    method save_checkpoints (line 349) | def save_checkpoints(self, args, exp, file_name, epoch, metadata, upda...
    method mlflow_save_pyfunc_model (line 382) | def mlflow_save_pyfunc_model(self, metadata, artifact_path, mlflow_out...
    method __del__ (line 407) | def __del__(self):

FILE: yolox/utils/model_utils.py
  function get_model_info (line 22) | def get_model_info(model: nn.Module, tsize: Sequence[int]) -> str:
  function fuse_conv_and_bn (line 35) | def fuse_conv_and_bn(conv: nn.Conv2d, bn: nn.BatchNorm2d) -> nn.Conv2d:
  function fuse_model (line 80) | def fuse_model(model: nn.Module) -> nn.Module:
  function replace_module (line 99) | def replace_module(module, replaced_module_type, new_module_type, replac...
  function freeze_module (line 131) | def freeze_module(module: nn.Module, name=None) -> nn.Module:
  function adjust_status (line 160) | def adjust_status(module: nn.Module, training: bool = False) -> nn.Module:

FILE: yolox/utils/setup_env.py
  function configure_nccl (line 16) | def configure_nccl():
  function configure_omp (line 28) | def configure_omp(num_threads=1):
  function configure_module (line 51) | def configure_module(ulimit_value=8192):

FILE: yolox/utils/visualize.py
  function vis (line 11) | def vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):