SYMBOL INDEX (569 symbols across 73 files)

FILE: MNN/mnn/include/AutoTime.hpp
  type MNN (line 16) | namespace MNN {
    function AutoTime (line 19) | class MNN_PUBLIC AutoTime {

FILE: MNN/mnn/include/Backend.hpp
  type MNN (line 21) | namespace MNN {
    type Op (line 23) | struct Op
    type GpuLibrary (line 24) | struct GpuLibrary
    class Execution (line 25) | class Execution
    class Backend (line 28) | class Backend : public NonCopyable {
      type Info (line 31) | struct Info {
        type Mode (line 38) | enum Mode {
      type StorageType (line 49) | enum StorageType {
      method Backend (line 78) | Backend(MNNForwardType type) : mType(type) {
      method onMeasure (line 95) | virtual std::pair<float, bool> onMeasure(const std::vector<Tensor*>&...
      method onResizeBegin (line 113) | virtual void onResizeBegin() {
      method onResizeEnd (line 119) | virtual void onResizeEnd() {
      method onWaitFinish (line 135) | virtual bool onWaitFinish() {
      method onLoadLibrary (line 143) | virtual bool onLoadLibrary(const GpuLibrary* library) {
      method onAllocateBuffer (line 168) | virtual bool onAllocateBuffer() {
      method MNNForwardType (line 190) | inline MNNForwardType type() const {
    class BackendCreator (line 199) | class BackendCreator {
      method onValid (line 219) | virtual bool onValid(Backend::Info& info) const {
      method BackendCreator (line 227) | BackendCreator() = default;

FILE: MNN/mnn/include/ErrorCode.hpp
  type MNN (line 12) | namespace MNN {
    type ErrorCode (line 13) | enum ErrorCode {

FILE: MNN/mnn/include/HalideRuntime.h
  type halide_buffer_t (line 52) | struct halide_buffer_t
  type halide_type_code_t (line 59) | typedef enum halide_type_code_t
  type halide_type_t (line 82) | struct halide_type_t {
  type halide_device_interface_impl_t (line 127) | struct halide_device_interface_impl_t
  type halide_device_interface_t (line 143) | struct halide_device_interface_t {
  type halide_dimension_t (line 167) | typedef struct halide_dimension_t {
  type halide_buffer_flags (line 195) | typedef enum {halide_buffer_flag_host_dirty = 1,
  type halide_buffer_t (line 203) | typedef struct halide_buffer_t {
  function halide_type_t (line 242) | halide_type_t halide_type_of() {

FILE: MNN/mnn/include/ImageProcess.hpp
  type MNN (line 16) | namespace MNN {
    type CV (line 17) | namespace CV {
      type ImageFormat (line 18) | enum ImageFormat {
      type Filter (line 27) | enum Filter { NEAREST = 0, BILINEAR = 1, BICUBIC = 2 }
      type Wrap (line 29) | enum Wrap { CLAMP_TO_EDGE = 0, ZERO = 1, REPEAT = 2 }
      function ImageProcess (line 38) | class MNN_PUBLIC ImageProcess {

FILE: MNN/mnn/include/Interpreter.hpp
  type MNN (line 19) | namespace MNN {
    type ScheduleConfig (line 22) | struct ScheduleConfig {
      type Path (line 31) | struct Path {
        type Mode (line 35) | enum Mode {
    class Session (line 66) | class Session
    type Content (line 67) | struct Content
    class Tensor (line 68) | class Tensor
    class Backend (line 69) | class Backend
    function OperatorInfo (line 71) | class MNN_PUBLIC OperatorInfo {
    function Interpreter (line 94) | class MNN_PUBLIC Interpreter {

FILE: MNN/mnn/include/MNNForwardType.h
  type MNNForwardType (line 14) | typedef enum {
  function namespace (line 46) | namespace MNN {

FILE: MNN/mnn/include/MNNSharedContext.h
  function VK_DEFINE_HANDLE (line 19) | VK_DEFINE_HANDLE(VkInstance)

FILE: MNN/mnn/include/Matrix.h
  function namespace (line 31) | namespace MNN {

FILE: MNN/mnn/include/NonCopyable.hpp
  type MNN (line 12) | namespace MNN {
    class NonCopyable (line 14) | class NonCopyable {
      method NonCopyable (line 16) | NonCopyable()                    = default;
      method NonCopyable (line 17) | NonCopyable(const NonCopyable&)  = delete;
      method NonCopyable (line 18) | NonCopyable(const NonCopyable&&) = delete;
      method NonCopyable (line 19) | NonCopyable& operator=(const NonCopyable&) = delete;
      method NonCopyable (line 20) | NonCopyable& operator=(const NonCopyable&&) = delete;

FILE: MNN/mnn/include/Rect.h
  type Point (line 37) | struct Point {
  function Rect (line 54) | struct MNN_PUBLIC Rect {

FILE: MNN/mnn/include/Tensor.hpp
  type MNN (line 16) | namespace MNN {
    function Tensor (line 25) | class MNN_PUBLIC Tensor {
    function elementSize (line 215) | inline int elementSize() const {
  function height (line 227) | inline int height() const {
  function channel (line 233) | inline int channel() const {
  function batch (line 239) | inline int batch() const {
  function stride (line 244) | inline int stride(int index) const {
  function length (line 247) | inline int length(int index) const {
  function setStride (line 250) | inline void setStride(int index, int stride) {
  function setLength (line 253) | inline void setLength(int index, int length) {
  type InsideDescribe (line 265) | struct InsideDescribe

FILE: MNN/mnn/include/revertMNNModel.hpp
  class Revert (line 14) | class Revert {

FILE: MNN/python/ultraface_py_mnn.py
  function define_img_size (line 42) | def define_img_size(image_size):
  function generate_priors (line 55) | def generate_priors(feature_map_list, shrinkage_list, image_size, min_bo...
  function predict (line 81) | def predict(width, height, confidences, boxes, prob_threshold, iou_thres...
  function inference (line 110) | def inference():

FILE: MNN/src/UltraFace.hpp
  type FaceInfo (line 25) | struct FaceInfo {
  class UltraFace (line 34) | class UltraFace {

FILE: MNN/src/main.cpp
  function main (line 10) | int main(int argc, char **argv) {

FILE: caffe/MyCaffe.py
  function param_name_dict (line 7) | def param_name_dict():
  function assign_proto (line 21) | def assign_proto(proto, name, val):
  class Function (line 46) | class Function(object):
    method __init__ (line 50) | def __init__(self, type_name, layer_name, inputs,outputs, **params):
    method _get_name (line 65) | def _get_name(self, names, autonames):
    method _get_top_name (line 73) | def _get_top_name(self, top, names, autonames):
    method _to_proto (line 79) | def _to_proto(self):
  class Layers (line 110) | class Layers(object):
    method __getattr__ (line 115) | def __getattr__(self, name):

FILE: caffe/convertCaffe.py
  function convertToCaffe (line 25) | def convertToCaffe(graph, prototxt_save_path, caffe_model_save_path):
  function getGraph (line 94) | def getGraph(onnx_path):

FILE: caffe/onnx2caffe/_error_utils.py
  class ErrorHandling (line 8) | class ErrorHandling(object):
    method __init__ (line 13) | def __init__(self,
    method unsupported_op (line 24) | def unsupported_op(self,
    method unsupported_op_configuration (line 40) | def unsupported_op_configuration(self,
    method missing_initializer (line 49) | def missing_initializer(self,

FILE: caffe/onnx2caffe/_graph.py
  class Transformer (line 12) | class Transformer(Protocol):
    method __call__ (line 13) | def __call__(self, graph):  # type: (Graph) -> Graph
  function _input_from_onnx_input (line 20) | def _input_from_onnx_input(input):  # type: (ValueInfoProto) -> EdgeInfo
  function _convertAttributeProto (line 27) | def _convertAttributeProto(onnx_arg):  # type: (AttributeProto) -> Attri...
  class Attributes (line 51) | class Attributes(Dict[Text, Any]):
    method from_onnx (line 53) | def from_onnx(args):  # type: (Iterable[AttributeProto]) -> Attributes
  class Node (line 60) | class Node(object):
    method __init__ (line 61) | def __init__(self,
    method add_parent (line 79) | def add_parent(self, parent_node):  # type: (Node) -> None
    method add_child (line 85) | def add_child(self, child_node):  # type: (Node) -> None
    method get_only_parent (line 91) | def get_only_parent(self):  # type: () -> Node
    method from_onnx (line 98) | def from_onnx(node):  # type: (NodeProto) -> Node
  class Graph (line 108) | class Graph(object):
    method __init__ (line 109) | def __init__(self,
    method transformed (line 138) | def transformed(self, transformers):  # type: (Iterable[Transformer]) ...
    method has_edge_name (line 144) | def has_edge_name(self, name):  # type: (Text) -> bool
    method get_unique_edge_name (line 159) | def get_unique_edge_name(self, name):  # type: (Text) -> Text
    method from_onnx (line 168) | def from_onnx(graph):  # type: (GraphProto) -> Graph

FILE: caffe/onnx2caffe/_operators.py
  function _compare (line 12) | def _compare(a, b, encoding="utf8"):  # type: (Text, Text, Text) -> bool
  function make_input (line 20) | def make_input(input):
  function _convert_conv (line 30) | def _convert_conv(node, graph, err):
  function _convert_relu (line 66) | def _convert_relu(node, graph, err):
  function _convert_sigmoid (line 84) | def _convert_sigmoid(node, graph, err):
  function _convert_BatchNorm (line 102) | def _convert_BatchNorm(node, graph, err):
  function _convert_Add (line 126) | def _convert_Add(node, graph, err):
  function _convert_Mul (line 153) | def _convert_Mul(node, graph, err):
  function _convert_Reshape (line 179) | def _convert_Reshape(node, graph, err):
  function _convert_Flatten (line 205) | def _convert_Flatten(node, graph, err):
  function _convert_Permute (line 219) | def _convert_Permute(node, graph, err):
  function _convert_Softmax (line 234) | def _convert_Softmax(node, graph, err):
  function _convert_pool (line 244) | def _convert_pool(node, graph, err):
  function _convert_dropout (line 270) | def _convert_dropout(node, graph, err):
  function _convert_gemm (line 280) | def _convert_gemm(node, graph, err):
  function _convert_upsample (line 314) | def _convert_upsample(node, graph, err):
  function _convert_concat (line 354) | def _convert_concat(node, graph, err):
  function _convert_conv_transpose (line 372) | def _convert_conv_transpose(node, graph, err):

FILE: caffe/onnx2caffe/_transformers.py
  class NodesFuser (line 14) | class NodesFuser(object):
    method __init__ (line 18) | def __init__(self,
    method __call__ (line 25) | def __call__(self, graph):  # type: (Graph) -> Graph
    method is_eligible (line 78) | def is_eligible(self, graph, nodes):  # type: (Graph, Sequence[Node]) ...
    method merge (line 82) | def merge(self, graph, nodes):  # type: (Graph, Sequence[Node]) -> Seq...
  class ConvAddFuser (line 88) | class ConvAddFuser(NodesFuser):
    method __init__ (line 92) | def __init__(self):  # type: () -> None
    method is_eligible (line 95) | def is_eligible(self, graph, nodes):  # type: (Graph, Sequence[Node]) ...
    method merge (line 122) | def merge(self, graph, nodes):  # type: (Graph, Sequence[Node]) -> Seq...
  class BNBroadcastedMulFuser (line 143) | class BNBroadcastedMulFuser(NodesFuser):
    method __init__ (line 147) | def __init__(self):  # type: () -> None
    method is_eligible (line 150) | def is_eligible(self, graph, nodes):  # type: (Graph, Sequence[Node]) ...
    method merge (line 172) | def merge(self, graph, nodes):  # type: (Graph, Sequence[Node]) -> Seq...
  class BNBroadcastedAddFuser (line 185) | class BNBroadcastedAddFuser(NodesFuser):
    method __init__ (line 189) | def __init__(self):  # type: () -> None
    method is_eligible (line 192) | def is_eligible(self, graph, nodes):  # type: (Graph, Sequence[Node]) ...
    method merge (line 214) | def merge(self, graph, nodes):  # type: (Graph, Sequence[Node]) -> Seq...
  class DropoutRemover (line 225) | class DropoutRemover(NodesFuser):
    method __init__ (line 229) | def __init__(self):  # type: () -> None
    method is_eligible (line 232) | def is_eligible(self, graph, nodes):  # type: (Graph, Sequence[Node]) ...
    method merge (line 236) | def merge(self, graph, nodes):  # type: (Graph, Sequence[Node]) -> Seq...
  class ReshapeInitTensorFuser (line 244) | class ReshapeInitTensorFuser(object):
    method __call__ (line 250) | def __call__(self, graph):  # type: (Graph) -> Graph
  class OutputRenamer (line 300) | class OutputRenamer(object):
    method __init__ (line 304) | def __init__(self,
    method __call__ (line 310) | def __call__(self, graph):  # type: (Graph) -> Graph
  class PixelShuffleFuser (line 331) | class PixelShuffleFuser(NodesFuser):
    method __init__ (line 336) | def __init__(self):  # type: () -> None
    method is_eligible (line 340) | def is_eligible(self, graph, nodes):  # type: (Graph, Sequence[Node]) ...
    method get_unique_edge_name (line 382) | def get_unique_edge_name(self, graph, name):  # type: (Graph, Text) ->...
    method merge (line 386) | def merge(self, graph, nodes):  # type: (Graph, Sequence[Node]) -> Seq...
  class AddModelInputsOutputs (line 450) | class AddModelInputsOutputs(object):
    method __call__ (line 454) | def __call__(self, graph):  # type: (Graph) -> Graph
  class ConstantsToInitializers (line 476) | class ConstantsToInitializers(object):
    method __call__ (line 480) | def __call__(self, graph):  # type: (Graph) -> Graph
  class ImageScalerRemover (line 494) | class ImageScalerRemover(object):
    method __call__ (line 499) | def __call__(self, graph):  # type: (Graph) -> Graph

FILE: caffe/onnx2caffe/_weightloader.py
  function _convert_conv (line 10) | def _convert_conv(net, node, graph, err):
  function _convert_relu (line 34) | def _convert_relu(net, node, graph, err):
  function _convert_sigmoid (line 38) | def _convert_sigmoid(net, node, graph, err):
  function _convert_BatchNorm (line 42) | def _convert_BatchNorm(net, node, graph, err):
  function _convert_Add (line 58) | def _convert_Add(net, node, graph, err):
  function _convert_Mul (line 62) | def _convert_Mul(net, node, graph, err):
  function _convert_Reshape (line 66) | def _convert_Reshape(net, node, graph, err):
  function _convert_Flatten (line 70) | def _convert_Flatten(net, node, graph, err):
  function _convert_pool (line 74) | def _convert_pool(net, node, graph, err):
  function _convert_dropout (line 78) | def _convert_dropout(net, node, graph, err):
  function _convert_Permute (line 82) | def _convert_Permute(net, node, graph, err):
  function _convert_Softmax (line 86) | def _convert_Softmax(net, node, graph, err):
  function _convert_gemm (line 90) | def _convert_gemm(net, node, graph, err):
  function _convert_upsample (line 112) | def _convert_upsample(net, node, graph, err):
  function _convert_concat (line 122) | def _convert_concat(net, node, graph, err):
  function _convert_conv_transpose (line 126) | def _convert_conv_transpose(net, node, graph, err):

FILE: caffe/ultra_face_caffe_inference.py
  function define_img_size (line 34) | def define_img_size(image_size):
  function generate_priors (line 47) | def generate_priors(feature_map_list, shrinkage_list, image_size, min_bo...
  function hard_nms (line 70) | def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
  function area_of (line 92) | def area_of(left_top, right_bottom):
  function iou_of (line 97) | def iou_of(boxes0, boxes1, eps=1e-5):
  function predict (line 107) | def predict(width, height, confidences, boxes, prob_threshold, iou_thres...
  function convert_locations_to_boxes (line 136) | def convert_locations_to_boxes(locations, priors, center_variance,
  function center_form_to_corner_form (line 146) | def center_form_to_corner_form(locations):
  function inference (line 151) | def inference():

FILE: caffe/ultra_face_opencvdnn_inference.py
  function define_img_size (line 30) | def define_img_size(image_size):
  function generate_priors (line 43) | def generate_priors(feature_map_list, shrinkage_list, image_size, min_bo...
  function hard_nms (line 66) | def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
  function area_of (line 88) | def area_of(left_top, right_bottom):
  function iou_of (line 93) | def iou_of(boxes0, boxes1, eps=1e-5):
  function predict (line 103) | def predict(width, height, confidences, boxes, prob_threshold, iou_thres...
  function convert_locations_to_boxes (line 132) | def convert_locations_to_boxes(locations, priors, center_variance,
  function center_form_to_corner_form (line 142) | def center_form_to_corner_form(locations):
  function inference (line 147) | def inference():

FILE: data/wider_face_2_voc_add_landmark.py
  function convertimgset (line 22) | def convertimgset(img_set="train"):
  function method_name (line 129) | def method_name(bboxes, filename, saveimg, vocannotationdir, lms, img_set):
  function generatetxt (line 273) | def generatetxt(img_set="train"):
  function generatevocsets (line 290) | def generatevocsets(img_set="train"):
  function convertdataset (line 311) | def convertdataset():

FILE: detect_imgs_onnx.py
  function predict (line 17) | def predict(width, height, confidences, boxes, prob_threshold, iou_thres...

FILE: masked_face/mafa2voc.py
  function expand_box (line 27) | def expand_box(square_box, scale_ratio=1.2):
  function fit_by_shifting (line 38) | def fit_by_shifting(box, rows, cols):
  function get_minimal_box (line 64) | def get_minimal_box(points):
  function points_in_box (line 76) | def points_in_box(points, box):
  function box_in_image (line 85) | def box_in_image(box, image):
  function box_is_valid (line 92) | def box_is_valid(image, points, box):
  function fit_by_shrinking (line 108) | def fit_by_shrinking(box, rows, cols):
  function fit_box (line 152) | def fit_box(box, image: object, points: object):
  function load_labels (line 185) | def load_labels(label_file, is_train):
  function parse_labels (line 209) | def parse_labels(raw_labels, is_train=True):
  function draw_face (line 296) | def draw_face(image, labels, color=(0, 255, 0)):
  function draw_mask (line 302) | def draw_mask(image, labels, color=(0, 0, 255)):
  function export_face (line 312) | def export_face(image, labels, export_file, occ_types=[1, 2, 3], min_siz...
  function write_voc_style_ann (line 359) | def write_voc_style_ann(labels, img_file_name, num_human_occ):

FILE: ncnn/src/UltraFace.hpp
  type FaceInfo (line 25) | struct FaceInfo {
  class UltraFace (line 35) | class UltraFace {

FILE: ncnn/src/main.cpp
  function main (line 13) | int main(int argc, char **argv) {

FILE: opencv_dnn/cv_dnn_ultraface.h
  type FaceInfo (line 16) | typedef struct FaceInfo {
  function class (line 26) | class UltraFace {

FILE: paddle/train.py
  function lr_poly (line 122) | def lr_poly(base_lr, iter):
  function adjust_learning_rate (line 135) | def adjust_learning_rate(optimizer, i_iter):
  function train (line 141) | def train(loader, net, criterion, optimizer, debug_steps=100, epoch=-1):
  function test (line 177) | def test(loader, net, criterion):

FILE: paddle/vision/datasets/voc_dataset.py
  class VOCDataset (line 10) | class VOCDataset(Dataset):
    method __init__ (line 11) | def __init__(self, root, transform=None, target_transform=None, is_tes...
    method __getitem__ (line 52) | def __getitem__(self, index):
    method get_image (line 65) | def get_image(self, index):
    method get_annotation (line 72) | def get_annotation(self, index):
    method __len__ (line 76) | def __len__(self):
    method _read_image_ids (line 80) | def _read_image_ids(image_sets_file):
    method _get_annotation (line 87) | def _get_annotation(self, image_id):
    method _read_image (line 114) | def _read_image(self, image_id):

FILE: paddle/vision/nn/mb_tiny.py
  class Mb_Tiny (line 5) | class Mb_Tiny(nn.Layer):
    method __init__ (line 7) | def __init__(self, num_classes=2):
    method forward (line 46) | def forward(self, x):

FILE: paddle/vision/nn/mb_tiny_RFB.py
  class BasicConv (line 6) | class BasicConv(nn.Layer):
    method __init__ (line 8) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi...
    method forward (line 20) | def forward(self, x):
  class BasicRFB (line 29) | class BasicRFB(nn.Layer):
    method __init__ (line 31) | def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_red...
    method forward (line 58) | def forward(self, x):
  class Mb_Tiny_RFB (line 72) | class Mb_Tiny_RFB(nn.Layer):
    method __init__ (line 74) | def __init__(self, num_classes=2):
    method forward (line 113) | def forward(self, x):

FILE: paddle/vision/nn/multibox_loss.py
  class MultiboxLoss (line 9) | class MultiboxLoss(nn.Layer):
    method __init__ (line 10) | def __init__(self, priors, neg_pos_ratio, center_variance, size_varian...
    method forward (line 22) | def forward(self, confidence, predicted_locations, labels, gt_locations):

FILE: paddle/vision/ssd/config/fd_config.py
  function define_img_size (line 18) | def define_img_size(size):

FILE: paddle/vision/ssd/data_preprocessing.py
  class TrainAugmentation (line 4) | class TrainAugmentation:
    method __init__ (line 5) | def __init__(self, size, mean=0, std=1.0):
    method __call__ (line 25) | def __call__(self, img, boxes, labels):
  class TestTransform (line 36) | class TestTransform:
    method __init__ (line 37) | def __init__(self, size, mean=0.0, std=1.0):
    method __call__ (line 46) | def __call__(self, image, boxes, labels):
  class PredictionTransform (line 50) | class PredictionTransform:
    method __init__ (line 51) | def __init__(self, size, mean=0.0, std=1.0):
    method __call__ (line 59) | def __call__(self, image):

FILE: paddle/vision/ssd/mb_tiny_RFB_fd.py
  function SeperableConv2d (line 9) | def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, ...
  function create_Mb_Tiny_RFB_fd (line 20) | def create_Mb_Tiny_RFB_fd(num_classes, is_test=False, device="cuda"):
  function create_Mb_Tiny_RFB_fd_predictor (line 56) | def create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=200, nms_method=...

FILE: paddle/vision/ssd/mb_tiny_fd.py
  function SeperableConv2d (line 9) | def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, ...
  function create_mb_tiny_fd (line 20) | def create_mb_tiny_fd(num_classes, is_test=False, device="cuda"):
  function create_mb_tiny_fd_predictor (line 56) | def create_mb_tiny_fd_predictor(net, candidate_size=200, nms_method=None...

FILE: paddle/vision/ssd/predictor.py
  class Predictor (line 8) | class Predictor:
    method __init__ (line 9) | def __init__(self, net, size, mean=0.0, std=1.0, nms_method=None,
    method predict (line 29) | def predict(self, image, top_k=-1, prob_threshold=None):

FILE: paddle/vision/ssd/ssd.py
  class SSD (line 14) | class SSD(nn.Layer):
    method __init__ (line 15) | def __init__(self, num_classes: int, base_net: nn.LayerList, source_la...
    method forward (line 42) | def forward(self, x: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Ten...
    method compute_header (line 103) | def compute_header(self, i, x):
    method init_from_base_net (line 114) | def init_from_base_net(self, model):
    method init_from_pretrained_ssd (line 121) | def init_from_pretrained_ssd(self, model):
    method init (line 130) | def init(self):
    method load (line 137) | def load(self, model):
    method save (line 140) | def save(self, model_path):
  class MatchPrior (line 144) | class MatchPrior(object):
    method __init__ (line 145) | def __init__(self, center_form_priors, center_variance, size_variance,...
    method __call__ (line 152) | def __call__(self, gt_boxes, gt_labels):
  function _xavier_init_ (line 160) | def _xavier_init_(m: nn.Layer):

FILE: paddle/vision/transforms/transforms.py
  function intersect (line 13) | def intersect(box_a, box_b):
  function jaccard_numpy (line 20) | def jaccard_numpy(box_a, box_b):
  function object_converage_numpy (line 40) | def object_converage_numpy(box_a, box_b):
  class Compose (line 59) | class Compose(object):
    method __init__ (line 70) | def __init__(self, transforms):
    method __call__ (line 73) | def __call__(self, img, boxes=None, labels=None):
  class Lambda (line 79) | class Lambda(object):
    method __init__ (line 82) | def __init__(self, lambd):
    method __call__ (line 86) | def __call__(self, img, boxes=None, labels=None):
  class ConvertFromInts (line 90) | class ConvertFromInts(object):
    method __call__ (line 91) | def __call__(self, image, boxes=None, labels=None):
  class SubtractMeans (line 95) | class SubtractMeans(object):
    method __init__ (line 96) | def __init__(self, mean):
    method __call__ (line 99) | def __call__(self, image, boxes=None, labels=None):
  class imgprocess (line 105) | class imgprocess(object):
    method __init__ (line 106) | def __init__(self, std):
    method __call__ (line 109) | def __call__(self, image, boxes=None, labels=None):
  class ToAbsoluteCoords (line 115) | class ToAbsoluteCoords(object):
    method __call__ (line 116) | def __call__(self, image, boxes=None, labels=None):
  class ToPercentCoords (line 126) | class ToPercentCoords(object):
    method __call__ (line 127) | def __call__(self, image, boxes=None, labels=None):
  class Resize (line 137) | class Resize(object):
    method __init__ (line 138) | def __init__(self, size=(300, 300)):
    method __call__ (line 141) | def __call__(self, image, boxes=None, labels=None):
  class RandomSaturation (line 147) | class RandomSaturation(object):
    method __init__ (line 148) | def __init__(self, lower=0.5, upper=1.5):
    method __call__ (line 154) | def __call__(self, image, boxes=None, labels=None):
  class RandomHue (line 161) | class RandomHue(object):
    method __init__ (line 162) | def __init__(self, delta=18.0):
    method __call__ (line 166) | def __call__(self, image, boxes=None, labels=None):
  class RandomLightingNoise (line 174) | class RandomLightingNoise(object):
    method __init__ (line 175) | def __init__(self):
    method __call__ (line 180) | def __call__(self, image, boxes=None, labels=None):
  class ConvertColor (line 188) | class ConvertColor(object):
    method __init__ (line 189) | def __init__(self, current, transform):
    method __call__ (line 193) | def __call__(self, image, boxes=None, labels=None):
  class RandomContrast (line 209) | class RandomContrast(object):
    method __init__ (line 210) | def __init__(self, lower=0.5, upper=1.5):
    method __call__ (line 217) | def __call__(self, image, boxes=None, labels=None):
  class RandomBrightness (line 224) | class RandomBrightness(object):
    method __init__ (line 225) | def __init__(self, delta=32):
    method __call__ (line 230) | def __call__(self, image, boxes=None, labels=None):
  class ToCV2Image (line 237) | class ToCV2Image(object):
    method __call__ (line 238) | def __call__(self, tensor, boxes=None, labels=None):
  class ToTensor (line 242) | class ToTensor(object):
    method __call__ (line 243) | def __call__(self, cvimage, boxes=None, labels=None):
  class RandomSampleCrop (line 247) | class RandomSampleCrop(object):
    method __init__ (line 261) | def __init__(self):
    method __call__ (line 274) | def __call__(self, image, boxes=None, labels=None):
  class RandomSampleCrop_v2 (line 352) | class RandomSampleCrop_v2(object):
    method __init__ (line 366) | def __init__(self):
    method __call__ (line 379) | def __call__(self, image, boxes=None, labels=None):
  class Expand (line 456) | class Expand(object):
    method __init__ (line 457) | def __init__(self, mean):
    method __call__ (line 460) | def __call__(self, image, boxes, labels):
  class RandomMirror (line 484) | class RandomMirror(object):
    method __call__ (line 485) | def __call__(self, image, boxes, classes):
  class SwapChannels (line 494) | class SwapChannels(object):
    method __init__ (line 502) | def __init__(self, swaps):
    method __call__ (line 505) | def __call__(self, image):
  class PhotometricDistort (line 520) | class PhotometricDistort(object):
    method __init__ (line 521) | def __init__(self):
    method __call__ (line 533) | def __call__(self, image, boxes, labels):

FILE: paddle/vision/utils/box_utils.py
  function generate_priors (line 6) | def generate_priors(feature_map_list, shrinkage_list, image_size, min_bo...
  function convert_locations_to_boxes (line 27) | def convert_locations_to_boxes(locations, priors, center_variance, size_...
  function convert_boxes_to_locations (line 52) | def convert_boxes_to_locations(center_form_boxes, center_form_priors, ce...
  function area_of (line 62) | def area_of(left_top, right_bottom) -> paddle.Tensor:
  function iou_of (line 76) | def iou_of(boxes0, boxes1, eps=1e-5):
  function assign_priors (line 102) | def assign_priors(gt_boxes, gt_labels, corner_form_priors,
  function hard_negative_mining (line 134) | def hard_negative_mining(loss, labels, neg_pos_ratio):
  function center_form_to_corner_form (line 158) | def center_form_to_corner_form(locations):
  function corner_form_to_center_form (line 163) | def corner_form_to_center_form(boxes):
  function hard_nms (line 168) | def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
  function nms (line 201) | def nms(box_scores, nms_method=None, score_threshold=None, iou_threshold...
  function soft_nms (line 209) | def soft_nms(box_scores, score_threshold, sigma=0.5, top_k=-1):

FILE: paddle/vision/utils/box_utils_numpy.py
  function convert_locations_to_boxes (line 4) | def convert_locations_to_boxes(locations, priors, center_variance, size_...
  function convert_boxes_to_locations (line 29) | def convert_boxes_to_locations(center_form_boxes, center_form_priors, ce...
  function area_of (line 39) | def area_of(left_top, right_bottom):
  function iou_of (line 53) | def iou_of(boxes0, boxes1, eps=1e-5):
  function center_form_to_corner_form (line 72) | def center_form_to_corner_form(locations):
  function corner_form_to_center_form (line 77) | def corner_form_to_center_form(boxes):
  function hard_nms (line 84) | def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):

FILE: paddle/vision/utils/misc.py
  function str2bool (line 6) | def str2bool(s):
  class Timer (line 10) | class Timer:
    method __init__ (line 11) | def __init__(self):
    method start (line 14) | def start(self, key="default"):
    method end (line 17) | def end(self, key="default"):
  function save_checkpoint (line 25) | def save_checkpoint(epoch, net_state_dict, optimizer_state_dict, best_sc...
  function load_checkpoint (line 35) | def load_checkpoint(checkpoint_path):
  function freeze_net_layers (line 39) | def freeze_net_layers(net):
  function store_labels (line 44) | def store_labels(path, labels):

FILE: run_video_face_detect_onnx.py
  function predict (line 16) | def predict(width, height, confidences, boxes, prob_threshold, iou_thres...

FILE: tf/backend/op.py
  function basic_conv (line 4) | def basic_conv(x, out_ch, kernel_size, stride=(1, 1), padding=0, dilatio...
  function basic_rfb (line 24) | def basic_rfb(x, in_ch, out_ch, stride=1, scale=0.1, map_reduce=8, visio...
  function separable_conv (line 60) | def separable_conv(x, out_ch, kernel_size, stride, padding, prefix='sepa...
  function conv_bn (line 73) | def conv_bn(x, out_ch, stride, padding=1, prefix='conv_bn'):
  function conv_dw (line 86) | def conv_dw(x, out_ch, stride, padding=1, prefix='conv_dw'):

FILE: tf/backend/utils.py
  function post_processing (line 8) | def post_processing(reg_list, cls_list, num_classes, image_size, feature...
  function decode_regression (line 44) | def decode_regression(reg, image_size, feature_map_w_h_list, min_boxes,
  function load_weight (line 80) | def load_weight(model, torch_path, mapping_table_path):

FILE: tf/convert_tensorflow.py
  function main (line 16) | def main():

FILE: tf/det_image.py
  function main (line 18) | def main():

FILE: tf/model/rfb_320.py
  function create_rfb_net (line 18) | def create_rfb_net(input_shape, base_channel, num_classes):

FILE: tf/model/slim_320.py
  function create_slim_net (line 18) | def create_slim_net(input_shape, base_channel, num_classes):

FILE: tflite/TFLiteFaceDetector.py
  class UltraLightFaceDetecion (line 7) | class UltraLightFaceDetecion():
    method __init__ (line 8) | def __init__(self, filepath, input_size=(320, 240), conf_threshold=0.6,
    method _generate_anchors (line 43) | def _generate_anchors(self):
    method _pre_processing (line 68) | def _pre_processing(self, img):
    method inference (line 76) | def inference(self, img):
    method _post_processing (line 94) | def _post_processing(self, boxes, scores):
    method _decode_regression (line 109) | def _decode_regression(self, reg):

FILE: tflite/inference_test.py
  function image_inference (line 18) | def image_inference(image_path, model_path, color=(125, 255, 0)):
  function video_inference (line 36) | def video_inference(video, model_path, color=(125, 255, 0)):

FILE: tflite/model/tflite_RFB_320_without_postprocessing.py
  function basic_conv (line 7) | def basic_conv(x, out_ch, kernel_size, stride=(1, 1), padding=0, dilatio...
  function basic_rfb (line 27) | def basic_rfb(x, in_ch, out_ch, stride=1, scale=0.1, map_reduce=8, visio...
  function separable_conv (line 63) | def separable_conv(x, out_ch, kernel_size, stride, padding, prefix='sepa...
  function conv_bn (line 76) | def conv_bn(x, out_ch, stride, padding=1, prefix='conv_bn'):
  function conv_dw (line 89) | def conv_dw(x, out_ch, stride, padding=1, prefix='conv_dw'):
  function create_rfb_net (line 104) | def create_rfb_net(input_shape, base_channel, num_classes):

FILE: tflite/model/tflite_slim_320_without_postprocessing.py
  function create_slim_net (line 8) | def create_slim_net(input_shape, base_channel, num_classes):

FILE: train.py
  function lr_poly (line 121) | def lr_poly(base_lr, iter):
  function adjust_learning_rate (line 125) | def adjust_learning_rate(optimizer, i_iter):
  function train (line 131) | def train(loader, net, criterion, optimizer, device, debug_steps=100, ep...
  function test (line 170) | def test(loader, net, criterion, device):

FILE: vision/datasets/voc_dataset.py
  class VOCDataset (line 10) | class VOCDataset:
    method __init__ (line 12) | def __init__(self, root, transform=None, target_transform=None, is_tes...
    method __getitem__ (line 53) | def __getitem__(self, index):
    method get_image (line 66) | def get_image(self, index):
    method get_annotation (line 73) | def get_annotation(self, index):
    method __len__ (line 77) | def __len__(self):
    method _read_image_ids (line 81) | def _read_image_ids(image_sets_file):
    method _get_annotation (line 88) | def _get_annotation(self, image_id):
    method _read_image (line 115) | def _read_image(self, image_id):

FILE: vision/nn/mb_tiny.py
  class Mb_Tiny (line 5) | class Mb_Tiny(nn.Module):
    method __init__ (line 7) | def __init__(self, num_classes=2):
    method forward (line 46) | def forward(self, x):

FILE: vision/nn/mb_tiny_RFB.py
  class BasicConv (line 6) | class BasicConv(nn.Module):
    method __init__ (line 8) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi...
    method forward (line 20) | def forward(self, x):
  class BasicRFB (line 29) | class BasicRFB(nn.Module):
    method __init__ (line 31) | def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_red...
    method forward (line 58) | def forward(self, x):
  class Mb_Tiny_RFB (line 72) | class Mb_Tiny_RFB(nn.Module):
    method __init__ (line 74) | def __init__(self, num_classes=2):
    method forward (line 113) | def forward(self, x):

FILE: vision/nn/multibox_loss.py
  class MultiboxLoss (line 8) | class MultiboxLoss(nn.Module):
    method __init__ (line 9) | def __init__(self, priors, neg_pos_ratio,
    method forward (line 23) | def forward(self, confidence, predicted_locations, labels, gt_locations):

FILE: vision/ssd/config/fd_config.py
  function define_img_size (line 18) | def define_img_size(size):

FILE: vision/ssd/data_preprocessing.py
  class TrainAugmentation (line 4) | class TrainAugmentation:
    method __init__ (line 5) | def __init__(self, size, mean=0, std=1.0):
    method __call__ (line 25) | def __call__(self, img, boxes, labels):
  class TestTransform (line 36) | class TestTransform:
    method __init__ (line 37) | def __init__(self, size, mean=0.0, std=1.0):
    method __call__ (line 46) | def __call__(self, image, boxes, labels):
  class PredictionTransform (line 50) | class PredictionTransform:
    method __init__ (line 51) | def __init__(self, size, mean=0.0, std=1.0):
    method __call__ (line 59) | def __call__(self, image):

FILE: vision/ssd/mb_tiny_RFB_fd.py
  function SeperableConv2d (line 9) | def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, ...
  function create_Mb_Tiny_RFB_fd (line 20) | def create_Mb_Tiny_RFB_fd(num_classes, is_test=False, device="cuda"):
  function create_Mb_Tiny_RFB_fd_predictor (line 56) | def create_Mb_Tiny_RFB_fd_predictor(net, candidate_size=200, nms_method=...

FILE: vision/ssd/mb_tiny_fd.py
  function SeperableConv2d (line 9) | def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, ...
  function create_mb_tiny_fd (line 20) | def create_mb_tiny_fd(num_classes, is_test=False, device="cuda"):
  function create_mb_tiny_fd_predictor (line 56) | def create_mb_tiny_fd_predictor(net, candidate_size=200, nms_method=None...

FILE: vision/ssd/predictor.py
  class Predictor (line 8) | class Predictor:
    method __init__ (line 9) | def __init__(self, net, size, mean=0.0, std=1.0, nms_method=None,
    method predict (line 29) | def predict(self, image, top_k=-1, prob_threshold=None):

FILE: vision/ssd/ssd.py
  class SSD (line 14) | class SSD(nn.Module):
    method __init__ (line 15) | def __init__(self, num_classes: int, base_net: nn.ModuleList, source_l...
    method forward (line 42) | def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    method compute_header (line 103) | def compute_header(self, i, x):
    method init_from_base_net (line 114) | def init_from_base_net(self, model):
    method init_from_pretrained_ssd (line 121) | def init_from_pretrained_ssd(self, model):
    method init (line 130) | def init(self):
    method load (line 137) | def load(self, model):
    method save (line 140) | def save(self, model_path):
  class MatchPrior (line 144) | class MatchPrior(object):
    method __init__ (line 145) | def __init__(self, center_form_priors, center_variance, size_variance,...
    method __call__ (line 152) | def __call__(self, gt_boxes, gt_labels):
  function _xavier_init_ (line 164) | def _xavier_init_(m: nn.Module):

FILE: vision/transforms/transforms.py
  function intersect (line 13) | def intersect(box_a, box_b):
  function jaccard_numpy (line 20) | def jaccard_numpy(box_a, box_b):
  function object_converage_numpy (line 40) | def object_converage_numpy(box_a, box_b):
  class Compose (line 59) | class Compose(object):
    method __init__ (line 70) | def __init__(self, transforms):
    method __call__ (line 73) | def __call__(self, img, boxes=None, labels=None):
  class Lambda (line 79) | class Lambda(object):
    method __init__ (line 82) | def __init__(self, lambd):
    method __call__ (line 86) | def __call__(self, img, boxes=None, labels=None):
  class ConvertFromInts (line 90) | class ConvertFromInts(object):
    method __call__ (line 91) | def __call__(self, image, boxes=None, labels=None):
  class SubtractMeans (line 95) | class SubtractMeans(object):
    method __init__ (line 96) | def __init__(self, mean):
    method __call__ (line 99) | def __call__(self, image, boxes=None, labels=None):
  class imgprocess (line 105) | class imgprocess(object):
    method __init__ (line 106) | def __init__(self, std):
    method __call__ (line 109) | def __call__(self, image, boxes=None, labels=None):
  class ToAbsoluteCoords (line 115) | class ToAbsoluteCoords(object):
    method __call__ (line 116) | def __call__(self, image, boxes=None, labels=None):
  class ToPercentCoords (line 126) | class ToPercentCoords(object):
    method __call__ (line 127) | def __call__(self, image, boxes=None, labels=None):
  class Resize (line 137) | class Resize(object):
    method __init__ (line 138) | def __init__(self, size=(300, 300)):
    method __call__ (line 141) | def __call__(self, image, boxes=None, labels=None):
  class RandomSaturation (line 147) | class RandomSaturation(object):
    method __init__ (line 148) | def __init__(self, lower=0.5, upper=1.5):
    method __call__ (line 154) | def __call__(self, image, boxes=None, labels=None):
  class RandomHue (line 161) | class RandomHue(object):
    method __init__ (line 162) | def __init__(self, delta=18.0):
    method __call__ (line 166) | def __call__(self, image, boxes=None, labels=None):
  class RandomLightingNoise (line 174) | class RandomLightingNoise(object):
    method __init__ (line 175) | def __init__(self):
    method __call__ (line 180) | def __call__(self, image, boxes=None, labels=None):
  class ConvertColor (line 188) | class ConvertColor(object):
    method __init__ (line 189) | def __init__(self, current, transform):
    method __call__ (line 193) | def __call__(self, image, boxes=None, labels=None):
  class RandomContrast (line 209) | class RandomContrast(object):
    method __init__ (line 210) | def __init__(self, lower=0.5, upper=1.5):
    method __call__ (line 217) | def __call__(self, image, boxes=None, labels=None):
  class RandomBrightness (line 224) | class RandomBrightness(object):
    method __init__ (line 225) | def __init__(self, delta=32):
    method __call__ (line 230) | def __call__(self, image, boxes=None, labels=None):
  class ToCV2Image (line 237) | class ToCV2Image(object):
    method __call__ (line 238) | def __call__(self, tensor, boxes=None, labels=None):
  class ToTensor (line 242) | class ToTensor(object):
    method __call__ (line 243) | def __call__(self, cvimage, boxes=None, labels=None):
  class RandomSampleCrop (line 247) | class RandomSampleCrop(object):
    method __init__ (line 261) | def __init__(self):
    method __call__ (line 274) | def __call__(self, image, boxes=None, labels=None):
  class RandomSampleCrop_v2 (line 352) | class RandomSampleCrop_v2(object):
    method __init__ (line 366) | def __init__(self):
    method __call__ (line 379) | def __call__(self, image, boxes=None, labels=None):
  class Expand (line 456) | class Expand(object):
    method __init__ (line 457) | def __init__(self, mean):
    method __call__ (line 460) | def __call__(self, image, boxes, labels):
  class RandomMirror (line 484) | class RandomMirror(object):
    method __call__ (line 485) | def __call__(self, image, boxes, classes):
  class SwapChannels (line 494) | class SwapChannels(object):
    method __init__ (line 502) | def __init__(self, swaps):
    method __call__ (line 505) | def __call__(self, image):
  class PhotometricDistort (line 520) | class PhotometricDistort(object):
    method __init__ (line 521) | def __init__(self):
    method __call__ (line 533) | def __call__(self, image, boxes, labels):

FILE: vision/utils/box_utils.py
  function generate_priors (line 6) | def generate_priors(feature_map_list, shrinkage_list, image_size, min_bo...
  function convert_locations_to_boxes (line 32) | def convert_locations_to_boxes(locations, priors, center_variance,
  function convert_boxes_to_locations (line 58) | def convert_boxes_to_locations(center_form_boxes, center_form_priors, ce...
  function area_of (line 68) | def area_of(left_top, right_bottom) -> torch.Tensor:
  function iou_of (line 82) | def iou_of(boxes0, boxes1, eps=1e-5):
  function assign_priors (line 101) | def assign_priors(gt_boxes, gt_labels, corner_form_priors,
  function hard_negative_mining (line 131) | def hard_negative_mining(loss, labels, neg_pos_ratio):
  function center_form_to_corner_form (line 156) | def center_form_to_corner_form(locations):
  function corner_form_to_center_form (line 161) | def corner_form_to_center_form(boxes):
  function hard_nms (line 168) | def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
  function nms (line 201) | def nms(box_scores, nms_method=None, score_threshold=None, iou_threshold...
  function soft_nms (line 209) | def soft_nms(box_scores, score_threshold, sigma=0.5, top_k=-1):

FILE: vision/utils/box_utils_numpy.py
  function convert_locations_to_boxes (line 4) | def convert_locations_to_boxes(locations, priors, center_variance,
  function convert_boxes_to_locations (line 30) | def convert_boxes_to_locations(center_form_boxes, center_form_priors, ce...
  function area_of (line 40) | def area_of(left_top, right_bottom):
  function iou_of (line 54) | def iou_of(boxes0, boxes1, eps=1e-5):
  function center_form_to_corner_form (line 73) | def center_form_to_corner_form(locations):
  function corner_form_to_center_form (line 78) | def corner_form_to_center_form(boxes):
  function hard_nms (line 85) | def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):

FILE: vision/utils/misc.py
  function str2bool (line 6) | def str2bool(s):
  class Timer (line 10) | class Timer:
    method __init__ (line 11) | def __init__(self):
    method start (line 14) | def start(self, key="default"):
    method end (line 17) | def end(self, key="default"):
  function save_checkpoint (line 25) | def save_checkpoint(epoch, net_state_dict, optimizer_state_dict, best_sc...
  function load_checkpoint (line 35) | def load_checkpoint(checkpoint_path):
  function freeze_net_layers (line 39) | def freeze_net_layers(net):
  function store_labels (line 44) | def store_labels(path, labels):

FILE: widerface_evaluate/evaluation.py
  function get_gt_boxes (line 17) | def get_gt_boxes(gt_dir):
  function get_gt_boxes_from_txt (line 36) | def get_gt_boxes_from_txt(gt_path, cache_dir):
  function read_pred_file (line 81) | def read_pred_file(filepath):
  function get_preds (line 103) | def get_preds(pred_dir):
  function norm_score (line 120) | def norm_score(pred):
  function image_eval (line 145) | def image_eval(pred, gt, ignore, iou_thresh):
  function img_pr_info (line 181) | def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
  function dataset_pr_info (line 198) | def dataset_pr_info(thresh_num, pr_curve, count_face):
  function voc_ap (line 206) | def voc_ap(rec, prec):
  function evaluation (line 226) | def evaluation(pred, gt_path, iou_thresh=0.5):