SYMBOL INDEX (471 symbols across 33 files)

FILE: EagleEye_normal_prune.py
  function obtain_avg_forward_time (line 13) | def obtain_avg_forward_time(input, model, repeat=200):
  function obtain_filters_mask (line 24) | def obtain_filters_mask(model, CBL_idx, prune_idx, idx_mask):
  function obtain_l1_mask (line 59) | def obtain_l1_mask(conv_module, random_rate):
  function performance_summary (line 73) | def performance_summary(model, opt=None, prefix=""):
  function rand_prune_and_eval (line 78) | def rand_prune_and_eval(model, min_rate, max_rate):

FILE: EagleEye_regular_prune.py
  function obtain_avg_forward_time (line 10) | def obtain_avg_forward_time(input, model, repeat=200):
  function obtain_filters_mask (line 21) | def obtain_filters_mask(model, CBL_idx, prune_idx, idx_mask):
  function obtain_l1_mask (line 52) | def obtain_l1_mask(conv_module, random_rate):
  function performance_summary (line 68) | def performance_summary(model, opt=None, prefix=""):
  function rand_prune_and_eval (line 72) | def rand_prune_and_eval(model, min_rate, max_rate):

FILE: EagleEye_slim_prune.py
  function obtain_avg_forward_time (line 11) | def obtain_avg_forward_time(input, model, repeat=200):
  function obtain_filters_mask (line 22) | def obtain_filters_mask(model, CBL_idx, prune_idx, idx_mask):
  function obtain_l1_mask (line 51) | def obtain_l1_mask(conv_module, random_rate):
  function performance_summary (line 65) | def performance_summary(model, opt=None, prefix=""):
  function rand_prune_and_eval (line 69) | def rand_prune_and_eval(model, min_rate, max_rate):

FILE: PTQ.py
  function PTQ (line 12) | def PTQ(cfg,

FILE: convert.py
  function convert (line 10) | def convert():

FILE: convert_FPGA.py
  function convert (line 9) | def convert():

FILE: convert_FPGA_2.py
  function convert (line 9) | def convert():

FILE: detect.py
  function detect (line 9) | def detect(save_img=False):

FILE: layer_channel_prune.py
  function obtain_filters_mask (line 13) | def obtain_filters_mask(model, thre, CBL_idx, prune_idx):
  function prune_and_eval (line 50) | def prune_and_eval(model, CBL_idx, CBLidx2mask):
  function prune_and_eval2 (line 64) | def prune_and_eval2(model, prune_shortcuts=[]):
  function obtain_filters_mask2 (line 80) | def obtain_filters_mask2(model, CBL_idx, prune_shortcuts):
  function obtain_avg_forward_time (line 95) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: layer_channel_regular_prune.py
  function obtain_filters_mask (line 15) | def obtain_filters_mask(model, thre, CBL_idx, shortcut_idx, prune_idx):
  function prune_and_eval (line 86) | def prune_and_eval(model, sorted_bn, shortcut_idx, percent=.0):
  function prune_and_eval2 (line 131) | def prune_and_eval2(model, prune_shortcuts=[]):
  function obtain_filters_mask2 (line 147) | def obtain_filters_mask2(model, CBL_idx, prune_shortcuts):
  function obtain_avg_forward_time (line 162) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: layer_prune.py
  function prune_and_eval (line 14) | def prune_and_eval(model, prune_shortcuts=[]):
  function obtain_filters_mask (line 30) | def obtain_filters_mask(model, CBL_idx, prune_shortcuts):
  function obtain_avg_forward_time (line 45) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: models.py
  function create_modules (line 11) | def create_modules(module_defs, img_size, cfg, quantized, quantizer_outp...
  class YOLOLayer (line 350) | class YOLOLayer(nn.Module):
    method __init__ (line 351) | def __init__(self, anchors, nc, img_size, yolo_index, layers, stride, ...
    method create_grids (line 367) | def create_grids(self, ng=(13, 13), device='cpu'):
    method forward (line 380) | def forward(self, p, out):
  class Darknet (line 440) | class Darknet(nn.Module):
    method __init__ (line 443) | def __init__(self, cfg, img_size=(416, 416), verbose=False, quantized=...
    method forward (line 478) | def forward(self, x, augment=False):
    method forward_once (line 508) | def forward_once(self, x, augment=False, verbose=False):
    method fuse (line 563) | def fuse(self):
    method info (line 579) | def info(self, verbose=False):
  function get_yolo_layers (line 583) | def get_yolo_layers(model):
  function load_darknet_weights (line 587) | def load_darknet_weights(self, weights, cutoff=-1, pt=False, quant=False):
  function save_weights (line 738) | def save_weights(self, path='model.weights', cutoff=-1):
  function convert (line 785) | def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weight...
  function attempt_download (line 816) | def attempt_download(weights):

FILE: normal_prune.py
  function prune_and_eval (line 15) | def prune_and_eval(model, sorted_bn, percent=.0):
  function obtain_filters_mask (line 41) | def obtain_filters_mask(model, thre, CBL_idx, prune_idx):
  function obtain_avg_forward_time (line 76) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: regular_prune.py
  function prune_and_eval (line 23) | def prune_and_eval(model, sorted_bn, percent=.0):
  function obtain_filters_mask (line 68) | def obtain_filters_mask(model, thre, CBL_idx, prune_idx):
  function obtain_avg_forward_time (line 124) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: shortcut_prune.py
  function prune_and_eval (line 28) | def prune_and_eval(model, sorted_bn, shortcut_idx, percent=.0):
  function obtain_filters_mask (line 73) | def obtain_filters_mask(model, thre, CBL_idx, shortcut_idx, prune_idx):
  function obtain_avg_forward_time (line 124) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: slim_prune.py
  function obtain_filters_mask (line 13) | def obtain_filters_mask(model, thre, CBL_idx, prune_idx):
  function prune_and_eval (line 50) | def prune_and_eval(model, CBL_idx, CBLidx2mask):
  function obtain_avg_forward_time (line 64) | def obtain_avg_forward_time(input, model, repeat=200):

FILE: test.py
  function test (line 10) | def test(cfg,

FILE: train.py
  function train (line 56) | def train(hyp):

FILE: utils/adabound.py
  class AdaBound (line 7) | class AdaBound(Optimizer):
    method __init__ (line 26) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, ...
    method __setstate__ (line 46) | def __setstate__(self, state):
    method step (line 51) | def step(self, closure=None):
  class AdaBoundW (line 122) | class AdaBoundW(Optimizer):
    method __init__ (line 141) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, ...
    method __setstate__ (line 161) | def __setstate__(self, state):
    method step (line 166) | def step(self, closure=None):

FILE: utils/datasets.py
  function exif_size (line 28) | def exif_size(img):
  class LoadImages (line 43) | class LoadImages:  # for inference
    method __init__ (line 44) | def __init__(self, path, img_size=416, is_gray_scale=False, rect=False):
    method __iter__ (line 69) | def __iter__(self):
    method __next__ (line 73) | def __next__(self):
    method new_video (line 118) | def new_video(self, path):
    method __len__ (line 123) | def __len__(self):
  class LoadWebcam (line 127) | class LoadWebcam:  # for inference
    method __init__ (line 128) | def __init__(self, pipe=0, img_size=416):
    method __iter__ (line 149) | def __iter__(self):
    method __next__ (line 153) | def __next__(self):
    method __len__ (line 188) | def __len__(self):
  class LoadStreams (line 192) | class LoadStreams:  # multiple IP or RTSP cameras
    method __init__ (line 193) | def __init__(self, sources='streams.txt', img_size=416):
    method update (line 226) | def update(self, index, cap):
    method __iter__ (line 238) | def __iter__(self):
    method __next__ (line 242) | def __next__(self):
    method __len__ (line 261) | def __len__(self):
  class LoadImagesAndLabels (line 265) | class LoadImagesAndLabels(Dataset):  # for training/testing
    method __init__ (line 266) | def __init__(self, path, img_size=416, batch_size=16, augment=False, h...
    method __len__ (line 410) | def __len__(self):
    method __getitem__ (line 419) | def __getitem__(self, index):
    method collate_fn (line 504) | def collate_fn(batch):
  function load_image (line 511) | def load_image(self, index, is_gray_scale=False):
  function augment_hsv (line 534) | def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
  function load_mosaic (line 553) | def load_mosaic(self, index, is_gray_scale=False):
  function letterbox (line 611) | def letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=Tru...
  function random_affine (line 649) | def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, s...
  function cutout (line 718) | def cutout(image, labels):
  class FenceMask (line 831) | class FenceMask(torch.nn.Module):
    method __init__ (line 832) | def __init__(self, batch_size, img_size, probability):
    method forward (line 881) | def forward(self, x):
    method set_prob (line 903) | def set_prob(self, epoch, max_epoch):
  class Grid (line 907) | class Grid(object):
    method __init__ (line 908) | def __init__(self, d1, d2, rotate=1, ratio=0.5, mode=0, prob=1.):
    method set_prob (line 916) | def set_prob(self, epoch, max_epoch):
    method __call__ (line 919) | def __call__(self, img):
  class GridMask (line 968) | class GridMask(torch.nn.Module):
    method __init__ (line 969) | def __init__(self, d1, d2, rotate=1, ratio=0.5, mode=0, prob=1.):
    method set_prob (line 977) | def set_prob(self, epoch, max_epoch):
    method forward (line 980) | def forward(self, x):
  function reduce_img_size (line 991) | def reduce_img_size(path='../data/sm4/images', img_size=1024):  # from u...
  function convert_images2bmp (line 1008) | def convert_images2bmp():  # from utils.datasets import *; convert_image...
  function recursive_dataset2bmp (line 1032) | def recursive_dataset2bmp(dataset='../data/sm4_bmp'):  # from utils.data...
  function imagelist2folder (line 1052) | def imagelist2folder(path='data/coco_64img.txt'):  # from utils.datasets...
  function create_folder (line 1061) | def create_folder(path='./new_folder'):

FILE: utils/google_utils.py
  function gdrive_download (line 11) | def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.z...
  function upload_blob (line 47) | def upload_blob(bucket_name, source_file_name, destination_blob_name):
  function download_blob (line 62) | def download_blob(bucket_name, source_blob_name, destination_file_name):

FILE: utils/layers.py
  function make_divisible (line 4) | def make_divisible(v, divisor):
  class Flatten (line 10) | class Flatten(nn.Module):
    method forward (line 12) | def forward(self, x):
  class Concat (line 16) | class Concat(nn.Module):
    method __init__ (line 18) | def __init__(self, dimension=1):
    method forward (line 22) | def forward(self, x):
  class FeatureConcat (line 26) | class FeatureConcat(nn.Module):
    method __init__ (line 27) | def __init__(self, layers, groups):
    method forward (line 33) | def forward(self, x, outputs):
  class Shortcut (line 43) | class Shortcut(nn.Module):  # weighted sum of 2 or more layers https://a...
    method __init__ (line 44) | def __init__(self, layers, weight=False):
    method forward (line 52) | def forward(self, x, outputs):
  class MixConv2d (line 75) | class MixConv2d(nn.Module):  # MixConv: Mixed Depthwise Convolutional Ke...
    method __init__ (line 76) | def __init__(self, in_ch, out_ch, k=(3, 5, 7), stride=1, dilation=1, b...
    method forward (line 99) | def forward(self, x):
  class SwishImplementation (line 104) | class SwishImplementation(torch.autograd.Function):
    method forward (line 106) | def forward(ctx, x):
    method backward (line 111) | def backward(ctx, grad_output):
  class MishImplementation (line 117) | class MishImplementation(torch.autograd.Function):
    method forward (line 119) | def forward(ctx, x):
    method backward (line 124) | def backward(ctx, grad_output):
  class MemoryEfficientSwish (line 131) | class MemoryEfficientSwish(nn.Module):
    method forward (line 132) | def forward(self, x):
  class MemoryEfficientMish (line 136) | class MemoryEfficientMish(nn.Module):
    method forward (line 137) | def forward(self, x):
  class Swish (line 141) | class Swish(nn.Module):
    method forward (line 142) | def forward(self, x):
  class Mish (line 146) | class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
    method forward (line 147) | def forward(self, x):
  class ReLU6 (line 151) | class ReLU6(nn.Module):
    method __init__ (line 152) | def __init__(self):
    method forward (line 155) | def forward(self, x):
  class HardSwish (line 159) | class HardSwish(nn.Module):
    method __init__ (line 160) | def __init__(self):
    method forward (line 163) | def forward(self, x):
  class HardSigmoid (line 167) | class HardSigmoid(nn.Module):
    method __init__ (line 168) | def __init__(self):
    method forward (line 171) | def forward(self, x):
  class SE (line 176) | class SE(nn.Module):
    method __init__ (line 177) | def __init__(self, channel, reduction=4):
    method forward (line 188) | def forward(self, x):

FILE: utils/output_upsample.py
  function Val_upsample (line 9) | def Val_upsample(cfg,TN):

FILE: utils/parse_config.py
  function parse_model_cfg (line 6) | def parse_model_cfg(path):
  function parse_data_cfg (line 54) | def parse_data_cfg(path):

FILE: utils/prune_utils.py
  function parse_module_defs2 (line 8) | def parse_module_defs2(module_defs):
  function parse_module_defs (line 53) | def parse_module_defs(module_defs):
  function parse_module_defs4 (line 91) | def parse_module_defs4(module_defs):
  function gather_bn_weights (line 107) | def gather_bn_weights(module_list, prune_idx):
  function write_cfg (line 119) | def write_cfg(cfg_file, module_defs):
  class BNOptimizer (line 130) | class BNOptimizer():
    method updateBN (line 133) | def updateBN(sr_flag, module_list, s, prune_idx):
  function obtain_quantiles (line 141) | def obtain_quantiles(bn_weights, num_quantile=5):
  function get_input_mask (line 155) | def get_input_mask(module_defs, idx, CBLidx2mask, is_gray_scale=False):
  function init_weights_from_loose_model (line 212) | def init_weights_from_loose_model(compact_model, loose_model, CBL_idx, O...
  function prune_model_keep_size (line 261) | def prune_model_keep_size(model, prune_idx, CBL_idx, CBLidx2mask):
  function obtain_bn_mask (line 338) | def obtain_bn_mask(bn_module, thre):
  function get_nearest_multiple (line 345) | def get_nearest_multiple(num, base):
  function merge_mask (line 355) | def merge_mask(model, CBLidx2mask, CBLidx2filters, base=1):
  function update_activation (line 422) | def update_activation(i, pruned_model, activation, CBL_idx):
  function prune_model_keep_size_forEagleEye (line 435) | def prune_model_keep_size_forEagleEye(model, prune_idx, CBLidx2mask):

FILE: utils/quantized/quantized_TPSQ.py
  class Round (line 15) | class Round(Function):
    method forward (line 18) | def forward(self, input):
    method backward (line 24) | def backward(self, grad_output):
  class Search_Pow2 (line 29) | class Search_Pow2(Function):
    method forward (line 32) | def forward(self, input):
    method backward (line 48) | def backward(self, grad_output):
  class Quantizer (line 66) | class Quantizer(nn.Module):
    method __init__ (line 67) | def __init__(self, bits, out_channels, warmup=False):
    method clamp (line 79) | def clamp(self, input):
    method quantize (line 89) | def quantize(self, input):
    method round (line 95) | def round(self, input):
    method dequantize (line 100) | def dequantize(self, input):
    method forward (line 106) | def forward(self, input):
    method get_quantize_value (line 120) | def get_quantize_value(self, input):
  class RangeTracker (line 133) | class RangeTracker(nn.Module):
    method __init__ (line 134) | def __init__(self):
    method update_range (line 137) | def update_range(self, min_val, max_val):
    method forward (line 141) | def forward(self, input):
  class GlobalRangeTracker (line 147) | class GlobalRangeTracker(RangeTracker):  # W,min_max_shape=(N, 1, 1, 1),...
    method __init__ (line 148) | def __init__(self):
    method update_range (line 154) | def update_range(self, min_val, max_val):
  class Bias_Quantizer (line 166) | class Bias_Quantizer(nn.Module):
    method __init__ (line 167) | def __init__(self, bits, range_tracker):
    method update_params (line 173) | def update_params(self):
    method quantize (line 189) | def quantize(self, input):
    method round (line 193) | def round(self, input):
    method clamp (line 198) | def clamp(self, input):
    method dequantize (line 205) | def dequantize(self, input):
    method forward (line 209) | def forward(self, input):
    method get_quantize_value (line 225) | def get_quantize_value(self, input):
    method get_scale (line 238) | def get_scale(self):
  class Weight_Quantizer (line 245) | class Weight_Quantizer(Quantizer):
    method __init__ (line 246) | def __init__(self, bits, out_channels, warmup):
    method forward (line 255) | def forward(self, input):
  class Activattion_Quantizer (line 290) | class Activattion_Quantizer(Quantizer):
    method __init__ (line 291) | def __init__(self, bits, out_channels, warmup):
    method forward (line 300) | def forward(self, input):
  function reshape_to_activation (line 336) | def reshape_to_activation(input):
  function reshape_to_weight (line 340) | def reshape_to_weight(input):
  function reshape_to_bias (line 344) | def reshape_to_bias(input):
  class TPSQ_BNFold_QuantizedConv2d_For_FPGA (line 349) | class TPSQ_BNFold_QuantizedConv2d_For_FPGA(nn.Conv2d):
    method __init__ (line 350) | def __init__(
    method forward (line 404) | def forward(self, input):
    method BN_fuse (line 574) | def BN_fuse(self):

FILE: utils/quantized/quantized_dorefa.py
  class Round (line 14) | class Round(Function):
    method forward (line 17) | def forward(self, input):
    method backward (line 23) | def backward(self, grad_output):
  class activation_quantize (line 29) | class activation_quantize(nn.Module):
    method __init__ (line 30) | def __init__(self, a_bits):
    method round (line 34) | def round(self, input):
    method get_quantize_value (line 38) | def get_quantize_value(self, input):
    method get_scale (line 47) | def get_scale(self):
    method forward (line 54) | def forward(self, input):
  class weight_quantize (line 70) | class weight_quantize(nn.Module):
    method __init__ (line 71) | def __init__(self, w_bits):
    method round (line 75) | def round(self, input):
    method get_quantize_value (line 79) | def get_quantize_value(self, input):
    method get_scale (line 90) | def get_scale(self):
    method forward (line 97) | def forward(self, input):
    method get_weights (line 113) | def get_weights(self, input):
  class DorefaConv2d (line 129) | class DorefaConv2d(nn.Conv2d):
    method __init__ (line 130) | def __init__(
    method forward (line 157) | def forward(self, input):
  function reshape_to_activation (line 175) | def reshape_to_activation(input):
  function reshape_to_weight (line 179) | def reshape_to_weight(input):
  function reshape_to_bias (line 183) | def reshape_to_bias(input):
  class BNFold_DorefaConv2d (line 187) | class BNFold_DorefaConv2d(DorefaConv2d):
    method __init__ (line 189) | def __init__(
    method forward (line 242) | def forward(self, input):
    method BN_fuse (line 427) | def BN_fuse(self):
  class DorefaLinear (line 445) | class DorefaLinear(nn.Linear):
    method __init__ (line 446) | def __init__(self, in_features, out_features, bias=True, a_bits=2, w_b...
    method forward (line 451) | def forward(self, input):

FILE: utils/quantized/quantized_google.py
  class RangeTracker (line 16) | class RangeTracker(nn.Module):
    method __init__ (line 17) | def __init__(self, q_level):
    method update_range (line 21) | def update_range(self, min_val, max_val):
    method forward (line 25) | def forward(self, input):
  class GlobalRangeTracker (line 35) | class GlobalRangeTracker(RangeTracker):  # W,min_max_shape=(N, 1, 1, 1),...
    method __init__ (line 36) | def __init__(self, q_level, out_channels):
    method update_range (line 46) | def update_range(self, min_val, max_val):
  class AveragedRangeTracker (line 58) | class AveragedRangeTracker(RangeTracker):  # A,min_max_shape=(1, 1, 1, 1...
    method __init__ (line 59) | def __init__(self, q_level, out_channels, momentum=0.1):
    method update_range (line 70) | def update_range(self, min_val, max_val):
  class Round (line 81) | class Round(Function):
    method forward (line 84) | def forward(self, input):
    method backward (line 90) | def backward(self, grad_output):
  class Quantizer (line 95) | class Quantizer(nn.Module):
    method __init__ (line 96) | def __init__(self, bits, range_tracker, out_channels, Scale_freeze_ste...
    method update_params (line 110) | def update_params(self):
    method quantize (line 114) | def quantize(self, input):
    method round (line 118) | def round(self, input):
    method clamp (line 123) | def clamp(self, input):
    method dequantize (line 134) | def dequantize(self, input):
    method forward (line 138) | def forward(self, input):
    method get_quantize_value (line 155) | def get_quantize_value(self, input):
    method get_scale (line 168) | def get_scale(self):
  class SymmetricQuantizer (line 176) | class SymmetricQuantizer(Quantizer):
    method update_params (line 178) | def update_params(self):
  class AsymmetricQuantizer (line 200) | class AsymmetricQuantizer(Quantizer):
    method update_params (line 202) | def update_params(self):
  function reshape_to_activation (line 222) | def reshape_to_activation(input):
  function reshape_to_weight (line 226) | def reshape_to_weight(input):
  function reshape_to_bias (line 230) | def reshape_to_bias(input):
  class BNFold_QuantizedConv2d_For_FPGA (line 235) | class BNFold_QuantizedConv2d_For_FPGA(nn.Conv2d):
    method __init__ (line 236) | def __init__(
    method forward (line 320) | def forward(self, input):
    method BN_fuse (line 821) | def BN_fuse(self):
  class QuantizedShortcut_max (line 839) | class QuantizedShortcut_max(nn.Module):  # weighted sum of 2 or more lay...
    method __init__ (line 840) | def __init__(self, layers, weight=False, bits=8,
    method quantize (line 863) | def quantize(self, input):
    method round (line 867) | def round(self, input):
    method clamp (line 872) | def clamp(self, input):
    method dequantize (line 879) | def dequantize(self, input):
    method forward (line 883) | def forward(self, x, outputs):
  class QuantizedShortcut_min (line 1066) | class QuantizedShortcut_min(nn.Module):  # weighted sum of 2 or more lay...
    method __init__ (line 1067) | def __init__(self, layers, weight=False, bits=8,
    method quantize (line 1091) | def quantize(self, input, featrure_in=False):
    method round (line 1098) | def round(self, input):
    method clamp (line 1103) | def clamp(self, input):
    method dequantize (line 1110) | def dequantize(self, input, featrure_in=False):
    method forward (line 1117) | def forward(self, x, outputs):
  class QuantizedFeatureConcat (line 1305) | class QuantizedFeatureConcat(nn.Module):
    method __init__ (line 1306) | def __init__(self, layers, groups, bits=8,
    method quantize (line 1324) | def quantize(self, input):
    method round (line 1328) | def round(self, input):
    method clamp (line 1333) | def clamp(self, input):
    method dequantize (line 1340) | def dequantize(self, input):
    method forward (line 1344) | def forward(self, x, outputs):

FILE: utils/quantized/quantized_lowbit.py
  class Ternarize (line 10) | class Ternarize(Function):
    method forward (line 17) | def forward(self, input):
    method backward (line 33) | def backward(self, grad_output):
  class Binarize (line 42) | class Binarize(Function):
    method forward (line 44) | def forward(self, input):
    method backward (line 52) | def backward(self, grad_output):
  class BinaryLeakyReLU (line 66) | class BinaryLeakyReLU(nn.LeakyReLU):
    method __init__ (line 67) | def __init__(self):
    method forward (line 70) | def forward(self, input):
  class BinaryLinear (line 76) | class BinaryLinear(nn.Linear):
    method forward (line 78) | def forward(self, input):
    method reset_parameters (line 85) | def reset_parameters(self):
  class BWNConv2d (line 97) | class BWNConv2d(nn.Conv2d):
    method forward (line 99) | def forward(self, input):
    method reset_parameters (line 106) | def reset_parameters(self):
  class BinaryConv2d (line 122) | class BinaryConv2d(nn.Conv2d):
    method forward (line 124) | def forward(self, input):
    method reset_parameters (line 130) | def reset_parameters(self):

FILE: utils/quantized/quantized_ptq.py
  class RangeTracker (line 15) | class RangeTracker(nn.Module):
    method __init__ (line 16) | def __init__(self, q_level):
    method update_range (line 20) | def update_range(self, min_val, max_val):
    method forward (line 24) | def forward(self, input):
  class GlobalRangeTracker (line 34) | class GlobalRangeTracker(RangeTracker):  # W,min_max_shape=(N, 1, 1, 1),...
    method __init__ (line 35) | def __init__(self, q_level, out_channels):
    method update_range (line 45) | def update_range(self, min_val, max_val):
  class AveragedRangeTracker (line 57) | class AveragedRangeTracker(RangeTracker):  # A,min_max_shape=(1, 1, 1, 1...
    method __init__ (line 58) | def __init__(self, q_level, out_channels, momentum=0.1):
    method update_range (line 69) | def update_range(self, min_val, max_val):
  class Round (line 80) | class Round(Function):
    method forward (line 83) | def forward(self, input):
  class Quantizer (line 89) | class Quantizer(nn.Module):
    method __init__ (line 90) | def __init__(self, bits, range_tracker, out_channels, FPGA, sign=True):
    method update_params (line 103) | def update_params(self):
    method quantize (line 107) | def quantize(self, input):
    method round (line 111) | def round(self, input):
    method clamp (line 116) | def clamp(self, input):
    method dequantize (line 127) | def dequantize(self, input):
    method forward (line 131) | def forward(self, input):
    method get_quantize_value (line 147) | def get_quantize_value(self, input):
    method get_scale (line 160) | def get_scale(self):
  class SymmetricQuantizer (line 168) | class SymmetricQuantizer(Quantizer):
    method update_params (line 170) | def update_params(self):
  class AsymmetricQuantizer (line 194) | class AsymmetricQuantizer(Quantizer):
    method update_params (line 196) | def update_params(self):
  class PTQuantizedConv2d (line 219) | class PTQuantizedConv2d(nn.Conv2d):
    method __init__ (line 220) | def __init__(
    method forward (line 260) | def forward(self, input):
  function reshape_to_activation (line 278) | def reshape_to_activation(input):
  function reshape_to_weight (line 282) | def reshape_to_weight(input):
  function reshape_to_bias (line 286) | def reshape_to_bias(input):
  class BNFold_PTQuantizedConv2d_For_FPGA (line 293) | class BNFold_PTQuantizedConv2d_For_FPGA(PTQuantizedConv2d):
    method __init__ (line 294) | def __init__(
    method forward (line 369) | def forward(self, input):
    method BN_fuse (line 813) | def BN_fuse(self):

FILE: utils/quantized/quantized_ptq_cos.py
  class Round (line 14) | class Round(Function):
    method forward (line 17) | def forward(self, input):
  class Quantizer (line 23) | class Quantizer(nn.Module):
    method __init__ (line 24) | def __init__(self, bits, out_channels):
    method update_params (line 35) | def update_params(self, step):
    method quantize (line 44) | def quantize(self, input):
    method round (line 48) | def round(self, input):
    method clamp (line 53) | def clamp(self, input):
    method dequantize (line 60) | def dequantize(self, input):
    method forward (line 64) | def forward(self, input):
    method get_quantize_value (line 95) | def get_quantize_value(self, input):
    method get_scale (line 109) | def get_scale(self):
  function reshape_to_activation (line 116) | def reshape_to_activation(input):
  function reshape_to_weight (line 120) | def reshape_to_weight(input):
  function reshape_to_bias (line 124) | def reshape_to_bias(input):
  class BNFold_COSPTQuantizedConv2d_For_FPGA (line 131) | class BNFold_COSPTQuantizedConv2d_For_FPGA(nn.Conv2d):
    method __init__ (line 132) | def __init__(
    method forward (line 193) | def forward(self, input):
    method BN_fuse (line 723) | def BN_fuse(self):
  class COSPTQuantizedShortcut_min (line 741) | class COSPTQuantizedShortcut_min(nn.Module):  # weighted sum of 2 or mor...
    method __init__ (line 742) | def __init__(self, layers, weight=False, bits=8,
    method quantize (line 773) | def quantize(self, input, type):
    method round (line 782) | def round(self, input):
    method clamp (line 787) | def clamp(self, input):
    method dequantize (line 794) | def dequantize(self, input, type):
    method update_params (line 804) | def update_params(self, step, type):
    method forward (line 821) | def forward(self, x, outputs):
  class COSPTQuantizedShortcut_max (line 1058) | class COSPTQuantizedShortcut_max(nn.Module):  # weighted sum of 2 or mor...
    method __init__ (line 1059) | def __init__(self, layers, weight=False, bits=8,
    method quantize (line 1088) | def quantize(self, input, type):
    method round (line 1097) | def round(self, input):
    method clamp (line 1102) | def clamp(self, input):
    method dequantize (line 1109) | def dequantize(self, input, type):
    method update_params (line 1119) | def update_params(self, step, type):
    method forward (line 1136) | def forward(self, x, outputs):
  class COSPTQuantizedFeatureConcat (line 1364) | class COSPTQuantizedFeatureConcat(nn.Module):
    method __init__ (line 1365) | def __init__(self, layers, groups, bits=8,
    method quantize (line 1383) | def quantize(self, input):
    method round (line 1387) | def round(self, input):
    method clamp (line 1392) | def clamp(self, input):
    method dequantize (line 1399) | def dequantize(self, input):
    method forward (line 1403) | def forward(self, x, outputs):

FILE: utils/torch_utils.py
  function init_seeds (line 7) | def init_seeds(seed=0):
  function select_device (line 16) | def select_device(device='', batch_size=None):
  function time_synchronized (line 43) | def time_synchronized():
  function initialize_weights (line 48) | def initialize_weights(model):
  function find_modules (line 60) | def find_modules(model, mclass=nn.Conv2d):
  function fuse_conv_and_bn (line 65) | def fuse_conv_and_bn(conv, bn):
  function model_info (line 92) | def model_info(model, verbose=False):
  function load_classifier (line 113) | def load_classifier(name='resnet101', n=2):
  function scale_img (line 130) | def scale_img(img, ratio=1.0, same_shape=True):  # img(16,3,256,416), r=...
  class ModelEMA (line 141) | class ModelEMA:
    method __init__ (line 159) | def __init__(self, model, decay=0.9999, device=''):
    method update (line 171) | def update(self, model):
    method update_attr (line 185) | def update_attr(self, model):

FILE: utils/utils.py
  function init_seeds (line 31) | def init_seeds(seed=0):
  function load_classes (line 37) | def load_classes(path):
  function labels_to_class_weights (line 44) | def labels_to_class_weights(labels, nc=80):
  function labels_to_image_weights (line 63) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
  function coco_class_weights (line 72) | def coco_class_weights():  # frequency of each class in coco train2014
  function coco80_to_coco91_class (line 86) | def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index...
  function xyxy2xywh (line 98) | def xyxy2xywh(x):
  function xywh2xyxy (line 108) | def xywh2xyxy(x):
  function scale_coords (line 138) | def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
  function clip_coords (line 154) | def clip_coords(boxes, img_shape):
  function ap_per_class (line 162) | def ap_per_class(tp, conf, pred_cls, target_cls):
  function compute_ap (line 225) | def compute_ap(recall, precision):
  function bbox_iou (line 254) | def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=Fal...
  function box_iou (line 300) | def box_iou(box1, box2):
  function wh_iou (line 325) | def wh_iou(wh1, wh2):
  class FocalLoss (line 333) | class FocalLoss(nn.Module):
    method __init__ (line 335) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 343) | def forward(self, pred, true):
  function smooth_BCE (line 363) | def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues...
  function compute_loss (line 368) | def compute_loss(p, targets, model):  # predictions, targets, model
  function compute_lost_KD (line 435) | def compute_lost_KD(output_s, output_t, num_classes, batch_size):
  function compute_lost_KD2 (line 446) | def compute_lost_KD2(model, targets, output_s, output_t):
  function compute_lost_KD3 (line 490) | def compute_lost_KD3(model, targets, output_s, output_t):
  function compute_lost_KD4 (line 524) | def compute_lost_KD4(model, targets, output_s, output_t, feature_s, feat...
  function indices_merge (line 567) | def indices_merge(indices):
  function fine_grained_imitation_feature_mask (line 578) | def fine_grained_imitation_feature_mask(feature_s, feature_t, indices, i...
  function compute_lost_KD5 (line 607) | def compute_lost_KD5(model, targets, output_s, output_t, feature_s, feat...
  function fine_grained_imitation_mask (line 657) | def fine_grained_imitation_mask(feature_s, feature_t, indices):
  function compute_lost_KD6 (line 671) | def compute_lost_KD6(model, targets, output_s, output_t, batch_size):
  function Failure_Case_Loss_FM (line 692) | def Failure_Case_Loss_FM(masks, imgs, targets):
  function build_targets (line 725) | def build_targets(p, targets, model):
  function non_max_suppression (line 782) | def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi...
  function get_yolo_layers (line 863) | def get_yolo_layers(model):
  function print_model_biases (line 868) | def print_model_biases(model):
  function strip_optimizer (line 887) | def strip_optimizer(f='weights/last.pt'):  # from utils.utils import *; ...
  function create_backbone (line 894) | def create_backbone(f='weights/last.pt'):  # from utils.utils import *; ...
  function coco_class_count (line 908) | def coco_class_count(path='../coco/labels/train2014/'):
  function coco_only_people (line 919) | def coco_only_people(path='../coco/labels/train2017/'):  # from utils.ut...
  function select_best_evolve (line 928) | def select_best_evolve(path='evolve*.txt'):  # from utils.utils import *...
  function crop_images_random (line 935) | def crop_images_random(path='../images/', scale=0.50):  # from utils.uti...
  function coco_single_class_labels (line 958) | def coco_single_class_labels(path='../coco/labels/train2014/', label_cla...
  function kmean_anchors (line 980) | def kmean_anchors(path='./data/coco64.txt', n=9, img_size=(320, 1024), t...
  function print_mutation (line 1059) | def print_mutation(hyp, results, bucket=''):
  function apply_classifier (line 1078) | def apply_classifier(x, model, img, im0):
  function fitness (line 1113) | def fitness(x):
  function output_to_target (line 1119) | def output_to_target(output, width, height):
  function plot_one_box (line 1149) | def plot_one_box(x, img, color=None, label=None, line_thickness=None):
  function plot_wh_methods (line 1163) | def plot_wh_methods():  # from utils.utils import *; plot_wh_methods()
  function plot_images (line 1183) | def plot_images(images, targets, paths=None, fname='images.jpg', names=N...
  function plot_test_txt (line 1265) | def plot_test_txt():  # from utils.utils import *; plot_test()
  function plot_targets_txt (line 1284) | def plot_targets_txt():  # from utils.utils import *; plot_targets_txt()
  function plot_evolution_results (line 1298) | def plot_evolution_results(hyp):  # from utils.utils import *; plot_evol...
  function plot_results_overlay (line 1318) | def plot_results_overlay(start=0, stop=0):  # from utils.utils import *;...
  function plot_results (line 1341) | def plot_results(start=0, stop=0, bucket='', id=()):  # from utils.utils...