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Repository: SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone
Branch: master
Commit: 0202ec6be7d6
Files: 103
Total size: 972.9 KB
Directory structure:
gitextract_flko7qgo/
├── EagleEye_normal_prune.py
├── EagleEye_regular_prune.py
├── EagleEye_slim_prune.py
├── LICENSE
├── PTQ.py
├── README.md
├── cfg/
│ ├── yolov2/
│ │ ├── yolov2-hand.cfg
│ │ ├── yolov2-tiny-hand.cfg
│ │ ├── yolov2-tiny.cfg
│ │ └── yolov2.cfg
│ ├── yolov3/
│ │ ├── yolov3-UAV.cfg
│ │ ├── yolov3-asff.cfg
│ │ ├── yolov3-bdd100k.cfg
│ │ ├── yolov3-hand.cfg
│ │ ├── yolov3-onDIOR.cfg
│ │ ├── yolov3-screw.cfg
│ │ ├── yolov3-ship.cfg
│ │ ├── yolov3-spp-matrix.cfg
│ │ ├── yolov3-spp-pan-scale.cfg
│ │ ├── yolov3-spp.cfg
│ │ ├── yolov3-spp3.cfg
│ │ ├── yolov3-visdrone.cfg
│ │ └── yolov3.cfg
│ ├── yolov3-ghostnet/
│ │ └── yolov3-ghost-coco.cfg
│ ├── yolov3-mobilenet/
│ │ ├── yolov3-mobilenet-UAV.cfg
│ │ ├── yolov3-mobilenet-coco.cfg
│ │ ├── yolov3-mobilenet-hand.cfg
│ │ ├── yolov3-mobilenet-screw.cfg
│ │ └── yolov3-mobilenet-visdrone.cfg
│ ├── yolov3-singlechannel/
│ │ └── yolov3-singlechannel.cfg
│ ├── yolov3tiny/
│ │ ├── yolov3-tiny-UAV.cfg
│ │ ├── yolov3-tiny-hand.cfg
│ │ ├── yolov3-tiny-ship-one.cfg
│ │ ├── yolov3-tiny-ship.cfg
│ │ ├── yolov3-tiny.cfg
│ │ ├── yolov3-tiny3.cfg
│ │ ├── yolov3-tiny_bdd100k.cfg
│ │ ├── yolov3-tiny_onDIOR.cfg
│ │ └── yolov3-tiny_visdrone.cfg
│ ├── yolov3tiny-efficientnetB0/
│ │ └── yolov3tiny-efficientnetB0.cfg
│ ├── yolov3tiny-mobilenet-small/
│ │ ├── yolov3tiny-mobilenet-small-UAV.cfg
│ │ ├── yolov3tiny-mobilenet-small-coco.cfg
│ │ ├── yolov3tiny-mobilenet-small-screw.cfg
│ │ └── yolov3tiny-mobilenet-small-visdrone.cfg
│ ├── yolov4/
│ │ ├── yolov4-hand.cfg
│ │ ├── yolov4-relu.cfg
│ │ ├── yolov4-visdrone.cfg
│ │ └── yolov4.cfg
│ └── yolov4tiny/
│ └── yolov4-tiny.cfg
├── convert.py
├── convert_FPGA.py
├── convert_FPGA_2.py
├── data/
│ ├── UAV_Samples_label.data
│ ├── UAV_Samples_label.names
│ ├── bdd100k.data
│ ├── bdd100k.names
│ ├── coco.names
│ ├── coco2014.data
│ ├── coco2017.data
│ ├── dior.data
│ ├── dior.names
│ ├── get_coco2014.sh
│ ├── get_coco2017.sh
│ ├── oxfordhand.data
│ ├── oxfordhand.names
│ ├── screw.data
│ ├── screw.names
│ ├── trainset.data
│ ├── trainset.names
│ ├── visdrone.data
│ └── visdrone.names
├── detect.py
├── info.py
├── layer_channel_prune.py
├── layer_channel_regular_prune.py
├── layer_prune.py
├── models.py
├── normal_prune.py
├── regular_prune.py
├── requirements.txt
├── shortcut_prune.py
├── slim_prune.py
├── test.py
├── train.py
├── utils/
│ ├── __init__.py
│ ├── adabound.py
│ ├── datasets.py
│ ├── gcp.sh
│ ├── google_utils.py
│ ├── layers.py
│ ├── output_upsample.py
│ ├── parse_config.py
│ ├── prune_utils.py
│ ├── quantized/
│ │ ├── __init__.py
│ │ ├── quantized_TPSQ.py
│ │ ├── quantized_dorefa.py
│ │ ├── quantized_google.py
│ │ ├── quantized_lowbit.py
│ │ ├── quantized_ptq.py
│ │ └── quantized_ptq_cos.py
│ ├── torch_utils.py
│ └── utils.py
└── weights/
└── pretrain_weights/
└── download_yolov3_weights.sh
================================================
FILE CONTENTS
================================================
================================================
FILE: EagleEye_normal_prune.py
================================================
from sys import float_repr_style
from models import *
from utils.utils import *
from utils.prune_utils import *
from utils.datasets import *
import os
import test
import argparse
from thop import profile
# from thop import profile
# from distiller.model_summaries import model_performance_summary
def obtain_avg_forward_time(input, model, repeat=200):
model.eval()
start = time.time()
with torch.no_grad():
for i in range(repeat):
output = model(input)
avg_infer_time = (time.time() - start) / repeat
return avg_infer_time, output
def obtain_filters_mask(model, CBL_idx, prune_idx, idx_mask):
pruned = 0
total = 0
num_filters = []
filters_mask = []
# CBL_idx存储的是所有带BN的卷积层（YOLO层的前一层卷积层是不带BN的）
for idx in CBL_idx:
bn_module = model.module_list[idx][1]
if idx in prune_idx:
mask = idx_mask[idx]
# mask = obtain_bn_mask(bn_module, thre).cpu().numpy()
remain = int(mask.sum())
pruned = pruned + mask.shape[0] - remain
if remain == 0:
print("Channels would be all pruned!")
raise Exception
# print(f'layer index: {idx:>3d} \t total channel: {mask.shape[0]:>4d} \t '
# f'remaining channel: {remain:>4d}')
else:
mask = torch.ones(bn_module.weight.data.shape)
remain = mask.shape[0]
total += mask.shape[0]
num_filters.append(remain)
filters_mask.append(mask.clone())
# 因此，这里求出的prune_ratio,需要裁剪的α参数/cbl_idx中所有的α参数
# prune_ratio = pruned / total
# print(f'Prune channels: {pruned}\tPrune ratio: {prune_ratio:.3f}')
return num_filters, filters_mask
def obtain_l1_mask(conv_module, random_rate):
w_copy = conv_module.weight.data.abs().clone()
w_copy = torch.sum(w_copy, dim=(1, 2, 3))
length = w_copy.cpu().numpy().shape[0]
num_retain = int(length * (1 - random_rate))
if num_retain == 0:
num_retain = 1
_, y = torch.topk(w_copy, num_retain)
mask = torch.zeros(length, dtype=torch.float32).to(w_copy.device)
mask[y] = 1
return mask
#macs = flops / 2
def performance_summary(model, opt=None, prefix=""):
macs, _ = profile(model, inputs=(torch.zeros(1, 3, 480, 640).to(device),), verbose=False)
return macs
def rand_prune_and_eval(model, min_rate, max_rate):
while True:
model_copy = deepcopy(model)
remain_num = 0
idx_new = dict()
for idx in prune_idx:
# bn_module = model_copy.module_list[idx][1]
conv_module = model_copy.module_list[idx][0]
random_rate = (max_rate - min_rate) * (np.random.rand(1)) + min_rate
mask = obtain_l1_mask(conv_module, random_rate)
idx_new[idx] = mask
remain_num += int(mask.sum())
conv_module.weight.data = conv_module.weight.data.permute(1, 2, 3, 0).mul(mask).float().permute(3, 0, 1, 2)
# bn_module.weight.data.mul_(mask)
# ---------------
num_filters, filters_mask = obtain_filters_mask(model_copy, CBL_idx, prune_idx, idx_new)
CBLidx2mask = {idx: mask for idx, mask in zip(CBL_idx, filters_mask)}
compact_module_defs = deepcopy(model.module_defs)
for idx, num in zip(CBL_idx, num_filters):
assert compact_module_defs[idx]['type'] == 'convolutional'
compact_module_defs[idx]['filters'] = str(num)
compact_model = Darknet([model.hyperparams.copy()] + compact_module_defs).to(device)
current_parameters = obtain_num_parameters(compact_model)
# print(current_parameters/origin_nparameters, end='；')
current_macs = performance_summary(compact_model)
# if current_parameters / origin_nparameters > remain_ratio + delta or current_parameters / origin_nparameters < remain_ratio - delta:
# macs = flops/2
if current_macs / origin_macs > remain_ratio + delta or current_macs / origin_macs < remain_ratio - delta:
# print('missing')
model_copy.cpu()
compact_model.cpu()
torch.cuda.empty_cache()
continue
print("yes---")
for i in CBLidx2mask:
CBLidx2mask[i] = CBLidx2mask[i].clone().cpu().numpy()
pruned_model = prune_model_keep_size_forEagleEye(model, prune_idx, CBLidx2mask)
init_weights_from_loose_model(compact_model, pruned_model, CBL_idx, Conv_idx, CBLidx2mask)
compact_model.train()
with torch.no_grad():
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader)):
imgs = imgs.cuda().float() / 255.0
compact_model(imgs)
if batch_i > steps:
break
del model_copy
torch.cuda.empty_cache()
break
return compact_module_defs, current_parameters, compact_model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='cfg/yolov3/yolov3.cfg', help='cfg file path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data file path')
parser.add_argument('--weights', type=str, default='weights/pretrain_weights/yolov3.weights',
help='sparse model weights')
parser.add_argument('--percent', type=float, default=0.5, help='global channel prune percent')
parser.add_argument('--delta', type=float, default=0.05, help='delta')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--batch-size', type=int, default=16, help='batch-size')
parser.add_argument('--number', type=int, default=200, help='number of subnetwork')
opt = parser.parse_args()
print(opt)
t0 = time.time()
remain_ratio = 1 - opt.percent
number = opt.number
img_size = opt.img_size
batch_size = opt.batch_size
delta = opt.delta
hyp = {'giou': 3.54, # giou loss gain
'cls': 37.4, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.20, # iou training threshold
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
'lrf': 0.0005, # final learning rate (with cos scheduler)
'momentum': 0.937, # SGD momentum
'weight_decay': 0.0005, # optimizer weight decay
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
'degrees': 1.98 * 0, # image rotation (+/- deg)
'translate': 0.05 * 0, # image translation (+/- fraction)
'scale': 0.05 * 0, # image scale (+/- gain)
'shear': 0.641 * 0} # image shear (+/- deg)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Darknet(opt.cfg).to(device)
if opt.weights:
if opt.weights.endswith(".pt"):
model.load_state_dict(torch.load(opt.weights, map_location=device)['model'])
else:
_ = load_darknet_weights(model, opt.weights)
data_config = parse_data_cfg(opt.data)
valid_path = data_config["valid"]
train_path = data_config["train"]
class_names = load_classes(data_config["names"])
steps = math.ceil((len(open(train_path).readlines()) / batch_size) * 0.1)
obtain_num_parameters = lambda model: sum([param.nelement() for param in model.parameters()])
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=False, # rectangular training
cache_images=False)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 16]),
shuffle=True, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
test_dataset = LoadImagesAndLabels(valid_path, img_size, batch_size,
hyp=hyp,
rect=True,
cache_images=False)
testloader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 8]),
shuffle=False,
pin_memory=True,
collate_fn=test_dataset.collate_fn)
with torch.no_grad():
origin_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=model,
dataloader=testloader,
rank=-1,
plot=False)
origin_nparameters = obtain_num_parameters(model)
origin_macs = performance_summary(model)
CBL_idx, Conv_idx, prune_idx = parse_module_defs(model.module_defs)
print("-------------------------------------------------------")
max_mAP = 0
for i in range(number):
compact_module_defs, current_parameters, compact_model = rand_prune_and_eval(model, 0, 1)
with torch.no_grad():
# 防止随机生成的较差的模型撑爆显存，增大nmsconf阈值
mAP = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
conf_thres=0.1,
model=compact_model,
dataloader=testloader,
rank=-1,
plot=False)[0][2]
print('candidate: ' + str(i), end=" ")
print('remain_ratio: ' + str(current_parameters / origin_nparameters))
print(f'mAP of the pruned model is {mAP:.4f}')
if mAP > max_mAP:
max_mAP = mAP
compact_model_winnner = deepcopy(compact_model)
cfg_name = 'cfg_backup/' + str(i) + '.cfg'
if not os.path.isdir('cfg_backup/'):
os.makedirs('cfg_backup/')
pruned_cfg_file = write_cfg(cfg_name, [model.hyperparams.copy()] + compact_module_defs)
del compact_model
torch.cuda.empty_cache()
# 获得原始模型的module_defs，并修改该defs中的卷积核数量
compact_module_defs = deepcopy(compact_model_winnner.module_defs)
compact_nparameters = obtain_num_parameters(compact_model_winnner)
compact_macs = performance_summary(compact_model_winnner)
compact_flops = compact_macs*2 / 1024**3
origin_flops = origin_macs*2 / 1024**3
random_input = torch.rand((16, 3, 416, 416)).to(device)
pruned_forward_time, pruned_output = obtain_avg_forward_time(random_input, model)
compact_forward_time, compact_output = obtain_avg_forward_time(random_input, compact_model_winnner)
# 在测试集上测试剪枝后的模型, 并统计模型的参数数量
with torch.no_grad():
compact_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=compact_model_winnner,
dataloader=testloader,
rank=-1,
plot=False)
# 比较剪枝前后参数数量的变化、指标性能的变化
metric_table = [
["Metric", "Before", "After"],
["mAP", f'{origin_model_metric[1].mean():.6f}', f'{compact_model_metric[1].mean():.6f}'],
["Parameters", f"{origin_nparameters}", f"{compact_nparameters}"],
["GFLOPs",f"{origin_flops}",f"{compact_flops}"],
["Inference", f'{pruned_forward_time:.4f}', f'{compact_forward_time:.4f}']
]
print(AsciiTable(metric_table).table)
# 生成剪枝后的cfg文件并保存模型
pruned_cfg_name = 'cfg/rand-normal_' + str(remain_ratio) + '_' + str(number) + '/' + 'rand-normal_-' + str(
remain_ratio) + '_' + str(number) + '.cfg'
# 创建存储目录
dir_name = 'cfg/rand-normal_' + str(remain_ratio) + '_' + str(number) + '/'
if not os.path.isdir(dir_name):
os.makedirs(dir_name)
# 由于原始的compact_module_defs将anchor从字符串变为了数组，因此这里将anchors重新变为字符串
file = open(opt.cfg, 'r')
lines = file.read().split('\n')
for line in lines:
if line.split(' = ')[0] == 'anchors':
anchor = line.split(' = ')[1]
break
if line.split('=')[0] == 'anchors':
anchor = line.split('=')[1]
break
file.close()
for item in compact_module_defs:
if item['type'] == 'shortcut':
item['from'] = str(item['from'][0])
elif item['type'] == 'route':
item['layers'] = ",".join('%s' % i for i in item['layers'])
elif item['type'] == 'yolo':
item['mask'] = ",".join('%s' % i for i in item['mask'])
item['anchors'] = anchor
pruned_cfg_file = write_cfg(pruned_cfg_name, [model.hyperparams.copy()] + compact_module_defs)
print(f'Config file has been saved: {pruned_cfg_file}')
weights_dir_name = dir_name.replace('cfg', 'weights')
if not os.path.isdir(weights_dir_name):
os.makedirs(weights_dir_name)
compact_model_name = weights_dir_name + 'rand-normal_' + str(remain_ratio) + '_' + str(number) + '.weights'
save_weights(compact_model_winnner, path=compact_model_name)
print(f'Compact model has been saved: {compact_model_name}')
print('%g sub networks completed in %.3f hours.\n' % (number, (time.time() - t0) / 3600))
================================================
FILE: EagleEye_regular_prune.py
================================================
from models import *
from utils.utils import *
from utils.prune_utils import *
from utils.datasets import *
import os
import test
import argparse
from thop import profile
def obtain_avg_forward_time(input, model, repeat=200):
model.eval()
start = time.time()
with torch.no_grad():
for i in range(repeat):
output = model(input)
avg_infer_time = (time.time() - start) / repeat
return avg_infer_time, output
def obtain_filters_mask(model, CBL_idx, prune_idx, idx_mask):
pruned = 0
total = 0
num_filters = []
filters_mask = []
# CBL_idx存储的是所有带BN的卷积层（YOLO层的前一层卷积层是不带BN的）
for idx in CBL_idx:
bn_module = model.module_list[idx][1]
if idx in prune_idx:
mask = idx_mask[idx]
# mask = obtain_bn_mask(bn_module, thre).cpu().numpy()
remain = int(mask.sum())
pruned = pruned + mask.shape[0] - remain
if remain == 0:
print("Channels would be all pruned!")
raise Exception
else:
mask = torch.ones(bn_module.weight.data.shape)
remain = mask.shape[0]
total += mask.shape[0]
num_filters.append(remain)
filters_mask.append(mask.clone())
return num_filters, filters_mask
def obtain_l1_mask(conv_module, random_rate):
w_copy = conv_module.weight.data.abs().clone()
w_copy = torch.sum(w_copy, dim=(1, 2, 3))
length = w_copy.cpu().numpy().shape[0]
num_retain = int(length * (1 - random_rate))
num_retain = get_nearest_multiple(num_retain,channel_base)
if num_retain > length:
num_retain = length
if num_retain == 0:
num_retain = channel_base
_, y = torch.topk(w_copy, num_retain)
mask = torch.zeros(length, dtype=torch.float32).to(w_copy.device)
mask[y] = 1
return mask
#macs = flops / 2
def performance_summary(model, opt=None, prefix=""):
macs, _ = profile(model, inputs=(torch.zeros(1, 3, 480, 640).to(device),), verbose=False)
return macs
def rand_prune_and_eval(model, min_rate, max_rate):
while True:
model_copy = deepcopy(model)
remain_num = 0
idx_new = dict()
for idx in prune_idx:
# bn_module = model_copy.module_list[idx][1]
conv_module = model_copy.module_list[idx][0]
random_rate = (max_rate - min_rate) * (np.random.rand(1)) + min_rate
mask = obtain_l1_mask(conv_module, random_rate)
idx_new[idx] = mask
remain_num += int(mask.sum())
conv_module.weight.data = conv_module.weight.data.permute(1, 2, 3, 0).mul(mask).float().permute(3, 0, 1, 2)
# bn_module.weight.data.mul_(mask)
# ---------------
num_filters, filters_mask = obtain_filters_mask(model_copy, CBL_idx, prune_idx, idx_new)
CBLidx2mask = {idx: mask for idx, mask in zip(CBL_idx, filters_mask)}
CBLidx2filters = {idx: filters for idx, filters in zip(CBL_idx, num_filters)}
compact_module_defs = deepcopy(model.module_defs)
for i in model_copy.module_defs:
if i['type'] == 'shortcut':
i['is_access'] = False
merge_mask(model_copy, CBLidx2mask, CBLidx2filters, base=channel_base)
for idx, num in CBLidx2filters.items():
#num = get_nearest_multiple(num,channel_base)
assert compact_module_defs[idx]['type'] == 'convolutional'
compact_module_defs[idx]['filters'] = str(num)
compact_model = Darknet([model.hyperparams.copy()] + compact_module_defs).to(device)
current_parameters = obtain_num_parameters(compact_model)
# print(current_parameters/origin_nparameters, end='；')
current_macs = performance_summary(compact_model)
# macs = flops/2
if current_macs / origin_macs > remain_ratio + delta or current_macs / origin_macs < remain_ratio - delta:
# print('missing')
del model_copy
del compact_model
torch.cuda.empty_cache()
continue
print("yes---")
for i in CBLidx2mask:
CBLidx2mask[i] = CBLidx2mask[i].clone().cpu().numpy()
pruned_model = prune_model_keep_size_forEagleEye(model, prune_idx, CBLidx2mask)
init_weights_from_loose_model(compact_model, pruned_model, CBL_idx, Conv_idx, CBLidx2mask)
compact_model.train()
with torch.no_grad():
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader)):
imgs = imgs.cuda().float() / 255.0
compact_model(imgs)
if batch_i > steps:
break
del model_copy
torch.cuda.empty_cache()
break
return compact_module_defs, current_parameters, compact_model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='cfg/yolov3/yolov3.cfg', help='cfg file path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data file path')
parser.add_argument('--weights', type=str, default='weights/pretrain_weights/yolov3.weights',
help='sparse model weights')
parser.add_argument('--percent', type=float, default=0.5, help='global channel prune percent')
parser.add_argument('--delta', type=float, default=0.05, help='delta')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--batch-size', type=int, default=16, help='batch-size')
parser.add_argument('--number', type=int, default=200, help='number of subnetwork')
parser.add_argument('--channel-base', type=int, default=8, help='channel of subnetwork is multiple of channel-base')
opt = parser.parse_args()
print(opt)
t0 = time.time()
remain_ratio = 1 - opt.percent
number = opt.number
img_size = opt.img_size
batch_size = opt.batch_size
delta = opt.delta
channel_base = opt.channel_base
hyp = {'giou': 3.54, # giou loss gain
'cls': 37.4, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.20, # iou training threshold
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
'lrf': 0.0005, # final learning rate (with cos scheduler)
'momentum': 0.937, # SGD momentum
'weight_decay': 0.0005, # optimizer weight decay
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
'degrees': 1.98 * 0, # image rotation (+/- deg)
'translate': 0.05 * 0, # image translation (+/- fraction)
'scale': 0.05 * 0, # image scale (+/- gain)
'shear': 0.641 * 0} # image shear (+/- deg)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Darknet(opt.cfg).to(device)
if opt.weights:
if opt.weights.endswith(".pt"):
model.load_state_dict(torch.load(opt.weights, map_location=device)['model'])
else:
_ = load_darknet_weights(model, opt.weights)
data_config = parse_data_cfg(opt.data)
valid_path = data_config["valid"]
train_path = data_config["train"]
class_names = load_classes(data_config["names"])
steps = math.ceil((len(open(train_path).readlines()) / batch_size) * 0.1)
obtain_num_parameters = lambda model: sum([param.nelement() for param in model.parameters()])
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=False, # rectangular training
cache_images=False)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 16]),
shuffle=True, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
test_dataset = LoadImagesAndLabels(valid_path, img_size, batch_size,
hyp=hyp,
rect=True,
cache_images=False)
testloader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 8]),
shuffle=False,
pin_memory=True,
collate_fn=test_dataset.collate_fn)
with torch.no_grad():
origin_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=model,
dataloader=testloader,
rank=-1,
plot=False)
origin_nparameters = obtain_num_parameters(model)
origin_macs = performance_summary(model)
CBL_idx, Conv_idx, prune_idx, _, _ = parse_module_defs2(model.module_defs)
print("-------------------------------------------------------")
max_mAP = 0
for i in range(number):
compact_module_defs, current_parameters, compact_model = rand_prune_and_eval(model, 0, 1)
with torch.no_grad():
# 防止随机生成的较差的模型撑爆显存，增大nmsconf阈值
mAP = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
conf_thres=0.1,
model=compact_model,
dataloader=testloader,
rank=-1,
plot=False)[0][2]
print('candidate: ' + str(i), end=" ")
print('remain_ratio: ' + str(current_parameters / origin_nparameters))
print(f'mAP of the pruned model is {mAP:.4f}')
if mAP > max_mAP:
max_mAP = mAP
compact_model_winnner = deepcopy(compact_model)
cfg_name = 'cfg_backup/' + str(i) + '.cfg'
if not os.path.isdir('cfg_backup/'):
os.makedirs('cfg_backup/')
pruned_cfg_file = write_cfg(cfg_name, [model.hyperparams.copy()] + compact_module_defs)
del compact_model
torch.cuda.empty_cache()
# 获得原始模型的module_defs，并修改该defs中的卷积核数量
compact_module_defs = deepcopy(compact_model_winnner.module_defs)
compact_nparameters = obtain_num_parameters(compact_model_winnner)
compact_macs = performance_summary(compact_model_winnner)
compact_flops = compact_macs*2 / 1024**3
origin_flops = origin_macs*2 / 1024**3
random_input = torch.rand((16, 3, 416, 416)).to(device)
pruned_forward_time, pruned_output = obtain_avg_forward_time(random_input, model)
compact_forward_time, compact_output = obtain_avg_forward_time(random_input, compact_model_winnner)
# 在测试集上测试剪枝后的模型, 并统计模型的参数数量
with torch.no_grad():
compact_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=compact_model_winnner,
dataloader=testloader,
rank=-1,
plot=False)
# 比较剪枝前后参数数量的变化、指标性能的变化
metric_table = [
["Metric", "Before", "After"],
["mAP", f'{origin_model_metric[1].mean():.6f}', f'{compact_model_metric[1].mean():.6f}'],
["Parameters", f"{origin_nparameters}", f"{compact_nparameters}"],
["GFLOPs",f"{origin_flops}",f"{compact_flops}"],
["Inference", f'{pruned_forward_time:.4f}', f'{compact_forward_time:.4f}']
]
print(AsciiTable(metric_table).table)
# 生成剪枝后的cfg文件并保存模型
pruned_cfg_name = 'cfg/rand-slim_' + str(remain_ratio) + '_' + str(number) + '/' + 'rand-slim_' + str(
remain_ratio) + '_' + str(number) + '.cfg'
# 创建存储目录
dir_name = 'cfg/rand-slim_' + str(remain_ratio) + '_' + str(number) + '/'
if not os.path.isdir(dir_name):
os.makedirs(dir_name)
# 由于原始的compact_module_defs将anchor从字符串变为了数组，因此这里将anchors重新变为字符串
file = open(opt.cfg, 'r')
lines = file.read().split('\n')
for line in lines:
if line.split(' = ')[0] == 'anchors':
anchor = line.split(' = ')[1]
break
if line.split('=')[0] == 'anchors':
anchor = line.split('=')[1]
break
file.close()
for item in compact_module_defs:
if item['type'] == 'shortcut':
item['from'] = str(item['from'][0])
elif item['type'] == 'route':
item['layers'] = ",".join('%s' % i for i in item['layers'])
elif item['type'] == 'yolo':
item['mask'] = ",".join('%s' % i for i in item['mask'])
item['anchors'] = anchor
pruned_cfg_file = write_cfg(pruned_cfg_name, [model.hyperparams.copy()] + compact_module_defs)
print(f'Config file has been saved: {pruned_cfg_file}')
weights_dir_name = dir_name.replace('cfg', 'weights')
if not os.path.isdir(weights_dir_name):
os.makedirs(weights_dir_name)
compact_model_name = weights_dir_name + 'rand-slim_' + str(remain_ratio) + '_' + str(number) + '.weights'
save_weights(compact_model_winnner, path=compact_model_name)
print(f'Compact model has been saved: {compact_model_name}')
print('%g sub networks completed in %.3f hours.\n' % (number, (time.time() - t0) / 3600))
================================================
FILE: EagleEye_slim_prune.py
================================================
from models import *
from utils.utils import *
from utils.prune_utils import *
from utils.datasets import *
import os
import test
import argparse
from thop import profile
def obtain_avg_forward_time(input, model, repeat=200):
model.eval()
start = time.time()
with torch.no_grad():
for i in range(repeat):
output = model(input)
avg_infer_time = (time.time() - start) / repeat
return avg_infer_time, output
def obtain_filters_mask(model, CBL_idx, prune_idx, idx_mask):
pruned = 0
total = 0
num_filters = []
filters_mask = []
# CBL_idx存储的是所有带BN的卷积层（YOLO层的前一层卷积层是不带BN的）
for idx in CBL_idx:
bn_module = model.module_list[idx][1]
if idx in prune_idx:
mask = idx_mask[idx]
# mask = obtain_bn_mask(bn_module, thre).cpu().numpy()
remain = int(mask.sum())
pruned = pruned + mask.shape[0] - remain
if remain == 0:
print("Channels would be all pruned!")
raise Exception
else:
mask = torch.ones(bn_module.weight.data.shape)
remain = mask.shape[0]
total += mask.shape[0]
num_filters.append(remain)
filters_mask.append(mask.clone())
return num_filters, filters_mask
def obtain_l1_mask(conv_module, random_rate):
w_copy = conv_module.weight.data.abs().clone()
w_copy = torch.sum(w_copy, dim=(1, 2, 3))
length = w_copy.cpu().numpy().shape[0]
num_retain = int(length * (1 - random_rate))
if num_retain == 0:
num_retain = 1
_, y = torch.topk(w_copy, num_retain)
mask = torch.zeros(length, dtype=torch.float32).to(w_copy.device)
mask[y] = 1
return mask
#macs = flops / 2
def performance_summary(model, opt=None, prefix=""):
macs, _ = profile(model, inputs=(torch.zeros(1, 3, 480, 640).to(device),), verbose=False)
return macs
def rand_prune_and_eval(model, min_rate, max_rate):
while True:
model_copy = deepcopy(model)
remain_num = 0
idx_new = dict()
for idx in prune_idx:
# bn_module = model_copy.module_list[idx][1]
conv_module = model_copy.module_list[idx][0]
random_rate = (max_rate - min_rate) * (np.random.rand(1)) + min_rate
mask = obtain_l1_mask(conv_module, random_rate)
idx_new[idx] = mask
remain_num += int(mask.sum())
conv_module.weight.data = conv_module.weight.data.permute(1, 2, 3, 0).mul(mask).float().permute(3, 0, 1, 2)
# bn_module.weight.data.mul_(mask)
# ---------------
num_filters, filters_mask = obtain_filters_mask(model_copy, CBL_idx, prune_idx, idx_new)
CBLidx2mask = {idx: mask for idx, mask in zip(CBL_idx, filters_mask)}
CBLidx2filters = {idx: filters for idx, filters in zip(CBL_idx, num_filters)}
compact_module_defs = deepcopy(model.module_defs)
for i in model_copy.module_defs:
if i['type'] == 'shortcut':
i['is_access'] = False
merge_mask(model_copy, CBLidx2mask, CBLidx2filters)
for idx, num in CBLidx2filters.items():
assert compact_module_defs[idx]['type'] == 'convolutional'
compact_module_defs[idx]['filters'] = str(num)
compact_model = Darknet([model.hyperparams.copy()] + compact_module_defs).to(device)
current_parameters = obtain_num_parameters(compact_model)
# print(current_parameters/origin_nparameters, end='；')
current_macs = performance_summary(compact_model)
# macs = flops/2
if current_macs / origin_macs > remain_ratio + delta or current_macs / origin_macs < remain_ratio - delta:
# print('missing')
del model_copy
del compact_model
torch.cuda.empty_cache()
continue
print("yes---")
for i in CBLidx2mask:
CBLidx2mask[i] = CBLidx2mask[i].clone().cpu().numpy()
pruned_model = prune_model_keep_size_forEagleEye(model, prune_idx, CBLidx2mask)
init_weights_from_loose_model(compact_model, pruned_model, CBL_idx, Conv_idx, CBLidx2mask)
compact_model.train()
with torch.no_grad():
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader)):
imgs = imgs.cuda().float() / 255.0
compact_model(imgs)
if batch_i > steps:
break
del model_copy
torch.cuda.empty_cache()
break
return compact_module_defs, current_parameters, compact_model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='cfg/yolov3/yolov3.cfg', help='cfg file path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data file path')
parser.add_argument('--weights', type=str, default='weights/pretrain_weights/yolov3.weights',
help='sparse model weights')
parser.add_argument('--percent', type=float, default=0.5, help='global channel prune percent')
parser.add_argument('--delta', type=float, default=0.05, help='delta')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--batch-size', type=int, default=16, help='batch-size')
parser.add_argument('--number', type=int, default=200, help='number of subnetwork')
opt = parser.parse_args()
print(opt)
t0 = time.time()
remain_ratio = 1 - opt.percent
number = opt.number
img_size = opt.img_size
batch_size = opt.batch_size
delta = opt.delta
hyp = {'giou': 3.54, # giou loss gain
'cls': 37.4, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.20, # iou training threshold
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
'lrf': 0.0005, # final learning rate (with cos scheduler)
'momentum': 0.937, # SGD momentum
'weight_decay': 0.0005, # optimizer weight decay
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
'degrees': 1.98 * 0, # image rotation (+/- deg)
'translate': 0.05 * 0, # image translation (+/- fraction)
'scale': 0.05 * 0, # image scale (+/- gain)
'shear': 0.641 * 0} # image shear (+/- deg)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Darknet(opt.cfg).to(device)
if opt.weights:
if opt.weights.endswith(".pt"):
model.load_state_dict(torch.load(opt.weights, map_location=device)['model'])
else:
_ = load_darknet_weights(model, opt.weights)
data_config = parse_data_cfg(opt.data)
valid_path = data_config["valid"]
train_path = data_config["train"]
class_names = load_classes(data_config["names"])
steps = math.ceil((len(open(train_path).readlines()) / batch_size) * 0.1)
obtain_num_parameters = lambda model: sum([param.nelement() for param in model.parameters()])
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=False, # rectangular training
cache_images=False)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 16]),
shuffle=True, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
test_dataset = LoadImagesAndLabels(valid_path, img_size, batch_size,
hyp=hyp,
rect=True,
cache_images=False)
testloader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 8]),
shuffle=False,
pin_memory=True,
collate_fn=test_dataset.collate_fn)
with torch.no_grad():
origin_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=model,
dataloader=testloader,
rank=-1,
plot=False)
origin_nparameters = obtain_num_parameters(model)
origin_macs = performance_summary(model)
CBL_idx, Conv_idx, prune_idx, _, _ = parse_module_defs2(model.module_defs)
print("-------------------------------------------------------")
max_mAP = 0
for i in range(number):
compact_module_defs, current_parameters, compact_model = rand_prune_and_eval(model, 0, 1)
with torch.no_grad():
# 防止随机生成的较差的模型撑爆显存，增大nmsconf阈值
mAP = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
conf_thres=0.1,
model=compact_model,
dataloader=testloader,
rank=-1,
plot=False)[0][2]
print('candidate: ' + str(i), end=" ")
print('remain_ratio: ' + str(current_parameters / origin_nparameters))
print(f'mAP of the pruned model is {mAP:.4f}')
if mAP > max_mAP:
max_mAP = mAP
compact_model_winnner = deepcopy(compact_model)
cfg_name = 'cfg_backup/' + str(i) + '.cfg'
if not os.path.isdir('cfg_backup/'):
os.makedirs('cfg_backup/')
pruned_cfg_file = write_cfg(cfg_name, [model.hyperparams.copy()] + compact_module_defs)
del compact_model
torch.cuda.empty_cache()
# 获得原始模型的module_defs，并修改该defs中的卷积核数量
compact_module_defs = deepcopy(compact_model_winnner.module_defs)
compact_nparameters = obtain_num_parameters(compact_model_winnner)
compact_macs = performance_summary(compact_model_winnner)
compact_flops = compact_macs*2 / 1024**3
origin_flops = origin_macs*2 / 1024**3
random_input = torch.rand((16, 3, 416, 416)).to(device)
pruned_forward_time, pruned_output = obtain_avg_forward_time(random_input, model)
compact_forward_time, compact_output = obtain_avg_forward_time(random_input, compact_model_winnner)
# 在测试集上测试剪枝后的模型, 并统计模型的参数数量
with torch.no_grad():
compact_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=compact_model_winnner,
dataloader=testloader,
rank=-1,
plot=False)
# 比较剪枝前后参数数量的变化、指标性能的变化
metric_table = [
["Metric", "Before", "After"],
["mAP", f'{origin_model_metric[1].mean():.6f}', f'{compact_model_metric[1].mean():.6f}'],
["Parameters", f"{origin_nparameters}", f"{compact_nparameters}"],
["GFLOPs",f"{origin_flops}",f"{compact_flops}"],
["Inference", f'{pruned_forward_time:.4f}', f'{compact_forward_time:.4f}']
]
print(AsciiTable(metric_table).table)
# 生成剪枝后的cfg文件并保存模型
pruned_cfg_name = 'cfg/rand-slim_' + str(remain_ratio) + '_' + str(number) + '/' + 'rand-slim_' + str(
remain_ratio) + '_' + str(number) + '.cfg'
# 创建存储目录
dir_name = 'cfg/rand-slim_' + str(remain_ratio) + '_' + str(number) + '/'
if not os.path.isdir(dir_name):
os.makedirs(dir_name)
# 由于原始的compact_module_defs将anchor从字符串变为了数组，因此这里将anchors重新变为字符串
file = open(opt.cfg, 'r')
lines = file.read().split('\n')
for line in lines:
if line.split(' = ')[0] == 'anchors':
anchor = line.split(' = ')[1]
break
if line.split('=')[0] == 'anchors':
anchor = line.split('=')[1]
break
file.close()
for item in compact_module_defs:
if item['type'] == 'shortcut':
item['from'] = str(item['from'][0])
elif item['type'] == 'route':
item['layers'] = ",".join('%s' % i for i in item['layers'])
elif item['type'] == 'yolo':
item['mask'] = ",".join('%s' % i for i in item['mask'])
item['anchors'] = anchor
pruned_cfg_file = write_cfg(pruned_cfg_name, [model.hyperparams.copy()] + compact_module_defs)
print(f'Config file has been saved: {pruned_cfg_file}')
weights_dir_name = dir_name.replace('cfg', 'weights')
if not os.path.isdir(weights_dir_name):
os.makedirs(weights_dir_name)
compact_model_name = weights_dir_name + 'rand-slim_' + str(remain_ratio) + '_' + str(number) + '.weights'
save_weights(compact_model_winnner, path=compact_model_name)
print(f'Compact model has been saved: {compact_model_name}')
print('%g sub networks completed in %.3f hours.\n' % (number, (time.time() - t0) / 3600))
================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
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================================================
FILE: PTQ.py
================================================
import argparse
import test
from torch.utils.data import DataLoader
from models import *
from utils.datasets import *
from utils.utils import *
wdir = 'weights' + os.sep # weights dir
PTQ_weights = wdir + 'PTQ.pt'
def PTQ(cfg,
data,
weights=None,
batch_size=64,
imgsz=416,
augment=False,
a_bit=8,
w_bit=8, ):
# Initialize/load model and set device
device = torch_utils.select_device(opt.device, batch_size=batch_size)
print('PTQ only support for one gpu!')
print('') # skip a line
# Initialize model
model = Darknet(cfg, is_gray_scale=opt.gray_scale, maxabsscaler=opt.maxabsscaler)
q_model = Darknet(cfg, quantized=3, a_bit=a_bit, w_bit=w_bit, is_gray_scale=opt.gray_scale,
maxabsscaler=opt.maxabsscaler,
shortcut_way=opt.shortcut_way)
# Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
q_model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
load_darknet_weights(model, weights)
load_darknet_weights(q_model, weights, quant=True)
model.to(device)
q_model.to(device)
# Configure run
data_dict = parse_data_cfg(data)
cali_path = data_dict['train']
test_path = data_dict['valid']
# Dataloader
cali_dataset = LoadImagesAndLabels(cali_path, imgsz, batch_size, rect=True,
is_gray_scale=True if opt.gray_scale else False, subset_len=opt.subset_len)
cali_batch_size = min(batch_size, len(cali_dataset))
cali_dataloader = DataLoader(cali_dataset,
batch_size=cali_batch_size,
num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
pin_memory=True,
collate_fn=cali_dataset.collate_fn)
test_dataset = LoadImagesAndLabels(test_path, imgsz, batch_size, rect=True,
is_gray_scale=True if opt.gray_scale else False)
test_batch_size = min(batch_size, len(test_dataset))
test_dataloader = DataLoader(test_dataset,
batch_size=test_batch_size,
num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
pin_memory=True,
collate_fn=test_dataset.collate_fn)
print('') # skip a line
print('<.....................test original model.......................>')
test.test(cfg,
data=opt.data,
batch_size=batch_size,
imgsz=imgsz,
model=model,
dataloader=test_dataloader,
rank=-1,
maxabsscaler=opt.maxabsscaler)
q_model.train()
print('') # skip a line
print('<.....................Quantize.......................>')
for batch_i, (imgs, _, _, _) in enumerate(tqdm(cali_dataloader)):
if opt.maxabsscaler:
imgs = imgs.to(device).float() / 256.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
imgs = imgs * 2 - 1
else:
imgs = imgs.to(device).float() / 256.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# Disable gradients
with torch.no_grad():
_, _ = q_model(imgs, augment=augment) # inference and training outputs
print('') # skip a line
print('<.....................test quantized model.......................>')
print('') # skip a line
test.test(cfg,
data=opt.data,
batch_size=batch_size,
imgsz=imgsz,
model=q_model,
dataloader=test_dataloader,
quantized=3,
a_bit=opt.a_bit,
w_bit=opt.w_bit,
rank=-1,
maxabsscaler=opt.maxabsscaler)
# Save model
if hasattr(q_model, 'module'):
model_temp = q_model.module.state_dict()
else:
model_temp = q_model.state_dict()
chkpt = {'epoch': None,
'best_fitness': None,
'training_results': None,
'model': model_temp,
'optimizer': None}
# Save last, best and delete
torch.save(chkpt, PTQ_weights)
del chkpt
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='test.py')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path')
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--a-bit', type=int, default=8,
help='a-bit')
parser.add_argument('--w-bit', type=int, default=8,
help='w-bit')
parser.add_argument('--subset_len', type=int, default=-1, help='calibration set len')
parser.add_argument('--gray_scale', action='store_true', help='gray scale trainning')
parser.add_argument('--maxabsscaler', '-mas', action='store_true', help='Standarize input to (-1,1)')
parser.add_argument('--shortcut_way', type=int, default=1, help='--shortcut quantization way')
opt = parser.parse_args()
opt.cfg = list(glob.iglob('./**/' + opt.cfg, recursive=True))[0] # find file
opt.data = list(glob.iglob('./**/' + opt.data, recursive=True))[0] # find file
print(opt)
PTQ(opt.cfg,
opt.data,
opt.weights,
opt.batch_size,
opt.img_size,
opt.augment,
a_bit=opt.a_bit,
w_bit=opt.w_bit)
================================================
FILE: README.md
================================================
# YOLOv3-ModelCompression-MultidatasetTraining
This project mainly include three parts.
1.Provides training methods for multiple mainstream object detection datasets(coco2017, coco2014, BDD100k, Visdrone,
Hand)
2.Provides a mainstream model compression algorithm including pruning, quantization, and knowledge distillation.
3.Provides multiple backbone for yolov3 including Darknet-YOLOv3，Tiny-YOLOv3，Mobilenetv3-YOLOv3
Source using Pytorch implementation to [ultralytics/yolov3](https://github.com/ultralytics/yolov3) for yolov3 source
code. Pruning method based on BN layer
by [coldlarry/YOLOv3-complete-pruning](https://github.com/coldlarry/YOLOv3-complete-pruning), thanks to both of you.
**If you can't download weights file and datasets from BaiDu, please send e-mail(spurslipu@pku.edu.cn) to me, I will
rely as soon as I can.**
# Update
January 4, 2020. Provides download links and training methods to the Visdrone dataset.
January 19, 2020. Dior, Bdd100k and Visdrone training will be provided, as well as the converted weights file.
March 1, 2020. Provides Mobilenetv3 backbone.
April 7, 2020. Implement two models based on Mobilenetv3: Yolov3-Mobilenet, and Yolov3tin-Mobilene-small, provide
pre-training weights, extend the normal pruning methods to the two Mobilenet-based models.
April 27, 2020. Update mobilenetv3 pre-training weights, add a layer pruning method, methods from
the [tanluren/yolov3-channel-and-layer-pruning/yolov3](https://github.com/tanluren/yolov3-channel-and-layer-pruning),
Thanks for sharing.
May 22, 2020. Updated some new optimizations from [ultralytics/yolov3](https://github.com/ultralytics/yolov3), update
cfg file and weights of YOLOv4.
May 22, 2020. The 8-bit quantization method was updated and some bugs were fixed.
July 12, 2020. The problem of mAP returning to 0 after pruning in yolov3-mobilenet was fixed. See issue#41 for more
details.
September 30, 2020. The BN_Fold training method was updated to reduce the precision loss caused by BN fusion, and the
POW (2) quantization method targeted at FPGA was updated. See the quantization section for details.
# Requirements
Our project based on [ultralytics/yolov3](https://github.com/ultralytics/yolov3),
see [ultralytics/yolov3](https://github.com/ultralytics/yolov3) for details. Here is a brief explanation:
- `numpy`
- `torch >= 1.1.0`
- `opencv-python`
- `tqdm`
# Current support
|<center>Function</center>|<center></center>|
| --- |--- |
|<center>Multi-Backbone training</center>|<center>√</center> |
|<center>Multi-Datasets</center>|<center>√</center> |
|<center>Pruning</center>|<center>√</center> |
|<center>Quantization</center>|<center>√</center> |
|<center>Knowledge Distillation</center>|<center>√</center> |
# Training
`python3 train.py --data ... --cfg ... `For training model command, the -pt command is required when using coco
pre-training model.
`python3 test.py --data ... --cfg ... ` For testing model command
`python3 detect.py --data ... --cfg ... --source ...` For detecting model command, the default address of source is
data/samples, the output result is saved in the /output, and the detection resource can be pictures and videos.
# Multi-Datasets
This project provides preprocessed datasets for the YOLOv3, configuration files (.cfg), dataset index files (.data),
dataset category files (.names), and anchor box sizes (including 9 boxes for YOLOv3 and 6 boxes for tiny- YOLOv3) that
are reclustered using the K-means algorithm.
mAP
|<center>Dataset</center>|<center>YOLOv3-640</center>|<center>YOLOv4-640</center>|<center>YOLOv3-mobilenet-640</center>|
| --- |--- |--- |--- |
|<center>Dior</center>|<center>0.749</center>|
|<center>bdd100k</center>|<center>0.543</center>|
|<center>visdrone</center>|<center>0.311</center>|<center>0.383</center>|<center>0.348</center>|
Datasets, download and unzip to /data.
- [COCO2017](https://pan.baidu.com/s/1KysFL6AmdbCBq4tHDebqlw)
Extract code：hjln
- [COCO2014](https://pan.baidu.com/s/1EoXOR77yEVokqPCaxg8QGg)
Extract code：rhqx
- [COCO weights](https://pan.baidu.com/s/1JZylwRQIgAd389oWUu0djg)
Extract code：k8ms
Training command
```bash
python3 train.py --data data/coco2017.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3.cfg --img-size ... --epochs ...
```
- [Dior](https://pan.baidu.com/s/1z0IQPBN16I-EctjwN9Idyg)
Extract code：vnuq
- [Dior weights](https://pan.baidu.com/s/12lYOgBAo1R5VkOZqDqCFJQ)
Extract code：l8wz
Training command
```bash
python3 train.py --data data/dior.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-onDIOR.cfg --img-size ... --epochs ...
```
- [bdd100k](https://pan.baidu.com/s/157Md2qeFgmcOv5UmnIGI_g)
Extract code：8duw
- [bdd100k weights](https://pan.baidu.com/s/1wWiHlLxIaK_WHy_mG2wmAA)
Extract code：xeqo
Training command
```bash
python3 train.py --data data/bdd100k.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-bdd100k.cfg --img-size ... --epochs ...
```
- [visdrone](https://pan.baidu.com/s/1CPGmS3tLI7my4_m7qDhB4Q)
Extract code：dy4c
- [YOLOv3-visdrone weights](https://pan.baidu.com/s/1N4qDP3b0tt8TIWuTFefDEw)
Extract code：87lf
- [YOLOv4-visdrone weights](https://pan.baidu.com/s/1zOFyt_AFiNk0fAFa8yE9RQ)
Extract code：xblu
- [YOLOv3-mobilenet-visdrone weights](https://pan.baidu.com/s/1BHC8b6xHmTuN8h74QJFt1g)
Extract code：fb6y
Training command
```bash
python train.py --data data/visdrone.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-visdrone.cfg --img-size ... --epochs ...
```
- [oxfordhand](https://pan.baidu.com/s/1JL4gFGh-W_gYEEsiIQssZw)
Extract code：3du4
Training command
```bash
python train.py --data data/oxfordhand.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-hand.cfg --img-size ... --epochs ...
```
## 1.Dior
The DIRO dataset is one of the largest, most diverse, and publicly available object detection datasets in the Earth
observation community. Among them, the number of instances of ships and vehicles is high, which achieves a good balance
between small instances and large ones. The images were collected from Google Earth.
[Introduction](https://cloud.tencent.com/developer/article/1509762)
### Test results
## 2.bdd100k
Bdd100 is a large, diverse data set of driving videos containing 100,000 videos. Each video was about 40 seconds long,
and the researchers marked bounding boxes for all 100,000 key frames of objects that often appeared on the road. The
data set covers different weather conditions, including sunny, cloudy and rainy days, and different times of day and
night.
[Website](http://bair.berkeley.edu/blog/2018/05/30/bdd/)
[Download](http://bdd-data.berkeley.edu)
[Paper](https://arxiv.org/abs/1805.04687)
### Test results
## 3.Visdrone
The VisDrone2019 dataset was collected by AISKYEYE team at the Machine Learning and Data Mining Laboratory at Tianjin
University, China. Benchmark data set contains 288 video clips, and consists of 261908 frames and 10209 frames a static
image, by all sorts of installed on the unmanned aerial vehicle (uav) camera capture, covers a wide range of aspects,
including location (thousands of kilometers apart from China in 14 different cities), environment (city and country),
object (pedestrians, vehicles, bicycles, etc.) and density (sparse and crowded scenario). This data set was collected
using a variety of uav platforms (i.e., uAvs with different models) in a variety of situations and under various weather
and light conditions. These frames are manually marked with more than 2.6 million border frames, which are often targets
of interest, such as pedestrians, cars, bicycles and tricycles. Some important attributes are also provided, including
scene visibility, object categories, and occlusion, to improve data utilization.
[Website](http://www.aiskyeye.com/)
### Test results of YOLOv3
### Test results of YOLOv4
# Multi-Backbone
Based on mobilenetv3, two network structures are designed.
|Structure |<center>backbone</center>|<center>Postprocessing</center> |<center>Parameters</center> |<center>GFLOPS</center> |<center>mAP0.5</center> |<center>mAP0.5:0.95</center> |<center>speed(inference/NMS/total)</center> |<center>FPS</center> |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|YOLOv3 |38.74M |20.39M |59.13M |117.3 |0.580 |0.340 |12.3/1.7/14.0 ms|71.4fps |
|YOLOv3tiny |6.00M |2.45M |8.45M |9.9 |0.347 |0.168 |3.5/1.8/5.3 ms |188.7fps |
|YOLOv3-mobilenetv3 |2.84M |20.25M |23.09M |32.2 |0.547 |0.346 |7.9/1.8/9.7 ms |103.1fps |
|YOLOv3tiny-mobilenetv3-small|0.92M |2.00M |2.92M |2.9 |0.379 |0.214 |5.2/1.9/7.1 ms |140.8fps |
|YOLOv4 |- |- |61.35M |107.1 |0.650 |0.438 |13.5/1.8/15.3 ms|65.4fps |
|YOLOv4-tiny |- |- |5.78M |12.3 |0.435 |0.225 |4.1/1.7/5.8 ms |172.4fps |
1. YOLOv3,YOLOv3tiny and YOLOv4 were trained and tested on coco2014, and Yolov3-Mobilenetv3 and YOLOv3tiny
Mobilenetv3-Small were trained and tested on coco2017.
2. The inference speed test on GTX2080ti*4, and image size is 608.
3. The training set should match the testing set, because mismatch will cause the mistakes of mAP.
Read [issue](https://github.com/ultralytics/yolov3/issues/970) for detial.
## Train command
1.YOLOv3
```bash
python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3-608.weights --cfg cfg/yolov3/yolov3.cfg --img_size ...
```
Weights Download
- [COCO pretraining weights](https://pan.baidu.com/s/1JZylwRQIgAd389oWUu0djg)
Extract code：k8ms
2.YOLOv3tiny
```bash
python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3tiny.weights --cfg cfg/yolov3tiny/yolov3-tiny.cfg --img_size ...
```
- [COCO pretraining weights](https://pan.baidu.com/s/1iWGxdjR3TWxEe37__msyRA)
Extract code：udfe
3.YOLOv3tiny-mobilenet-small
```bash
python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3tiny-mobilenet-small.weights --cfg cfg/yolov3tiny-mobilenet-small/yolov3tiny-mobilenet-small-coco.cfg --img_size ...
```
- [COCO pretraining weights](https://pan.baidu.com/s/1mSFjWLU91H2OhNemsAeiiQ)
Extract code：pxz4
4.YOLOv3-mobilenet
```bash
python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3-mobilenet.weights --cfg cfg/yolov3-mobilenet/yolov3-mobilenet-coco.cfg --img_size ...
```
- [COCO pretraining weights](https://pan.baidu.com/s/1EI2Xh1i18CRLoZo_P3NVHw)
Extract code：3vm8
5.YOLOv4
```bash
python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov4.weights --cfg cfg/yolov4/yolov4.cfg --img_size ...
```
- [COCO pretraining weights](https://pan.baidu.com/s/1jAGNNC19oQhAIgBfUrkzmQ)
Extract code：njdg
# Model Compression
## 1. Pruning
### Features
|<center>method</center> |<center>advantage</center>|<center>disadvantage</center> |
| --- | --- | --- |
|Normal pruning |Not prune for shortcut layer. It has a considerable and stable compression rate but requires no fine tuning.|The compression rate is limited. |
|Shortcut pruning |Very high compression rate. |Fine-tuning is necessary. |
|Silmming |Shortcut fusion method is used to improve the precision of shear planting.|Best way for shortcut pruning|
|Regular pruning |Designed for hardware deployment, the number of filters after pruning is a multiple of 2, no fine-tuning, support tiny-yolov3 and Mobilenet.|Part of the compression ratio is sacrificed for regularization. |
|layer pruning |ResBlock is used as the basic unit for purning, which is conducive to hardware deployment. |It can only cut backbone. |
|layer-channel pruning |First, use channel pruning and then use layer pruning, and pruning rate was very high. |Accuracy may be affected. |
### Step
1.Training
```bash
python3 train.py --data ... -pt --batch-size ... --weights ... --cfg ...
```
2.Sparse training
`--s`Specifies the sparsity factor，`--prune`Specify the sparsity type.
`--prune 0` is the sparsity of normal pruning and regular pruning.
`--prune 1` is the sparsity of shortcut pruning.
`--prune 2` is the sparsity of layer pruning.
command：
```bash
python3 train.py --data ... -pt --batch-size 32 --weights ... --cfg ... --s 0.001 --prune 0
```
3.Pruning
- normal pruning
```bash
python3 normal_prune.py --cfg ... --data ... --weights ... --percent ...
```
- regular pruning
```bash
python3 regular_prune.py --cfg ... --data ... --weights ... --percent ...
```
- shortcut pruning
```bash
python3 shortcut_prune.py --cfg ... --data ... --weights ... --percent ...
```
- silmming
```bash
python3 slim_prune.py --cfg ... --data ... --weights ... --percent ...
```
- layer pruning
```bash
python3 layer_prune.py --cfg ... --data ... --weights ... --shortcut ...
```
- layer-channel pruning
```bash
python3 layer_channel_prune.py --cfg ... --data ... --weights ... --shortcut ... --percent ...
```
It is important to note that the cfg and weights variables in OPT need to be pointed to the cfg and weights files
generated by step 2.
In addition, you can get more compression by increasing the percent value in the code.
(If the sparsity is not enough and the percent value is too high, the program will report an error.)
### Pruning experiment
1.normal pruning oxfordhand，img_size = 608，test on GTX2080Ti*4
|<center>model</center> |<center>parameter before pruning</center> |<center>mAP before pruning</center>|<center>inference time before pruning</center>|<center>percent</center> |<center>parameter after pruning</center> |<center>mAP after pruning</center> |<center>inference time after pruning</center>
| --- | --- | --- | --- | --- | --- | --- | --- |
|yolov3(without fine tuning) |58.67M |0.806 |0.1139s |0.8 |10.32M |0.802 |0.0844s |
|yolov3-mobilenet(fine tuning) |22.75M |0.812 |0.0345s |0.97 |2.72M |0.795 |0.0211s |
|yolov3tiny(fine tuning) |8.27M |0.708 |0.0144s |0.5 |1.13M |0.641 |0.0116s |
2.regular pruning oxfordhand，img_size = 608，test ong GTX2080Ti*4
|<center>model</center> |<center>parameter before pruning</center> |<center>mAP before pruning</center>|<center>inference time before pruning</center>|<center>percent</center> |<center>parameter after pruning</center> |<center>mAP after pruning</center> |<center>inference time after pruning</center>
| --- | --- | --- | --- | --- | --- | --- | --- |
|yolov3(without fine tuning) |58.67M |0.806 |0.1139s |0.8 |12.15M |0.805 |0.0874s |
|yolov3-mobilenet(fine tuning) |22.75M |0.812 |0.0345s |0.97 |2.75M |0.803 |0.0208s |
|yolov3tiny(fine tuning) |8.27M |0.708 |0.0144s |0.5 |1.82M |0.703 |0.0122s |
3.shortcut pruning oxfordhand，img_size = 608，test ong GTX2080Ti*4
|<center>model</center> |<center>parameter before pruning</center> |<center>mAP before pruning</center>|<center>inference time before pruning</center>|<center>percent</center> |<center>parameter after pruning</center> |<center>mAP after pruning</center> |<center>inference time after pruning</center>
| --- | --- | --- | --- | --- | --- | --- | --- |
|yolov3 |58.67M |0.806 | |0.8 |6.35M |0.816 | |
|yolov4 |60.94M |0.896 | |0.6 |13.97M |0.855 | |
## 2.quantization
`--quantized 2` Dorefa quantization method
```bash
python train.py --data ... --batch-size ... --weights ... --cfg ... --img-size ... --epochs ... --quantized 2
```
`--quantized 1` Google quantization method
```bash
python train.py --data ... --batch-size ... --weights ... --cfg ... --img-size ... --epochs ... --quantized 1
```
`--FPGA` Pow(2) quantization for FPGA.
### experiment
oxfordhand, yolov3, 640image-size
|<center>method</center> |<center>mAP</center> |
| --- | --- |
|Baseline |0.847 |
|Google8bit |0.851 |
|Google8bit + BN Flod |0.851 |
|Google8bit + BN Flod + FPGA |0.852 |
|Google4bit + BN Flod + FPGA |0.842 |
## 3.Knowledge Distillation
### Knowledge Distillation
The distillation method is based on the basic distillation method proposed by Hinton in 2015, and has been partially
improved in combination with the detection network.
Distilling the Knowledge in a Neural Network
[paper](https://arxiv.org/abs/1503.02531)
command : `--t_cfg --t_weights --KDstr`
`--t_cfg` cfg file of teacher model
`--t_weights` weights file of teacher model
`--KDstr` KD strategy
`--KDstr 1` KLloss can be obtained directly from the output of teacher network and the output of student network and added to the overall loss.
`--KDstr 2` To distinguish between box loss and class loss, the student does not learn directly from the teacher. L2 distance is calculated respectively for student, teacher and GT. When student is greater than teacher, an additional loss is added for student and GT.
`--KDstr 3` To distinguish between Boxloss and ClassLoss, the student learns directly from the teacher.
`--KDstr 4` KDloss is divided into three categories, box loss, class loss and feature loss.
`--KDstr 5` On the basis of KDstr 4, the fine-grain-mask is added into the feature
example:
```bash
python train.py --data ... --batch-size ... --weights ... --cfg ... --img-size ... --epochs ... --t_cfg ... --t_weights ...
```
Usually, the pre-compression model is used as the teacher model, and the post-compression model is used as the student
model for distillation training to improve the mAP of student network.
### experiment
oxfordhand，yolov3tiny as teacher model，normal pruning yolov3tiny as student model
|<center>teacher model</center> |<center>mAP of teacher model</center> |<center>student model</center>|<center>directly fine tuning</center>|<center>KDstr 1</center> |<center>KDstr 2</center> |<center>KDstr 3</center> |<center>KDstr 4(L1)</center> |<center>KDstr 5(L1)</center> |
| --- | --- | --- | --- | --- | --- | --- |--- |--- |
|yolov3tiny608 |0.708 |normal pruning yolov3tiny608 |0.658 |0.666 |0.661 |0.672 |0.673 |0.674 |
================================================
FILE: cfg/yolov2/yolov2-hand.cfg
================================================
[net]
# Testing
batch = 1
subdivisions = 1
# Training
# batch=64
# subdivisions=8
width = 608
height = 608
channels = 3
momentum = 0.9
decay = 0.0005
angle = 0
saturation = 1.5
exposure = 1.5
hue = .1
learning_rate = 0.001
burn_in = 1000
max_batches = 500200
policy = steps
steps = 400000,450000
scales = .1,.1
[convolutional]
batch_normalize = 1
filters = 32
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 64
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 64
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
#######
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 30
activation = linear
[yolo]
mask = 0,1,2,3,4
anchors = 9,13, 15,21, 24,29, 38,43, 70,74
classes = 1
num = 5
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
================================================
FILE: cfg/yolov2/yolov2-tiny-hand.cfg
================================================
[net]
batch = 64
subdivisions = 8
width = 416
height = 416
channels = 3
momentum = 0.9
decay = 0.0005
angle = 0
saturation = 1.5
exposure = 1.5
hue = .1
learning_rate = 0.001
max_batches = 40200
policy = steps
steps = -1,100,20000,30000
scales = .1,10,.1,.1
[convolutional]
batch_normalize = 1
filters = 16
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 32
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 64
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 1
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
###########
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 30
activation = linear
[yolo]
mask = 0,1,2,3,4
anchors = 9,13, 15,21, 24,29, 38,43, 70,74
classes = 1
num = 5
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
================================================
FILE: cfg/yolov2/yolov2-tiny.cfg
================================================
[net]
batch = 64
subdivisions = 8
width = 416
height = 416
channels = 3
momentum = 0.9
decay = 0.0005
angle = 0
saturation = 1.5
exposure = 1.5
hue = .1
learning_rate = 0.001
max_batches = 40200
policy = steps
steps = -1,100,20000,30000
scales = .1,10,.1,.1
[convolutional]
batch_normalize = 1
filters = 16
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 32
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 64
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 1
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
###########
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 425
activation = linear
[yolo]
mask = 0,1,2,3,4
anchors = 7,12, 19,30, 45,61, 90,141, 240,279
classes = 80
num = 5
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
================================================
FILE: cfg/yolov2/yolov2.cfg
================================================
[net]
# Testing
batch = 1
subdivisions = 1
# Training
# batch=64
# subdivisions=8
width = 608
height = 608
channels = 3
momentum = 0.9
decay = 0.0005
angle = 0
saturation = 1.5
exposure = 1.5
hue = .1
learning_rate = 0.001
burn_in = 1000
max_batches = 500200
policy = steps
steps = 400000,450000
scales = .1,.1
[convolutional]
batch_normalize = 1
filters = 32
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 64
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 64
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[maxpool]
size = 2
stride = 2
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
#######
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 425
activation = linear
[yolo]
mask = 0,1,2,3,4
anchors = 7,12, 19,30, 45,61, 90,141, 240,279
classes = 80
num = 5
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
================================================
FILE: cfg/yolov3/yolov3-UAV.cfg
================================================
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=16
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear
[yolo]
mask = 6,7,8
anchors = 5,6, 8,12, 9,8, 10,10, 11,12, 13,16, 15,13, 18,18, 22,25
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear
[yolo]
mask = 3,4,5
anchors = 5,6, 8,12, 9,8, 10,10, 11,12, 13,16, 15,13, 18,18, 22,25
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear
[yolo]
mask = 0,1,2
anchors = 5,6, 8,12, 9,8, 10,10, 11,12, 13,16, 15,13, 18,18, 22,25
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
================================================
FILE: cfg/yolov3/yolov3-asff.cfg
================================================
# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=608
height=608
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
# SPP --------------------------------------------------------------------------
[maxpool]
stride=1
size=5
[route]
layers=-2
[maxpool]
stride=1
size=9
[route]
layers=-4
[maxpool]
stride=1
size=13
[route]
layers=-1,-3,-5,-6
# SPP --------------------------------------------------------------------------
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=258
activation=linear
# YOLO -------------------------------------------------------------------------
[route]
layers = -3
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=258
activation=linear
# YOLO -------------------------------------------------------------------------
[route]
layers = -3
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=258
activation=linear
[yolo]
from=88,99,110
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
[yolo]
from=88,99,110
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
[yolo]
from=88,99,110
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
================================================
FILE: cfg/yolov3/yolov3-bdd100k.cfg
================================================
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=16
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=45
activation=linear
[yolo]
mask = 6,7,8
anchors = 3,7, 5,18, 6,9, 10,32, 11,14, 17,21, 24,36, 45,59, 93,132
classes=10
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=45
activation=linear
[yolo]
mask = 3,4,5
anchors = 3,7, 5,18, 6,9, 10,32, 11,14, 17,21, 24,36, 45,59, 93,132
classes=10
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=45
activation=linear
[yolo]
mask = 0,1,2
anchors = 3,7, 5,18, 6,9, 10,32, 11,14, 17,21, 24,36, 45,59, 93,132
classes=10
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
================================================
FILE: cfg/yolov3/yolov3-hand.cfg
================================================
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=16
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear
[yolo]
mask = 6,7,8
anchors = 8,13, 14,20, 22,25, 26,36, 35,49, 40,31, 51,69, 63,47, 94,103
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear
[yolo]
mask = 3,4,5
anchors = 8,13, 14,20, 22,25, 26,36, 35,49, 40,31, 51,69, 63,47, 94,103
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear
[yolo]
mask = 0,1,2
anchors = 8,13, 14,20, 22,25, 26,36, 35,49, 40,31, 51,69, 63,47, 94,103
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
================================================
FILE: cfg/yolov3/yolov3-onDIOR.cfg
================================================
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=16
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=75
activation=linear
[yolo]
mask = 6,7,8
anchors = 5,5, 6,13, 10,26, 13,6, 15,15, 27,10, 28,33, 61,74, 167,169
classes=20
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=75
activation=linear
[yolo]
mask = 3,4,5
anchors = 5,5, 6,13, 10,26, 13,6, 15,15, 27,10, 28,33, 61,74, 167,169
classes=20
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=75
activation=linear
[yolo]
mask = 0,1,2
anchors = 5,5, 6,13, 10,26, 13,6, 15,15, 27,10, 28,33, 61,74, 167,169
classes=20
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
================================================
FILE: cfg/yolov3/yolov3-screw.cfg
================================================
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=256
height=256
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
# learning_rate=0.0001
# burn_in=1000
# max_batches = 50200
# policy=steps
# steps=40000,45000
# scales=.1,.1
learning_rate=0.0001
burn_in=1000
max_batches = 40000
policy=steps
steps=20000,30000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 6,7,8
anchors = 20,29, 26,34, 29,40, 33,44, 35,47, 37,50, 39,52, 43,57, 74,87
classes=2
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 3,4,5
anchors = 20,29, 26,34, 29,40, 33,44, 35,47, 37,50, 39,52, 43,57, 74,87
classes=2
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 0,1,2
anchors = 20,29, 26,34, 29,40, 33,44, 35,47, 37,50, 39,52, 43,57, 74,87
classes=2
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
================================================
FILE: cfg/yolov3/yolov3-ship.cfg
================================================
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch = 16
subdivisions = 1
width = 416
height = 416
channels = 3
momentum = 0.9
decay = 0.0005
angle = 0
saturation = 1.5
exposure = 1.5
hue = .1
learning_rate = 0.001
burn_in = 1000
max_batches = 500200
policy = steps
steps = 400000,450000
scales = .1,.1
[convolutional]
batch_normalize = 1
filters = 32
size = 3
stride = 1
pad = 1
activation = leaky
# Downsample
[convolutional]
batch_normalize = 1
filters = 64
size = 3
stride = 2
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 32
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 64
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
# Downsample
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 2
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 64
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 64
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
# Downsample
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 2
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
# Downsample
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 2
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
# Downsample
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 2
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
filters = 1024
size = 3
stride = 1
pad = 1
activation = leaky
[shortcut]
from = -3
activation = linear
######################
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
batch_normalize = 1
filters = 512
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 1024
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 30
activation = linear
[yolo]
mask = 6,7,8
anchors = 112,107, 148,331, 184,196, 234,284, 297,135, 297,350, 352,261, 358,193, 377,352
classes = 5
num = 9
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
[route]
layers = -4
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[upsample]
stride = 2
[route]
layers = -1, 61
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 512
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 512
activation = leaky
[convolutional]
batch_normalize = 1
filters = 256
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 512
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 30
activation = linear
[yolo]
mask = 3,4,5
anchors = 112,107, 148,331, 184,196, 234,284, 297,135, 297,350, 352,261, 358,193, 377,352
classes = 5
num = 9
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
[route]
layers = -4
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[upsample]
stride = 2
[route]
layers = -1, 36
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 256
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 256
activation = leaky
[convolutional]
batch_normalize = 1
filters = 128
size = 1
stride = 1
pad = 1
activation = leaky
[convolutional]
batch_normalize = 1
size = 3
stride = 1
pad = 1
filters = 256
activation = leaky
[convolutional]
size = 1
stride = 1
pad = 1
filters = 30
activation = linear
[yolo]
mask = 0,1,2
anchors = 112,107, 148,331, 184,196, 234,284, 297,135, 297,350, 352,261, 358,193, 377,352
classes = 5
num = 9
jitter = .3
ignore_thresh = .7
truth_thresh = 1
random = 1
================================================
FILE: cfg/yolov3/yolov3-spp-matrix.cfg
================================================
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500500
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
### SPP ###
[maxpool]
stride=1
size=5
[route]
layers=-2
[maxpool]
stride=1
size=9
[route]
layers=-4
[maxpool]
stride=1
size=13
[route]
layers=-1,-3,-5,-6
### End SPP ###
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
# 89
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
# 101
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
# 113
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
##################
[route]
layers = 110
# 115
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
# 116
[convolutional]
batch_normalize=1
filters=128
size=1
stride_x=1
stride_y=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 9,10,11
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = 110
# 121
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
# 122
[convolutional]
batch_normalize=1
filters=128
size=1
stride_x=2
stride_y=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 12,13,14
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
##################
[route]
layers = 98
[convolutional]
share_index=115
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
share_index=116
batch_normalize=1
filters=128
size=1
stride_x=1
stride_y=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 15,16,17
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = 98
[convolutional]
share_index=121
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
share_index=122
batch_normalize=1
filters=128
size=1
stride_x=2
stride_y=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 18,19,20
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
##################
[route]
layers = 86
[convolutional]
share_index=115
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
share_index=116
batch_normalize=1
filters=128
size=1
stride_x=1
stride_y=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 21,22,23
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = 86
[convolutional]
share_index=121
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
share_index=122
batch_normalize=1
filters=128
size=1
stride_x=2
stride_y=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 24,25,26
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326, 10,7, 16,15, 33,12, 5,13, 8,30, 17,23, 30,31, 62,23, 59,60, 15,61, 31,45, 30,119, 116,45, 156,99, 373,163, 58,90, 78,198, 187,326
classes=80
num=27
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
================================================
FILE: cfg/yolov3/yolov3-spp-pan-scale.cfg
================================================
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=32
width=544
height=544
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 10000
policy=steps
steps=8000,9000
scales=.1,.1
#policy=sgdr
#sgdr_cycle=1000
#sgdr_mult=2
#steps=4000,6000,8000,9000
#scales=1, 1, 0.1, 0.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
### SPP ###
[maxpool]
stride=1
size=5
[route]
layers=-2
[maxpool]
stride=1
size=9
[route]
layers=-4
[maxpool]
stride=1
size=13
[route]
layers=-1,-3,-5,-6
### End SPP ###
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
########### to [yolo-3]
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
########### to [yolo-2]
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
########### to [yolo-1]
########### features of different layers
[route]
layers=1
[reorg3d]
stride=2
[route]
layers=5,-1
[reorg3d]
stride=2
[route]
layers=12,-1
[reorg3d]
stride=2
[route]
layers=37,-1
[reorg3d]
stride=2
[route]
layers=62,-1
########### [yolo-1]
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=4
[route]
layers = -1,-12
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=340
activation=linear
[yolo]
mask = 0,1,2,3
anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326
classes=80
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.05
random=0
########### [yolo-2]
[route]
layers = -7
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1,-28
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=340
activation=linear
[yolo]
mask = 4,5,6,7
anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326
classes=80
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.1
random=0
########### [yolo-3]
[route]
layers = -14
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[route]
layers = -1,-43
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=340
activation=linear
[yolo]
mask = 8,9,10,11
anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 59,119, 80,80, 116,90, 156,198, 373,326
classes=80
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.2
random=0
================================================
FILE: cfg/yolov3/yolov3-spp.cfg
================================================
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=608
height=608
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
a
gitextract_flko7qgo/
├── EagleEye_normal_prune.py
├── EagleEye_regular_prune.py
├── EagleEye_slim_prune.py
├── LICENSE
├── PTQ.py
├── README.md
├── cfg/
│ ├── yolov2/
│ │ ├── yolov2-hand.cfg
│ │ ├── yolov2-tiny-hand.cfg
│ │ ├── yolov2-tiny.cfg
│ │ └── yolov2.cfg
│ ├── yolov3/
│ │ ├── yolov3-UAV.cfg
│ │ ├── yolov3-asff.cfg
│ │ ├── yolov3-bdd100k.cfg
│ │ ├── yolov3-hand.cfg
│ │ ├── yolov3-onDIOR.cfg
│ │ ├── yolov3-screw.cfg
│ │ ├── yolov3-ship.cfg
│ │ ├── yolov3-spp-matrix.cfg
│ │ ├── yolov3-spp-pan-scale.cfg
│ │ ├── yolov3-spp.cfg
│ │ ├── yolov3-spp3.cfg
│ │ ├── yolov3-visdrone.cfg
│ │ └── yolov3.cfg
│ ├── yolov3-ghostnet/
│ │ └── yolov3-ghost-coco.cfg
│ ├── yolov3-mobilenet/
│ │ ├── yolov3-mobilenet-UAV.cfg
│ │ ├── yolov3-mobilenet-coco.cfg
│ │ ├── yolov3-mobilenet-hand.cfg
│ │ ├── yolov3-mobilenet-screw.cfg
│ │ └── yolov3-mobilenet-visdrone.cfg
│ ├── yolov3-singlechannel/
│ │ └── yolov3-singlechannel.cfg
│ ├── yolov3tiny/
│ │ ├── yolov3-tiny-UAV.cfg
│ │ ├── yolov3-tiny-hand.cfg
│ │ ├── yolov3-tiny-ship-one.cfg
│ │ ├── yolov3-tiny-ship.cfg
│ │ ├── yolov3-tiny.cfg
│ │ ├── yolov3-tiny3.cfg
│ │ ├── yolov3-tiny_bdd100k.cfg
│ │ ├── yolov3-tiny_onDIOR.cfg
│ │ └── yolov3-tiny_visdrone.cfg
│ ├── yolov3tiny-efficientnetB0/
│ │ └── yolov3tiny-efficientnetB0.cfg
│ ├── yolov3tiny-mobilenet-small/
│ │ ├── yolov3tiny-mobilenet-small-UAV.cfg
│ │ ├── yolov3tiny-mobilenet-small-coco.cfg
│ │ ├── yolov3tiny-mobilenet-small-screw.cfg
│ │ └── yolov3tiny-mobilenet-small-visdrone.cfg
│ ├── yolov4/
│ │ ├── yolov4-hand.cfg
│ │ ├── yolov4-relu.cfg
│ │ ├── yolov4-visdrone.cfg
│ │ └── yolov4.cfg
│ └── yolov4tiny/
│ └── yolov4-tiny.cfg
├── convert.py
├── convert_FPGA.py
├── convert_FPGA_2.py
├── data/
│ ├── UAV_Samples_label.data
│ ├── UAV_Samples_label.names
│ ├── bdd100k.data
│ ├── bdd100k.names
│ ├── coco.names
│ ├── coco2014.data
│ ├── coco2017.data
│ ├── dior.data
│ ├── dior.names
│ ├── get_coco2014.sh
│ ├── get_coco2017.sh
│ ├── oxfordhand.data
│ ├── oxfordhand.names
│ ├── screw.data
│ ├── screw.names
│ ├── trainset.data
│ ├── trainset.names
│ ├── visdrone.data
│ └── visdrone.names
├── detect.py
├── info.py
├── layer_channel_prune.py
├── layer_channel_regular_prune.py
├── layer_prune.py
├── models.py
├── normal_prune.py
├── regular_prune.py
├── requirements.txt
├── shortcut_prune.py
├── slim_prune.py
├── test.py
├── train.py
├── utils/
│ ├── __init__.py
│ ├── adabound.py
│ ├── datasets.py
│ ├── gcp.sh
│ ├── google_utils.py
│ ├── layers.py
│ ├── output_upsample.py
│ ├── parse_config.py
│ ├── prune_utils.py
│ ├── quantized/
│ │ ├── __init__.py
│ │ ├── quantized_TPSQ.py
│ │ ├── quantized_dorefa.py
│ │ ├── quantized_google.py
│ │ ├── quantized_lowbit.py
│ │ ├── quantized_ptq.py
│ │ └── quantized_ptq_cos.py
│ ├── torch_utils.py
│ └── utils.py
└── weights/
└── pretrain_weights/
└── download_yolov3_weights.sh
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...
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"path": "EagleEye_regular_prune.py",
"chars": 14538,
"preview": "from models import *\nfrom utils.utils import *\nfrom utils.prune_utils import *\nfrom utils.datasets import *\nimport os\nim"
},
{
"path": "EagleEye_slim_prune.py",
"chars": 14177,
"preview": "from models import *\nfrom utils.utils import *\nfrom utils.prune_utils import *\nfrom utils.datasets import *\nimport os\nim"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "PTQ.py",
"chars": 6207,
"preview": "import argparse\nimport test\nfrom torch.utils.data import DataLoader\nfrom models import *\nfrom utils.datasets import *\nfr"
},
{
"path": "README.md",
"chars": 18971,
"preview": "# YOLOv3-ModelCompression-MultidatasetTraining\n\nThis project mainly include three parts.\n\n1.Provides training methods fo"
},
{
"path": "cfg/yolov2/yolov2-hand.cfg",
"chars": 2764,
"preview": "[net]\n# Testing\nbatch = 1\nsubdivisions = 1\n# Training\n# batch=64\n# subdivisions=8\nwidth = 608\nheight = 608\nchannels = 3\n"
},
{
"path": "cfg/yolov2/yolov2-tiny-hand.cfg",
"chars": 1466,
"preview": "[net]\nbatch = 64\nsubdivisions = 8\nwidth = 416\nheight = 416\nchannels = 3\nmomentum = 0.9\ndecay = 0.0005\nangle = 0\nsaturati"
},
{
"path": "cfg/yolov2/yolov2-tiny.cfg",
"chars": 1471,
"preview": "[net]\nbatch = 64\nsubdivisions = 8\nwidth = 416\nheight = 416\nchannels = 3\nmomentum = 0.9\ndecay = 0.0005\nangle = 0\nsaturati"
},
{
"path": "cfg/yolov2/yolov2.cfg",
"chars": 2769,
"preview": "[net]\n# Testing\nbatch = 1\nsubdivisions = 1\n# Training\n# batch=64\n# subdivisions=8\nwidth = 608\nheight = 608\nchannels = 3\n"
},
{
"path": "cfg/yolov3/yolov3-UAV.cfg",
"chars": 8277,
"preview": "\n[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0."
},
{
"path": "cfg/yolov3/yolov3-asff.cfg",
"chars": 9310,
"preview": "# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3\n# def kmean_anchors"
},
{
"path": "cfg/yolov3/yolov3-bdd100k.cfg",
"chars": 8281,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3/yolov3-hand.cfg",
"chars": 8292,
"preview": "\n[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0."
},
{
"path": "cfg/yolov3/yolov3-onDIOR.cfg",
"chars": 8287,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3/yolov3-screw.cfg",
"chars": 8400,
"preview": "[net]\n# Testing\n# batch=1\n# subdivisions=1\n# Training\nbatch=64\nsubdivisions=16\nwidth=256\nheight=256\nchannels=3\nmomentu"
},
{
"path": "cfg/yolov3/yolov3-ship.cfg",
"chars": 9386,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch = 16\nsubdivisions = 1\nwidth = 416\nheight = 416\nchannels = 3\nmo"
},
{
"path": "cfg/yolov3/yolov3-spp-matrix.cfg",
"chars": 13427,
"preview": "[net]\n# Testing\n# batch=1\n# subdivisions=1\n# Training\nbatch=64\nsubdivisions=16\nwidth=416\nheight=416\nchannels=3\nmomentum="
},
{
"path": "cfg/yolov3/yolov3-spp-pan-scale.cfg",
"chars": 9600,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=32\nwidth=544\nheight=544\nchannels=3\nmomentum=0."
},
{
"path": "cfg/yolov3/yolov3-spp.cfg",
"chars": 8605,
"preview": "[net]\n# Testing\n# batch=1\n# subdivisions=1\n# Training\nbatch=64\nsubdivisions=16\nwidth=608\nheight=608\nchannels=3\nmomentum="
},
{
"path": "cfg/yolov3/yolov3-spp3.cfg",
"chars": 8959,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=16\nwidth=608\nheight=608\nchannels=3\nmomentum="
},
{
"path": "cfg/yolov3/yolov3-visdrone.cfg",
"chars": 8335,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3/yolov3.cfg",
"chars": 8338,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-ghostnet/yolov3-ghost-coco.cfg",
"chars": 13258,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-mobilenet/yolov3-mobilenet-UAV.cfg",
"chars": 7553,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-mobilenet/yolov3-mobilenet-coco.cfg",
"chars": 7616,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-mobilenet/yolov3-mobilenet-hand.cfg",
"chars": 7568,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-mobilenet/yolov3-mobilenet-screw.cfg",
"chars": 7568,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-mobilenet/yolov3-mobilenet-visdrone.cfg",
"chars": 7613,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3-singlechannel/yolov3-singlechannel.cfg",
"chars": 9386,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch = 16\nsubdivisions = 1\nwidth = 416\nheight = 416\nchannels = 1\nmo"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny-UAV.cfg",
"chars": 2027,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9\nd"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny-hand.cfg",
"chars": 2029,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\nbatch=16\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9\nd"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny-ship-one.cfg",
"chars": 1429,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=2\nwidth=416\nheight=416\nchannels=3\nmomentum=0"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny-ship.cfg",
"chars": 1917,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=2\nwidth=416\nheight=416\nchannels=3\nmomentum=0"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny.cfg",
"chars": 1915,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=2\nwidth=416\nheight=416\nchannels=3\nmomentum=0"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny3.cfg",
"chars": 2457,
"preview": "[net]\n# Testing\n# batch=1\n# subdivisions=1\n# Training\nbatch=64\nsubdivisions=16\nwidth=608\nheight=608\nchannels=3\nmomentum="
},
{
"path": "cfg/yolov3tiny/yolov3-tiny_bdd100k.cfg",
"chars": 1997,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=2\nwidth=416\nheight=416\nchannels=3\nmomentum=0"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny_onDIOR.cfg",
"chars": 2011,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=2\nwidth=416\nheight=416\nchannels=3\nmomentum=0"
},
{
"path": "cfg/yolov3tiny/yolov3-tiny_visdrone.cfg",
"chars": 1879,
"preview": "[net]\n# Testing\nbatch=1\nsubdivisions=1\n# Training\n# batch=64\n# subdivisions=2\nwidth=416\nheight=416\nchannels=3\nmomentum=0"
},
{
"path": "cfg/yolov3tiny-efficientnetB0/yolov3tiny-efficientnetB0.cfg",
"chars": 12353,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=8\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov3tiny-mobilenet-small/yolov3tiny-mobilenet-small-UAV.cfg",
"chars": 5186,
"preview": "[net]\n# Testing\nbatch = 1\nsubdivisions = 1\n# Training\n# batch=64\n# subdivisions=2\nwidth = 416\nheight = 416\nchannels = 3\n"
},
{
"path": "cfg/yolov3tiny-mobilenet-small/yolov3tiny-mobilenet-small-coco.cfg",
"chars": 5212,
"preview": "[net]\n# Testing\nbatch = 1\nsubdivisions = 1\n# Training\n# batch=64\n# subdivisions=2\nwidth = 416\nheight = 416\nchannels = 3\n"
},
{
"path": "cfg/yolov3tiny-mobilenet-small/yolov3tiny-mobilenet-small-screw.cfg",
"chars": 5190,
"preview": "[net]\n# Testing\nbatch = 1\nsubdivisions = 1\n# Training\n# batch=64\n# subdivisions=2\nwidth = 416\nheight = 416\nchannels = 3\n"
},
{
"path": "cfg/yolov3tiny-mobilenet-small/yolov3tiny-mobilenet-small-visdrone.cfg",
"chars": 5176,
"preview": "[net]\n# Testing\nbatch = 1\nsubdivisions = 1\n# Training\n# batch=64\n# subdivisions=2\nwidth = 416\nheight = 416\nchannels = 3\n"
},
{
"path": "cfg/yolov4/yolov4-hand.cfg",
"chars": 12154,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=8\nwidth=608\nheight=608\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov4/yolov4-relu.cfg",
"chars": 12279,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=8\nwidth=608\nheight=608\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov4/yolov4-visdrone.cfg",
"chars": 12205,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=8\nwidth=608\nheight=608\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov4/yolov4.cfg",
"chars": 12209,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=8\nwidth=608\nheight=608\nchannels=3\nmomentum=0.9"
},
{
"path": "cfg/yolov4tiny/yolov4-tiny.cfg",
"chars": 3026,
"preview": "[net]\n# Testing\n#batch=1\n#subdivisions=1\n# Training\nbatch=64\nsubdivisions=1\nwidth=416\nheight=416\nchannels=3\nmomentum=0.9"
},
{
"path": "convert.py",
"chars": 1517,
"preview": "# Author:LiPu\nimport argparse\nfrom sys import platform\n\nfrom models import *\nfrom utils.datasets import *\nfrom utils.uti"
},
{
"path": "convert_FPGA.py",
"chars": 10026,
"preview": "import argparse\nimport struct\n\nfrom models import * # set ONNX_EXPORT in models.py\n\nfrom utils.utils import *\n\n\ndef con"
},
{
"path": "convert_FPGA_2.py",
"chars": 9934,
"preview": "import argparse\nimport struct\n\nfrom models import * # set ONNX_EXPORT in models.py\n\nfrom utils.utils import *\n\n\ndef con"
},
{
"path": "data/UAV_Samples_label.data",
"chars": 123,
"preview": "classes= 1\ntrain=data/UAV_Samples_label/train.txt\nvalid=data/UAV_Samples_label/test.txt\nnames=data/UAV_Samples_label.nam"
},
{
"path": "data/UAV_Samples_label.names",
"chars": 4,
"preview": "UAV\n"
},
{
"path": "data/bdd100k.data",
"chars": 94,
"preview": "classes= 10\ntrain=data/bdd100k/train.txt\nvalid=data/bdd100k/test.txt\nnames=data/bdd100k.names\n"
},
{
"path": "data/bdd100k.names",
"chars": 71,
"preview": "car\nbus\nperson\nbike\ntruck\nmotor\ntrain\nrider\ntraffic sign\ntraffic light\n"
},
{
"path": "data/coco.names",
"chars": 621,
"preview": "person\nbicycle\ncar\nmotorcycle\nairplane\nbus\ntrain\ntruck\nboat\ntraffic light\nfire hydrant\nstop sign\nparking meter\nbench\nbir"
},
{
"path": "data/coco2014.data",
"chars": 99,
"preview": "classes=80\ntrain=data/coco2014/train2014.txt\nvalid=data/coco2014/val2014.txt\nnames=data/coco.names\n"
},
{
"path": "data/coco2017.data",
"chars": 99,
"preview": "classes=80\ntrain=data/coco2017/train2017.txt\nvalid=data/coco2017/val2017.txt\nnames=data/coco.names\n"
},
{
"path": "data/dior.data",
"chars": 95,
"preview": "classes= 20\ntrain=data/DIOR-full/train.txt\nvalid=data/DIOR-full/test.txt\nnames=data/dior.names\n"
},
{
"path": "data/dior.names",
"chars": 224,
"preview": "airplane\nairport\nbaseballfield\nbasketballcourt\nbridge\nchimney\ndam\nExpressway-Service-area\nExpressway-toll-station\ngolffi"
},
{
"path": "data/get_coco2014.sh",
"chars": 824,
"preview": "#!/bin/bash\n# Zip coco folder\n# zip -r coco.zip coco\n# tar -czvf coco.tar.gz coco\n\n# Download labels from Google Drive, "
},
{
"path": "data/get_coco2017.sh",
"chars": 824,
"preview": "#!/bin/bash\n# Zip coco folder\n# zip -r coco.zip coco\n# tar -czvf coco.tar.gz coco\n\n# Download labels from Google Drive, "
},
{
"path": "data/oxfordhand.data",
"chars": 91,
"preview": "classes= 1\ntrain=data/hand/train.txt\nvalid=data/hand/valid.txt\nnames=data/oxfordhand.names\n"
},
{
"path": "data/oxfordhand.names",
"chars": 5,
"preview": "hand\n"
},
{
"path": "data/screw.data",
"chars": 113,
"preview": "classes= 2\ntrain = data/screw/train.txt\nvalid = data/screw/valid.txt\nnames = data/screw.names\nbackup = backup\n\n"
},
{
"path": "data/screw.names",
"chars": 13,
"preview": "noscrew\nscrew"
},
{
"path": "data/trainset.data",
"chars": 95,
"preview": "classes=5\ntrain=data/trainset/train.txt\nvalid=data/trainset/test.txt\nnames=data/trainset.names\n"
},
{
"path": "data/trainset.names",
"chars": 38,
"preview": "Freedom\nBurke\nNimitz\nWasp\nTiconderoga\n"
},
{
"path": "data/visdrone.data",
"chars": 97,
"preview": "classes= 10\ntrain=data/visdrone/train.txt\nvalid=data/visdrone/test.txt\nnames=data/visdrone.names\n"
},
{
"path": "data/visdrone.names",
"chars": 75,
"preview": "pedestrian\npeople\nbicycle\ncar\nvan\ntruck\ntricycle\nawning-tricycle\nbus\nmotor\n"
},
{
"path": "detect.py",
"chars": 10722,
"preview": "import argparse\nfrom utils import output_upsample\n\nfrom models import *\nfrom utils.datasets import *\nfrom utils.utils im"
},
{
"path": "info.py",
"chars": 560,
"preview": "# Author:LiPu\nimport argparse\nfrom models import *\nfrom torchsummary import summary\n\nparser = argparse.ArgumentParser()\n"
},
{
"path": "layer_channel_prune.py",
"chars": 12996,
"preview": "from models import *\nfrom utils.utils import *\nimport numpy as np\nfrom copy import deepcopy\nfrom test import test\nfrom t"
},
{
"path": "layer_channel_regular_prune.py",
"chars": 15990,
"preview": "from models import *\nfrom utils.utils import *\nimport numpy as np\nfrom copy import deepcopy\nfrom test import test\nfrom t"
},
{
"path": "layer_prune.py",
"chars": 8676,
"preview": "from models import *\nfrom utils.utils import *\nimport torch\nimport numpy as np\nfrom copy import deepcopy\nfrom test impor"
},
{
"path": "models.py",
"chars": 48007,
"preview": "from utils.google_utils import *\nfrom utils.parse_config import *\nfrom utils.quantized.quantized_google import *\nfrom ut"
},
{
"path": "normal_prune.py",
"chars": 8395,
"preview": "from models import *\nfrom utils.utils import *\nimport torch\nimport numpy as np\nfrom copy import deepcopy\nfrom test impor"
},
{
"path": "regular_prune.py",
"chars": 10431,
"preview": "from models import *\nfrom utils.utils import *\nimport torch\nimport numpy as np\nfrom copy import deepcopy\nfrom test impor"
},
{
"path": "requirements.txt",
"chars": 692,
"preview": "# pip install -U -r requirements.txt\nCython\nnumpy==1.17\nopencv-python\ntorch>=1.5.1\nmatplotlib\npillow\ntensorboard\ntorchvi"
},
{
"path": "shortcut_prune.py",
"chars": 9892,
"preview": "import argparse\n\nfrom models import *\nfrom utils.utils import *\nimport torch\nimport numpy as np\nfrom copy import deepcop"
},
{
"path": "slim_prune.py",
"chars": 7957,
"preview": "from models import *\nfrom utils.utils import *\nimport numpy as np\nfrom copy import deepcopy\nfrom test import test\nfrom t"
},
{
"path": "test.py",
"chars": 13908,
"preview": "import argparse\nimport json\n\nfrom torch.utils.data import DataLoader\nfrom models import *\nfrom utils.datasets import *\nf"
},
{
"path": "train.py",
"chars": 35963,
"preview": "import argparse\n\nimport torch.optim as optim\nimport torch.optim.lr_scheduler as lr_scheduler\nfrom torch.utils.tensorboar"
},
{
"path": "utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "utils/adabound.py",
"chars": 11353,
"preview": "import math\n\nimport torch\nfrom torch.optim.optimizer import Optimizer\n\n\nclass AdaBound(Optimizer):\n \"\"\"Implements Ada"
},
{
"path": "utils/datasets.py",
"chars": 44242,
"preview": "import glob\nimport math\nimport os\nimport random\nimport shutil\nimport time\nfrom pathlib import Path\nfrom threading import"
},
{
"path": "utils/gcp.sh",
"chars": 2170,
"preview": "#!/usr/bin/env bash\n\n# New VM\nrm -rf sample_data yolov3\ngit clone https://github.com/ultralytics/yolov3\n# git clone -b t"
},
{
"path": "utils/google_utils.py",
"chars": 2666,
"preview": "# This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries\n# pip install --upgrade goo"
},
{
"path": "utils/layers.py",
"chars": 6277,
"preview": "from utils.utils import *\n\n\ndef make_divisible(v, divisor):\n # Function ensures all layers have a channel number that"
},
{
"path": "utils/output_upsample.py",
"chars": 3916,
"preview": "import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport os\nfrom utils.parse_config import *\n\n\n# cfg = './"
},
{
"path": "utils/parse_config.py",
"chars": 3000,
"preview": "import os\n\nimport numpy as np\n\n\ndef parse_model_cfg(path):\n # Parse the yolo *.cfg file and return module definitions"
},
{
"path": "utils/prune_utils.py",
"chars": 18748,
"preview": "import torch\nfrom terminaltables import AsciiTable\nfrom copy import deepcopy\nimport numpy as np\nimport torch.nn.function"
},
{
"path": "utils/quantized/__init__.py",
"chars": 14,
"preview": "# Author:LiPu\n"
},
{
"path": "utils/quantized/quantized_TPSQ.py",
"chars": 23696,
"preview": "# Author:LiPu\nimport numpy as np\nimport os\nimport time\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.fu"
},
{
"path": "utils/quantized/quantized_dorefa.py",
"chars": 17962,
"preview": "# Author:LiPu\nimport time\nimport numpy as np\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as"
},
{
"path": "utils/quantized/quantized_google.py",
"chars": 73442,
"preview": "import copy\nimport math\nimport time\nimport numpy as np\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.fun"
},
{
"path": "utils/quantized/quantized_lowbit.py",
"chars": 3948,
"preview": "# Author:LiPu\nimport math\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Function\nimport torch.nn.functio"
},
{
"path": "utils/quantized/quantized_ptq.py",
"chars": 40920,
"preview": "# Author:LiPu\nimport math\nimport time\nimport numpy as np\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.f"
},
{
"path": "utils/quantized/quantized_ptq_cos.py",
"chars": 74927,
"preview": "# Author:LiPu\nimport math\nimport numpy as np\nimport os\nimport copy\nimport torch\nimport torch.nn as nn\nimport torch.nn.fu"
},
{
"path": "utils/torch_utils.py",
"chars": 8241,
"preview": "from copy import deepcopy\n\nimport torch.backends.cudnn as cudnn\nfrom utils.quantized.quantized_google import *\n\n\ndef ini"
},
{
"path": "utils/utils.py",
"chars": 57832,
"preview": "import glob\nimport math\nimport os\nimport random\nimport shutil\nimport subprocess\nfrom pathlib import Path\nfrom sys import"
},
{
"path": "weights/pretrain_weights/download_yolov3_weights.sh",
"chars": 896,
"preview": "#!/bin/bash\n\n# make '/weights' directory if it does not exist and cd into it\n# mkdir -p weights && cd weights\n\n# copy da"
}
]
About this extraction
This page contains the full source code of the SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 103 files (972.9 KB), approximately 288.6k tokens, and a symbol index with 471 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.