def __init__(self, cfg=None, phase='train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(Retina, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg=None, phase='train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.cfg = cfg
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
elif cfg['name'] == 'effnet':
self.body = load_effnetm()
elif cfg['name'] == 'Res2Net':
from .res2net import res2net101_26w_4s
model = res2net101_26w_4s(pretrained=True)
self.body = _utils.IntermediateLayerGetter(model,
cfg['return_layers'])
if cfg['name'] == 'mobilenet0.25' or cfg['name'] == 'Resnet50':
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
if cfg['name'] == 'effnet':
in_channels_list = [64, 176, 512]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list, out_channels, cfg)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(
fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg=None, phase='train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
# 获取中间层输出
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
# 参考口罩分支，ssh单独新建
self.ssh1_pose = SSH(out_channels, out_channels)
self.ssh2_pose = SSH(out_channels, out_channels)
self.ssh3_pose = SSH(out_channels, out_channels)
# 对应ssh 的 框-关键点-2分类
self.ClassHead = self._make_class_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(
fpn_num=3, inchannels=cfg['out_channel'])
# 对应ssh_pose 的 yaw-pitch-roll 的 3个66分类
self.Pose_yaw_Head = self._make_pose_yaw_pitch_roll_head(
fpn_num=3, inchannels=cfg['out_channel'])
self.Pose_pitch_Head = self._make_pose_yaw_pitch_roll_head(
fpn_num=3, inchannels=cfg['out_channel'])
self.Pose_roll_Head = self._make_pose_yaw_pitch_roll_head(
fpn_num=3, inchannels=cfg['out_channel'])
def _make_body(self, cfg):
backbone = None
model_name = cfg['name']
if model_name == 'mobilenet0.25' or model_name == 'Resnet50':
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
return in_channels_list
elif model_name == 'mobilenetv3':
backbone = MobileNetV3_Small()
if cfg['pretrain']:
checkpoint = torch.load("./weights/mbv3_small.pth.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
backbone.load_state_dict(new_state_dict)
print("Loaded pretrained model!")
self.body = backbone
elif model_name == 'ghostnet':
backbone = ghost_net(width_mult=cfg['width_mult'])
self.body = backbone
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2,
in_channels_stage2 * 2,
in_channels_stage2 * 4,
]
return in_channels_list
def __init__(self, cfg = None, phase = 'train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace,self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
elif cfg['name'] == 'detnas':
print('You were using DetNas as backbone, this is exprimental, may not work.')
self.backbone = ShuffleNetV2DetNAS(model_size='VOC_RetinaNet_300M')
detnas_pretrained = 'pretrained/VOC_RetinaNet_300M.pkl'
if os.path.exists(detnas_pretrained):
checkpoint = torch.load("VOC_RetinaNet_300M.pkl", map_location=torch.device('cpu'))
else:
print('{} does not exist, pls download it so that DetNas can be used. see readme under pretrained/')
exit(0)
self.backbone.load_state_dict(checkpoint)
self.backbone = self.backbone.features
return_layers = {'6': 1, '9': 2, '16': 3}
in_channels_stage2 = 80
self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list,out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg=None, phase='train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
# 改成绝对路径
checkpoint = torch.load(os.path.join(
base_config.root_dir,
r"core\weights\retinaface\mobilenetV1X0.25_pretrain.tar"),
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(
fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg=None, phase="train"):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = None
if cfg["name"] == "mobilenet0.25":
backbone = MobileNetV1()
if cfg["pretrain"]:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device("cpu"),
)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint["state_dict"].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg["name"] == "Resnet50":
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg["pretrain"])
self.body = _utils.IntermediateLayerGetter(backbone, cfg["return_layers"])
in_channels_stage2 = cfg["in_channel"]
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg["out_channel"]
self.fpn = FPN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg["out_channel"])
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg["out_channel"])
self.LandmarkHead = self._make_landmark_head(
fpn_num=3, inchannels=cfg["out_channel"]
)
def __init__(self,
cfg=None,
fpn_level=5,
head=HeadBasic,
outputs=[4, 2, 10]):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(SinvNet, self).__init__()
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels = cfg['in_channel'] * 2
out_channels = cfg['out_channel']
self.downsample = nn.AvgPool2d(3, stride=2, padding=1)
self.conv = conv_bn1X1(in_channels, out_channels, stride=1, leaky=0)
self.fpn_level = fpn_level
self.fpn = FPNLevel(out_channels, fpn_level)
self.ssh = SSHLight(out_channels, out_channels)
self.dropout = nn.Dropout(p=0.3)
self.Heads = nn.ModuleList()
for num_output in outputs:
self.Heads.append(
head(inchannels=out_channels,
num_anchors=1,
num_output=num_output))
def __init__(self, cfg = None, phase = 'train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace,self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'ShuffleNet':
self.body = ShuffleNetV2(scale=1, in_channels=3, c_tag=0.5, num_classes=2, activation=nn.ReLU,
SE=False, residual=False).cuda()
elif cfg['name'] == 'mobilenet0.25':
self.body = MobileNetV1()
# if cfg['pretrain']:
# checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
# from collections import OrderedDict
# new_state_dict = OrderedDict()
# for k, v in checkpoint['state_dict'].items():
# name = k[7:] # remove module.
# new_state_dict[name] = v
# # load params
# backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
# self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list,out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg=None, phase='train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
elif cfg['name'] == 'selecsls60':
self.body = timm.create_model('selecsls60',
features_only=True,
out_indices=(cfg['return_layers']),
pretrained=cfg['pretrain'])
in_channels_list = self.body.feature_info.channels()
elif cfg['name'] == 'mobilenetv3_large_100':
self.body = timm.create_model('mobilenetv3_large_100',
features_only=True,
out_indices=(cfg['return_layers']),
pretrained=cfg['pretrain'])
in_channels_list = self.body.feature_info.channels()
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(
fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg=None, phase='train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(
"./weights/mobilenetV1X0.25_pretrain.tar",
map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
elif cfg['name'] == 'mobilenetv3':
backbone = MobileNetV3()
# bbweight = backbone.state_dict()
# layername = []
# oldln = []
# for i,key in enumerate(bbweight):
# print("Layer {} : {}; Size : {}".format(i,key,bbweight[key].size()))
# layername.append(key)
# cpk = torch.load("/home/hwits/Documents/ImageRetrieval/mobilenetv3/mbv3_small.pth.tar")['state_dict']
# # print(cpk.keys())
# print(cpk['epoch'])
# print("="*200)
# for i,key in enumerate(cpk):
# print("Old Layer {} : {}; Size : {}".format(i,key,cpk[key].size()))
# if i < len(layername):
# oldln.append(key)
# print(bbweight.keys())
# print(oldln)
#
# import collections
# mbv3_cpk = collections.OrderedDict()
# for i in range(len(layername)):
# mbv3_cpk[layername[i]] = cpk[oldln[i]]
#
# torch.save(mbv3_cpk,"mobilenetv3_truck.pth")
if cfg['pretrain']:
checkpoint = torch.load("./weights/mobilenetv3_truck.pth",
map_location=torch.device('cpu'))
print("Loading Pretrained Weights ...")
backbone.load_state_dict(checkpoint)
elif cfg['name'] == 'mobilenetv2_0.1':
backbone = MobileNetV2()
if cfg['pretrain']:
checkpoint = torch.load("./weights/mobilenetv2_0.1_face.pth",
map_location=torch.device('cpu'))
model_dict = backbone.state_dict()
pretrained_dict = {
k: v
for k, v in checkpoint.items() if k in model_dict
} # 用于过滤掉修改结构处的权重
model_dict.update(pretrained_dict)
# print("Pre-Trained:",pretrained_dict.keys())
backbone.load_state_dict(model_dict)
elif cfg['name'] == 'efficientnetb0':
backbone = EfficientNet.from_name("efficientnet-b0")
if cfg['pretrain']:
checkpoint = torch.load("./weights/efficientnetb0_face.pth",
map_location=torch.device('cpu'))
backbone.load_state_dict(checkpoint)
print("succeed loaded weights...")
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
if cfg['name'] == 'mobilenet0.25':
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
# in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
elif cfg['name'] == 'mobilenetv2_0.1':
in_channels_stage3 = cfg['in_channel3']
in_channels_stage4 = cfg['in_channel4']
in_channels_list = [in_channels_stage3, in_channels_stage4]
elif cfg['name'] == 'mobilenetv3':
in_channels_stage1 = cfg['in_channel0']
in_channels_stage2 = cfg['in_channel1']
in_channels_stage3 = cfg['in_channel2']
in_channels_list = [
in_channels_stage1,
in_channels_stage2,
in_channels_stage3,
]
elif cfg['name'] == 'efficientnetb0':
# in_channels_stage2 = cfg['in_channel1']
in_channels_stage3 = cfg['in_channel2']
in_channels_list = [
# in_channels_stage2 * 2,
# in_channels_stage2,
in_channels_stage3,
]
out_channels = cfg['out_channel']
self.pan = PAN(in_channels_list, out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(
fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, cfg = None, phase = 'train'):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace,self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
elif cfg['name'] == 'mobilenetv3':
backbone = MobileNetV3()
# print(backbone)
if cfg['pretrain']:
checkpoint = torch.load("./weights/mobilenetv3.pth", map_location=torch.device('cpu'))
print("Pretrained Weights : ",type(checkpoint))
backbone.load_state_dict(checkpoint)
elif cfg['name'] == 'mobilenetv2_0.1':
backbone = MobileNetV2()
if cfg['pretrain']:
checkpoint = torch.load("./weights/mobilenetv2_0.1_face.pth", map_location=torch.device('cpu'))
backbone.load_state_dict(checkpoint)
elif cfg['name'] == 'efficientnetb0':
backbone = EfficientNet.from_name("efficientnet-b0")
if cfg['pretrain']:
checkpoint = torch.load("./weights/efficientnetb0_face.pth", map_location=torch.device('cpu'))
backbone.load_state_dict(checkpoint)
print("succeed loaded weights...")
self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
if cfg['name'] == 'mobilenet0.25':
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
# in_channels_stage2 * 2,
in_channels_stage2*4,
in_channels_stage2*8,
]
elif cfg['name'] == 'mobilenetv2_0.1':
in_channels_stage2 = cfg['in_channel1']
in_channels_stage3 = cfg['in_channel2']
in_channels_list = [
# in_channels_stage2 * 2,
in_channels_stage2,
in_channels_stage3,
]
elif cfg['name'] == 'mobilenetv3':
in_channels_stage2 = cfg['in_channel1']
in_channels_stage3 = cfg['in_channel2']
in_channels_list = [
# in_channels_stage2 * 2,
in_channels_stage2,
in_channels_stage3,
]
elif cfg['name'] == 'efficientnetb0':
in_channels_stage2 = cfg['in_channel1']
in_channels_stage3 = cfg['in_channel2']
in_channels_list = [
# in_channels_stage2 * 2,
in_channels_stage2,
in_channels_stage3,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list,out_channels)
# self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=2, inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=2, inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=2, inchannels=cfg['out_channel'])
