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, encoder):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super(RetinaFace,self).__init__()
backbone = None
if encoder == "mobilenet":
self.cfg = cfg_mnet
backbone = MobileNetV1()
elif encoder == "resnet":
import torchvision.models as models
self.cfg = cfg_re50
backbone = models.resnet50(pretrained=False)
else:
raise NotImplementedError("UNKOWN BACKBONE ARCHITECTURE: {}".format(encoder))
self.body = _utils.IntermediateLayerGetter(backbone, self.cfg['return_layers'])
in_channels_stage2 = self.cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = self.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=self.cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=self.cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=self.cfg['out_channel'])
def __init__(self, cfg):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
super().__init__()
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=False)
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(TinyRetinaFace, self).__init__()
self.phase = phase
backbone = TinyMobileNetV1()
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.ssh1 = TinySSH(in_channels_list[0], out_channels)
self.ssh2 = TinySSH(in_channels_list[1], out_channels)
self.ssh3 = TinySSH(in_channels_list[2], 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,backbone,return_layers,anchor_nums=3):
super(RetinaFace,self).__init__()
# if backbone_name == 'resnet50':
# self.backbone = resnet.resnet50(pretrained)
# self.backbone = resnet.__dict__[backbone_name](pretrained=pretrained)
# self.return_layers = {'layer1': 0, 'layer2': 1, 'layer3': 2, 'layer4': 3}
assert backbone,'Backbone can not be none!'
assert len(return_layers)>0,'There must be at least one return layers'
self.body = _utils.IntermediateLayerGetter(backbone, return_layers)
in_channels_stage2 = 256
# in_channels_stage2 = 64
in_channels_list = [
#in_channels_stage2,
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = 256
self.fpn = FeaturePyramidNetwork(in_channels_list,out_channels)
# self.ClassHead = ClassHead()
# self.BboxHead = BboxHead()
# self.LandmarkHead = LandmarkHead()
self.ClassHead = self._make_class_head()
self.BboxHead = self._make_bbox_head()
self.LandmarkHead = self._make_landmark_head()
self.anchors = Anchors()
self.regressBoxes = RegressionTransform()
self.losslayer = LossLayer()
def __init__(self, cfg=None):
"""
:param cfg: Network related settings.
"""
super(RetinaFace, self).__init__()
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 = models.mobilenet_v2(pretrained=cfg['pretrain'], width_mult=cfg['width_mult']).features
self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
in_channels_list = [
cfg['in_channel'][0],
cfg['in_channel'][1],
cfg['in_channel'][2],
]
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 select_model(model_name: str) -> t.NoReturn:
"""
API function for Algonauts; for a given model name, change the model of this module to the selected one;
to get a list of all viable models, refer to viable_models
"""
global model_, currently_selected_model, backbone_layer_keys, backbone_return_layers, intermediate_layer_getter
global backbone
model_ = torch.hub.load(torch_dir, model_name, pretrained=True)
model_.to(device)
model_.eval()
currently_selected_model = model_name
if hasattr(model_, "backbone"):
backbone = model_.backbone
elif hasattr(model_, 'features'):
backbone = model_.features
else:
backbone = model_
print("backbone could not be found; defaulting to usual model output")
backbone.to(device)
backbone.eval()
# the names of the layers, that can be extracted from the backbone
backbone_layer_keys = [name for name, module in backbone.named_children()]
# preparing the model, that can return all layers specified in backbone_layer_keys
backbone_return_layers = {
layer_name: layer_name
for layer_name in backbone_layer_keys
}
intermediate_layer_getter = _utils.IntermediateLayerGetter(
backbone, backbone_return_layers) # used to generate all features
intermediate_layer_getter.to(device)
intermediate_layer_getter.eval()
def __init__(self):
super(RetinaFace,self).__init__()
backbone = MobileNetV1()
return_layers = {'stage1': 1, 'stage2': 2, 'stage3': 3}
in_channels = 32
out_channels = 64
self.body = _utils.IntermediateLayerGetter(backbone, return_layers)
in_channels_stage2 = in_channels
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = out_channels
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=out_channels)
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=out_channels)
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=out_channels)
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.phase = phase
backbone = None
if cfg["name"] == "mobilenet0.25":
backbone = MobileNetV1()
elif cfg["name"] == "Resnet50":
import torchvision.models as models
backbone = models.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,
]
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, pretrained = False, mode = 'train'):
super(RetinaFace,self).__init__()
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if pretrained:
checkpoint = torch.load("./model_data/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:]
new_state_dict[name] = v
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
backbone = models.resnet50(pretrained=pretrained)
self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
# 获得每个初步有效特征层的通道数
in_channels_list = [cfg['in_channel'] * 2, cfg['in_channel'] * 4, cfg['in_channel'] * 8]
self.fpn = FPN(in_channels_list, cfg['out_channel'])
# 利用ssh模块提高模型感受野
self.ssh1 = SSH(cfg['out_channel'], cfg['out_channel'])
self.ssh2 = SSH(cfg['out_channel'], cfg['out_channel'])
self.ssh3 = SSH(cfg['out_channel'], cfg['out_channel'])
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'])
self.mode = mode
def __init__(self, name: str, pretrained: bool, in_channels: int,
return_layers: Dict[str, int], out_channels: int) -> None:
super().__init__()
if name == "Resnet50":
backbone = models.resnet50(pretrained=pretrained)
else:
raise NotImplementedError(
f"Only Resnet50 backbone is supported but got {name}")
self.body = _utils.IntermediateLayerGetter(backbone, return_layers)
in_channels_stage2 = in_channels
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
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,
in_channels=out_channels)
self.BboxHead = self._make_bbox_head(fpn_num=3,
in_channels=out_channels)
self.LandmarkHead = self._make_landmark_head(fpn_num=3,
in_channels=out_channels)
def __init__(self, phase = 'train', net = 'mnet0.25', return_layers = {'stage1': 1, 'stage2': 2, 'stage3': 3}):
super(RetinaFace,self).__init__()
self.phase = phase
backbone = None
if net == 'mnet0.25':
backbone = MobileNetV1()
if True:
checkpoint = torch.load("model_best.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
# load params
backbone.load_state_dict(new_state_dict)
self.body = _utils.IntermediateLayerGetter(backbone, return_layers)
in_channels_stage2 = 32
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = 64
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()
self.BboxHead = self._make_bbox_head()
self.LandmarkHead = self._make_landmark_head()
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',
calculate_prior_boxes=False,
backbone=None,
):
"""
: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.cfg = cfg
self.calculate_prior_boxes = calculate_prior_boxes
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(RetinaTrackNet, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(backbone,
cfg['return_layers'])
else:
pass
anchorNum = cfg[
'anchorNum_per_stage'] ##not total anchor,indicate per stage anchors
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)
##task shared
self.task_shared = task_shared(out_channels,
out_channels,
anchorNum=anchorNum)
##task specific
self.cls_task = task_specific_cls(out_channels, out_channels)
self.loc_task = task_specific_loc(out_channels, out_channels)
self.emb_task = task_specific_emb(out_channels, out_channels)
###head
self.cls_heads = make_cls_head(inp=out_channels,
fpnNum=len(in_channels_list),
anchorNum=anchorNum)
self.loc_heads = make_loc_head(inp=out_channels,
fpnNum=len(in_channels_list),
anchorNum=anchorNum)
self.emb_heads = make_emb_head(inp=out_channels,
fpnNum=len(in_channels_list),
anchorNum=anchorNum)
###classifier ,ndim of emb dims and num of ids
self.classifier = nn.Linear(256, 7)
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, 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,
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'):
"""
phase: train / test
"""
super(RetinaFace, self).__init__()
print('RetinaFace work model : {}'.format(phase))
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
print(
'Loading pretrain model: ./weights/mobilenetV1X0.25_pretrain.tar'
)
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,
]
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, class_num=3, 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'] == 'mobilenet':
backbone = MobileNetV1()
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
elif cfg['name'] == 'resnet18':
import torchvision.models as models
backbone = models.resnet18(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(
backbone, cfg['return_layers']) # get output of each 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']
# in_channel 256 512 1024
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(class_num,
fpn_num=3,
inchannels=cfg['out_channel'],
anchor_num=5)
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'],
anchor_num=5)
def __init__(self):
super(RetinaFaceModel, self).__init__()
backbone = models.resnet50(pretrained=False)
self.body = _utils.IntermediateLayerGetter(
model=backbone,
return_layers=CONFIG_RESNET_50['backbone_layers_to_extract'])
in_channels_stage2 = CONFIG_RESNET_50['in_channel']
in_channels_list = (in_channels_stage2 * 2, in_channels_stage2 * 4,
in_channels_stage2 * 8)
out_channels = CONFIG_RESNET_50['out_channel']
self.fpn = FPN(in_channels_list=in_channels_list,
out_channels=out_channels)
self.ssh1 = SSH(in_channels=out_channels, out_channels=out_channels)
self.ssh2 = SSH(in_channels=out_channels, out_channels=out_channels)
self.ssh3 = SSH(in_channels=out_channels, out_channels=out_channels)
self.ClassHead = self.__make_class_head(
heads_num=3, in_channels=CONFIG_RESNET_50['out_channel'])
self.BboxHead = self.__make_bbox_head(
heads_num=3, in_channels=CONFIG_RESNET_50['out_channel'])
self.LandmarkHead = self.__make_landmark_head(
heads_num=3, in_channels=CONFIG_RESNET_50['out_channel'])
def __init__(self, cfg=None):
super(Retina, self).__init__()
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, phase='test', weights=None):
"""
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = MobileNetV1()
# if weights is not None:
# 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, {
'stage1': 1,
'stage2': 2,
'stage3': 3
})
in_channels_stage2 = 32
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = 64
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=64)
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=64)
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=64)
def __init__(self):
super(RetinaFace, self).__init__()
backbone = models.resnet50(pretrained=False)
self.body = _utils.IntermediateLayerGetter(backbone, {
'layer2': 1,
'layer3': 2,
'layer4': 3
})
in_channels_stage2 = 256
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = 256
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=256)
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=256)
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=256)
def __init__(self, cfg=None, phase='train'):
super(RetinaFace, self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25_retinaface':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load('', 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_ratinaface':
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.BoxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
def __init__(self, model, phase='train'):
"""
:param model: name of features extractor model.
:param phase: train or test.
"""
super(RetinaFace, self).__init__()
self.phase = phase
backbone = model
self.body = _utils.IntermediateLayerGetter(backbone, {
'stage2': 1,
'stage3': 2,
'stage4': 3
})
in_channels_stage2 = 58
in_channels_list = [
in_channels_stage2 * 2, in_channels_stage2 * 4,
in_channels_stage2 * 8
]
out_channels = 116
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=out_channels)
self.BboxHead = self._make_bbox_head(fpn_num=3,
inchannels=out_channels)
self.LandmarkHead = self._make_landmark_head(fpn_num=3,
inchannels=out_channels)
