def __init__(self, config, device='cuda:0'):
super().__init__()
self.num_joints = config.model.backbone.num_joints
self.volume_aggregation_method = config.model.volume_aggregation_method
# volume
self.volume_softmax = config.model.volume_softmax
self.volume_multiplier = config.model.volume_multiplier
self.volume_size = config.model.volume_size
self.cuboid_side = config.model.cuboid_side
self.kind = config.model.kind
# heatmap
self.heatmap_softmax = config.model.heatmap_softmax
self.heatmap_multiplier = config.model.heatmap_multiplier
# modules
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
self.backbone = pose_resnet.get_pose_net(config.model.backbone,
device=device)
for p in self.backbone.final_layer.parameters():
p.requires_grad = False
self.process_features = nn.Sequential(nn.Conv2d(256, 32, 1))
self.volume_net = V2VModel(32, self.num_joints)
self.inputImageShape = [384, 384]
self.outputHeatmapShape = [96, 96]
def __init__(self, config):
super().__init__()
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
self.backbone = pose_resnet.get_pose_net(config.model.backbone)
# self.return_confidences = config.backbone.return_confidences
self.direct_optimization = config.model.direct_optimization
def __init__(self, config, device='cuda:0'):
super().__init__()
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
self.backbone = pose_resnet.get_pose_net(config.model.backbone,
device=device)
self.direct_optimization = config.model.direct_optimization
def __init__(self, config, device='cuda:0'):
super().__init__()
self.use_confidences = config.model.use_confidences
if config.model.use_default_backbone:
self.backbone = pose_resnet.get_pose_net(config.model.backbone,
config.opt.batch_size,
device=device)
else:
self.backbone = pose_hrnet.get_pose_net(device=device)
self.heatmap_softmax = config.model.heatmap_softmax
self.heatmap_multiplier = config.model.heatmap_multiplier
def __init__(self, config):
super().__init__()
self.use_confidences = config.model.use_confidences
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
if self.use_confidences:
config.model.backbone.alg_confidences = True
self.backbone = pose_resnet.get_pose_net(config.model.backbone)
self.heatmap_softmax = config.model.heatmap_softmax # true
self.heatmap_multiplier = config.model.heatmap_multiplier
def __init__(self, config, device='cuda:0'):
super().__init__()
self.num_joints = config.model.backbone.num_joints
self.volume_aggregation_method = config.model.volume_net.volume_aggregation_method
# volume
self.volume_softmax = config.model.volume_net.volume_softmax
self.volume_multiplier = config.model.volume_net.volume_multiplier
self.volume_size = config.model.volume_net.volume_size
self.cuboid_size = config.model.volume_net.cuboid_size
self.kind = config.model.kind
self.use_gt_pelvis = config.model.volume_net.use_gt_pelvis
# heatmap
self.heatmap_softmax = config.model.heatmap_softmax
self.heatmap_multiplier = config.model.heatmap_multiplier
# transfer
self.transfer_cmu_to_human36m = config.dataset.transfer_cmu_to_human36m
# modules
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
if self.volume_aggregation_method.startswith('conf'):
config.model.backbone.vol_confidences = True
self.backbone = pose_resnet.get_pose_net(config.model.backbone)
self.use_feature = config.model.volume_net.use_feature_v2v
if config.model.backbone.fix_weights:
for p in self.backbone.parameters():
p.requires_grad = False
elif self.use_feature:
for p in self.backbone.final_layer.parameters():
p.requires_grad = False
v2v_in_channel = self.num_joints
if self.use_feature:
self.process_features = nn.Sequential(
nn.Conv2d(256, 32, 1)
)
v2v_in_channel = 32
self.volume_net = V2VNet(v2v_in_channel, self.num_joints, config)
def __init__(self, config, device='cuda:0'):
super().__init__()
self.num_joints = config.model.backbone.num_joints
self.volume_aggregation_method = config.model.volume_aggregation_method
# volume
self.volume_softmax = config.model.volume_softmax
self.volume_multiplier = config.model.volume_multiplier
self.volume_size = config.model.volume_size
self.cuboid_side = config.model.cuboid_side
self.kind = config.model.kind
self.use_gt_pelvis = config.model.use_gt_pelvis
# heatmap
self.heatmap_softmax = config.model.heatmap_softmax
self.heatmap_multiplier = config.model.heatmap_multiplier
# transfer
self.transfer_cmu_to_human36m = config.model.transfer_cmu_to_human36m if hasattr(
config.model, "transfer_cmu_to_human36m") else False
# modules
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
if self.volume_aggregation_method.startswith('conf'):
config.model.backbone.vol_confidences = True
self.backbone = pose_resnet.get_pose_net(config.model.backbone,
config.opt.batch_size,
device=device)
for p in self.backbone.final_layer.parameters():
p.requires_grad = False
self.process_features = nn.Sequential(nn.Conv2d(256, 32, 1))
self.volume_net = V2VModel(32, self.num_joints)
def __init__(self, config, device='cpu'):
super().__init__()
self.num_joints = 17
self.volume_aggregation_method = "softmax"
# volume
self.volume_softmax = True
self.volume_multiplier = 1.0
self.volume_size = 64
self.cuboid_side = 2500.0
self.kind = "mpii"
self.use_gt_pelvis = False
# heatmap
self.heatmap_softmax = True
self.heatmap_multiplier = 1.0
# transfer
self.transfer_cmu_to_human36m = False
# modules
config.model.backbone.alg_confidences = False
config.model.backbone.vol_confidences = False
if self.volume_aggregation_method.startswith('conf'):
config.model.backbone.vol_confidences = True
self.backbone = pose_resnet.get_pose_net(config.model.backbone,
device=device)
for p in self.backbone.final_layer.parameters():
p.requires_grad = False
self.process_features = nn.Sequential(nn.Conv2d(256, 32, 1))
self.volume_net = V2VModel(32, self.num_joints)
