def DPT_Hybrid_NYU(pretrained=True, **kwargs):
""" # This docstring shows up in hub.help()
MiDaS DPT-Hybrid model for monocular depth estimation
pretrained (bool): load pretrained weights into model
"""
model = DPTDepthModel(
path=None,
scale=0.000305,
shift=0.1378,
invert=True,
backbone="vitb_rn50_384",
non_negative=True,
)
if pretrained:
checkpoint = (
"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_nyu-2ce69ec7.pt"
)
state_dict = torch.hub.load_state_dict_from_url(
checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
)
model.load_state_dict(state_dict)
return model
def DPT_Large(pretrained=True, **kwargs):
""" # This docstring shows up in hub.help()
MiDaS DPT-Large model for monocular depth estimation
pretrained (bool): load pretrained weights into model
"""
model = DPTDepthModel(
path=None,
backbone="vitl16_384",
non_negative=True,
)
if pretrained:
checkpoint = (
"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt"
)
state_dict = torch.hub.load_state_dict_from_url(
checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
)
model.load_state_dict(state_dict)
return model