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hubconf.py
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hubconf.py
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dependencies = ["torch"]
import torch
from midas.dpt_depth import DPTDepthModel
from midas.midas_net import MidasNet
from midas.midas_net_custom import MidasNet_small
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/isl-org/MiDaS/releases/download/v3/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
def DPT_Hybrid(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,
backbone="vitb_rn50_384",
non_negative=True,
)
if pretrained:
checkpoint = (
"https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.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 MiDaS(pretrained=True, **kwargs):
""" # This docstring shows up in hub.help()
MiDaS v2.1 model for monocular depth estimation
pretrained (bool): load pretrained weights into model
"""
model = MidasNet()
if pretrained:
checkpoint = (
"https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.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 MiDaS_small(pretrained=True, **kwargs):
""" # This docstring shows up in hub.help()
MiDaS small model for monocular depth estimation on resource-constrained devices
pretrained (bool): load pretrained weights into model
"""
model = MidasNet_small(None, features=64, backbone="efficientnet_lite3", exportable=True, non_negative=True, blocks={'expand': True})
if pretrained:
checkpoint = (
"https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small-70d6b9c8.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 transforms():
import cv2
from torchvision.transforms import Compose
from midas.transforms import Resize, NormalizeImage, PrepareForNet
from midas import transforms
transforms.default_transform = Compose(
[
lambda img: {"image": img / 255.0},
Resize(
384,
384,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
]
)
transforms.small_transform = Compose(
[
lambda img: {"image": img / 255.0},
Resize(
256,
256,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="upper_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
]
)
transforms.dpt_transform = Compose(
[
lambda img: {"image": img / 255.0},
Resize(
384,
384,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="minimal",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
PrepareForNet(),
lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
]
)
return transforms