import cv2
import torch
from midas.dpt_depth import DPTDepthModel
from midas.midas_net import MidasNet
from midas.midas_net_custom import MidasNet_small
from midas.transforms import Resize, NormalizeImage, PrepareForNet
from torchvision.transforms import Compose
default_models = {
"dpt_beit_large_512": "weights/dpt_beit_large_512.pt",
"dpt_beit_large_384": "weights/dpt_beit_large_384.pt",
"dpt_beit_base_384": "weights/dpt_beit_base_384.pt",
"dpt_swin2_large_384": "weights/dpt_swin2_large_384.pt",
"dpt_swin2_base_384": "weights/dpt_swin2_base_384.pt",
"dpt_swin2_tiny_256": "weights/dpt_swin2_tiny_256.pt",
"dpt_swin_large_384": "weights/dpt_swin_large_384.pt",
"dpt_next_vit_large_384": "weights/dpt_next_vit_large_384.pt",
"dpt_levit_224": "weights/dpt_levit_224.pt",
"dpt_large_384": "weights/dpt_large_384.pt",
"dpt_hybrid_384": "weights/dpt_hybrid_384.pt",
"midas_v21_384": "weights/midas_v21_384.pt",
"midas_v21_small_256": "weights/midas_v21_small_256.pt",
"openvino_midas_v21_small_256": "weights/openvino_midas_v21_small_256.xml",
}
def load_model(device, model_path, model_type="dpt_large_384", optimize=True, height=None, square=False):
"""Load the specified network.
Args:
device (device): the torch device used
model_path (str): path to saved model
model_type (str): the type of the model to be loaded
optimize (bool): optimize the model to half-integer on CUDA?
height (int): inference encoder image height
square (bool): resize to a square resolution?
Returns:
The loaded network, the transform which prepares images as input to the network and the dimensions of the
network input
"""
if "openvino" in model_type:
from openvino.runtime import Core
keep_aspect_ratio = not square
if model_type == "dpt_beit_large_512":
model = DPTDepthModel(
path=model_path,
backbone="beitl16_512",
non_negative=True,
)
net_w, net_h = 512, 512
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_beit_large_384":
model = DPTDepthModel(
path=model_path,
backbone="beitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_beit_base_384":
model = DPTDepthModel(
path=model_path,
backbone="beitb16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_swin2_large_384":
model = DPTDepthModel(
path=model_path,
backbone="swin2l24_384",
non_negative=True,
)
net_w, net_h = 384, 384
keep_aspect_ratio = False
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_swin2_base_384":
model = DPTDepthModel(
path=model_path,
backbone="swin2b24_384",
non_negative=True,
)
net_w, net_h = 384, 384
keep_aspect_ratio = False
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_swin2_tiny_256":
model = DPTDepthModel(
path=model_path,
backbone="swin2t16_256",
non_negative=True,
)
net_w, net_h = 256, 256
keep_aspect_ratio = False
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_swin_large_384":
model = DPTDepthModel(
path=model_path,
backbone="swinl12_384",
non_negative=True,
)
net_w, net_h = 384, 384
keep_aspect_ratio = False
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_next_vit_large_384":
model = DPTDepthModel(
path=model_path,
backbone="next_vit_large_6m",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
# We change the notation from dpt_levit_224 (MiDaS notation) to levit_384 (timm notation) here, where the 224 refers
# to the resolution 224x224 used by LeViT and 384 is the first entry of the embed_dim, see _cfg and model_cfgs of
# https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/levit.py
# (commit id: 927f031293a30afb940fff0bee34b85d9c059b0e)
elif model_type == "dpt_levit_224":
model = DPTDepthModel(
path=model_path,
backbone="levit_384",
non_negative=True,
head_features_1=64,
head_features_2=8,
)
net_w, net_h = 224, 224
keep_aspect_ratio = False
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_large_384":
model = DPTDepthModel(
path=model_path,
backbone="vitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid_384":
model = DPTDepthModel(
path=model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21_384":
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif model_type == "midas_v21_small_256":
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif model_type == "openvino_midas_v21_small_256":
ie = Core()
uncompiled_model = ie.read_model(model=model_path)
model = ie.compile_model(uncompiled_model, "CPU")
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
if not "openvino" in model_type:
print("Model loaded, number of parameters = {:.0f}M".format(sum(p.numel() for p in model.parameters()) / 1e6))
else:
print("Model loaded, optimized with OpenVINO")
if "openvino" in model_type:
keep_aspect_ratio = False
if height is not None:
net_w, net_h = height, height
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=keep_aspect_ratio,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
if not "openvino" in model_type:
model.eval()
if optimize and (device == torch.device("cuda")):
if not "openvino" in model_type:
model = model.to(memory_format=torch.channels_last)
model = model.half()
else:
print("Error: OpenVINO models are already optimized. No optimization to half-float possible.")
exit()
if not "openvino" in model_type:
model.to(device)
return model, transform, net_w, net_h