Converting Depth-Anything To CoreML

I'm trying to convert existing depth-anything PyTorch model to CoreML format. I decided to use Google Colab and took the following note for inferencing depth-anything model. However, I meet some exception while trying to import it on iOS side. Here is my code snippet for converting:

# Installing all needed extensions
!pip install coremltools
# ...

import coremltools as ct
import torch

# Convert the PyTorch model to TorchScript
traced_model = torch.jit.trace(depth_anything, torch.rand(1, 3, 518, 518))

# Convert the TorchScript model to CoreML
model_coreml = ct.convert(
    traced_model,
    inputs=[ct.ImageType(name="input_1", shape=(1, 3, 518, 518), scale=1/255.0)]
)

output = model_coreml._spec.description.output[0]
output.type.imageType.colorSpace = ct.proto.FeatureTypes_pb2.ImageFeatureType.ColorSpace.Value('RGB')
output.type.imageType.width = 518
output.type.imageType.height = 518

# Save the modified CoreML model
print(model_coreml)
model_coreml.save('/content/drive/MyDrive/trained_models/depth9.mlpackage')

I've tried to specify input parameters straight as I do for the output one like this:

# Create a dictionary for the input schema
input_schema = {'input_name': 'input', 'input_type': ct.TensorType(shape=(1, 3, 518, 518))}

# Add the input schema to the model's metadata
model_coreml.user_defined_metadata['inputSchema'] = str(input_schema)

Or to use convert_to option with setting up neuralnetwork like this:

model_coreml = ct.convert(
    traced_model,
    inputs=[ct.ImageType(name="input_1", shape=(1, 3, 518, 518), scale=1/255.0)],
    convert_to='neuralnetwork'
)

Or to set ct.proto.FeatureTypes_pb2.ImageFeatureType.ColorSpace.Value('RGB') with BGR/GRAYSCALE
Nothing helps.

If I try to import the model with neuralnetwork backend I just receive an infinite loading. If I try to import the model with mlprogram backend (default, if not specified) I receive the following:

I look forward for any advices and help since all I need is just to convert existing depth-anything model with no adjustments or changes to CoreML format. Thanks!

Well, actually using neuralnetwork backend and knowing the fact that depth shape is 1xHxW the following modifications made for shape and scale values did the trick:

import coremltools as ct
import torch

x = torch.rand(1, 3, 518, 518)
traced_model = torch.jit.trace(depth_anything, x, strict=False)

mlmodel = ct.convert(traced_model,inputs=[ct.ImageType(shape=x.shape,bias=[-0.485/0.229,-0.456/0.224,-0.406/0.225],scale=1.0/255.0/0.226)], convert_to='neuralnetwork')

mlmodel.save('/content/drive/MyDrive/trained_models/depth_anything.mlmodel')

I'm not sure it's a good solution, but as a workaround I've achieved it had covered all my needs