Pra-tham/roughness_model

import torch from huggingface_hub import hf_hub_download

Download the model file from Hugging Face Hub

repo_name = "roughness_model" downloaded_file = hf_hub_download( repo_id=f"Pra-tham/{repo_name}", # Replace with your Hugging Face username filename="roughness_model.pth" ) print(f"Model downloaded from Hugging Face Hub: {downloaded_file}")

Initialize the model and load the state_dict

model.load_state_dict(torch.load(downloaded_file)) model.eval() # Set to evaluation mode print("Model loaded successfully from Hugging Face Hub!")

Set the computation device

device0 = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

Load the pretrained model checkpoint

load_path = "/kaggle/working/metric_depth_vit_large_800k.pth" checkpoint = torch.load(load_path, map_location="cpu")

Load the model configuration

cfg_large = Config.fromfile('/kaggle/working/Texture_training/training/mono/configs/RAFTDecoder/vit.raft5.large.py')

Initialize the DepthModel

model = DepthModel(cfg_large, None)

Load the model's state dictionary

ckpt_state_dict = checkpoint['model_state_dict'] model.load_state_dict(ckpt_state_dict, strict=False)

Print the model architecture

#print(model)