Sidra Aleem

Hi @muellerdo Thanks for your response. **The below image shows the preprocessing step of data load**  **The below image shows the neural network.**  **Below image for the structure...

@heyoeyo , I didnot pass any prompts. I am just passing the input image to model as: ``` for idx in tqdm(range(len(dataset)), desc= f"Processing images", unit= "image"): input_samples, gt_samples, voxel...

I passed the box prompts, and it solved the issue.

@edwardjhu I did that as: ``` lora_b =self.lora_B.detach().cpu() lora_b.shape (48, 6) ``` ``` lora_a =self.lora_A.detach().cpu() lora_a.shape (6, 3) ``` Given the dimensions `lora_b @ lora_a` are comaptible for matrix multiplication....

no.

@edwardjhu , I am first loading the model as: `model.load_state_dict(torch.load( "/home/sidra/Documents/Domain_Apatation/UDAS/src/checkpoints/base_model_mms_2023-07-06_12-45-28_PM/dc_model.pth"), strict=False)`. Below is the structure of a part of loaded model: ``` UNet2D( (init_path): Sequential( (0): Conv2d(1, 16, kernel_size=(3,...

How did you do it? As Conv2d looks like this: ``` class Conv2d(ConvLoRA): def __init__(self, *args, **kwargs): super(Conv2d, self).__init__(nn.Conv2d, *args, **kwargs) ```