SAMProcessor padding for rectangular aspect input images isn't symmetric 
Author: jeb2112
Pull request: no
Labels: name: Vision

name: Processing

### System Info

 `transformers` version: 4.46.2
- Platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.37
- Python version: 3.9.20
- Huggingface_hub version: 0.26.2
- Safetensors version: 0.4.5
- Accelerate version: not installed
- Accelerate config: not found
- PyTorch version (GPU?): 2.5.1+cu118 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: no
- Using GPU in script?: yes
- GPU type: NVIDIA GeForce GTX 1650


### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

```
from transformers import SamProcessor, SamModel
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F

def upsample_mask(pred_mask, target_size):
    return F.interpolate(pred_mask.unsqueeze(0).unsqueeze(0), size=target_size, mode="nearest").squeeze(1)

# Load the processor and model
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
model = SamModel.from_pretrained("facebook/sam-vit-base")

# Prepare inputs
input_points = [[[115, 92], [117, 108]]]
input_labels = [[1, 0]]

input_image = np.zeros((233,197,3),dtype=np.uint8)
input_image[20:210,20:177,:] = 128
input_image[88:96,111:118,:] = 255

# optionally crop to square aspect
if False:
    input_image = input_image[:197,:,:]

inputs = processor(
    images=input_image,
    input_points=input_points,
    input_labels=input_labels,
    return_tensors="pt"
)

check_image = np.copy(inputs['pixel_values'])[0]
check_image = np.moveaxis(check_image,0,-1)
pts = np.array(inputs['input_points'][0]).astype('int')

# display the control points
for p in pts[0]:
    check_image[p[1]-4:p[1]+4,p[0]-4:p[0]+4,:] = np.array([255,0,0],dtype='uint8')

# Forward pass with multimask_output=False
outputs = model(**inputs, multimask_output=False)

# Retrieve masks
pred_mask = np.squeeze(outputs["pred_masks"]) 
pred_mask_sig = torch.sigmoid(upsample_mask(pred_mask, np.shape(input_image)[:2])).cpu().detach().numpy().squeeze()
thresh_mask = (pred_mask_sig > 0.5).astype(np.uint8) * 255

plt.figure(8)
plt.subplot(1,4,1)
plt.imshow(input_image)

plt.subplot(1,4,2)
plt.imshow(check_image)

plt.subplot(1,4,3)
plt.imshow(pred_mask.cpu().detach().numpy().squeeze())

plt.subplot(1,4,4)
plt.imshow(thresh_mask)
plt.show()
```

### Expected behavior

I expected the padding of the up-scaled working image to be symmetric, if the input image is asymmetric. When I resample the predicted mask back to the starting asymmetric matrix size, there is now an offset between prediction and the original image/control points. I looked at line 106 of image_processing_sam.py on github but the pad_size arg doesn't seem to have an option for symmetric versus asymmetric. I can correct for this manually of course, just thought I wouldn't have to do that. 

Comments:
OK, on further thought I guess this should only be a feature request. 

Hey @jeb2112, thanks for opening the issue! I'm not sure I understand the problem, so let me know if I missed something! The image is padded on the bottom and right sides. Then you have to use the `post_process_masks` method to remove the padding and resize the resulting masks to the image size.

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.