Spaces:
Build error
Build error
from transformers import SamModel, SamConfig, SamProcessor | |
import torch | |
import numpy as np | |
import app | |
import os | |
from PIL import Image | |
def pred(src): | |
# Load the model configuration | |
cache_dir = "/code/cache" | |
model_config = SamConfig.from_pretrained("facebook/sam-vit-base", cache_dir=cache_dir) | |
processor = SamProcessor.from_pretrained("facebook/sam-vit-base", cache_dir=cache_dir) | |
# Create an instance of the model architecture with the loaded configuration | |
my_sam_model = SamModel(config=model_config) | |
# #Update the model by loading the weights from saved file. | |
my_sam_model.load_state_dict(torch.load("sam_model.pth", map_location=torch.device('cpu'))) | |
new_image = np.array(Image.open(src).convert("RGB")) | |
inputs = processor(new_image, return_tensors="pt") | |
my_sam_model.eval() | |
# # forward pass | |
with torch.no_grad(): | |
outputs = my_sam_model(**inputs, multimask_output=False) | |
# # apply sigmoid | |
single_patch_prob = torch.sigmoid(outputs.pred_masks.squeeze(1)) | |
# # convert soft mask to hard mask | |
single_patch_prob = single_patch_prob.cpu().numpy().squeeze() | |
single_patch_prediction = (single_patch_prob > 0.5).astype(np.uint8) | |
return single_patch_prob, single_patch_prediction | |