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| import numpy as np | |
| import gradio as gr | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| def inference(repo_id, model_name, img): | |
| model_path = hf_hub_download(repo_id=repo_id, filename=model_name) | |
| model = torch.jit.load(model_path, map_location='cpu') | |
| n_channels = len(model.norm.mean) | |
| # Remove redundant channels | |
| img = img[...,:n_channels] | |
| inp = torch.from_numpy(img).float() | |
| with torch.inference_mode(): | |
| argmax, softmax, stdeviation = model(inp) | |
| pred = argmax.cpu().numpy() | |
| std = stdeviation.cpu().numpy() | |
| # Adjust mask for binary cases | |
| if model.num_classes==2: pred *= 255 | |
| return pred, std | |
| title="deepflash2" | |
| description='deepflash2 is a deep-learning pipeline for the segmentation of ambiguous microscopic images.\n deepflash2 uses deep model ensembles to achieve more accurate and reliable results. Thus, inference time will be more than a minute in this space.' | |
| examples=[['matjesg/deepflash2_demo', 'cFOS_in_HC_ensemble_small.pt', 'cFOS_example.png'], | |
| ['matjesg/deepflash2_demo', 'YFP_in_CTX_ensemble_small.pt', 'YFP_example.png'] | |
| ] | |
| gr.Interface(inference, | |
| [gr.inputs.Textbox(placeholder='e.g., matjesg/cFOS_in_HC', label='repo_id'), | |
| gr.inputs.Textbox(placeholder='e.g., cFOS_in_HC_ensemble.pt', label='model_name'), | |
| gr.inputs.Image(type='numpy', label='Input image') | |
| ], | |
| [gr.outputs.Image(type='numpy', label='Segmentation Mask'), | |
| gr.outputs.Image(type='numpy', label='Uncertainty Map')], | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| ).launch() |