| import gradio as gr | |
| import random | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from transformers import (SegformerFeatureExtractor, | |
| SegformerForSemanticSegmentation) | |
| MODEL_PATH="./best_model_test/" | |
| device = torch.device("cpu") | |
| preprocessor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") | |
| model = SegformerForSemanticSegmentation.from_pretrained(MODEL_PATH) | |
| model.eval() | |
| def upscale_logits(logit_outputs, size): | |
| """Escala los logits a (4W)x(4H) para recobrar dimensiones originales del input""" | |
| return nn.functional.interpolate( | |
| logit_outputs, | |
| size=size, | |
| mode="bilinear", | |
| align_corners=False | |
| ) | |
| def visualize_instance_seg_mask(mask): | |
| """Agrega colores RGB a cada una de las clases en la mask""" | |
| image = np.zeros((mask.shape[0], mask.shape[1], 3)) | |
| labels = np.unique(mask) | |
| label2color = {label: (random.randint(0, 1), | |
| random.randint(0, 255), | |
| random.randint(0, 255)) for label in labels} | |
| for i in range(image.shape[0]): | |
| for j in range(image.shape[1]): | |
| image[i, j, :] = label2color[mask[i, j]] | |
| image = image / 255 | |
| return image | |
| def query_image(img): | |
| """Función para generar predicciones a la escala origina""" | |
| inputs = preprocessor(images=img, return_tensors="pt") | |
| with torch.no_grad(): | |
| preds = model(**inputs)["logits"] | |
| preds_upscale = upscale_logits(preds, preds.shape[2]) | |
| predict_label = torch.argmax(preds_upscale, dim=1).to(device) | |
| result = predict_label[0,:,:].detach().cpu().numpy() | |
| return visualize_instance_seg_mask(result) | |
| demo = gr.Interface( | |
| query_image, | |
| inputs=[gr.Image(type="pil")], | |
| outputs="image", | |
| title="SegFormer Model for rock glacier image segmentation" | |
| ) | |
| demo.launch() | |