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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()
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