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from fastai.vision.all import *
from icevision.all import *
from fastai.basics import *
from fastai.callback import *
from icevision import models
import PIL
import gradio as gr
class_map=ClassMap(['kangaroo'])
# Cargamos el learner
model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True),num_classes=len(class_map))
state_dict = torch.load('fasterRCNNKangaroo.pth',map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
# Definimos una función que se encarga de llevar a cabo las predicciones
infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()])
def predict(img):
img = PILImage.create(img)
pred_dict = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=class_map, detection_threshold=0.5)
return pred_dict['img']
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(),examples=['00004.jpg','00014.jpg']).launch(share=False) |