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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
from icevision.all import *
from icevision.models.checkpoint import *
import PIL
checkpoint_path = "fasterRCNN_resnet18_Raccoons.pth"
model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True),
num_classes=2)
state_dict = torch.load(checkpoint_path, map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()])
# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
img = PIL.Image.fromarray(img, "RGB")
pred_dict = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=ClassMap(['raccoon']), 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(type="pil", label="VFNet Inference")],
examples=['raccoon1.jpg','raccoon2.jpg']).launch(share=False) |