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SOULOFCINDER 2023-03-29 17:29:30 +08:00
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import gradio as gr
from PIL import Image
from transformers import BeitImageProcessor, BeitForImageClassification
from PIL import Image
def inference(img):
pretrained_model_path = "beit-base-patch16-224-pt22k-ft22k"
processor = BeitImageProcessor.from_pretrained(pretrained_model_path)
model = BeitForImageClassification.from_pretrained(pretrained_model_path)
inputs = processor(images=img, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 21,841 ImageNet-22k classes
predicted_class_idx = logits.argmax(-1).item()
# print("Predicted class:", model.config.id2label[predicted_class_idx])
return model.config.id2label[predicted_class_idx]
title = "beit-base-patch16-224-pt22k-ft22k"
description = "Gradio Demo for beit-base-patch16-224-pt22k-ft22k. To use it, simply upload your image, or click one of the examples to load them."
article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
examples=[['example_cat.jpg'],['Masahiro.png']]
demo = gr.Interface(
fn=inference,
inputs=[gr.inputs.Image(type="pil")],
outputs=gr.outputs.Textbox(),
title=title,
description=description,
article=article,
examples=examples)
demo.launch()
##
# model_dir = "hub/animegan2-pytorch-main"
# model_dir_weight = "hub/checkpoints/face_paint_512_v1.pt"
#
# model2 = torch.hub.load(
# model_dir,
# "generator",
# pretrained=True,
# progress=False,
# source="local"
# )
# model1 = torch.load(model_dir_weight)
# face2paint = torch.hub.load(
# model_dir, 'face2paint',
# size=512,side_by_side=False,
# source="local"
# )
#
# def inference(img, ver):
# if ver == 'version 2 (🔺 robustness,🔻 stylization)':
# out = face2paint(model2, img)
# else:
# out = face2paint(model1, img)
# return out
#

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