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import gradio as gr
from transformers import ViltProcessor, ViltForImagesAndTextClassification
import torch
# NLRV2 example images
torch.hub.download_url_to_file('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg', 'image1.jpg')
torch.hub.download_url_to_file('https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg', 'image2.jpg')
torch.hub.download_url_to_file('https://lil.nlp.cornell.edu/nlvr/exs/acorns_1.jpg', 'image3.jpg')
torch.hub.download_url_to_file('https://lil.nlp.cornell.edu/nlvr/exs/acorns_6.jpg', 'image4.jpg')
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
def predict(image1, image2, text):
# prepare inputs
encoding = processor([image1, image2], text, return_tensors="pt")
# forward pass
with torch.no_grad():
outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1)
output = dict()
for label, id in model.config.label2id.items():
output[label] = probs[:,id].item()
return output
images = [gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")]
text = gr.inputs.Textbox(lines=2, label="Sentence")
label = gr.outputs.Label(num_top_classes=2, type="confidences")
example_sentence_1 = "The left image contains twice the number of dogs as the right image, and at least two dogs in total are standing."
example_sentence_2 = "One image shows exactly two brown acorns in back-to-back caps on green foliage."
examples = [["image1.jpg", "image2.jpg", example_sentence_1], ["image3.jpg", "image4.jpg", example_sentence_2]]
title = "Interactive demo: natural language visual reasoning with ViLT"
description = "Gradio Demo for ViLT (Vision and Language Transformer), fine-tuned on NLVR2. To use it, simply upload a pair of images and type a sentence and click 'submit', or click one of the examples to load them. The model will predict whether the sentence is true or false, based on the 2 images. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2102.03334' target='_blank'>ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision</a> | <a href='https://github.com/dandelin/ViLT' target='_blank'>Github Repo</a></p>"
interface = gr.Interface(fn=predict,
inputs=images + [text],
outputs=label,
examples=examples,
title=title,
description=description,
article=article,
theme="default",
enable_queue=True)
interface.launch(debug=True) |