| from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, PreTrainedTokenizerFast | |
| model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") | |
| tokenizer = PreTrainedTokenizerFast.from_pretrained("distilgpt2") | |
| def vit2distilgpt2(img): | |
| pixel_values = vit_feature_extractor(images=img, return_tensors="pt").pixel_values | |
| encoder_outputs = model.generate(pixel_values.to('cpu'), num_beams=5, num_return_sequences=3) | |
| generated_sentences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True) | |
| return generated_sentences | |
| import gradio as gr | |
| inputs = [ | |
| gr.inputs.Image(type="pil", label="Original Images") | |
| ] | |
| outputs = [ | |
| gr.outputs.Textbox(label="Caption 1"), | |
| gr.outputs.Textbox(label="Caption 2"), | |
| gr.outputs.Textbox(label="Caption 3") | |
| ] | |
| title = "Image Captioning using ViT + GPT2" | |
| description = "ViT and GPT2 are used to generate Image Caption for the uploaded image. COCO DataSet is used for Training" | |
| examples = [ | |
| ["Image1.png"], | |
| ["Image2.png"], | |
| ["Image3.png"] | |
| ] | |
| gr.Interface( | |
| vit2distilgpt2, | |
| inputs, | |
| outputs, | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| theme="huggingface", | |
| ).launch(debug=True, enable_queue=True) | |