Update

README.md

@@ -4,7 +4,7 @@ emoji: 📊
 colorFrom: gray
 colorTo: green
 sdk: gradio
-sdk_version: 3.15.0
+sdk_version: 3.35.2
 app_file: app.py
 pinned: false
 duplicated_from: nielsr/comparing-VQA-models

app.py

@@ -1,98 +1,72 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoModelForCausalLM, BlipForQuestionAnswering, ViltForQuestionAnswering
 import torch
+from transformers import (AutoProcessor, BlipForQuestionAnswering,
+                          ViltForQuestionAnswering)
+
+torch.hub.download_url_to_file(
+    'http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+torch.hub.download_url_to_file(
+    'https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png',
+    'stop_sign.png')
+torch.hub.download_url_to_file(
+    'https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg',
+    'astronaut.jpg')
+
+blip_processor_large = AutoProcessor.from_pretrained(
+    'Salesforce/blip-vqa-capfilt-large')
+blip_model_large = BlipForQuestionAnswering.from_pretrained(
+    'Salesforce/blip-vqa-capfilt-large')
+
+vilt_processor = AutoProcessor.from_pretrained(
+    'dandelin/vilt-b32-finetuned-vqa')
+vilt_model = ViltForQuestionAnswering.from_pretrained(
+    'dandelin/vilt-b32-finetuned-vqa')
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
-torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
-torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
-torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg')
-
-git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-vqav2")
-git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vqav2")
-
-git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-vqav2")
-git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-vqav2")
-
-blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
-blip_model_base = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
-
-blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
-blip_model_large = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large")
-
-vilt_processor = AutoProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
-vilt_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-git_model_base.to(device)
-blip_model_base.to(device)
-git_model_large.to(device)
 blip_model_large.to(device)
 vilt_model.to(device)
 
-def generate_answer_git(processor, model, image, question):
-    # prepare image
-    pixel_values = processor(images=image, return_tensors="pt").pixel_values
-
-    # prepare question
-    input_ids = processor(text=question, add_special_tokens=False).input_ids
-    input_ids = [processor.tokenizer.cls_token_id] + input_ids
-    input_ids = torch.tensor(input_ids).unsqueeze(0)
-    
-    generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
-    generated_answer = processor.batch_decode(generated_ids, skip_special_tokens=True)
-   
-    return generated_answer
-
 
+@torch.inference_mode()
 def generate_answer_blip(processor, model, image, question):
-    # prepare image + question
-    inputs = processor(images=image, text=question, return_tensors="pt")
-    
+    inputs = processor(images=image, text=question,
+                       return_tensors='pt').to(device)
     generated_ids = model.generate(**inputs, max_length=50)
-    generated_answer = processor.batch_decode(generated_ids, skip_special_tokens=True)
-   
-    return generated_answer
+    generated_answer = processor.batch_decode(generated_ids,
+                                              skip_special_tokens=True)
+    return generated_answer[0]
 
 
+@torch.inference_mode()
 def generate_answer_vilt(processor, model, image, question):
-    # prepare image + question
-    encoding = processor(images=image, text=question, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**encoding)
-
+    encoding = processor(images=image, text=question,
+                         return_tensors='pt').to(device)
+    outputs = model(**encoding)
     predicted_class_idx = outputs.logits.argmax(-1).item()
-    
     return model.config.id2label[predicted_class_idx]
 
 
 def generate_answers(image, question):
-    answer_git_base = generate_answer_git(git_processor_base, git_model_base, image, question)
-
-    answer_git_large = generate_answer_git(git_processor_large, git_model_large, image, question)
-
-    answer_blip_base = generate_answer_blip(blip_processor_base, blip_model_base, image, question)
-
-    answer_blip_large = generate_answer_blip(blip_processor_large, blip_model_large, image, question)
-
-    answer_vilt = generate_answer_vilt(vilt_processor, vilt_model, image, question)
-
-    return answer_git_base, answer_git_large, answer_blip_base, answer_blip_large, answer_vilt
-
-   
-examples = [["cats.jpg", "How many cats are there?"], ["stop_sign.png", "What's behind the stop sign?"], ["astronaut.jpg", "What's the astronaut riding on?"]]
-outputs = [gr.outputs.Textbox(label="Answer generated by GIT-base"), gr.outputs.Textbox(label="Answer generated by GIT-large"), gr.outputs.Textbox(label="Answer generated by BLIP-base"), gr.outputs.Textbox(label="Answer generated by BLIP-large"), gr.outputs.Textbox(label="Answer generated by ViLT")] 
-
-title = "Interactive demo: comparing visual question answering (VQA) models"
-description = "Gradio Demo to compare GIT, BLIP and ViLT, 3 state-of-the-art vision+language models. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://huggingface.co/docs/transformers/main/model_doc/blip' target='_blank'>BLIP docs</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/git' target='_blank'>GIT docs</a></p>"
-
-interface = gr.Interface(fn=generate_answers, 
-                         inputs=[gr.inputs.Image(type="pil"), gr.inputs.Textbox(label="Question")],
-                         outputs=outputs,
-                         examples=examples, 
-                         title=title,
-                         description=description,
-                         article=article, 
-                         enable_queue=True)
-interface.launch(debug=True)
+    answer_blip_large = generate_answer_blip(blip_processor_large,
+                                             blip_model_large, image, question)
+    answer_vilt = generate_answer_vilt(vilt_processor, vilt_model, image,
+                                       question)
+    return answer_blip_large, answer_vilt
+
+
+demo = gr.Interface(
+    fn=generate_answers,
+    inputs=[gr.Image(type='pil'),
+            gr.Textbox(label='Question')],
+    outputs=[
+        gr.Textbox(label='Answer generated by BLIP-large'),
+        gr.Textbox(label='Answer generated by ViLT')
+    ],
+    examples=[
+        ['cats.jpg', 'How many cats are there?'],
+        ['stop_sign.png', "What's behind the stop sign?"],
+        ['astronaut.jpg', "What's the astronaut riding on?"],
+    ],
+    title='Interactive demo: comparing visual question answering (VQA) models')
+demo.queue().launch()

requirements.txt

@@ -1,2 +1,2 @@
-git+https://github.com/huggingface/transformers.git
-torch
+torch
+transformers