from transformers import Blip2ForConditionalGeneration from transformers import Blip2Processor from peft import PeftModel import streamlit as st from PIL import Image #import torch import os preprocess_ckp = "Salesforce/blip2-opt-2.7b" #Checkpoint path used for perprocess image base_model_ckp = "./model/blip2-opt-2.7b-fp16-sharded" #Base model checkpoint path peft_model_ckp = "./model/blip2_peft" #PEFT model checkpoint path sample_img_path = "./sample_images" map_sampleid_name = { 'dress' : '00fe223d-9d1f-4bd3-a556-7ece9d28e6fb.jpeg', 'earrings': '0b3862ae-f89e-419c-bc1e-57418abd4180.jpeg', 'sweater': '0c21ba7b-ceb6-4136-94a4-1d4394499986.jpeg', 'sunglasses': '0e44ec10-e53b-473a-a77f-ac8828bb5e01.jpeg', 'shoe': '4cd37d6d-e7ea-4c6e-aab2-af700e480bc1.jpeg', 'hat': '69aeb517-c66c-47b8-af7d-bdf1fde57ed0.jpeg', 'heels':'447abc42-6ac7-4458-a514-bdcd570b1cd1.jpeg', 'socks': 'd188836c-b734-4031-98e5-423d5ff1239d.jpeg', 'tee': 'e2d8637a-5478-429d-a2a8-3d5859dbc64d.jpeg', 'bracelet': 'e78518ac-0f54-4483-a233-fad6511f0b86.jpeg' } #init_model_required = True def init_model(): #if init_model_required: #Preprocess input processor = Blip2Processor.from_pretrained(preprocess_ckp) #Model #Inferance on GPU device. Will give error in CPU system, as "load_in_8bit" is an setting of bitsandbytes library and only works for GPU #model = Blip2ForConditionalGeneration.from_pretrained(base_model_ckp, load_in_8bit = True, device_map = "auto") #Inferance on CPU device model = Blip2ForConditionalGeneration.from_pretrained(base_model_ckp) model = PeftModel.from_pretrained(model, peft_model_ckp) #init_model_required = False return processor, model def main(): st.title("Fashion Image Caption using BLIP2") processor, model = init_model() #Select few sample images for the catagory of cloths st.text("Select image:") option = st.selectbox('From sample', ('None', 'dress', 'earrings', 'sweater', 'sunglasses', 'shoe', 'hat', 'heels', 'socks', 'tee', 'bracelet'), index = 0) st.text("OR") file_name = st.file_uploader("Upload an image") image = None if file_name is not None: image = Image.open(file_name) elif option is not 'None': file_name = os.path.join(sample_img_path, map_sampleid_name[option]) image = Image.open(file_name) if image is not None: image_col, caption_text = st.columns(2) image_col.header("Image") image_col.image(image, use_column_width = True) #Preprocess the image #Inferance on GPU. When used this on GPU will get errors like: "slow_conv2d_cpu" not implemented for 'Half'" , " Input type (float) and bias type (struct c10::Half)" #inputs = processor(images = image, return_tensors = "pt").to('cuda', torch.float16) #Inferance on CPU inputs = processor(images = image, return_tensors = "pt") pixel_values = inputs.pixel_values #Predict the caption for the imahe generated_ids = model.generate(pixel_values = pixel_values, max_length = 25) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] #Output the predict text caption_text.header("Generated Caption") caption_text.text(generated_caption) if __name__ == "__main__": main()