| """
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| # Copyright (c) 2022, salesforce.com, inc.
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| # All rights reserved.
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| # SPDX-License-Identifier: BSD-3-Clause
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| # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
| """
|
|
|
| import math
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|
|
| import numpy as np
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| import streamlit as st
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| from lavis.models.blip_models.blip_image_text_matching import compute_gradcam
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| from lavis.processors import load_processor
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| from PIL import Image
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|
|
| from app import device, load_demo_image
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| from app.utils import getAttMap, init_bert_tokenizer, load_blip_itm_model
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|
|
|
|
| def app():
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| model_type = st.sidebar.selectbox("Model:", ["BLIP_base", "BLIP_large"])
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|
|
| values = list(range(1, 12))
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| default_layer_num = values.index(7)
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| layer_num = (
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| st.sidebar.selectbox("Layer number", values, index=default_layer_num) - 1
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| )
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|
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| st.markdown(
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| "<h1 style='text-align: center;'>Text Localization</h1>", unsafe_allow_html=True
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| )
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|
|
| vis_processor = load_processor("blip_image_eval").build(image_size=384)
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| text_processor = load_processor("blip_caption")
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|
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| tokenizer = init_bert_tokenizer()
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|
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| instructions = "Try the provided image and text or use your own ones."
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| file = st.file_uploader(instructions)
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|
|
| query = st.text_input(
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| "Try a different input.", "A girl playing with her dog on the beach."
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| )
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|
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| submit_button = st.button("Submit")
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|
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| col1, col2 = st.columns(2)
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|
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| if file:
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| raw_img = Image.open(file).convert("RGB")
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| else:
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| raw_img = load_demo_image()
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|
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| col1.header("Image")
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| w, h = raw_img.size
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| scaling_factor = 720 / w
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| resized_image = raw_img.resize((int(w * scaling_factor), int(h * scaling_factor)))
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| col1.image(resized_image, use_column_width=True)
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|
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| col2.header("GradCam")
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|
|
| if submit_button:
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| if model_type.startswith("BLIP"):
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| blip_type = model_type.split("_")[1]
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| model = load_blip_itm_model(device, model_type=blip_type)
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|
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| img = vis_processor(raw_img).unsqueeze(0).to(device)
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| qry = text_processor(query)
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|
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| qry_tok = tokenizer(qry, return_tensors="pt").to(device)
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|
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| norm_img = np.float32(resized_image) / 255
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|
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| gradcam, _ = compute_gradcam(model, img, qry, qry_tok, block_num=layer_num)
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|
|
| avg_gradcam = getAttMap(norm_img, gradcam[0][1], blur=True)
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| col2.image(avg_gradcam, use_column_width=True, clamp=True)
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|
|
| num_cols = 4.0
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| num_tokens = len(qry_tok.input_ids[0]) - 2
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|
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| num_rows = int(math.ceil(num_tokens / num_cols))
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|
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| gradcam_iter = iter(gradcam[0][2:-1])
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| token_id_iter = iter(qry_tok.input_ids[0][1:-1])
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|
|
| for _ in range(num_rows):
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| with st.container():
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| for col in st.columns(int(num_cols)):
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| token_id = next(token_id_iter, None)
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| if not token_id:
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| break
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| gradcam_img = next(gradcam_iter)
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|
|
| word = tokenizer.decode([token_id])
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| gradcam_todraw = getAttMap(norm_img, gradcam_img, blur=True)
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|
|
| new_title = (
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| '<p style="text-align: center; font-size: 25px;">{}</p>'.format(
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| word
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| )
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| )
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| col.markdown(new_title, unsafe_allow_html=True)
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|
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| col.image(gradcam_todraw, use_column_width=True, clamp=True)
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|
|
