""" # Copyright (c) 2022, salesforce.com, inc. # All rights reserved. # SPDX-License-Identifier: BSD-3-Clause # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause """ import plotly.graph_objects as go import requests import streamlit as st import torch from lavis.models import load_model from lavis.processors import load_processor from lavis.processors.blip_processors import BlipCaptionProcessor from PIL import Image from app import device, load_demo_image from app.utils import load_blip_itm_model from lavis.processors.clip_processors import ClipImageEvalProcessor @st.cache() def load_demo_image(img_url=None): if not img_url: img_url = "https://img.atlasobscura.com/yDJ86L8Ou6aIjBsxnlAy5f164w1rjTgcHZcx2yUs4mo/rt:fit/w:1200/q:81/sm:1/scp:1/ar:1/aHR0cHM6Ly9hdGxh/cy1kZXYuczMuYW1h/em9uYXdzLmNvbS91/cGxvYWRzL3BsYWNl/X2ltYWdlcy85MDll/MDRjOS00NTJjLTQx/NzQtYTY4MS02NmQw/MzI2YWIzNjk1ZGVk/MGZhMTJiMTM5MmZi/NGFfUmVhcl92aWV3/X29mX3RoZV9NZXJs/aW9uX3N0YXR1ZV9h/dF9NZXJsaW9uX1Bh/cmssX1NpbmdhcG9y/ZSxfd2l0aF9NYXJp/bmFfQmF5X1NhbmRz/X2luX3RoZV9kaXN0/YW5jZV8tXzIwMTQw/MzA3LmpwZw.jpg" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") return raw_image @st.cache( hash_funcs={ torch.nn.parameter.Parameter: lambda parameter: parameter.data.detach() .cpu() .numpy() }, allow_output_mutation=True, ) def load_model_cache(model_type, device): if model_type == "blip": model = load_model( "blip_feature_extractor", model_type="base", is_eval=True, device=device ) elif model_type == "albef": model = load_model( "albef_feature_extractor", model_type="base", is_eval=True, device=device ) elif model_type == "CLIP_ViT-B-32": model = load_model( "clip_feature_extractor", "ViT-B-32", is_eval=True, device=device ) elif model_type == "CLIP_ViT-B-16": model = load_model( "clip_feature_extractor", "ViT-B-16", is_eval=True, device=device ) elif model_type == "CLIP_ViT-L-14": model = load_model( "clip_feature_extractor", "ViT-L-14", is_eval=True, device=device ) return model def app(): model_type = st.sidebar.selectbox( "Model:", ["ALBEF", "BLIP_Base", "CLIP_ViT-B-32", "CLIP_ViT-B-16", "CLIP_ViT-L-14"], ) score_type = st.sidebar.selectbox("Score type:", ["Cosine", "Multimodal"]) # ===== layout ===== st.markdown( "

Zero-shot Classification

", unsafe_allow_html=True, ) instructions = """Try the provided image or upload your own:""" file = st.file_uploader(instructions) st.header("Image") if file: raw_img = Image.open(file).convert("RGB") else: raw_img = load_demo_image() st.image(raw_img) # , use_column_width=True) col1, col2 = st.columns(2) col1.header("Categories") cls_0 = col1.text_input("category 1", value="merlion") cls_1 = col1.text_input("category 2", value="sky") cls_2 = col1.text_input("category 3", value="giraffe") cls_3 = col1.text_input("category 4", value="fountain") cls_4 = col1.text_input("category 5", value="marina bay") cls_names = [cls_0, cls_1, cls_2, cls_3, cls_4] cls_names = [cls_nm for cls_nm in cls_names if len(cls_nm) > 0] if len(cls_names) != len(set(cls_names)): st.error("Please provide unique class names") return button = st.button("Submit") col2.header("Prediction") # ===== event ===== if button: if model_type.startswith("BLIP"): text_processor = BlipCaptionProcessor(prompt="A picture of ") cls_prompt = [text_processor(cls_nm) for cls_nm in cls_names] if score_type == "Cosine": vis_processor = load_processor("blip_image_eval").build(image_size=224) img = vis_processor(raw_img).unsqueeze(0).to(device) feature_extractor = load_model_cache(model_type="blip", device=device) sample = {"image": img, "text_input": cls_prompt} with torch.no_grad(): image_features = feature_extractor.extract_features( sample, mode="image" ).image_embeds_proj[:, 0] text_features = feature_extractor.extract_features( sample, mode="text" ).text_embeds_proj[:, 0] sims = (image_features @ text_features.t())[ 0 ] / feature_extractor.temp else: vis_processor = load_processor("blip_image_eval").build(image_size=384) img = vis_processor(raw_img).unsqueeze(0).to(device) model = load_blip_itm_model(device) output = model(img, cls_prompt, match_head="itm") sims = output[:, 1] sims = torch.nn.Softmax(dim=0)(sims) inv_sims = [sim * 100 for sim in sims.tolist()[::-1]] elif model_type.startswith("ALBEF"): vis_processor = load_processor("blip_image_eval").build(image_size=224) img = vis_processor(raw_img).unsqueeze(0).to(device) text_processor = BlipCaptionProcessor(prompt="A picture of ") cls_prompt = [text_processor(cls_nm) for cls_nm in cls_names] feature_extractor = load_model_cache(model_type="albef", device=device) sample = {"image": img, "text_input": cls_prompt} with torch.no_grad(): image_features = feature_extractor.extract_features( sample, mode="image" ).image_embeds_proj[:, 0] text_features = feature_extractor.extract_features( sample, mode="text" ).text_embeds_proj[:, 0] st.write(image_features.shape) st.write(text_features.shape) sims = (image_features @ text_features.t())[0] / feature_extractor.temp sims = torch.nn.Softmax(dim=0)(sims) inv_sims = [sim * 100 for sim in sims.tolist()[::-1]] elif model_type.startswith("CLIP"): if model_type == "CLIP_ViT-B-32": model = load_model_cache(model_type="CLIP_ViT-B-32", device=device) elif model_type == "CLIP_ViT-B-16": model = load_model_cache(model_type="CLIP_ViT-B-16", device=device) elif model_type == "CLIP_ViT-L-14": model = load_model_cache(model_type="CLIP_ViT-L-14", device=device) else: raise ValueError(f"Unknown model type {model_type}") if score_type == "Cosine": # image_preprocess = ClipImageEvalProcessor(image_size=336) image_preprocess = ClipImageEvalProcessor(image_size=224) img = image_preprocess(raw_img).unsqueeze(0).to(device) sample = {"image": img, "text_input": cls_names} with torch.no_grad(): clip_features = model.extract_features(sample) image_features = clip_features.image_embeds_proj text_features = clip_features.text_embeds_proj sims = (100.0 * image_features @ text_features.T)[0].softmax(dim=-1) inv_sims = sims.tolist()[::-1] else: st.warning("CLIP does not support multimodal scoring.") return fig = go.Figure( go.Bar( x=inv_sims, y=cls_names[::-1], text=["{:.2f}".format(s) for s in inv_sims], orientation="h", ) ) fig.update_traces( textfont_size=12, textangle=0, textposition="outside", cliponaxis=False, ) col2.plotly_chart(fig, use_container_width=True)