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import matplotlib.pyplot as plt
import nmslib
import numpy as np
import os
import streamlit as st
from transformers import CLIPProcessor, FlaxCLIPModel
BASELINE_MODEL = "openai/clip-vit-base-patch32"
# MODEL_PATH = "/home/shared/models/clip-rsicd/bs128x8-lr5e-6-adam/ckpt-1"
MODEL_PATH = "flax-community/clip-rsicd-v2"
# IMAGE_VECTOR_FILE = "/home/shared/data/vectors/test-baseline.tsv"
# IMAGE_VECTOR_FILE = "/home/shared/data/vectors/test-bs128x8-lr5e-6-adam-ckpt-1.tsv"
IMAGE_VECTOR_FILE = "./vectors/test-bs128x8-lr5e-6-adam-ckpt-1.tsv"
# IMAGES_DIR = "/home/shared/data/rsicd_images"
IMAGES_DIR = "./images"
@st.cache(allow_output_mutation=True)
def load_index():
filenames, image_vecs = [], []
fvec = open(IMAGE_VECTOR_FILE, "r")
for line in fvec:
cols = line.strip().split('\t')
filename = cols[0]
image_vec = np.array([float(x) for x in cols[1].split(',')])
filenames.append(filename)
image_vecs.append(image_vec)
V = np.array(image_vecs)
index = nmslib.init(method='hnsw', space='cosinesimil')
index.addDataPointBatch(V)
index.createIndex({'post': 2}, print_progress=True)
return filenames, index
@st.cache(allow_output_mutation=True)
def load_model():
model = FlaxCLIPModel.from_pretrained(MODEL_PATH)
processor = CLIPProcessor.from_pretrained(BASELINE_MODEL)
return model, processor
def app():
filenames, index = load_index()
model, processor = load_model()
st.title("Text to Image Retrieval")
st.markdown("""
The CLIP model from OpenAI is trained in a self-supervised manner using
contrastive learning to project images and caption text onto a common
embedding space. We have fine-tuned the model using the RSICD dataset
(10k images and ~50k captions from the remote sensing domain).
This demo shows the image to text retrieval capabilities of this model, i.e.,
given a text query, we use our fine-tuned CLIP model to project the text query
to the image/caption embedding space and search for nearby images (by
cosine similarity) in this space.
Our fine-tuned CLIP model was previously used to generate image vectors for
our demo, and NMSLib was used for fast vector access.
Some suggested queries to start you off with -- "ships", "school house",
"military installations", "mountains", "beaches", "airports", "lakes", etc.
""")
query = st.text_input("Text Query:")
if st.button("Query"):
inputs = processor(text=[query], images=None, return_tensors="jax", padding=True)
query_vec = model.get_text_features(**inputs)
query_vec = np.asarray(query_vec)
ids, distances = index.knnQuery(query_vec, k=10)
result_filenames = [filenames[id] for id in ids]
images, captions = [], []
for result_filename, score in zip(result_filenames, distances):
images.append(
plt.imread(os.path.join(IMAGES_DIR, result_filename)))
captions.append("{:s} (score: {:.3f})".format(result_filename, 1.0 - score))
st.image(images[0:3], caption=captions[0:3])
st.image(images[3:6], caption=captions[3:6])
st.image(images[6:9], caption=captions[6:9])
st.image(images[9:], caption=captions[9:])
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