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"""
# 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 os
import numpy as np
import streamlit as st
import torch
import torch.nn.functional as F
from app import cache_root, device
from app.utils import (
getAttMap,
init_bert_tokenizer,
load_blip_itm_model,
read_img,
resize_img,
)
from lavis.models import load_model
from lavis.processors import load_processor
@st.cache(
hash_funcs={
torch.nn.parameter.Parameter: lambda parameter: parameter.data.detach()
.cpu()
.numpy()
},
allow_output_mutation=True,
)
def load_feat():
from lavis.common.utils import download_url
dirname = os.path.join(os.path.dirname(__file__), "assets")
filename = "path2feat_coco_train2014.pth"
filepath = os.path.join(dirname, filename)
url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/path2feat_coco_train2014.pth"
if not os.path.exists(filepath):
download_url(url=url, root=dirname, filename="path2feat_coco_train2014.pth")
path2feat = torch.load(filepath)
paths = sorted(path2feat.keys())
all_img_feats = torch.stack([path2feat[k] for k in paths], dim=0).to(device)
return path2feat, paths, all_img_feats
@st.cache(
hash_funcs={
torch.nn.parameter.Parameter: lambda parameter: parameter.data.detach()
.cpu()
.numpy()
},
allow_output_mutation=True,
)
def load_feature_extractor_model(device):
model_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth"
model = load_model(
"blip_feature_extractor", model_type="base", is_eval=True, device=device
)
model.load_from_pretrained(model_url)
return model
def app():
# === layout ===
model_type = st.sidebar.selectbox("Model:", ["BLIP_base", "BLIP_large"])
file_root = os.path.join(cache_root, "coco/images/train2014/")
values = [12, 24, 48]
default_layer_num = values.index(24)
num_display = st.sidebar.selectbox(
"Number of images:", values, index=default_layer_num
)
show_gradcam = st.sidebar.selectbox("Show GradCam:", [True, False], index=1)
itm_ranking = st.sidebar.selectbox("Multimodal re-ranking:", [True, False], index=0)
# st.title('Multimodal Search')
st.markdown(
"<h1 style='text-align: center;'>Multimodal Search</h1>", unsafe_allow_html=True
)
# === event ===
vis_processor = load_processor("blip_image_eval").build(image_size=384)
text_processor = load_processor("blip_caption")
user_question = st.text_input(
"Search query", "A dog running on the grass.", help="Type something to search."
)
user_question = text_processor(user_question)
feature_extractor = load_feature_extractor_model(device)
# ======= ITC =========
sample = {"text_input": user_question}
with torch.no_grad():
text_feature = feature_extractor.extract_features(
sample, mode="text"
).text_embeds_proj[0, 0]
path2feat, paths, all_img_feats = load_feat()
all_img_feats.to(device)
all_img_feats = F.normalize(all_img_feats, dim=1)
num_cols = 4
num_rows = int(num_display / num_cols)
similarities = text_feature @ all_img_feats.T
indices = torch.argsort(similarities, descending=True)[:num_display]
top_paths = [paths[ind.detach().cpu().item()] for ind in indices]
sorted_similarities = [similarities[idx] for idx in indices]
filenames = [os.path.join(file_root, p) for p in top_paths]
# ========= ITM and GradCam ==========
bsz = 4 # max number of images to avoid cuda oom
if model_type.startswith("BLIP"):
blip_type = model_type.split("_")[1]
itm_model = load_blip_itm_model(device, model_type=blip_type)
tokenizer = init_bert_tokenizer()
queries_batch = [user_question] * bsz
queries_tok_batch = tokenizer(queries_batch, return_tensors="pt").to(device)
num_batches = int(num_display / bsz)
avg_gradcams = []
all_raw_images = []
itm_scores = []
for i in range(num_batches):
filenames_in_batch = filenames[i * bsz : (i + 1) * bsz]
raw_images, images = read_and_process_images(filenames_in_batch, vis_processor)
gradcam, itm_output = compute_gradcam_batch(
itm_model, images, queries_batch, queries_tok_batch
)
all_raw_images.extend([resize_img(r_img) for r_img in raw_images])
norm_imgs = [np.float32(r_img) / 255 for r_img in raw_images]
for norm_img, grad_cam in zip(norm_imgs, gradcam):
avg_gradcam = getAttMap(norm_img, grad_cam[0], blur=True)
avg_gradcams.append(avg_gradcam)
with torch.no_grad():
itm_score = torch.nn.functional.softmax(itm_output, dim=1)
itm_scores.append(itm_score)
# ========= ITM re-ranking =========
itm_scores = torch.cat(itm_scores)[:, 1]
if itm_ranking:
itm_scores_sorted, indices = torch.sort(itm_scores, descending=True)
avg_gradcams_sorted = []
all_raw_images_sorted = []
for idx in indices:
avg_gradcams_sorted.append(avg_gradcams[idx])
all_raw_images_sorted.append(all_raw_images[idx])
avg_gradcams = avg_gradcams_sorted
all_raw_images = all_raw_images_sorted
if show_gradcam:
images_to_show = iter(avg_gradcams)
else:
images_to_show = iter(all_raw_images)
for _ in range(num_rows):
with st.container():
for col in st.columns(num_cols):
col.image(next(images_to_show), use_column_width=True, clamp=True)
def read_and_process_images(image_paths, vis_processor):
raw_images = [read_img(path) for path in image_paths]
images = [vis_processor(r_img) for r_img in raw_images]
images_tensors = torch.stack(images).to(device)
return raw_images, images_tensors
def compute_gradcam_batch(model, visual_input, text_input, tokenized_text, block_num=6):
model.text_encoder.base_model.base_model.encoder.layer[
block_num
].crossattention.self.save_attention = True
output = model({"image": visual_input, "text_input": text_input}, match_head="itm")
loss = output[:, 1].sum()
model.zero_grad()
loss.backward()
with torch.no_grad():
mask = tokenized_text.attention_mask.view(
tokenized_text.attention_mask.size(0), 1, -1, 1, 1
) # (bsz,1,token_len, 1,1)
token_length = mask.sum() - 2
token_length = token_length.cpu()
# grads and cams [bsz, num_head, seq_len, image_patch]
grads = model.text_encoder.base_model.base_model.encoder.layer[
block_num
].crossattention.self.get_attn_gradients()
cams = model.text_encoder.base_model.base_model.encoder.layer[
block_num
].crossattention.self.get_attention_map()
# assume using vit large with 576 num image patch
cams = cams[:, :, :, 1:].reshape(visual_input.size(0), 12, -1, 24, 24) * mask
grads = (
grads[:, :, :, 1:].clamp(0).reshape(visual_input.size(0), 12, -1, 24, 24)
* mask
)
gradcam = cams * grads
# [enc token gradcam, average gradcam across token, gradcam for individual token]
# gradcam = torch.cat((gradcam[0:1,:], gradcam[1:token_length+1, :].sum(dim=0, keepdim=True)/token_length, gradcam[1:, :]))
gradcam = gradcam.mean(1).cpu().detach()
gradcam = (
gradcam[:, 1 : token_length + 1, :].sum(dim=1, keepdim=True) / token_length
)
return gradcam, output
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