File size: 8,292 Bytes
846a3aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
"""
# 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(
"<h1 style='text-align: center;'>Zero-shot Classification</h1>",
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)
|