Spaces:
Build error
Build error
File size: 8,516 Bytes
de36b67 a46cb3f de36b67 388d884 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
import gradio as gr
from transformers import ViltProcessor, ViltForQuestionAnswering
import torch
import gradio as gr
import torch
import copy
import time
import requests
import io
import numpy as np
import re
from PIL import Image
from vilt.config import ex
from vilt.modules import ViLTransformerSS
from vilt.modules.objectives import cost_matrix_cosine, ipot
from vilt.transforms import pixelbert_transform
from vilt.datamodules.datamodule_base import get_pretrained_tokenizer
@ex.automain
def main(_config):
_config = copy.deepcopy(_config)
loss_names = {
"itm": 0,
"mlm": 0.5,
"mpp": 0,
"vqa": 0,
"imgcls": 0,
"nlvr2": 0,
"irtr": 0,
"arc": 0,
}
tokenizer = get_pretrained_tokenizer(_config["tokenizer"])
_config.update(
{
"loss_names": loss_names,
}
)
model = ViLTransformerSS(_config)
model.setup("test")
model.eval()
device = "cpu"
model.to(device)
def infer(url, mp_text, hidx):
try:
res = requests.get(url)
image = Image.open(io.BytesIO(res.content)).convert("RGB")
img = pixelbert_transform(size=384)(image)
img = img.unsqueeze(0).to(device)
except:
return False
batch = {"text": [""], "image": [None]}
tl = len(re.findall("\[MASK\]", mp_text))
inferred_token = [mp_text]
batch["image"][0] = img
with torch.no_grad():
for i in range(tl):
batch["text"] = inferred_token
encoded = tokenizer(inferred_token)
batch["text_ids"] = torch.tensor(encoded["input_ids"]).to(device)
batch["text_labels"] = torch.tensor(encoded["input_ids"]).to(device)
batch["text_masks"] = torch.tensor(encoded["attention_mask"]).to(device)
encoded = encoded["input_ids"][0][1:-1]
infer = model(batch)
mlm_logits = model.mlm_score(infer["text_feats"])[0, 1:-1]
mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
mlm_values[torch.tensor(encoded) != 103] = 0
select = mlm_values.argmax().item()
encoded[select] = mlm_ids[select].item()
inferred_token = [tokenizer.decode(encoded)]
selected_token = ""
encoded = tokenizer(inferred_token)
if hidx > 0 and hidx < len(encoded["input_ids"][0][:-1]):
with torch.no_grad():
batch["text"] = inferred_token
batch["text_ids"] = torch.tensor(encoded["input_ids"]).to(device)
batch["text_labels"] = torch.tensor(encoded["input_ids"]).to(device)
batch["text_masks"] = torch.tensor(encoded["attention_mask"]).to(device)
infer = model(batch)
txt_emb, img_emb = infer["text_feats"], infer["image_feats"]
txt_mask, img_mask = (
infer["text_masks"].bool(),
infer["image_masks"].bool(),
)
for i, _len in enumerate(txt_mask.sum(dim=1)):
txt_mask[i, _len - 1] = False
txt_mask[:, 0] = False
img_mask[:, 0] = False
txt_pad, img_pad = ~txt_mask, ~img_mask
cost = cost_matrix_cosine(txt_emb.float(), img_emb.float())
joint_pad = txt_pad.unsqueeze(-1) | img_pad.unsqueeze(-2)
cost.masked_fill_(joint_pad, 0)
txt_len = (txt_pad.size(1) - txt_pad.sum(dim=1, keepdim=False)).to(
dtype=cost.dtype
)
img_len = (img_pad.size(1) - img_pad.sum(dim=1, keepdim=False)).to(
dtype=cost.dtype
)
T = ipot(
cost.detach(),
txt_len,
txt_pad,
img_len,
img_pad,
joint_pad,
0.1,
1000,
1,
)
plan = T[0]
plan_single = plan * len(txt_emb)
cost_ = plan_single.t()
cost_ = cost_[hidx][1:].cpu()
patch_index, (H, W) = infer["patch_index"]
heatmap = torch.zeros(H, W)
for i, pidx in enumerate(patch_index[0]):
h, w = pidx[0].item(), pidx[1].item()
heatmap[h, w] = cost_[i]
heatmap = (heatmap - heatmap.mean()) / heatmap.std()
heatmap = np.clip(heatmap, 1.0, 3.0)
heatmap = (heatmap - heatmap.min()) / (heatmap.max() - heatmap.min())
_w, _h = image.size
overlay = Image.fromarray(np.uint8(heatmap * 255), "L").resize(
(_w, _h), resample=Image.NEAREST
)
image_rgba = image.copy()
image_rgba.putalpha(overlay)
image = image_rgba
selected_token = tokenizer.convert_ids_to_tokens(
encoded["input_ids"][0][hidx]
)
return [np.array(image), inferred_token[0], selected_token]
inputs = [
gr.inputs.Textbox(
label="Url of an image.",
lines=5,
),
gr.inputs.Textbox(label="Caption with [MASK] tokens to be filled.", lines=5),
gr.inputs.Slider(
minimum=0,
maximum=38,
step=1,
label="Index of token for heatmap visualization (ignored if zero)",
),
]
outputs = [
gr.outputs.Image(label="Image"),
gr.outputs.Textbox(label="description"),
gr.outputs.Textbox(label="selected token"),
]
interface = gr.Interface(
fn=infer,
inputs=inputs,
outputs=outputs,
examples=[
[
"https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg",
"a display of flowers growing out and over the [MASK] [MASK] in front of [MASK] on a [MASK] [MASK].",
0,
],
[
"https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg",
"a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.",
4,
],
[
"https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg",
"a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.",
11,
],
[
"https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg",
"a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.",
15,
],
[
"https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg",
"a display of flowers growing out and over the retaining wall in front of cottages on a cloudy day.",
18,
],
[
"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg",
"a room with a [MASK], a [MASK], a [MASK], and a [MASK].",
0,
],
[
"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg",
"a room with a rug, a chair, a painting, and a plant.",
5,
],
[
"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg",
"a room with a rug, a chair, a painting, and a plant.",
8,
],
[
"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg",
"a room with a rug, a chair, a painting, and a plant.",
11,
],
[
"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Living_Room.jpg/800px-Living_Room.jpg",
"a room with a rug, a chair, a painting, and a plant.",
15,
],
],
)
interface.launch(debug=True)
ex.run() |