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91c1e9e 06b25e4 91c1e9e 06b25e4 91c1e9e 06b25e4 91c1e9e 06b25e4 91c1e9e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | from PIL import Image
from itertools import zip_longest
import os
import requests
from urllib.parse import urlparse
import torch
from torch import nn
import torch.nn.functional as F
import cv2
import numpy as np
from model.llava.mm_utils import tokenizer_image_token
from model.SAM.utils.transforms import ResizeLongestSide
from dataset.utils.blip_processors import Blip2ImageEvalProcessor
from tools.prompting import write_list as _write_list
from tools.utils import (
DEFAULT_IM_END_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_TOKEN,
IMAGE_TOKEN_INDEX,
)
def is_url(image):
try:
result = urlparse(image)
return all([result.scheme, result.netloc])
except:
return False
class Chat:
IMG_MEAN = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1)
IMG_STD = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1)
IMG_SIZE = 1024
IGNORE_LABEL = 255
WHITE_BAND_WIDTH = 10
def __init__(
self,
model,
tokenizer,
qformer_tokenizer,
args,
device="cuda:0",
):
self.device = device
self.model = model
self.global_enc_processor = Blip2ImageEvalProcessor()
self.tokenizer = tokenizer
self.qformer_tokenizer = qformer_tokenizer
image_size = args.image_size
self.transform = ResizeLongestSide(image_size)
self.use_mm_start_end = args.use_mm_start_end
# Match each image stream to the corresponding encoder dtype/device.
try:
first_param = next(self.model.parameters())
self.runtime_dtype = first_param.dtype
self.runtime_device = first_param.device
except Exception:
self.runtime_dtype = torch.bfloat16
self.runtime_device = torch.device(self.device)
try:
ge_param = next(
self.base_model.model.grounding_encoder.image_encoder.parameters()
)
self.grounding_dtype = ge_param.dtype
self.grounding_device = ge_param.device
except Exception:
self.grounding_dtype = self.runtime_dtype
self.grounding_device = self.runtime_device
def _load_image_to_rgb(self, image):
if is_url(image):
response = requests.get(image)
image_np = np.frombuffer(response.content, np.uint8)
image_np = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
else:
image_np = cv2.imread(image)
if image_np is None:
raise ValueError(f"Unable to load image: {image}")
return cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
def _global_enc_from_array(self, image_np: np.ndarray) -> torch.Tensor:
return self.global_enc_processor(Image.fromarray(image_np))
def _prepare_encodings(self, image_np: np.ndarray, image_identifier: str):
original_size = image_np.shape[:2]
global_enc_image = self._global_enc_from_array(image_np)
global_enc_image = global_enc_image.to(
device=self.runtime_device, dtype=self.runtime_dtype
)
grounding_image = self.transform.apply_image(image_np)
resize = grounding_image.shape[:2]
grounding_enc_image = self.grounding_enc_processor(
torch.from_numpy(grounding_image).permute(2, 0, 1).contiguous()
).to(device=self.grounding_device, dtype=self.grounding_dtype)
return (
global_enc_image,
grounding_enc_image,
original_size,
resize,
image_np,
image_identifier,
)
def grounding_enc_processor(self, x: torch.Tensor) -> torch.Tensor:
x = (x - self.IMG_MEAN) / self.IMG_STD
h, w = x.shape[-2:]
x = F.pad(x, (0, self.IMG_SIZE - w, 0, self.IMG_SIZE - h))
return x
@property
def base_model(self):
if isinstance(self.model, nn.DataParallel):
return self.model.module
else:
return self.model
def ask(self, text, conv, num_images=2):
text = text.replace("<", "<").replace(">", ">")
prompt = text
if len(conv.messages) == 0:
if num_images > 1:
img_tokens = [
f"{DEFAULT_IMAGE_TOKEN} (IMAGE{i + 1})" for i in range(num_images)
]
img_tokens_list = _write_list(img_tokens)
begin_sentence = (
f"The {img_tokens_list} provide an overview of the pictures.\n"
)
elif num_images == 1:
begin_sentence = (
f"The {DEFAULT_IMAGE_TOKEN} (IMAGE1) provides an overview of the picture.\n"
)
else:
begin_sentence = ""
prompt = begin_sentence + prompt
if self.use_mm_start_end:
replace_token = (
DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
)
prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
conv.append_message(conv.roles[0], prompt)
def answer(
self,
conv,
img_list,
question,
answer_prefix: str = "",
max_new_tokens=512,
max_length=2300,
):
conv.append_message(conv.roles[1], "")
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, self.tokenizer, return_tensors="pt")
if answer_prefix:
if DEFAULT_IMAGE_TOKEN in answer_prefix:
prefix_ids = tokenizer_image_token(answer_prefix, self.tokenizer)
else:
prefix_ids = self.tokenizer(answer_prefix).input_ids
if len(prefix_ids) > 0 and prefix_ids[0] == self.tokenizer.bos_token_id:
prefix_ids = prefix_ids[1:]
if len(prefix_ids) > 0 and prefix_ids[-1] == self.tokenizer.eos_token_id:
prefix_ids = prefix_ids[:-1]
if len(prefix_ids) > 0:
prefix_ids = torch.tensor(prefix_ids, dtype=input_ids.dtype)
input_ids = torch.cat([input_ids, prefix_ids], dim=0)
input_ids = input_ids.unsqueeze(0).to(self.device)
current_max_len = input_ids.shape[1] + max_new_tokens
begin_idx = max(0, current_max_len - max_length)
input_ids = input_ids[:, begin_idx:]
attention_masks = input_ids.ne(self.tokenizer.pad_token_id)
attention_masks = attention_masks.to(self.device)
conv_offset = torch.tensor([0, 1], device=self.device)
questions_list = question if isinstance(question, list) else [question]
if self.qformer_tokenizer is not None:
qformer_inputs = self.qformer_tokenizer(
questions_list, padding=True, truncation=True, return_tensors="pt"
)
qformer_input_ids = qformer_inputs.input_ids.to(self.device)
qformer_attention_masks = qformer_inputs.attention_mask.to(self.device)
else:
qformer_input_ids = None
qformer_attention_masks = None
if len(img_list) > 0:
if len(img_list) > 1:
(
global_enc_images,
grounding_enc_images,
original_sizes,
resizes,
_,
image_paths,
) = list(zip(*img_list))
global_enc_images = torch.stack(global_enc_images, dim=0)
grounding_enc_images = torch.stack(grounding_enc_images, dim=0)
original_size_list = list(zip_longest([None, *original_sizes]))
resize_list = list(zip_longest([None, *resizes]))
else:
(
global_enc_image,
grounding_enc_image,
original_size,
resize,
_,
image_path,
) = img_list[0]
global_enc_images = global_enc_image.unsqueeze(0)
grounding_enc_images = grounding_enc_image.unsqueeze(0)
image_paths = [image_path]
resizes = [resize]
original_sizes = [original_size]
slots = [None, *original_sizes]
resize_slots = [None, *resizes]
original_size_list = list(zip_longest(slots))
resize_list = list(zip_longest(resize_slots))
image_paths = [image_paths]
image_offset = torch.tensor(
[0, global_enc_images.shape[0]], device=self.device
)
grounding_offset = torch.tensor(
[0, grounding_enc_images.shape[0]], device=self.device
)
output_ids, pred_masks = self.model.evaluate(
global_enc_images,
grounding_enc_images,
input_ids,
attention_masks,
resize_list,
original_size_list,
conv_offset,
image_offset,
grounding_offset,
qformer_input_ids,
qformer_attention_masks,
max_new_tokens=max_new_tokens,
image_paths=image_paths,
eos_token_id=self.tokenizer.eos_token_id,
)
output_ids = output_ids[0][output_ids[0] != IMAGE_TOKEN_INDEX]
else:
image_offset = torch.tensor([0, 0], device=self.device)
grounding_offset = torch.tensor([0, 0], device=self.device)
output_ids, _ = self.model.evaluate(
None,
None,
input_ids,
attention_masks,
None,
None,
conv_offset,
image_offset,
grounding_offset,
qformer_input_ids,
qformer_attention_masks,
max_new_tokens=max_new_tokens,
eos_token_id=self.tokenizer.eos_token_id,
)
output_ids = output_ids[0]
pred_masks = None
text_output = self.tokenizer.decode(output_ids, skip_special_tokens=False)
text_output = " ".join(text_output.split())
chunks = text_output.split("</s>")
llm_message = chunks[0] if len(chunks) <= 2 else chunks[1]
llm_message = llm_message.split("ASSISTANT: ")[-1].strip()
conv.messages.pop()
conv.append_message(conv.roles[1], llm_message)
return llm_message, pred_masks, text_output
def upload_img(self, image, conv, img_list):
image_np = self._load_image_to_rgb(image)
img_list.append(self._prepare_encodings(image_np, image))
msg = "Received."
return msg
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