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"""glamm.py.
File for providing model implementations for any models using AutoModel.
"""
import logging
import os
import sys
import cv2
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, CLIPImageProcessor
from src.models.base import ModelBase
from src.models.config import Config
sys.path.append(os.path.join(os.path.dirname(__file__), 'groundingLMM'))
from model.GLaMM import GLaMMForCausalLM # noqa: E402
from model.llava.mm_utils import tokenizer_image_token # noqa: E402
from model.SAM.utils.transforms import ResizeLongestSide # noqa: E402
from tools.utils import DEFAULT_IM_END_TOKEN # noqa: E402
from tools.utils import DEFAULT_IM_START_TOKEN # noqa: E402
from tools.utils import DEFAULT_IMAGE_TOKEN # noqa: E402
def grounding_enc_processor(x: torch.Tensor) -> torch.Tensor:
"""Preprocess function.
Args:
x (torch.Tensor): Input tensor to preprocess.
Returns:
torch.Tensor: The preprocessed tensor.
"""
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
x = (x - IMG_MEAN) / IMG_STD
h, w = x.shape[-2:]
x = F.pad(x, (0, IMG_SIZE - w, 0, IMG_SIZE - h))
return x
def prepare_model_for_inference(model: GLaMMForCausalLM, args: dict) -> GLaMMForCausalLM:
"""Initialize vision tower.
Args:
model (GLaMMForCausalLM): The model to prepare.
args (dict): The arguments containing configuration options.
Returns:
GLaMMForCausalLM: The prepared model.
"""
print(
'\033[92m' + '---- Initialized Global Image Encoder (vision tower) from: {} ----'.format(
args['vision_tower']
) + '\033[0m'
)
model.get_model().initialize_vision_modules(model.get_model().config)
vision_tower = model.get_model().get_vision_tower()
vision_tower.to(dtype=torch.bfloat16, device=args['local_rank'])
model = model.bfloat16().cuda()
return model
class GlammModel(ModelBase):
"""Glamm model implementation."""
def __init__(self, config: Config) -> None:
"""Initialization of the llava model.
Args:
config (Config): Parsed config
"""
super().__init__(config)
def _load_specific_model(self) -> None:
"""Overridden function to populate self.model."""
# set up tokenizer first
self.tokenizer = AutoTokenizer.from_pretrained(
self.config.model_path,
model_max_length=self.config.model['model_max_length'],
padding_side='right',
use_fast=False
)
self.tokenizer.pad_token = self.tokenizer.unk_token
self.config.model['bbox_token_idx'] = self.tokenizer('<bbox>', add_special_tokens=False).input_ids[0]
self.config.model['seg_token_idx'] = self.tokenizer('[SEG]', add_special_tokens=False).input_ids[0]
self.config.model['bop_token_idx'] = self.tokenizer('<p>', add_special_tokens=False).input_ids[0]
self.config.model['eop_token_idx'] = self.tokenizer('</p>', add_special_tokens=False).input_ids[0]
model_args = {
'seg_token_idx': self.config.model['seg_token_idx'],
'bbox_token_idx': self.config.model['bbox_token_idx'],
'eop_token_idx': self.config.model['eop_token_idx'],
'bop_token_idx': self.config.model['bop_token_idx'],
}
self.model = GLaMMForCausalLM.from_pretrained(
self.config.model_path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
**model_args
)
self.model = prepare_model_for_inference(self.model, self.config.model)
def _init_processor(self) -> None:
"""Set the self.processor to follow the example given.
This should follow the processor setting and tokenizers under:
https://github.com/mbzuai-oryx/groundingLMM/blob/main/app.py
"""
processor = {
'global_enc_processor': CLIPImageProcessor.from_pretrained(self.config.model['vision_tower']),
'grounding_transform': ResizeLongestSide(self.config.model['image_size'])
}
self.processor = processor
def _generate_prompt(self, prompt: str) -> str:
"""Generates the GLaMM model prompt which will not use the chat template.
Args:
prompt (str): The input prompt string.
Returns:
str: The prompt to return, set by the config.
"""
prompt = f'The {DEFAULT_IMAGE_TOKEN} provides an overview of the picture.\n{prompt}'
if self.config.model['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)
return prompt
def _generate_processor_output(self, prompt: str, img_path: str) -> dict:
"""Generate the processor argument to be input into the processor.
Args:
prompt (str): The generated prompt string with the input text and the image labels.
img_path (str): The specified image path.
Returns:
dict: The corresponding processor arguments per image and prompt.
Raises:
ValueError: If the image path is not defined.
"""
if img_path is None:
raise ValueError('GLAMM cannot have text-only generation.')
image_np = cv2.imread(img_path)
image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
orig_h, orig_w = image_np.shape[:2]
original_size_list = [(orig_h, orig_w)]
# Global encoder
global_enc_image = self.processor['global_enc_processor'].preprocess(
image_np, return_tensors='pt')['pixel_values'][0].unsqueeze(0).cuda().bfloat16()
# Grounding encoder
grounding_input = self.processor['grounding_transform'].apply_image(image_np)
resize_list = [grounding_input.shape[:2]]
grounding_enc_image = grounding_enc_processor(
torch.from_numpy(grounding_input).permute(2, 0, 1).contiguous()
).unsqueeze(0).cuda().bfloat16()
input_ids = tokenizer_image_token(prompt, self.tokenizer, return_tensors='pt').unsqueeze(0).cuda()
return {
'input_ids': input_ids,
'global_enc_image': global_enc_image,
'grounding_enc_image': grounding_enc_image,
'resize_list': resize_list,
'original_size_list': original_size_list,
'bboxes': None
}
def _forward(self, data: dict) -> None:
"""Given some input data, performs a single forward pass.
This function itself can be overriden, while _hook_and_eval should be left in tact.
Args:
data (dict): The given data tensor.
"""
with torch.no_grad():
output_ids, _ = self.model.evaluate(
data['global_enc_image'],
data['grounding_enc_image'],
data['input_ids'],
data['resize_list'],
data['original_size_list'],
max_tokens_new=self.config.forward['max_new_tokens'],
bboxes=data['bboxes']
)
logging.debug('Completed forward pass')
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