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import hydra
import torch
import os
import pyrootutils
from PIL import Image
import re
import cv2
import numpy as np
from omegaconf import OmegaConf
from diffusers import AutoencoderKL, UNet2DConditionModel, EulerDiscreteScheduler
from any_res import process_anyres_image
pyrootutils.setup_root(__file__, indicator='.project-root', pythonpath=True)
def visualize_bbox(image, bboxes, save_path):
img_width, img_height = image.size
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
for bbox in bboxes:
x_center, y_center, box_width, box_height = bbox
x_center = x_center / 224 * img_width
y_center = y_center / 224 * img_height
box_width = box_width /224 * img_width
box_height = box_height / 224 * img_height
x1 = int(x_center - box_width / 2)
y1 = int(y_center - box_height / 2)
x2 = int(x_center + box_width / 2)
y2 = int(y_center + box_height / 2)
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.imwrite(save_path, image)
def extract_box(output_str):
boxes = re.findall('<box_start>(.*?)<box_end>', output_str)
if len(boxes) >0:
bboxes = [[int(num) for num in re.findall('<loc-(\d+)>', box)] for box in boxes]
else:
bboxes = None
return bboxes
BOI_TOKEN = '<img>'
BOP_TOKEN = '<patch>'
EOI_TOKEN = '</img>'
EOP_TOKEN = '</patch>'
IMG_TOKEN = '<img_{:05d}>'
instruction_prompt = '[INST] {instruction} [/INST]\n'
resolution_grids = ['1x1', '1x2', '1x3', '2x1', '3x1', '1x4', '4x1', '2x2']
base_resolution = 448
device = 'cuda:0'
device1 = 'cuda:1'
dtype = torch.float16
dtype_str = 'fp16'
num_img_in_tokens = 64
num_img_out_tokens = 64
tokenizer_cfg_path = 'configs/tokenizer/clm_llama_tokenizer_224loc_anyres.yaml'
image_transform_cfg_path = 'configs/processer/qwen_448_transform.yaml'
visual_encoder_cfg_path = 'configs/visual_encoder/qwen_vitg_448.yaml'
llm_cfg_path = 'configs/clm_models/llm_seed_x_i.yaml'
agent_cfg_path = 'configs/clm_models/agent_seed_x_i.yaml'
adapter_cfg_path = 'configs/sdxl_adapter/sdxl_qwen_vit_resampler_l4_q64_pretrain_no_normalize.yaml'
discrete_model_cfg_path = 'configs/discrete_model/discrete_identity.yaml'
diffusion_model_path = 'pretrained/stable-diffusion-xl-base-1.0'
tokenizer_cfg = OmegaConf.load(tokenizer_cfg_path)
tokenizer = hydra.utils.instantiate(tokenizer_cfg)
image_transform_cfg = OmegaConf.load(image_transform_cfg_path)
image_transform = hydra.utils.instantiate(image_transform_cfg)
visual_encoder_cfg = OmegaConf.load(visual_encoder_cfg_path)
visual_encoder = hydra.utils.instantiate(visual_encoder_cfg)
visual_encoder.eval().to(device1, dtype=dtype)
print('Init visual encoder done')
llm_cfg = OmegaConf.load(llm_cfg_path)
llm = hydra.utils.instantiate(llm_cfg, torch_dtype=dtype)
print('Init llm done.')
agent_model_cfg = OmegaConf.load(agent_cfg_path)
agent_model = hydra.utils.instantiate(agent_model_cfg, llm=llm)
agent_model.eval().to(device, dtype=dtype)
print('Init agent mdoel Done')
noise_scheduler = EulerDiscreteScheduler.from_pretrained(diffusion_model_path, subfolder="scheduler")
print('init vae')
vae = AutoencoderKL.from_pretrained(diffusion_model_path, subfolder="vae").to(device1, dtype=dtype)
print('init unet')
unet = UNet2DConditionModel.from_pretrained(diffusion_model_path, subfolder="unet").to(device1, dtype=dtype)
adapter_cfg = OmegaConf.load(adapter_cfg_path)
adapter = hydra.utils.instantiate(adapter_cfg, unet=unet).to(device1, dtype=dtype).eval()
discrete_model_cfg = OmegaConf.load(discrete_model_cfg_path)
discrete_model = hydra.utils.instantiate(discrete_model_cfg).to(device1).eval()
print('Init adapter done')
adapter.init_pipe(vae=vae,
scheduler=noise_scheduler,
visual_encoder=visual_encoder,
image_transform=image_transform,
discrete_model=discrete_model,
dtype=dtype,
device=device1)
print('Init adapter pipe done')
boi_token_id = tokenizer.encode(BOI_TOKEN, add_special_tokens=False)[0]
eoi_token_id = tokenizer.encode(EOI_TOKEN, add_special_tokens=False)[0]
bop_token_id = tokenizer.encode(BOP_TOKEN, add_special_tokens=False)[0]
eop_token_id = tokenizer.encode(EOP_TOKEN, add_special_tokens=False)[0]
grid_pinpoints = []
for scale in resolution_grids:
s1, s2 = scale.split('x')
grid_pinpoints.append([int(s1)*base_resolution, int(s2)*base_resolution])
grid_pinpoints = grid_pinpoints
# image comprehension
image_path = 'demo_images/advisor.png'
image = Image.open(image_path).convert('RGB')
image_tensor, patch_pos_tensor = process_anyres_image(image, image_transform, grid_pinpoints, base_resolution)
embeds_cmp_mask = torch.tensor([True]*image_tensor.shape[0]).to(device, dtype=torch.bool)
patch_pos = [patch_pos_tensor]
patch_position = torch.cat(patch_pos, dim=0)
image_tensor = image_tensor.to(device1, dtype=dtype)
patch_length = image_tensor.shape[0]
image_tokens = ''
for _ in range(patch_length-1):
image_tokens += BOP_TOKEN + ''.join(IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)) + EOP_TOKEN
image_tokens += BOI_TOKEN + ''.join(IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)) + EOI_TOKEN
question = 'Can I conntect with an advisor on Sunday?'
prompt = instruction_prompt.format_map({'instruction': image_tokens + question})
input_ids = tokenizer.encode(prompt, add_special_tokens=False)
input_ids = [tokenizer.bos_token_id] + input_ids
input_ids = torch.tensor(input_ids).to(device, dtype=torch.long)
ids_cmp_mask = torch.zeros_like(input_ids, dtype=torch.bool)
boi_indices = torch.where(torch.logical_or(input_ids == boi_token_id, input_ids == bop_token_id))[0].tolist()
eoi_indices = torch.where(torch.logical_or(input_ids == eoi_token_id, input_ids == eop_token_id))[0].tolist()
for boi_idx, eoi_idx in zip(boi_indices, eoi_indices):
ids_cmp_mask[boi_idx + 1:eoi_idx] = True
input_ids = input_ids.unsqueeze(0)
ids_cmp_mask = ids_cmp_mask.unsqueeze(0)
with torch.no_grad():
image_embeds = visual_encoder(image_tensor)
image_embeds = image_embeds.to(device)
output = agent_model.generate(tokenizer=tokenizer,
input_ids=input_ids,
image_embeds=image_embeds,
embeds_cmp_mask=embeds_cmp_mask,
patch_positions=patch_position,
ids_cmp_mask=ids_cmp_mask,
max_new_tokens=512,
num_img_gen_tokens=num_img_out_tokens)
text = re.sub('<[^>]*>', '', output['text'])
print(text)
# detection
image_path = 'demo_images/ground.png'
image = Image.open(image_path).convert('RGB')
image_tensor, patch_pos_tensor = process_anyres_image(image, image_transform, grid_pinpoints, base_resolution)
embeds_cmp_mask = torch.tensor([True]*image_tensor.shape[0]).to(device, dtype=torch.bool)
patch_pos = [patch_pos_tensor]
patch_position = torch.cat(patch_pos, dim=0)
image_tensor = image_tensor.to(device1, dtype=dtype)
patch_length = image_tensor.shape[0]
image_tokens = ''
for _ in range(patch_length-1):
image_tokens += BOP_TOKEN + ''.join(IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)) + EOP_TOKEN
image_tokens += BOI_TOKEN + ''.join(IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)) + EOI_TOKEN
question = 'Is there anything in the image that can protect me from catching the flu virus when I go out? Show me the location.'
prompt = instruction_prompt.format_map({'instruction': image_tokens + question})
input_ids = tokenizer.encode(prompt, add_special_tokens=False)
input_ids = [tokenizer.bos_token_id] + input_ids
input_ids = torch.tensor(input_ids).to(device, dtype=torch.long)
ids_cmp_mask = torch.zeros_like(input_ids, dtype=torch.bool)
boi_indices = torch.where(torch.logical_or(input_ids == boi_token_id, input_ids == bop_token_id))[0].tolist()
eoi_indices = torch.where(torch.logical_or(input_ids == eoi_token_id, input_ids == eop_token_id))[0].tolist()
for boi_idx, eoi_idx in zip(boi_indices, eoi_indices):
ids_cmp_mask[boi_idx + 1:eoi_idx] = True
input_ids = input_ids.unsqueeze(0)
ids_cmp_mask = ids_cmp_mask.unsqueeze(0)
with torch.no_grad():
image_embeds = visual_encoder(image_tensor)
image_embeds = image_embeds.to(device)
output = agent_model.generate(tokenizer=tokenizer,
input_ids=input_ids,
image_embeds=image_embeds,
embeds_cmp_mask=embeds_cmp_mask,
patch_positions=patch_position,
ids_cmp_mask=ids_cmp_mask,
max_new_tokens=512,
num_img_gen_tokens=num_img_out_tokens)
print(output['text'])
bbox = extract_box(output['text'])
if bbox is not None:
save_path = 'vis/ground.png'
visualize_bbox(image, bbox, save_path)
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