demo/seagull_inference.py

import torch
from seagull.utils import disable_torch_init
from transformers import AutoTokenizer, CLIPImageProcessor
from seagull.model.language_model.seagull_llama import SeagullLlamaForCausalLM
from seagull.mm_utils import tokenizer_image_token
from seagull.conversation import conv_templates, SeparatorStyle
from seagull.constants import IMAGE_TOKEN_INDEX
from seagull.train.train import DataArguments

from functools import partial
import os
import numpy as np
import cv2
from typing import List
from PIL import Image
from pycocotools import mask as mask_utils

class Seagull():
    def __init__(self, model_path, device='cuda'):
        disable_torch_init()
        model_path = os.path.expanduser(model_path)
        self.tokenizer = AutoTokenizer.from_pretrained(model_path, model_max_length=2048, padding_side="right", use_fast=True)
        self.model = SeagullLlamaForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16,).to(device)
        self.tokenizer.pad_token = self.tokenizer.unk_token

        self.image_processor = CLIPImageProcessor(do_resize=True, size={"shortest_edge":512}, resample=3,  do_center_crop=True, crop_size={"height": 512, "width": 512},
                                                    do_rescale=True, rescale_factor=0.00392156862745098, do_normalize=True, image_mean=[0.48145466, 0.4578275, 0.40821073],
                                                    image_std=[0.26862954, 0.26130258, 0.27577711], do_convert_rgb=True, )
        
        spi_tokens = ['<global>', '<local>']
        self.tokenizer.add_tokens(spi_tokens, special_tokens=True)
        
        for m in self.model.modules():
            m.tokenizer = self.tokenizer

        vision_tower = self.model.get_vision_tower()
        if not vision_tower.is_loaded:
            vision_tower.load_model()
        vision_tower.to(dtype=torch.float16, device=device)

        begin_str = "<image>\nThis provides an overview of the image.\n Please answer the following questions about the provided region. Note: Distortions include: blur, colorfulness, compression, contrast exposure and noise.\n Here is the region <global><local>. "
        
        instruction = {
            'distortion': 'Provide the distortion type of this region.',
            'quality': 'Analyze the quality of this region.',
            'importance': 'Consider the impact of this region on the overall image quality. Analyze its importance to the overall image quality.'
        }
        
        self.ids_input = {}
        for ins_type, ins in instruction.items():
            conv = conv_templates['seagull_v1'].copy()
            qs = begin_str + ins
            conv.append_message(conv.roles[0], qs)
            conv.append_message(conv.roles[1], None)
            prompt = conv.get_prompt()
            self.ids_input[ins_type] = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.model.device)

        self.stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        
    def init_image(self, img):
        if isinstance(img, dict):
            img = img['image']
        elif isinstance(img, List):
            img = cv2.imread(img[0])
            img = img[:, :, ::-1]
        h_, w_ = img.shape[:2]
        if h_ > 512:
            ratio = 512 / h_
            new_h, new_w = int(h_ * ratio), int(w_ * ratio)
            preprocessed_img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
        else:
            preprocessed_img = img.copy()

        return (preprocessed_img, preprocessed_img, preprocessed_img, preprocessed_img)

    def preprocess(self, img):
        image = self.image_processor.preprocess(img,
                                do_center_crop=False,
                                return_tensors='pt')['pixel_values'][0]

        image = torch.nn.functional.interpolate(image.unsqueeze(0),
                                                size=(512, 512),
                                                mode='bilinear',
                                                align_corners=False).squeeze(0)
        
        return image
        
    def seagull_predict(self, img, mask, instruct_type, mask_type='rle'):
        if isinstance(img, str):
            img = cv2.imread(img)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            h, w, _ = img.shape
            
            if mask_type == 'rle': # use the mask to indicate the roi
                compressed_rle = {'size' : [h, w], 'counts' : mask}
                mask = mask_utils.decode(compressed_rle)
            elif mask_type == 'points': # use the point to indicate the roi
                x_min, y_min, w1, h1 = mask
                x_max, y_max = x_min + w1, y_min + h1
                mask = np.zeros_like(img[:, :, 0])
                mask[max(0, y_min):min(y_max, mask.shape[0]), max(0, x_min):min(x_max, mask.shape[1])] = 1
            
        image = self.preprocess(img)
        mask = np.array(mask, dtype=np.int)
        
        ys, xs = np.where(mask > 0)
        if len(xs) > 0 and len(ys) > 0:
            x_min, x_max = np.min(xs), np.max(xs)
            y_min, y_max = np.min(ys), np.max(ys)
            w1 = x_max - x_min
            h1 = y_max - y_min
            bounding_box = (x_min, y_min, w1, h1)
        else:
            bounding_box = None
            
        mask = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_NEAREST)
        mask = np.array(mask > 0.1, dtype=np.uint8)
        masks = torch.Tensor(mask).unsqueeze(0).to(self.model.device)
        
        input_ids = self.ids_input[instruct_type.split()[0].lower()]
        
        x1, y1, w1, h1 = list(map(int, bounding_box))  # x y w h
        cropped_img = img[y1:y1 + h1, x1:x1 + w1]
        cropped_img = Image.fromarray(cropped_img)
        cropped_img = self.preprocess(cropped_img)
            
        with torch.inference_mode():

            self.model.orig_forward = self.model.forward
            self.model.forward = partial(self.model.orig_forward,
                                        img_metas=[None],
                                        masks=[masks.half()],
                                        cropped_img=cropped_img.unsqueeze(0)
                                        )
            output_ids = self.model.generate(
                input_ids,
                images=image.unsqueeze(0).half().to(self.model.device),
                do_sample=False,
                temperature=1,
                max_new_tokens=2048,
                use_cache=True,
                num_beams=1,
                top_k = 0,
                top_p = 1,
                )

            self.model.forward = self.model.orig_forward

        input_token_len = input_ids.shape[1]
        n_diff_input_output = (
            input_ids != output_ids[:, :input_token_len]).sum().item()
        if n_diff_input_output > 0:
            print(
                f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
        outputs = self.tokenizer.batch_decode(output_ids[:, input_token_len:],
                                            skip_special_tokens=True)[0]
    
        outputs = outputs.strip()
        if outputs.endswith(self.stop_str):
            outputs = outputs[:-len(self.stop_str)]
        outputs = outputs.strip()
        if ':' in outputs:
            outputs = outputs.split(':')[1]

        outputs_list = outputs.split('.')
        outputs_list_final = []
        outputs_str = ''
        for output in outputs_list:
            if output not in outputs_list_final:
                if output=='':
                    continue
                outputs_list_final.append(output)
                outputs_str+=output+'.'
            else:
                break
        return outputs_str