Add modeling_deepseekocr.py for standalone loading

modeling_deepseekocr.py

@@ -0,0 +1,1037 @@
+from .modeling_deepseekv2 import DeepseekV2Model, DeepseekV2ForCausalLM
+from .configuration_deepseek_v2 import DeepseekV2Config
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from typing import List, Optional, Tuple, Union
+from transformers.cache_utils import Cache
+import requests
+from PIL import Image, ImageOps, ImageDraw, ImageFont
+from io import BytesIO
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+import os
+from .deepencoder import build_sam_vit_b, build_clip_l, MlpProjector
+from addict import Dict
+from transformers import TextStreamer
+from .conversation import get_conv_template
+from abc import ABC
+import math
+import re
+from tqdm import tqdm
+import numpy as np
+import time
+
+
+def load_image(image_path):
+
+    try:
+        image = Image.open(image_path)
+        
+        corrected_image = ImageOps.exif_transpose(image)
+        
+        return corrected_image
+        
+    except Exception as e:
+        print(f"error: {e}")
+        try:
+            return Image.open(image_path)
+        except:
+            return None
+
+
+def re_match(text):
+    pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
+    matches = re.findall(pattern, text, re.DOTALL)
+
+    # pattern1 = r'<\|ref\|>.*?<\|/ref\|>\n'
+    # new_text1 = re.sub(pattern1, '', text, flags=re.DOTALL)
+
+    mathes_image = []
+    mathes_other = []
+    for a_match in matches:
+        if '<|ref|>image<|/ref|>' in a_match[0]:
+            mathes_image.append(a_match[0])
+        else:
+            mathes_other.append(a_match[0])
+    return matches, mathes_image, mathes_other
+
+
+def extract_coordinates_and_label(ref_text, image_width, image_height):
+
+    try:
+        label_type = ref_text[1]
+        cor_list = eval(ref_text[2])
+    except Exception as e:
+        print(e)
+        return None
+
+    return (label_type, cor_list)
+
+
+def draw_bounding_boxes(image, refs, ouput_path):
+
+    image_width, image_height = image.size
+    
+    img_draw = image.copy()
+    draw = ImageDraw.Draw(img_draw)
+
+    overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
+    draw2 = ImageDraw.Draw(overlay)
+    
+    # try:
+    # except IOError:
+    #     try:
+    #         font = ImageFont.truetype("DejaVuSans.ttf", 20) 
+    #     except IOError:
+    font = ImageFont.load_default()
+
+    img_idx = 0
+    
+    for i, ref in enumerate(refs):
+        try:
+            result = extract_coordinates_and_label(ref, image_width, image_height)
+            if result:
+                label_type, points_list = result
+                
+                color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
+
+                color_a = color + (20, )
+                for points in points_list:
+                    x1, y1, x2, y2 = points
+
+                    x1 = int(x1 / 999 * image_width)
+                    y1 = int(y1 / 999 * image_height)
+
+                    x2 = int(x2 / 999 * image_width)
+                    y2 = int(y2 / 999 * image_height)
+
+                    if label_type == 'image':
+                        try:
+                            cropped = image.crop((x1, y1, x2, y2))
+                            cropped.save(f"{ouput_path}/images/{img_idx}.jpg")
+                        except Exception as e:
+                            print(e)
+                            pass
+                        img_idx += 1
+                        
+                    try:
+                        if label_type == 'title':
+                            draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
+                            draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
+                        else:
+                            draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
+                            draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
+                        text_x = x1
+                        text_y = max(0, y1 - 15)
+                            
+                        
+                        text_bbox = draw.textbbox((0, 0), label_type, font=font)
+                        text_width = text_bbox[2] - text_bbox[0]
+                        text_height = text_bbox[3] - text_bbox[1]
+                        draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height], 
+                                    fill=(255, 255, 255, 30))
+                        
+                        draw.text((text_x, text_y), label_type, font=font, fill=color)
+                    except:
+                        pass
+        except:
+            continue
+    img_draw.paste(overlay, (0, 0), overlay)
+    return img_draw
+
+
+def process_image_with_refs(image, ref_texts, output_path):
+
+    result_image = draw_bounding_boxes(image, ref_texts, output_path)
+    
+    return result_image
+
+
+
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    # print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
+    return best_ratio
+
+
+def dynamic_preprocess(image, min_num=2, max_num=9, image_size=640, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    # print(target_ratios)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # print(target_aspect_ratio)
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images, target_aspect_ratio
+
+
+
+def normalize_transform(mean, std):
+    if mean is None and std is None:
+        transform = None
+    elif mean is None and std is not None:
+        mean = [0.] * len(std)
+        transform = transforms.Normalize(mean=mean, std=std)
+    elif mean is not None and std is None:
+        std = [1.] * len(mean)
+        transform = transforms.Normalize(mean=mean, std=std)
+    else:
+        transform = transforms.Normalize(mean=mean, std=std)
+
+    return transform
+
+
+
+def format_messages(
+        conversations: List[Dict[str, str]],
+        sft_format: str = "deepseek",
+        system_prompt: str = "",
+):
+    """
+    Applies the SFT template to conversation.
+
+    Args:
+        conversations (List[Dict]): A List of messages.
+        sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek".
+        system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "".
+
+    Returns:
+        sft_prompt (str): The formatted text.
+    """
+
+    conv = get_conv_template(sft_format)
+    conv.set_system_message(system_prompt)
+    for message in conversations:
+        conv.append_message(message["role"], message["content"].strip())
+    sft_prompt = conv.get_prompt().strip()
+
+    return sft_prompt
+
+
+def text_encode(tokenizer, text: str, bos: bool = True, eos: bool = False):
+    t = tokenizer.encode(text, add_special_tokens=False)
+    bos_id = 0
+    eos_id = 1
+    if bos:
+        t = [bos_id] + t
+    if eos:
+        t = t + [eos_id]
+
+    return t
+
+def load_pil_images(conversations: List[Dict[str, str]]) -> List[Image.Image]:
+    """
+
+    Args:
+        conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
+            [
+                {
+                    "role": "User",
+                    "content": "<image_placeholder>\nExtract all information from this image and convert them into markdown format.",
+                    "images": ["./examples/table_datasets.png"]
+                },
+                {"role": "Assistant", "content": ""},
+            ]
+
+    Returns:
+        pil_images (List[PIL.Image.Image]): the list of PIL images.
+
+    """
+
+    pil_images = []
+
+    for message in conversations:
+        if "images" not in message:
+            continue
+
+        for image_path in message["images"]:
+            # print('----------------')
+            # print(image_path)
+            # print('----------------')
+            # exit()
+            
+            # pil_img = Image.open(image_path)
+            pil_img = load_image(image_path)
+            pil_img = pil_img.convert("RGB")
+            pil_images.append(pil_img)
+
+    return pil_images
+
+
+class BaseTransform(ABC):
+
+    def set_rng(self, *args, **kwargs):
+        pass
+
+    def __call__(self, *args, **kwargs) -> torch.Tensor:
+        pass
+
+    @property
+    def default_shape(self):
+        raise NotImplementedError
+
+
+class BasicImageTransform(BaseTransform):
+    def __init__(
+        self, 
+        mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
+        std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
+        normalize: bool = True
+    ):
+        self.mean = mean
+        self.std = std
+    
+        transform_pipelines = [
+            transforms.ToTensor()
+        ]
+
+        normalize = normalize_transform(mean, std) if normalize else nn.Identity()
+        if normalize is not None:
+            transform_pipelines.append(normalize)
+
+        self.transform = transforms.Compose(transform_pipelines)
+    
+    def __call__(self, x):
+        x = self.transform(x)
+        return x
+
+class NoEOSTextStreamer(TextStreamer):
+    def on_finalized_text(self, text: str, stream_end: bool = False):
+
+        eos_text = self.tokenizer.decode([self.tokenizer.eos_token_id], skip_special_tokens=False)
+        text = text.replace(eos_text, "\n")
+        print(text, flush=True, end="")
+
+
+class DeepseekOCRConfig(DeepseekV2Config):
+    model_type = "DeepseekOCR"
+
+class DeepseekOCRModel(DeepseekV2Model):
+    config_class = DeepseekOCRConfig
+
+    def __init__(self, config: DeepseekV2Config):
+        super(DeepseekOCRModel, self).__init__(config)
+
+        self.sam_model = build_sam_vit_b()
+        self.vision_model = build_clip_l()
+        # self.conv_2 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=2, stride=2)
+        n_embed = 1280
+        self.projector =  MlpProjector(Dict(projector_type="linear", input_dim=2048, n_embed=n_embed))
+        embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
+        self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
+        self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
+
+
+
+    
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        images_seq_mask: Optional[torch.FloatTensor] = None,
+        images_spatial_crop: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+
+
+
+
+        if inputs_embeds is None:
+            # inputs_embeds = self.embed_tokens(input_ids)
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+
+
+        sam_model = getattr(self, 'sam_model', None)
+        # sam_model = self.sam_model
+        vision_model = getattr(self, 'vision_model', None)
+
+
+
+        if sam_model is not None and (input_ids.shape[1] != 1 or self.training) and torch.sum(images[0][1]).item() != 0:
+
+            idx = 0
+            
+            # sam_model = torch.jit.script(sam_model)
+            
+            # start_time = time.time()
+            for image, crop_shape in zip(images, images_spatial_crop):
+                images_in_this_batch = []
+
+                patches = image[0]
+                image_ori = image[1]
+
+                with torch.no_grad():
+                # with torch.inference_mode(): 
+                    
+                    if torch.sum(patches).item() != 0:
+                        # P, C, H, W = patches.shape
+                        crop_flag = 1
+                        local_features_1 = sam_model(patches)
+
+                        local_features_2 = vision_model(patches, local_features_1)  
+                        # vit_time = time.time()
+                        local_features = torch.cat((local_features_2[:, 1:], local_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
+                        local_features = self.projector(local_features)
+
+
+                        global_features_1 = sam_model(image_ori)
+                        global_features_2 = vision_model(image_ori, global_features_1) 
+                        global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
+                        global_features = self.projector(global_features)
+
+                        print('=====================')
+                        print('BASE: ', global_features.shape)
+                        print('PATCHES: ', local_features.shape)
+                        print('=====================')
+
+                        _, hw, n_dim = global_features.shape
+                        h = w = int(hw ** 0.5)
+
+                        _2, hw2, n_dim2 = local_features.shape
+                        h2 = w2 = int(hw2 ** 0.5)
+
+                        width_crop_num, height_crop_num = crop_shape[0], crop_shape[1]
+
+                        global_features = global_features.view(h, w, n_dim)
+
+                        global_features = torch.cat(
+                            [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
+                        )
+
+                        global_features = global_features.view(-1, n_dim)
+
+
+                        local_features = local_features.view(height_crop_num, width_crop_num, h2, w2, n_dim2).permute(0, 2, 1, 3, 4).reshape(height_crop_num*h2, width_crop_num*w2, n_dim2)
+                        local_features = torch.cat(
+                            [local_features, self.image_newline[None, None, :].expand(height_crop_num * h2, 1, n_dim2)], dim=1
+                        )
+                        local_features = local_features.view(-1, n_dim2)
+
+                        global_local_features = torch.cat([local_features, global_features, self.view_seperator[None, :]], dim=0)
+
+                        # end_time = time.time()
+
+                        # print('sam: ', sam_time - start_time)
+                        # print('vit: ', vit_time - sam_time)
+                        # print('all: ', end_time - start_time)
+
+                        # exit()
+                   
+                    else:
+                        global_features_1 = sam_model(image_ori)
+                        global_features_2 = vision_model(image_ori, global_features_1) 
+                        global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
+                        global_features = self.projector(global_features)
+                        print('=====================')
+                        print('BASE: ', global_features.shape)
+                        print('NO PATCHES')
+                        print('=====================')
+                        _, hw, n_dim = global_features.shape
+                        h = w = int(hw ** 0.5)
+
+
+                        global_features = global_features.view(h, w, n_dim)
+
+                        global_features = torch.cat(
+                            [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
+                        )
+
+                        global_features = global_features.view(-1, n_dim)
+
+                        global_local_features = torch.cat([global_features, self.view_seperator[None, :]], dim=0)
+
+                    images_in_this_batch.append(global_local_features)
+                
+
+                # print(inputs_embeds.shape)
+
+                if images_in_this_batch:
+                    images_in_this_batch = torch.cat(images_in_this_batch, dim=0)
+                    # exit()
+
+                    inputs_embeds[idx].masked_scatter_(images_seq_mask[idx].unsqueeze(-1).cuda(), images_in_this_batch)
+
+                idx += 1
+            
+
+        return super(DeepseekOCRModel, self).forward(
+            input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids,
+            output_attentions=output_attentions, output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+    
+
+class DeepseekOCRForCausalLM(DeepseekV2ForCausalLM):
+
+    config_class = DeepseekOCRConfig
+    # supports_gradient_checkpointing = True
+
+    def __init__(self, config):
+        super(DeepseekV2ForCausalLM, self).__init__(config)
+        self.model = DeepseekOCRModel(config)
+
+        self.vocab_size = config.vocab_size
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        images_seq_mask: Optional[torch.FloatTensor] = None,
+        images_spatial_crop: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+        
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+
+        outputs  = self.model(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            images=images,
+            images_seq_mask = images_seq_mask,
+            images_spatial_crop = images_spatial_crop,
+            return_dict=return_dict
+            
+        )
+
+
+        
+        # print(transformer_outputs)
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        # logits
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if self.generation_config.cache_implementation == "static":
+        #     # generation with static cache
+        #     cache_position = kwargs.get("cache_position", None)
+        #     if cache_position is None:
+        #         past_length = 0
+        #     else:
+        #         past_length = cache_position[-1] + 1
+        #     input_ids = input_ids[:, past_length:]
+        #     position_ids = position_ids[:, past_length:]
+
+        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
+        # same goes for position ids. Could also help with continued generation.
+        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "images": kwargs.get("images", None),
+                "images_seq_mask": kwargs.get("images_seq_mask", None),
+                "images_spatial_crop": kwargs.get("images_spatial_crop", None),
+            }
+        )
+        return model_inputs
+    
+
+    def disable_torch_init(self):
+        """
+        Disable the redundant torch default initialization to accelerate model creation.
+        """
+        import torch
+        setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
+        setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
+
+
+
+    def infer(self, tokenizer, prompt='', image_file='', output_path = '', base_size=1024, image_size=640, crop_mode=True, test_compress=False, save_results=False, eval_mode=False):
+        self.disable_torch_init()
+
+        os.makedirs(output_path, exist_ok=True)
+        os.makedirs(f'{output_path}/images', exist_ok=True)
+
+        if prompt and image_file:
+            conversation = [
+                {
+                    "role": "<|User|>",
+                    # "content": "<image>\n<|grounding|>Given the layout of the image. ",
+                    "content": f'{prompt}',
+                    # "content": "君不见黄河之水天上来的下一句是什么？",
+                    # "content": "<image>\nFree OCR. ",
+                    # "content": "<image>\nParse the figure. ",
+                    # "content": "<image>\nExtract the text in the image. ",
+                    "images": [f'{image_file}'],
+                },
+                {"role": "<|Assistant|>", "content": ""},
+            ]
+        
+        elif prompt:
+            conversation = [
+                {
+                    "role": "<|User|>",
+                    # "content": "<image>\n<|grounding|>Given the layout of the image. ",
+                    "content": f'{prompt}',
+                    # "content": "君不见黄河之水天上来的下一句是什么？",
+                    # "content": "<image>\nFree OCR. ",
+                    # "content": "<image>\nParse the figure. ",
+                    # "content": "<image>\nExtract the text in the image. ",
+                    # "images": [f'{image_file}'],
+                },
+                {"role": "<|Assistant|>", "content": ""},
+            ]
+        else:
+            assert False, f'prompt is none!'
+        
+        prompt = format_messages(conversations=conversation, sft_format='plain', system_prompt='')
+
+        patch_size = 16
+        downsample_ratio = 4
+        images = load_pil_images(conversation)
+
+        valid_img_tokens = 0
+        ratio = 1
+
+        image_draw = images[0].copy()
+
+        w,h = image_draw.size
+        # print(w, h)
+        ratio = 1 - ((max(w, h) - min(w, h)) / (max(w, h)))
+    
+
+        image_transform=BasicImageTransform(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), normalize=True)
+        images_seq_mask = []
+
+        image_token = '<image>'
+        image_token_id = 128815
+        text_splits = prompt.split(image_token)
+
+        images_list, images_crop_list, images_seq_mask = [], [], []
+        tokenized_str = []
+        images_spatial_crop = []
+        for text_sep, image in zip(text_splits, images):
+
+            tokenized_sep = text_encode(tokenizer, text_sep, bos=False, eos=False)
+            tokenized_str += tokenized_sep
+            images_seq_mask += [False] * len(tokenized_sep)
+
+            if crop_mode:
+
+                if image.size[0] <= 640 and image.size[1] <= 640:
+                    crop_ratio = [1, 1]
+
+                else:
+                    if crop_mode:
+                        # best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
+                        images_crop_raw, crop_ratio = dynamic_preprocess(image)
+                    else:
+                        # best_width, best_height = self.image_size, self.image_size
+                        crop_ratio = [1, 1]
+                
+                """process the global view"""
+                # image = image.resize((base_size, base_size))
+                global_view = ImageOps.pad(image, (base_size, base_size),
+                                        color=tuple(int(x * 255) for x in image_transform.mean))
+                
+                if base_size == 1024:
+                    valid_img_tokens += int(256 * ratio)
+                elif base_size == 1280:
+                    valid_img_tokens += int(400 * ratio)
+                # elif base_size == 640:
+                #     valid_img_tokens += int(100 * ratio)
+                
+
+
+
+                
+                images_list.append(image_transform(global_view).to(torch.bfloat16))
+
+                # global_view_tensor = image_transform(global_view).to(torch.bfloat16)
+
+                width_crop_num, height_crop_num = crop_ratio
+
+                images_spatial_crop.append([width_crop_num, height_crop_num])
+                
+                
+                if width_crop_num > 1 or height_crop_num > 1:
+                    """process the local views"""
+                    
+                    for i in range(len(images_crop_raw)):
+                        images_crop_list.append(image_transform(images_crop_raw[i]).to(torch.bfloat16))
+                
+                if image_size == 640:
+                    valid_img_tokens += len(images_crop_list) * 100
+
+                num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
+                num_queries_base = math.ceil((base_size // patch_size) / downsample_ratio)
+
+
+
+                """add image tokens"""
+
+                
+
+                tokenized_image = ([image_token_id] * num_queries_base + [image_token_id]) * num_queries_base
+                tokenized_image += [image_token_id]
+                if width_crop_num > 1 or height_crop_num > 1:
+                    tokenized_image += ([image_token_id] * (num_queries * width_crop_num) + [image_token_id]) * (
+                                num_queries * height_crop_num)
+                tokenized_str += tokenized_image
+                images_seq_mask += [True] * len(tokenized_image)
+                # num_image_tokens.append(len(tokenized_image))
+
+            else:
+                # best_width, best_height = self.image_size, self.image_size
+                # print(image.size, (best_width, best_height)) # check the select_best_resolutions func
+
+                """process the global view"""
+                if image_size <= 640:
+                    print('directly resize')
+                    image = image.resize((image_size, image_size))
+                # else:
+                global_view = ImageOps.pad(image, (image_size, image_size),
+                                        color=tuple(int(x * 255) for x in image_transform.mean))
+                images_list.append(image_transform(global_view).to(torch.bfloat16))
+
+                if base_size == 1024:
+                    valid_img_tokens += int(256 * ratio)
+                elif base_size == 1280:
+                    valid_img_tokens += int(400 * ratio)
+                elif base_size == 640:
+                    valid_img_tokens += int(100 * 1)
+                elif base_size == 512:
+                    valid_img_tokens += int(64 * 1)
+
+                width_crop_num, height_crop_num = 1, 1
+
+                images_spatial_crop.append([width_crop_num, height_crop_num])
+
+
+                """add image tokens"""
+                num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
+
+                tokenized_image = ([image_token_id] * num_queries + [image_token_id]) * num_queries
+                tokenized_image += [image_token_id]
+                # tokenized_image += ([self.image_token_id] * (num_queries * width_crop_num) + [self.image_token_id]) * (
+                #             num_queries * height_crop_num)
+                tokenized_str += tokenized_image
+                images_seq_mask += [True] * len(tokenized_image)
+                # num_image_tokens.append(len(tokenized_image))
+        
+
+        """process the last text split"""
+        tokenized_sep = text_encode(tokenizer, text_splits[-1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos tokens"""
+        bos_id = 0
+        tokenized_str = [bos_id] + tokenized_str 
+        images_seq_mask = [False] + images_seq_mask
+
+
+
+        input_ids = torch.LongTensor(tokenized_str)
+
+
+        
+
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+
+        if len(images_list) == 0:
+            images_ori = torch.zeros((1, 3, image_size, image_size))
+            images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
+            images_crop = torch.zeros((1, 3, base_size, base_size))
+
+        else:
+            images_ori = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+            if images_crop_list:
+                images_crop = torch.stack(images_crop_list, dim=0)
+            else:
+                images_crop = torch.zeros((1, 3, base_size, base_size))
+
+
+
+        if not eval_mode:
+            streamer = NoEOSTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                with torch.no_grad():
+                    output_ids = self.generate(
+                        input_ids.unsqueeze(0).cuda(),
+                        images=[(images_crop.cuda(), images_ori.cuda())],
+                        images_seq_mask = images_seq_mask.unsqueeze(0).cuda(),
+                        images_spatial_crop = images_spatial_crop,
+                        # do_sample=False,
+                        # num_beams = 1,
+                        temperature=0.0,
+                        eos_token_id=tokenizer.eos_token_id,
+                        streamer=streamer,
+                        max_new_tokens=8192,
+                        no_repeat_ngram_size = 20,
+                        use_cache = True
+                        )
+
+        else:
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                with torch.no_grad():
+                    output_ids = self.generate(
+                        input_ids.unsqueeze(0).cuda(),
+                        images=[(images_crop.cuda(), images_ori.cuda())],
+                        images_seq_mask = images_seq_mask.unsqueeze(0).cuda(),
+                        images_spatial_crop = images_spatial_crop,
+                        # do_sample=False,
+                        # num_beams = 1,
+                        temperature=0.0,
+                        eos_token_id=tokenizer.eos_token_id,
+                        max_new_tokens=8192,
+                        no_repeat_ngram_size = 35,
+                        use_cache = True
+                        )
+                
+
+        if '<image>' in conversation[0]['content'] and eval_mode:
+                outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+                stop_str = '<｜end▁of▁sentence｜>'
+                if outputs.endswith(stop_str):
+                    outputs = outputs[:-len(stop_str)]
+                # re_match
+                outputs = outputs.strip()
+
+                return outputs
+        
+        if '<image>' in conversation[0]['content'] and test_compress:
+            outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+            pure_texts_outputs_token_length = len(text_encode(tokenizer, outputs, bos=False, eos=False))
+            print('='*50)
+            print('image size: ', (w, h))
+            print('valid image tokens: ', int(valid_img_tokens))
+            print('output texts tokens (valid): ', pure_texts_outputs_token_length)
+            print('compression ratio: ', round(pure_texts_outputs_token_length/valid_img_tokens, 2))
+            print('='*50)
+
+
+        if '<image>' in conversation[0]['content'] and save_results:
+            outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
+            stop_str = '<｜end▁of▁sentence｜>'
+
+            print('='*15 + 'save results:' + '='*15)
+            
+            # # # # conv.messages[-1][-1] = outputs
+            if outputs.endswith(stop_str):
+                outputs = outputs[:-len(stop_str)]
+            outputs = outputs.strip()
+
+            matches_ref, matches_images, mathes_other = re_match(outputs)
+            # print(matches_ref)
+            result = process_image_with_refs(image_draw, matches_ref, output_path)
+
+
+            for idx, a_match_image in enumerate(tqdm(matches_images, desc="image")):
+                outputs = outputs.replace(a_match_image, '![](images/' + str(idx) + '.jpg)\n')
+            
+            for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
+                outputs = outputs.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
+
+
+            # if 'structural formula' in conversation[0]['content']:
+            #     outputs = '<smiles>' + outputs + '</smiles>'
+            with open(f'{output_path}/result.mmd', 'w', encoding = 'utf-8') as afile:
+                afile.write(outputs)
+
+            if 'line_type' in outputs:
+                import matplotlib.pyplot as plt
+                lines = eval(outputs)['Line']['line']
+
+                line_type = eval(outputs)['Line']['line_type']
+                # print(lines)
+
+                endpoints = eval(outputs)['Line']['line_endpoint']
+
+                fig, ax = plt.subplots(figsize=(3,3), dpi=200)
+                ax.set_xlim(-15, 15)
+                ax.set_ylim(-15, 15)
+
+                for idx, line in enumerate(lines):
+                    try:
+                        p0 = eval(line.split(' -- ')[0])
+                        p1 = eval(line.split(' -- ')[-1])
+
+                        if line_type[idx] == '--':
+                            ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth=0.8, color='k')
+                        else:
+                            ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth = 0.8, color = 'k')
+
+                        ax.scatter(p0[0], p0[1], s=5, color = 'k')
+                        ax.scatter(p1[0], p1[1], s=5, color = 'k')
+                    except:
+                        pass
+
+                for endpoint in endpoints:
+
+                    label = endpoint.split(': ')[0]
+                    (x, y) = eval(endpoint.split(': ')[1])
+                    ax.annotate(label, (x, y), xytext=(1, 1), textcoords='offset points', 
+                                fontsize=5, fontweight='light')
+                
+
+                plt.savefig(f'{output_path}/geo.jpg')
+                plt.close()
+
+            result.save(f"{output_path}/result_with_boxes.jpg")