model.py

import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer

from internvl2_patches import InternVLChatModel

import config


# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
path = config.path
model = InternVLChatModel.from_pretrained(
    path,
    torch_dtype=config.dtype,
    # low_cpu_mem_usage=True,
    use_flash_attn=True,
    ignore_mismatched_sizes=True,
    revision='7f49802f5bf1e6e3d20b6f69268701c7eb67e037').to(config.device)
tokenizer = AutoTokenizer.from_pretrained('OpenGVLab/InternVL2-4B', trust_remote_code=True, use_fast=False, 
                                          revision='7f49802f5bf1e6e3d20b6f69268701c7eb67e037')
tokenizer.padding_side = 'left'

img_context_token_id = tokenizer.convert_tokens_to_ids('<IMG_CONTEXT>')
model.img_context_token_id = img_context_token_id

model.mlp1 = model.mlp1.to(torch.float32)
# model.vision_model.encoder = model.vision_model.encoder.to(torch.float32)
print(model.mlp1,)

params = list(model.mlp1.parameters())# + list(model.vision_model.encoder.parameters())

print(f'Training: {params}')
# we will drop all but last patch & train mlp1; mlp1 will be where we do vector arythmetic and probes.
optimizer = torch.optim.AdamW(params, lr=config.lr)


IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

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
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, 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)
    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)

    # 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

# TODO can make a batch process within data pipeline
def load_image(image_file, pil_image=None, input_size=224, max_num=12):
    if not pil_image:
        pil_image = Image.open(image_file)
    image = pil_image.convert('RGB')
    transform = build_transform(input_size=input_size)
    # images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in [image]]
    pixel_values = torch.stack(pixel_values)
    return pixel_values