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| from PIL import Image | |
| from io import BytesIO | |
| import base64 | |
| import torch | |
| from transformers import StoppingCriteria | |
| from tinyllava.constants import IMAGE_TOKEN_INDEX | |
| import math | |
| import ast | |
| ## added by llava-1.6 | |
| def select_best_resolution(original_size, possible_resolutions): | |
| """ | |
| Selects the best resolution from a list of possible resolutions based on the original size. | |
| Args: | |
| original_size (tuple): The original size of the image in the format (width, height). | |
| possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...]. | |
| Returns: | |
| tuple: The best fit resolution in the format (width, height). | |
| """ | |
| original_width, original_height = original_size | |
| best_fit = None | |
| max_effective_resolution = 0 | |
| min_wasted_resolution = float('inf') | |
| for width, height in possible_resolutions: | |
| scale = min(width / original_width, height / original_height) | |
| downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale) | |
| effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height) | |
| wasted_resolution = (width * height) - effective_resolution | |
| if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution): | |
| max_effective_resolution = effective_resolution | |
| min_wasted_resolution = wasted_resolution | |
| best_fit = (width, height) | |
| return best_fit | |
| ## added by llava-1.6 | |
| def resize_and_pad_image(image, target_resolution): | |
| """ | |
| Resize and pad an image to a target resolution while maintaining aspect ratio. | |
| Args: | |
| image (PIL.Image.Image): The input image. | |
| target_resolution (tuple): The target resolution (width, height) of the image. | |
| Returns: | |
| PIL.Image.Image: The resized and padded image. | |
| """ | |
| original_width, original_height = image.size | |
| target_width, target_height = target_resolution | |
| scale_w = target_width / original_width | |
| scale_h = target_height / original_height | |
| if scale_w < scale_h: | |
| new_width = target_width | |
| new_height = min(math.ceil(original_height * scale_w), target_height) | |
| else: | |
| new_height = target_height | |
| new_width = min(math.ceil(original_width * scale_h), target_width) | |
| # Resize the image | |
| resized_image = image.resize((new_width, new_height)) | |
| new_image = Image.new('RGB', (target_width, target_height), (0, 0, 0)) | |
| paste_x = (target_width - new_width) // 2 | |
| paste_y = (target_height - new_height) // 2 | |
| new_image.paste(resized_image, (paste_x, paste_y)) | |
| return new_image | |
| ## added by llava-1.6 | |
| def divide_to_patches(image, patch_size): | |
| """ | |
| Divides an image into patches of a specified size. | |
| Args: | |
| image (PIL.Image.Image): The input image. | |
| patch_size (int): The size of each patch. | |
| Returns: | |
| list: A list of PIL.Image.Image objects representing the patches. | |
| """ | |
| patches = [] | |
| width, height = image.size | |
| for i in range(0, height, patch_size): | |
| for j in range(0, width, patch_size): | |
| box = (j, i, j + patch_size, i + patch_size) | |
| patch = image.crop(box) | |
| patches.append(patch) | |
| return patches | |
| ## added by llava-1.6 | |
| def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): | |
| """ | |
| Calculate the shape of the image patch grid after the preprocessing for images of any resolution. | |
| Args: | |
| image_size (tuple): The size of the input image in the format (width, height). | |
| grid_pinpoints (str): A string representation of a list of possible resolutions. | |
| patch_size (int): The size of each image patch. | |
| Returns: | |
| tuple: The shape of the image patch grid in the format (width, height). | |
| """ | |
| if type(grid_pinpoints) is list: | |
| possible_resolutions = grid_pinpoints | |
| else: | |
| possible_resolutions = ast.literal_eval(grid_pinpoints) | |
| width, height = select_best_resolution(image_size, possible_resolutions) | |
| return width // patch_size, height // patch_size | |
| ## added by llava-1.6 | |
| def process_anyres_image(image, processor, grid_pinpoints): | |
| """ | |
| Process an image with variable resolutions. | |
| Args: | |
| image (PIL.Image.Image): The input image to be processed. | |
| processor: The image processor object. | |
| grid_pinpoints (str): A string representation of a list of possible resolutions. | |
| Returns: | |
| torch.Tensor: A tensor containing the processed image patches. | |
| """ | |
| if type(grid_pinpoints) is list: | |
| possible_resolutions = grid_pinpoints | |
| else: | |
| possible_resolutions = ast.literal_eval(grid_pinpoints) | |
| best_resolution = select_best_resolution(image.size, possible_resolutions) | |
| image_padded = resize_and_pad_image(image, best_resolution) | |
| patches = divide_to_patches(image_padded, processor.crop_size['height']) | |
| image_original_resize = image.resize((processor.size['shortest_edge'], processor.size['shortest_edge'])) | |
| image_patches = [image_original_resize] + patches | |
| image_patches = [processor.preprocess(image_patch, return_tensors='pt')['pixel_values'][0] | |
| for image_patch in image_patches] | |
| return torch.stack(image_patches, dim=0) | |
| def load_image_from_base64(image): | |
| return Image.open(BytesIO(base64.b64decode(image))) | |
| def expand2square(pil_img, background_color): | |
| width, height = pil_img.size | |
| if width == height: | |
| return pil_img | |
| elif width > height: | |
| result = Image.new(pil_img.mode, (width, width), background_color) | |
| result.paste(pil_img, (0, (width - height) // 2)) | |
| return result | |
| else: | |
| result = Image.new(pil_img.mode, (height, height), background_color) | |
| result.paste(pil_img, ((height - width) // 2, 0)) | |
| return result | |
| def process_images(images, image_processor, model_cfg): | |
| image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None) | |
| new_images = [] | |
| if image_aspect_ratio == 'pad': | |
| for image in images: | |
| image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean)) | |
| image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] | |
| new_images.append(image) | |
| elif image_aspect_ratio == "anyres": | |
| for image in images: | |
| image = process_anyres_image(image, image_processor, model_cfg.image_grid_pinpoints) | |
| new_images.append(image) | |
| else: | |
| return image_processor(images, return_tensors='pt')['pixel_values'] | |
| if all(x.shape == new_images[0].shape for x in new_images): | |
| new_images = torch.stack(new_images, dim=0) | |
| return new_images | |
| def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): | |
| prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')] | |
| def insert_separator(X, sep): | |
| return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1] | |
| input_ids = [] | |
| offset = 0 | |
| if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: | |
| offset = 1 | |
| input_ids.append(prompt_chunks[0][0]) | |
| for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): | |
| input_ids.extend(x[offset:]) | |
| if return_tensors is not None: | |
| if return_tensors == 'pt': | |
| return torch.tensor(input_ids, dtype=torch.long) | |
| raise ValueError(f'Unsupported tensor type: {return_tensors}') | |
| return input_ids | |
| def get_model_name_from_path(model_path): | |
| model_path = model_path.strip("/") | |
| model_paths = model_path.split("/") | |
| if model_paths[-1].startswith('checkpoint-'): | |
| return model_paths[-2] + "_" + model_paths[-1] | |
| else: | |
| return model_paths[-1] | |
| class KeywordsStoppingCriteria(StoppingCriteria): | |
| def __init__(self, keywords, tokenizer, input_ids): | |
| self.keywords = keywords | |
| self.keyword_ids = [] | |
| self.max_keyword_len = 0 | |
| for keyword in keywords: | |
| cur_keyword_ids = tokenizer(keyword).input_ids | |
| if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: | |
| cur_keyword_ids = cur_keyword_ids[1:] | |
| if len(cur_keyword_ids) > self.max_keyword_len: | |
| self.max_keyword_len = len(cur_keyword_ids) | |
| self.keyword_ids.append(torch.tensor(cur_keyword_ids)) | |
| self.tokenizer = tokenizer | |
| self.start_len = input_ids.shape[1] | |
| def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len) | |
| self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] | |
| for keyword_id in self.keyword_ids: | |
| if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all(): | |
| return True | |
| outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] | |
| for keyword in self.keywords: | |
| if keyword in outputs: | |
| return True | |
| return False | |
| def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| outputs = [] | |
| for i in range(output_ids.shape[0]): | |
| outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores)) | |
| return all(outputs) | |