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Running
on
Zero
from PIL import Image | |
from io import BytesIO | |
import base64 | |
import torch | |
import math | |
import ast | |
from transformers import StoppingCriteria | |
from llava.constants import IMAGE_TOKEN_INDEX | |
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 | |
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 | |
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 | |
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 | |
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: | |
truncated_output_ids = output_ids[0, -keyword_id.shape[0]:] | |
if torch.equal(truncated_output_ids, keyword_id): | |
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) | |