Update llava/mm_utils.py

llava/mm_utils.py

@@ -1,247 +1,248 @@
-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)
+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)
+    image_aspect_ratio = 'pad'
+    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)