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import base64
import torch
import math
import ast
from PIL import Image
from io import BytesIO
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 select_best_resolution_v2(original_size, possible_resolutions):
"""
Selects the best resolution from a list of possible resolutions based on the original size and aspect ratio.
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
original_aspect_ratio = original_height / original_width
original_area = original_width * original_height
best_fit = None
min_aspect_ratio_diff = float('inf')
min_area_ratio = float('inf')
for width, height in possible_resolutions:
aspect_ratio = height / width
area = width * height
aspect_ratio_diff = max(aspect_ratio, original_aspect_ratio) / min(aspect_ratio, original_aspect_ratio)
area_ratio = max(area, original_area) / min(area, original_area)
if aspect_ratio_diff < min_aspect_ratio_diff or (aspect_ratio_diff == min_aspect_ratio_diff and area_ratio < min_area_ratio):
min_aspect_ratio_diff = aspect_ratio_diff
min_area_ratio = area_ratio
best_fit = (width, height)
return best_fit
def resize_and_pad_image(image, target_resolution, keep_ratio=False):
"""
Resize and pad an image to a target resolution
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
if keep_ratio:
# maintaining aspect ratio
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))
else:
# not maintaining aspect ratio
new_image = image.resize((target_width, target_height))
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)
width1, height1 = select_best_resolution(image_size, possible_resolutions)
width2, height2 = select_best_resolution_v2(image_size, possible_resolutions)
if width1*height1 > width2*height2:
width, height = width2, height2
else:
width, height = width1, height1
return width // patch_size, height // patch_size
def process_anyres_image(image, image_transform, grid_pinpoints, base_image_size):
"""
Process an image with variable resolutions.
Args:
image (PIL.Image.Image): The input image to be processed.
image_transform: 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)
width1, height1 = select_best_resolution(image.size, possible_resolutions)
width2, height2 = select_best_resolution_v2(image.size, possible_resolutions)
if width1*height1 > width2*height2:
width, height = width2, height2
else:
width, height = width1, height1
best_resolution = [width, height]
image_padded = resize_and_pad_image(image, best_resolution)
patches = divide_to_patches(image_padded, base_image_size)
image_original_resize = image.resize((base_image_size, base_image_size))
image_patches = patches + [image_original_resize] # add the original image as the last patch
image_patches = [image_transform(image_patch)
for image_patch in image_patches]
patch_grid = (best_resolution[0]//base_image_size, best_resolution[1]//base_image_size)
x_index = (torch.arange(patch_grid[0]).repeat(patch_grid[1], 1) + 0.5)/patch_grid[0]
y_index = (torch.arange(patch_grid[1]).unsqueeze(1).repeat(1, patch_grid[0]) + 0.5)/patch_grid[1]
patch_pos = torch.stack([x_index, y_index], dim=-1).flatten(0, 1) # h*w, 2
origin_pos = torch.tensor([[0.5, 0.5]])
patch_pos = torch.cat([patch_pos, origin_pos], dim=0) # h*w+1, 2
return torch.stack(image_patches, dim=0), patch_pos
def load_image_from_base64(image):
return Image.open(BytesIO(base64.b64decode(image)))
def anyres_data_collate(batch, tokenizer, dataset_name=None):
results = {}
keys = batch[0].keys()
for key in keys:
cur = [batch[i][key] for i in range(len(batch)) if batch[i][key] is not None]
if len(cur) == 0:
results[key] = None
elif isinstance(cur[0], torch.Tensor):
if key in ['embeds_gen_mask', 'embeds_cmp_mask', 'images', 'images_patch_length', 'patch_position', 'image_size']:
results[key] = torch.cat(cur, dim=0)
else:
if key in ['input_ids']:
results[key] = torch.nn.utils.rnn.pad_sequence(cur, batch_first=True, padding_value=tokenizer.pad_token_id)
elif key in ['attention_mask']:
results[key] = torch.nn.utils.rnn.pad_sequence(cur, batch_first=True, padding_value=0)
elif key in ['labels']:
results[key] = torch.nn.utils.rnn.pad_sequence(cur, batch_first=True, padding_value=-100)
elif key in ['ids_gen_mask', 'ids_cmp_mask']:
results[key] = torch.nn.utils.rnn.pad_sequence(cur, batch_first=True, padding_value=False)
else:
results[key] = torch.stack(cur, dim=0)
else:
results[key] = cur
results['dataset_name'] = dataset_name
return results
def anyres_data_collate_old(batch, dataset_name=None):
results = {}
keys = batch[0].keys()
for key in keys:
cur = [batch[i][key] for i in range(len(batch)) if batch[i][key] is not None]
if len(cur) == 0:
results[key] = None
elif isinstance(cur[0], torch.Tensor):
if key in ['embeds_gen_mask', 'embeds_cmp_mask', 'images', 'images_patch_length', 'patch_position', 'image_size']:
results[key] = torch.cat(cur, dim=0)
else:
results[key] = torch.stack(cur, dim=0)
else:
results[key] = cur
results['dataset_name'] = dataset_name
return results
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