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import torch
import ast
import math
from PIL import Image
def has_fn(model, fn_name):
"""Check if model has a function fn_name"""
return callable(getattr(model, fn_name, None))
def exists(val):
return val is not None
def num_params(module, filter_to_trainable=False):
"""Returns the number of parameters in the module, or optionally only the trainable parameters"""
if filter_to_trainable:
return sum(p.numel() for p in module.parameters() if p.requires_grad)
else:
return sum(p.numel() for p in module.parameters())
def hasattr_recursive(obj, att):
"""
Check if obj has nested attribute
Example: hasattr_recursive(obj, 'a.b.c') is equivalent to hasattr(obj, 'a') and hasattr(obj.a, 'b') and hasattr(obj.a.b, 'c')
"""
if att == "":
return True
i = att.find(".")
if i < 0:
return hasattr(obj, att)
else:
try:
return hasattr_recursive(getattr(obj, att[:i]), att[i + 1 :])
except:
return False
def getattr_recursive(obj, att):
"""
Return nested attribute of obj
Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c
"""
if att == "":
return obj
i = att.find(".")
if i < 0:
return getattr(obj, att)
else:
return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])
def setattr_recursive(obj, att, val):
"""
Set nested attribute of obj
Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val
"""
if "." in att:
obj = getattr_recursive(obj, ".".join(att.split(".")[:-1]))
setattr(obj, att.split(".")[-1], val)
def stack_with_padding(list_of_tensors, padding_value=0, padding_side="right"):
"""
Stack a list of tensors with padding on one side
Args:
list_of_tensors (list[torch.Tensor]): List of tensors to stack
padding_value (int, optional): Value to pad with. Defaults to 0.
padding_side (str, optional): Side to pad on. Defaults to "right".
Returns:
torch.Tensor: Stacked tensors
"""
max_tokens = max(tensor.size(0) for tensor in list_of_tensors)
padded_tensors = []
for tensor in list_of_tensors:
num_tokens = tensor.size(0)
if len(tensor.size()) == 1:
padding = torch.full(
(max_tokens - num_tokens,),
padding_value,
dtype=tensor.dtype,
device=tensor.device,
)
else:
padding = torch.full(
(max_tokens - num_tokens, tensor.size(1)),
padding_value,
dtype=tensor.dtype,
device=tensor.device,
)
padded_tensor = (
torch.cat((tensor, padding), dim=0)
if padding_side == "right"
else torch.cat((padding, tensor), dim=0)
)
padded_tensors.append(padded_tensor)
return torch.stack(padded_tensors)
def check_embedding_fns(lang_model):
"""Checks for and attempts to set {get/set}_{input/output}_embeddings functions to the model"""
if not has_fn(lang_model, "get_input_embeddings"):
if hasattr_recursive(lang_model, "transformer.wte"): # MPT
lang_model.get_input_embeddings = lambda: lang_model.transformer.wte
elif hasattr_recursive(lang_model, "model.decoder.embed_tokens"): # OPT
lang_model.get_input_embeddings = lambda: lang_model.decoder.embed_tokens
else:
raise ValueError(
"We require the language encoder to have a get_input_embeddings method but we couldn't determine the name of the input embeddings attribute. Please supply this manually in factory.py."
)
if not has_fn(lang_model, "set_input_embeddings"):
if hasattr_recursive(lang_model, "transformer.wte"): # MPT
lang_model.set_input_embeddings = lambda x: setattr_recursive(
lang_model, "transformer.wte", x
)
elif hasattr_recursive(lang_model, "model.decoder.embed_tokens"): # OPT
lang_model.set_input_embeddings = lambda x: setattr_recursive(
lang_model, "model.decoder.embed_tokens", x
)
else:
raise ValueError(
"We require the language encoder to have a set_input_embeddings method but we couldn't determine the name of the input embeddings attribute. Please supply this manually in factory.py."
)
if not has_fn(lang_model, "get_output_embeddings"):
if hasattr_recursive(lang_model, "lm_head"):
lang_model.get_output_embeddings = lambda: lang_model.lm_head
else:
raise ValueError(
"We require the language encoder to have a get_output_embeddings method but we couldn't determine the name of the output embeddings attribute. Please supply this manually in factory.py."
)
if not has_fn(lang_model, "set_output_embeddings"):
if hasattr_recursive(lang_model, "lm_head"):
lang_model.set_output_embeddings = lambda x: setattr_recursive(
lang_model, "lm_head", x
)
else:
raise ValueError(
"We require the language encoder to have a set_output_embeddings method but we couldn't determine the name of the output embeddings attribute. Please supply this manually in factory.py."
)
def has_fn(model, fn_name):
"""Check if model has a function fn_name"""
return callable(getattr(model, fn_name, None))
# Adopted from https://github.com/haotian-liu/LLaVA. Below is the original copyright:
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def unpad_image(tensor, original_size, keep_original_shape=False):
"""
Unpads a PyTorch tensor of a padded and resized image.
Args:
tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
original_size (tuple): The original size of the image (height, width).
Returns:
torch.Tensor: The unpadded image tensor.
"""
original_width, original_height = original_size
current_height, current_width = tensor.shape[1:]
original_aspect_ratio = original_width / original_height
current_aspect_ratio = current_width / current_height
if original_aspect_ratio > current_aspect_ratio:
scale_factor = current_width / original_width
new_height = int(original_height * scale_factor)
padding = (current_height - new_height) // 2
if keep_original_shape:
attention_mask = torch.ones((current_height, current_width), device=tensor.device)
attention_mask[:padding, :] = 0
attention_mask[current_height - padding:, :] = 0
return tensor, attention_mask
else:
unpadded_tensor = tensor[:, padding:current_height - padding, :]
return unpadded_tensor, None
else:
scale_factor = current_height / original_height
new_width = int(original_width * scale_factor)
padding = (current_width - new_width) // 2
if keep_original_shape:
attention_mask = torch.ones((current_height, current_width), device=tensor.device)
attention_mask[:, :padding] = 0
attention_mask[:, current_width - padding:] = 0
return tensor, attention_mask
else:
unpadded_tensor = tensor[:, :, padding:current_width - padding]
return unpadded_tensor, None
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.
"""
# FIXME: determine grid_pinpoints from image sizes.
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)
processor_size = processor.transforms[0].size
patches = divide_to_patches(image_padded, processor_size[0])
image_original_resize = image.resize((processor_size[0], processor_size[0]))
image_patches = [image_original_resize] + patches
image_patches = [processor(image_patch)
for image_patch in image_patches]
return torch.stack(image_patches, dim=0)
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.transforms[-1].mean))
image = image_processor(image)
new_images.append(image)
elif image_aspect_ratio in ["anyres", "anyres-legacy"]:
base_img_size = image_processor.transforms[0].size[0]
for image in images:
image = process_anyres_image(image, image_processor, [[base_img_size,base_img_size*2],
[base_img_size*2,base_img_size],
[base_img_size*2,base_img_size*2],
[base_img_size*3,base_img_size],
[base_img_size,base_img_size*3]])
# Debug any res inference by only using 672x672.
# image = process_anyres_image(image, image_processor, [[base_img_size*2,base_img_size*2]])
new_images.append(image)
else:
return image_processor(images)
if all(x.shape == new_images[0].shape for x in new_images):
new_images = torch.stack(new_images, dim=0)
return new_images