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from abc import ABC, abstractmethod
import torch
import torch.nn as nn
class ProcessorWrapper:
def __init__(
self,
transform,
height=378,
width=378,
image_mean=[0.48145466, 0.4578275, 0.40821073],
):
self._crop_size = {
"height": height,
"width": width,
}
self._transforms = transform
# print(transform)
self.image_mean = image_mean
@property
def crop_size(self):
return self._crop_size
def preprocess(self, image, return_tensors="pt"):
# Ensure image is a PIL Image
output = {}
output["pixel_values"] = [self._transforms(image)]
return output
class BaseVisionTower(nn.Module):
def __init__(self, vision_tower_name, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.args = args
self.vision_tower_name = vision_tower_name
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
self.unfreeze_mm_vision_tower = getattr(args, "unfreeze_mm_vision_tower", False)
self.delay_load = delay_load
@abstractmethod
def load_model(self, device_map=None):
raise NotImplementedError("Subclasses must implement load_model")
@abstractmethod
def _forward(self, images):
raise NotImplementedError("Subclasses must implement forward")
def forward(self, images):
if type(images) is list:
image_features = [self._forward(image.unsqueeze(0)) for image in images]
else:
image_features = self._forward(images)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
# Dynamically infer the dtype from the first parameter, if not explicitly specified
if hasattr(self.vision_tower, "dtype"):
return self.vision_tower.dtype
else:
params = list(self.vision_tower.parameters())
return (
params[0].dtype if len(params) > 0 else torch.float32
) # Default to torch.float32 if no parameters
@property
def device(self):
# Dynamically infer the device from the first parameter, if not explicitly specified
if hasattr(self.vision_tower, "device"):
return self.vision_tower.device
else:
params = list(self.vision_tower.parameters())
return (
params[0].device if len(params) > 0 else torch.device("cpu")
) # Default to CPU if no parameters
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
try:
return self.config.hidden_size
except:
return self._hidden_size
@property
def image_size(self): # resolution
# return self.config.image_size
try:
return self.config.image_size
except:
return self._image_size
@property
def patch_size(self):
# return self.config.patch_size
try:
return self.config.patch_size
except:
return self._patch_size
@property
def num_patches_per_side(self):
if self._interp_size is not None:
return int(self._interp_size**0.5)
try:
return self.image_size // self.patch_size
except:
return self._num_patches_per_side
@property
def num_patches(self):
if self._interp_size is not None:
return self._interp_size
try:
return self.num_patches_per_side**2
except:
return self._num_patches
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