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Running
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Zero
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import torch
import torch.nn as nn
from transformers import SiglipImageProcessor, SiglipVisionConfig, SiglipVisionModel
from vita.util.s2wrapper import forward as multiscale_forward
class SiglipVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = -2
if not delay_load:
self.load_model()
else:
self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self):
self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
self.image_processor.crop_size = self.image_processor.size
self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(
image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
output_hidden_states=True,
)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(
images.to(device=self.device, dtype=self.dtype), output_hidden_states=True
)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
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):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
class SiglipVisionTowerS2(SiglipVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
self.s2_scales = getattr(args, "s2_scales", "384,768,1152")
self.s2_scales = list(map(int, self.s2_scales.split(",")))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
super().__init__(vision_tower, args, delay_load)
self.multiscale_forward = multiscale_forward
if not delay_load:
self.image_processor.size["height"] = self.image_processor.size[
"width"
] = self.s2_image_size
self.image_processor.crop_size["height"] = self.image_processor.crop_size[
"width"
] = self.s2_image_size
def load_model(self):
self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
self.image_processor.crop_size = self.image_processor.size
self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name)
self.vision_tower.requires_grad_(False)
self.image_processor.size["height"] = self.image_processor.size[
"width"
] = self.s2_image_size
self.image_processor.crop_size["height"] = self.image_processor.crop_size[
"width"
] = self.s2_image_size
self.is_loaded = True
@torch.no_grad()
def forward_feature(self, images):
image_forward_outs = self.vision_tower(
images.to(device=self.device, dtype=self.dtype), output_hidden_states=True
)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = self.multiscale_forward(
self.forward_feature,
image.unsqueeze(0),
img_sizes=self.s2_scales,
max_split_size=self.s2_split_size,
)
image_features.append(image_feature)
else:
image_features = self.multiscale_forward(
self.forward_feature,
images,
img_sizes=self.s2_scales,
max_split_size=self.s2_split_size,
)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)
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