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import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from .modeling_siglip import SiglipVisionModel
from .configuration_siglip import SiglipVisionConfig
import math
import torch
import torch.nn.functional as F
from typing import List, Optional
import os
class SiglipVisionTower(nn.Module):
# We use the same wrapper as the default clip encoder.
# See `clip_encoder.py` in the same folder
def __init__(self, vision_tower, args, delay_load=False, raw_config=None):
super().__init__()
self.is_loaded = False
self.freeze_vision=args.freeze_vision
self.input_image_size=args.input_image_size
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.name = 'siglip'
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
self.delay_load = delay_load
self.raw_config = raw_config
if not delay_load:
self.load_model()
else:
if os.path.isfile(self.vision_tower_name):
self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name, local_files_only=True)
else:
self.cfg_only = SiglipVisionConfig(**self.raw_config.vision_config.siglip_vision_config)
def load_model(self):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
# self.image_processor = SiglipImageProcessor(size=1024)
# self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name, local_files_only=True, torch_dtype=torch.bfloat16)
if self.delay_load:
# cfg = SiglipVisionConfig.from_pretrained(self.vision_tower_name, local_files_only=True)
self.vision_tower = SiglipVisionModel(self.cfg_only)
else:
self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name, local_files_only=True)
if self.freeze_vision:
self.vision_tower.requires_grad_(False)
self.vision_tower.vision_model.encoder.gradient_checkpointing = True
self.is_loaded = True
def forward(self, images):
return self.vision_tower(
pixel_values=images,
output_hidden_states=False,
return_dict=True).last_hidden_state
@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_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
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