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# Copyright 2024 Xi Zhang
#
# 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.
import torch
import torch.nn as nn
from transformers import AutoImageProcessor, AutoModel, AutoConfig
class DINOVisionTower(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 = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
if not delay_load:
self.load_model()
elif getattr(args, 'unfreeze_mm_vision_tower', False):
self.load_model()
else:
self.cfg_only = AutoConfig.from_pretrained(self.vision_tower_name)
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 = AutoImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def get_features(self, images):
outputs = self.vision_tower(images, output_hidden_states=True)
hidden_states = outputs.hidden_states
if self.select_layer == "all":
if self.select_feature == "patch":
all_layers_features = [hidden_state[:, 1:, :].contiguous() for hidden_state in hidden_states[1:]]
elif self.select_feature == "cls_patch":
all_layers_features = [hidden_state.contiguous() for hidden_state in hidden_states[1:]]
else:
raise ValueError(f"Unexpected select feature: {self.select_feature}")
return torch.stack(all_layers_features)
else:
selected_layer_features = hidden_states[int(self.select_layer)]
if self.select_feature == "patch":
selected_layer_features = selected_layer_features[:, 1:]
elif self.select_feature == "cls_patch":
selected_layer_features = selected_layer_features
else:
raise ValueError(f"Unexpected select feature: {self.select_feature}")
return torch.stack([selected_layer_features])
@torch.no_grad()
def forward(self, images):
if images.shape[0] != 2:
raise ValueError(
f"Expected images.shape[0] == 2, but got {images.shape}. "
"Ensure the input includes both current and previous images."
)
cur_images = images[0]
prev_images = images[1]
cur_features = self.get_features(cur_images)
prev_features = self.get_features(prev_images)
cur_features = cur_features.permute(1, 0, 2, 3)
prev_features = prev_features.permute(1, 0, 2, 3)
# Stack current and previous images along a new dimension
images_features = torch.stack([cur_features, prev_features])
return images_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
@property
def num_layers(self):
return self.config.num_hidden_layers |