vl-7b-4bit / deepseek_vl /models /clip_encoder.py
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# Copyright (c) 2023-2024 DeepSeek.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
from typing import Dict, List, Literal, Optional, Tuple, Union
import torch
import torch.nn as nn
import torchvision.transforms
from einops import rearrange
from deepseek_vl.models.sam import create_sam_vit
from deepseek_vl.models.siglip_vit import create_siglip_vit
class CLIPVisionTower(nn.Module):
def __init__(
self,
model_name: str = "siglip_large_patch16_384",
image_size: Union[Tuple[int, int], int] = 336,
select_feature: str = "patch",
select_layer: int = -2,
select_layers: list = None,
ckpt_path: str = "",
pixel_mean: Optional[List[float]] = None,
pixel_std: Optional[List[float]] = None,
**kwargs,
):
super().__init__()
self.model_name = model_name
self.select_feature = select_feature
self.select_layer = select_layer
self.select_layers = select_layers
vision_tower_params = {
"model_name": model_name,
"image_size": image_size,
"ckpt_path": ckpt_path,
"select_layer": select_layer,
}
vision_tower_params.update(kwargs)
self.vision_tower, self.forward_kwargs = self.build_vision_tower(
vision_tower_params
)
if pixel_mean is not None and pixel_std is not None:
image_norm = torchvision.transforms.Normalize(
mean=pixel_mean, std=pixel_std
)
else:
image_norm = None
self.image_norm = image_norm
def build_vision_tower(self, vision_tower_params):
if self.model_name.startswith("siglip"):
self.select_feature = "same"
vision_tower = create_siglip_vit(**vision_tower_params)
forward_kwargs = dict()
elif self.model_name.startswith("sam"):
vision_tower = create_sam_vit(**vision_tower_params)
forward_kwargs = dict()
else: # huggingface
from transformers import CLIPVisionModel
vision_tower = CLIPVisionModel.from_pretrained(**vision_tower_params)
forward_kwargs = dict(output_hidden_states=True)
return vision_tower, forward_kwargs
def feature_select(self, image_forward_outs):
if isinstance(image_forward_outs, torch.Tensor):
# the output has been the self.select_layer"s features
image_features = image_forward_outs
else:
image_features = image_forward_outs.hidden_states[self.select_layer]
if self.select_feature == "patch":
# if the output has cls_token
image_features = image_features[:, 1:]
elif self.select_feature == "cls_patch":
image_features = image_features
elif self.select_feature == "same":
image_features = image_features
else:
raise ValueError(f"Unexpected select feature: {self.select_feature}")
return image_features
def forward(self, images):
"""
Args:
images (torch.Tensor): [b, 3, H, W]
Returns:
image_features (torch.Tensor): [b, n_patch, d]
"""
if self.image_norm is not None:
images = self.image_norm(images)
image_forward_outs = self.vision_tower(images, **self.forward_kwargs)
image_features = self.feature_select(image_forward_outs)
return image_features
class HybridVisionTower(nn.Module):
def __init__(
self,
high_res_cfg: Dict,
low_res_cfg: Dict,
freeze_high: bool = False,
freeze_low: bool = False,
concat_type: Literal["feature", "sequence", "add", "tuple"] = "tuple",
**ignore_kwargs,
):
super().__init__()
self.vision_tower_high = CLIPVisionTower(**high_res_cfg)
self.vision_tower_low = CLIPVisionTower(**low_res_cfg)
self.low_res_size = low_res_cfg["image_size"]
self.concat_type = concat_type
self.high_layer_norm = nn.LayerNorm(high_res_cfg.get("output_dim", 1024))
self.low_layer_norm = nn.LayerNorm(low_res_cfg.get("output_dim", 1024))
if freeze_high:
for p_name, p in self.vision_tower_high.named_parameters():
p.requires_grad = False
self.vision_tower_high = self.vision_tower_high.eval()
else:
# train donwsamples and neck
for p_name, p in self.vision_tower_high.named_parameters():
if "downsamples" in p_name or "neck" in p_name:
p.requires_grad = True
else:
p.requires_grad = False
if freeze_low:
for p in self.vision_tower_low.parameters():
p.requires_grad = False
self.vision_tower_low = self.vision_tower_low.eval()
self.resize = torchvision.transforms.Resize(self.low_res_size, antialias=True)
def forward(self, images: torch.Tensor):
"""
Args:
images (torch.Tensor): [bs, 3, H, W]
Returns:
res (torch.Tensor): [bs, t, c]
"""
# [bs, c, h, w]
high_images = images
# [bs, c, h_low, w_low]
low_images = self.resize(images)
# separately run two vision towers
# run high_res vision tower
high_res = self.vision_tower_high(high_images)
# [bs, c, h, w] -> [bs, h*w, c]
high_res = rearrange(high_res, "b c h w -> b (h w) c")
# run low_res vision tower
low_res = self.vision_tower_low(low_images)
if self.concat_type == "feature":
images_features = torch.cat([high_res, low_res], dim=-1)
elif self.concat_type == "sequence":
images_features = torch.cat([high_res, low_res], dim=1)
elif self.concat_type == "add":
images_features = high_res + low_res
elif self.concat_type == "tuple":
images_features = (high_res, low_res)
else:
raise ValueError(
"Currently only support `feature`, `sequence`, `add` and `tuple` concat type."
)
return images_features
if __name__ == "__main__":
image_size = 1024
x = torch.zeros(2, 3, image_size, image_size).bfloat16().cuda()
high_res_cfg = dict(
model_name="sam_b_downsample",
select_feature="same",
image_size=image_size,
pixel_mean=(0.48145466, 0.4578275, 0.40821073),
pixel_std=(0.26862954, 0.26130258, 0.27577711),
select_layer=-1,
ckpt_path="",
)
low_res_cfg = dict(
model_name="siglip_large_patch16_384",
select_feature="same",
image_size=384,
pixel_mean=(0.5, 0.5, 0.5),
pixel_std=(0.5, 0.5, 0.5),
select_layer=-1,
ckpt_path="",
)
net = (
HybridVisionTower(
high_res_cfg=high_res_cfg,
low_res_cfg=low_res_cfg,
freeze_high=True,
freeze_low=True,
concat_type="tuple",
)
.bfloat16()
.cuda()
)
high_x, low_x = net(x)
print(x.shape, high_x.shape, low_x.shape)