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import torch
import torch.nn as nn
from transformers import CLIPImageProcessor
from llava.utils import rank0_print
try:
import open_clip
import torchvision
from open_clip.transformer import _expand_token
except ImportError:
print("OpenCLIP not installed")
open_clip = None
HIDDEN_SIZE_DICT = {
"ViT-H-14-378-quickgelu": 1280,
}
class OpenCLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.model_name = vision_tower.replace("open_clip_hub:", "")
self.pretrained = args.vision_tower_pretrained
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
if not delay_load:
rank0_print(f"Loading vision tower: {vision_tower}")
self.load_model()
elif getattr(args, "unfreeze_mm_vision_tower", False):
# TODO: better detector is needed.
rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.")
self.load_model()
elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`.")
self.load_model()
def load_model(self, device_map="auto"):
rank0_print(f"Loading OpenCLIP model: {self.model_name}")
rank0_print(f"Pretrained: {self.pretrained}")
vision_tower, _, image_processor = open_clip.create_model_and_transforms(model_name=self.model_name, pretrained=self.pretrained, precision="fp32", device="cuda")
resize_transform = [t for t in image_processor.transforms if isinstance(t, torchvision.transforms.Resize)][0]
normalize_transform = [t for t in image_processor.transforms if isinstance(t, torchvision.transforms.Normalize)][0]
self.resize_transform_size = resize_transform.size # 224 or 384
self.patch_size = vision_tower.visual.conv1.kernel_size[0] # 14 or 16
self.image_processor = CLIPImageProcessor.from_pretrained(
"openai/clip-vit-large-patch14",
crop_size=resize_transform.size,
size={"shortest_edge": resize_transform.size},
image_mean=list(normalize_transform.mean),
image_std=list(normalize_transform.std),
)
rank0_print(f"Loaded image processor: {self.image_processor}")
self.vision_tower = vision_tower.visual
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs[self.select_layer]
if self.select_feature == "patch":
image_features = image_features[:, 1:]
elif self.select_feature == "cls_patch":
image_features = image_features
elif self.select_feature == "conv_flatten":
image_features = image_features.flatten(2).transpose(1, 2)
else:
raise ValueError(f"Unexpected select feature: {self.select_feature}")
return image_features
def forward_visual(self, x, output_hidden_states=False):
if hasattr(self.vision_tower, "trunk") and hasattr(self.vision_tower.trunk, "_intermediate_layers"):
return self.vision_tower.trunk._intermediate_layers(x, abs(self.select_layer))
else:
def forward_openclip(self, x: torch.Tensor):
features = []
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
# class embeddings and positional embeddings
x = torch.cat(
[_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x],
dim=1,
)
# shape = [*, grid ** 2 + 1, width]
x = x + self.positional_embedding.to(x.dtype)
x = self.patch_dropout(x)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
for r in self.transformer.resblocks:
x = r(x, attn_mask=None)
features.append(x)
return features
return forward_openclip(self.vision_tower, x)
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.forward_visual(image.to(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.forward_visual(images.to(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):
if hasattr(self.vision_tower, "conv1"):
return self.vision_tower.conv1.weight.dtype
if hasattr(self.vision_tower, "trunk"):
return self.vision_tower.trunk.patch_embed.proj.weight.dtype
raise NotImplementedError
@property
def device(self):
if hasattr(self.vision_tower, "conv1"):
return self.vision_tower.conv1.weight.device
if hasattr(self.vision_tower, "trunk"):
return self.vision_tower.trunk.patch_embed.proj.weight.device
raise NotImplementedError
@property
def config(self):
return None
@property
def hidden_size(self):
if self.model_name in HIDDEN_SIZE_DICT:
return HIDDEN_SIZE_DICT[self.model_name]
else:
raise NotImplementedError
@property
def num_patches(self):
image_size = self.resize_transform_size if isinstance(self.resize_transform_size, int) else self.resize_transform_size[0]
_num_patches = (image_size // self.patch_size) ** 2
if "cls_patch" in self.select_feature:
_num_patches += 1
return _num_patches
@property
def image_size(self):
return self.resize_transform_size
@property
def num_patches_per_side(self):
return self.resize_transform_size // self.patch_size
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