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import os
import torch
import torch.nn as nn
import os
from omegaconf import OmegaConf
from starvector.model.image_encoder.clip_model import convert_weights_to_precision
from starvector.data.util import ImageTrainProcessor
class ImageEncoder(nn.Module):
def __init__(self, config, **kwargs):
super(ImageEncoder, self).__init__()
image_size = config.image_size
torch_dtype = kwargs.get('model_precision', config.torch_dtype)
self.image_encoder_type = config.image_encoder_type
if self.image_encoder_type == 'clip':
self.visual_encoder, self.ln_vision = self.build_clip_encoder(image_size=image_size)
convert_weights_to_precision(self, torch_dtype)
self.processor = ImageTrainProcessor(size=config.image_size)
elif self.image_encoder_type == 'vqgan':
self.visual_encoder = self.build_vqgan_encoder()
self.ln_vision = None
self.processor = ImageTrainProcessor(size=config.image_size)
elif self.image_encoder_type == 'convnext':
self.visual_encoder = self.build_vqgan_encoder()
self.ln_vision = None
self.processor = ImageTrainProcessor(size=config.image_size)
elif 'siglip' in self.image_encoder_type:
if self.image_encoder_type == 'siglip_512':
model_name = "google/siglip-base-patch16-512"
elif self.image_encoder_type == 'siglip_384':
model_name = "google/siglip-large-patch16-384"
elif self.image_encoder_type == 'siglip_256':
model_name = "google/siglip-base-patch16-256"
from transformers import AutoProcessor, AutoModel
self.visual_encoder = AutoModel.from_pretrained(
model_name, torch_dtype = torch_dtype
).vision_model
self.processor = AutoProcessor.from_pretrained(
model_name, torch_dtype = torch_dtype
)
def build_clip_encoder(self, image_size):
from starvector.model.image_encoder.clip_model import VisionTransformer, LayerNorm
visual_encoder = VisionTransformer(
input_resolution=image_size,
patch_size=14,
width=1024,
layers=23,
heads=16,
use_grad_checkpointing=False)
ln_vision = LayerNorm(visual_encoder.num_features)
return visual_encoder, ln_vision
def build_vqgan_encoder(self):
from taming.modules.diffusionmodules.model import Encoder
VQGAN_CHECKPOINT = "/path/to/vqgan_checkpoint" # You can download the checkpoint from https://github.com/EleutherAI/vqgan-clip/blob/main/README.md
vqgan_chkp_path = VQGAN_CHECKPOINT
files_in_directory = os.listdir(vqgan_chkp_path + '/configs')
vqgan_config_file = [file for file in files_in_directory if file.endswith('project.yaml')][0]
vqgan_config = OmegaConf.load(os.path.join(vqgan_chkp_path, 'configs', vqgan_config_file))
visual_encoder = Encoder(**vqgan_config.model.params.ddconfig)
# Load checkpoint weights
checkpoint = torch.load(os.path.join(vqgan_chkp_path, 'checkpoints', 'last.ckpt'))['state_dict']
# Create a new state_dict with modified keys
new_state_dict = {}
for key, value in checkpoint.items():
if key.startswith('encoder.'):
new_key = key[len('encoder.'):]
new_state_dict[new_key] = value
# Load weights
visual_encoder.load_state_dict(new_state_dict)
return visual_encoder
def build_convnext_encoder(self):
import open_clip
model, _, _ = open_clip.create_model_and_transforms('convnext_base_w', pretrained='laion2b_s13b_b82k')
return model.visual
def forward(self, image):
if self.image_encoder_type == 'clip':
embeds = self.visual_encoder(image)
out = self.ln_vision(embeds)
elif self.image_encoder_type == 'open-clip':
out = self.visual_encoder(image)[1]
out = self.ln_vision(out)
elif self.image_encoder_type == 'vqgan':
out = self.visual_encoder(image)
size = out.size()
out = out.view(size[0], size[1], -1)
out = out.permute(0, 2, 1)
elif self.image_encoder_type == 'convnext':
out = self.visual_encoder.trunk.forward_features(image)
size = out.size()
out = out.view(size[0], size[1], -1)
out = out.permute(0, 2, 1)
elif 'siglip' in self.image_encoder_type:
out = self.visual_encoder(image)["last_hidden_state"]
return out
def process_images(self, images):
if self.image_encoder_type == 'clip':
res = []
for image in images:
res.append(self.processor(image).unsqueeze(0)) # B, 3, H, W
return res
else:
return self.processor(images=images, return_tensors="pt").pixel_values.unsqueeze(0)
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