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Running
on
Zero
File size: 6,508 Bytes
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# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // 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
import torch.nn.functional as F
from einops import rearrange
from torchvision.transforms import Resize
from transformers import AutoConfig, AutoModel, Siglip2VisionConfig, Siglip2VisionModel
from . import models
from .utils import ScalingLayer
class TextAlignedTokenizer(nn.Module):
def __init__(
self,
bottleneck,
bottleneck_token_num=256,
input_size=384,
teacher='google/siglip2-so400m-patch14-384',
input_type='quant', # choose from ['quant', 'rec', 'indices']
pool_scale=1, # choose from [1, 2, 3]
decoder_depth=3,
select_layer_id=-2,
*args,
**kwargs
):
super().__init__()
self.input_size = input_size
self.bottleneck_token_num = bottleneck_token_num
self.teacher = teacher
self.input_type = input_type
self.pool_scale = pool_scale
self.decoder_depth = decoder_depth
self.select_layer_id = select_layer_id
self.bottleneck_dim = bottleneck['args']['bottleneck_dim']
self.encoder_config = AutoConfig.from_pretrained(teacher)
self.encoder = AutoModel.from_config(self.encoder_config).vision_model
self.encoder_hidden_dim = self.encoder.config.hidden_size
self.decoder_config = Siglip2VisionConfig()
self.decoder_config.update({
'patch_size': 1,
'num_hidden_layers': self.decoder_depth,
'num_channels': self.bottleneck_dim,
'hidden_size': self.encoder_hidden_dim,
})
self.decoder = Siglip2VisionModel(self.decoder_config)
self.encode_task_layer = nn.Sequential(
nn.Linear(self.encoder_hidden_dim, self.encoder_hidden_dim),
nn.Tanh())
self.decode_task_layer = nn.Sequential(
nn.Linear(self.encoder_hidden_dim, self.encoder_hidden_dim),
nn.Tanh(),
nn.Linear(self.encoder_hidden_dim, self.encoder_hidden_dim))
bottleneck_args = {
'token_nums': self.bottleneck_token_num,
'input_dim': self.encoder_hidden_dim,
'output_dim': self.bottleneck_dim}
self.bottleneck = models.make(bottleneck, args=bottleneck_args)
self.scale_layer = ScalingLayer(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
self.image_resize = Resize((self.input_size, self.input_size))
def set_vq_eval_deterministic(self, deterministic=True):
self.bottleneck.regularizer.set_eval_deterministic(deterministic)
@property
def device(self):
return next(self.parameters()).device
@property
def dtype(self):
return next(self.parameters()).dtype
@classmethod
def from_checkpoint(cls, ckpt, load_teacher=True, **kwargs):
ckpt = torch.load(ckpt, map_location='cpu')
ckpt_kwargs = ckpt["model"]["args"]
model = cls(**kwargs, **ckpt_kwargs)
sd = ckpt["model"]["sd"]
if not load_teacher:
sd = {k: v for k, v in sd.items() if not k.startswith('teacher')}
model.load_state_dict(sd, strict=True)
return model
def encode(self, x, **kwargs):
if x.ndim == 5:
x = rearrange(x, 'b c t h w -> (b t) c h w')
x = self.scale_layer(x)
if tuple(x.shape[-2:]) != (self.input_size, self.input_size):
x = self.image_resize(x)
vq_feats = self.encoder(x, output_hidden_states=True).hidden_states[self.select_layer_id]
pool_scale = self.pool_scale
pool_scale = kwargs.get("pool_scale", pool_scale)
if pool_scale != 1:
vq_feats = self.avg_pool(vq_feats, pool_scale)
vq_feats = self.encode_task_layer(vq_feats.to(x))
bottleneck_out = self.bottleneck(vq_feats)
z = bottleneck_out.pop('output')
return {'encoded': z, 'pool_scale': pool_scale, 'vq_feats': vq_feats, **bottleneck_out}
def avg_pool(self, z, pool_scale=1):
if z.ndim == 3:
b, n, c = z.shape
p = int(n ** 0.5)
z = rearrange(z, 'b (p1 p2) c -> b c p1 p2', p1=p, p2=p)
else:
b, c, p, _ = z.shape
p_s = int(p // pool_scale)
z = F.avg_pool2d(
z,
kernel_size=(pool_scale, pool_scale),
stride=(pool_scale, pool_scale)
).contiguous()
z = rearrange(z, 'b c p1 p2 -> b (p1 p2) c')
return z
def decode(self, z):
if z.ndim == 4:
z = rearrange(z, 'b c p1 p2 -> b (p1 p2) c')
attention_mask = torch.ones(z.shape[:2], dtype=torch.int, device=z.device)
p = int(z.shape[1]**0.5)
spatial_shape = torch.tensor([[p, p]]*z.shape[0], device=self.device)
z = self.decoder(z, attention_mask, spatial_shape, output_hidden_states=True).last_hidden_state
z = self.decode_task_layer(z)
return z
def decode_from_bottleneck(self, bottleneck_rep):
z = self.bottleneck.decode(bottleneck_rep) # (b, n, c)
p = int(z.shape[1]**0.5)
z = rearrange(z, 'b (p1 p2) c -> b c p1 p2', p1=p, p2=p)
return self.decode(z)
def forward(self, data, **kwargs):
# data: video in shape (b, c, t, h, w)
encode_output = self.encode(data, **kwargs)
vq_feats = encode_output['encoded']
p = int(vq_feats.shape[1] ** 0.5)
vq_feats = rearrange(vq_feats, 'b (h w) c -> b c h w', h=p, w=p)
pred_feats = self.decode(vq_feats)
if self.input_type == 'quant':
z = encode_output["regularized_z"] # [b, n, c]
elif self.input_type == 'indices':
z = encode_output["bottleneck_rep"] # [b, n]
elif self.input_type == 'rec':
z = pred_feats # [b, n, c]
encode_output['encoded'] = z
return encode_output |