# Open Source Model Licensed under the Apache License Version 2.0
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
# The below software and/or models in this distribution may have been
# modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C) THL A29 Limited.
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms
from transformers import (
CLIPVisionModelWithProjection,
CLIPVisionConfig,
Dinov2Model,
Dinov2Config,
)
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000 ** omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
return np.concatenate([emb_sin, emb_cos], axis=1)
class ImageEncoder(nn.Module):
def __init__(
self,
version=None,
config=None,
use_cls_token=True,
image_size=224,
**kwargs,
):
super().__init__()
if config is None:
self.model = self.MODEL_CLASS.from_pretrained(version)
else:
self.model = self.MODEL_CLASS(self.MODEL_CONFIG_CLASS.from_dict(config))
self.model.eval()
self.model.requires_grad_(False)
self.use_cls_token = use_cls_token
self.size = image_size // 14
self.num_patches = (image_size // 14) ** 2
if self.use_cls_token:
self.num_patches += 1
self.transform = transforms.Compose(
[
transforms.Resize(image_size, transforms.InterpolationMode.BILINEAR, antialias=True),
transforms.CenterCrop(image_size),
transforms.Normalize(
mean=self.mean,
std=self.std,
),
]
)
def forward(self, image, mask=None, value_range=(-1, 1), **kwargs):
if value_range is not None:
low, high = value_range
image = (image - low) / (high - low)
image = image.to(self.model.device, dtype=self.model.dtype)
inputs = self.transform(image)
outputs = self.model(inputs)
last_hidden_state = outputs.last_hidden_state
if not self.use_cls_token:
last_hidden_state = last_hidden_state[:, 1:, :]
return last_hidden_state
def unconditional_embedding(self, batch_size, **kwargs):
device = next(self.model.parameters()).device
dtype = next(self.model.parameters()).dtype
zero = torch.zeros(
batch_size,
self.num_patches,
self.model.config.hidden_size,
device=device,
dtype=dtype,
)
return zero
class CLIPImageEncoder(ImageEncoder):
MODEL_CLASS = CLIPVisionModelWithProjection
MODEL_CONFIG_CLASS = CLIPVisionConfig
mean = [0.48145466, 0.4578275, 0.40821073]
std = [0.26862954, 0.26130258, 0.27577711]
class DinoImageEncoder(ImageEncoder):
MODEL_CLASS = Dinov2Model
MODEL_CONFIG_CLASS = Dinov2Config
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
class DinoImageEncoderMV(DinoImageEncoder):
def __init__(
self,
version=None,
config=None,
use_cls_token=True,
image_size=224,
view_num=4,
**kwargs,
):
super().__init__(version, config, use_cls_token, image_size, **kwargs)
self.view_num = view_num
self.num_patches = self.num_patches
pos = np.arange(self.view_num, dtype=np.float32)
view_embedding = torch.from_numpy(
get_1d_sincos_pos_embed_from_grid(self.model.config.hidden_size, pos)).float()
view_embedding = view_embedding.unsqueeze(1).repeat(1, self.num_patches, 1)
self.view_embed = view_embedding.unsqueeze(0)
def forward(self, image, mask=None, value_range=(-1, 1), view_idxs=None):
if value_range is not None:
low, high = value_range
image = (image - low) / (high - low)
image = image.to(self.model.device, dtype=self.model.dtype)
bs, num_views, c, h, w = image.shape
image = image.view(bs * num_views, c, h, w)
inputs = self.transform(image)
outputs = self.model(inputs)
last_hidden_state = outputs.last_hidden_state
last_hidden_state = last_hidden_state.view(
bs, num_views, last_hidden_state.shape[-2],
last_hidden_state.shape[-1]
)
view_embedding = self.view_embed.to(last_hidden_state.dtype).to(last_hidden_state.device)
if view_idxs is not None:
assert len(view_idxs) == bs
view_embeddings = []
for i in range(bs):
view_idx = view_idxs[i]
assert num_views == len(view_idx)
view_embeddings.append(self.view_embed[:, view_idx, ...])
view_embedding = torch.cat(view_embeddings, 0).to(last_hidden_state.dtype).to(last_hidden_state.device)
if num_views != self.view_num:
view_embedding = view_embedding[:, :num_views, ...]
last_hidden_state = last_hidden_state + view_embedding
last_hidden_state = last_hidden_state.view(bs, num_views * last_hidden_state.shape[-2],
last_hidden_state.shape[-1])
return last_hidden_state
def unconditional_embedding(self, batch_size, view_idxs=None, **kwargs):
device = next(self.model.parameters()).device
dtype = next(self.model.parameters()).dtype
zero = torch.zeros(
batch_size,
self.num_patches * len(view_idxs[0]),
self.model.config.hidden_size,
device=device,
dtype=dtype,
)
return zero
def build_image_encoder(config):
if config['type'] == 'CLIPImageEncoder':
return CLIPImageEncoder(**config['kwargs'])
elif config['type'] == 'DinoImageEncoder':
return DinoImageEncoder(**config['kwargs'])
elif config['type'] == 'DinoImageEncoderMV':
return DinoImageEncoderMV(**config['kwargs'])
else:
raise ValueError(f'Unknown image encoder type: {config["type"]}')
class DualImageEncoder(nn.Module):
def __init__(
self,
main_image_encoder,
additional_image_encoder,
):
super().__init__()
self.main_image_encoder = build_image_encoder(main_image_encoder)
self.additional_image_encoder = build_image_encoder(additional_image_encoder)
def forward(self, image, mask=None, **kwargs):
outputs = {
'main': self.main_image_encoder(image, mask=mask, **kwargs),
'additional': self.additional_image_encoder(image, mask=mask, **kwargs),
}
return outputs
def unconditional_embedding(self, batch_size, **kwargs):
outputs = {
'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs),
'additional': self.additional_image_encoder.unconditional_embedding(batch_size, **kwargs),
}
return outputs
class SingleImageEncoder(nn.Module):
def __init__(
self,
main_image_encoder,
):
super().__init__()
self.main_image_encoder = build_image_encoder(main_image_encoder)
def forward(self, image, mask=None, **kwargs):
outputs = {
'main': self.main_image_encoder(image, mask=mask, **kwargs),
}
return outputs
def unconditional_embedding(self, batch_size, **kwargs):
outputs = {
'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs),
}
return outputs