hy3dshape/models/conditioner.py

# 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.
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# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
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import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms
from transformers import (
    CLIPVisionModelWithProjection,
    CLIPVisionConfig,
    Dinov2Model,
    Dinov2Config,
)
from transformers import AutoImageProcessor, AutoModel


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 = AutoModel.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,
        drop_ratio=0.0
    ):
        super().__init__()
        self.main_image_encoder = build_image_encoder(main_image_encoder)
        self.drop_ratio = drop_ratio
        self.disable_drop = True

    def forward(self, image, mask=None, **kwargs):
        outputs = {
            'main': self.main_image_encoder(image, mask=mask, **kwargs),
        }
        if self.disable_drop:
            return outputs
        else:
            random_p = torch.rand(len(image), device='cuda')
            remain_bool_tensor = random_p > self.drop_ratio
            outputs['main'] *= remain_bool_tensor.view(-1,1,1)
        return outputs

        
        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