ExampleCode/example1/model/DesiEncoder.py

import torch
import torch.nn as nn
from functools import partial
from timm.models.vision_transformer import PatchEmbed, Block
import torch.nn.functional as F
import numpy as np
import torch


import numpy as np
import skimage
import numpy as np
import cv2 as cv

def sdss_rgb(imgs, bands, scales=None,
             m=0.02):
    """
    Transformation from raw image data (nanomaggies) to the rgb values displayed
    at the legacy viewer https://www.legacysurvey.org/viewer

    Code copied from
    https://github.com/legacysurvey/imagine/blob/master/map/views.py
    """

    rgbscales = {'u': (2, 1.5),  # 1.0,
                 'g': (2, 2.5),
                 'r': (1, 1.5),
                 'i': (0, 1.0),
                 'z': (0, 0.4),  # 0.3
                 }

    if scales is not None:
        rgbscales.update(scales)

    I = 0
    for img, band in zip(imgs, bands):
        plane, scale = rgbscales[band]
        img = np.maximum(0, img * scale + m)
        I = I + img
    I /= len(bands)

    Q = 20
    fI = np.arcsinh(Q * I) / np.sqrt(Q)
    I += (I == 0.) * 1e-6
    H, W = I.shape
    rgb = np.zeros((H, W, 3), np.float32)

    for img, band in zip(imgs, bands):
        plane, scale = rgbscales[band]
        rgb[:, :, plane] = (img * scale + m) * fI / I

    rgb = np.clip(rgb, 0, 1)

    return rgb

def dr2_rgb(rimgs, bands, **ignored):
    return sdss_rgb(rimgs, bands, scales=dict(g=(2, 6.0), r=(1, 3.4), z=(0, 2.2)), m=0.03)


# --------------------------------------------------------
# 2D sine-cosine position embedding
# References:
# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
# MoCo v3: https://github.com/facebookresearch/moco-v3
# --------------------------------------------------------
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
    return emb


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=float)
    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)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


# --------------------------------------------------------
# Interpolate position embeddings for high-resolution
# References:
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------
def interpolate_pos_embed(model, checkpoint_model):
    if 'pos_embed' in checkpoint_model:
        pos_embed_checkpoint = checkpoint_model['pos_embed']
        embedding_size = pos_embed_checkpoint.shape[-1]
        num_patches = model.patch_embed.num_patches
        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
        # height (== width) for the checkpoint position embedding
        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
        # height (== width) for the new position embedding
        new_size = int(num_patches ** 0.5)
        # class_token and dist_token are kept unchanged
        if orig_size != new_size:
            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
            # only the position tokens are interpolated
            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
            pos_tokens = torch.nn.functional.interpolate(
                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
            checkpoint_model['pos_embed'] = new_pos_embed

class MaskedAutoEncoderViT(nn.Module):
    """ Masked Autoencoder with VisionTransformer backbone
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, 
                 embed_dim=1024, depth=24, num_heads=16,
                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                 mlp_ratio=4.0, norm_layer=nn.LayerNorm, norm_pix_loss=False,lambda_consistency=1.0):
        super().__init__()
        
        # --------------------------------------------------------------------------
        # MAE encoder specifics
        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)   # fixed sin-cos embedding

        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MAE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))

        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)   # fixed sin-cos embedding

        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(decoder_depth)
        ])

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True)  # decoder to patch
        # --------------------------------------------------------------------------

        self.norm_pix_loss = norm_pix_loss
        self.lambda_consistency = lambda_consistency

        self.initialize_weights()
    
    def initialize_weights(self):

        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        self.apply(self._init_weights)
        
    def _init_weights(self, m):
        if isinstance(m, nn.Linear):

            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
    
    def random_masking(self, x, mask_ratio):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape   # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))

        noise = torch.rand(N, L, device=x.device)   # noise in [0, 1]

        ids_shuffle = torch.argsort(noise, dim=1)   # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0

        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore
    
    def patchify(self, imgs):
        """
        imgs: (N, 3, H, W)
        x: (N, L, patch_size**2 *3)
        """
        p = self.patch_embed.patch_size[0]
        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0

        h = w = imgs.shape[2] // p
        x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
        x = torch.einsum('nchpwq->nhwpqc', x)
        x = x.reshape(shape=(imgs.shape[0], h*w, p**2*3))
        return x
    
    def unpatchify(self, x):
        """
        x: (N, L, patch_size**2 *3)
        imgs: (N, 3, H, W)
        """
        p = self.patch_embed.patch_size[0]
        h = w = int(x.shape[1]**0.5)
        assert h *w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
        return imgs
    
    def forward_encoder(self, x, mask_ratio):

        x = self.patch_embed(x)

        x = x + self.pos_embed[:, 1:, :]

        x, mask, ids_restore = self.random_masking(x, mask_ratio)

        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)

        return x, mask, ids_restore
    
    def forward_decoder(self, x, ids_restore):
        x = self.decoder_embed(x)

        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)   # no cls token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))   # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)   # append cls token

        x = x + self.decoder_pos_embed

        for blk in self.decoder_blocks:
            x = blk(x)
        x = self.decoder_norm(x)
        x = self.decoder_pred(x)

        x = x[:, 1:, :]

        return x
    
    def forward_loss(self, imgs, pred, mask):
        """
        imgs: [N, 3, H, W]
        pred: [N, L, p*p*3]
        mask: [N, L], 0 is keep, 1 is move.
        """
        target = self.patchify(imgs)
        if self.norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6)**0.5
        
        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)   # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()   # mean loss on removed patches
        return loss


    def consistency_loss(self, latent1, latent2, use_cls=False):
        """
        latent1, latent2: [N, L, D]
        use_cls: 是否使用 cls_token，如果 False 就用平均 patch 特征
        """
        if use_cls:
            z1 = latent1[:, 0]   # [N, D] cls token
            z2 = latent2[:, 0]
        else:
            z1 = latent1[:, 1:].mean(dim=1)   # [N, D] 平均所有 patch 特征
            z2 = latent2[:, 1:].mean(dim=1)

        z1 = F.normalize(z1, dim=-1)
        z2 = F.normalize(z2, dim=-1)

        loss = 2 - 2 * (z1 * z2).sum(dim=-1).mean()
        return loss

    def forward(self, imgs, mask_ratio=0.75):
        # --- 第一次 mask ---
        latent1, mask1, ids_restore1 = self.forward_encoder(imgs, mask_ratio)
        pred1 = self.forward_decoder(latent1, ids_restore1)
        loss_recon1 = self.forward_loss(imgs, pred1, mask1)

        # --- 第二次 mask ---
        latent2, mask2, ids_restore2 = self.forward_encoder(imgs, mask_ratio)
        pred2 = self.forward_decoder(latent2, ids_restore2)
        loss_recon2 = self.forward_loss(imgs, pred2, mask2)

        # --- 一致性损失 ---
        loss_cons = self.consistency_loss(latent1, latent2)

        # --- 总 loss ---
        loss_total = (loss_recon1 + loss_recon2) / 2 + self.lambda_consistency * loss_cons

        return loss_total, pred1, mask1
    

    def forward_encoder_with_given_mask(self, x, given_patch_mask):
        """
        Forward encoder using a given patch-level mask.
        
        Args:
            x: (N, 3, H, W)
            given_patch_mask: (N, L), 1 for masked patches, 0 for kept
        
        Returns:
            x: encoded tokens with cls token (N, len_keep + 1, embed_dim)
            mask: (N, L), same as input
            ids_restore: (N, L), mapping for unshuffling
        """

        x = self.patch_embed(x)  # (N, L, D)
        x = x + self.pos_embed[:, 1:, :]  # (N, L, D)

        N, L, D = x.shape

        noise = torch.rand(N, L, device=x.device)

        mask_float = given_patch_mask.float()
        ids_shuffle = torch.argsort(mask_float * (noise.max() + 1) + noise, dim=1)  # (N, L)
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        len_keep = L - given_patch_mask.sum(dim=1).max().int().item()
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x_masked), dim=1)

        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)

        return x, given_patch_mask, ids_restore
    
    def forward_with_given_mask(self, imgs, given_patch_mask):

        latent, mask, ids_restore = self.forward_encoder_with_given_mask(imgs, given_patch_mask)
        pred = self.forward_decoder(latent, ids_restore)
        loss = self.forward_loss(imgs, pred, mask)
        return loss, pred, mask
    
    


def mae_vit_base_patch16(**kwargs):
    model = MaskedAutoEncoderViT(
        patch_size=16, embed_dim=768, depth=12, num_heads=12,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model