Blame for model/Vision_encoder/models_mae.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------

from functools import partial

import torch
import torch.nn as nn
import numpy as np

from timm.models.vision_transformer import PatchEmbed, Block

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., norm_layer=partial(nn.LayerNorm, eps=1e-6), norm_pix_loss=False, 
                 quantile=False, quantile_head_num=9,**kwargs):
        super().__init__()

        # add for quantile forecasting
        self.norm_pix_loss = norm_pix_loss
        self.quantile = quantile
        self.quantile_head_num = quantile_head_num

        # --------------------------------------------------------------------------
        # 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)

        # prediction head
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True)  # decoder to patch
        if self.quantile:
            # adds quantile outputs:
            # we need 9 outputs in total, corresponding to 10%, 20%, ..., 90%
            # So other 8 heads are needed to generate other quantile outputs.
            self.decoder_pred_quantile_list = nn.ModuleList()
            for i in range(self.quantile_head_num-1):
                self.decoder_pred_quantile_list.append(nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True))  # decoder to patch
        
        # --------------------------------------------------------------------------

        self.initialize_weights()
    

    def initialize_weights(self):
        # initialization
        # initialize (and freeze) pos_embed by sin-cos embedding
        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))

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            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 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, n_channels=3):
        """
        x: (N, L, patch_size**2 * n_channels)
        imgs: (N, 3, H, W)
        """
        p = self.patch_embed.patch_size[0]
        h = w = int(x.shape[1]**.5)
        assert h * w == x.shape[1]
        
        # x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
        x = x.reshape(shape=(x.shape[0], h, w, p, p, n_channels))
        x = torch.einsum('nhwpqc->nchpwq', x)
        # imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
        imgs = x.reshape(shape=(x.shape[0], n_channels, h * p, h * p))
        return imgs

    def random_masking(self, x, mask_ratio, noise=None):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        noise: [N, L]
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(round(L * (1 - mask_ratio)))
        
        if noise is None:
            noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
        
        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

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

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_encoder(self, x, mask_ratio, noise=None,use_multiscale=True):
        # embed patches
        x = self.patch_embed(x)

        x = x + self.pos_embed[:, 1:, :]
       
        # x = x + self.pos_embed[:, 1:, :]+pos
        # masking: length -> length * mask_ratio
        x, mask, ids_restore = self.random_masking(x, mask_ratio, noise)

        # append cls token
        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)

        total_depth = len(self.blocks)
        part_size = total_depth // 4
        intermediate_features = []

        # apply Transformer blocks
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if use_multiscale and (i + 1) % part_size == 0:
                intermediate_features.append(x)

        if use_multiscale:
            fused = torch.stack(intermediate_features, dim=0).sum(dim=0)  # [N, L, D]
            fused = self.norm(fused)
            return fused, mask, ids_restore
        else:      
            x = self.norm(x)
            return x, mask, ids_restore

    def forward_decoder(self, x, ids_restore):
        # embed tokens
        x = self.decoder_embed(x)

        # append mask tokens to sequence
        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

        # add pos embed
        x = x + self.decoder_pos_embed

        # apply Transformer blocks
        for blk in self.decoder_blocks:
            x = blk(x)
        x = self.decoder_norm(x)

        # predictor projection
        if not self.quantile:
            x = self.decoder_pred(x)
            x = x[:, 1:, :]  # remove cls token
            return x
        else:
            # first calculate the 50% quantile value
            x_mid = self.decoder_pred(x)[:, 1:, :]  # [batch, ]
            
            # then calculate the other quantile values
            x_quantile_list = []
            for i in range(self.quantile_head_num-1):
                x_quantile = self.decoder_pred_quantile_list[i](x)[:, 1:, :]
                x_quantile_list.append(x_quantile)
            
            # this will generate 9 groups of data
            return x_mid, x_quantile_list

    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 remove, 
        """
        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)**.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 forward(self, imgs, mask_ratio=0.0, noise=None):
        latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio, noise)
        pred = self.forward_decoder(latent, ids_restore)  # [N, L, p*p*3]
        # loss = self.forward_loss(imgs, pred, mask)
        return latent, pred, mask

from transformers import PretrainedConfig

class MAEConfig(PretrainedConfig):
    model_type = "mae_vit"  

    def __init__(
        self,
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        norm_layer_eps=1e-6,
        decoder_embed_dim=512,
        decoder_depth=8,
        decoder_num_heads=16,
        in_chans=3,
        img_size=224,
        mask_ratio=0.4,
        **kwargs
    ):
        super().__init__(**kwargs)  
        self.patch_size = patch_size
        self.embed_dim = embed_dim
        self.depth = depth
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio
        self.norm_layer_eps = norm_layer_eps
        self.decoder_embed_dim = decoder_embed_dim
        self.decoder_depth = decoder_depth
        self.decoder_num_heads = decoder_num_heads
        self.in_chans = in_chans
        self.img_size = img_size
        self.mask_ratio = mask_ratio


def get_mae_base_config():
    return dict(
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        decoder_embed_dim=512,
        decoder_depth=8,
        decoder_num_heads=16,
        mlp_ratio=4.0,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
    )

def get_mae_large_config():
    config = get_mae_base_config()
    config.update(
        embed_dim=1024,
        depth=24,
        num_heads=16,
    )
    return config

def get_mae_huge_config():
    config = get_mae_base_config()
    config.update(
        patch_size=14,
        embed_dim=1280,
        depth=32,
        num_heads=16,
    )
    return config

MAE_ARCH = {
    "mae_visualize_base.pth": get_mae_base_config(),
    "mae_visualize_large.pth": get_mae_large_config(),
    "mae_visualize_huge.pth": get_mae_huge_config(),
}


import numpy as np

# --------------------------------------------------------
# 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=np.float32)
    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