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# 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