rmaphoh
251 lines
9.5 KiB
Python
251 lines
9.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
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# DeiT: https://github.com/facebookresearch/deit
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# --------------------------------------------------------
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from functools import partial
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import torch
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import torch.nn as nn
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from timm.models.vision_transformer import PatchEmbed, Block
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from util.pos_embed import get_2d_sincos_pos_embed
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class MaskedAutoencoderViT(nn.Module):
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""" Masked Autoencoder with VisionTransformer backbone
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3,
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embed_dim=1024, depth=24, num_heads=16,
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decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
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mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
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super().__init__()
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# --------------------------------------------------------------------------
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# MAE encoder specifics
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self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding
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self.blocks = nn.ModuleList([
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Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim)
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# --------------------------------------------------------------------------
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# --------------------------------------------------------------------------
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# MAE decoder specifics
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self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)
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self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
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self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding
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self.decoder_blocks = nn.ModuleList([
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Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
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for i in range(decoder_depth)])
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self.decoder_norm = norm_layer(decoder_embed_dim)
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self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch
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# --------------------------------------------------------------------------
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self.norm_pix_loss = norm_pix_loss
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self.initialize_weights()
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def initialize_weights(self):
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# initialization
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# initialize (and freeze) pos_embed by sin-cos embedding
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pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
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self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
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decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
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self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
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# initialize patch_embed like nn.Linear (instead of nn.Conv2d)
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w = self.patch_embed.proj.weight.data
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torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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# timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
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torch.nn.init.normal_(self.cls_token, std=.02)
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torch.nn.init.normal_(self.mask_token, std=.02)
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# initialize nn.Linear and nn.LayerNorm
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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# we use xavier_uniform following official JAX ViT:
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torch.nn.init.xavier_uniform_(m.weight)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def patchify(self, imgs):
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"""
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imgs: (N, 3, H, W)
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x: (N, L, patch_size**2 *3)
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"""
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p = self.patch_embed.patch_size[0]
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assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
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h = w = imgs.shape[2] // p
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x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
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x = torch.einsum('nchpwq->nhwpqc', x)
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x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3))
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return x
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def unpatchify(self, x):
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"""
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x: (N, L, patch_size**2 *3)
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imgs: (N, 3, H, W)
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"""
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p = self.patch_embed.patch_size[0]
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h = w = int(x.shape[1]**.5)
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assert h * w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
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x = torch.einsum('nhwpqc->nchpwq', x)
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imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
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return imgs
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def random_masking(self, x, mask_ratio):
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"""
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Perform per-sample random masking by per-sample shuffling.
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Per-sample shuffling is done by argsort random noise.
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x: [N, L, D], sequence
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"""
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N, L, D = x.shape # batch, length, dim
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len_keep = int(L * (1 - mask_ratio))
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noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
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# sort noise for each sample
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ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
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ids_restore = torch.argsort(ids_shuffle, dim=1)
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# keep the first subset
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ids_keep = ids_shuffle[:, :len_keep]
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x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
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# generate the binary mask: 0 is keep, 1 is remove
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mask = torch.ones([N, L], device=x.device)
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mask[:, :len_keep] = 0
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# unshuffle to get the binary mask
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mask = torch.gather(mask, dim=1, index=ids_restore)
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return x_masked, mask, ids_restore
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def forward_encoder(self, x, mask_ratio):
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# embed patches
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x = self.patch_embed(x)
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# add pos embed w/o cls token
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x = x + self.pos_embed[:, 1:, :]
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# masking: length -> length * mask_ratio
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x, mask, ids_restore = self.random_masking(x, mask_ratio)
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# append cls token
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cls_token = self.cls_token + self.pos_embed[:, :1, :]
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cls_tokens = cls_token.expand(x.shape[0], -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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# apply Transformer blocks
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for blk in self.blocks:
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x = blk(x)
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x = self.norm(x)
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return x, mask, ids_restore
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def forward_decoder(self, x, ids_restore):
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# embed tokens
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x = self.decoder_embed(x)
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# append mask tokens to sequence
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mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
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x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # no cls token
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x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle
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x = torch.cat([x[:, :1, :], x_], dim=1) # append cls token
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# add pos embed
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x = x + self.decoder_pos_embed
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# apply Transformer blocks
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for blk in self.decoder_blocks:
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x = blk(x)
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x = self.decoder_norm(x)
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# predictor projection
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x = self.decoder_pred(x)
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# remove cls token
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x = x[:, 1:, :]
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return x
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def forward_loss(self, imgs, pred, mask):
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"""
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imgs: [N, 3, H, W]
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pred: [N, L, p*p*3]
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mask: [N, L], 0 is keep, 1 is remove,
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"""
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target = self.patchify(imgs)
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if self.norm_pix_loss:
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mean = target.mean(dim=-1, keepdim=True)
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var = target.var(dim=-1, keepdim=True)
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target = (target - mean) / (var + 1.e-6)**.5
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loss = (pred - target) ** 2
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loss = loss.mean(dim=-1) # [N, L], mean loss per patch
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loss = (loss * mask).sum() / mask.sum() # mean loss on removed patches
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return loss
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def forward(self, imgs, mask_ratio=0.75):
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latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
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pred = self.forward_decoder(latent, ids_restore) # [N, L, p*p*3]
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loss = self.forward_loss(imgs, pred, mask)
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return loss, pred, mask
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def mae_vit_base_patch16_dec512d8b(**kwargs):
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model = MaskedAutoencoderViT(
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patch_size=16, embed_dim=768, depth=12, num_heads=12,
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decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
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mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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return model
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def mae_vit_large_patch16_dec512d8b(**kwargs):
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model = MaskedAutoencoderViT(
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patch_size=16, embed_dim=1024, depth=24, num_heads=16,
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decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
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mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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return model
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def mae_vit_huge_patch14_dec512d8b(**kwargs):
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model = MaskedAutoencoderViT(
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patch_size=14, embed_dim=1280, depth=32, num_heads=16,
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decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
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mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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return model
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# set recommended archs
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mae_vit_base_patch16 = mae_vit_base_patch16_dec512d8b # decoder: 512 dim, 8 blocks
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mae_vit_large_patch16 = mae_vit_large_patch16_dec512d8b # decoder: 512 dim, 8 blocks
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mae_vit_huge_patch14 = mae_vit_huge_patch14_dec512d8b # decoder: 512 dim, 8 blocks
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