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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
- from this import d
-
- import torch
- import torch.nn as nn
-
- from timm.models.vision_transformer import PatchEmbed, Block
-
- from util.pos_embed import get_2d_sincos_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., norm_layer=nn.LayerNorm, norm_pix_loss=False, dropout=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.dropout = nn.Dropout(dropout)
-
- self.blocks = nn.ModuleList([
- Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, drop=dropout, attn_drop=dropout,
- drop_path=dropout)
- 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, qk_scale=None, norm_layer=norm_layer, drop=dropout, attn_drop=dropout,
- drop_path=dropout)
- 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.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):
- """
- 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]**.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 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]
-
- # 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):
- # embed patches
- x = self.patch_embed(x)
-
- # add pos embed w/o cls token
- x = x + self.pos_embed[:, 1:, :]
-
- # masking: length -> length * mask_ratio
- x, mask, ids_restore = self.random_masking(x, mask_ratio)
-
- # 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)
-
- # apply Transformer blocks
- for blk in self.blocks:
- x = blk(x)
- x = self.dropout(x)
- 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
- x = self.decoder_pred(x)
-
- # remove cls token
- 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 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=0.75):
- latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
- pred = self.forward_decoder(latent, ids_restore) # [N, L, p*p*3]
- loss = self.forward_loss(imgs, pred, mask)
- return loss, pred, mask
-
-
- def mae_vit_base_patch16_dec512d8b(**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
-
- def client1_mae_vit_base_patch16_dec512d8b(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=16, embed_dim=640, depth=12, num_heads=10,
- 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
-
- def client2_mae_vit_base_patch16_dec512d8b(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=16, embed_dim=640, depth=12, num_heads=10,
- 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
-
-
- def mae_vit_large_patch16_dec512d8b(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=16, 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), **kwargs)
- return model
-
-
- def mae_vit_huge_patch14_dec512d8b(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=14, embed_dim=1280, depth=32, 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), **kwargs)
- return model
-
- def client1_mae_vit_large_patch16_dec512d8b(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=16, 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), **kwargs)
- return model
-
- def client2_mae_vit_large_patch16_dec512d8b(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=16, embed_dim=1024, depth=24, num_heads=14,
- 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
-
- def mae_vit_test(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=8, embed_dim=64, depth=3, num_heads=8,
- decoder_embed_dim=128, decoder_depth=2, decoder_num_heads=4,
- mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
- return model
-
- def mae_vit_test_client1(**kwargs):
- model = MaskedAutoencoderViT(
- patch_size=8, embed_dim=32, depth=3, num_heads=2,
- decoder_embed_dim=128, decoder_depth=2, decoder_num_heads=2,
- mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
- return model
-
- # set recommended archs
- mae_vit_base_patch16 = mae_vit_base_patch16_dec512d8b # decoder: 512 dim, 8 blocks
- mae_vit_large_patch16 = mae_vit_large_patch16_dec512d8b # decoder: 512 dim, 8 blocks
- mae_vit_huge_patch14 = mae_vit_huge_patch14_dec512d8b # decoder: 512 dim, 8 blocks
-
- client1_mae_vit_large_patch16 = client1_mae_vit_large_patch16_dec512d8b
- client2_mae_vit_large_patch16 = client2_mae_vit_large_patch16_dec512d8b
-
- client1_mae_vit_base_patch16 = client1_mae_vit_base_patch16_dec512d8b
- client2_mae_vit_base_patch16 = client2_mae_vit_base_patch16_dec512d8b
-
- mae_test = mae_vit_test
- mae_client_test = mae_vit_test_client1
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