import torch import torch.nn as nn from functools import partial 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=4.0, norm_layer=nn.LayerNorm, norm_pix_loss=False): super().__init__() 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) 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.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 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 forward_encoder_with_given_mask(self, x, given_patch_mask): 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 def mae_vit_large_patch16(**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(**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