from functools import partial import torch import torch.nn as nn import numpy as np import torch.utils.checkpoint from timm.models.swin_transformer import SwinTransformerBlock from timm.models.vision_transformer import Block from timm.models.layers import to_2tuple class PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) self.patch_hw = (img_size[1] // patch_size[1], img_size[0] // patch_size[0]) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): B, C, H, W = x.shape x = self.proj(x).flatten(2).transpose(1, 2) return x 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=float) 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 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_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_flexible(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[0], dtype=np.float32) grid_w = np.arange(grid_size[1], 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[0], grid_size[1]]) 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 class SwinTransformerBlockWrapper(torch.nn.Module): """ Wrap SwinTransformerBlock to fit the input shape of [B, N, C] like TransformerBlock. The SwinTransformerBlock takes the input shape of [B, H, W, C], and TransformerBlock takes the input shape of [B, N, C]. """ def __init__(self, block: SwinTransformerBlock): super().__init__() self.block = block self.input_resolution = block.input_resolution def forward(self, x): """ :param x: [B, N, C] :return: [B, N, C] """ B, N, C = x.shape x = x.reshape(B, *self.input_resolution, C) x = self.block(x) x = x.reshape(B, N, C) return x class MaskedAutoencoderViT(nn.Module): """ Masked Autoencoder with VisionTransformer backbone """ def __init__( self, img_size=224, patch_size=16, in_chans=3, # input channels. 1 for audio, 3 for image embed_dim=1024, depth=24, # transformer depth num_heads=16, decoder_mode=0, # 0: transformer (global attn), 1: swin-transformer (swined local attn) no_shift=False, # invalid when decoder_mode=0. swin-transformer. shift patch or not decoder_embed_dim=512, decoder_depth=8, # invalid when decoder_mode=1. It will be fixed to 16 when decoder_mode=1. decoder_num_heads=16, # invalid when decoder_mode=1. It will be fixed to 16 when decoder_mode=1. mlp_ratio=4., # hidden dimension / embed dimension in feedforward layer of transformer norm_layer=nn.LayerNorm, norm_pix_loss=False, # use (per-patch) normalized pixels as targets for computing loss pos_trainable=False, ): super().__init__() self.img_size = to_2tuple(img_size) self.embed_dim = embed_dim self.decoder_embed_dim = decoder_embed_dim # 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=pos_trainable) # fixed sin-cos embedding self.encoder_depth = depth self.blocks = nn.ModuleList([ Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer) for _ 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=pos_trainable) # fixed sin-cos embedding self.no_shift = no_shift self.decoder_mode = decoder_mode window_size = (4, 4) feat_size = (self.img_size[0] // patch_size, 8) if self.decoder_mode == 1: decoder_modules = [] for index in range(16): if self.no_shift: shift_size = (0, 0) else: if (index % 2) == 0: shift_size = (0, 0) else: shift_size = (2, 0) # shift_size = tuple([0 if ((index % 2) == 0) else w // 2 for w in window_size]) decoder_modules.append( SwinTransformerBlockWrapper( SwinTransformerBlock( dim=decoder_embed_dim, input_resolution=feat_size, num_heads=16, window_size=window_size, shift_size=shift_size, mlp_ratio=mlp_ratio, proj_drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=norm_layer, ) ) ) self.decoder_blocks = nn.ModuleList(decoder_modules) else: # Transformer self.decoder_blocks = nn.ModuleList([ Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer) for _ 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.patch_size = patch_size self.initialize_weights() def initialize_weights(self): # initialize (and freeze) pos_embed by sin-cos embedding pos_embed = get_2d_sincos_pos_embed_flexible(self.pos_embed.shape[-1], self.patch_embed.patch_hw, cls_token=True) self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) decoder_pos_embed = get_2d_sincos_pos_embed_flexible(self.decoder_pos_embed.shape[-1], self.patch_embed.patch_hw, 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) L = (H/p)*(W/p) """ p = self.patch_embed.patch_size[0] h = imgs.shape[2] // p w = imgs.shape[3] // p # h,w = self.patch_embed.patch_hw x = imgs.reshape(shape=(imgs.shape[0], 1, h, p, w, p)) x = torch.einsum('nchpwq->nhwpqc', x) x = x.reshape(imgs.shape[0], h * w, p ** 2 * 1) return x def unpatchify(self, x): """ x: (N, L, patch_size**2 *3) specs: (N, 1, H, W) """ p = self.patch_embed.patch_size[0] h = self.img_size[0] // p w = 128 // p x = x.reshape(shape=(x.shape[0], h, w, p, p, 1)) x = torch.einsum('nhwpqc->nchpwq', x) specs = x.reshape(x.shape[0], 1, h * p, w * p) return specs 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): """ :param x: [N, C, H, W] :param mask_ratio: float. ratio of masked patches :return: tuple. x: [N, L', D], mask: [N, L], ids_restore: [N, L], None """ # embed patches x = self.patch_embed(x) B, L, D = x.shape # add pos embed w/o cls token x = x + self.pos_embed[:, 1:L + 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.norm(x) return x, mask, ids_restore def forward_encoder_no_mask( self, x, header='mean' ): """ :param x: [N, C, H, W] :param header: str. 'cls' or 'mean' :param key_padding_mask: [N, L], 0 is keep, 1 is remove :return: contextual_emb: [N, L, D] """ # embed patches x = self.patch_embed(x) B, L, D = x.shape # add pos embed w/o cls token x = x + self.pos_embed[:, 1:L + 1, :] # 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 n, blk in enumerate(self.blocks): x = blk(x) x = self.norm(x) if header == 'cls': emb = x[:, 0, :] elif header == 'mean': emb = x[:, 1:, :].mean(dim=1) else: raise NotImplementedError return emb def forward_decoder(self, x, ids_restore): """ :param x: [N, L, D] :param ids_restore: [N, L] :return: pred: [N, L, p*p*3], None, None """ # embed tokens x = self.decoder_embed(x) # [N, L, D] -> [N, L, D'] # 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 B, L, D = x.shape # add pos embed x = x + self.decoder_pos_embed[:, :L, :] if self.decoder_mode != 0: B, L, D = x.shape x = x[:, 1:, :] if self.decoder_mode > 3: # mvit x = self.decoder_blocks(x) else: # apply Transformer blocks for blk in self.decoder_blocks: x = blk(x) x = self.decoder_norm(x) # predictor projection pred = self.decoder_pred(x) # remove cls token if self.decoder_mode == 0: pred = pred[:, 1:, :] return pred def forward_loss(self, imgs, pred, mask, norm_pix_loss=False): """ 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 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=0.8): """ :param imgs: [N, C, H, W] :param mask_ratio: float. ratio of masked patches :return: tuple. loss_recon: float, pred: [N, L, p*p*3], mask: [N, L], None """ emb_enc, mask, ids_restore = self.forward_encoder(imgs, mask_ratio) pred = self.forward_decoder(emb_enc, ids_restore) # [N, L, p*p*3] loss_recon = self.forward_loss(imgs, pred, mask, norm_pix_loss=self.norm_pix_loss) return loss_recon, pred, mask if __name__ == '__main__': device = 'cpu' # device = 'cuda' # Model audio_mae = MaskedAutoencoderViT( img_size=(2048, 128), patch_size=16, in_chans=1, embed_dim=768, depth=12, num_heads=12, decoder_mode=1, no_shift=False, decoder_embed_dim=512, norm_layer=partial(nn.LayerNorm, eps=1e-6), norm_pix_loss=False, pos_trainable=False, ) # Load pre-trained weights ckpt_path = 'music-mae-32kHz.pth' audio_mae.load_state_dict(torch.load(ckpt_path, map_location='cpu')) audio_mae.to(device) # Generate a batch of random inputs: (N, C, H, W), N=4 (batch size), C=1 (channel), H=2048, W=128 # Each input is a mel spectrogram with shape (2048, 128) x = torch.randn(4, 1, 2048, 128).to(device) # Compute the representation of the input batch emb = audio_mae.forward_encoder_no_mask(x, header='mean') # torch.Size([4, 768])