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mae.py

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+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])

requirements.txt

@@ -0,0 +1,5 @@
+torch==2.1.1
+timm==0.9.12
+numpy==1.24.4
+librosa==0.10.1
+miniaudio==1.59

test.py

@@ -0,0 +1,103 @@
+from typing import Union, Tuple
+import numpy as np
+from numpy.typing import NDArray
+import torch
+from torch import nn
+from functools import partial
+import matplotlib.pyplot as plt
+from PIL import Image
+import librosa
+import miniaudio
+
+from mae import MaskedAutoencoderViT
+
+
+def load_audio(
+        path: str,
+        sr: int = 32000,
+        duration: int = 20,
+) -> (np.ndarray, int):
+    g = miniaudio.stream_file(path, output_format=miniaudio.SampleFormat.FLOAT32, nchannels=1,
+                              sample_rate=sr, frames_to_read=sr * duration)
+    signal = np.array(next(g))
+    return signal
+
+
+def mel_spectrogram(
+        signal: np.ndarray,
+        sr: int = 32000,
+        n_fft: int = 800,
+        hop_length: int = 320,
+        n_mels: int = 128,
+) -> np.ndarray:
+    mel_spec = librosa.feature.melspectrogram(
+        y=signal, sr=sr, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels,
+        window='hann', pad_mode='constant'
+    )
+    mel_spec = librosa.power_to_db(mel_spec)  # (freq, time)
+    return mel_spec.T  # (time, freq)
+
+
+def display_image(
+        img: Union[NDArray, Image.Image],
+        figsize: Tuple[float, float] = (5, 5),
+) -> None:
+    plt.figure(figsize=figsize)
+    plt.imshow(img, origin='lower', aspect='auto')  # cmp = 'viridis', 'coolwarm'
+    plt.axis('off')
+    plt.colorbar()
+    plt.tight_layout()
+    plt.show()
+
+
+def normalize(arr: np.ndarray, eps: float = 1e-8) -> np.ndarray:
+    return (arr - arr.mean()) / (arr.std() + eps)
+
+
+if __name__ == '__main__':
+    mp3_file = "/Users/chenjing22/Downloads/songs/See You Again.mp3"
+    mel_spec = mel_spectrogram(load_audio(mp3_file, duration=21))  # (time, freq)
+
+    # padding or truncating
+    length = mel_spec.shape[0]
+    target_length = 2048
+    mel_spec = mel_spec[:target_length] if length > target_length else np.pad(
+        mel_spec, ((0, target_length - length), (0, 0)), mode='constant', constant_values=mel_spec.min()
+    )
+
+    # normalize
+    mel_spec = normalize(mel_spec)  # (2048, 128)
+
+    display_image(mel_spec.T, figsize=(10, 4))
+
+    # Model
+    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'
+    mae.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
+
+    device = 'cpu'  # 'cuda'
+    mae.to(device)
+
+    x = torch.from_numpy(mel_spec).unsqueeze(0).unsqueeze(0).to(device)  # (1, 1, 2048, 128)
+    mse_loss, y, mask = mae(x, mask_ratio=0.7)  # y: (1, 1024, 256), mask: (1, 1024)
+
+    y[mask == 0.] = mae.patchify(x)[mask == 0.]
+    x_reconstructed = mae.unpatchify(y).squeeze(0).squeeze(0).detach().numpy()
+
+    print(f'mse_loss: {mse_loss.item()}')
+    display_image(x_reconstructed.T, figsize=(10, 4))