Create model.py

model.py

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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
+
+import torch
+import torch.nn as nn
+import numpy as np
+from timm.models.vision_transformer import PatchEmbed, Block
+
+from huggingface_hub import PyTorchModelHubMixin
+from timm.models.layers import DropPath
+import math
+import torch.nn.functional as F
+
+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_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_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=np.float32)
+    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
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.hidden_features = hidden_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+class Attention(nn.Module):
+    def __init__(
+            self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
+            proj_drop=0., attn_head_dim=None):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+class NormalCell(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, class_token=False, group=1, 
+                tokens_type='transformer', kernel=3, mlp_hidden_dim=None):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.class_token = class_token
+        if tokens_type == 'transformer':
+            self.attn = Attention(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        else:
+            raise NotImplementedError()
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = mlp_hidden_dim if mlp_hidden_dim is not None else int(dim * mlp_ratio)
+        PCM_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        self.PCM = nn.Sequential(
+                            nn.Conv2d(dim, PCM_dim, kernel, 1, kernel//2, 1, group),
+                            nn.BatchNorm2d(PCM_dim),
+                            nn.SiLU(inplace=True),
+                            nn.Conv2d(PCM_dim, dim, kernel, 1, kernel//2, 1, group),
+                            )
+
+    def forward(self, x):
+        b, n, c = x.shape
+        if self.class_token:
+            n = n - 1
+            wh = int(math.sqrt(n))
+            convX = self.drop_path(self.PCM(x[:, 1:, :].view(b, wh, wh, c).permute(0, 3, 1, 2).contiguous()).permute(0, 2, 3, 1).contiguous().view(b, n, c))
+            x = x + self.drop_path(self.attn(self.norm1(x)))
+            x[:, 1:] = x[:, 1:] + convX
+        else:
+            wh = int(math.sqrt(n))
+            x_2d = x.view(b, wh, wh, c).permute(0, 3, 1, 2).contiguous()
+            convX = self.drop_path(self.PCM(x_2d).permute(0, 2, 3, 1).contiguous().view(b, n, c))
+            x = x + self.drop_path(self.attn(self.norm1(x)))
+            x = x + convX
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+class MaskedAutoencoderViTAE(nn.Module, PyTorchModelHubMixin):
+    """ Masked Autoencoder with VisionTransformer backbone
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3,
+                 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), norm_pix_loss=False, kernel=3, mlp_hidden_dim=None):
+        '''
+        @Param kernel: int, control the kernel size in PCM
+        @Param mlp_hidden_dim: int, the hidden dimenison of FFN, overwrites mlp ratio, default None 
+        '''
+        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.blocks = nn.ModuleList([
+            NormalCell(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, kernel=kernel, class_token=True, group=embed_dim // 4, mlp_hidden_dim=mlp_hidden_dim)
+            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, 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) # encoder to decoder
+        # --------------------------------------------------------------------------
+
+        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).expand(-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.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).expand(-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