Upload Prithvi.py

Prithvi.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
+
+from timm.models.vision_transformer import Block
+from timm.models.layers import to_2tuple, _assert
+
+import numpy as np
+
+from einops import rearrange
+
+def get_3d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+    """
+    grid_size: 3d tuple of grid size: t, h, w
+    return:
+    pos_embed: L, D
+    """
+
+    assert embed_dim % 16 == 0
+
+    t_size, h_size, w_size = grid_size
+
+    w_embed_dim = embed_dim // 16 * 6
+    h_embed_dim = embed_dim // 16 * 6
+    t_embed_dim = embed_dim // 16 * 4
+
+    w_pos_embed = get_1d_sincos_pos_embed_from_grid(w_embed_dim, np.arange(w_size))
+    h_pos_embed = get_1d_sincos_pos_embed_from_grid(h_embed_dim, np.arange(h_size))
+    t_pos_embed = get_1d_sincos_pos_embed_from_grid(t_embed_dim, np.arange(t_size))
+
+    w_pos_embed = np.tile(w_pos_embed, (t_size * h_size, 1))
+    h_pos_embed = np.tile(np.repeat(h_pos_embed, w_size, axis=0), (t_size, 1))
+    t_pos_embed = np.repeat(t_pos_embed, h_size * w_size, axis=0)
+
+    pos_embed = np.concatenate((w_pos_embed, h_pos_embed, t_pos_embed), axis=1)
+
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+class PatchEmbed(nn.Module):
+    """ Frames of 2D Images to Patch Embedding
+    The 3D version of timm.models.vision_transformer.PatchEmbed
+    """
+    def __init__(
+            self,
+            img_size=224,
+            patch_size=16,
+            num_frames=3,
+            tubelet_size=1,
+            in_chans=3,
+            embed_dim=768,
+            norm_layer=None,
+            flatten=True,
+            bias=True,
+    ):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_frames = num_frames
+        self.tubelet_size = tubelet_size
+        self.grid_size = (num_frames // tubelet_size, img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+        self.num_patches = self.grid_size[0] * self.grid_size[1] * self.grid_size[2]
+        self.flatten = flatten
+
+        self.proj = nn.Conv3d(in_chans, embed_dim,
+                              kernel_size=(tubelet_size, patch_size[0], patch_size[1]),
+                              stride=(tubelet_size, patch_size[0], patch_size[1]), bias=bias)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        B, C, T, H, W = x.shape
+        _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
+        _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # B,C,T,H,W -> B,C,L -> B,L,C
+        x = self.norm(x)
+        return x
+
+
+class MaskedAutoencoderViT(nn.Module):
+    """ Masked Autoencoder with VisionTransformer backbone
+    """
+    def __init__(self, img_size=224, patch_size=16,
+                 num_frames=3, tubelet_size=1,
+                 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):
+        super().__init__()
+
+        # --------------------------------------------------------------------------
+        # MAE encoder specifics
+        self.patch_embed = PatchEmbed(img_size, patch_size,num_frames, tubelet_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)
+        # --------------------------------------------------------------------------
+
+        # --------------------------------------------------------------------------
+        # 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, tubelet_size * patch_size * patch_size * 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_3d_sincos_pos_embed(self.pos_embed.shape[-1], self.patch_embed.grid_size, cls_token=True)
+        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        decoder_pos_embed = get_3d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], self.patch_embed.grid_size, 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: B, C, T, H, W
+        x: B, L, D
+        """
+        p = self.patch_embed.patch_size[0]
+        tub = self.patch_embed.tubelet_size
+        x = rearrange(imgs, 'b c (t tub) (h p) (w q) -> b (t h w) (tub p q c)', tub=tub, p=p, q=p)
+
+        return x
+
+    def unpatchify(self, x):
+        """
+        x: B, L, D
+        imgs: B, C, T, H, W
+        """
+        p = self.patch_embed.patch_size[0]
+        num_p = self.patch_embed.img_size[0] // p
+        tub = self.patch_embed.tubelet_size
+        imgs = rearrange(x, 'b (t h w) (tub p q c) -> b c (t tub) (h p) (w q)', h=num_p, w=num_p, tub=tub, p=p, q=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.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: B, C, T, H, W
+        target: B, L, D
+        pred: B, L, D
+        mask: B, 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)
+        loss = self.forward_loss(imgs, pred, mask)
+        return loss, pred, mask