model/vision_transformer.py

# 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.
#

import math
from functools import partial
import numpy as np

import torch
import torch.nn as nn

from model.tensors import (
    trunc_normal_,
    repeat_interleave_batch
)
from model.utils import apply_masks

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=float)
    grid_w = np.arange(grid_size, dtype=float)
    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(embed_dim, grid_size, cls_token=False):
    """
    grid_size: int of the grid length
    return:
    pos_embed: [grid_size, embed_dim] or [1+grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid = np.arange(grid_size, dtype=float)
    pos_embed = get_1d_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_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 drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


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.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.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x, attn

# class Block(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):
#         super().__init__()
#         self.norm1 = norm_layer(dim)
#         self.attn = Attention(
#             dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
#         self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
#         self.norm2 = norm_layer(dim)
#         mlp_hidden_dim = int(dim * mlp_ratio)
#         self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

#     def forward(self, x, return_attention=False):
#         y, attn = self.attn(self.norm1(x))
#         if return_attention:
#             return attn
#         x = x + self.drop_path(y)
#         x = x + self.drop_path(self.mlp(self.norm2(x)))
#         return x

class Block(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,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)

        self.attn = Attention(
            dim, num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop)
        self.attn_returns_weights = True

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2     = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp       = MLP(in_features=dim, hidden_features=mlp_hidden_dim,
                             act_layer=act_layer, drop=drop)

    def forward(self, x, return_attention=False):
        if self.attn_returns_weights:
            y, attn = self.attn(self.norm1(x))
            if return_attention:
                return attn
        else:                                       
            y  = self.attn(self.norm1(x))
            attn = None
        x = x + self.drop_path(y)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x if not return_attention else attn


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__()
        num_patches = (img_size // patch_size) * (img_size // patch_size)
        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


class ConvEmbed(nn.Module):
    """
    3x3 Convolution stems for ViT following ViTC models
    """

    def __init__(self, channels, strides, img_size=224, in_chans=3, batch_norm=True):
        super().__init__()
        # Build the stems
        stem = []
        channels = [in_chans] + channels
        for i in range(len(channels) - 2):
            stem += [nn.Conv2d(channels[i], channels[i+1], kernel_size=3,
                               stride=strides[i], padding=1, bias=(not batch_norm))]
            if batch_norm:
                stem += [nn.BatchNorm2d(channels[i+1])]
            stem += [nn.ReLU(inplace=True)]
        stem += [nn.Conv2d(channels[-2], channels[-1], kernel_size=1, stride=strides[-1])]
        self.stem = nn.Sequential(*stem)

        # Comptute the number of patches
        stride_prod = int(np.prod(strides))
        self.num_patches = (img_size[0] // stride_prod)**2

    def forward(self, x):
        p = self.stem(x)
        return p.flatten(2).transpose(1, 2)


class VisionTransformerPredictor(nn.Module):
    """ Vision Transformer """
    def __init__(
        self,
        num_patches,
        embed_dim=768,
        predictor_embed_dim=384,
        depth=6,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=nn.LayerNorm,
        init_std=0.02,
        **kwargs
    ):
        super().__init__()
        self.predictor_embed = nn.Linear(embed_dim, predictor_embed_dim, bias=True)
        self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        # --
        self.predictor_pos_embed = nn.Parameter(torch.zeros(1, num_patches, predictor_embed_dim),
                                                requires_grad=False)
        predictor_pos_embed = get_2d_sincos_pos_embed(self.predictor_pos_embed.shape[-1],
                                                      int(num_patches**.5),
                                                      cls_token=False)
        self.predictor_pos_embed.data.copy_(torch.from_numpy(predictor_pos_embed).float().unsqueeze(0))
        # --
        self.predictor_blocks = nn.ModuleList([
            Block(
                dim=predictor_embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
            for i in range(depth)])
        self.predictor_norm = norm_layer(predictor_embed_dim)
        self.predictor_proj = nn.Linear(predictor_embed_dim, embed_dim, bias=True)
        # ------
        self.init_std = init_std
        trunc_normal_(self.mask_token, std=self.init_std)
        self.apply(self._init_weights)
        self.fix_init_weight()

    def fix_init_weight(self):
        def rescale(param, layer_id):
            param.div_(math.sqrt(2.0 * layer_id))

        for layer_id, layer in enumerate(self.predictor_blocks):
            rescale(layer.attn.proj.weight.data, layer_id + 1)
            rescale(layer.mlp.fc2.weight.data, layer_id + 1)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=self.init_std)
            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)
        elif isinstance(m, nn.Conv2d):
            trunc_normal_(m.weight, std=self.init_std)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x, masks_x, masks):
        assert (masks is not None) and (masks_x is not None), 'Cannot run predictor without mask indices'

        if not isinstance(masks_x, list):
            masks_x = [masks_x]

        if not isinstance(masks, list):
            masks = [masks]

        # -- Batch Size
        B = len(x) // len(masks_x)

        # -- map from encoder-dim to pedictor-dim
        x = self.predictor_embed(x)

        # -- add positional embedding to x tokens
        x_pos_embed = self.predictor_pos_embed.repeat(B, 1, 1)
        x += apply_masks(x_pos_embed, masks_x[0].unsqueeze(1))

        _, N_ctxt, D = x.shape

        # -- concat mask tokens to x
        pos_embs = self.predictor_pos_embed.repeat(B, 1, 1)
        pos_embs = apply_masks(pos_embs, masks[0])
        
        # --
        pred_tokens = self.mask_token.repeat(pos_embs.size(0), pos_embs.size(1), 1)
        # --
        pred_tokens += pos_embs
        x = x.repeat(masks[0].shape[1], 1, 1)
        x = torch.cat([x, pred_tokens], dim=1)

        # -- fwd prop
        for blk in self.predictor_blocks:
            x = blk(x)
        x = self.predictor_norm(x)
        
        # -- return preds for mask tokens
        x = x[:, N_ctxt:]
        x = self.predictor_proj(x)

        return x

def gather_tokens_multiK(x_full: torch.Tensor,
                         idx:     torch.Tensor) -> torch.Tensor:
    """
    x_full : [B, N_tot, D]
    idx    : [B, V, K, N_q]   (int64 indices)
    Returns
    -------
    out    : [B, V, K, N_q, D]
    """
    B, N_tot, D = x_full.shape
    B2, V, K, N_q = idx.shape
    assert B == B2, "batch mismatch"

    # 1) expand indices for gather
    idx_exp = idx.unsqueeze(-1).expand(-1, -1, -1, -1, D)        # [B,V,K,N_q,D]

    # 2) broadcast x_full to [B, V, K, N_tot, D]
    x_exp = x_full[:, None, None]                                # [B,1,1,N_tot,D]
    x_exp = x_exp.expand(B, V, K, N_tot, D)                      # [B,V,K,N_tot,D]

    # 3) gather along the patch dimension (=3)
    gathered = torch.gather(x_exp, 3, idx_exp)                   # [B,V,K,N_q,D]
    return gathered

class VisionTransformerPredictorMV(nn.Module):
    """
    Multi‑view predictor for JEPA.

    * Context sequence  = visible tokens from **all views and all K_enc sets**  
    * Target  sequence  = one mask token per **K_pred set** per view
    """

    def __init__(
        self,
        num_patches,
        n_views,                       # ← NEW: number of camera views
        embed_dim               = 768,
        predictor_embed_dim     = 384,
        depth                   = 3,
        num_heads               = 12,
        mlp_ratio               = 4.0,
        qkv_bias                = True,
        qk_scale                = None,
        drop_rate               = 0.0,
        attn_drop_rate          = 0.0,
        drop_path_rate          = 0.0,
        norm_layer              = nn.LayerNorm,
        init_std                = 0.02,
        **kwargs,               # forward‑compat
    ):
        super().__init__()
        P = predictor_embed_dim

        # ---- linear proj + learned mask token -----------------------------
        self.proj_in  = nn.Linear(embed_dim, P, bias=True)
        self.mask_tok = nn.Parameter(torch.zeros(1, 1, P))

        # ---- transformer blocks -------------------------------------------
        dpr = [x.item() for x in torch.linspace(0.0, drop_path_rate, depth)]
        self.blocks = nn.ModuleList([
            Block(
                dim             = P,
                num_heads       = num_heads,
                mlp_ratio       = mlp_ratio,
                qkv_bias        = qkv_bias,
                qk_scale        = qk_scale,
                drop            = drop_rate,
                attn_drop       = attn_drop_rate,
                drop_path       = dpr[i],
                norm_layer      = norm_layer
            )
            for i in range(depth)
        ])
        self.norm     = norm_layer(P)
        self.proj_out = nn.Linear(P, embed_dim, bias=True)

        trunc_normal_(self.mask_tok, std=init_std)
        self.apply(self._init_weights)

    # -----------------------------------------------------------------------
    @staticmethod
    def _init_weights(m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.weight, 1.0)
            nn.init.constant_(m.bias,   0.0)

    def forward(
        self,
        z_ctx: torch.Tensor,        # [B, V, N_vis, embed_dim]
        masks_pred: torch.Tensor,   # [B, V, K_pred, N_q]    (indices)
    ):
        """
        Returns
        -------
        pred : [B, V*K_pred*N_q, embed_dim]   (flattened)  – or reshape as needed
        """
        # 1) project encoder‑dim → predictor‑dim
        z_ctx = self.proj_in(z_ctx)                        # [B,V,N_vis,P]

        ctx_tokens = (z_ctx.unsqueeze(2))
        B, V, K_enc, N_vis, P = ctx_tokens.shape
        ctx_tokens = ctx_tokens.view(B, V * K_enc * N_vis, P)
        N_ctx = ctx_tokens.size(1)

        B, V, N_q, P = masks_pred.shape
        D = self.mask_tok.shape[-1]
        M = V * N_q * P  
        tgt_tok  = self.mask_tok.expand(B, M, D)
        
        # 4) transformer over [ctx | tgt]
        seq = torch.cat([ctx_tokens, tgt_tok], dim=1)    # [B, N_ctx+N_tgt, P]
        for blk in self.blocks:
            seq = blk(seq)
        seq = self.norm(seq)

        pred = seq[:, N_ctx:]                            # target part
        pred = self.proj_out(pred)                       # -> embed_dim
        return pred

    
def vit_predictor(**kwargs):
    model = VisionTransformerPredictorMV(
        mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    return model

# # ---------------------------- configurable --------------------------------

if __name__ == '__main__':
    B, V        = 2, 4
    N_tot       = 196
    N_vis       = 31
    K_enc       = 1
    K_pred      = 4
    N_q         = 36
    E           = 768

    torch.manual_seed(0)
    device = "cuda"          # or torch.device("cuda:0")
    dtype  = torch.float16

    z_ctx       = torch.randn(B, V, N_vis, E).to(device, dtype)
    masks_enc   = torch.randint(0, N_tot, (B, V, K_enc, N_vis)).to(device)
    masks_pred  = torch.randint(0, N_tot, (B, V, K_pred, N_q)).to(device)
    
    pred_mv = VisionTransformerPredictorMV(
        num_patches          = N_tot,
        n_views              = V,
        embed_dim            = E,
        predictor_embed_dim  = 384,
        depth                = 4,
        num_heads            = 8
    ).to(device).to(dtype)

    out = pred_mv(z_ctx, masks_enc, masks_pred)
    print(out.shape)   # torch.Size([2, 4*4*36, 768]) → [B, V*K_pred*N_q, E]