video_mae.py

# From: https://github.com/OpenGVLab/VideoMAEv2/blob/master/models/modeling_finetune.py
# --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
from functools import partial

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from timm.layers import drop_path, to_2tuple, trunc_normal_


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 400,
        'input_size': (3, 224, 224),
        'pool_size': None,
        'crop_pct': .9,
        'interpolation': 'bicubic',
        'mean': (0.5, 0.5, 0.5),
        'std': (0.5, 0.5, 0.5),
        **kwargs
    }


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)

    def extra_repr(self) -> str:
        return 'p={}'.format(self.drop_prob)


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)
        # commit this for the orignal BERT implement
        x = self.fc2(x)
        x = self.drop(x)
        return x


class CosAttention(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
        # DO NOT RENAME [self.scale] (for no weight decay)
        if qk_scale is None:
            self.scale = nn.Parameter(
                torch.log(10 * torch.ones((num_heads, 1, 1))),
                requires_grad=True)
        else:
            self.scale = qk_scale

        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 = 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.unbind(0)

        attn = (
            F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))

        # torch.log(torch.tensor(1. / 0.01)) = 4.6052
        logit_scale = torch.clamp(self.scale, max=4.6052).exp()

        attn = attn * logit_scale

        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 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 = 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.unbind(0)

        x = F.scaled_dot_product_attention(
            q,
            k,
            v,
            dropout_p=self.attn_drop.p if self.training else 0.0,
            scale=self.scale,
        )
        
        x = x.transpose(1, 2).reshape(B, N, -1)

        x = self.proj(x)
        x = self.proj_drop(x)
        return x

from alternative_attn_modules import ExplicitAttention, AttentionUsingScaledDotProduct, AttentionUsingMHAForward, AttentionUsingMHALayer

## Uncomment and change this to use the desired attention module
# Attention = AttentionUsingMHALayer
# print(f"Using {Attention} as attention module")

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.,
                 init_values=None,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 attn_head_dim=None,
                 cos_attn=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        if cos_attn:
            self.attn = CosAttention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                proj_drop=drop,
                attn_head_dim=attn_head_dim)
        else:
            self.attn = Attention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                proj_drop=drop,
                attn_head_dim=attn_head_dim)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        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)

        if init_values > 0:
            self.gamma_1 = nn.Parameter(
                init_values * torch.ones((dim)), requires_grad=True)
            self.gamma_2 = nn.Parameter(
                init_values * torch.ones((dim)), requires_grad=True)
        else:
            self.gamma_1, self.gamma_2 = None, None

    def forward(self, x):
        if self.gamma_1 is None:
            x = x + self.drop_path(self.attn(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_chans=3,
                 embed_dim=768,
                 num_frames=16,
                 tubelet_size=2):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_spatial_patches = (img_size[0] // patch_size[0]) * (
            img_size[1] // patch_size[1])
        num_patches = num_spatial_patches * (num_frames // tubelet_size)

        self.img_size = img_size
        self.tubelet_size = tubelet_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.proj = nn.Conv3d(
            in_channels=in_chans,
            out_channels=embed_dim,
            kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]),
            stride=(self.tubelet_size, patch_size[0], patch_size[1]))

    def forward(self, x, **kwargs):
        B, C, T, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[
            1], f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        # b, c, l -> b, l, c
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


def get_sinusoid_encoding(n_position: int, embed_dims: int) -> torch.Tensor:
    """Generate sinusoid encoding table.

    Sinusoid encoding is a kind of relative position encoding method came from
    `Attention Is All You Need<https://arxiv.org/abs/1706.03762>`_.
    Args:
        n_position (int): The length of the input token.
        embed_dims (int): The position embedding dimension.
    Returns:
        :obj:`torch.FloatTensor`: The sinusoid encoding table of size
        (1, n_position, embed_dims)
    """

    vec = torch.arange(embed_dims, dtype=torch.float64)
    vec = (vec - vec % 2) / embed_dims
    vec = torch.pow(10000, -vec).view(1, -1)

    sinusoid_table = torch.arange(n_position).view(-1, 1) * vec
    sinusoid_table[:, 0::2].sin_()  # dim 2i
    sinusoid_table[:, 1::2].cos_()  # dim 2i+1

    sinusoid_table = sinusoid_table.to(torch.float32)

    return sinusoid_table.unsqueeze(0)


class VisionTransformer(nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_chans=3,
                 num_classes=1000,
                 embed_dim=768,
                 depth=12,
                 num_heads=12,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 head_drop_rate=0.,
                 norm_layer=nn.LayerNorm,
                 init_values=0.,
                 use_learnable_pos_emb=False,
                 init_scale=0.,
                 all_frames=16,
                 tubelet_size=2,
                 use_mean_pooling=True,
                 with_cp=False,
                 cos_attn=False):
        super().__init__()
        self.num_classes = num_classes
        self.all_frames = all_frames
        # num_features for consistency with other models
        self.num_features = self.embed_dim = embed_dim
        self.tubelet_size = tubelet_size
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            num_frames=all_frames,
            tubelet_size=tubelet_size)
        self.num_patches = self.patch_embed.num_patches
        self.with_cp = with_cp

        pos_embed = get_sinusoid_encoding(self.num_patches, embed_dim)
        self.register_buffer("pos_embed", pos_embed)

        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
               ]  # stochastic depth decay rule
        self.blocks = nn.ModuleList([
            Block(
                dim=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,
                init_values=init_values,
                cos_attn=cos_attn) for i in range(depth)
        ])
        self.norm = nn.Identity() if use_mean_pooling else norm_layer(
            embed_dim)
        self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
        self.head_dropout = nn.Dropout(head_drop_rate)
        self.head = nn.Linear(
            embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        if use_learnable_pos_emb:
            trunc_normal_(self.pos_embed, std=.02)

        self.apply(self._init_weights)

        self.head.weight.data.mul_(init_scale)
        self.head.bias.data.mul_(init_scale)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            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 get_num_layers(self):
        return len(self.blocks)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(
            self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        B = x.size(0)

        x = self.patch_embed(x)

        if self.pos_embed is not None:
            x = x + self.pos_embed
        
        x = self.pos_drop(x)

        for blk in self.blocks:
            if self.with_cp:
                x = cp.checkpoint(blk, x)
            else:
                x = blk(x)

        if self.fc_norm is not None:
            return self.fc_norm(x.mean(1))
        else:
            return self.norm(x[:, 0])

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head_dropout(x)
        x = self.head(x)
        return x


def vit_small_patch16_224(pretrained=False, **kwargs) -> VisionTransformer:
    model = VisionTransformer(
        patch_size=16,
        embed_dim=384,
        depth=12,
        num_heads=6,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model


def vit_base_patch16_224(pretrained=False, **kwargs) -> VisionTransformer:
    model = VisionTransformer(
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model