mmclassification/mmpretrain/models/backbones/t2t_vit.py

# Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from typing import Sequence

import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn.bricks.transformer import FFN
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import trunc_normal_

from mmpretrain.registry import MODELS
from ..utils import (MultiheadAttention, build_norm_layer, resize_pos_embed,
                     to_2tuple)
from .base_backbone import BaseBackbone


class T2TTransformerLayer(BaseModule):
    """Transformer Layer for T2T_ViT.

    Comparing with :obj:`TransformerEncoderLayer` in ViT, it supports
    different ``input_dims`` and ``embed_dims``.

    Args:
        embed_dims (int): The feature dimension.
        num_heads (int): Parallel attention heads.
        feedforward_channels (int): The hidden dimension for FFNs
        input_dims (int, optional): The input token dimension.
            Defaults to None.
        drop_rate (float): Probability of an element to be zeroed
            after the feed forward layer. Defaults to 0.
        attn_drop_rate (float): The drop out rate for attention output weights.
            Defaults to 0.
        drop_path_rate (float): Stochastic depth rate. Defaults to 0.
        num_fcs (int): The number of fully-connected layers for FFNs.
            Defaults to 2.
        qkv_bias (bool): enable bias for qkv if True. Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``(input_dims // num_heads) ** -0.5`` if set. Defaults to None.
        act_cfg (dict): The activation config for FFNs.
            Defaults to ``dict(type='GELU')``.
        norm_cfg (dict): Config dict for normalization layer.
            Defaults to ``dict(type='LN')``.
        init_cfg (dict, optional): Initialization config dict.
            Defaults to None.

    Notes:
        In general, ``qk_scale`` should be ``head_dims ** -0.5``, i.e.
        ``(embed_dims // num_heads) ** -0.5``. However, in the official
        code, it uses ``(input_dims // num_heads) ** -0.5``, so here we
        keep the same with the official implementation.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 input_dims=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 num_fcs=2,
                 qkv_bias=False,
                 qk_scale=None,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 init_cfg=None):
        super(T2TTransformerLayer, self).__init__(init_cfg=init_cfg)

        self.v_shortcut = True if input_dims is not None else False
        input_dims = input_dims or embed_dims

        self.ln1 = build_norm_layer(norm_cfg, input_dims)

        self.attn = MultiheadAttention(
            input_dims=input_dims,
            embed_dims=embed_dims,
            num_heads=num_heads,
            attn_drop=attn_drop_rate,
            proj_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            qkv_bias=qkv_bias,
            qk_scale=qk_scale or (input_dims // num_heads)**-0.5,
            v_shortcut=self.v_shortcut)

        self.ln2 = build_norm_layer(norm_cfg, embed_dims)

        self.ffn = FFN(
            embed_dims=embed_dims,
            feedforward_channels=feedforward_channels,
            num_fcs=num_fcs,
            ffn_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            act_cfg=act_cfg)

    def forward(self, x):
        if self.v_shortcut:
            x = self.attn(self.ln1(x))
        else:
            x = x + self.attn(self.ln1(x))
        x = self.ffn(self.ln2(x), identity=x)
        return x


class T2TModule(BaseModule):
    """Tokens-to-Token module.

    "Tokens-to-Token module" (T2T Module) can model the local structure
    information of images and reduce the length of tokens progressively.

    Args:
        img_size (int): Input image size
        in_channels (int): Number of input channels
        embed_dims (int): Embedding dimension
        token_dims (int): Tokens dimension in T2TModuleAttention.
        use_performer (bool): If True, use Performer version self-attention to
            adopt regular self-attention. Defaults to False.
        init_cfg (dict, optional): The extra config for initialization.
            Default: None.

    Notes:
        Usually, ``token_dim`` is set as a small value (32 or 64) to reduce
        MACs
    """

    def __init__(
        self,
        img_size=224,
        in_channels=3,
        embed_dims=384,
        token_dims=64,
        use_performer=False,
        init_cfg=None,
    ):
        super(T2TModule, self).__init__(init_cfg)

        self.embed_dims = embed_dims

        self.soft_split0 = nn.Unfold(
            kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
        self.soft_split1 = nn.Unfold(
            kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        self.soft_split2 = nn.Unfold(
            kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))

        if not use_performer:
            self.attention1 = T2TTransformerLayer(
                input_dims=in_channels * 7 * 7,
                embed_dims=token_dims,
                num_heads=1,
                feedforward_channels=token_dims)

            self.attention2 = T2TTransformerLayer(
                input_dims=token_dims * 3 * 3,
                embed_dims=token_dims,
                num_heads=1,
                feedforward_channels=token_dims)

            self.project = nn.Linear(token_dims * 3 * 3, embed_dims)
        else:
            raise NotImplementedError("Performer hasn't been implemented.")

        # there are 3 soft split, stride are 4,2,2 separately
        out_side = img_size // (4 * 2 * 2)
        self.init_out_size = [out_side, out_side]
        self.num_patches = out_side**2

    @staticmethod
    def _get_unfold_size(unfold: nn.Unfold, input_size):
        h, w = input_size
        kernel_size = to_2tuple(unfold.kernel_size)
        stride = to_2tuple(unfold.stride)
        padding = to_2tuple(unfold.padding)
        dilation = to_2tuple(unfold.dilation)

        h_out = (h + 2 * padding[0] - dilation[0] *
                 (kernel_size[0] - 1) - 1) // stride[0] + 1
        w_out = (w + 2 * padding[1] - dilation[1] *
                 (kernel_size[1] - 1) - 1) // stride[1] + 1
        return (h_out, w_out)

    def forward(self, x):
        # step0: soft split
        hw_shape = self._get_unfold_size(self.soft_split0, x.shape[2:])
        x = self.soft_split0(x).transpose(1, 2)

        for step in [1, 2]:
            # re-structurization/reconstruction
            attn = getattr(self, f'attention{step}')
            x = attn(x).transpose(1, 2)
            B, C, _ = x.shape
            x = x.reshape(B, C, hw_shape[0], hw_shape[1])

            # soft split
            soft_split = getattr(self, f'soft_split{step}')
            hw_shape = self._get_unfold_size(soft_split, hw_shape)
            x = soft_split(x).transpose(1, 2)

        # final tokens
        x = self.project(x)
        return x, hw_shape


def get_sinusoid_encoding(n_position, embed_dims):
    """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.
    """

    def get_position_angle_vec(position):
        return [
            position / np.power(10000, 2 * (i // 2) / embed_dims)
            for i in range(embed_dims)
        ]

    sinusoid_table = np.array(
        [get_position_angle_vec(pos) for pos in range(n_position)])
    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

    return torch.FloatTensor(sinusoid_table).unsqueeze(0)


@MODELS.register_module()
class T2T_ViT(BaseBackbone):
    """Tokens-to-Token Vision Transformer (T2T-ViT)

    A PyTorch implementation of `Tokens-to-Token ViT: Training Vision
    Transformers from Scratch on ImageNet <https://arxiv.org/abs/2101.11986>`_

    Args:
        img_size (int | tuple): The expected input image shape. Because we
            support dynamic input shape, just set the argument to the most
            common input image shape. Defaults to 224.
        in_channels (int): Number of input channels.
        embed_dims (int): Embedding dimension.
        num_layers (int): Num of transformer layers in encoder.
            Defaults to 14.
        out_indices (Sequence | int): Output from which stages.
            Defaults to -1, means the last stage.
        drop_rate (float): Dropout rate after position embedding.
            Defaults to 0.
        drop_path_rate (float): stochastic depth rate. Defaults to 0.
        norm_cfg (dict): Config dict for normalization layer. Defaults to
            ``dict(type='LN')``.
        final_norm (bool): Whether to add a additional layer to normalize
            final feature map. Defaults to True.
        out_type (str): The type of output features. Please choose from

            - ``"cls_token"``: The class token tensor with shape (B, C).
            - ``"featmap"``: The feature map tensor from the patch tokens
              with shape (B, C, H, W).
            - ``"avg_featmap"``: The global averaged feature map tensor
              with shape (B, C).
            - ``"raw"``: The raw feature tensor includes patch tokens and
              class tokens with shape (B, L, C).

            Defaults to ``"cls_token"``.
        with_cls_token (bool): Whether concatenating class token into image
            tokens as transformer input. Defaults to True.
        interpolate_mode (str): Select the interpolate mode for position
            embeding vector resize. Defaults to "bicubic".
        t2t_cfg (dict): Extra config of Tokens-to-Token module.
            Defaults to an empty dict.
        layer_cfgs (Sequence | dict): Configs of each transformer layer in
            encoder. Defaults to an empty dict.
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.
    """
    OUT_TYPES = {'raw', 'cls_token', 'featmap', 'avg_featmap'}

    def __init__(self,
                 img_size=224,
                 in_channels=3,
                 embed_dims=384,
                 num_layers=14,
                 out_indices=-1,
                 drop_rate=0.,
                 drop_path_rate=0.,
                 norm_cfg=dict(type='LN'),
                 final_norm=True,
                 out_type='cls_token',
                 with_cls_token=True,
                 interpolate_mode='bicubic',
                 t2t_cfg=dict(),
                 layer_cfgs=dict(),
                 init_cfg=None):
        super().__init__(init_cfg)

        # Token-to-Token Module
        self.tokens_to_token = T2TModule(
            img_size=img_size,
            in_channels=in_channels,
            embed_dims=embed_dims,
            **t2t_cfg)
        self.patch_resolution = self.tokens_to_token.init_out_size
        num_patches = self.patch_resolution[0] * self.patch_resolution[1]

        # Set out type
        if out_type not in self.OUT_TYPES:
            raise ValueError(f'Unsupported `out_type` {out_type}, please '
                             f'choose from {self.OUT_TYPES}')
        self.out_type = out_type

        # Set cls token
        if with_cls_token:
            self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
            self.num_extra_tokens = 1
        elif out_type != 'cls_token':
            self.cls_token = None
            self.num_extra_tokens = 0
        else:
            raise ValueError(
                'with_cls_token must be True when `out_type="cls_token"`.')

        # Set position embedding
        self.interpolate_mode = interpolate_mode
        sinusoid_table = get_sinusoid_encoding(
            num_patches + self.num_extra_tokens, embed_dims)
        self.register_buffer('pos_embed', sinusoid_table)
        self._register_load_state_dict_pre_hook(self._prepare_pos_embed)

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

        if isinstance(out_indices, int):
            out_indices = [out_indices]
        assert isinstance(out_indices, Sequence), \
            f'"out_indices" must be a sequence or int, ' \
            f'get {type(out_indices)} instead.'
        for i, index in enumerate(out_indices):
            if index < 0:
                out_indices[i] = num_layers + index
            assert 0 <= out_indices[i] <= num_layers, \
                f'Invalid out_indices {index}'
        self.out_indices = out_indices

        # stochastic depth decay rule
        dpr = [x for x in np.linspace(0, drop_path_rate, num_layers)]

        self.encoder = ModuleList()
        for i in range(num_layers):
            if isinstance(layer_cfgs, Sequence):
                layer_cfg = layer_cfgs[i]
            else:
                layer_cfg = deepcopy(layer_cfgs)
            layer_cfg = {
                'embed_dims': embed_dims,
                'num_heads': 6,
                'feedforward_channels': 3 * embed_dims,
                'drop_path_rate': dpr[i],
                'qkv_bias': False,
                'norm_cfg': norm_cfg,
                **layer_cfg
            }

            layer = T2TTransformerLayer(**layer_cfg)
            self.encoder.append(layer)

        self.final_norm = final_norm
        if final_norm:
            self.norm = build_norm_layer(norm_cfg, embed_dims)
        else:
            self.norm = nn.Identity()

    def init_weights(self):
        super().init_weights()

        if (isinstance(self.init_cfg, dict)
                and self.init_cfg['type'] == 'Pretrained'):
            # Suppress custom init if use pretrained model.
            return

        trunc_normal_(self.cls_token, std=.02)

    def _prepare_pos_embed(self, state_dict, prefix, *args, **kwargs):
        name = prefix + 'pos_embed'
        if name not in state_dict.keys():
            return

        ckpt_pos_embed_shape = state_dict[name].shape
        if self.pos_embed.shape != ckpt_pos_embed_shape:
            from mmengine.logging import MMLogger
            logger = MMLogger.get_current_instance()
            logger.info(
                f'Resize the pos_embed shape from {ckpt_pos_embed_shape} '
                f'to {self.pos_embed.shape}.')

            ckpt_pos_embed_shape = to_2tuple(
                int(np.sqrt(ckpt_pos_embed_shape[1] - self.num_extra_tokens)))
            pos_embed_shape = self.tokens_to_token.init_out_size

            state_dict[name] = resize_pos_embed(state_dict[name],
                                                ckpt_pos_embed_shape,
                                                pos_embed_shape,
                                                self.interpolate_mode,
                                                self.num_extra_tokens)

    def forward(self, x):
        B = x.shape[0]
        x, patch_resolution = self.tokens_to_token(x)

        if self.cls_token is not None:
            # stole cls_tokens impl from Phil Wang, thanks
            cls_token = self.cls_token.expand(B, -1, -1)
            x = torch.cat((cls_token, x), dim=1)

        x = x + resize_pos_embed(
            self.pos_embed,
            self.patch_resolution,
            patch_resolution,
            mode=self.interpolate_mode,
            num_extra_tokens=self.num_extra_tokens)
        x = self.drop_after_pos(x)

        outs = []
        for i, layer in enumerate(self.encoder):
            x = layer(x)

            if i == len(self.encoder) - 1 and self.final_norm:
                x = self.norm(x)

            if i in self.out_indices:
                outs.append(self._format_output(x, patch_resolution))

        return tuple(outs)

    def _format_output(self, x, hw):
        if self.out_type == 'raw':
            return x
        if self.out_type == 'cls_token':
            return x[:, 0]

        patch_token = x[:, self.num_extra_tokens:]
        if self.out_type == 'featmap':
            B = x.size(0)
            # (B, N, C) -> (B, H, W, C) -> (B, C, H, W)
            return patch_token.reshape(B, *hw, -1).permute(0, 3, 1, 2)
        if self.out_type == 'avg_featmap':
            return patch_token.mean(dim=1)