annotator/uniformer/mmseg/models/backbones/vit.py

"""Modified from https://github.com/rwightman/pytorch-image-
models/blob/master/timm/models/vision_transformer.py."""

import math

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from annotator.uniformer.mmcv.cnn import (Conv2d, Linear, build_activation_layer, build_norm_layer,
                      constant_init, kaiming_init, normal_init)
from annotator.uniformer.mmcv.runner import _load_checkpoint
from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm

from annotator.uniformer.mmseg.utils import get_root_logger
from ..builder import BACKBONES
from ..utils import DropPath, trunc_normal_


class Mlp(nn.Module):
    """MLP layer for Encoder block.

    Args:
        in_features(int): Input dimension for the first fully
            connected layer.
        hidden_features(int): Output dimension for the first fully
            connected layer.
        out_features(int): Output dementsion for the second fully
            connected layer.
        act_cfg(dict): Config dict for activation layer.
            Default: dict(type='GELU').
        drop(float): Drop rate for the dropout layer. Dropout rate has
            to be between 0 and 1. Default: 0.
    """

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_cfg=dict(type='GELU'),
                 drop=0.):
        super(Mlp, self).__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = Linear(in_features, hidden_features)
        self.act = build_activation_layer(act_cfg)
        self.fc2 = 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):
    """Attention layer for Encoder block.

    Args:
        dim (int): Dimension for the input vector.
        num_heads (int): Number of parallel attention heads.
        qkv_bias (bool): Enable bias for qkv if True. Default: False.
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        attn_drop (float): Drop rate for attention output weights.
            Default: 0.
        proj_drop (float): Drop rate for output weights. Default: 0.
    """

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super(Attention, self).__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 = 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


class Block(nn.Module):
    """Implements encoder block with residual connection.

    Args:
        dim (int): The feature dimension.
        num_heads (int): Number of parallel attention heads.
        mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        drop (float): Drop rate for mlp output weights. Default: 0.
        attn_drop (float): Drop rate for attention output weights.
            Default: 0.
        proj_drop (float): Drop rate for attn layer output weights.
            Default: 0.
        drop_path (float): Drop rate for paths of model.
            Default: 0.
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='GELU').
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN', requires_grad=True).
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
    """

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 proj_drop=0.,
                 drop_path=0.,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN', eps=1e-6),
                 with_cp=False):
        super(Block, self).__init__()
        self.with_cp = with_cp
        _, self.norm1 = build_norm_layer(norm_cfg, dim)
        self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop,
                              proj_drop)
        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
        _, self.norm2 = build_norm_layer(norm_cfg, dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_cfg=act_cfg,
            drop=drop)

    def forward(self, x):

        def _inner_forward(x):
            out = x + self.drop_path(self.attn(self.norm1(x)))
            out = out + self.drop_path(self.mlp(self.norm2(out)))
            return out

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)

        return out


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

    Args:
        img_size (int | tuple): Input image size.
            default: 224.
        patch_size (int): Width and height for a patch.
            default: 16.
        in_channels (int): Input channels for images. Default: 3.
        embed_dim (int): The embedding dimension. Default: 768.
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_channels=3,
                 embed_dim=768):
        super(PatchEmbed, self).__init__()
        if isinstance(img_size, int):
            self.img_size = (img_size, img_size)
        elif isinstance(img_size, tuple):
            self.img_size = img_size
        else:
            raise TypeError('img_size must be type of int or tuple')
        h, w = self.img_size
        self.patch_size = (patch_size, patch_size)
        self.num_patches = (h // patch_size) * (w // patch_size)
        self.proj = Conv2d(
            in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        return self.proj(x).flatten(2).transpose(1, 2)


@BACKBONES.register_module()
class VisionTransformer(nn.Module):
    """Vision transformer backbone.

    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for
        Image Recognition at Scale` - https://arxiv.org/abs/2010.11929

    Args:
        img_size (tuple): input image size. Default: (224, 224).
        patch_size (int, tuple): patch size. Default: 16.
        in_channels (int): number of input channels. Default: 3.
        embed_dim (int): embedding dimension. Default: 768.
        depth (int): depth of transformer. Default: 12.
        num_heads (int): number of attention heads. Default: 12.
        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
            Default: 4.
        out_indices (list | tuple | int): Output from which stages.
            Default: -1.
        qkv_bias (bool): enable bias for qkv if True. Default: True.
        qk_scale (float): override default qk scale of head_dim ** -0.5 if set.
        drop_rate (float): dropout rate. Default: 0.
        attn_drop_rate (float): attention dropout rate. Default: 0.
        drop_path_rate (float): Rate of DropPath. Default: 0.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN', eps=1e-6, requires_grad=True).
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='GELU').
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Default: False.
        final_norm (bool):  Whether to add a additional layer to normalize
            final feature map. Default: False.
        interpolate_mode (str): Select the interpolate mode for position
            embeding vector resize. Default: bicubic.
        with_cls_token (bool): If concatenating class token into image tokens
            as transformer input. Default: True.
        with_cp (bool): Use checkpoint or not. Using checkpoint
            will save some memory while slowing down the training speed.
            Default: False.
    """

    def __init__(self,
                 img_size=(224, 224),
                 patch_size=16,
                 in_channels=3,
                 embed_dim=768,
                 depth=12,
                 num_heads=12,
                 mlp_ratio=4,
                 out_indices=11,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 norm_cfg=dict(type='LN', eps=1e-6, requires_grad=True),
                 act_cfg=dict(type='GELU'),
                 norm_eval=False,
                 final_norm=False,
                 with_cls_token=True,
                 interpolate_mode='bicubic',
                 with_cp=False):
        super(VisionTransformer, self).__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.features = self.embed_dim = embed_dim
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_channels=in_channels,
            embed_dim=embed_dim)

        self.with_cls_token = with_cls_token
        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
        self.pos_embed = nn.Parameter(
            torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        if isinstance(out_indices, int):
            self.out_indices = [out_indices]
        elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
            self.out_indices = out_indices
        else:
            raise TypeError('out_indices must be type of int, list or tuple')

        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=dpr[i],
                attn_drop=attn_drop_rate,
                act_cfg=act_cfg,
                norm_cfg=norm_cfg,
                with_cp=with_cp) for i in range(depth)
        ])

        self.interpolate_mode = interpolate_mode
        self.final_norm = final_norm
        if final_norm:
            _, self.norm = build_norm_layer(norm_cfg, embed_dim)

        self.norm_eval = norm_eval
        self.with_cp = with_cp

    def init_weights(self, pretrained=None):
        if isinstance(pretrained, str):
            logger = get_root_logger()
            checkpoint = _load_checkpoint(pretrained, logger=logger)
            if 'state_dict' in checkpoint:
                state_dict = checkpoint['state_dict']
            else:
                state_dict = checkpoint

            if 'pos_embed' in state_dict.keys():
                if self.pos_embed.shape != state_dict['pos_embed'].shape:
                    logger.info(msg=f'Resize the pos_embed shape from \
{state_dict["pos_embed"].shape} to {self.pos_embed.shape}')
                    h, w = self.img_size
                    pos_size = int(
                        math.sqrt(state_dict['pos_embed'].shape[1] - 1))
                    state_dict['pos_embed'] = self.resize_pos_embed(
                        state_dict['pos_embed'], (h, w), (pos_size, pos_size),
                        self.patch_size, self.interpolate_mode)

            self.load_state_dict(state_dict, False)

        elif pretrained is None:
            # We only implement the 'jax_impl' initialization implemented at
            # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353  # noqa: E501
            trunc_normal_(self.pos_embed, std=.02)
            trunc_normal_(self.cls_token, std=.02)
            for n, m in self.named_modules():
                if isinstance(m, Linear):
                    trunc_normal_(m.weight, std=.02)
                    if m.bias is not None:
                        if 'mlp' in n:
                            normal_init(m.bias, std=1e-6)
                        else:
                            constant_init(m.bias, 0)
                elif isinstance(m, Conv2d):
                    kaiming_init(m.weight, mode='fan_in')
                    if m.bias is not None:
                        constant_init(m.bias, 0)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
                    constant_init(m.bias, 0)
                    constant_init(m.weight, 1.0)
        else:
            raise TypeError('pretrained must be a str or None')

    def _pos_embeding(self, img, patched_img, pos_embed):
        """Positiong embeding method.

        Resize the pos_embed, if the input image size doesn't match
            the training size.
        Args:
            img (torch.Tensor): The inference image tensor, the shape
                must be [B, C, H, W].
            patched_img (torch.Tensor): The patched image, it should be
                shape of [B, L1, C].
            pos_embed (torch.Tensor): The pos_embed weighs, it should be
                shape of [B, L2, c].
        Return:
            torch.Tensor: The pos encoded image feature.
        """
        assert patched_img.ndim == 3 and pos_embed.ndim == 3, \
            'the shapes of patched_img and pos_embed must be [B, L, C]'
        x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]
        if x_len != pos_len:
            if pos_len == (self.img_size[0] // self.patch_size) * (
                    self.img_size[1] // self.patch_size) + 1:
                pos_h = self.img_size[0] // self.patch_size
                pos_w = self.img_size[1] // self.patch_size
            else:
                raise ValueError(
                    'Unexpected shape of pos_embed, got {}.'.format(
                        pos_embed.shape))
            pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:],
                                              (pos_h, pos_w), self.patch_size,
                                              self.interpolate_mode)
        return self.pos_drop(patched_img + pos_embed)

    @staticmethod
    def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size, mode):
        """Resize pos_embed weights.

        Resize pos_embed using bicubic interpolate method.
        Args:
            pos_embed (torch.Tensor): pos_embed weights.
            input_shpae (tuple): Tuple for (input_h, intput_w).
            pos_shape (tuple): Tuple for (pos_h, pos_w).
            patch_size (int): Patch size.
        Return:
            torch.Tensor: The resized pos_embed of shape [B, L_new, C]
        """
        assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
        input_h, input_w = input_shpae
        pos_h, pos_w = pos_shape
        cls_token_weight = pos_embed[:, 0]
        pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
        pos_embed_weight = pos_embed_weight.reshape(
            1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
        pos_embed_weight = F.interpolate(
            pos_embed_weight,
            size=[input_h // patch_size, input_w // patch_size],
            align_corners=False,
            mode=mode)
        cls_token_weight = cls_token_weight.unsqueeze(1)
        pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
        pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
        return pos_embed

    def forward(self, inputs):
        B = inputs.shape[0]

        x = self.patch_embed(inputs)

        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = self._pos_embeding(inputs, x, self.pos_embed)

        if not self.with_cls_token:
            # Remove class token for transformer input
            x = x[:, 1:]

        outs = []
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if i == len(self.blocks) - 1:
                if self.final_norm:
                    x = self.norm(x)
            if i in self.out_indices:
                if self.with_cls_token:
                    # Remove class token and reshape token for decoder head
                    out = x[:, 1:]
                else:
                    out = x
                B, _, C = out.shape
                out = out.reshape(B, inputs.shape[2] // self.patch_size,
                                  inputs.shape[3] // self.patch_size,
                                  C).permute(0, 3, 1, 2)
                outs.append(out)

        return tuple(outs)

    def train(self, mode=True):
        super(VisionTransformer, self).train(mode)
        if mode and self.norm_eval:
            for m in self.modules():
                if isinstance(m, nn.LayerNorm):
                    m.eval()