import math from functools import partial import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint from timm.models.layers import DropPath, trunc_normal_ from easycv.models.utils import Mlp from easycv.utils.checkpoint import load_checkpoint from easycv.utils.logger import get_root_logger from ..registry import BACKBONES def window_partition(x, window_size): """ Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition """ B, H, W, C = x.shape pad_h = (window_size - H % window_size) % window_size pad_w = (window_size - W % window_size) % window_size if pad_h > 0 or pad_w > 0: x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) Hp, Wp = H + pad_h, W + pad_w x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows, (Hp, Wp) def window_unpartition(windows, window_size, pad_hw, hw): """ Window unpartition into original sequences and removing padding. Args: x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C]. """ Hp, Wp = pad_hw H, W = hw B = windows.shape[0] // (Hp * Wp // window_size // window_size) x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) if Hp > H or Wp > W: x = x[:, :H, :W, :].contiguous() return x def get_rel_pos(q_size, k_size, rel_pos): """ Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of query q. k_size (int): size of key k. rel_pos (Tensor): relative position embeddings (L, C). Returns: Extracted positional embeddings according to relative positions. """ max_rel_dist = int(2 * max(q_size, k_size) - 1) # Interpolate rel pos if needed. if rel_pos.shape[0] != max_rel_dist: # Interpolate rel pos. rel_pos_resized = F.interpolate( rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), size=max_rel_dist, mode='linear', ) rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) else: rel_pos_resized = rel_pos # Scale the coords with short length if shapes for q and k are different. q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) return rel_pos_resized[relative_coords.long()] def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size): """ Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 Args: attn (Tensor): attention map. q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. q_size (Tuple): spatial sequence size of query q with (q_h, q_w). k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: attn (Tensor): attention map with added relative positional embeddings. """ q_h, q_w = q_size k_h, k_w = k_size Rh = get_rel_pos(q_h, k_h, rel_pos_h) Rw = get_rel_pos(q_w, k_w, rel_pos_w) B, _, dim = q.shape r_q = q.reshape(B, q_h, q_w, dim) rel_h = torch.einsum('bhwc,hkc->bhwk', r_q, Rh) rel_w = torch.einsum('bhwc,wkc->bhwk', r_q, Rw) attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(B, q_h * q_w, k_h * k_w) return attn def get_abs_pos(abs_pos, has_cls_token, hw): """ Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Args: abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. hw (Tuple): size of input image tokens. Returns: Absolute positional embeddings after processing with shape (1, H, W, C) """ h, w = hw if has_cls_token: abs_pos = abs_pos[:, 1:] xy_num = abs_pos.shape[1] size = int(math.sqrt(xy_num)) assert size * size == xy_num if size != h or size != w: new_abs_pos = F.interpolate( abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2), size=(h, w), mode='bicubic', align_corners=False, ) return new_abs_pos.permute(0, 2, 3, 1) else: return abs_pos.reshape(1, h, w, -1) class PatchEmbed(nn.Module): """ Image to Patch Embedding. """ def __init__(self, kernel_size=(16, 16), stride=(16, 16), padding=(0, 0), in_chans=3, embed_dim=768): """ Args: kernel_size (Tuple): kernel size of the projection layer. stride (Tuple): stride of the projection layer. padding (Tuple): padding size of the projection layer. in_chans (int): Number of input image channels. embed_dim (int): embed_dim (int): Patch embedding dimension. """ super().__init__() self.proj = nn.Conv2d( in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding) def forward(self, x): x = self.proj(x) # B C H W -> B H W C x = x.permute(0, 2, 3, 1) return x class Attention(nn.Module): """Multi-head Attention block with relative position embeddings.""" def __init__( self, dim, num_heads=8, qkv_bias=True, use_rel_pos=False, rel_pos_zero_init=True, input_size=None, ): """ Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. qkv_bias (bool: If True, add a learnable bias to query, key, value. rel_pos (bool): If True, add relative positional embeddings to the attention map. rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. input_size (int or None): Input resolution for calculating the relative positional parameter size. """ super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.proj = nn.Linear(dim, dim) self.use_rel_pos = use_rel_pos if self.use_rel_pos: # initialize relative positional embeddings self.rel_pos_h = nn.Parameter( torch.zeros(2 * input_size[0] - 1, head_dim)) self.rel_pos_w = nn.Parameter( torch.zeros(2 * input_size[1] - 1, head_dim)) if not rel_pos_zero_init: trunc_normal_(self.rel_pos_h, std=0.02) trunc_normal_(self.rel_pos_w, std=0.02) def forward(self, x): B, H, W, _ = x.shape # qkv with shape (3, B, nHead, H * W, C) qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # q, k, v with shape (B * nHead, H * W, C) q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) attn = (q * self.scale) @ k.transpose(-2, -1) if self.use_rel_pos: attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) attn = attn.softmax(dim=-1) x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) x = self.proj(x) return x class Block(nn.Module): """Transformer blocks with support of window attention and residual propagation blocks""" def __init__( self, dim, num_heads, mlp_ratio=4.0, qkv_bias=True, drop_path=0.0, norm_layer=nn.LayerNorm, act_layer=nn.GELU, use_rel_pos=False, rel_pos_zero_init=True, window_size=0, use_residual_block=False, input_size=None, ): """ Args: dim (int): Number of input channels. num_heads (int): Number of attention heads in each ViT block. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. drop_path (float): Stochastic depth rate. norm_layer (nn.Module): Normalization layer. act_layer (nn.Module): Activation layer. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. window_size (int): Window size for window attention blocks. If it equals 0, then not use window attention. use_residual_block (bool): If True, use a residual block after the MLP block. input_size (int or None): Input resolution for calculating the relative positional parameter size. """ super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, use_rel_pos=use_rel_pos, rel_pos_zero_init=rel_pos_zero_init, input_size=input_size if window_size == 0 else (window_size, window_size), ) self.drop_path = DropPath( drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) self.mlp = Mlp( in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer) self.window_size = window_size self.use_residual_block = use_residual_block def forward(self, x): shortcut = x x = self.norm1(x) # Window partition if self.window_size > 0: H, W = x.shape[1], x.shape[2] x, pad_hw = window_partition(x, self.window_size) x = self.attn(x) # Reverse window partition if self.window_size > 0: x = window_unpartition(x, self.window_size, pad_hw, (H, W)) x = shortcut + self.drop_path(x) x = x + self.drop_path(self.mlp(self.norm2(x))) if self.use_residual_block: x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) return x @BACKBONES.register_module() class ViTDet(nn.Module): """ This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. "Exploring Plain Vision Transformer Backbones for Object Detection", https://arxiv.org/abs/2203.16527 """ def __init__( self, img_size=1024, patch_size=16, in_chans=3, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, drop_path_rate=0.0, norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, use_abs_pos=True, use_rel_pos=False, rel_pos_zero_init=True, window_size=0, window_block_indexes=(), residual_block_indexes=(), use_act_checkpoint=False, pretrain_img_size=224, pretrain_use_cls_token=True, pretrained=None, ): """ Args: img_size (int): Input image size. patch_size (int): Patch size. in_chans (int): Number of input image channels. embed_dim (int): Patch embedding dimension. depth (int): Depth of ViT. num_heads (int): Number of attention heads in each ViT block. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. drop_path_rate (float): Stochastic depth rate. norm_layer (nn.Module): Normalization layer. act_layer (nn.Module): Activation layer. use_abs_pos (bool): If True, use absolute positional embeddings. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. window_size (int): Window size for window attention blocks. window_block_indexes (list): Indexes for blocks using window attention. residual_block_indexes (list): Indexes for blocks using conv propagation. use_act_checkpoint (bool): If True, use activation checkpointing. pretrain_img_size (int): input image size for pretraining models. pretrain_use_cls_token (bool): If True, pretrainig models use class token. """ super().__init__() self.pretrain_use_cls_token = pretrain_use_cls_token self.use_act_checkpoint = use_act_checkpoint self.patch_embed = PatchEmbed( kernel_size=(patch_size, patch_size), stride=(patch_size, patch_size), in_chans=in_chans, embed_dim=embed_dim, ) if use_abs_pos: # Initialize absolute positional embedding with pretrain image size. num_patches = (pretrain_img_size // patch_size) * ( pretrain_img_size // patch_size) num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches self.pos_embed = nn.Parameter( torch.zeros(1, num_positions, embed_dim)) else: self.pos_embed = None # stochastic depth decay rule dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] self.blocks = nn.ModuleList() for i in range(depth): block = Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, use_rel_pos=use_rel_pos, rel_pos_zero_init=rel_pos_zero_init, window_size=window_size if i in window_block_indexes else 0, use_residual_block=i in residual_block_indexes, input_size=(img_size // patch_size, img_size // patch_size), ) self.blocks.append(block) if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=0.02) self.apply(self._init_weights) self.pretrained = pretrained def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) 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 init_weights(self): if isinstance(self.pretrained, str): logger = get_root_logger() load_checkpoint(self, self.pretrained, strict=False, logger=logger) def forward(self, x): x = self.patch_embed(x) if self.pos_embed is not None: x = x + get_abs_pos(self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])) for blk in self.blocks: if self.use_act_checkpoint: x = checkpoint.checkpoint(blk, x) else: x = blk(x) outputs = [x.permute(0, 3, 1, 2)] return outputs