import logging import math from functools import partial import fvcore.nn.weight_init as weight_init import torch import torch.nn as nn import torch.nn.functional as F from detectron2.layers import CNNBlockBase, Conv2d, get_norm from detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous from detectron2.modeling.backbone import Backbone from .eva_02_utils import ( PatchEmbed, add_decomposed_rel_pos, get_abs_pos, window_partition, window_unpartition, VisionRotaryEmbeddingFast, ) try: import xformers.ops as xops HAS_XFORMER=True except: HAS_XFORMER=False pass logger = logging.getLogger(__name__) __all__ = ["EVA02_ViT", "SimpleFeaturePyramid", "get_vit_lr_decay_rate"] class SwiGLU(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., norm_layer=nn.LayerNorm, subln=False ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.w1 = nn.Linear(in_features, hidden_features) self.w2 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity() self.w3 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x1 = self.w1(x) x2 = self.w2(x) hidden = self.act(x1) * x2 x = self.ffn_ln(hidden) x = self.w3(x) x = self.drop(x) return x class Attention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=True, qk_scale=None, attn_head_dim=None, rope=None, xattn=True, ): 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.q_proj = nn.Linear(dim, all_head_dim, bias=False) self.k_proj = nn.Linear(dim, all_head_dim, bias=False) self.v_proj = nn.Linear(dim, all_head_dim, 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.rope = rope self.xattn = xattn self.proj = nn.Linear(all_head_dim, dim) if not HAS_XFORMER: self.xattn = False def forward(self, x): B, H, W, C = x.shape x = x.view(B, -1, C) N = H * W q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias) k = F.linear(input=x, weight=self.k_proj.weight, bias=None) v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias) q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) ## rope q = self.rope(q).type_as(v) k = self.rope(k).type_as(v) if self.xattn: q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C k = k.permute(0, 2, 1, 3) v = v.permute(0, 2, 1, 3) x = xops.memory_efficient_attention(q, k, v) x = x.reshape(B, N, -1) else: q = q * self.scale attn = (q @ k.transpose(-2, -1)) attn = attn.softmax(dim=-1).type_as(x) x = (attn @ v).transpose(1, 2).reshape(B, N, -1) x = self.proj(x) x = x.view(B, H, W, C) return x class ResBottleneckBlock(CNNBlockBase): """ The standard bottleneck residual block without the last activation layer. It contains 3 conv layers with kernels 1x1, 3x3, 1x1. """ def __init__( self, in_channels, out_channels, bottleneck_channels, norm="LN", act_layer=nn.GELU, ): """ Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. bottleneck_channels (int): number of output channels for the 3x3 "bottleneck" conv layers. norm (str or callable): normalization for all conv layers. See :func:`layers.get_norm` for supported format. act_layer (callable): activation for all conv layers. """ super().__init__(in_channels, out_channels, 1) self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False) self.norm1 = get_norm(norm, bottleneck_channels) self.act1 = act_layer() self.conv2 = Conv2d( bottleneck_channels, bottleneck_channels, 3, padding=1, bias=False, ) self.norm2 = get_norm(norm, bottleneck_channels) self.act2 = act_layer() self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False) self.norm3 = get_norm(norm, out_channels) for layer in [self.conv1, self.conv2, self.conv3]: weight_init.c2_msra_fill(layer) for layer in [self.norm1, self.norm2]: layer.weight.data.fill_(1.0) layer.bias.data.zero_() # zero init last norm layer. self.norm3.weight.data.zero_() self.norm3.bias.data.zero_() def forward(self, x): out = x for layer in self.children(): out = layer(out) out = x + out return out class Block(nn.Module): """Transformer blocks with support of window attention and residual propagation blocks""" def __init__( self, dim, num_heads, mlp_ratio=4*2/3, qkv_bias=True, drop_path=0.0, norm_layer=partial(nn.LayerNorm, eps=1e-6), window_size=0, use_residual_block=False, rope=None, xattn=True, ): """ 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, rope=rope, xattn=xattn, ) from timm.models.layers import DropPath self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) self.mlp = SwiGLU( in_features=dim, hidden_features=int(dim * mlp_ratio), subln=True, norm_layer=norm_layer, ) self.window_size = window_size self.use_residual_block = use_residual_block if use_residual_block: # Use a residual block with bottleneck channel as dim // 2 self.residual = ResBottleneckBlock( in_channels=dim, out_channels=dim, bottleneck_channels=dim // 2, norm="LN", ) 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 class EVA02_ViT(Backbone): """ 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*2/3, 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, rope=True, pt_hw_seq_len=16, intp_freq=True, window_size=0, window_block_indexes=(), residual_block_indexes=(), use_act_checkpoint=False, pretrain_img_size=224, pretrain_use_cls_token=True, out_feature="last_feat", xattn=True, ): """ 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. out_feature (str): name of the feature from the last block. """ super().__init__() self.pretrain_use_cls_token = pretrain_use_cls_token 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 half_head_dim = embed_dim // num_heads // 2 hw_seq_len = img_size // patch_size self.rope_win = VisionRotaryEmbeddingFast( dim=half_head_dim, pt_seq_len=pt_hw_seq_len, ft_seq_len=window_size if intp_freq else None, ) self.rope_glb = VisionRotaryEmbeddingFast( dim=half_head_dim, pt_seq_len=pt_hw_seq_len, ft_seq_len=hw_seq_len if intp_freq else 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, window_size=window_size if i in window_block_indexes else 0, use_residual_block=i in residual_block_indexes, rope=self.rope_win if i in window_block_indexes else self.rope_glb, xattn=xattn ) if use_act_checkpoint: # TODO: use torch.utils.checkpoint from fairscale.nn.checkpoint import checkpoint_wrapper block = checkpoint_wrapper(block) self.blocks.append(block) self._out_feature_channels = {out_feature: embed_dim} self._out_feature_strides = {out_feature: patch_size} self._out_features = [out_feature] if self.pos_embed is not None: nn.init.trunc_normal_(self.pos_embed, std=0.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): nn.init.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 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: x = blk(x) outputs = {self._out_features[0]: x.permute(0, 3, 1, 2)} return outputs class SimpleFeaturePyramid(Backbone): """ This module implements SimpleFeaturePyramid in :paper:`vitdet`. It creates pyramid features built on top of the input feature map. """ def __init__( self, net, in_feature, out_channels, scale_factors, top_block=None, norm="LN", square_pad=0, ): """ Args: net (Backbone): module representing the subnetwork backbone. Must be a subclass of :class:`Backbone`. in_feature (str): names of the input feature maps coming from the net. out_channels (int): number of channels in the output feature maps. scale_factors (list[float]): list of scaling factors to upsample or downsample the input features for creating pyramid features. top_block (nn.Module or None): if provided, an extra operation will be performed on the output of the last (smallest resolution) pyramid output, and the result will extend the result list. The top_block further downsamples the feature map. It must have an attribute "num_levels", meaning the number of extra pyramid levels added by this block, and "in_feature", which is a string representing its input feature (e.g., p5). norm (str): the normalization to use. square_pad (int): If > 0, require input images to be padded to specific square size. """ super(SimpleFeaturePyramid, self).__init__() assert isinstance(net, Backbone) self.scale_factors = scale_factors input_shapes = net.output_shape() strides = [int(input_shapes[in_feature].stride / scale) for scale in scale_factors] _assert_strides_are_log2_contiguous(strides) dim = input_shapes[in_feature].channels self.stages = [] use_bias = norm == "" for idx, scale in enumerate(scale_factors): out_dim = dim if scale == 4.0: layers = [ nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2), get_norm(norm, dim // 2), nn.GELU(), nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2), ] out_dim = dim // 4 elif scale == 2.0: layers = [nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2)] out_dim = dim // 2 elif scale == 1.0: layers = [] elif scale == 0.5: layers = [nn.MaxPool2d(kernel_size=2, stride=2)] else: raise NotImplementedError(f"scale_factor={scale} is not supported yet.") layers.extend( [ Conv2d( out_dim, out_channels, kernel_size=1, bias=use_bias, norm=get_norm(norm, out_channels), ), Conv2d( out_channels, out_channels, kernel_size=3, padding=1, bias=use_bias, norm=get_norm(norm, out_channels), ), ] ) layers = nn.Sequential(*layers) stage = int(math.log2(strides[idx])) self.add_module(f"simfp_{stage}", layers) self.stages.append(layers) self.net = net self.in_feature = in_feature self.top_block = top_block # Return feature names are "p", like ["p2", "p3", ..., "p6"] self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} # top block output feature maps. if self.top_block is not None: for s in range(stage, stage + self.top_block.num_levels): self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) self._out_features = list(self._out_feature_strides.keys()) self._out_feature_channels = {k: out_channels for k in self._out_features} self._size_divisibility = strides[-1] self._square_pad = square_pad @property def padding_constraints(self): return { "size_divisiblity": self._size_divisibility, "square_size": self._square_pad, } def forward(self, x): """ Args: x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. Returns: dict[str->Tensor]: mapping from feature map name to pyramid feature map tensor in high to low resolution order. Returned feature names follow the FPN convention: "p", where stage has stride = 2 ** stage e.g., ["p2", "p3", ..., "p6"]. """ bottom_up_features = self.net(x) features = bottom_up_features[self.in_feature] results = [] for stage in self.stages: results.append(stage(features)) if self.top_block is not None: if self.top_block.in_feature in bottom_up_features: top_block_in_feature = bottom_up_features[self.top_block.in_feature] else: top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] results.extend(self.top_block(top_block_in_feature)) assert len(self._out_features) == len(results) return {f: res for f, res in zip(self._out_features, results)} def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12): """ Calculate lr decay rate for different ViT blocks. Args: name (string): parameter name. lr_decay_rate (float): base lr decay rate. num_layers (int): number of ViT blocks. Returns: lr decay rate for the given parameter. """ layer_id = num_layers + 1 if name.startswith("backbone"): if ".pos_embed" in name or ".patch_embed" in name: layer_id = 0 elif ".blocks." in name and ".residual." not in name: layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 return lr_decay_rate ** (num_layers + 1 - layer_id)