295 lines
10 KiB
Python
295 lines
10 KiB
Python
"""ViTDet backbone adapted from Detectron2"""
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from functools import partial
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from typing import List, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from sam2.modeling.backbones.utils import (
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get_abs_pos,
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PatchEmbed,
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window_partition,
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window_unpartition,
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)
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from sam2.modeling.sam2_utils import DropPath, LayerScale, MLP
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class Attention(nn.Module):
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"""Multi-head Attention block with relative position embeddings."""
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=True,
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use_rel_pos=False,
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rel_pos_zero_init=True,
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input_size=None,
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):
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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qkv_bias (bool: If True, add a learnable bias to query, key, value.
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rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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input_size (int or None): Input resolution for calculating the relative positional
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parameter size.
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attn_type: Type of attention operation, e.g. "vanilla", "vanilla-xformer".
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"""
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.proj = nn.Linear(dim, dim)
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self.use_rel_pos = use_rel_pos
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def forward(self, x):
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B, H, W, _ = x.shape
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# qkv with shape (3, B, nHead, H * W, C)
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qkv = (
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self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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)
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# q, k, v with shape (B * nHead, H * W, C)
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q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
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q = q.view(B, self.num_heads, H * W, -1)
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k = k.view(B, self.num_heads, H * W, -1)
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v = v.view(B, self.num_heads, H * W, -1)
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x = F.scaled_dot_product_attention(q, k, v)
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x = (
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x.view(B, self.num_heads, H, W, -1)
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.permute(0, 2, 3, 1, 4)
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.reshape(B, H, W, -1)
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)
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x = self.proj(x)
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return x
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class Block(nn.Module):
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"""Transformer blocks with support of window attention"""
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.0,
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qkv_bias=True,
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drop_path=0.0,
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norm_layer=nn.LayerNorm,
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act_layer=nn.GELU,
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use_rel_pos=False,
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rel_pos_zero_init=True,
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window_size=0,
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input_size=None,
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dropout=0.0,
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init_values=None,
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):
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads in each ViT block.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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drop_path (float): Stochastic depth rate.
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norm_layer (nn.Module): Normalization layer.
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act_layer (nn.Module): Activation layer.
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use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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window_size (int): Window size for window attention blocks. If it equals 0, then not
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use window attention.
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input_size (int or None): Input resolution for calculating the relative positional
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parameter size.
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dropout (float): Dropout rate.
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"""
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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use_rel_pos=use_rel_pos,
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rel_pos_zero_init=rel_pos_zero_init,
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input_size=input_size if window_size == 0 else (window_size, window_size),
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)
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self.ls1 = (
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LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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)
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.mlp = MLP(
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dim,
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int(dim * mlp_ratio),
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dim,
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num_layers=2,
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activation=act_layer,
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)
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self.ls2 = (
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LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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)
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self.dropout = nn.Dropout(dropout)
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self.window_size = window_size
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def forward(self, x):
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shortcut = x
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x = self.norm1(x)
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# Window partition
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if self.window_size > 0:
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H, W = x.shape[1], x.shape[2]
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x, pad_hw = window_partition(x, self.window_size)
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x = self.ls1(self.attn(x))
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# Reverse window partition
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if self.window_size > 0:
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x = window_unpartition(x, self.window_size, pad_hw, (H, W))
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x = shortcut + self.dropout(self.drop_path(x))
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x = x + self.dropout(self.drop_path(self.ls2(self.mlp(self.norm2(x)))))
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return x
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class ViT(nn.Module):
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"""
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This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.
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"Exploring Plain Vision Transformer Backbones for Object Detection",
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https://arxiv.org/abs/2203.16527
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"""
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def __init__(
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self,
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img_size=1024,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.0,
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qkv_bias=True,
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drop_path_rate=0.0,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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act_layer=nn.GELU,
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use_abs_pos=True,
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use_rel_pos=False,
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rel_pos_zero_init=True,
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window_size=14,
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window_block_indexes=(0, 1, 3, 4, 6, 7, 9, 10),
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use_act_checkpoint=False,
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pretrain_img_size=224,
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pretrain_use_cls_token=True,
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dropout=0.0,
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weights_path=None,
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return_interm_layers=False,
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init_values=None,
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):
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"""
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Args:
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img_size (int): Input image size. Only relevant for rel pos.
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patch_size (int): Patch size.
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in_chans (int): Number of input image channels.
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embed_dim (int): Patch embedding dimension.
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depth (int): Depth of ViT.
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num_heads (int): Number of attention heads in each ViT block.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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drop_path_rate (float): Stochastic depth rate.
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norm_layer (nn.Module): Normalization layer.
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act_layer (nn.Module): Activation layer.
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use_abs_pos (bool): If True, use absolute positional embeddings.
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use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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window_size (int): Window size for window attention blocks.
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window_block_indexes (list): Indexes for blocks using window attention.
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residual_block_indexes (list): Indexes for blocks using conv propagation.
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use_act_checkpoint (bool): If True, use activation checkpointing.
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pretrain_img_size (int): input image size for pretraining models.
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pretrain_use_cls_token (bool): If True, pretrainig models use class token.
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dropout (float): Dropout rate. Applied in residual blocks of attn, mlp and inside the mlp.
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path (str or None): Path to the pretrained weights.
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return_interm_layers (bool): Whether to return intermediate layers (all global attention blocks).
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freezing (BackboneFreezingType): Type of freezing.
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"""
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super().__init__()
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self.pretrain_use_cls_token = pretrain_use_cls_token
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self.patch_embed = PatchEmbed(
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kernel_size=(patch_size, patch_size),
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stride=(patch_size, patch_size),
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padding=(0, 0),
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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if use_abs_pos:
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# Initialize absolute positional embedding with pretrain image size.
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num_patches = (pretrain_img_size // patch_size) * (
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pretrain_img_size // patch_size
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)
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num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
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self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
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else:
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self.pos_embed = None
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# stochastic depth decay rule
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
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self.blocks = nn.ModuleList()
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self.full_attn_ids = []
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cur_stage = 1
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for i in range(depth):
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block = Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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act_layer=act_layer,
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use_rel_pos=use_rel_pos,
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rel_pos_zero_init=rel_pos_zero_init,
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window_size=window_size if i in window_block_indexes else 0,
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input_size=(img_size // patch_size, img_size // patch_size),
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dropout=dropout,
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init_values=init_values,
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)
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if i not in window_block_indexes:
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self.full_attn_ids.append(i)
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cur_stage += 1
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self.blocks.append(block)
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self.return_interm_layers = return_interm_layers
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self.channel_list = (
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[embed_dim] * len(self.full_attn_ids)
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if return_interm_layers
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else [embed_dim]
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)
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def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
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x = self.patch_embed(x)
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if self.pos_embed is not None:
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x = x + get_abs_pos(
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self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])
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)
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outputs = []
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for i, blk in enumerate(self.blocks):
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x = blk(x)
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if (i == self.full_attn_ids[-1]) or (
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self.return_interm_layers and i in self.full_attn_ids
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):
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feats = x.permute(0, 3, 1, 2)
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outputs.append(feats)
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return outputs |