1063 lines
39 KiB
Python
1063 lines
39 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
|
|
# The implementation here is modified based on timm,
|
|
# originally Apache 2.0 License and publicly available at
|
|
# https://github.com/naver-ai/vidt/blob/vidt-plus/methods/swin_w_ram.py
|
|
|
|
import math
|
|
import os
|
|
|
|
import numpy as np
|
|
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, to_2tuple, trunc_normal_
|
|
|
|
|
|
class Mlp(nn.Module):
|
|
""" Multilayer perceptron."""
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
hidden_features=None,
|
|
out_features=None,
|
|
act_layer=nn.GELU,
|
|
drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.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
|
|
|
|
|
|
def masked_sin_pos_encoding(x,
|
|
mask,
|
|
num_pos_feats,
|
|
temperature=10000,
|
|
scale=2 * math.pi):
|
|
""" Masked Sinusoidal Positional Encoding
|
|
|
|
Args:
|
|
x: [PATCH] tokens
|
|
mask: the padding mask for [PATCH] tokens
|
|
num_pos_feats: the size of channel dimension
|
|
temperature: the temperature value
|
|
scale: the normalization scale
|
|
|
|
Returns:
|
|
pos: Sinusoidal positional encodings
|
|
"""
|
|
|
|
num_pos_feats = num_pos_feats // 2
|
|
not_mask = ~mask
|
|
|
|
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
|
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
|
|
|
eps = 1e-6
|
|
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * scale
|
|
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * scale
|
|
|
|
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=x.device)
|
|
dim_t = temperature**(2 * (dim_t // 2) / num_pos_feats)
|
|
|
|
pos_x = x_embed[:, :, :, None] / dim_t
|
|
pos_y = y_embed[:, :, :, None] / dim_t
|
|
|
|
pos_x = torch.stack(
|
|
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
|
|
dim=4).flatten(3)
|
|
pos_y = torch.stack(
|
|
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
|
|
dim=4).flatten(3)
|
|
pos = torch.cat((pos_y, pos_x), dim=3)
|
|
|
|
return pos
|
|
|
|
|
|
def window_partition(x, window_size):
|
|
"""
|
|
Args:
|
|
x: (B, H, W, C)
|
|
window_size (int): window size
|
|
|
|
Returns:
|
|
windows: (num_windows*B, window_size, window_size, C)
|
|
"""
|
|
B, H, W, C = x.shape
|
|
x = x.view(B, H // window_size, window_size, W // window_size, window_size,
|
|
C)
|
|
windows = x.permute(0, 1, 3, 2, 4,
|
|
5).contiguous().view(-1, window_size, window_size, C)
|
|
return windows
|
|
|
|
|
|
def window_reverse(windows, window_size, H, W):
|
|
"""
|
|
Args:
|
|
windows: (num_windows*B, window_size, window_size, C)
|
|
window_size (int): Window size
|
|
H (int): Height of image
|
|
W (int): Width of image
|
|
|
|
Returns:
|
|
x: (B, H, W, C)
|
|
"""
|
|
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
|
x = windows.view(B, H // window_size, W // window_size, window_size,
|
|
window_size, -1)
|
|
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
|
return x
|
|
|
|
|
|
class ReconfiguredAttentionModule(nn.Module):
|
|
""" Window based multi-head self attention (W-MSA) module with relative position bias -> extended with RAM.
|
|
It supports both of shifted and non-shifted window.
|
|
|
|
!!!!!!!!!!! IMPORTANT !!!!!!!!!!!
|
|
The original attention module in Swin is replaced with the reconfigured attention module in Section 3.
|
|
All the Args are shared, so only the forward function is modified.
|
|
See https://arxiv.org/pdf/2110.03921.pdf
|
|
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
window_size (tuple[int]): The height and width of the window.
|
|
num_heads (int): Number of attention heads.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
|
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
|
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
|
"""
|
|
|
|
def __init__(self,
|
|
dim,
|
|
window_size,
|
|
num_heads,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
attn_drop=0.,
|
|
proj_drop=0.):
|
|
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.window_size = window_size # Wh, Ww
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
# define a parameter table of relative position bias
|
|
self.relative_position_bias_table = nn.Parameter(
|
|
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
|
|
num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
|
|
|
# get pair-wise relative position index for each token inside the window
|
|
coords_h = torch.arange(self.window_size[0])
|
|
coords_w = torch.arange(self.window_size[1])
|
|
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
|
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
|
relative_coords = coords_flatten[:, :,
|
|
None] - coords_flatten[:,
|
|
None, :] # 2, Wh*Ww, Wh*Ww
|
|
relative_coords = relative_coords.permute(
|
|
1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
|
relative_coords[:, :,
|
|
0] += self.window_size[0] - 1 # shift to start from 0
|
|
relative_coords[:, :, 1] += self.window_size[1] - 1
|
|
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
|
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
|
self.register_buffer('relative_position_index',
|
|
relative_position_index)
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
|
self.attn_drop = nn.Dropout(attn_drop)
|
|
self.proj = nn.Linear(dim, dim)
|
|
self.proj_drop = nn.Dropout(proj_drop)
|
|
|
|
trunc_normal_(self.relative_position_bias_table, std=.02)
|
|
self.softmax = nn.Softmax(dim=-1)
|
|
|
|
def forward(self,
|
|
x,
|
|
det,
|
|
mask=None,
|
|
cross_attn=False,
|
|
cross_attn_mask=None):
|
|
""" Forward function.
|
|
RAM module receives [Patch] and [DET] tokens and returns their calibrated ones
|
|
|
|
Args:
|
|
x: [PATCH] tokens
|
|
det: [DET] tokens
|
|
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None -> mask for shifted window attention
|
|
|
|
"additional inputs for RAM"
|
|
cross_attn: whether to use cross-attention [det x patch] (for selective cross-attention)
|
|
cross_attn_mask: mask for cross-attention
|
|
|
|
Returns:
|
|
patch_x: the calibrated [PATCH] tokens
|
|
det_x: the calibrated [DET] tokens
|
|
"""
|
|
|
|
assert self.window_size[0] == self.window_size[1]
|
|
window_size = self.window_size[0]
|
|
local_map_size = window_size * window_size
|
|
|
|
# projection before window partitioning
|
|
if not cross_attn:
|
|
B, H, W, C = x.shape
|
|
N = H * W
|
|
x = x.view(B, N, C)
|
|
x = torch.cat([x, det], dim=1)
|
|
full_qkv = self.qkv(x)
|
|
patch_qkv, det_qkv = full_qkv[:, :N, :], full_qkv[:, N:, :]
|
|
else:
|
|
B, H, W, C = x[0].shape
|
|
N = H * W
|
|
_, ori_H, ori_W, _ = x[1].shape
|
|
ori_N = ori_H * ori_W
|
|
|
|
shifted_x = x[0].view(B, N, C)
|
|
cross_x = x[1].view(B, ori_N, C)
|
|
x = torch.cat([shifted_x, cross_x, det], dim=1)
|
|
full_qkv = self.qkv(x)
|
|
patch_qkv, cross_patch_qkv, det_qkv = \
|
|
full_qkv[:, :N, :], full_qkv[:, N:N + ori_N, :], full_qkv[:, N + ori_N:, :]
|
|
patch_qkv = patch_qkv.view(B, H, W, -1)
|
|
|
|
# window partitioning for [PATCH] tokens
|
|
patch_qkv = window_partition(
|
|
patch_qkv, window_size) # nW*B, window_size, window_size, C
|
|
B_ = patch_qkv.shape[0]
|
|
patch_qkv = patch_qkv.reshape(B_, window_size * window_size, 3,
|
|
self.num_heads, C // self.num_heads)
|
|
_patch_qkv = patch_qkv.permute(2, 0, 3, 1, 4)
|
|
patch_q, patch_k, patch_v = _patch_qkv[0], _patch_qkv[1], _patch_qkv[2]
|
|
|
|
# [PATCH x PATCH] self-attention using window partitions
|
|
patch_q = patch_q * self.scale
|
|
patch_attn = (patch_q @ patch_k.transpose(-2, -1))
|
|
# add relative pos bias for [patch x patch] self-attention
|
|
relative_position_bias = self.relative_position_bias_table[
|
|
self.relative_position_index.view(-1)].view(
|
|
self.window_size[0] * self.window_size[1],
|
|
self.window_size[0] * self.window_size[1],
|
|
-1) # Wh*Ww,Wh*Ww,nH
|
|
relative_position_bias = relative_position_bias.permute(
|
|
2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
|
patch_attn = patch_attn + relative_position_bias.unsqueeze(0)
|
|
|
|
# if shifted window is used, it needs to apply the mask
|
|
if mask is not None:
|
|
nW = mask.shape[0]
|
|
tmp0 = patch_attn.view(B_ // nW, nW, self.num_heads,
|
|
local_map_size, local_map_size)
|
|
tmp1 = mask.unsqueeze(1).unsqueeze(0)
|
|
patch_attn = tmp0 + tmp1
|
|
patch_attn = patch_attn.view(-1, self.num_heads, local_map_size,
|
|
local_map_size)
|
|
|
|
patch_attn = self.softmax(patch_attn)
|
|
patch_attn = self.attn_drop(patch_attn)
|
|
patch_x = (patch_attn @ patch_v).transpose(1, 2).reshape(
|
|
B_, window_size, window_size, C)
|
|
|
|
# extract qkv for [DET] tokens
|
|
det_qkv = det_qkv.view(B, -1, 3, self.num_heads, C // self.num_heads)
|
|
det_qkv = det_qkv.permute(2, 0, 3, 1, 4)
|
|
det_q, det_k, det_v = det_qkv[0], det_qkv[1], det_qkv[2]
|
|
|
|
# if cross-attention is activated
|
|
if cross_attn:
|
|
|
|
# reconstruct the spatial form of [PATCH] tokens for global [DET x PATCH] attention
|
|
cross_patch_qkv = cross_patch_qkv.view(B, ori_H, ori_W, 3,
|
|
self.num_heads,
|
|
C // self.num_heads)
|
|
patch_kv = cross_patch_qkv[:, :, :,
|
|
1:, :, :].permute(3, 0, 4, 1, 2,
|
|
5).contiguous()
|
|
patch_kv = patch_kv.view(2, B, self.num_heads, ori_H * ori_W, -1)
|
|
|
|
# extract "key and value" of [PATCH] tokens for cross-attention
|
|
cross_patch_k, cross_patch_v = patch_kv[0], patch_kv[1]
|
|
|
|
# bind key and value of [PATCH] and [DET] tokens for [DET X [PATCH, DET]] attention
|
|
det_k, det_v = torch.cat([cross_patch_k, det_k],
|
|
dim=2), torch.cat([cross_patch_v, det_v],
|
|
dim=2)
|
|
|
|
# [DET x DET] self-attention or binded [DET x [PATCH, DET]] attention
|
|
det_q = det_q * self.scale
|
|
det_attn = (det_q @ det_k.transpose(-2, -1))
|
|
# apply cross-attention mask if available
|
|
if cross_attn_mask is not None:
|
|
det_attn = det_attn + cross_attn_mask
|
|
det_attn = self.softmax(det_attn)
|
|
det_attn = self.attn_drop(det_attn)
|
|
det_x = (det_attn @ det_v).transpose(1, 2).reshape(B, -1, C)
|
|
|
|
# reverse window for [PATCH] tokens <- the output of [PATCH x PATCH] self attention
|
|
patch_x = window_reverse(patch_x, window_size, H, W)
|
|
|
|
# projection for outputs from multi-head
|
|
x = torch.cat([patch_x.view(B, H * W, C), det_x], dim=1)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
|
|
# decompose after FFN into [PATCH] and [DET] tokens
|
|
patch_x = x[:, :H * W, :].view(B, H, W, C)
|
|
det_x = x[:, H * W:, :]
|
|
|
|
return patch_x, det_x
|
|
|
|
|
|
class SwinTransformerBlock(nn.Module):
|
|
""" Swin Transformer Block.
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
num_heads (int): Number of attention heads.
|
|
window_size (int): Window size.
|
|
shift_size (int): Shift size for SW-MSA.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
|
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
|
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
"""
|
|
|
|
def __init__(self,
|
|
dim,
|
|
num_heads,
|
|
window_size=7,
|
|
shift_size=0,
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop=0.,
|
|
attn_drop=0.,
|
|
drop_path=0.,
|
|
act_layer=nn.GELU,
|
|
norm_layer=nn.LayerNorm):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.num_heads = num_heads
|
|
self.window_size = window_size
|
|
self.shift_size = shift_size
|
|
self.mlp_ratio = mlp_ratio
|
|
assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size'
|
|
|
|
self.norm1 = norm_layer(dim)
|
|
self.attn = ReconfiguredAttentionModule(
|
|
dim,
|
|
window_size=to_2tuple(self.window_size),
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop)
|
|
|
|
self.drop_path = DropPath(
|
|
drop_path) if drop_path > 0. else nn.Identity()
|
|
self.norm2 = norm_layer(dim)
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = Mlp(
|
|
in_features=dim,
|
|
hidden_features=mlp_hidden_dim,
|
|
act_layer=act_layer,
|
|
drop=drop)
|
|
|
|
self.H = None
|
|
self.W = None
|
|
|
|
def forward(self, x, mask_matrix, pos, cross_attn, cross_attn_mask):
|
|
""" Forward function.
|
|
|
|
Args:
|
|
x: Input feature, tensor size (B, H*W + DET, C). i.e., binded [PATCH, DET] tokens
|
|
H, W: Spatial resolution of the input feature.
|
|
mask_matrix: Attention mask for cyclic shift.
|
|
|
|
"additional inputs'
|
|
pos: (patch_pos, det_pos)
|
|
cross_attn: whether to use cross attn [det x [det + patch]]
|
|
cross_attn_mask: attention mask for cross-attention
|
|
|
|
Returns:
|
|
x: calibrated & binded [PATCH, DET] tokens
|
|
"""
|
|
|
|
B, L, C = x.shape
|
|
H, W = self.H, self.W
|
|
|
|
assert L == H * W + self.det_token_num, 'input feature has wrong size'
|
|
|
|
shortcut = x
|
|
x = self.norm1(x)
|
|
x, det = x[:, :H * W, :], x[:, H * W:, :]
|
|
x = x.view(B, H, W, C)
|
|
orig_x = x
|
|
|
|
# pad feature maps to multiples of window size
|
|
pad_l = pad_t = 0
|
|
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
|
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
|
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
|
_, Hp, Wp, _ = x.shape
|
|
|
|
# projection for det positional encodings: make the channel size suitable for the current layer
|
|
patch_pos, det_pos = pos
|
|
det_pos = self.det_pos_linear(det_pos)
|
|
|
|
# cyclic shift
|
|
if self.shift_size > 0:
|
|
shifted_x = torch.roll(
|
|
x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
|
attn_mask = mask_matrix
|
|
else:
|
|
shifted_x = x
|
|
attn_mask = None
|
|
|
|
# prepare cross-attn and add positional encodings
|
|
if cross_attn:
|
|
# patch token (for cross-attention) + Sinusoidal pos encoding
|
|
cross_patch = orig_x + patch_pos
|
|
# det token + learnable pos encoding
|
|
det = det + det_pos
|
|
shifted_x = (shifted_x, cross_patch)
|
|
else:
|
|
# it cross_attn is deativated, only [PATCH] and [DET] self-attention are performed
|
|
det = det + det_pos
|
|
shifted_x = shifted_x
|
|
|
|
# W-MSA/SW-MSA
|
|
shifted_x, det = self.attn(
|
|
shifted_x,
|
|
mask=attn_mask,
|
|
# additional args
|
|
det=det,
|
|
cross_attn=cross_attn,
|
|
cross_attn_mask=cross_attn_mask)
|
|
|
|
# reverse cyclic shift
|
|
if self.shift_size > 0:
|
|
x = torch.roll(
|
|
shifted_x,
|
|
shifts=(self.shift_size, self.shift_size),
|
|
dims=(1, 2))
|
|
else:
|
|
x = shifted_x
|
|
|
|
if pad_r > 0 or pad_b > 0:
|
|
x = x[:, :H, :W, :].contiguous()
|
|
|
|
x = x.view(B, H * W, C)
|
|
x = torch.cat([x, det], dim=1)
|
|
|
|
# FFN
|
|
x = shortcut + self.drop_path(x)
|
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
|
|
return x
|
|
|
|
|
|
class PatchMerging(nn.Module):
|
|
""" Patch Merging Layer
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
"""
|
|
|
|
def __init__(self, dim, norm_layer=nn.LayerNorm, expand=True):
|
|
super().__init__()
|
|
self.dim = dim
|
|
|
|
# if expand is True, the channel size will be expanded, otherwise, return 256 size of channel
|
|
expand_dim = 2 * dim if expand else 256
|
|
self.reduction = nn.Linear(4 * dim, expand_dim, bias=False)
|
|
self.norm = norm_layer(4 * dim)
|
|
|
|
# added for detection token [please ignore, not used for training]
|
|
# not implemented yet.
|
|
self.expansion = nn.Linear(dim, expand_dim, bias=False)
|
|
self.norm2 = norm_layer(dim)
|
|
|
|
def forward(self, x, H, W):
|
|
""" Forward function.
|
|
|
|
Args:
|
|
x: Input feature, tensor size (B, H*W, C), i.e., binded [PATCH, DET] tokens
|
|
H, W: Spatial resolution of the input feature.
|
|
|
|
Returns:
|
|
x: merged [PATCH, DET] tokens;
|
|
only [PATCH] tokens are reduced in spatial dim, while [DET] tokens is fix-scale
|
|
"""
|
|
|
|
B, L, C = x.shape
|
|
assert L == H * W + self.det_token_num, 'input feature has wrong size'
|
|
|
|
x, det = x[:, :H * W, :], x[:, H * W:, :]
|
|
x = x.view(B, H, W, C)
|
|
|
|
# padding
|
|
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
|
if pad_input:
|
|
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
|
|
|
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
|
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
|
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
|
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
|
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
|
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
|
|
|
# simply repeating for DET tokens
|
|
det = det.repeat(1, 1, 4)
|
|
|
|
x = torch.cat([x, det], dim=1)
|
|
x = self.norm(x)
|
|
x = self.reduction(x)
|
|
|
|
return x
|
|
|
|
|
|
class BasicLayer(nn.Module):
|
|
""" A basic Swin Transformer layer for one stage.
|
|
|
|
Args:
|
|
dim (int): Number of feature channels
|
|
depth (int): Depths of this stage.
|
|
num_heads (int): Number of attention head.
|
|
window_size (int): Local window size. Default: 7.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
|
"""
|
|
|
|
def __init__(self,
|
|
dim,
|
|
depth,
|
|
num_heads,
|
|
window_size=7,
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop=0.,
|
|
attn_drop=0.,
|
|
drop_path=0.,
|
|
norm_layer=nn.LayerNorm,
|
|
downsample=None,
|
|
last=False,
|
|
use_checkpoint=False):
|
|
|
|
super().__init__()
|
|
self.window_size = window_size
|
|
self.shift_size = window_size // 2
|
|
self.depth = depth
|
|
self.dim = dim
|
|
self.use_checkpoint = use_checkpoint
|
|
|
|
# build blocks
|
|
self.blocks = nn.ModuleList([
|
|
SwinTransformerBlock(
|
|
dim=dim,
|
|
num_heads=num_heads,
|
|
window_size=window_size,
|
|
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop,
|
|
attn_drop=attn_drop,
|
|
drop_path=drop_path[i]
|
|
if isinstance(drop_path, list) else drop_path,
|
|
norm_layer=norm_layer) for i in range(depth)
|
|
])
|
|
|
|
# patch merging layer
|
|
if downsample is not None:
|
|
self.downsample = downsample(
|
|
dim=dim, norm_layer=norm_layer, expand=(not last))
|
|
else:
|
|
self.downsample = None
|
|
|
|
def forward(self, x, H, W, det_pos, input_mask, cross_attn=False):
|
|
""" Forward function.
|
|
|
|
Args:
|
|
x: Input feature, tensor size (B, H*W, C).
|
|
H, W: Spatial resolution of the input feature.
|
|
det_pos: pos encoding for det token
|
|
input_mask: padding mask for inputs
|
|
cross_attn: whether to use cross attn [det x [det + patch]]
|
|
"""
|
|
|
|
B = x.shape[0]
|
|
|
|
# calculate attention mask for SW-MSA
|
|
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
|
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
|
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
|
|
h_slices = (slice(0, -self.window_size),
|
|
slice(-self.window_size,
|
|
-self.shift_size), slice(-self.shift_size, None))
|
|
w_slices = (slice(0, -self.window_size),
|
|
slice(-self.window_size,
|
|
-self.shift_size), slice(-self.shift_size, None))
|
|
cnt = 0
|
|
for h in h_slices:
|
|
for w in w_slices:
|
|
img_mask[:, h, w, :] = cnt
|
|
cnt += 1
|
|
|
|
# mask for cyclic shift
|
|
mask_windows = window_partition(img_mask, self.window_size)
|
|
mask_windows = mask_windows.view(-1,
|
|
self.window_size * self.window_size)
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0,
|
|
float(-100.0)).masked_fill(
|
|
attn_mask == 0, float(0.0))
|
|
|
|
# compute sinusoidal pos encoding and cross-attn mask here to avoid redundant computation
|
|
if cross_attn:
|
|
|
|
_H, _W = input_mask.shape[1:]
|
|
if not (_H == H and _W == W):
|
|
input_mask = F.interpolate(
|
|
input_mask[None].float(), size=(H, W)).to(torch.bool)[0]
|
|
|
|
# sinusoidal pos encoding for [PATCH] tokens used in cross-attention
|
|
patch_pos = masked_sin_pos_encoding(x, input_mask, self.dim)
|
|
|
|
# attention padding mask due to the zero padding in inputs
|
|
# the zero (padded) area is masked by 1.0 in 'input_mask'
|
|
cross_attn_mask = input_mask.float()
|
|
cross_attn_mask = cross_attn_mask.masked_fill(cross_attn_mask != 0.0, float(-100.0)). \
|
|
masked_fill(cross_attn_mask == 0.0, float(0.0))
|
|
|
|
# pad for detection token (this padding is required to process the binded [PATCH, DET] attention
|
|
cross_attn_mask = cross_attn_mask.view(
|
|
B, H * W).unsqueeze(1).unsqueeze(2)
|
|
cross_attn_mask = F.pad(
|
|
cross_attn_mask, (0, self.det_token_num), value=0)
|
|
|
|
else:
|
|
patch_pos = None
|
|
cross_attn_mask = None
|
|
|
|
# zip pos encodings
|
|
pos = (patch_pos, det_pos)
|
|
|
|
for n_blk, blk in enumerate(self.blocks):
|
|
blk.H, blk.W = H, W
|
|
|
|
# for selective cross-attention
|
|
if cross_attn:
|
|
_cross_attn = True
|
|
_cross_attn_mask = cross_attn_mask
|
|
_pos = pos # i.e., (patch_pos, det_pos)
|
|
else:
|
|
_cross_attn = False
|
|
_cross_attn_mask = None
|
|
_pos = (None, det_pos)
|
|
|
|
if self.use_checkpoint:
|
|
x = checkpoint.checkpoint(
|
|
blk,
|
|
x,
|
|
attn_mask,
|
|
# additional inputs
|
|
pos=_pos,
|
|
cross_attn=_cross_attn,
|
|
cross_attn_mask=_cross_attn_mask)
|
|
else:
|
|
x = blk(
|
|
x,
|
|
attn_mask,
|
|
# additional inputs
|
|
pos=_pos,
|
|
cross_attn=_cross_attn,
|
|
cross_attn_mask=_cross_attn_mask)
|
|
|
|
# reduce the number of patch tokens, but maintaining a fixed-scale det tokens
|
|
# meanwhile, the channel dim increases by a factor of 2
|
|
if self.downsample is not None:
|
|
x_down = self.downsample(x, H, W)
|
|
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
|
return x, H, W, x_down, Wh, Ww
|
|
else:
|
|
return x, H, W, x, H, W
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
""" Image to Patch Embedding
|
|
|
|
Args:
|
|
patch_size (int): Patch token size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
|
"""
|
|
|
|
def __init__(self,
|
|
patch_size=4,
|
|
in_chans=3,
|
|
embed_dim=96,
|
|
norm_layer=None):
|
|
super().__init__()
|
|
patch_size = to_2tuple(patch_size)
|
|
self.patch_size = patch_size
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
self.proj = nn.Conv2d(
|
|
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
if norm_layer is not None:
|
|
self.norm = norm_layer(embed_dim)
|
|
else:
|
|
self.norm = None
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
|
|
# padding
|
|
_, _, H, W = x.size()
|
|
if W % self.patch_size[1] != 0:
|
|
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
|
if H % self.patch_size[0] != 0:
|
|
x = F.pad(x,
|
|
(0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
|
|
|
x = self.proj(x) # B C Wh Ww
|
|
if self.norm is not None:
|
|
Wh, Ww = x.size(2), x.size(3)
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.norm(x)
|
|
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
|
|
|
return x
|
|
|
|
|
|
class SwinTransformer(nn.Module):
|
|
""" Swin Transformer backbone.
|
|
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
|
https://arxiv.org/pdf/2103.14030
|
|
|
|
Args:
|
|
pretrain_img_size (int): Input image size for training the pretrained model,
|
|
used in absolute postion embedding. Default 224.
|
|
patch_size (int | tuple(int)): Patch size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
depths (tuple[int]): Depths of each Swin Transformer stage.
|
|
num_heads (tuple[int]): Number of attention head of each stage.
|
|
window_size (int): Window size. Default: 7.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop_rate (float): Dropout rate.
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
|
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
|
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
|
out_indices (Sequence[int]): Output from which stages.
|
|
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
|
-1 means not freezing any args.
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
pretrain_img_size=224,
|
|
patch_size=4,
|
|
in_chans=3,
|
|
embed_dim=96,
|
|
depths=[2, 2, 6, 2],
|
|
num_heads=[3, 6, 12, 24],
|
|
window_size=7,
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.2,
|
|
norm_layer=nn.LayerNorm,
|
|
ape=False,
|
|
patch_norm=True,
|
|
out_indices=[1, 2,
|
|
3], # not used in the current version, please ignore.
|
|
frozen_stages=-1,
|
|
use_checkpoint=False):
|
|
super().__init__()
|
|
|
|
self.pretrain_img_size = pretrain_img_size
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.ape = ape
|
|
self.patch_norm = patch_norm
|
|
self.out_indices = out_indices
|
|
self.frozen_stages = frozen_stages
|
|
|
|
# split image into non-overlapping patches
|
|
self.patch_embed = PatchEmbed(
|
|
patch_size=patch_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None)
|
|
|
|
# absolute position embedding
|
|
if self.ape:
|
|
pretrain_img_size = to_2tuple(pretrain_img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
patches_resolution = [
|
|
pretrain_img_size[0] // patch_size[0],
|
|
pretrain_img_size[1] // patch_size[1]
|
|
]
|
|
|
|
self.absolute_pos_embed = nn.Parameter(
|
|
torch.zeros(1, embed_dim, patches_resolution[0],
|
|
patches_resolution[1]))
|
|
trunc_normal_(self.absolute_pos_embed, std=.02)
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
# stochastic depth
|
|
dpr = [
|
|
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
|
|
] # stochastic depth decay rule
|
|
|
|
# build layers
|
|
self.layers = nn.ModuleList()
|
|
for i_layer in range(self.num_layers):
|
|
layer = BasicLayer(
|
|
dim=int(embed_dim * 2**i_layer),
|
|
depth=depths[i_layer],
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_size,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
|
norm_layer=norm_layer,
|
|
# modified by ViDT
|
|
downsample=PatchMerging if
|
|
(i_layer < self.num_layers) else None,
|
|
last=None if (i_layer < self.num_layers - 1) else True,
|
|
#
|
|
use_checkpoint=use_checkpoint)
|
|
self.layers.append(layer)
|
|
|
|
num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
|
|
self.num_features = num_features
|
|
|
|
# add a norm layer for each output
|
|
# Not used in the current version -> please ignore. this error will be fixed later
|
|
# we leave this lines to load the pre-trained model ...
|
|
for i_layer in out_indices:
|
|
layer = norm_layer(num_features[i_layer])
|
|
layer_name = f'norm{i_layer}'
|
|
self.add_module(layer_name, layer)
|
|
|
|
self._freeze_stages()
|
|
|
|
def _freeze_stages(self):
|
|
if self.frozen_stages >= 0:
|
|
self.patch_embed.eval()
|
|
for param in self.patch_embed.parameters():
|
|
param.requires_grad = False
|
|
|
|
if self.frozen_stages >= 1 and self.ape:
|
|
self.absolute_pos_embed.requires_grad = False
|
|
|
|
if self.frozen_stages >= 2:
|
|
self.pos_drop.eval()
|
|
for i in range(0, self.frozen_stages - 1):
|
|
m = self.layers[i]
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'det_pos_embed', 'det_token'}
|
|
|
|
def finetune_det(self,
|
|
method,
|
|
det_token_num=100,
|
|
pos_dim=256,
|
|
cross_indices=[3]):
|
|
""" A funtion to add neccessary (leanable) variables to Swin Transformer for object detection
|
|
|
|
Args:
|
|
method: vidt or vidt_wo_neck
|
|
det_token_num: the number of object to detect, i.e., number of object queries
|
|
pos_dim: the channel dimension of positional encodings for [DET] and [PATCH] tokens
|
|
cross_indices: the indices where to use the [DET X PATCH] cross-attention
|
|
there are four possible stages in [0, 1, 2, 3]. 3 indicates Stage 4 in the ViDT paper.
|
|
"""
|
|
|
|
# which method?
|
|
self.method = method
|
|
|
|
# how many object we detect?
|
|
self.det_token_num = det_token_num
|
|
self.det_token = nn.Parameter(
|
|
torch.zeros(1, det_token_num, self.num_features[0]))
|
|
self.det_token = trunc_normal_(self.det_token, std=.02)
|
|
|
|
# dim size of pos encoding
|
|
self.pos_dim = pos_dim
|
|
|
|
# learnable positional encoding for detection tokens
|
|
det_pos_embed = torch.zeros(1, det_token_num, pos_dim)
|
|
det_pos_embed = trunc_normal_(det_pos_embed, std=.02)
|
|
self.det_pos_embed = torch.nn.Parameter(det_pos_embed)
|
|
|
|
# info for detection
|
|
self.num_channels = [
|
|
self.num_features[i + 1]
|
|
for i in range(len(self.num_features) - 1)
|
|
]
|
|
if method == 'vidt':
|
|
self.num_channels.append(
|
|
self.pos_dim) # default: 256 (same to the default pos_dim)
|
|
self.cross_indices = cross_indices
|
|
# divisor to reduce the spatial size of the mask
|
|
self.mask_divisor = 2**(len(self.layers) - len(self.cross_indices))
|
|
|
|
# projection matrix for det pos encoding in each Swin layer (there are 4 blocks)
|
|
for layer in self.layers:
|
|
layer.det_token_num = det_token_num
|
|
if layer.downsample is not None:
|
|
layer.downsample.det_token_num = det_token_num
|
|
for block in layer.blocks:
|
|
block.det_token_num = det_token_num
|
|
block.det_pos_linear = nn.Linear(pos_dim, block.dim)
|
|
|
|
# neck-free model do not require downsamling at the last stage.
|
|
if method == 'vidt_wo_neck':
|
|
self.layers[-1].downsample = None
|
|
|
|
def forward(self, x, mask):
|
|
""" Forward function.
|
|
|
|
Args:
|
|
x: input rgb images
|
|
mask: input padding masks [0: rgb values, 1: padded values]
|
|
|
|
Returns:
|
|
patch_outs: multi-scale [PATCH] tokens (four scales are used)
|
|
these tokens are the first input of the neck decoder
|
|
det_tgt: final [DET] tokens obtained at the last stage
|
|
this tokens are the second input of the neck decoder
|
|
det_pos: the learnable pos encoding for [DET] tokens.
|
|
these encodings are used to generate reference points in deformable attention
|
|
"""
|
|
|
|
# original input shape
|
|
B, _, _ = x.shape[0], x.shape[2], x.shape[3]
|
|
|
|
# patch embedding
|
|
x = self.patch_embed(x)
|
|
|
|
Wh, Ww = x.size(2), x.size(3)
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.pos_drop(x)
|
|
|
|
# expand det_token for all examples in the batch
|
|
det_token = self.det_token.expand(B, -1, -1)
|
|
|
|
# det pos encoding -> will be projected in each block
|
|
det_pos = self.det_pos_embed
|
|
|
|
# prepare a mask for cross attention
|
|
mask = F.interpolate(
|
|
mask[None].float(),
|
|
size=(Wh // self.mask_divisor,
|
|
Ww // self.mask_divisor)).to(torch.bool)[0]
|
|
|
|
patch_outs = []
|
|
for stage in range(self.num_layers):
|
|
layer = self.layers[stage]
|
|
|
|
# whether to use cross-attention
|
|
cross_attn = True if stage in self.cross_indices else False
|
|
|
|
# concat input
|
|
x = torch.cat([x, det_token], dim=1)
|
|
|
|
# inference
|
|
x_out, H, W, x, Wh, Ww = layer(
|
|
x,
|
|
Wh,
|
|
Ww,
|
|
# additional input for VIDT
|
|
input_mask=mask,
|
|
det_pos=det_pos,
|
|
cross_attn=cross_attn)
|
|
|
|
x, det_token = x[:, :-self.det_token_num, :], x[:, -self.
|
|
det_token_num:, :]
|
|
|
|
# Aggregate intermediate outputs
|
|
if stage > 0:
|
|
patch_out = x_out[:, :-self.det_token_num, :].view(
|
|
B, H, W, -1).permute(0, 3, 1, 2)
|
|
patch_outs.append(patch_out)
|
|
|
|
# patch token reduced from last stage output
|
|
patch_outs.append(x.view(B, Wh, Ww, -1).permute(0, 3, 1, 2))
|
|
|
|
# det token
|
|
det_tgt = x_out[:, -self.det_token_num:, :].permute(0, 2, 1)
|
|
|
|
# det token pos encoding
|
|
det_pos = det_pos.permute(0, 2, 1)
|
|
|
|
features_0, features_1, features_2, features_3 = patch_outs
|
|
return features_0, features_1, features_2, features_3, det_tgt, det_pos
|
|
|
|
def train(self, mode=True):
|
|
"""Convert the model into training mode while keep layers freezed."""
|
|
super(SwinTransformer, self).train(mode)
|
|
self._freeze_stages()
|
|
|
|
# not working in the current version
|
|
def flops(self):
|
|
flops = 0
|
|
flops += self.patch_embed.flops()
|
|
for i, layer in enumerate(self.layers):
|
|
flops += layer.flops()
|
|
flops += self.num_features * self.patches_resolution[
|
|
0] * self.patches_resolution[1] // (2**self.num_layers)
|
|
flops += self.num_features * self.num_classes
|
|
return flops
|