# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ------------------------------------------------------------------------------------------------
# Copyright (c) 2022 Microsoft
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/FocalNet/FocalNet-DINO/blob/main/models/dino/focal.py
# ------------------------------------------------------------------------------------------------
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_
from detectron2.modeling.backbone import Backbone
class Mlp(nn.Module):
"""Multilayer perceptron."""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0
):
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
class FocalModulation(nn.Module):
""" Focal Modulation
Args:
dim (int): Number of input channels.
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
focal_level (int): Number of focal levels
focal_window (int): Focal window size at focal level 1
focal_factor (int, default=2): Step to increase the focal window
use_postln (bool, default=False): Whether use post-modulation layernorm
"""
def __init__(
self,
dim,
proj_drop=0.,
focal_level=2,
focal_window=7,
focal_factor=2,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False
):
super().__init__()
self.dim = dim
# specific args for focalv3
self.focal_level = focal_level
self.focal_window = focal_window
self.focal_factor = focal_factor
self.use_postln_in_modulation = use_postln_in_modulation
self.normalize_modulator = normalize_modulator
self.f = nn.Linear(dim, 2*dim+(self.focal_level+1), bias=True)
self.h = nn.Conv2d(dim, dim, kernel_size=1, stride=1, padding=0, groups=1, bias=True)
self.act = nn.GELU()
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.focal_layers = nn.ModuleList()
if self.use_postln_in_modulation:
self.ln = nn.LayerNorm(dim)
for k in range(self.focal_level):
kernel_size = self.focal_factor*k + self.focal_window
self.focal_layers.append(
nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, groups=dim,
padding=kernel_size//2, bias=False),
nn.GELU(),
)
)
def forward(self, x):
""" Forward function.
Args:
x: input features with shape of (B, H, W, C)
"""
B, nH, nW, C = x.shape
x = self.f(x)
x = x.permute(0, 3, 1, 2).contiguous()
q, ctx, gates = torch.split(x, (C, C, self.focal_level+1), 1)
ctx_all = 0
for l in range(self.focal_level):
ctx = self.focal_layers[l](ctx)
ctx_all = ctx_all + ctx*gates[:, l:l+1]
ctx_global = self.act(ctx.mean(2, keepdim=True).mean(3, keepdim=True))
ctx_all = ctx_all + ctx_global*gates[:,self.focal_level:]
if self.normalize_modulator:
ctx_all = ctx_all / (self.focal_level+1)
x_out = q * self.h(ctx_all)
x_out = x_out.permute(0, 2, 3, 1).contiguous()
if self.use_postln_in_modulation:
x_out = self.ln(x_out)
x_out = self.proj(x_out)
x_out = self.proj_drop(x_out)
return x_out
class FocalModulationBlock(nn.Module):
""" Focal Modulation Block.
Args:
dim (int): Number of input channels.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): 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
focal_level (int): number of focal levels
focal_window (int): focal kernel size at level 1
"""
def __init__(
self,
dim,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
focal_level=2,
focal_window=9,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
use_layerscale=False,
layerscale_value=1e-4
):
super().__init__()
self.dim = dim
self.mlp_ratio = mlp_ratio
self.focal_window = focal_window
self.focal_level = focal_level
self.use_postln = use_postln
self.use_layerscale = use_layerscale
self.norm1 = norm_layer(dim)
self.modulation = FocalModulation(
dim,
focal_window=self.focal_window,
focal_level=self.focal_level,
proj_drop=drop,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator,
)
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
self.gamma_1 = 1.0
self.gamma_2 = 1.0
if self.use_layerscale:
self.gamma_1 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)
self.gamma_2 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)
def forward(self, x):
""" Forward function.
Args:
x: Input feature, tensor size (B, H*W, C).
H, W: Spatial resolution of the input feature.
"""
B, L, C = x.shape
H, W = self.H, self.W
assert L == H * W, "input feature has wrong size"
shortcut = x
if not self.use_postln:
x = self.norm1(x)
x = x.view(B, H, W, C)
# FM
x = self.modulation(x).view(B, H * W, C)
if self.use_postln:
x = self.norm1(x)
# FFN
x = shortcut + self.drop_path(self.gamma_1 * x)
if self.use_postln:
x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
else:
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class BasicLayer(nn.Module):
""" A basic focal modulation layer for one stage.
Args:
dim (int): Number of feature channels
depth (int): Depths of this stage.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
drop (float, optional): 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
focal_level (int): Number of focal levels
focal_window (int): Focal window size at focal level 1
use_conv_embed (bool): Use overlapped convolution for patch embedding or now. Default: False
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
"""
def __init__(self,
dim,
depth,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
focal_window=9,
focal_level=2,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
use_layerscale=False,
use_checkpoint=False
):
super().__init__()
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
FocalModulationBlock(
dim=dim,
mlp_ratio=mlp_ratio,
drop=drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
focal_window=focal_window,
focal_level=focal_level,
use_postln=use_postln,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator,
use_layerscale=use_layerscale,
norm_layer=norm_layer)
for i in range(depth)])
# patch merging layer
if downsample is not None:
self.downsample = downsample(
patch_size=2,
in_chans=dim, embed_dim=2*dim,
use_conv_embed=use_conv_embed,
norm_layer=norm_layer,
is_stem=False
)
else:
self.downsample = None
def forward(self, x, H, W):
""" Forward function.
Args:
x: Input feature, tensor size (B, H*W, C).
H, W: Spatial resolution of the input feature.
"""
for blk in self.blocks:
blk.H, blk.W = H, W
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
if self.downsample is not None:
x_reshaped = x.transpose(1, 2).view(x.shape[0], x.shape[-1], H, W)
x_down = self.downsample(x_reshaped)
x_down = x_down.flatten(2).transpose(1, 2)
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
use_conv_embed (bool): Whether use overlapped convolution for patch embedding. Default: False
is_stem (bool): Is the stem block or not.
"""
def __init__(
self,
patch_size=4,
in_chans=3,
embed_dim=96,
norm_layer=None,
use_conv_embed=False,
is_stem=False
):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.in_chans = in_chans
self.embed_dim = embed_dim
if use_conv_embed:
# if we choose to use conv embedding, then we treat the stem and non-stem differently
if is_stem:
kernel_size = 7; padding = 2; stride = 4
else:
kernel_size = 3; padding = 1; stride = 2
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
else:
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."""
_, _, 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
[docs]class FocalNet(Backbone):
"""Implement paper `Focal Modulation Networks <https://arxiv.org/pdf/2203.11926.pdf>`_
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.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
drop_rate (float): Dropout rate.
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
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 parameters.
focal_levels (Sequence[int]): Number of focal levels at four stages
focal_windows (Sequence[int]): Focal window sizes at first focal level at four stages
use_conv_embed (bool): Whether use overlapped convolution for patch embedding
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(
self,
pretrain_img_size=1600,
patch_size=4,
in_chans=3,
embed_dim=96,
depths=[2, 2, 6, 2],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.3, # 0.3 or 0.4 works better for large+ models
norm_layer=nn.LayerNorm,
patch_norm=True,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
focal_levels=[3, 3, 3, 3],
focal_windows=[3, 3, 3, 3],
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
use_checkpoint=False,
):
super().__init__()
self.pretrain_img_size = pretrain_img_size
self.num_layers = len(depths)
self.embed_dim = embed_dim
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,
use_conv_embed=use_conv_embed, is_stem=True)
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],
mlp_ratio=mlp_ratio,
drop=drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchEmbed if (i_layer < self.num_layers - 1) else None,
focal_window=focal_windows[i_layer],
focal_level=focal_levels[i_layer],
use_conv_embed=use_conv_embed,
use_postln=use_postln,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator,
use_layerscale=use_layerscale,
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
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()
# add basic info
self._out_features = ["p{}".format(i) for i in self.out_indices]
self._out_feature_channels = {
"p{}".format(i): self.embed_dim * 2**i for i in self.out_indices
}
self._out_feature_strides = {"p{}".format(i): 2 ** (i + 2) for i in self.out_indices}
self._size_devisibility = 32
self.apply(self._init_weights)
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 >= 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
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)
[docs] def forward(self, x):
"""Forward function of `FocalNet`
Args:
x (torch.Tensor): the input tensor for feature extraction.
Returns:
dict[str->Tensor]: mapping from feature name (e.g., "p1") to tensor
"""
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)
outs = {}
for i in range(self.num_layers):
layer = self.layers[i]
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
if i in self.out_indices:
norm_layer = getattr(self, f"norm{i}")
x_out = norm_layer(x_out)
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
outs["p{}".format(i)] = out
return outs