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"""Implementation of MSCAN from SegNeXt: Rethinking Convolutional Attention Design for Semantic
Segmentation (NeurIPS 2022)
based on: https://github.com/Visual-Attention-Network/SegNeXt
"""
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair as to_2tuple
from siclib.models import BaseModel
from siclib.models.utils.modules import DropPath, DWConv
# flake8: noqa
# mypy: ignore-errors
class Mlp(nn.Module):
def __init__(
self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
):
"""Initialize the MLP."""
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
def forward(self, x):
"""Forward pass."""
x = self.fc1(x)
x = self.dwconv(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class StemConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(StemConv, self).__init__()
self.proj = nn.Sequential(
nn.Conv2d(
in_channels, out_channels // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
),
nn.BatchNorm2d(out_channels // 2),
nn.GELU(),
nn.Conv2d(
out_channels // 2, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
),
nn.BatchNorm2d(out_channels),
)
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.size()
x = x.flatten(2).transpose(1, 2)
return x, H, W
class AttentionModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
self.conv0_1 = nn.Conv2d(dim, dim, (1, 7), padding=(0, 3), groups=dim)
self.conv0_2 = nn.Conv2d(dim, dim, (7, 1), padding=(3, 0), groups=dim)
self.conv1_1 = nn.Conv2d(dim, dim, (1, 11), padding=(0, 5), groups=dim)
self.conv1_2 = nn.Conv2d(dim, dim, (11, 1), padding=(5, 0), groups=dim)
self.conv2_1 = nn.Conv2d(dim, dim, (1, 21), padding=(0, 10), groups=dim)
self.conv2_2 = nn.Conv2d(dim, dim, (21, 1), padding=(10, 0), groups=dim)
self.conv3 = nn.Conv2d(dim, dim, 1)
def forward(self, x):
u = x.clone()
attn = self.conv0(x)
attn_0 = self.conv0_1(attn)
attn_0 = self.conv0_2(attn_0)
attn_1 = self.conv1_1(attn)
attn_1 = self.conv1_2(attn_1)
attn_2 = self.conv2_1(attn)
attn_2 = self.conv2_2(attn_2)
attn = attn + attn_0 + attn_1 + attn_2
attn = self.conv3(attn)
return attn * u
class SpatialAttention(nn.Module):
def __init__(self, d_model):
super().__init__()
self.d_model = d_model
self.proj_1 = nn.Conv2d(d_model, d_model, 1)
self.activation = nn.GELU()
self.spatial_gating_unit = AttentionModule(d_model)
self.proj_2 = nn.Conv2d(d_model, d_model, 1)
def forward(self, x):
shorcut = x.clone()
x = self.proj_1(x)
x = self.activation(x)
x = self.spatial_gating_unit(x)
x = self.proj_2(x)
x = x + shorcut
return x
class Block(nn.Module):
def __init__(
self,
dim,
mlp_ratio=4.0,
drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
):
super().__init__()
self.norm1 = nn.BatchNorm2d(dim)
self.attn = SpatialAttention(dim)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = nn.BatchNorm2d(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
)
layer_scale_init_value = 1e-2
self.layer_scale_1 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True
)
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True
)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.permute(0, 2, 1).view(B, C, H, W)
x = x + self.drop_path(
self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.attn(self.norm1(x))
)
x = x + self.drop_path(
self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x))
)
x = x.view(B, C, N).permute(0, 2, 1)
return x
class OverlapPatchEmbed(nn.Module):
"""Image to Patch Embedding"""
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2),
)
self.norm = nn.BatchNorm2d(embed_dim)
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = self.norm(x)
x = x.flatten(2).transpose(1, 2)
return x, H, W
class MSCAN(BaseModel):
default_conf = {
"in_channels": 3,
"embed_dims": [64, 128, 320, 512],
"mlp_ratios": [8, 8, 4, 4],
"drop_rate": 0.0,
"drop_path_rate": 0.1,
"depths": [3, 3, 12, 3],
"num_stages": 4,
}
required_data_keys = ["image"]
def _init(self, conf):
self.depths = conf.depths
self.num_stages = conf.num_stages
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, conf.drop_path_rate, sum(conf.depths))]
cur = 0
for i in range(conf.num_stages):
if i == 0:
patch_embed = StemConv(3, conf.embed_dims[0])
else:
patch_embed = OverlapPatchEmbed(
patch_size=7 if i == 0 else 3,
stride=4 if i == 0 else 2,
in_chans=conf.in_chans if i == 0 else conf.embed_dims[i - 1],
embed_dim=conf.embed_dims[i],
)
block = nn.ModuleList(
[
Block(
dim=conf.embed_dims[i],
mlp_ratio=conf.mlp_ratios[i],
drop=conf.drop_rate,
drop_path=dpr[cur + j],
)
for j in range(conf.depths[i])
]
)
norm = nn.LayerNorm(conf.embed_dims[i])
cur += conf.depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"block{i + 1}", block)
setattr(self, f"norm{i + 1}", norm)
def _forward(self, data):
img = data["image"]
# rgb -> bgr and from [0, 1] to [0, 255]
x = img[:, [2, 1, 0], :, :] * 255.0
B = x.shape[0]
outs = []
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
block = getattr(self, f"block{i + 1}")
norm = getattr(self, f"norm{i + 1}")
x, H, W = patch_embed(x)
for blk in block:
x = blk(x, H, W)
x = norm(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
return {"features": outs}
def loss(self, pred, data):
"""Compute the loss."""
raise NotImplementedError
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