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archs.py
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import torch
from torch import nn
import torch
import torchvision
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.utils import save_image
import torch.nn.functional as F
import os
import matplotlib.pyplot as plt
from utils import *
import timm
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import types
import math
from abc import ABCMeta, abstractmethod
# from mmcv.cnn import ConvModule
from pdb import set_trace as st
from kan import KANLinear, KAN
from torch.nn import init
class KANLayer(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., no_kan=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.dim = in_features
grid_size=5
spline_order=3
scale_noise=0.1
scale_base=1.0
scale_spline=1.0
base_activation=torch.nn.SiLU
grid_eps=0.02
grid_range=[-1, 1]
if not no_kan:
self.fc1 = KANLinear(
in_features,
hidden_features,
grid_size=grid_size,
spline_order=spline_order,
scale_noise=scale_noise,
scale_base=scale_base,
scale_spline=scale_spline,
base_activation=base_activation,
grid_eps=grid_eps,
grid_range=grid_range,
)
self.fc2 = KANLinear(
hidden_features,
out_features,
grid_size=grid_size,
spline_order=spline_order,
scale_noise=scale_noise,
scale_base=scale_base,
scale_spline=scale_spline,
base_activation=base_activation,
grid_eps=grid_eps,
grid_range=grid_range,
)
self.fc3 = KANLinear(
hidden_features,
out_features,
grid_size=grid_size,
spline_order=spline_order,
scale_noise=scale_noise,
scale_base=scale_base,
scale_spline=scale_spline,
base_activation=base_activation,
grid_eps=grid_eps,
grid_range=grid_range,
)
# # TODO
# self.fc4 = KANLinear(
# hidden_features,
# out_features,
# grid_size=grid_size,
# spline_order=spline_order,
# scale_noise=scale_noise,
# scale_base=scale_base,
# scale_spline=scale_spline,
# base_activation=base_activation,
# grid_eps=grid_eps,
# grid_range=grid_range,
# )
else:
self.fc1 = nn.Linear(in_features, hidden_features)
self.fc2 = nn.Linear(hidden_features, out_features)
self.fc3 = nn.Linear(hidden_features, out_features)
# TODO
# self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv_1 = DW_bn_relu(hidden_features)
self.dwconv_2 = DW_bn_relu(hidden_features)
self.dwconv_3 = DW_bn_relu(hidden_features)
# # TODO
# self.dwconv_4 = DW_bn_relu(hidden_features)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
# pdb.set_trace()
B, N, C = x.shape
x = self.fc1(x.reshape(B*N,C))
x = x.reshape(B,N,C).contiguous()
x = self.dwconv_1(x, H, W)
x = self.fc2(x.reshape(B*N,C))
x = x.reshape(B,N,C).contiguous()
x = self.dwconv_2(x, H, W)
x = self.fc3(x.reshape(B*N,C))
x = x.reshape(B,N,C).contiguous()
x = self.dwconv_3(x, H, W)
# # TODO
# x = x.reshape(B,N,C).contiguous()
# x = self.dwconv_4(x, H, W)
return x
class KANBlock(nn.Module):
def __init__(self, dim, drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, no_kan=False):
super().__init__()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim)
self.layer = KANLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, no_kan=no_kan)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = x + self.drop_path(self.layer(self.norm2(x), H, W))
return x
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
return x
class DW_bn_relu(nn.Module):
def __init__(self, dim=768):
super(DW_bn_relu, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
self.bn = nn.BatchNorm2d(dim)
self.relu = nn.ReLU()
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W)
x = self.dwconv(x)
x = self.bn(x)
x = self.relu(x)
x = x.flatten(2).transpose(1, 2)
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
self.num_patches = self.H * self.W
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.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
class ConvLayer(nn.Module):
def __init__(self, in_ch, out_ch):
super(ConvLayer, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, input):
return self.conv(input)
class D_ConvLayer(nn.Module):
def __init__(self, in_ch, out_ch):
super(D_ConvLayer, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, in_ch, 3, padding=1),
nn.BatchNorm2d(in_ch),
nn.ReLU(inplace=True),
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, input):
return self.conv(input)
class UKAN(nn.Module):
def __init__(self, num_classes, input_channels=3, deep_supervision=False, img_size=224, patch_size=16, in_chans=3, embed_dims=[256, 320, 512], no_kan=False,
drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, depths=[1, 1, 1], **kwargs):
super().__init__()
kan_input_dim = embed_dims[0]
self.encoder1 = ConvLayer(3, kan_input_dim//8)
self.encoder2 = ConvLayer(kan_input_dim//8, kan_input_dim//4)
self.encoder3 = ConvLayer(kan_input_dim//4, kan_input_dim)
self.norm3 = norm_layer(embed_dims[1])
self.norm4 = norm_layer(embed_dims[2])
self.dnorm3 = norm_layer(embed_dims[1])
self.dnorm4 = norm_layer(embed_dims[0])
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
self.block1 = nn.ModuleList([KANBlock(
dim=embed_dims[1],
drop=drop_rate, drop_path=dpr[0], norm_layer=norm_layer
)])
self.block2 = nn.ModuleList([KANBlock(
dim=embed_dims[2],
drop=drop_rate, drop_path=dpr[1], norm_layer=norm_layer
)])
self.dblock1 = nn.ModuleList([KANBlock(
dim=embed_dims[1],
drop=drop_rate, drop_path=dpr[0], norm_layer=norm_layer
)])
self.dblock2 = nn.ModuleList([KANBlock(
dim=embed_dims[0],
drop=drop_rate, drop_path=dpr[1], norm_layer=norm_layer
)])
self.patch_embed3 = PatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0], embed_dim=embed_dims[1])
self.patch_embed4 = PatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1], embed_dim=embed_dims[2])
self.decoder1 = D_ConvLayer(embed_dims[2], embed_dims[1])
self.decoder2 = D_ConvLayer(embed_dims[1], embed_dims[0])
self.decoder3 = D_ConvLayer(embed_dims[0], embed_dims[0]//4)
self.decoder4 = D_ConvLayer(embed_dims[0]//4, embed_dims[0]//8)
self.decoder5 = D_ConvLayer(embed_dims[0]//8, embed_dims[0]//8)
self.final = nn.Conv2d(embed_dims[0]//8, num_classes, kernel_size=1)
self.soft = nn.Softmax(dim =1)
def forward(self, x):
B = x.shape[0]
### Encoder
### Conv Stage
### Stage 1
out = F.relu(F.max_pool2d(self.encoder1(x), 2, 2))
t1 = out
### Stage 2
out = F.relu(F.max_pool2d(self.encoder2(out), 2, 2))
t2 = out
### Stage 3
out = F.relu(F.max_pool2d(self.encoder3(out), 2, 2))
t3 = out
### Tokenized KAN Stage
### Stage 4
out, H, W = self.patch_embed3(out)
for i, blk in enumerate(self.block1):
out = blk(out, H, W)
out = self.norm3(out)
out = out.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
t4 = out
### Bottleneck
out, H, W= self.patch_embed4(out)
for i, blk in enumerate(self.block2):
out = blk(out, H, W)
out = self.norm4(out)
out = out.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
### Stage 4
out = F.relu(F.interpolate(self.decoder1(out), scale_factor=(2,2), mode ='bilinear'))
out = torch.add(out, t4)
_, _, H, W = out.shape
out = out.flatten(2).transpose(1,2)
for i, blk in enumerate(self.dblock1):
out = blk(out, H, W)
### Stage 3
out = self.dnorm3(out)
out = out.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
out = F.relu(F.interpolate(self.decoder2(out),scale_factor=(2,2),mode ='bilinear'))
out = torch.add(out,t3)
_,_,H,W = out.shape
out = out.flatten(2).transpose(1,2)
for i, blk in enumerate(self.dblock2):
out = blk(out, H, W)
out = self.dnorm4(out)
out = out.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
out = F.relu(F.interpolate(self.decoder3(out),scale_factor=(2,2),mode ='bilinear'))
out = torch.add(out,t2)
out = F.relu(F.interpolate(self.decoder4(out),scale_factor=(2,2),mode ='bilinear'))
out = torch.add(out,t1)
out = F.relu(F.interpolate(self.decoder5(out),scale_factor=(2,2),mode ='bilinear'))
return self.final(out)