Unex_net.py

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 *
from torchsummary import summary
from torch.utils.tensorboard import SummaryWriter
path = os.getcwd()+'\\runs'
writer = SummaryWriter(path+'\\experiment')
__all__ = ['UNext']

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
# import pdb


def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False)


def shift(dim):
    x_shift = [torch.roll(x_c, shift, dim) for x_c, shift in zip(xs, range(-self.pad, self.pad + 1))]
    x_cat = torch.cat(x_shift, 1)
    x_cat = torch.narrow(x_cat, 2, self.pad, H)
    x_cat = torch.narrow(x_cat, 3, self.pad, W)
    return x_cat


class shiftmlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., shift_size=5):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.dim = in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

        self.shift_size = shift_size
        self.pad = shift_size // 2

        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 shift(x, dim):
        x = F.pad(x, "constant", 0)
        x = torch.chunk(x, shift_size, 1)
        x = [torch.roll(x_c, shift, dim) for x_s, shift in zip(x, range(-pad, pad + 1))]
        x = torch.cat(x, 1)
        return x[:, :, pad:-pad, pad:-pad]

    def forward(self, x, H, W):
        # pdb.set_trace()
        B, N, C = x.shape

        xn = x.transpose(1, 2).view(B, C, H, W).contiguous()
        xn = F.pad(xn, (self.pad, self.pad, self.pad, self.pad), "constant", 0)
        xs = torch.chunk(xn, self.shift_size, 1)
        x_shift = [torch.roll(x_c, shift, 2) for x_c, shift in zip(xs, range(-self.pad, self.pad + 1))]
        x_cat = torch.cat(x_shift, 1)
        x_cat = torch.narrow(x_cat, 2, self.pad, H)
        x_s = torch.narrow(x_cat, 3, self.pad, W)

        x_s = x_s.reshape(B, C, H * W).contiguous()
        x_shift_r = x_s.transpose(1, 2)

        x = self.fc1(x_shift_r)

        x = self.dwconv(x, H, W)
        x = self.act(x)
        x = self.drop(x)

        xn = x.transpose(1, 2).view(B, C, H, W).contiguous()
        xn = F.pad(xn, (self.pad, self.pad, self.pad, self.pad), "constant", 0)
        xs = torch.chunk(xn, self.shift_size, 1)
        x_shift = [torch.roll(x_c, shift, 3) for x_c, shift in zip(xs, range(-self.pad, self.pad + 1))]
        x_cat = torch.cat(x_shift, 1)
        x_cat = torch.narrow(x_cat, 2, self.pad, H)
        x_s = torch.narrow(x_cat, 3, self.pad, W)
        x_s = x_s.reshape(B, C, H * W).contiguous()
        x_shift_c = x_s.transpose(1, 2)

        x = self.fc2(x_shift_c)
        x = self.drop(x)
        return x


class shiftedBlock(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        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 * mlp_ratio)
        self.mlp = shiftmlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=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):

        x = x + self.drop_path(self.mlp(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 OverlapPatchEmbed(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 UNext(nn.Module):

    ## Conv 3 + MLP 2 + shifted MLP

    def __init__(self, num_classes, input_channels=1, deep_supervision=False, img_size=224, patch_size=16, in_chans=3,
                 embed_dims=[32, 64, 128, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[1, 1, 1], sr_ratios=[8, 4, 2, 1], **kwargs):
        super().__init__()

        self.encoder1 = nn.Conv2d(input_channels, 8, 3, stride=1, padding=1)
        self.encoder2 = nn.Conv2d(8, 16, 3, stride=1, padding=1)
        self.encoder3 = nn.Conv2d(16, 32, 3, stride=1, padding=1)

        self.ebn1 = nn.BatchNorm2d(8)
        self.ebn2 = nn.BatchNorm2d(16)
        self.ebn3 = nn.BatchNorm2d(32)

        self.norm3 = norm_layer(embed_dims[1])
        self.norm4 = norm_layer(embed_dims[2])

        self.dnorm3 = norm_layer(64)
        self.dnorm4 = norm_layer(32)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]

        self.block1 = nn.ModuleList([shiftedBlock(
            dim=embed_dims[1], num_heads=num_heads[0], mlp_ratio=1, qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[0], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])])

        self.block2 = nn.ModuleList([shiftedBlock(
            dim=embed_dims[2], num_heads=num_heads[0], mlp_ratio=1, qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[1], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])])

        self.dblock1 = nn.ModuleList([shiftedBlock(
            dim=embed_dims[1], num_heads=num_heads[0], mlp_ratio=1, qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[0], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])])

        self.dblock2 = nn.ModuleList([shiftedBlock(
            dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=1, qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[1], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])])

        self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
                                              embed_dim=embed_dims[1])
        self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
                                              embed_dim=embed_dims[2])

        self.decoder1 = nn.Conv2d(128, 64, 3, stride=1, padding=1)
        self.decoder2 = nn.Conv2d(64, 32, 3, stride=1, padding=1)
        self.decoder3 = nn.Conv2d(32, 16, 3, stride=1, padding=1)
        self.decoder4 = nn.Conv2d(16, 8, 3, stride=1, padding=1)
        self.decoder5 = nn.Conv2d(8, 8, 3, stride=1, padding=1)

        self.dbn1 = nn.BatchNorm2d(64)
        self.dbn2 = nn.BatchNorm2d(32)
        self.dbn3 = nn.BatchNorm2d(16)
        self.dbn4 = nn.BatchNorm2d(8)

        self.final = nn.Conv2d(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.ebn1(self.encoder1(x)), 2, 2))
        t1 = out
        ### Stage 2
        out = F.relu(F.max_pool2d(self.ebn2(self.encoder2(out)), 2, 2))
        t2 = out
        ### Stage 3
        out = F.relu(F.max_pool2d(self.ebn3(self.encoder3(out)), 2, 2))
        t3 = out

        ### Tokenized MLP 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.dbn1(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.dbn2(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.dbn3(self.decoder3(out)), scale_factor=(2, 2), mode='bilinear'))
        out = torch.add(out, t2)
        out = F.relu(F.interpolate(self.dbn4(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)
model=UNext(1)
# # model = Unet()
# dummy_input = torch.rand(1, 1, 224, 224)
# with SummaryWriter(comment='Unet') as w:
#     w.add_graph(model, (dummy_input, ))
# print(dummy_input.shape)
# tensorboard --logdir=D:\opencv-python\条纹投影\Unet_test\runs\Apr05_11-53-43_LAPTOP-85DL9FARUnet
# ch = 1
# h = 224
# w = 224
# summary(model, input_size=(ch, h, w), device='cpu')