archs.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn.functional as F

__all__ = ['ClusterSeg','UNet_3Plus_DeepSup_CGM','UNext','Unet','TA_Unet']

import copy
import torch
import torch.nn as nn
import numpy as np
from torch.nn import Dropout, Softmax, Linear, Conv2d, LayerNorm
from torch.nn.modules.utils import _pair
from torch.nn import init
from scipy import ndimage
try:
    from itertools import ifilterfalse
except ImportError:  # py3k
    from itertools import filterfalse as ifilterfalse



# download pretrained vit model from https://console.cloud.google.com/storage/vit_models/
ATTENTION_Q = "MultiHeadDotProductAttention_1/query"
ATTENTION_K = "MultiHeadDotProductAttention_1/key"
ATTENTION_V = "MultiHeadDotProductAttention_1/value"
ATTENTION_OUT = "MultiHeadDotProductAttention_1/out"
FC_0 = "MlpBlock_3/Dense_0"
FC_1 = "MlpBlock_3/Dense_1"
ATTENTION_NORM = "LayerNorm_0"
MLP_NORM = "LayerNorm_2"


def np2th(weights, conv=False):
    """Possibly convert HWIO to OIHW."""
    if conv:
        weights = weights.transpose([3, 2, 0, 1])
    return torch.from_numpy(weights)


def swish(x):
    return x * torch.sigmoid(x)


ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish}


class Attention(nn.Module):
    def __init__(self, config, vis):
        super(Attention, self).__init__()
        self.vis = vis
        self.num_attention_heads = config.transformer["num_heads"]
        self.attention_head_size = int(config.hidden_size / self.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = Linear(config.hidden_size, self.all_head_size)
        self.key = Linear(config.hidden_size, self.all_head_size)
        self.value = Linear(config.hidden_size, self.all_head_size)

        self.out = Linear(config.hidden_size, config.hidden_size)
        self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"])
        self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"])

        self.softmax = Softmax(dim=-1)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, hidden_states):
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        attention_probs = self.softmax(attention_scores)
        weights = attention_probs if self.vis else None
        attention_probs = self.attn_dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        attention_output = self.out(context_layer)
        attention_output = self.proj_dropout(attention_output)
        return attention_output, weights


class Mlp(nn.Module):
    def __init__(self, config):
        super(Mlp, self).__init__()
        self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"])
        self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size)
        self.act_fn = ACT2FN["gelu"]
        self.dropout = Dropout(config.transformer["dropout_rate"])

        self._init_weights()

    def _init_weights(self):
        nn.init.xavier_uniform_(self.fc1.weight)
        nn.init.xavier_uniform_(self.fc2.weight)
        nn.init.normal_(self.fc1.bias, std=1e-6)
        nn.init.normal_(self.fc2.bias, std=1e-6)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act_fn(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        return x


class Embeddings(nn.Module):
    def __init__(self, config, in_channels):
        super(Embeddings, self).__init__()
        self.config = config
        img_size = self.config.img_size
        if config.patches.get("grid") is not None:  # ResNet
            grid_size = config.patches["grid"]
            patch_size = (img_size[0] // 8 // grid_size[0], img_size[1] // 8 // grid_size[1])
            patch_size_real = (patch_size[0] * 16, patch_size[1] * 16)
            n_patches = (img_size[0] // patch_size_real[0]) * (img_size[1] // patch_size_real[1])
        else:
            patch_size = _pair(config.patches["size"])
            n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
        self.patch_embeddings = Conv2d(in_channels=in_channels,
                                       out_channels=config.hidden_size,
                                       kernel_size=patch_size,
                                       stride=patch_size)
        self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches, config.hidden_size))
        self.dropout = Dropout(config.transformer["dropout_rate"])

    def forward(self, x):
        x = self.patch_embeddings(x)  # (B, hidden. n_patches^(1/2), n_patches^(1/2))
        x = x.flatten(2)
        x = x.transpose(-1, -2)  # (B, n_patches, hidden)

        embeddings = x + self.position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings


class Block(nn.Module):
    def __init__(self, config, vis):
        super(Block, self).__init__()
        self.hidden_size = config.hidden_size
        self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)
        self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)
        self.ffn = Mlp(config)
        self.attn = Attention(config, vis)

    def forward(self, x):
        h = x
        x = self.attention_norm(x)
        x, weights = self.attn(x)
        x = x + h

        h = x
        x = self.ffn_norm(x)
        x = self.ffn(x)
        x = x + h
        return x, weights

    def load_from(self, weights, n_block):
        ROOT = "Transformer/encoderblock_{}".format(n_block)
        with torch.no_grad():
            query_weight = np2th(weights["{}/{}/kernel".format(ROOT, ATTENTION_Q)]).view(self.hidden_size, self.hidden_size).t()
            key_weight = np2th(weights["{}/{}/kernel".format(ROOT, ATTENTION_K)]).view(self.hidden_size, self.hidden_size).t()
            value_weight = np2th(weights["{}/{}/kernel".format(ROOT, ATTENTION_V)]).view(self.hidden_size, self.hidden_size).t()
            out_weight = np2th(weights["{}/{}/kernel".format(ROOT, ATTENTION_OUT)]).view(self.hidden_size, self.hidden_size).t()

            query_bias = np2th(weights["{}/{}/bias".format(ROOT, ATTENTION_Q)]).view(-1)
            key_bias = np2th(weights["{}/{}/bias".format(ROOT, ATTENTION_K)]).view(-1)
            value_bias = np2th(weights["{}/{}/bias".format(ROOT, ATTENTION_V)]).view(-1)
            out_bias = np2th(weights["{}/{}/bias".format(ROOT, ATTENTION_OUT)]).view(-1)

            self.attn.query.weight.copy_(query_weight)
            self.attn.key.weight.copy_(key_weight)
            self.attn.value.weight.copy_(value_weight)
            self.attn.out.weight.copy_(out_weight)
            self.attn.query.bias.copy_(query_bias)
            self.attn.key.bias.copy_(key_bias)
            self.attn.value.bias.copy_(value_bias)
            self.attn.out.bias.copy_(out_bias)

            mlp_weight_0 = np2th(weights["{}/{}/kernel".format(ROOT, FC_0)]).t()
            mlp_weight_1 = np2th(weights["{}/{}/kernel".format(ROOT, FC_1)]).t()
            mlp_bias_0 = np2th(weights["{}/{}/bias".format(ROOT, FC_0)]).t()
            mlp_bias_1 = np2th(weights["{}/{}/bias".format(ROOT, FC_1)]).t()

            self.ffn.fc1.weight.copy_(mlp_weight_0)
            self.ffn.fc2.weight.copy_(mlp_weight_1)
            self.ffn.fc1.bias.copy_(mlp_bias_0)
            self.ffn.fc2.bias.copy_(mlp_bias_1)

            self.attention_norm.weight.copy_(np2th(weights["{}/{}/scale".format(ROOT, ATTENTION_NORM)]))
            self.attention_norm.bias.copy_(np2th(weights["{}/{}/bias".format(ROOT, ATTENTION_NORM)]))
            self.ffn_norm.weight.copy_(np2th(weights["{}/{}/scale".format(ROOT, MLP_NORM)]))
            self.ffn_norm.bias.copy_(np2th(weights["{}/{}/bias".format(ROOT, MLP_NORM)]))


class Encoder(nn.Module):
    def __init__(self, config, vis):
        super(Encoder, self).__init__()
        self.vis = vis
        self.layer = nn.ModuleList()
        self.encoder_norm = LayerNorm(config.hidden_size, eps=1e-6)
        for _ in range(config.transformer["num_layers"]):
            layer = Block(config, vis)
            self.layer.append(copy.deepcopy(layer))

    def forward(self, hidden_states):
        attn_weights = []
        for layer_block in self.layer:
            hidden_states, weights = layer_block(hidden_states)
            if self.vis:
                attn_weights.append(weights)
        encoded = self.encoder_norm(hidden_states)
        return encoded, attn_weights


class Transformer(nn.Module):
    def __init__(self, config, in_channels, vis):
        super(Transformer, self).__init__()
        self.embeddings = Embeddings(config, in_channels)
        self.encoder = Encoder(config, vis)

    def forward(self, input_ids):
        embedding_output = self.embeddings(input_ids)
        encoded, attn_weights = self.encoder(embedding_output)  # (B, n_patch, hidden)
        return encoded, attn_weights


class Conv2dReLU(nn.Sequential):
    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            padding=0,
            stride=1,
            use_batchnorm=True,
    ):
        conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=not (use_batchnorm),
        )
        relu = nn.ReLU(inplace=True)
        bn = nn.BatchNorm2d(out_channels)
        super(Conv2dReLU, self).__init__(conv, bn, relu)


class ConvMore(nn.Module):
    def __init__(self, config):
        super(ConvMore, self).__init__()
        self.config = config
        self.head_channels = config.head_channels
        self.conv = Conv2dReLU(
            config.hidden_size,
            self.head_channels,
            kernel_size=3,
            padding=1,
            use_batchnorm=True
        )

    def forward(self, hidden_states):
        B, n_patch, hidden = hidden_states.size()  # reshape from (B, n_patch, hidden) to (B, h, w, hidden)
        h, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch))
        x = hidden_states.permute(0, 2, 1)
        x = x.contiguous().view(B, hidden, h, w)
        x = self.conv(x)
        return x


def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('Linear') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def weights_init_kaiming(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
    elif classname.find('Linear') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def init_weights(net, init_type='normal'):
    if init_type == 'normal':
        net.apply(weights_init_normal)
    elif init_type == 'kaiming':
        net.apply(weights_init_kaiming)
    else:
        raise NotImplementedError('initialization method [%s] is not implemented' % init_type)

class unetConv2(nn.Module):
    def __init__(self, in_size, out_size, is_batchnorm, n=2, ks=3, stride=1, padding=1):
        super(unetConv2, self).__init__()
        self.n = n
        self.ks = ks
        self.stride = stride
        self.padding = padding
        s = stride
        p = padding
        if is_batchnorm:
            for i in range(1, n + 1):
                conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p),
                                     nn.BatchNorm2d(out_size),
                                     nn.ReLU(inplace=True), )
                setattr(self, 'conv%d' % i, conv)
                in_size = out_size
        else:
            for i in range(1, n + 1):
                conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p),
                                     nn.ReLU(inplace=True), )
                setattr(self, 'conv%d' % i, conv)
                in_size = out_size
        # initialise the blocks
        for m in self.children():
            init_weights(m, init_type='kaiming')

    def forward(self, inputs):
        x = inputs
        for i in range(1, self.n + 1):
            conv = getattr(self, 'conv%d' % i)
            x = conv(x)
        return x

class unetUp_origin(nn.Module):
    def __init__(self, in_size, out_size, is_deconv=False, n_concat=2):
        super(unetUp_origin, self).__init__()
        # self.conv = unetConv2(out_size*2, out_size, False)
        if is_deconv:
            self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False)
            self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=4, stride=2, padding=1)
        else:
            self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False)
            self.up = nn.UpsamplingBilinear2d(scale_factor=2)

        # initialise the blocks
        for m in self.children():
            if m.__class__.__name__.find('unetConv2') != -1: continue
            init_weights(m, init_type='kaiming')

    def forward(self, inputs0, *input):
        outputs0 = self.up(inputs0)
        for i in range(len(input)):
            outputs0 = torch.cat([outputs0, input[i]], 1)
        return self.conv(outputs0)

class ClusterSeg(nn.Module):
    def __init__(self, config, num_classes=1,img_size=160,in_channels=1, vis=False):
        super(ClusterSeg, self).__init__()
        self.config = config
        self.img_size = img_size
        self.num_classes = num_classes
        self.in_channels = in_channels
        self.vis = vis
        self.classifier = config.classifier
        self.is_deconv = config.is_deconv
        self.is_batchnorm = config.is_batchnorm
        self.is_ds = config.is_ds
        # self.encoder = Encoder(config, vis)

        filters = [32, 64, 128, 256, 512]

        self.transformer = Transformer(config, in_channels=filters[3], vis=vis)
        self.convmore = ConvMore(config)

        self.conv00 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
        self.maxpool0 = nn.MaxPool2d(kernel_size=2)
        self.conv10 = unetConv2(filters[0], filters[1], self.is_batchnorm)
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
        self.conv20 = unetConv2(filters[1], filters[2], self.is_batchnorm)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)
        self.conv30 = unetConv2(filters[2], filters[3], self.is_batchnorm)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2)

        # upsampling
        # mask path
        self.up_concat01 = unetUp_origin(filters[1], filters[0], self.is_deconv)
        self.up_concat11 = unetUp_origin(filters[2], filters[1], self.is_deconv)
        self.up_concat21 = unetUp_origin(filters[3], filters[2], self.is_deconv)
        self.up_concat31 = unetUp_origin(filters[4], filters[3], self.is_deconv)

        self.up_concat02 = unetUp_origin(filters[1], filters[0], self.is_deconv, 3)
        self.up_concat12 = unetUp_origin(filters[2], filters[1], self.is_deconv, 3)
        self.up_concat22 = unetUp_origin(filters[3], filters[2], self.is_deconv, 3)

        self.up_concat03 = unetUp_origin(filters[1], filters[0], self.is_deconv, 4)
        self.up_concat13 = unetUp_origin(filters[2], filters[1], self.is_deconv, 4)

        self.up_concat04 = unetUp_origin(filters[1], filters[0], self.is_deconv, 5)

        # boundary path
        self.upb_concat01 = unetUp_origin(filters[1], filters[0], self.is_deconv)
        self.upb_concat11 = unetUp_origin(filters[2], filters[1], self.is_deconv)
        self.upb_concat21 = unetUp_origin(filters[3], filters[2], self.is_deconv)
        self.upb_concat31 = unetUp_origin(filters[4], filters[3], self.is_deconv)

        self.upb_concat02 = unetUp_origin(filters[1], filters[0], self.is_deconv, 3)
        self.upb_concat12 = unetUp_origin(filters[2], filters[1], self.is_deconv, 3)
        self.upb_concat22 = unetUp_origin(filters[3], filters[2], self.is_deconv, 3)

        self.upb_concat03 = unetUp_origin(filters[1], filters[0], self.is_deconv, 4)
        self.upb_concat13 = unetUp_origin(filters[2], filters[1], self.is_deconv, 4)

        self.upb_concat04 = unetUp_origin(filters[1], filters[0], self.is_deconv, 5)

        # three class path
        self.upt_concat31 = unetUp_origin(filters[4], filters[3], self.is_deconv)
        self.upt_concat22 = unetUp_origin(filters[3], filters[2], self.is_deconv, 3)
        self.upt_concat13 = unetUp_origin(filters[2], filters[1], self.is_deconv, 4)
        self.upt_concat04 = unetUp_origin(filters[1], filters[0], self.is_deconv, 5)

        # final conv (without any concat)
        self.final_4 = nn.Conv2d(filters[0], self.num_classes, 1)
        self.finalb_1 = nn.Conv2d(filters[0], self.num_classes, 1)
        self.finalb_2 = nn.Conv2d(filters[0], self.num_classes, 1)
        self.finalb_3 = nn.Conv2d(filters[0], self.num_classes, 1)
        self.finalb_4 = nn.Conv2d(filters[0], self.num_classes, 1)
        self.finalt_4 = nn.Conv2d(filters[0], self.num_classes, 1)

        # initialise weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init_weights(m)
            elif isinstance(m, nn.BatchNorm2d):
                init_weights(m)

    def forward(self, inputs):
        X_00 = self.conv00(inputs)  # [B, 32, s, s]
        maxpool0 = self.maxpool0(X_00)  # [B, 32, s/2, s/2]
        X_10 = self.conv10(maxpool0)  # [B, 64, s/2, s/2]
        maxpool1 = self.maxpool1(X_10)  # [B, 64, s/4, s/4]
        X_20 = self.conv20(maxpool1)  # [B, 128, s/4, s/4]
        maxpool2 = self.maxpool2(X_20)  # [B, 128, s/8, s/8]
        X_30 = self.conv30(maxpool2)  # [B, 256, s/8, s/8]
        maxpool3 = self.maxpool3(X_30)  # [B, 256, s/16, s/16]
        x, attn_weights = self.transformer(maxpool3)  # [B, s/16 * s/16, 768]
        X_40 = self.convmore(x)  # [B, 512, s/16, s/16]

        # column 1
        X_01 = self.up_concat01(X_10, X_00)
        X_11 = self.up_concat11(X_20, X_10)
        X_21 = self.up_concat21(X_30, X_20)
        X_31 = self.up_concat31(X_40, X_30)

        # Y_01 = self.upb_concat01(X_10, X_00)
        # Y_11 = self.upb_concat11(X_20, X_10)
        # Y_21 = self.upb_concat21(X_30, X_20)
        # Y_31 = self.upb_concat31(X_40, X_30)
        #
        # Z_31 = self.upt_concat31(X_40, X_30)

        # column 2
        X_02 = self.up_concat02(X_11, X_00, X_01)
        X_12 = self.up_concat12(X_21, X_10, X_11)
        X_22 = self.up_concat22(X_31, X_20, X_21)

        # Y_02 = self.upb_concat02(Y_11, X_00, Y_01)
        # Y_12 = self.upb_concat12(Y_21, X_10, Y_11)
        # Y_22 = self.upb_concat22(Y_31, X_20, Y_21)
        #
        # Z_22 = self.upt_concat22(Z_31, X_20, Y_21)

        # column 3
        X_03 = self.up_concat03(X_12, X_00, X_01, X_02)
        X_13 = self.up_concat13(X_22, X_10, X_11, X_12)

        # Y_03 = self.upb_concat03(Y_12, X_00, Y_01, Y_02)
        # Y_13 = self.upb_concat13(Y_22, X_10, Y_11, Y_12)
        #
        # Z_13 = self.upt_concat13(Z_22, X_10, Y_11, Y_12)

        # column 4
        X_04 = self.up_concat04(X_13, X_00, X_01, X_02, X_03)
        # Y_04 = self.upb_concat04(Y_13, X_00, Y_01, Y_02, Y_03)
        # Z_04 = self.upt_concat04(Z_13, X_00, Y_01, Y_02, Y_03)

        # final column
        final_m = self.final_4(X_04)
        # final_b1 = self.finalb_1(Y_01)
        # final_b2 = self.finalb_2(Y_02)
        # final_b3 = self.finalb_3(Y_03)
        # final_b4 = self.finalb_4(Y_04)
        # final_t = self.finalt_4(Z_04)

        return final_m

    def load_from(self, weights):
        with torch.no_grad():
            self.transformer.encoder.encoder_norm.weight.copy_(np2th(weights["Transformer/encoder_norm/scale"]))
            self.transformer.encoder.encoder_norm.bias.copy_(np2th(weights["Transformer/encoder_norm/bias"]))

            posemb = np2th(weights["Transformer/posembed_input/pos_embedding"])

            posemb_new = self.transformer.embeddings.position_embeddings
            if posemb.size() == posemb_new.size():
                self.transformer.embeddings.position_embeddings.copy_(posemb)
            elif posemb.size()[1] - 1 == posemb_new.size()[1]:
                posemb = posemb[:, 1:]
                self.transformer.embeddings.position_embeddings.copy_(posemb)
            else:
                ntok_new = posemb_new.size(1)
                if self.classifier == "seg":
                    _, posemb_grid = posemb[:, :1], posemb[0, 1:]
                gs_old = int(np.sqrt(len(posemb_grid)))
                gs_new = int(np.sqrt(ntok_new))
                print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new))
                posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)
                zoom = (gs_new / gs_old, gs_new / gs_old, 1)
                posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1)  # th2np
                posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1)
                posemb = posemb_grid
                self.transformer.embeddings.position_embeddings.copy_(np2th(posemb))

            for bname, block in self.transformer.encoder.named_children():
                for uname, unit in block.named_children():
                    unit.load_from(weights, n_block=uname)

def weights_init_normal(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('Linear') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def weights_init_xavier(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.xavier_normal_(m.weight.data, gain=1)
    elif classname.find('Linear') != -1:
        init.xavier_normal_(m.weight.data, gain=1)
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def weights_init_kaiming(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
    elif classname.find('Linear') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def weights_init_orthogonal(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.orthogonal_(m.weight.data, gain=1)
    elif classname.find('Linear') != -1:
        init.orthogonal_(m.weight.data, gain=1)
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def init_weights(net, init_type='normal'):
    #print('initialization method [%s]' % init_type)
    if init_type == 'normal':
        net.apply(weights_init_normal)
    elif init_type == 'xavier':
        net.apply(weights_init_xavier)
    elif init_type == 'kaiming':
        net.apply(weights_init_kaiming)
    elif init_type == 'orthogonal':
        net.apply(weights_init_orthogonal)
    else:
        raise NotImplementedError('initialization method [%s] is not implemented' % init_type)

class unetConv2(nn.Module):
    def __init__(self, in_size, out_size, is_batchnorm, n=2, ks=3, stride=1, padding=1):
        super(unetConv2, self).__init__()
        self.n = n
        self.ks = ks
        self.stride = stride
        self.padding = padding
        s = stride
        p = padding
        if is_batchnorm:
            for i in range(1, n + 1):
                conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p),
                                     nn.BatchNorm2d(out_size),
                                     nn.ReLU(inplace=True), )
                setattr(self, 'conv%d' % i, conv)
                in_size = out_size

        else:
            for i in range(1, n + 1):
                conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p),
                                     nn.ReLU(inplace=True), )
                setattr(self, 'conv%d' % i, conv)
                in_size = out_size

        # initialise the blocks
        for m in self.children():
            init_weights(m, init_type='kaiming')

    def forward(self, inputs):
        x = inputs
        for i in range(1, self.n + 1):
            conv = getattr(self, 'conv%d' % i)
            x = conv(x)

        return x

class unetUp(nn.Module):
    def __init__(self, in_size, out_size, is_deconv, n_concat=2):
        super(unetUp, self).__init__()
        # self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False)
        self.conv = unetConv2(out_size * 2, out_size, False)
        if is_deconv:
            self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=4, stride=2, padding=1)
        else:
            self.up = nn.UpsamplingBilinear2d(scale_factor=2)

        # initialise the blocks
        for m in self.children():
            if m.__class__.__name__.find('unetConv2') != -1: continue
            init_weights(m, init_type='kaiming')

    def forward(self, inputs0, *input):
        # print(self.n_concat)
        # print(inputs)
        outputs0 = self.up(inputs0)
        for i in range(len(input)):
            outputs0 = torch.cat([outputs0, input[i]], 1)
        return self.conv(outputs0)

class unetUp_origin(nn.Module):
    def __init__(self, in_size, out_size, is_deconv, n_concat=2):
        super(unetUp_origin, self).__init__()
        # self.conv = unetConv2(out_size*2, out_size, False)
        if is_deconv:
            self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False)
            self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=4, stride=2, padding=1)
        else:
            self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False)
            self.up = nn.UpsamplingBilinear2d(scale_factor=2)

        # initialise the blocks
        for m in self.children():
            if m.__class__.__name__.find('unetConv2') != -1: continue
            init_weights(m, init_type='kaiming')

    def forward(self, inputs0, *input):
        # print(self.n_concat)
        # print(inputs)
        outputs0 = self.up(inputs0)
        for i in range(len(input)):
            outputs0 = torch.cat([outputs0, input[i]], 1)
        return self.conv(outputs0)
'''
    UNet 3+ with deep supervision and class-guided module
'''

class UNet_3Plus_DeepSup_CGM(nn.Module):

    def __init__(self,num_classes, img_size=224,   embed_dims=[128, 160, 256],
                 num_heads=[1, 2, 4, 8], 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(UNet_3Plus_DeepSup_CGM, self).__init__()

        in_channels = 1
        n_classes = num_classes
        feature_scale=4
        is_deconv=True
        is_batchnorm=True

        self.is_deconv = is_deconv
        self.in_channels = in_channels
        self.is_batchnorm = is_batchnorm
        self.feature_scale = feature_scale

        filters = [32, 64, 128, 256, 512]

        ## -------------Encoder--------------
        self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)

        self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)

        self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2)

        self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm)
        self.maxpool4 = nn.MaxPool2d(kernel_size=2)

        self.conv5 = unetConv2(filters[3], filters[4], self.is_batchnorm)

        ## -------------Decoder--------------
        self.CatChannels = filters[0]
        self.CatBlocks = 5
        self.UpChannels = self.CatChannels * self.CatBlocks

        '''stage 4d'''
        # h1->320*320, hd4->40*40, Pooling 8 times
        self.h1_PT_hd4 = nn.MaxPool2d(8, 8, ceil_mode=True)
        self.h1_PT_hd4_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
        self.h1_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
        self.h1_PT_hd4_relu = nn.ReLU(inplace=True)

        # h2->160*160, hd4->40*40, Pooling 4 times
        self.h2_PT_hd4 = nn.MaxPool2d(4, 4, ceil_mode=True)
        self.h2_PT_hd4_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
        self.h2_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
        self.h2_PT_hd4_relu = nn.ReLU(inplace=True)

        # h3->80*80, hd4->40*40, Pooling 2 times
        self.h3_PT_hd4 = nn.MaxPool2d(2, 2, ceil_mode=True)
        self.h3_PT_hd4_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
        self.h3_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
        self.h3_PT_hd4_relu = nn.ReLU(inplace=True)

        # h4->40*40, hd4->40*40, Concatenation
        self.h4_Cat_hd4_conv = nn.Conv2d(filters[3], self.CatChannels, 3, padding=1)
        self.h4_Cat_hd4_bn = nn.BatchNorm2d(self.CatChannels)
        self.h4_Cat_hd4_relu = nn.ReLU(inplace=True)

        # hd5->20*20, hd4->40*40, Upsample 2 times
        self.hd5_UT_hd4 = nn.Upsample(scale_factor=2, mode='bilinear')  # 14*14
        self.hd5_UT_hd4_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
        self.hd5_UT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd5_UT_hd4_relu = nn.ReLU(inplace=True)

        # fusion(h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4)
        self.conv4d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1)  # 16
        self.bn4d_1 = nn.BatchNorm2d(self.UpChannels)
        self.relu4d_1 = nn.ReLU(inplace=True)

        '''stage 3d'''
        # h1->320*320, hd3->80*80, Pooling 4 times
        self.h1_PT_hd3 = nn.MaxPool2d(4, 4, ceil_mode=True)
        self.h1_PT_hd3_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
        self.h1_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
        self.h1_PT_hd3_relu = nn.ReLU(inplace=True)

        # h2->160*160, hd3->80*80, Pooling 2 times
        self.h2_PT_hd3 = nn.MaxPool2d(2, 2, ceil_mode=True)
        self.h2_PT_hd3_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
        self.h2_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
        self.h2_PT_hd3_relu = nn.ReLU(inplace=True)

        # h3->80*80, hd3->80*80, Concatenation
        self.h3_Cat_hd3_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
        self.h3_Cat_hd3_bn = nn.BatchNorm2d(self.CatChannels)
        self.h3_Cat_hd3_relu = nn.ReLU(inplace=True)

        # hd4->40*40, hd4->80*80, Upsample 2 times
        self.hd4_UT_hd3 = nn.Upsample(scale_factor=2, mode='bilinear')  # 14*14
        self.hd4_UT_hd3_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
        self.hd4_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd4_UT_hd3_relu = nn.ReLU(inplace=True)

        # hd5->20*20, hd4->80*80, Upsample 4 times
        self.hd5_UT_hd3 = nn.Upsample(scale_factor=4, mode='bilinear')  # 14*14
        self.hd5_UT_hd3_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
        self.hd5_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd5_UT_hd3_relu = nn.ReLU(inplace=True)

        # fusion(h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3)
        self.conv3d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1)  # 16
        self.bn3d_1 = nn.BatchNorm2d(self.UpChannels)
        self.relu3d_1 = nn.ReLU(inplace=True)

        '''stage 2d '''
        # h1->320*320, hd2->160*160, Pooling 2 times
        self.h1_PT_hd2 = nn.MaxPool2d(2, 2, ceil_mode=True)
        self.h1_PT_hd2_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
        self.h1_PT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
        self.h1_PT_hd2_relu = nn.ReLU(inplace=True)

        # h2->160*160, hd2->160*160, Concatenation
        self.h2_Cat_hd2_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
        self.h2_Cat_hd2_bn = nn.BatchNorm2d(self.CatChannels)
        self.h2_Cat_hd2_relu = nn.ReLU(inplace=True)

        # hd3->80*80, hd2->160*160, Upsample 2 times
        self.hd3_UT_hd2 = nn.Upsample(scale_factor=2, mode='bilinear')  # 14*14
        self.hd3_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
        self.hd3_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd3_UT_hd2_relu = nn.ReLU(inplace=True)

        # hd4->40*40, hd2->160*160, Upsample 4 times
        self.hd4_UT_hd2 = nn.Upsample(scale_factor=4, mode='bilinear')  # 14*14
        self.hd4_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
        self.hd4_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd4_UT_hd2_relu = nn.ReLU(inplace=True)

        # hd5->20*20, hd2->160*160, Upsample 8 times
        self.hd5_UT_hd2 = nn.Upsample(scale_factor=8, mode='bilinear')  # 14*14
        self.hd5_UT_hd2_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
        self.hd5_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd5_UT_hd2_relu = nn.ReLU(inplace=True)

        # fusion(h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2)
        self.conv2d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1)  # 16
        self.bn2d_1 = nn.BatchNorm2d(self.UpChannels)
        self.relu2d_1 = nn.ReLU(inplace=True)

        '''stage 1d'''
        # h1->320*320, hd1->320*320, Concatenation
        self.h1_Cat_hd1_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
        self.h1_Cat_hd1_bn = nn.BatchNorm2d(self.CatChannels)
        self.h1_Cat_hd1_relu = nn.ReLU(inplace=True)

        # hd2->160*160, hd1->320*320, Upsample 2 times
        self.hd2_UT_hd1 = nn.Upsample(scale_factor=2, mode='bilinear')  # 14*14
        self.hd2_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
        self.hd2_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd2_UT_hd1_relu = nn.ReLU(inplace=True)

        # hd3->80*80, hd1->320*320, Upsample 4 times
        self.hd3_UT_hd1 = nn.Upsample(scale_factor=4, mode='bilinear')  # 14*14
        self.hd3_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
        self.hd3_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd3_UT_hd1_relu = nn.ReLU(inplace=True)

        # hd4->40*40, hd1->320*320, Upsample 8 times
        self.hd4_UT_hd1 = nn.Upsample(scale_factor=8, mode='bilinear')  # 14*14
        self.hd4_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
        self.hd4_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd4_UT_hd1_relu = nn.ReLU(inplace=True)

        # hd5->20*20, hd1->320*320, Upsample 16 times
        self.hd5_UT_hd1 = nn.Upsample(scale_factor=16, mode='bilinear')  # 14*14
        self.hd5_UT_hd1_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
        self.hd5_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
        self.hd5_UT_hd1_relu = nn.ReLU(inplace=True)

        # fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1)
        self.conv1d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1)  # 16
        self.bn1d_1 = nn.BatchNorm2d(self.UpChannels)
        self.relu1d_1 = nn.ReLU(inplace=True)

        # -------------Bilinear Upsampling--------------
        self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear')  ###
        self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear')
        self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear')
        self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear')
        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')

        # DeepSup
        self.outconv1 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
        self.outconv2 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
        self.outconv3 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
        self.outconv4 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
        self.outconv5 = nn.Conv2d(filters[4], n_classes, 3, padding=1)

        self.cls = nn.Sequential(
            nn.Dropout(p=0.5),
            nn.Conv2d(filters[4], 2, 1),
            nn.AdaptiveMaxPool2d(1),
            nn.Sigmoid())

        # initialise weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init_weights(m, init_type='kaiming')
            elif isinstance(m, nn.BatchNorm2d):
                init_weights(m, init_type='kaiming')

    def dotProduct(self, seg, cls):
        B, N, H, W = seg.size()
        seg = seg.view(B, 1, N * H * W)
        final = torch.einsum("ijk,ij->ijk", [seg, cls])
        final = final.view(B, N, H, W)
        return final

    def forward(self, inputs):
        ## -------------Encoder-------------
        h1 = self.conv1(inputs)  # h1->320*320*64

        h2 = self.maxpool1(h1)
        h2 = self.conv2(h2)  # h2->160*160*128

        h3 = self.maxpool2(h2)
        h3 = self.conv3(h3)  # h3->80*80*256

        h4 = self.maxpool3(h3)
        h4 = self.conv4(h4)  # h4->40*40*512

        h5 = self.maxpool4(h4)
        hd5 = self.conv5(h5)  # h5->20*20*1024

        # -------------Classification-------------
        cls_branch = self.cls(hd5).squeeze(3).squeeze(2)  # (B,N,1,1)->(B,N)
        cls_branch_max = cls_branch.argmax(dim=1)
        cls_branch_max = cls_branch_max[:, np.newaxis].float()

        ## -------------Decoder-------------
        h1_PT_hd4 = self.h1_PT_hd4_relu(self.h1_PT_hd4_bn(self.h1_PT_hd4_conv(self.h1_PT_hd4(h1))))
        h2_PT_hd4 = self.h2_PT_hd4_relu(self.h2_PT_hd4_bn(self.h2_PT_hd4_conv(self.h2_PT_hd4(h2))))
        h3_PT_hd4 = self.h3_PT_hd4_relu(self.h3_PT_hd4_bn(self.h3_PT_hd4_conv(self.h3_PT_hd4(h3))))
        h4_Cat_hd4 = self.h4_Cat_hd4_relu(self.h4_Cat_hd4_bn(self.h4_Cat_hd4_conv(h4)))
        hd5_UT_hd4 = self.hd5_UT_hd4_relu(self.hd5_UT_hd4_bn(self.hd5_UT_hd4_conv(self.hd5_UT_hd4(hd5))))
        hd4 = self.relu4d_1(self.bn4d_1(self.conv4d_1(
            torch.cat((h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4), 1))))  # hd4->40*40*UpChannels

        h1_PT_hd3 = self.h1_PT_hd3_relu(self.h1_PT_hd3_bn(self.h1_PT_hd3_conv(self.h1_PT_hd3(h1))))
        h2_PT_hd3 = self.h2_PT_hd3_relu(self.h2_PT_hd3_bn(self.h2_PT_hd3_conv(self.h2_PT_hd3(h2))))
        h3_Cat_hd3 = self.h3_Cat_hd3_relu(self.h3_Cat_hd3_bn(self.h3_Cat_hd3_conv(h3)))
        hd4_UT_hd3 = self.hd4_UT_hd3_relu(self.hd4_UT_hd3_bn(self.hd4_UT_hd3_conv(self.hd4_UT_hd3(hd4))))
        hd5_UT_hd3 = self.hd5_UT_hd3_relu(self.hd5_UT_hd3_bn(self.hd5_UT_hd3_conv(self.hd5_UT_hd3(hd5))))
        hd3 = self.relu3d_1(self.bn3d_1(self.conv3d_1(
            torch.cat((h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3), 1))))  # hd3->80*80*UpChannels

        h1_PT_hd2 = self.h1_PT_hd2_relu(self.h1_PT_hd2_bn(self.h1_PT_hd2_conv(self.h1_PT_hd2(h1))))
        h2_Cat_hd2 = self.h2_Cat_hd2_relu(self.h2_Cat_hd2_bn(self.h2_Cat_hd2_conv(h2)))
        hd3_UT_hd2 = self.hd3_UT_hd2_relu(self.hd3_UT_hd2_bn(self.hd3_UT_hd2_conv(self.hd3_UT_hd2(hd3))))
        hd4_UT_hd2 = self.hd4_UT_hd2_relu(self.hd4_UT_hd2_bn(self.hd4_UT_hd2_conv(self.hd4_UT_hd2(hd4))))
        hd5_UT_hd2 = self.hd5_UT_hd2_relu(self.hd5_UT_hd2_bn(self.hd5_UT_hd2_conv(self.hd5_UT_hd2(hd5))))
        hd2 = self.relu2d_1(self.bn2d_1(self.conv2d_1(
            torch.cat((h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2), 1))))  # hd2->160*160*UpChannels

        h1_Cat_hd1 = self.h1_Cat_hd1_relu(self.h1_Cat_hd1_bn(self.h1_Cat_hd1_conv(h1)))
        hd2_UT_hd1 = self.hd2_UT_hd1_relu(self.hd2_UT_hd1_bn(self.hd2_UT_hd1_conv(self.hd2_UT_hd1(hd2))))
        hd3_UT_hd1 = self.hd3_UT_hd1_relu(self.hd3_UT_hd1_bn(self.hd3_UT_hd1_conv(self.hd3_UT_hd1(hd3))))
        hd4_UT_hd1 = self.hd4_UT_hd1_relu(self.hd4_UT_hd1_bn(self.hd4_UT_hd1_conv(self.hd4_UT_hd1(hd4))))
        hd5_UT_hd1 = self.hd5_UT_hd1_relu(self.hd5_UT_hd1_bn(self.hd5_UT_hd1_conv(self.hd5_UT_hd1(hd5))))
        hd1 = self.relu1d_1(self.bn1d_1(self.conv1d_1(
            torch.cat((h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1), 1))))  # hd1->320*320*UpChannels

        d5 = self.outconv5(hd5)
        d5 = self.upscore5(d5)  # 16->256

        d4 = self.outconv4(hd4)
        d4 = self.upscore4(d4)  # 32->256

        d3 = self.outconv3(hd3)
        d3 = self.upscore3(d3)  # 64->256

        d2 = self.outconv2(hd2)
        d2 = self.upscore2(d2)  # 128->256

        d1 = self.outconv1(hd1)  # 256

        d1 = self.dotProduct(d1, cls_branch_max)
        d2 = self.dotProduct(d2, cls_branch_max)
        d3 = self.dotProduct(d3, cls_branch_max)
        d4 = self.dotProduct(d4, cls_branch_max)
        d5 = self.dotProduct(d5, cls_branch_max)
        return d1

import torch
import torch.nn.functional as F
import torch.nn as nn

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import math

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=3, deep_supervision=False, img_size=224, patch_size=16, in_chans=3,
                 embed_dims=[128, 160, 256],
                 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(1, 16, 3, stride=1, padding=1)
        self.encoder2 = nn.Conv2d(16, 32, 3, stride=1, padding=1)
        self.encoder3 = nn.Conv2d(32, 128, 3, stride=1, padding=1)

        self.ebn1 = nn.BatchNorm2d(16)
        self.ebn2 = nn.BatchNorm2d(32)
        self.ebn3 = nn.BatchNorm2d(128)

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

        self.dnorm3 = norm_layer(160)
        self.dnorm4 = norm_layer(128)

        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(256, 160, 3, stride=1, padding=1)
        self.decoder2 = nn.Conv2d(160, 128, 3, stride=1, padding=1)
        self.decoder3 = nn.Conv2d(128, 32, 3, stride=1, padding=1)
        self.decoder4 = nn.Conv2d(32, 16, 3, stride=1, padding=1)
        self.decoder5 = nn.Conv2d(16, 16, 3, stride=1, padding=1)

        self.dbn1 = nn.BatchNorm2d(160)
        self.dbn2 = nn.BatchNorm2d(128)
        self.dbn3 = nn.BatchNorm2d(32)
        self.dbn4 = nn.BatchNorm2d(16)

        self.final = nn.Conv2d(16, 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)

class _Interpolate(nn.Module):
    def __init__(self, scale_factor=2, mode='bilinear', align_corners=True):
        super(_Interpolate, self).__init__()
        self.scale_factor = scale_factor
        self.mode = mode
        self.align_corners = align_corners

    def forward(self, x):
        x = nn.functional.interpolate(x, scale_factor=self.scale_factor, mode=self.mode,
                                      align_corners=self.align_corners)
        return x
class _Up(nn.Module):
    def __init__(self, x1_ch, x2_ch, out_ch):
        super(_Up, self).__init__()
        self.up = _Interpolate()
        self.conv1x1 = nn.Conv2d(in_channels=x2_ch, out_channels=x1_ch, kernel_size=1)
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=x1_ch, out_channels=out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(num_features=out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=out_ch, out_channels=out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(num_features=out_ch),
            nn.ReLU(inplace=True)
        )

    def forward(self, x1, x2):
        x1 = self.up(x1)
        x2 = self.conv1x1(x2)
        x = x1 + x2
        x = self.conv(x)
        return x
class Downsample_block(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Downsample_block, self).__init__()
        #卷积，激励，正则，卷积，激励，正则，最大池化
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        y = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(y, 2, stride=2)

        return x, y
class Upsample_block(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Upsample_block, self).__init__()
        #逆卷积，合并，卷积，正则化，激励，卷积，正则化，激励
        self.transconv = nn.ConvTranspose2d(in_channels, out_channels, 4, padding=1, stride=2)
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x, y):
        x = self.transconv(x)
        x = torch.cat((x, y), dim=1)
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))

        return x
class Unet(nn.Module):
    def __init__(self,  num_classes,input_channels,deep_supervision):
        in_chan = 1
        out_chan = num_classes
        super(Unet, self).__init__()
        # 四个降采样，卷积，丢弃，正则，卷积，丢弃，四个上采样。
        self.down1 = Downsample_block(in_chan, 64)
        self.down2 = Downsample_block(64, 128)
        self.down3 = Downsample_block(128, 256)
        self.down4 = Downsample_block(256, 512)
        self.conv1 = nn.Conv2d(512, 1024, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(1024)
        self.conv2 = nn.Conv2d(1024, 1024, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(1024)
        self.up4 = Upsample_block(1024, 512)
        self.up3 = Upsample_block(512, 256)
        self.up2 = Upsample_block(256, 128)
        self.up1 = Upsample_block(128, 64)
        self.outconv = nn.Conv2d(64, out_chan, 1)
        # self.out = nn.functional.softmax()
        self.outconvp1 = nn.Conv2d(64, out_chan, 1)
        self.outconvm1 = nn.Conv2d(64, out_chan, 1)

        self.up1_conv = nn.Conv2d(in_channels=512, out_channels=out_chan, kernel_size=1)
        self.up2_conv = nn.Conv2d(in_channels=256, out_channels=out_chan, kernel_size=1)
        self.up3_conv = nn.Conv2d(in_channels=128, out_channels=out_chan, kernel_size=1)
        self.up4_conv = nn.Conv2d(in_channels=64, out_channels=out_chan, kernel_size=1)

    def forward(self, x):
        x, y1 = self.down1(x)
        x, y2 = self.down2(x)
        x, y3 = self.down3(x)
        x, y4 = self.down4(x)
        x = F.dropout2d(F.relu(self.bn1(self.conv1(x))))
        x = F.dropout2d(F.relu(self.bn2(self.conv2(x))))
        x5 = self.up4(x, y4)
        x6 = self.up3(x5, y3)
        x7 = self.up2(x6, y2)
        x8 = self.up1(x7, y1)
        x1 = self.outconv(x8)
        # x1 = self.out(x1)

        return x1

class BasicConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
        super(BasicConv, self).__init__()
        self.out_channels = out_channels
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_channels,eps=1e-5, momentum=0.01, affine=True) if bn else None
        self.relu = nn.ReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x

class ChannelPool(nn.Module):
    def forward(self, x):
        return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )

class SpatialGate(nn.Module):
    def __init__(self):
        super(SpatialGate, self).__init__()
        kernel_size = 7
        self.compress = ChannelPool()
        self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False)
    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.spatial(x_compress)
        scale = torch.sigmoid_(x_out)
        return x * scale

class TripletAttention(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):
        super(TripletAttention, self).__init__()
        self.ChannelGateH = SpatialGate()
        self.ChannelGateW = SpatialGate()
        self.no_spatial=no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialGate()
    def forward(self, x):
        x_perm1 = x.permute(0,2,1,3).contiguous()
        x_out1 = self.ChannelGateH(x_perm1)
        x_out11 = x_out1.permute(0,2,1,3).contiguous()
        x_perm2 = x.permute(0,3,2,1).contiguous()
        x_out2 = self.ChannelGateW(x_perm2)
        x_out21 = x_out2.permute(0,3,2,1).contiguous()
        if not self.no_spatial:
            x_out = self.SpatialGate(x)
            x_out = (1/3)*(x_out + x_out11 + x_out21)
        else:
            x_out = (1/2)*(x_out11 + x_out21)
        return x_out

class Downsample_block(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Downsample_block, self).__init__()
        #卷积，激励，正则，卷积，激励，正则，最大池化
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.ta = TripletAttention(out_channels,16)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        y = F.relu(self.bn2(self.conv2(x)))
        z = self.ta(y)
        x = F.max_pool2d(z, 2, stride=2)
        return x, z


class Upsample_block(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Upsample_block, self).__init__()
        #逆卷积，合并，卷积，正则化，激励，卷积，正则化，激励
        self.transconv = nn.ConvTranspose2d(in_channels, out_channels, 4, padding=1, stride=2)
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.ta = TripletAttention(out_channels,16)
        #self.se = SELayer(out_channels)

    def forward(self, x, y):
        x = self.transconv(x)
        x = self.ta(x)
        x = torch.cat((x, y), dim=1)
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        return x


class TA_Unet(nn.Module):
    def __init__(self,num_classes,input_channels,deep_supervision):
        in_chan = 1
        out_chan = 1
        super(TA_Unet, self).__init__()
        #四个降采样，卷积，丢弃，正则，卷积，丢弃，四个上采样。
        self.down1 = Downsample_block(in_chan, 64)
        self.down2 = Downsample_block(64, 128)
        self.down3 = Downsample_block(128, 256)
        self.down4 = Downsample_block(256, 512)
        self.conv1 = nn.Conv2d(512, 1024, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(1024)
        self.conv2 = nn.Conv2d(1024, 1024, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(1024)
        self.up4 = Upsample_block(1024, 512)
        self.up3 = Upsample_block(512, 256)
        self.up2 = Upsample_block(256, 128)
        self.up1 = Upsample_block(128, 64)
        self.outconv = nn.Conv2d(64, out_chan, 1)

    def forward(self, x):
        x, y1 = self.down1(x)
        x, y2 = self.down2(x)
        x, y3 = self.down3(x)
        x, y4 = self.down4(x)
        x = F.dropout2d(F.relu(self.bn1(self.conv1(x))))
        x = F.dropout2d(F.relu(self.bn2(self.conv2(x))))
        x5 = self.up4(x, y4)
        x6 = self.up3(x5, y3)
        x7 = self.up2(x6, y2)
        x8 = self.up1(x7, y1)
        cls = self.outconv(x8)


        return cls

if __name__ == '__main__':
    # model = Generic_UNet(1, 32, 3, 5, 2, 2, nn.Conv3d, nn.BatchNorm3d, {'eps': 1e-5, 'affine': True}, nn.Dropout3d,
    #                      {'p': 0, 'inplace': True},
    #                      nn.LeakyReLU, {'negative_slope': 0.01, 'inplace': True}, True, False, lambda x: x,
    #                      InitWeights_He(1e-2),
    #                      [[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]],
    #                      [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], False, True, True)
    # print(model)
    #
    # x_np = np.ones((2, 1, 64, 192, 160), dtype=np.float32)
    # x_tensor = torch.tensor(x_np)
    # y = model(x_tensor)
    # print(y)
    # --------------------nnUnet2D网络做测试-----------------------------------------
    from config import get_config
    config = get_config()
    model = ClusterSeg(config)
    print(model)

    # x_np = np.ones((2,1,192,192),dtype=np.float32)
    # x_tensor = torch.tensor(x_np)(64, 192,160)
    x_tensor = torch.randn((6, 1, 160, 160))
    y = model(x_tensor)
    print(y)
    print(y.shape)
    # import torch
    from thop import profile

    #
    # inputs = torch.randn(2, 1, 192, 192)
    # # model = DenseUNet(1, 3)
    #
    flops, params = profile(model, inputs=(x_tensor,))
    print('flops: {}, params: {}'.format(flops, params))