utils.py

import torch
from torch import nn
import numpy as np
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.vision_transformer import _cfg, Mlp, Block

def get_n_params(model):
    pp=0
    for p in list(model.parameters()):
        nn=1
        for s in list(p.size()):
            nn = nn*s
        pp += nn
    return pp


############ Swin Transformer ############
class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module): # W-MSA in the paper
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C) >>> (B * 32*32, 4*4, 192)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)  #AMBIGUOUS X)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.
    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
#         print(x.shape, end = " | ")
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
#         print(x.shape)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


############ DLF ############
class CrossAttention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.wq = nn.Linear(dim, dim, bias=qkv_bias)
        self.wk = nn.Linear(dim, dim, bias=qkv_bias)
        self.wv = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):

        B, N, C = x.shape
        q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)  # B1C -> B1H(C/H) -> BH1(C/H)
        k = self.wk(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)
        v = self.wv(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)

        attn = (q @ k.transpose(-2, -1)) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, 1, C)   # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class CrossAttentionBlock(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, has_mlp=True):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = CrossAttention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.has_mlp = has_mlp
        if has_mlp:
            self.norm2 = norm_layer(dim)
            mlp_hidden_dim = int(dim * mlp_ratio)
            self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
        if self.has_mlp:
            x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class MultiScaleBlock(nn.Module):

    def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()

        num_branches = len(dim)
        self.num_branches = num_branches

        self.blocks = nn.ModuleList()
        for d in range(num_branches):
            tmp = []
            for i in range(depth[d]):
                tmp.append(
                    Block(dim=dim[d], num_heads=num_heads[d], mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, 
                          attn_drop=attn_drop, drop_path=drop_path[i], norm_layer=norm_layer))
            if len(tmp) != 0:
                self.blocks.append(nn.Sequential(*tmp))

        if len(self.blocks) == 0:
            self.blocks = None

        self.projs = nn.ModuleList()
        for d in range(num_branches):
            if dim[d] == dim[(d+1) % num_branches] and False:
                tmp = [nn.Identity()]
            else:
                tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d+1) % num_branches])]
            self.projs.append(nn.Sequential(*tmp))

        self.fusion = nn.ModuleList()
        for d in range(num_branches):
            d_ = (d+1) % num_branches
            nh = num_heads[d_]
            if depth[-1] == 0:  # backward capability:
                self.fusion.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                       drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer,
                                                       has_mlp=False))
            else:
                tmp = []
                for _ in range(depth[-1]):
                    tmp.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                   drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer,
                                                   has_mlp=False))
                self.fusion.append(nn.Sequential(*tmp))

        self.revert_projs = nn.ModuleList()
        for d in range(num_branches):
            if dim[(d+1) % num_branches] == dim[d] and False:
                tmp = [nn.Identity()]
            else:
                tmp = [norm_layer(dim[(d+1) % num_branches]), act_layer(), nn.Linear(dim[(d+1) % num_branches], dim[d])]
            self.revert_projs.append(nn.Sequential(*tmp))

    def forward(self, x):
        inp = x
        
        # only take the cls token out
        proj_cls_token = [proj(x[:, 0:1]) for x, proj in zip(inp, self.projs)]

        # cross attention
        outs = []
        for i in range(self.num_branches):
            tmp = torch.cat((proj_cls_token[i], inp[(i + 1) % self.num_branches][:, 1:, ...]), dim=1)
            tmp = self.fusion[i](tmp)
            reverted_proj_cls_token = self.revert_projs[i](tmp[:, 0:1, ...])
            tmp = torch.cat((reverted_proj_cls_token, inp[i][:, 1:, ...]), dim=1)
            outs.append(tmp)
            
        outs_b = [block(x_) for x_, block in zip(outs, self.blocks)]
        return outs


############ Test ############
import numpy as np
import torch
from medpy import metric
from scipy.ndimage import zoom
import torch.nn as nn
import SimpleITK as sitk


class DiceLoss(nn.Module):
    def __init__(self, n_classes):
        super(DiceLoss, self).__init__()
        self.n_classes = n_classes

    def _one_hot_encoder(self, input_tensor):
        tensor_list = []
        for i in range(self.n_classes):
            temp_prob = input_tensor == i  # * torch.ones_like(input_tensor)
            tensor_list.append(temp_prob.unsqueeze(1))
        output_tensor = torch.cat(tensor_list, dim=1)
        return output_tensor.float()

    def _dice_loss(self, score, target):
        target = target.float()
        smooth = 1e-5
        intersect = torch.sum(score * target)
        y_sum = torch.sum(target * target)
        z_sum = torch.sum(score * score)
        loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth)
        loss = 1 - loss
        return loss

    def forward(self, inputs, target, weight=None, softmax=False):
        if softmax:
            inputs = torch.softmax(inputs, dim=1)
        target = self._one_hot_encoder(target)
        if weight is None:
            weight = [1] * self.n_classes
        assert inputs.size() == target.size(), 'predict {} & target {} shape do not match'.format(inputs.size(), target.size())
        class_wise_dice = []
        loss = 0.0
        for i in range(0, self.n_classes):
            dice = self._dice_loss(inputs[:, i], target[:, i])
            class_wise_dice.append(1.0 - dice.item())
            loss += dice * weight[i]
        return loss / self.n_classes


def calculate_metric_percase(pred, gt):
    pred[pred > 0] = 1
    gt[gt > 0] = 1
    if pred.sum() > 0 and gt.sum()>0:
        dice = metric.binary.dc(pred, gt)
        hd95 = metric.binary.hd95(pred, gt)
        return dice, hd95
    elif pred.sum() > 0 and gt.sum()==0:
        return 1, 0
    else:
        return 0, 0


def test_single_volume(image, label, net, classes, patch_size=[256, 256]):
    image, label = image.squeeze(0).cpu().detach().numpy(), label.squeeze(0).cpu().detach().numpy()
    if len(image.shape) == 3:
        prediction = np.zeros_like(label)
        for ind in range(image.shape[0]):
            slice = image[ind, :, :]
            x, y = slice.shape[0], slice.shape[1]
            if x != patch_size[0] or y != patch_size[1]:
                slice = zoom(slice, (patch_size[0] / x, patch_size[1] / y), order=3)  # previous using 0
            input = torch.from_numpy(slice).unsqueeze(0).unsqueeze(0).float().cuda()

            B, C, H, W = input.shape
            input = input.expand(B, 3, H, W)

            net.eval()
            with torch.no_grad():
                outputs = net(input)
                out = torch.argmax(torch.softmax(outputs, dim=1), dim=1).squeeze(0)
                out = out.cpu().detach().numpy()
                if x != patch_size[0] or y != patch_size[1]:
                    pred = zoom(out, (x / patch_size[0], y / patch_size[1]), order=0)
                else:
                    pred = out
                prediction[ind] = pred
    else:
        input = torch.from_numpy(image).unsqueeze(
            0).unsqueeze(0).float().cuda()
        net.eval()
        with torch.no_grad():
            out = torch.argmax(torch.softmax(net(input), dim=1), dim=1).squeeze(0)
            prediction = out.cpu().detach().numpy()
    metric_list = []
    for i in range(1, classes):
        metric_list.append(calculate_metric_percase(prediction == i, label == i))

    return metric_list

def random_split_array(data, ratios=(0.8, 0.1, 0.1)):
      np.random.shuffle(data)  # Shuffle for randomness
      split_indices = np.cumsum(ratios[:-1]) * len(data)
      return np.split(data, split_indices.astype(int))