utils.py

""" helper function

author junde
"""

import sys

import numpy

import torch
import torch.nn as nn
from torch.autograd import Function
from torch.optim.lr_scheduler import _LRScheduler
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torchvision.utils as vutils
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torch import autograd
import random
import math
import PIL
import matplotlib.pyplot as plt
import seaborn as sns

import collections
import logging
import cv2
import math
import os
import time
from datetime import datetime

import dateutil.tz

from typing import Union, Optional, List, Tuple, Text, BinaryIO
import pathlib
import warnings
import numpy as np
from scipy.ndimage import label, find_objects
from PIL import Image, ImageDraw, ImageFont, ImageColor
# from lucent.optvis.param.spatial import pixel_image, fft_image, init_image
# from lucent.optvis.param.color import to_valid_rgb
# from lucent.optvis import objectives, transform, param
# from lucent.misc.io import show
from torchvision.models import vgg19
import torch.nn.functional as F
import cfg

import warnings
from collections import OrderedDict
import numpy as np
from tqdm import tqdm
from PIL import Image
import torch

# from precpt import run_precpt
from models.discriminator import Discriminator
# from siren_pytorch import SirenNet, SirenWrapper

import shutil
import tempfile

import matplotlib.pyplot as plt
from tqdm import tqdm

from monai.losses import DiceCELoss
from monai.inferers import sliding_window_inference
from monai.transforms import (
    AsDiscrete,
    Compose,
    CropForegroundd,
    LoadImaged,
    Orientationd,
    RandFlipd,
    RandCropByPosNegLabeld,
    RandShiftIntensityd,
    ScaleIntensityRanged,
    Spacingd,
    RandRotate90d,
    EnsureTyped,
)

from monai.config import print_config
from monai.metrics import DiceMetric
from monai.networks.nets import SwinUNETR

from monai.data import (
    ThreadDataLoader,
    CacheDataset,
    load_decathlon_datalist,
    decollate_batch,
    set_track_meta,
)




args = cfg.parse_args()
device = torch.device('cuda', args.gpu_device)

'''preparation of domain loss'''
# cnn = vgg19(pretrained=True).features.to(device).eval()
# cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
# cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)

# netD = Discriminator(1).to(device)
# netD.apply(init_D)
# beta1 = 0.5
# dis_lr = 0.0002
# optimizerD = optim.Adam(netD.parameters(), lr=dis_lr, betas=(beta1, 0.999))
'''end'''

def get_network(args, net, use_gpu=True, gpu_device = 0, distribution = True):
    """ return given network
    """

    if net == 'sam':
        from models.sam import SamPredictor, sam_model_registry
        from models.sam.utils.transforms import ResizeLongestSide

        net = sam_model_registry['vit_b'](args,checkpoint=args.sam_ckpt).to(device)
    else:
        print('the network name you have entered is not supported yet')
        sys.exit()

    if use_gpu:
        #net = net.cuda(device = gpu_device)
        if distribution != 'none':
            net = torch.nn.DataParallel(net,device_ids=[int(id) for id in args.distributed.split(',')])
            net = net.to(device=gpu_device)
        else:
            net = net.to(device=gpu_device)

    return net


def get_decath_loader(args):

    train_transforms = Compose(
        [   
            LoadImaged(keys=["image", "label"], ensure_channel_first=True),
            ScaleIntensityRanged(
                keys=["image"],
                a_min=-175,
                a_max=250,
                b_min=0.0,
                b_max=1.0,
                clip=True,
            ),
            CropForegroundd(keys=["image", "label"], source_key="image"),
            Orientationd(keys=["image", "label"], axcodes="RAS"),
            Spacingd(
                keys=["image", "label"],
                pixdim=(1.5, 1.5, 2.0),
                mode=("bilinear", "nearest"),
            ),
            EnsureTyped(keys=["image", "label"], device=device, track_meta=False),
            RandCropByPosNegLabeld(
                keys=["image", "label"],
                label_key="label",
                spatial_size=(args.roi_size, args.roi_size, args.chunk),
                pos=1,
                neg=1,
                num_samples=args.num_sample,
                image_key="image",
                image_threshold=0,
            ),
            RandFlipd(
                keys=["image", "label"],
                spatial_axis=[0],
                prob=0.10,
            ),
            RandFlipd(
                keys=["image", "label"],
                spatial_axis=[1],
                prob=0.10,
            ),
            RandFlipd(
                keys=["image", "label"],
                spatial_axis=[2],
                prob=0.10,
            ),
            RandRotate90d(
                keys=["image", "label"],
                prob=0.10,
                max_k=3,
            ),
            RandShiftIntensityd(
                keys=["image"],
                offsets=0.10,
                prob=0.50,
            ),
        ]
    )
    val_transforms = Compose(
        [
            LoadImaged(keys=["image", "label"], ensure_channel_first=True),
            ScaleIntensityRanged(
                keys=["image"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True
            ),
            CropForegroundd(keys=["image", "label"], source_key="image"),
            Orientationd(keys=["image", "label"], axcodes="RAS"),
            Spacingd(
                keys=["image", "label"],
                pixdim=(1.5, 1.5, 2.0),
                mode=("bilinear", "nearest"),
            ),
            EnsureTyped(keys=["image", "label"], device=device, track_meta=True),
        ]
    )



    data_dir = args.data_path
    split_JSON = "dataset_0.json"

    datasets = os.path.join(data_dir, split_JSON)
    datalist = load_decathlon_datalist(datasets, True, "training")
    val_files = load_decathlon_datalist(datasets, True, "validation")
    train_ds = CacheDataset(
        data=datalist,
        transform=train_transforms,
        cache_num=24,
        cache_rate=1.0,
        num_workers=8,
    )
    train_loader = ThreadDataLoader(train_ds, num_workers=0, batch_size=args.b, shuffle=True)
    val_ds = CacheDataset(
        data=val_files, transform=val_transforms, cache_num=2, cache_rate=1.0, num_workers=0
    )
    val_loader = ThreadDataLoader(val_ds, num_workers=0, batch_size=1)

    set_track_meta(False)

    return train_loader, val_loader, train_transforms, val_transforms, datalist, val_files


def cka_loss(gram_featureA, gram_featureB):

    scaled_hsic = torch.dot(torch.flatten(gram_featureA),torch.flatten(gram_featureB))
    normalization_x = gram_featureA.norm()
    normalization_y = gram_featureB.norm()
    return scaled_hsic / (normalization_x * normalization_y)


class WarmUpLR(_LRScheduler):
    """warmup_training learning rate scheduler
    Args:
        optimizer: optimzier(e.g. SGD)
        total_iters: totoal_iters of warmup phase
    """
    def __init__(self, optimizer, total_iters, last_epoch=-1):

        self.total_iters = total_iters
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """we will use the first m batches, and set the learning
        rate to base_lr * m / total_iters
        """
        return [base_lr * self.last_epoch / (self.total_iters + 1e-8) for base_lr in self.base_lrs]

def gram_matrix(input):
    a, b, c, d = input.size()  # a=batch size(=1)
    # b=number of feature maps
    # (c,d)=dimensions of a f. map (N=c*d)

    features = input.view(a * b, c * d)  # resise F_XL into \hat F_XL

    G = torch.mm(features, features.t())  # compute the gram product

    # we 'normalize' the values of the gram matrix
    # by dividing by the number of element in each feature maps.
    return G.div(a * b * c * d)



@torch.no_grad()
def make_grid(
    tensor: Union[torch.Tensor, List[torch.Tensor]],
    nrow: int = 8,
    padding: int = 2,
    normalize: bool = False,
    value_range: Optional[Tuple[int, int]] = None,
    scale_each: bool = False,
    pad_value: int = 0,
    **kwargs
) -> torch.Tensor:
    if not (torch.is_tensor(tensor) or
            (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')

    if "range" in kwargs.keys():
        warning = "range will be deprecated, please use value_range instead."
        warnings.warn(warning)
        value_range = kwargs["range"]

    # if list of tensors, convert to a 4D mini-batch Tensor
    if isinstance(tensor, list):
        tensor = torch.stack(tensor, dim=0)

    if tensor.dim() == 2:  # single image H x W
        tensor = tensor.unsqueeze(0)
    if tensor.dim() == 3:  # single image
        if tensor.size(0) == 1:  # if single-channel, convert to 3-channel
            tensor = torch.cat((tensor, tensor, tensor), 0)
        tensor = tensor.unsqueeze(0)

    if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
        tensor = torch.cat((tensor, tensor, tensor), 1)

    if normalize is True:
        tensor = tensor.clone()  # avoid modifying tensor in-place
        if value_range is not None:
            assert isinstance(value_range, tuple), \
                "value_range has to be a tuple (min, max) if specified. min and max are numbers"

        def norm_ip(img, low, high):
            img.clamp(min=low, max=high)
            img.sub_(low).div_(max(high - low, 1e-5))

        def norm_range(t, value_range):
            if value_range is not None:
                norm_ip(t, value_range[0], value_range[1])
            else:
                norm_ip(t, float(t.min()), float(t.max()))

        if scale_each is True:
            for t in tensor:  # loop over mini-batch dimension
                norm_range(t, value_range)
        else:
            norm_range(tensor, value_range)

    if tensor.size(0) == 1:
        return tensor.squeeze(0)

    # make the mini-batch of images into a grid
    nmaps = tensor.size(0)
    xmaps = min(nrow, nmaps)
    ymaps = int(math.ceil(float(nmaps) / xmaps))
    height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)
    num_channels = tensor.size(1)
    grid = tensor.new_full((num_channels, height * ymaps + padding, width * xmaps + padding), pad_value)
    k = 0
    for y in range(ymaps):
        for x in range(xmaps):
            if k >= nmaps:
                break
            # Tensor.copy_() is a valid method but seems to be missing from the stubs
            # https://pytorch.org/docs/stable/tensors.html#torch.Tensor.copy_
            grid.narrow(1, y * height + padding, height - padding).narrow(  # type: ignore[attr-defined]
                2, x * width + padding, width - padding
            ).copy_(tensor[k])
            k = k + 1
    return grid


@torch.no_grad()
def save_image(
    tensor: Union[torch.Tensor, List[torch.Tensor]],
    fp: Union[Text, pathlib.Path, BinaryIO],
    format: Optional[str] = None,
    **kwargs
) -> None:
    """
    Save a given Tensor into an image file.
    Args:
        tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
            saves the tensor as a grid of images by calling ``make_grid``.
        fp (string or file object): A filename or a file object
        format(Optional):  If omitted, the format to use is determined from the filename extension.
            If a file object was used instead of a filename, this parameter should always be used.
        **kwargs: Other arguments are documented in ``make_grid``.
    """

    grid = make_grid(tensor, **kwargs)
    # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
    ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
    im = Image.fromarray(ndarr)
    im.save(fp, format=format)
    

def create_logger(log_dir, phase='train'):
    time_str = time.strftime('%Y-%m-%d-%H-%M')
    log_file = '{}_{}.log'.format(time_str, phase)
    final_log_file = os.path.join(log_dir, log_file)
    head = '%(asctime)-15s %(message)s'
    logging.basicConfig(filename=str(final_log_file),
                        format=head)
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)
    console = logging.StreamHandler()
    logging.getLogger('').addHandler(console)

    return logger


def set_log_dir(root_dir, exp_name):
    path_dict = {}
    os.makedirs(root_dir, exist_ok=True)

    # set log path
    exp_path = os.path.join(root_dir, exp_name)
    now = datetime.now(dateutil.tz.tzlocal())
    timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
    prefix = exp_path + '_' + timestamp
    os.makedirs(prefix)
    path_dict['prefix'] = prefix

    # set checkpoint path
    ckpt_path = os.path.join(prefix, 'Model')
    os.makedirs(ckpt_path)
    path_dict['ckpt_path'] = ckpt_path

    log_path = os.path.join(prefix, 'Log')
    os.makedirs(log_path)
    path_dict['log_path'] = log_path

    # set sample image path for fid calculation
    sample_path = os.path.join(prefix, 'Samples')
    os.makedirs(sample_path)
    path_dict['sample_path'] = sample_path

    return path_dict


def save_checkpoint(states, is_best, output_dir,
                    filename='checkpoint.pth'):
    torch.save(states, os.path.join(output_dir, filename))
    if is_best:
        torch.save(states, os.path.join(output_dir, 'checkpoint_best.pth'))


class RunningStats:
    def __init__(self, WIN_SIZE):
        self.mean = 0
        self.run_var = 0
        self.WIN_SIZE = WIN_SIZE

        self.window = collections.deque(maxlen=WIN_SIZE)

    def clear(self):
        self.window.clear()
        self.mean = 0
        self.run_var = 0

    def is_full(self):
        return len(self.window) == self.WIN_SIZE

    def push(self, x):

        if len(self.window) == self.WIN_SIZE:
            # Adjusting variance
            x_removed = self.window.popleft()
            self.window.append(x)
            old_m = self.mean
            self.mean += (x - x_removed) / self.WIN_SIZE
            self.run_var += (x + x_removed - old_m - self.mean) * (x - x_removed)
        else:
            # Calculating first variance
            self.window.append(x)
            delta = x - self.mean
            self.mean += delta / len(self.window)
            self.run_var += delta * (x - self.mean)

    def get_mean(self):
        return self.mean if len(self.window) else 0.0

    def get_var(self):
        return self.run_var / len(self.window) if len(self.window) > 1 else 0.0

    def get_std(self):
        return math.sqrt(self.get_var())

    def get_all(self):
        return list(self.window)

    def __str__(self):
        return "Current window values: {}".format(list(self.window))

def iou(outputs: np.array, labels: np.array):
    
    SMOOTH = 1e-6
    intersection = (outputs & labels).sum((1, 2))
    union = (outputs | labels).sum((1, 2))

    iou = (intersection + SMOOTH) / (union + SMOOTH)


    return iou.mean()

class DiceCoeff(Function):
    """Dice coeff for individual examples"""

    def forward(self, input, target):
        self.save_for_backward(input, target)
        eps = 0.0001
        self.inter = torch.dot(input.view(-1), target.view(-1))
        self.union = torch.sum(input) + torch.sum(target) + eps

        t = (2 * self.inter.float() + eps) / self.union.float()
        return t

    # This function has only a single output, so it gets only one gradient
    def backward(self, grad_output):

        input, target = self.saved_variables
        grad_input = grad_target = None

        if self.needs_input_grad[0]:
            grad_input = grad_output * 2 * (target * self.union - self.inter) \
                         / (self.union * self.union)
        if self.needs_input_grad[1]:
            grad_target = None

        return grad_input, grad_target


def dice_coeff(input, target):
    """Dice coeff for batches"""
    if input.is_cuda:
        s = torch.FloatTensor(1).to(device = input.device).zero_()
    else:
        s = torch.FloatTensor(1).zero_()

    for i, c in enumerate(zip(input, target)):
        s = s + DiceCoeff().forward(c[0], c[1])

    return s / (i + 1)

'''parameter'''
def para_image(w, h=None, img = None, mode = 'multi', seg = None, sd=None, batch=None,
          fft = False, channels=None, init = None):
    h = h or w
    batch = batch or 1
    ch = channels or 3
    shape = [batch, ch, h, w]
    param_f = fft_image if fft else pixel_image
    if init is not None:
        param_f = init_image
        params, maps_f = param_f(init)
    else:
        params, maps_f = param_f(shape, sd=sd)
    if mode == 'multi':
        output = to_valid_out(maps_f,img,seg)
    elif mode == 'seg':
        output = gene_out(maps_f,img)
    elif mode == 'raw':
        output = raw_out(maps_f,img)
    return params, output

def to_valid_out(maps_f,img,seg): #multi-rater
    def inner():
        maps = maps_f()
        maps = maps.to(device = img.device)
        maps = torch.nn.Softmax(dim = 1)(maps)
        final_seg = torch.multiply(seg,maps).sum(dim = 1, keepdim = True)
        return torch.cat((img,final_seg),1)
        # return torch.cat((img,maps),1)
    return inner

def gene_out(maps_f,img): #pure seg
    def inner():
        maps = maps_f()
        maps = maps.to(device = img.device)
        # maps = torch.nn.Sigmoid()(maps)
        return torch.cat((img,maps),1)
        # return torch.cat((img,maps),1)
    return inner

def raw_out(maps_f,img): #raw
    def inner():
        maps = maps_f()
        maps = maps.to(device = img.device)
        # maps = torch.nn.Sigmoid()(maps)
        return maps
        # return torch.cat((img,maps),1)
    return inner    


class CompositeActivation(torch.nn.Module):

    def forward(self, x):
        x = torch.atan(x)
        return torch.cat([x/0.67, (x*x)/0.6], 1)
        # return x


def cppn(args, size, img = None, seg = None, batch=None, num_output_channels=1, num_hidden_channels=128, num_layers=8,
         activation_fn=CompositeActivation, normalize=False, device = "cuda:0"):

    r = 3 ** 0.5

    coord_range = torch.linspace(-r, r, size)
    x = coord_range.view(-1, 1).repeat(1, coord_range.size(0))
    y = coord_range.view(1, -1).repeat(coord_range.size(0), 1)

    input_tensor = torch.stack([x, y], dim=0).unsqueeze(0).repeat(batch,1,1,1).to(device)

    layers = []
    kernel_size = 1
    for i in range(num_layers):
        out_c = num_hidden_channels
        in_c = out_c * 2 # * 2 for composite activation
        if i == 0:
            in_c = 2
        if i == num_layers - 1:
            out_c = num_output_channels
        layers.append(('conv{}'.format(i), torch.nn.Conv2d(in_c, out_c, kernel_size)))
        if normalize:
            layers.append(('norm{}'.format(i), torch.nn.InstanceNorm2d(out_c)))
        if i < num_layers - 1:
            layers.append(('actv{}'.format(i), activation_fn()))
        else:
            layers.append(('output', torch.nn.Sigmoid()))

    # Initialize model
    net = torch.nn.Sequential(OrderedDict(layers)).to(device)
    # Initialize weights
    def weights_init(module):
        if isinstance(module, torch.nn.Conv2d):
            torch.nn.init.normal_(module.weight, 0, np.sqrt(1/module.in_channels))
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
    net.apply(weights_init)
    # Set last conv2d layer's weights to 0
    torch.nn.init.zeros_(dict(net.named_children())['conv{}'.format(num_layers - 1)].weight)
    outimg = raw_out(lambda: net(input_tensor),img) if args.netype == 'raw' else to_valid_out(lambda: net(input_tensor),img,seg)
    return net.parameters(), outimg

def get_siren(args):
    wrapper = get_network(args, 'siren', use_gpu=args.gpu, gpu_device=torch.device('cuda', args.gpu_device), distribution = args.distributed)
    '''load init weights'''
    checkpoint = torch.load('./logs/siren_train_init_2022_08_19_21_00_16/Model/checkpoint_best.pth')
    wrapper.load_state_dict(checkpoint['state_dict'],strict=False)
    '''end'''

    '''load prompt'''
    checkpoint = torch.load('./logs/vae_standard_refuge1_2022_08_21_17_56_49/Model/checkpoint500')
    vae = get_network(args, 'vae', use_gpu=args.gpu, gpu_device=torch.device('cuda', args.gpu_device), distribution = args.distributed)
    vae.load_state_dict(checkpoint['state_dict'],strict=False)
    '''end'''

    return wrapper, vae


def siren(args, wrapper, vae, img = None, seg = None, batch=None, num_output_channels=1, num_hidden_channels=128, num_layers=8,
         activation_fn=CompositeActivation, normalize=False, device = "cuda:0"):
    vae_img = torchvision.transforms.Resize(64)(img)
    latent = vae.encoder(vae_img).view(-1).detach()
    outimg = raw_out(lambda: wrapper(latent = latent),img) if args.netype == 'raw' else to_valid_out(lambda: wrapper(latent = latent),img,seg)
    # img = torch.randn(1, 3, 256, 256)
    # loss = wrapper(img)
    # loss.backward()

    # # after much training ...
    # # simply invoke the wrapper without passing in anything

    # pred_img = wrapper() # (1, 3, 256, 256)
    return wrapper.parameters(), outimg
        

'''adversary'''
def render_vis(
    args,
    model,
    objective_f,
    real_img,
    param_f=None,
    optimizer=None,
    transforms=None,
    thresholds=(256,),
    verbose=True,
    preprocess=True,
    progress=True,
    show_image=True,
    save_image=False,
    image_name=None,
    show_inline=False,
    fixed_image_size=None,
    label = 1,
    raw_img = None,
    prompt = None
):
    if label == 1:
        sign = 1
    elif label == 0:
        sign = -1
    else:
        print('label is wrong, label is',label)
    if args.reverse:
        sign = -sign
    if args.multilayer:
        sign = 1

    '''prepare'''
    now = datetime.now()
    date_time = now.strftime("%m-%d-%Y, %H:%M:%S")

    netD, optD = pre_d()
    '''end'''

    if param_f is None:
        param_f = lambda: param.image(128)
    # param_f is a function that should return two things
    # params - parameters to update, which we pass to the optimizer
    # image_f - a function that returns an image as a tensor
    params, image_f = param_f()
    
    if optimizer is None:
        optimizer = lambda params: torch.optim.Adam(params, lr=5e-1)
    optimizer = optimizer(params)

    if transforms is None:
        transforms = []
    transforms = transforms.copy()

    # Upsample images smaller than 224
    image_shape = image_f().shape

    if fixed_image_size is not None:
        new_size = fixed_image_size
    elif image_shape[2] < 224 or image_shape[3] < 224:
        new_size = 224
    else:
        new_size = None
    if new_size:
        transforms.append(
            torch.nn.Upsample(size=new_size, mode="bilinear", align_corners=True)
        )

    transform_f = transform.compose(transforms)

    hook = hook_model(model, image_f)
    objective_f = objectives.as_objective(objective_f)

    if verbose:
        model(transform_f(image_f()))
        print("Initial loss of ad: {:.3f}".format(objective_f(hook)))

    images = []
    try:
        for i in tqdm(range(1, max(thresholds) + 1), disable=(not progress)):
            optimizer.zero_grad()
            try:
                model(transform_f(image_f()))
            except RuntimeError as ex:
                if i == 1:
                    # Only display the warning message
                    # on the first iteration, no need to do that
                    # every iteration
                    warnings.warn(
                        "Some layers could not be computed because the size of the "
                        "image is not big enough. It is fine, as long as the non"
                        "computed layers are not used in the objective function"
                        f"(exception details: '{ex}')"
                    )
            if args.disc:
                '''dom loss part'''
                # content_img = raw_img
                # style_img = raw_img
                # precpt_loss = run_precpt(cnn, cnn_normalization_mean, cnn_normalization_std, content_img, style_img, transform_f(image_f()))
                for p in netD.parameters():
                    p.requires_grad = True
                for _ in range(args.drec):
                    netD.zero_grad()
                    real = real_img
                    fake = image_f()
                    # for _ in range(6):
                    #     errD, D_x, D_G_z1 = update_d(args, netD, optD, real, fake)

                    # label = torch.full((args.b,), 1., dtype=torch.float, device=device)
                    # label.fill_(1.)
                    # output = netD(fake).view(-1)
                    # errG = nn.BCELoss()(output, label)
                    # D_G_z2 = output.mean().item()
                    # dom_loss = err
                    one = torch.tensor(1, dtype=torch.float)
                    mone = one * -1
                    one = one.cuda(args.gpu_device)
                    mone = mone.cuda(args.gpu_device)

                    d_loss_real = netD(real)
                    d_loss_real = d_loss_real.mean()
                    d_loss_real.backward(mone)

                    d_loss_fake = netD(fake)
                    d_loss_fake = d_loss_fake.mean()
                    d_loss_fake.backward(one)

                    # Train with gradient penalty
                    gradient_penalty = calculate_gradient_penalty(netD, real.data, fake.data)
                    gradient_penalty.backward()


                    d_loss = d_loss_fake - d_loss_real + gradient_penalty
                    Wasserstein_D = d_loss_real - d_loss_fake
                    optD.step()

                # Generator update
                for p in netD.parameters():
                    p.requires_grad = False  # to avoid computation

                fake_images = image_f()
                g_loss = netD(fake_images)
                g_loss = -g_loss.mean()
                dom_loss = g_loss
                g_cost = -g_loss

                if i% 5 == 0:
                    print(f' loss_fake: {d_loss_fake}, loss_real: {d_loss_real}')
                    print(f'Generator g_loss: {g_loss}')
                '''end'''



            '''ssim loss'''

            '''end'''

            if args.disc:
                loss = sign * objective_f(hook) + args.pw * dom_loss
                # loss = args.pw * dom_loss
            else:
                loss = sign * objective_f(hook)
                # loss = args.pw * dom_loss

            loss.backward()

            # #video the images
            # if i % 5 == 0:
            #     print('1')
            #     image_name = image_name[0].split('\\')[-1].split('.')[0] + '_' + str(i) + '.png'
            #     img_path = os.path.join(args.path_helper['sample_path'], str(image_name))
            #     export(image_f(), img_path)
            # #end
            # if i % 50 == 0:
            #     print('Loss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
            #       % (errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

            optimizer.step()
            if i in thresholds:
                image = tensor_to_img_array(image_f())
                # if verbose:
                #     print("Loss at step {}: {:.3f}".format(i, objective_f(hook)))
                if save_image:
                    na = image_name[0].split('\\')[-1].split('.')[0] + '_' + str(i) + '.png'
                    na = date_time + na
                    outpath = args.quickcheck if args.quickcheck else args.path_helper['sample_path']
                    img_path = os.path.join(outpath, str(na))
                    export(image_f(), img_path)
                
                images.append(image)
    except KeyboardInterrupt:
        print("Interrupted optimization at step {:d}.".format(i))
        if verbose:
            print("Loss at step {}: {:.3f}".format(i, objective_f(hook)))
        images.append(tensor_to_img_array(image_f()))

    if save_image:
        na = image_name[0].split('\\')[-1].split('.')[0] + '.png'
        na = date_time + na
        outpath = args.quickcheck if args.quickcheck else args.path_helper['sample_path']
        img_path = os.path.join(outpath, str(na))
        export(image_f(), img_path)
    if show_inline:
        show(tensor_to_img_array(image_f()))
    elif show_image:
        view(image_f())
    return image_f()


def tensor_to_img_array(tensor):
    image = tensor.cpu().detach().numpy()
    image = np.transpose(image, [0, 2, 3, 1])
    return image


def view(tensor):
    image = tensor_to_img_array(tensor)
    assert len(image.shape) in [
        3,
        4,
    ], "Image should have 3 or 4 dimensions, invalid image shape {}".format(image.shape)
    # Change dtype for PIL.Image
    image = (image * 255).astype(np.uint8)
    if len(image.shape) == 4:
        image = np.concatenate(image, axis=1)
    Image.fromarray(image).show()


def export(tensor, img_path=None):
    # image_name = image_name or "image.jpg"
    c = tensor.size(1)
    # if c == 7:
    #     for i in range(c):
    #         w_map = tensor[:,i,:,:].unsqueeze(1)
    #         w_map = tensor_to_img_array(w_map).squeeze()
    #         w_map = (w_map * 255).astype(np.uint8)
    #         image_name = image_name[0].split('/')[-1].split('.')[0] + str(i)+ '.png'
    #         wheat = sns.heatmap(w_map,cmap='coolwarm')
    #         figure = wheat.get_figure()    
    #         figure.savefig ('./fft_maps/weightheatmap/'+str(image_name), dpi=400)
    #         figure = 0
    # else:
    if c == 3:
        vutils.save_image(tensor, fp = img_path)
    else:
        image = tensor[:,0:3,:,:]
        w_map = tensor[:,-1,:,:].unsqueeze(1)
        image = tensor_to_img_array(image)
        w_map = 1 - tensor_to_img_array(w_map).squeeze()
        # w_map[w_map==1] = 0
        assert len(image.shape) in [
            3,
            4,
        ], "Image should have 3 or 4 dimensions, invalid image shape {}".format(image.shape)
        # Change dtype for PIL.Image
        image = (image * 255).astype(np.uint8)
        w_map = (w_map * 255).astype(np.uint8)

        Image.fromarray(w_map,'L').save(img_path)


class ModuleHook:
    def __init__(self, module):
        self.hook = module.register_forward_hook(self.hook_fn)
        self.module = None
        self.features = None


    def hook_fn(self, module, input, output):
        self.module = module
        self.features = output


    def close(self):
        self.hook.remove()


def hook_model(model, image_f):
    features = OrderedDict()
    # recursive hooking function
    def hook_layers(net, prefix=[]):
        if hasattr(net, "_modules"):
            for name, layer in net._modules.items():
                if layer is None:
                    # e.g. GoogLeNet's aux1 and aux2 layers
                    continue
                features["_".join(prefix + [name])] = ModuleHook(layer)
                hook_layers(layer, prefix=prefix + [name])

    hook_layers(model)

    def hook(layer):
        if layer == "input":
            out = image_f()
        elif layer == "labels":
            out = list(features.values())[-1].features
        else:
            assert layer in features, f"Invalid layer {layer}. Retrieve the list of layers with `lucent.modelzoo.util.get_model_layers(model)`."
            out = features[layer].features
        assert out is not None, "There are no saved feature maps. Make sure to put the model in eval mode, like so: `model.to(device).eval()`. See README for example."
        return out

    return hook

def vis_image(imgs, pred_masks, gt_masks, save_path, reverse = False, points = None,thre=0.5):
    
    b,c,h,w = pred_masks.size()
    dev = pred_masks.get_device()
    row_num = min(b, 4)

    if torch.max(pred_masks) > 1 or torch.min(pred_masks) < 0:
        pred_masks = torch.sigmoid(pred_masks)
    
    pred_masks = torch.tensor(pred_masks>thre)

    if reverse == True:
        pred_masks = 1 - pred_masks
        gt_masks = 1 - gt_masks
    if c == 2:
        pred_disc, pred_cup = pred_masks[:,0,:,:].unsqueeze(1).expand(b,3,h,w), pred_masks[:,1,:,:].unsqueeze(1).expand(b,3,h,w)
        gt_disc, gt_cup = gt_masks[:,0,:,:].unsqueeze(1).expand(b,3,h,w), gt_masks[:,1,:,:].unsqueeze(1).expand(b,3,h,w)
        tup = (imgs[:row_num,:,:,:],pred_disc[:row_num,:,:,:], pred_cup[:row_num,:,:,:], gt_disc[:row_num,:,:,:], gt_cup[:row_num,:,:,:])
        # compose = torch.cat((imgs[:row_num,:,:,:],pred_disc[:row_num,:,:,:], pred_cup[:row_num,:,:,:], gt_disc[:row_num,:,:,:], gt_cup[:row_num,:,:,:]),0)
        compose = torch.cat((pred_disc[:row_num,:,:,:], pred_cup[:row_num,:,:,:], gt_disc[:row_num,:,:,:], gt_cup[:row_num,:,:,:]),0)
        vutils.save_image(compose, fp = save_path, nrow = row_num, padding = 10)
    else:
        imgs = torchvision.transforms.Resize((h,w))(imgs)
        if imgs.size(1) == 1:
            imgs = imgs[:,0,:,:].unsqueeze(1).expand(b,3,h,w)
        pred_masks = pred_masks[:,0,:,:].unsqueeze(1).expand(b,3,h,w)
        gt_masks = gt_masks[:,0,:,:].unsqueeze(1).expand(b,3,h,w)
        if points != None:
            for i in range(b):
                if args.thd:
                    p = np.round(points.cpu()/args.roi_size * args.out_size).to(dtype = torch.int)
                else:
                    p = np.round(points.cpu()/args.image_size * args.out_size).to(dtype = torch.int)
                # gt_masks[i,:,points[i,0]-5:points[i,0]+5,points[i,1]-5:points[i,1]+5] = torch.Tensor([255, 0, 0]).to(dtype = torch.float32, device = torch.device('cuda:' + str(dev)))
                for pmt_id in range(p.shape[1]):
                    gt_masks[i,0,p[i,pmt_id,0]-3:p[i,pmt_id,0]+3,p[i,pmt_id,1]-3:p[i,pmt_id,1]+3] = 255
                    gt_masks[i,1,p[i,pmt_id,0]-3:p[i,pmt_id,0]+3,p[i,pmt_id,1]-3:p[i,pmt_id,1]+3] = 0
                    gt_masks[i,2,p[i,pmt_id,0]-3:p[i,pmt_id,0]+3,p[i,pmt_id,1]-3:p[i,pmt_id,1]+3] = 0
        tup = (imgs[:row_num,:,:,:],pred_masks[:row_num,:,:,:], gt_masks[:row_num,:,:,:])
        # compose = torch.cat((imgs[:row_num,:,:,:],pred_disc[:row_num,:,:,:], pred_cup[:row_num,:,:,:], gt_disc[:row_num,:,:,:], gt_cup[:row_num,:,:,:]),0)
        compose = torch.cat(tup,0)
        vutils.save_image(compose, fp = save_path, nrow = row_num, padding = 10)

    return

def eval_seg(pred,true_mask_p,threshold):
    '''
    threshold: a int or a tuple of int
    masks: [b,2,h,w]
    pred: [b,2,h,w]
    '''
    b, c, h, w = pred.size()
    if c == 2:
        iou_d, iou_c, disc_dice, cup_dice = 0,0,0,0
        for th in threshold:

            gt_vmask_p = (true_mask_p > th).float()
            vpred = (pred > th).float()
            vpred_cpu = vpred.cpu()
            disc_pred = vpred_cpu[:,0,:,:].numpy().astype('int32')
            cup_pred = vpred_cpu[:,1,:,:].numpy().astype('int32')

            disc_mask = gt_vmask_p [:,0,:,:].squeeze(1).cpu().numpy().astype('int32')
            cup_mask = gt_vmask_p [:, 1, :, :].squeeze(1).cpu().numpy().astype('int32')
    
            '''iou for numpy'''
            iou_d += iou(disc_pred,disc_mask)
            iou_c += iou(cup_pred,cup_mask)

            '''dice for torch'''
            disc_dice += dice_coeff(vpred[:,0,:,:], gt_vmask_p[:,0,:,:]).item()
            cup_dice += dice_coeff(vpred[:,1,:,:], gt_vmask_p[:,1,:,:]).item()
            
        return iou_d / len(threshold), iou_c / len(threshold), disc_dice / len(threshold), cup_dice / len(threshold)
    else:
        eiou, edice = 0,0
        for th in threshold:

            gt_vmask_p = (true_mask_p > th).float()
            vpred = (pred > th).float()
            vpred_cpu = vpred.cpu()
            disc_pred = vpred_cpu[:,0,:,:].numpy().astype('int32')

            disc_mask = gt_vmask_p [:,0,:,:].squeeze(1).cpu().numpy().astype('int32')
    
            '''iou for numpy'''
            eiou += iou(disc_pred,disc_mask)

            '''dice for torch'''
            edice += dice_coeff(vpred[:,0,:,:], gt_vmask_p[:,0,:,:]).item()
            
        return eiou / len(threshold), edice / len(threshold)

# @objectives.wrap_objective()
def dot_compare(layer, batch=1, cossim_pow=0):
    def inner(T):
        dot = (T(layer)[batch] * T(layer)[0]).sum()
        mag = torch.sqrt(torch.sum(T(layer)[0]**2))
        cossim = dot/(1e-6 + mag)
        return -dot * cossim ** cossim_pow
    return inner

def init_D(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

def pre_d():
    netD = Discriminator(3).to(device)
    # netD.apply(init_D)
    beta1 = 0.5
    dis_lr = 0.00002
    optimizerD = optim.Adam(netD.parameters(), lr=dis_lr, betas=(beta1, 0.999))
    return netD, optimizerD

def update_d(args, netD, optimizerD, real, fake):
    criterion = nn.BCELoss()

    label = torch.full((args.b,), 1., dtype=torch.float, device=device)
    output = netD(real).view(-1)
    # Calculate loss on all-real batch
    errD_real = criterion(output, label)
    # Calculate gradients for D in backward pass
    errD_real.backward()
    D_x = output.mean().item()

    label.fill_(0.)
    # Classify all fake batch with D
    output = netD(fake.detach()).view(-1)
    # Calculate D's loss on the all-fake batch
    errD_fake = criterion(output, label)
    # Calculate the gradients for this batch, accumulated (summed) with previous gradients
    errD_fake.backward()
    D_G_z1 = output.mean().item()
    # Compute error of D as sum over the fake and the real batches
    errD = errD_real + errD_fake
    # Update D
    optimizerD.step()

    return errD, D_x, D_G_z1

def calculate_gradient_penalty(netD, real_images, fake_images):
    eta = torch.FloatTensor(args.b,1,1,1).uniform_(0,1)
    eta = eta.expand(args.b, real_images.size(1), real_images.size(2), real_images.size(3)).to(device = device)

    interpolated = (eta * real_images + ((1 - eta) * fake_images)).to(device = device)

    # define it to calculate gradient
    interpolated = Variable(interpolated, requires_grad=True)

    # calculate probability of interpolated examples
    prob_interpolated = netD(interpolated)

    # calculate gradients of probabilities with respect to examples
    gradients = autograd.grad(outputs=prob_interpolated, inputs=interpolated,
                            grad_outputs=torch.ones(
                                prob_interpolated.size()).to(device = device),
                            create_graph=True, retain_graph=True)[0]

    grad_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * 10
    return grad_penalty


def random_click(mask, point_labels = 1, inout = 1):
    indices = np.argwhere(mask == inout)
    return indices[np.random.randint(len(indices))]


def generate_click_prompt(img, msk, pt_label = 1):
    # return: prompt, prompt mask
    pt_list = []
    msk_list = []
    b, c, h, w, d = msk.size()
    msk = msk[:,0,:,:,:]
    for i in range(d):
        pt_list_s = []
        msk_list_s = []
        for j in range(b):
            msk_s = msk[j,:,:,i]
            indices = torch.nonzero(msk_s)
            if indices.size(0) == 0:
                # generate a random array between [0-h, 0-h]:
                random_index = torch.randint(0, h, (2,)).to(device = msk.device)
                new_s = msk_s
            else:
                random_index = random.choice(indices)
                label = msk_s[random_index[0], random_index[1]]
                new_s = torch.zeros_like(msk_s)
                # convert bool tensor to int
                new_s = (msk_s == label).to(dtype = torch.float)
                # new_s[msk_s == label] = 1
            pt_list_s.append(random_index)
            msk_list_s.append(new_s)
        pts = torch.stack(pt_list_s, dim=0)
        msks = torch.stack(msk_list_s, dim=0)
        pt_list.append(pts)
        msk_list.append(msks)
    pt = torch.stack(pt_list, dim=-1)
    msk = torch.stack(msk_list, dim=-1)

    msk = msk.unsqueeze(1)

    return img, pt, msk #[b, 2, d], [b, c, h, w, d]



def drawContour(m,s,RGB,size,a=0.8):
    """Draw edges of contour 'c' from segmented image 's' onto 'm' in colour 'RGB'"""
    # Fill contour "c" with white, make all else black
    
    #ratio = int(255/np.max(s))
    #s = np.uint(s*ratio)

    # Find edges of this contour and make into Numpy array
    contours, _ = cv2.findContours(np.uint8(s),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
    m_old = m.copy()
    # Paint locations of found edges in color "RGB" onto "main"
    cv2.drawContours(m,contours,-1,RGB,size)
    m = cv2.addWeighted(np.uint8(m), a, np.uint8(m_old), 1-a,0)
    return m

def IOU(pm, gt):
    a = np.sum(np.bitwise_and(pm, gt))
    b = np.sum(pm) + np.sum(gt) - a +1e-8
    return a / b


def inverse_normalize(tensor, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
    mean = torch.as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
    std = torch.as_tensor(std, dtype=tensor.dtype, device=tensor.device)
    if mean.ndim == 1:
        mean = mean.view(-1, 1, 1)
    if std.ndim == 1:
        std = std.view(-1, 1, 1)
    tensor.mul_(std).add_(mean)
    return tensor



def remove_small_objects(array_2d, min_size=30):
    """
    Removes small objects from a 2D array using only NumPy.

    :param array_2d: Input 2D array.
    :param min_size: Minimum size of objects to keep.
    :return: 2D array with small objects removed.
    """
    # Label connected components
    structure = np.ones((3, 3), dtype=int)  # Define connectivity
    labeled, ncomponents = label(array_2d, structure)

    # Iterate through labeled components and remove small ones
    for i in range(1, ncomponents + 1):
        locations = np.where(labeled == i)
        if len(locations[0]) < min_size:
            array_2d[locations] = 0

    return array_2d

def create_box_mask(boxes,imgs):
    b,_,w,h = imgs.shape
    box_mask = torch.zeros((b,w,h))
    for k in range(b):
        k_box = boxes[k]
        for box in k_box:
            x1,y1,x2,y2 = int(box[0]),int(box[1]),int(box[2]),int(box[3])
            box_mask[k,y1:y2,x1:x2] = 1
    return box_mask