# Copyright 2020 MONAI Consortium

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from init import Options

from networks import build_net, update_learning_rate, build_UNETR

# from networks import build_net

import logging

import os

import sys

import tempfile

from glob import glob

import nibabel as nib

import numpy as np

import torch

from torch.utils.data import DataLoader

from torch.utils.tensorboard import SummaryWriter

import monai

from monai.data import create_test_image_3d, list_data_collate, decollate_batch

from monai.inferers import sliding_window_inference

from monai.metrics import DiceMetric

from monai.transforms import (EnsureType, Compose, LoadImaged, AddChanneld, Transpose,Activations,AsDiscrete, RandGaussianSmoothd, CropForegroundd, SpatialPadd,

                              ScaleIntensityd, ToTensord, RandSpatialCropd, Rand3DElasticd, RandAffined, RandZoomd,

    Spacingd, Orientationd, Resized, ThresholdIntensityd, RandShiftIntensityd, BorderPadd, RandGaussianNoised, RandAdjustContrastd,NormalizeIntensityd,RandFlipd)

from monai.visualize import plot_2d_or_3d_image

def main():

    opt = Options().parse()

    # monai.config.print_config()

    logging.basicConfig(stream=sys.stdout, level=logging.INFO)

    # check gpus

    if opt.gpu_ids != '-1':

        num_gpus = len(opt.gpu_ids.split(','))

    else:

        num_gpus = 0

    print('number of GPU:', num_gpus)

    # Data loader creation

    # train images

    train_images = sorted(glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))

    train_segs = sorted(glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))

    train_images_for_dice = sorted(glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))

    train_segs_for_dice = sorted(glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))

    # validation images

    val_images = sorted(glob(os.path.join(opt.images_folder, 'val', 'image*.nii')))

    val_segs = sorted(glob(os.path.join(opt.labels_folder, 'val', 'label*.nii')))

    # test images

    test_images = sorted(glob(os.path.join(opt.images_folder, 'test', 'image*.nii')))

    test_segs = sorted(glob(os.path.join(opt.labels_folder, 'test', 'label*.nii')))

    # augment the data list for training

    for i in range(int(opt.increase_factor_data)):

        train_images.extend(train_images)

        train_segs.extend(train_segs)

    print('Number of training patches per epoch:', len(train_images))

    print('Number of training images per epoch:', len(train_images_for_dice))

    print('Number of validation images per epoch:', len(val_images))

    print('Number of test images per epoch:', len(test_images))

    # Creation of data directories for data_loader

    train_dicts = [{'image': image_name, 'label': label_name}

                  for image_name, label_name in zip(train_images, train_segs)]

    train_dice_dicts = [{'image': image_name, 'label': label_name}

                   for image_name, label_name in zip(train_images_for_dice, train_segs_for_dice)]

    val_dicts = [{'image': image_name, 'label': label_name}

                   for image_name, label_name in zip(val_images, val_segs)]

    test_dicts = [{'image': image_name, 'label': label_name}

                 for image_name, label_name in zip(test_images, test_segs)]

    # Transforms list

    if opt.resolution is not None:

        train_transforms = [

            LoadImaged(keys=['image', 'label']),

            AddChanneld(keys=['image', 'label']),

            # ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),  # CT HU filter

            # ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),

            CropForegroundd(keys=['image', 'label'], source_key='image'),               # crop CropForeground

            NormalizeIntensityd(keys=['image']),                                          # augmentation

            ScaleIntensityd(keys=['image']),                                              # intensity

            Spacingd(keys=['image', 'label'], pixdim=opt.resolution, mode=('bilinear', 'nearest')),  # resolution

            RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=1),

            RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=0),

            RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=2),

            RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                        rotate_range=(np.pi / 36, np.pi / 36, np.pi * 2), padding_mode="zeros"),

            RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                        rotate_range=(np.pi / 36, np.pi / 2, np.pi / 36), padding_mode="zeros"),

            RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                        rotate_range=(np.pi / 2, np.pi / 36, np.pi / 36), padding_mode="zeros"),

            Rand3DElasticd(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                           sigma_range=(5, 8), magnitude_range=(100, 200), scale_range=(0.15, 0.15, 0.15),

                           padding_mode="zeros"),

            RandGaussianSmoothd(keys=["image"], sigma_x=(0.5, 1.15), sigma_y=(0.5, 1.15), sigma_z=(0.5, 1.15), prob=0.1,),

            RandAdjustContrastd(keys=['image'], gamma=(0.5, 2.5), prob=0.1),

            RandGaussianNoised(keys=['image'], prob=0.1, mean=np.random.uniform(0, 0.5), std=np.random.uniform(0, 15)),

            RandShiftIntensityd(keys=['image'], offsets=np.random.uniform(0,0.3), prob=0.1),

            SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'),  # pad if the image is smaller than patch

            RandSpatialCropd(keys=['image', 'label'], roi_size=opt.patch_size, random_size=False),

            ToTensord(keys=['image', 'label'])

        ]

        val_transforms = [

            LoadImaged(keys=['image', 'label']),

            AddChanneld(keys=['image', 'label']),

            # ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),

            # ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),

            CropForegroundd(keys=['image', 'label'], source_key='image'),                   # crop CropForeground

            NormalizeIntensityd(keys=['image']),                                      # intensity

            ScaleIntensityd(keys=['image']),

            Spacingd(keys=['image', 'label'], pixdim=opt.resolution, mode=('bilinear', 'nearest')),  # resolution

            SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'),  # pad if the image is smaller than patch

            ToTensord(keys=['image', 'label'])

        ]

    else:

        train_transforms = [

            LoadImaged(keys=['image', 'label']),

            AddChanneld(keys=['image', 'label']),

            # ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),

            # ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),

            CropForegroundd(keys=['image', 'label'], source_key='image'),               # crop CropForeground

            NormalizeIntensityd(keys=['image']),                                          # augmentation

            ScaleIntensityd(keys=['image']),                                              # intensity

            RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=1),

            RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=0),

            RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=2),

            RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                        rotate_range=(np.pi / 36, np.pi / 36, np.pi * 2), padding_mode="zeros"),

            RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                        rotate_range=(np.pi / 36, np.pi / 2, np.pi / 36), padding_mode="zeros"),

            RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                        rotate_range=(np.pi / 2, np.pi / 36, np.pi / 36), padding_mode="zeros"),

            Rand3DElasticd(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,

                           sigma_range=(5, 8), magnitude_range=(100, 200), scale_range=(0.15, 0.15, 0.15),

                           padding_mode="zeros"),

            RandGaussianSmoothd(keys=["image"], sigma_x=(0.5, 1.15), sigma_y=(0.5, 1.15), sigma_z=(0.5, 1.15), prob=0.1,),

            RandAdjustContrastd(keys=['image'], gamma=(0.5, 2.5), prob=0.1),

            RandGaussianNoised(keys=['image'], prob=0.1, mean=np.random.uniform(0, 0.5), std=np.random.uniform(0, 1)),

            RandShiftIntensityd(keys=['image'], offsets=np.random.uniform(0,0.3), prob=0.1),

            SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'),  # pad if the image is smaller than patch

            RandSpatialCropd(keys=['image', 'label'], roi_size=opt.patch_size, random_size=False),

            ToTensord(keys=['image', 'label'])

        ]

        val_transforms = [

            LoadImaged(keys=['image', 'label']),

            AddChanneld(keys=['image', 'label']),

            # ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),

            # ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),

            CropForegroundd(keys=['image', 'label'], source_key='image'),                   # crop CropForeground

            NormalizeIntensityd(keys=['image']),                                      # intensity

            ScaleIntensityd(keys=['image']),

            SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'),  # pad if the image is smaller than patch

            ToTensord(keys=['image', 'label'])

        ]

    train_transforms = Compose(train_transforms)

    val_transforms = Compose(val_transforms)

    # create a training data loader

    check_train = monai.data.Dataset(data=train_dicts, transform=train_transforms)

    train_loader = DataLoader(check_train, batch_size=opt.batch_size, shuffle=True, collate_fn=list_data_collate, num_workers=opt.workers, pin_memory=False)

    # create a training_dice data loader

    check_val = monai.data.Dataset(data=train_dice_dicts, transform=val_transforms)

    train_dice_loader = DataLoader(check_val, batch_size=1, num_workers=opt.workers, collate_fn=list_data_collate, pin_memory=False)

    # create a validation data loader

    check_val = monai.data.Dataset(data=val_dicts, transform=val_transforms)

    val_loader = DataLoader(check_val, batch_size=1, num_workers=opt.workers, collate_fn=list_data_collate, pin_memory=False)

    # create a validation data loader

    check_val = monai.data.Dataset(data=test_dicts, transform=val_transforms)

    test_loader = DataLoader(check_val, batch_size=1, num_workers=opt.workers, collate_fn=list_data_collate, pin_memory=False)

    # build the network

    if opt.network is 'nnunet':

        net = build_net()  # nn build_net

    elif opt.network is 'unetr':

        net = build_UNETR() # UneTR

    net.cuda()

    if num_gpus > 1:

        net = torch.nn.DataParallel(net)

    if opt.preload is not None:

        net.load_state_dict(torch.load(opt.preload))

    dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)

    post_trans = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold_values=True)])

    loss_function = monai.losses.DiceCELoss(sigmoid=True)

    torch.backends.cudnn.benchmark = opt.benchmark

    if opt.network is 'nnunet':

        optim = torch.optim.SGD(net.parameters(), lr=opt.lr, momentum=0.99, weight_decay=3e-5, nesterov=True,)

        net_scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=lambda epoch: (1 - epoch / opt.epochs) ** 0.9)

    elif opt.network is 'unetr':

        optim = torch.optim.AdamW(net.parameters(), lr=1e-4, weight_decay=1e-5)

    # start a typical PyTorch training

    val_interval = 1

    best_metric = -1

    best_metric_epoch = -1

    epoch_loss_values = list()

    writer = SummaryWriter()

    for epoch in range(opt.epochs):

        print("-" * 10)

        print(f"epoch {epoch + 1}/{opt.epochs}")

        net.train()

        epoch_loss = 0

        step = 0

        for batch_data in train_loader:

            step += 1

            inputs, labels = batch_data["image"].cuda(), batch_data["label"].cuda()

            optim.zero_grad()

            outputs = net(inputs)

            loss = loss_function(outputs, labels)

            loss.backward()

            optim.step()

            epoch_loss += loss.item()

            epoch_len = len(check_train) // train_loader.batch_size

            print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")

            writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)

        epoch_loss /= step

        epoch_loss_values.append(epoch_loss)

        print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")

        if opt.network is 'nnunet':

            update_learning_rate(net_scheduler, optim)

        if (epoch + 1) % val_interval == 0:

            net.eval()

            with torch.no_grad():

                def plot_dice(images_loader):

                    val_images = None

                    val_labels = None

                    val_outputs = None

                    for data in images_loader:

                        val_images, val_labels = data["image"].cuda(), data["label"].cuda()

                        roi_size = opt.patch_size

                        sw_batch_size = 4

                        val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, net)

                        val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]

                        dice_metric(y_pred=val_outputs, y=val_labels)

                    # aggregate the final mean dice result

                    metric = dice_metric.aggregate().item()

                    # reset the status for next validation round

                    dice_metric.reset()

                    return metric, val_images, val_labels, val_outputs

                metric, val_images, val_labels, val_outputs = plot_dice(val_loader)

                # Save best model

                if metric > best_metric:

                    best_metric = metric

                    best_metric_epoch = epoch + 1

                    torch.save(net.state_dict(), "best_metric_model.pth")

                    print("saved new best metric model")

                metric_train, train_images, train_labels, train_outputs = plot_dice(train_dice_loader)

                metric_test, test_images, test_labels, test_outputs = plot_dice(test_loader)

                # Logger bar

                print(

                    "current epoch: {} Training dice: {:.4f} Validation dice: {:.4f} Testing dice: {:.4f} Best Validation dice: {:.4f} at epoch {}".format(

                        epoch + 1, metric_train, metric, metric_test, best_metric, best_metric_epoch

                    )

                )

                writer.add_scalar("Mean_epoch_loss", epoch_loss, epoch + 1)

                writer.add_scalar("Testing_dice", metric_test, epoch + 1)

                writer.add_scalar("Training_dice", metric_train, epoch + 1)

                writer.add_scalar("Validation_dice", metric, epoch + 1)

                # plot the last model output as GIF image in TensorBoard with the corresponding image and label

                # val_outputs = (val_outputs.sigmoid() >= 0.5).float()

                plot_2d_or_3d_image(val_images, epoch + 1, writer, index=0, tag="validation image")

                plot_2d_or_3d_image(val_labels, epoch + 1, writer, index=0, tag="validation label")

                plot_2d_or_3d_image(val_outputs, epoch + 1, writer, index=0, tag="validation inference")

                plot_2d_or_3d_image(test_images, epoch + 1, writer, index=0, tag="test image")

                plot_2d_or_3d_image(test_labels, epoch + 1, writer, index=0, tag="test label")

                plot_2d_or_3d_image(test_outputs, epoch + 1, writer, index=0, tag="test inference")

    print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")

    writer.close()

if __name__ == "__main__":

    main()