DIS/IS-Net/train_valid_inference_main.py

import os
import time
import numpy as np
from skimage import io
import time

import torch, gc
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F

from data_loader_cache import get_im_gt_name_dict, create_dataloaders, GOSRandomHFlip, GOSResize, GOSRandomCrop, GOSNormalize #GOSDatasetCache,
from basics import  f1_mae_torch #normPRED, GOSPRF1ScoresCache,f1score_torch,
from models import *

def get_gt_encoder(train_dataloaders, train_datasets, valid_dataloaders, valid_datasets, hypar, train_dataloaders_val, train_datasets_val): #model_path, model_save_fre, max_ite=1000000):

    # train_dataloaders, train_datasets = create_dataloaders(train_nm_im_gt_list,
    #                                                      cache_size = hypar["cache_size"],
    #                                                      cache_boost = hypar["cache_boost_train"],
    #                                                      my_transforms = [
    #                                                                      GOSRandomHFlip(),
    #                                                                      # GOSResize(hypar["input_size"]),
    #                                                                      # GOSRandomCrop(hypar["crop_size"]),
    #                                                                       GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
    #                                                                       ],
    #                                                      batch_size = hypar["batch_size_train"],
    #                                                      shuffle = True)

    torch.manual_seed(hypar["seed"])
    if torch.cuda.is_available():
        torch.cuda.manual_seed(hypar["seed"])

    print("define gt encoder ...")
    net = ISNetGTEncoder() #UNETGTENCODERCombine()
    ## load the existing model gt encoder
    if(hypar["gt_encoder_model"]!=""):
        model_path = hypar["model_path"]+"/"+hypar["gt_encoder_model"]
        if torch.cuda.is_available():
            net.load_state_dict(torch.load(model_path))
            net.cuda()
        else:
            net.load_state_dict(torch.load(model_path,map_location="cpu"))
        print("gt encoder restored from the saved weights ...")
        return net ############

    if torch.cuda.is_available():
        net.cuda()

    print("--- define optimizer for GT Encoder---")
    optimizer = optim.Adam(net.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)

    model_path = hypar["model_path"]
    model_save_fre = hypar["model_save_fre"]
    max_ite = hypar["max_ite"]
    batch_size_train = hypar["batch_size_train"]
    batch_size_valid = hypar["batch_size_valid"]

    if(not os.path.exists(model_path)):
        os.mkdir(model_path)

    ite_num = hypar["start_ite"] # count the total iteration number
    ite_num4val = 0 #
    running_loss = 0.0 # count the toal loss
    running_tar_loss = 0.0 # count the target output loss
    last_f1 = [0 for x in range(len(valid_dataloaders))]

    train_num = train_datasets[0].__len__()

    net.train()

    start_last = time.time()
    gos_dataloader = train_dataloaders[0]
    epoch_num = hypar["max_epoch_num"]
    notgood_cnt = 0
    for epoch in range(epoch_num): ## set the epoch num as 100000

        for i, data in enumerate(gos_dataloader):

            if(ite_num >= max_ite):
                print("Training Reached the Maximal Iteration Number ", max_ite)
                exit()

            # start_read = time.time()
            ite_num = ite_num + 1
            ite_num4val = ite_num4val + 1

            # get the inputs
            labels = data['label']

            if(hypar["model_digit"]=="full"):
                labels = labels.type(torch.FloatTensor)
            else:
                labels = labels.type(torch.HalfTensor)

            # wrap them in Variable
            if torch.cuda.is_available():
                labels_v = Variable(labels.cuda(), requires_grad=False)
            else:
                labels_v = Variable(labels, requires_grad=False)

            # print("time lapse for data preparation: ", time.time()-start_read, ' s')

            # y zero the parameter gradients
            start_inf_loss_back = time.time()
            optimizer.zero_grad()

            ds, fs = net(labels_v)#net(inputs_v)
            loss2, loss = net.compute_loss(ds, labels_v)

            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            running_tar_loss += loss2.item()

            # del outputs, loss
            del ds, loss2, loss
            end_inf_loss_back = time.time()-start_inf_loss_back

            print("GT Encoder Training>>>"+model_path.split('/')[-1]+" - [epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f, time-per-iter: %3f s, time_read: %3f" % (
            epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, time.time()-start_last, time.time()-start_last-end_inf_loss_back))
            start_last = time.time()

            if ite_num % model_save_fre == 0:  # validate every 2000 iterations
                notgood_cnt += 1
                # net.eval()
                # tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time = valid_gt_encoder(net, valid_dataloaders, valid_datasets, hypar, epoch)
                tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time = valid_gt_encoder(net, train_dataloaders_val, train_datasets_val, hypar, epoch)

                net.train()  # resume train

                tmp_out = 0
                print("last_f1:",last_f1)
                print("tmp_f1:",tmp_f1)
                for fi in range(len(last_f1)):
                    if(tmp_f1[fi]>last_f1[fi]):
                        tmp_out = 1
                print("tmp_out:",tmp_out)
                if(tmp_out):
                    notgood_cnt = 0
                    last_f1 = tmp_f1
                    tmp_f1_str = [str(round(f1x,4)) for f1x in tmp_f1]
                    tmp_mae_str = [str(round(mx,4)) for mx in tmp_mae]
                    maxf1 = '_'.join(tmp_f1_str)
                    meanM = '_'.join(tmp_mae_str)
                    # .cpu().detach().numpy()
                    model_name = "/GTENCODER-gpu_itr_"+str(ite_num)+\
                                "_traLoss_"+str(np.round(running_loss / ite_num4val,4))+\
                                "_traTarLoss_"+str(np.round(running_tar_loss / ite_num4val,4))+\
                                "_valLoss_"+str(np.round(val_loss /(i_val+1),4))+\
                                "_valTarLoss_"+str(np.round(tar_loss /(i_val+1),4)) + \
                                "_maxF1_" + maxf1 + \
                                "_mae_" + meanM + \
                                "_time_" + str(np.round(np.mean(np.array(tmp_time))/batch_size_valid,6))+".pth"
                    torch.save(net.state_dict(), model_path + model_name)

                running_loss = 0.0
                running_tar_loss = 0.0
                ite_num4val = 0

                if(tmp_f1[0]>0.99):
                    print("GT encoder is well-trained and obtained...")
                    return net

                if(notgood_cnt >= hypar["early_stop"]):
                    print("No improvements in the last "+str(notgood_cnt)+" validation periods, so training stopped !")
                    exit()

    print("Training Reaches The Maximum Epoch Number")
    return net

def valid_gt_encoder(net, valid_dataloaders, valid_datasets, hypar, epoch=0):
    net.eval()
    print("Validating...")
    epoch_num = hypar["max_epoch_num"]

    val_loss = 0.0
    tar_loss = 0.0


    tmp_f1 = []
    tmp_mae = []
    tmp_time = []

    start_valid = time.time()
    for k in range(len(valid_dataloaders)):

        valid_dataloader = valid_dataloaders[k]
        valid_dataset = valid_datasets[k]

        val_num = valid_dataset.__len__()
        mybins = np.arange(0,256)
        PRE = np.zeros((val_num,len(mybins)-1))
        REC = np.zeros((val_num,len(mybins)-1))
        F1 = np.zeros((val_num,len(mybins)-1))
        MAE = np.zeros((val_num))

        val_cnt = 0.0
        for i_val, data_val in enumerate(valid_dataloader):

            # imidx_val, inputs_val, labels_val, shapes_val = data_val['imidx'], data_val['image'], data_val['label'], data_val['shape']
            imidx_val, labels_val, shapes_val = data_val['imidx'], data_val['label'], data_val['shape']

            if(hypar["model_digit"]=="full"):
                labels_val = labels_val.type(torch.FloatTensor)
            else:
                labels_val = labels_val.type(torch.HalfTensor)

            # wrap them in Variable
            if torch.cuda.is_available():
                labels_val_v = Variable(labels_val.cuda(), requires_grad=False)
            else:
                labels_val_v = Variable(labels_val,requires_grad=False)

            t_start = time.time()
            ds_val = net(labels_val_v)[0]
            t_end = time.time()-t_start
            tmp_time.append(t_end)

            # loss2_val, loss_val = muti_loss_fusion(ds_val, labels_val_v)
            loss2_val, loss_val = net.compute_loss(ds_val, labels_val_v)

            # compute F measure
            for t in range(hypar["batch_size_valid"]):
                val_cnt = val_cnt + 1.0
                print("num of val: ", val_cnt)
                i_test = imidx_val[t].data.numpy()

                pred_val = ds_val[0][t,:,:,:] # B x 1 x H x W

                ## recover the prediction spatial size to the orignal image size
                pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[t][0],shapes_val[t][1]),mode='bilinear'))

                ma = torch.max(pred_val)
                mi = torch.min(pred_val)
                pred_val = (pred_val-mi)/(ma-mi) # max = 1
                # pred_val = normPRED(pred_val)

                gt = np.squeeze(io.imread(valid_dataset.dataset["ori_gt_path"][i_test])) # max = 255
                with torch.no_grad():
                    gt = torch.tensor(gt).cuda()

                pre,rec,f1,mae = f1_mae_torch(pred_val*255, gt, valid_dataset, i_test, mybins, hypar)

                PRE[i_test,:]=pre
                REC[i_test,:] = rec
                F1[i_test,:] = f1
                MAE[i_test] = mae

            del ds_val, gt
            gc.collect()
            torch.cuda.empty_cache()

            # if(loss_val.data[0]>1):
            val_loss += loss_val.item()#data[0]
            tar_loss += loss2_val.item()#data[0]

            print("[validating: %5d/%5d] val_ls:%f, tar_ls: %f, f1: %f, mae: %f, time: %f"% (i_val, val_num, val_loss / (i_val + 1), tar_loss / (i_val + 1), np.amax(F1[i_test,:]), MAE[i_test],t_end))

            del loss2_val, loss_val

        print('============================')
        PRE_m = np.mean(PRE,0)
        REC_m = np.mean(REC,0)
        f1_m = (1+0.3)*PRE_m*REC_m/(0.3*PRE_m+REC_m+1e-8)
        # print('--------------:', np.mean(f1_m))
        tmp_f1.append(np.amax(f1_m))
        tmp_mae.append(np.mean(MAE))
        print("The max F1 Score: %f"%(np.max(f1_m)))
        print("MAE: ", np.mean(MAE))

    # print('[epoch: %3d/%3d, ite: %5d] tra_ls: %3f, val_ls: %3f, tar_ls: %3f, maxf1: %3f, val_time: %6f'% (epoch + 1, epoch_num, ite_num, running_loss / ite_num4val, val_loss/val_cnt, tar_loss/val_cnt, tmp_f1[-1], time.time()-start_valid))

    return tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time

def train(net, optimizer, train_dataloaders, train_datasets, valid_dataloaders, valid_datasets, hypar,train_dataloaders_val, train_datasets_val): #model_path, model_save_fre, max_ite=1000000):

    if hypar["interm_sup"]:
        print("Get the gt encoder ...")
        featurenet = get_gt_encoder(train_dataloaders, train_datasets, valid_dataloaders, valid_datasets, hypar,train_dataloaders_val, train_datasets_val)
        ## freeze the weights of gt encoder
        for param in featurenet.parameters():
            param.requires_grad=False


    model_path = hypar["model_path"]
    model_save_fre = hypar["model_save_fre"]
    max_ite = hypar["max_ite"]
    batch_size_train = hypar["batch_size_train"]
    batch_size_valid = hypar["batch_size_valid"]

    if(not os.path.exists(model_path)):
        os.mkdir(model_path)

    ite_num = hypar["start_ite"] # count the toal iteration number
    ite_num4val = 0 #
    running_loss = 0.0 # count the toal loss
    running_tar_loss = 0.0 # count the target output loss
    last_f1 = [0 for x in range(len(valid_dataloaders))]

    train_num = train_datasets[0].__len__()

    net.train()

    start_last = time.time()
    gos_dataloader = train_dataloaders[0]
    epoch_num = hypar["max_epoch_num"]
    notgood_cnt = 0
    for epoch in range(epoch_num): ## set the epoch num as 100000

        for i, data in enumerate(gos_dataloader):

            if(ite_num >= max_ite):
                print("Training Reached the Maximal Iteration Number ", max_ite)
                exit()

            # start_read = time.time()
            ite_num = ite_num + 1
            ite_num4val = ite_num4val + 1

            # get the inputs
            inputs, labels = data['image'], data['label']

            if(hypar["model_digit"]=="full"):
                inputs = inputs.type(torch.FloatTensor)
                labels = labels.type(torch.FloatTensor)
            else:
                inputs = inputs.type(torch.HalfTensor)
                labels = labels.type(torch.HalfTensor)

            # wrap them in Variable
            if torch.cuda.is_available():
                inputs_v, labels_v = Variable(inputs.cuda(), requires_grad=False), Variable(labels.cuda(), requires_grad=False)
            else:
                inputs_v, labels_v = Variable(inputs, requires_grad=False), Variable(labels, requires_grad=False)

            # print("time lapse for data preparation: ", time.time()-start_read, ' s')

            # y zero the parameter gradients
            start_inf_loss_back = time.time()
            optimizer.zero_grad()

            if hypar["interm_sup"]:
                # forward + backward + optimize
                ds,dfs = net(inputs_v)
                _,fs = featurenet(labels_v) ## extract the gt encodings
                loss2, loss = net.compute_loss_kl(ds, labels_v, dfs, fs, mode='MSE')
            else:
                # forward + backward + optimize
                ds,_ = net(inputs_v)
                loss2, loss = muti_loss_fusion(ds, labels_v)

            loss.backward()
            optimizer.step()

            # # print statistics
            running_loss += loss.item()
            running_tar_loss += loss2.item()

            # del outputs, loss
            del ds, loss2, loss
            end_inf_loss_back = time.time()-start_inf_loss_back

            print(">>>"+model_path.split('/')[-1]+" - [epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f, time-per-iter: %3f s, time_read: %3f" % (
            epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, time.time()-start_last, time.time()-start_last-end_inf_loss_back))
            start_last = time.time()

            if ite_num % model_save_fre == 0:  # validate every 2000 iterations
                notgood_cnt += 1
                net.eval()
                tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time = valid(net, valid_dataloaders, valid_datasets, hypar, epoch)
                net.train()  # resume train

                tmp_out = 0
                print("last_f1:",last_f1)
                print("tmp_f1:",tmp_f1)
                for fi in range(len(last_f1)):
                    if(tmp_f1[fi]>last_f1[fi]):
                        tmp_out = 1
                print("tmp_out:",tmp_out)
                if(tmp_out):
                    notgood_cnt = 0
                    last_f1 = tmp_f1
                    tmp_f1_str = [str(round(f1x,4)) for f1x in tmp_f1]
                    tmp_mae_str = [str(round(mx,4)) for mx in tmp_mae]
                    maxf1 = '_'.join(tmp_f1_str)
                    meanM = '_'.join(tmp_mae_str)
                    # .cpu().detach().numpy()
                    model_name = "/gpu_itr_"+str(ite_num)+\
                                "_traLoss_"+str(np.round(running_loss / ite_num4val,4))+\
                                "_traTarLoss_"+str(np.round(running_tar_loss / ite_num4val,4))+\
                                "_valLoss_"+str(np.round(val_loss /(i_val+1),4))+\
                                "_valTarLoss_"+str(np.round(tar_loss /(i_val+1),4)) + \
                                "_maxF1_" + maxf1 + \
                                "_mae_" + meanM + \
                                "_time_" + str(np.round(np.mean(np.array(tmp_time))/batch_size_valid,6))+".pth"
                    torch.save(net.state_dict(), model_path + model_name)

                running_loss = 0.0
                running_tar_loss = 0.0
                ite_num4val = 0

                if(notgood_cnt >= hypar["early_stop"]):
                    print("No improvements in the last "+str(notgood_cnt)+" validation periods, so training stopped !")
                    exit()

    print("Training Reaches The Maximum Epoch Number")

def valid(net, valid_dataloaders, valid_datasets, hypar, epoch=0):
    net.eval()
    print("Validating...")
    epoch_num = hypar["max_epoch_num"]

    val_loss = 0.0
    tar_loss = 0.0
    val_cnt = 0.0

    tmp_f1 = []
    tmp_mae = []
    tmp_time = []

    start_valid = time.time()

    for k in range(len(valid_dataloaders)):

        valid_dataloader = valid_dataloaders[k]
        valid_dataset = valid_datasets[k]

        val_num = valid_dataset.__len__()
        mybins = np.arange(0,256)
        PRE = np.zeros((val_num,len(mybins)-1))
        REC = np.zeros((val_num,len(mybins)-1))
        F1 = np.zeros((val_num,len(mybins)-1))
        MAE = np.zeros((val_num))

        for i_val, data_val in enumerate(valid_dataloader):
            val_cnt = val_cnt + 1.0
            imidx_val, inputs_val, labels_val, shapes_val = data_val['imidx'], data_val['image'], data_val['label'], data_val['shape']

            if(hypar["model_digit"]=="full"):
                inputs_val = inputs_val.type(torch.FloatTensor)
                labels_val = labels_val.type(torch.FloatTensor)
            else:
                inputs_val = inputs_val.type(torch.HalfTensor)
                labels_val = labels_val.type(torch.HalfTensor)

            # wrap them in Variable
            if torch.cuda.is_available():
                inputs_val_v, labels_val_v = Variable(inputs_val.cuda(), requires_grad=False), Variable(labels_val.cuda(), requires_grad=False)
            else:
                inputs_val_v, labels_val_v = Variable(inputs_val, requires_grad=False), Variable(labels_val,requires_grad=False)

            t_start = time.time()
            ds_val = net(inputs_val_v)[0]
            t_end = time.time()-t_start
            tmp_time.append(t_end)

            # loss2_val, loss_val = muti_loss_fusion(ds_val, labels_val_v)
            loss2_val, loss_val = net.compute_loss(ds_val, labels_val_v)

            # compute F measure
            for t in range(hypar["batch_size_valid"]):
                i_test = imidx_val[t].data.numpy()

                pred_val = ds_val[0][t,:,:,:] # B x 1 x H x W

                ## recover the prediction spatial size to the orignal image size
                pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[t][0],shapes_val[t][1]),mode='bilinear'))

                # pred_val = normPRED(pred_val)
                ma = torch.max(pred_val)
                mi = torch.min(pred_val)
                pred_val = (pred_val-mi)/(ma-mi) # max = 1

                gt = np.squeeze(io.imread(valid_dataset.dataset["ori_gt_path"][i_test])) # max = 255
                with torch.no_grad():
                    gt = torch.tensor(gt).cuda()

                pre,rec,f1,mae = f1_mae_torch(pred_val*255, gt, valid_dataset, i_test, mybins, hypar)


                PRE[i_test,:]=pre
                REC[i_test,:] = rec
                F1[i_test,:] = f1
                MAE[i_test] = mae

                del ds_val, gt
                gc.collect()
                torch.cuda.empty_cache()

            # if(loss_val.data[0]>1):
            val_loss += loss_val.item()#data[0]
            tar_loss += loss2_val.item()#data[0]

            print("[validating: %5d/%5d] val_ls:%f, tar_ls: %f, f1: %f, mae: %f, time: %f"% (i_val, val_num, val_loss / (i_val + 1), tar_loss / (i_val + 1), np.amax(F1[i_test,:]), MAE[i_test],t_end))

            del loss2_val, loss_val

        print('============================')
        PRE_m = np.mean(PRE,0)
        REC_m = np.mean(REC,0)
        f1_m = (1+0.3)*PRE_m*REC_m/(0.3*PRE_m+REC_m+1e-8)

        tmp_f1.append(np.amax(f1_m))
        tmp_mae.append(np.mean(MAE))

    return tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time

def main(train_datasets,
         valid_datasets,
         hypar): # model: "train", "test"

    ### --- Step 1: Build datasets and dataloaders ---
    dataloaders_train = []
    dataloaders_valid = []

    if(hypar["mode"]=="train"):
        print("--- create training dataloader ---")
        ## collect training dataset
        train_nm_im_gt_list = get_im_gt_name_dict(train_datasets, flag="train")
        ## build dataloader for training datasets
        train_dataloaders, train_datasets = create_dataloaders(train_nm_im_gt_list,
                                                             cache_size = hypar["cache_size"],
                                                             cache_boost = hypar["cache_boost_train"],
                                                             my_transforms = [
                                                                             GOSRandomHFlip(), ## this line can be uncommented for horizontal flip augmetation
                                                                             # GOSResize(hypar["input_size"]),
                                                                             # GOSRandomCrop(hypar["crop_size"]), ## this line can be uncommented for randomcrop augmentation
                                                                              GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
                                                                              ],
                                                             batch_size = hypar["batch_size_train"],
                                                             shuffle = True)
        train_dataloaders_val, train_datasets_val = create_dataloaders(train_nm_im_gt_list,
                                                             cache_size = hypar["cache_size"],
                                                             cache_boost = hypar["cache_boost_train"],
                                                             my_transforms = [
                                                                              GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
                                                                              ],
                                                             batch_size = hypar["batch_size_valid"],
                                                             shuffle = False)
        print(len(train_dataloaders), " train dataloaders created")

    print("--- create valid dataloader ---")
    ## build dataloader for validation or testing
    valid_nm_im_gt_list = get_im_gt_name_dict(valid_datasets, flag="valid")
    ## build dataloader for training datasets
    valid_dataloaders, valid_datasets = create_dataloaders(valid_nm_im_gt_list,
                                                          cache_size = hypar["cache_size"],
                                                          cache_boost = hypar["cache_boost_valid"],
                                                          my_transforms = [
                                                                           GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
                                                                           # GOSResize(hypar["input_size"])
                                                                           ],
                                                          batch_size=hypar["batch_size_valid"],
                                                          shuffle=False)
    print(len(valid_dataloaders), " valid dataloaders created")
    # print(valid_datasets[0]["data_name"])

    ### --- Step 2: Build Model and Optimizer ---
    print("--- build model ---")
    net = hypar["model"]#GOSNETINC(3,1)

    # convert to half precision
    if(hypar["model_digit"]=="half"):
        net.half()
        for layer in net.modules():
          if isinstance(layer, nn.BatchNorm2d):
            layer.float()

    if torch.cuda.is_available():
        net.cuda()

    if(hypar["restore_model"]!=""):
        print("restore model from:")
        print(hypar["model_path"]+"/"+hypar["restore_model"])
        if torch.cuda.is_available():
            net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"]))
        else:
            net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"],map_location="cpu"))

    print("--- define optimizer ---")
    optimizer = optim.Adam(net.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)

    ### --- Step 3: Train or Valid Model ---
    if(hypar["mode"]=="train"):
        train(net,
              optimizer,
              train_dataloaders,
              train_datasets,
              valid_dataloaders,
              valid_datasets,
              hypar,
              train_dataloaders_val, train_datasets_val)
    else:
        valid(net,
              valid_dataloaders,
              valid_datasets,
              hypar)


if __name__ == "__main__":

    ### --------------- STEP 1: Configuring the Train, Valid and Test datasets ---------------
    ## configure the train, valid and inference datasets
    train_datasets, valid_datasets = [], []
    dataset_1, dataset_1 = {}, {}

    dataset_tr = {"name": "DIS5K-TR",
                 "im_dir": "../DIS5K/DIS-TR/im",
                 "gt_dir": "../DIS5K/DIS-TR/gt",
                 "im_ext": ".jpg",
                 "gt_ext": ".png",
                 "cache_dir":"../DIS5K-Cache/DIS-TR"}

    dataset_vd = {"name": "DIS5K-VD",
                 "im_dir": "../DIS5K/DIS-VD/im",
                 "gt_dir": "../DIS5K/DIS-VD/gt",
                 "im_ext": ".jpg",
                 "gt_ext": ".png",
                 "cache_dir":"../DIS5K-Cache/DIS-VD"}

    dataset_te1 = {"name": "DIS5K-TE1",
                 "im_dir": "../DIS5K/DIS-TE1/im",
                 "gt_dir": "../DIS5K/DIS-TE1/gt",
                 "im_ext": ".jpg",
                 "gt_ext": ".png",
                 "cache_dir":"../DIS5K-Cache/DIS-TE1"}

    dataset_te2 = {"name": "DIS5K-TE2",
                 "im_dir": "../DIS5K/DIS-TE2/im",
                 "gt_dir": "../DIS5K/DIS-TE2/gt",
                 "im_ext": ".jpg",
                 "gt_ext": ".png",
                 "cache_dir":"../DIS5K-Cache/DIS-TE2"}

    dataset_te3 = {"name": "DIS5K-TE3",
                 "im_dir": "../DIS5K/DIS-TE3/im",
                 "gt_dir": "../DIS5K/DIS-TE3/gt",
                 "im_ext": ".jpg",
                 "gt_ext": ".png",
                 "cache_dir":"../DIS5K-Cache/DIS-TE3"}

    dataset_te4 = {"name": "DIS5K-TE4",
                 "im_dir": "../DIS5K/DIS-TE4/im",
                 "gt_dir": "../DIS5K/DIS-TE4/gt",
                 "im_ext": ".jpg",
                 "gt_ext": ".png",
                 "cache_dir":"../DIS5K-Cache/DIS-TE4"}

    train_datasets = [dataset_tr] ## users can create mutiple dictionary for setting a list of datasets as training set
    # valid_datasets = [dataset_vd] ## users can create mutiple dictionary for setting a list of datasets as vaidation sets or inference sets
    valid_datasets = [dataset_vd] #, dataset_te1, dataset_te2, dataset_te3, dataset_te4] # and hypar["mode"] = "valid" for inference,

    ### --------------- STEP 2: Configuring the hyperparamters for Training, validation and inferencing ---------------
    hypar = {}

    ## -- 2.1. configure the model saving or restoring path --
    hypar["mode"] = "train"
    ## "train": for training,
    ## "valid": for validation and inferening,
    ## in "valid" mode, it will calculate the accuracy as well as save the prediciton results into the "hypar["valid_out_dir"]", which shouldn't be ""
    ## otherwise only accuracy will be calculated and no predictions will be saved
    hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision

    if hypar["mode"] == "train":
        hypar["valid_out_dir"] = "" ## for "train" model leave it as "", for "valid"("inference") mode: set it according to your local directory
        hypar["model_path"] ="../saved_models/IS-Net-test" ## model weights saving (or restoring) path
        hypar["restore_model"] = "" ## name of the segmentation model weights .pth for resume training process from last stop or for the inferencing
        hypar["start_ite"] = 0 ## start iteration for the training, can be changed to match the restored training process
        hypar["gt_encoder_model"] = ""
    else: ## configure the segmentation output path and the to-be-used model weights path
        hypar["valid_out_dir"] = "../DIS5K-Results-test" ## output inferenced segmentation maps into this fold
        hypar["model_path"] ="../saved_models/IS-Net" ## load trained weights from this path
        hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights

    # if hypar["restore_model"]!="":
    #     hypar["start_ite"] = int(hypar["restore_model"].split("_")[2])

    ## -- 2.2. choose floating point accuracy --
    hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
    hypar["seed"] = 0

    ## -- 2.3. cache data spatial size --
    ## To handle large size input images, which take a lot of time for loading in training,
    #  we introduce the cache mechanism for pre-convering and resizing the jpg and png images into .pt file
    hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
    hypar["cache_boost_train"] = False ## "True" or "False", indicates wheather to load all the training datasets into RAM, True will greatly speed the training process while requires more RAM
    hypar["cache_boost_valid"] = False ## "True" or "False", indicates wheather to load all the validation datasets into RAM, True will greatly speed the training process while requires more RAM

    ## --- 2.4. data augmentation parameters ---
    hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
    hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
    hypar["random_flip_h"] = 1 ## horizontal flip, currently hard coded in the dataloader and it is not in use
    hypar["random_flip_v"] = 0 ## vertical flip , currently not in use

    ## --- 2.5. define model  ---
    print("building model...")
    hypar["model"] = ISNetDIS() #U2NETFASTFEATURESUP()
    hypar["early_stop"] = 20 ## stop the training when no improvement in the past 20 validation periods, smaller numbers can be used here e.g., 5 or 10.
    hypar["model_save_fre"] = 2000 ## valid and save model weights every 2000 iterations

    hypar["batch_size_train"] = 8 ## batch size for training
    hypar["batch_size_valid"] = 1 ## batch size for validation and inferencing
    print("batch size: ", hypar["batch_size_train"])

    hypar["max_ite"] = 10000000 ## if early stop couldn't stop the training process, stop it by the max_ite_num
    hypar["max_epoch_num"] = 1000000 ## if early stop and max_ite couldn't stop the training process, stop it by the max_epoch_num

    main(train_datasets,
         valid_datasets,
         hypar=hypar)