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 * device = 'cuda' if torch.cuda.is_available() else 'cpu' 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 i_val = None 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 if gt.max()==1: gt=gt*255 with torch.no_grad(): gt = torch.tensor(gt).to(device) 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 = net.compute_loss(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 if len(valid_dataset.dataset["ori_gt_path"]) != 0: gt = np.squeeze(io.imread(valid_dataset.dataset["ori_gt_path"][i_test])) # max = 255 if gt.max()==1: gt=gt*255 else: gt = np.zeros((shapes_val[t][0],shapes_val[t][1])) with torch.no_grad(): gt = torch.tensor(gt).to(device) 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"} ### test your own dataset dataset_demo = {"name": "your-dataset", "im_dir": "../your-dataset/im", "gt_dir": "", "im_ext": ".jpg", "gt_ext": "", "cache_dir":"../your-dataset/cache"} 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_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 ## in-dicate 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"] = "../your-results/"##"../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"##"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)