def load_model(model_dir): net = BASNet(3, 1) net.load_state_dict(torch.load(model_dir)) if torch.cuda.is_available(): net.cuda() net.eval() return net
def main(): image_dir = '/media/markytools/New Volume/Courses/EE298CompVis/finalproject/datasets/DUTS/DUTS-TE/DUTS-TE-Image/' prediction_dir = './predictionout/' model_dir = './saved_models/basnet_bsi_dataaugandarchi/basnet_bsi_epoch_207_itr_272000_train_9.345837_tar_1.082428.pth' img_name_list = glob.glob(image_dir + '*.jpg') # --------- 2. dataloader --------- #1. dataload test_salobj_dataset = SalObjDataset( img_name_list=img_name_list, lbl_name_list=[], transform=transforms.Compose([RescaleT(256), ToTensorLab(flag=0)]), category="test") test_salobj_dataloader = DataLoader(test_salobj_dataset, batch_size=1, shuffle=False, num_workers=1) # --------- 3. model define --------- print("...load BASNet...") net = BASNet(3, 1) net.load_state_dict(torch.load(model_dir)) if torch.cuda.is_available(): net.cuda() net.eval() # --------- 4. inference for each image --------- for i_test, data_test in enumerate(test_salobj_dataloader): print("inferencing:", img_name_list[i_test].split("/")[-1]) inputs_test = data_test['image'] inputs_test = inputs_test.type(torch.FloatTensor) if torch.cuda.is_available(): inputs_test = Variable(inputs_test.cuda()) else: inputs_test = Variable(inputs_test) # d1,d2,d3,d4,d5,d6,d7,d8 = net(inputs_test) d1, d2, d3, d4, d5, d6, d7, d8, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct = net( inputs_test) # normalization pred = d1[:, 0, :, :] pred = normPRED(pred) # save results to test_results folder save_output(img_name_list[i_test], pred, prediction_dir) del d1, d2, d3, d4, d5, d6, d7, d8
def load_BASNet(): global net, BASNet_loaded if BASNet_loaded: print('BASNet is already loaded') else: print("Loading BASNet...") net = BASNet(3, 1) net.load_state_dict(torch.load(model_dir)) if torch.cuda.is_available(): net.cuda() net.eval()
def run_prediction(files): img_name_list = files # --------- 2. dataloader --------- #1. dataload test_salobj_dataset = SalObjDataset( img_name_list=img_name_list, lbl_name_list=[], transform=transforms.Compose([RescaleT(256), ToTensorLab(flag=0)])) test_salobj_dataloader = DataLoader(test_salobj_dataset, batch_size=1, shuffle=False, num_workers=1) # --------- 3. model define --------- print("...load BASNet...") net = BASNet(3, 1) net.load_state_dict(torch.load(model_dir)) if torch.cuda.is_available(): net.cuda() net.eval() # --------- 4. inference for each image --------- for i_test, data_test in enumerate(test_salobj_dataloader): print("inferencing:", img_name_list[i_test].split("/")[-1]) inputs_test = data_test['image'] inputs_test = inputs_test.type(torch.FloatTensor) if torch.cuda.is_available(): inputs_test = Variable(inputs_test.cuda()) else: inputs_test = Variable(inputs_test) d1, d2, d3, d4, d5, d6, d7, d8 = net(inputs_test) # normalization pred = d1[:, 0, :, :] pred = normPRED(pred) # save results to test_results folder save_output(img_name_list[i_test], pred, prediction_dir) del d1, d2, d3, d4, d5, d6, d7, d8
prediction_dir = './test_data/test_results/' model_dir = './saved_models/basnet_bsi_original/basnet_bsi_itr_574000_train_1.013724_tar_0.054404.pth' img_name_list = glob.glob(image_dir + '*.jpg') # --------- 2. dataloader --------- # 1. dataload test_salobj_dataset = SalObjDataset(img_name_list=img_name_list, lbl_name_list=[ ], transform=transforms.Compose([RescaleT(256), ToTensorLab(flag=0)])) test_salobj_dataloader = DataLoader( test_salobj_dataset, batch_size=1, shuffle=False, num_workers=1) # --------- 3. model define --------- print("...load BASNet...") net = BASNet(3, 1) net.load_state_dict(torch.load(model_dir)) if torch.cuda.is_available(): net.cuda() net.eval() # --------- 4. inference for each image --------- for i_test, data_test in enumerate(test_salobj_dataloader): print("inferencing:", img_name_list[i_test].split("/")[-1]) inputs_test = data_test['image'] inputs_test = inputs_test.type(torch.FloatTensor) if torch.cuda.is_available(): inputs_test = Variable(inputs_test.cuda()) else:
def train(): if os.name == 'nt': data_dir = 'C:/Users/marky/Documents/Courses/saliency/datasets/DUTS/' else: data_dir = os.getenv( "HOME") + '/Documents/Courses/EE298-CV/finalproj/datasets/DUTS/' tra_image_dir = 'DUTS-TR/DUTS-TR-Image/' tra_label_dir = 'DUTS-TR/DUTS-TR-Mask/' test_image_dir = 'DUTS-TE/DUTS-TE-Image/' test_label_dir = 'DUTS-TE/DUTS-TE-Mask/' image_ext = '.jpg' label_ext = '.png' model_dir = "./saved_models/basnet_bsi_aug/" resume_train = False resume_model_path = model_dir + "basnet_bsi_epoch_81_itr_106839_train_1.511335_tar_0.098392.pth" last_epoch = 1 epoch_num = 100000 batch_size_train = 8 batch_size_val = 1 train_num = 0 val_num = 0 enableInpaintAug = False device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu") # ------- 5. training process -------- print("---start training...") test_increments = 6250 ite_num = 0 running_loss = 0.0 running_tar_loss = 0.0 ite_num4val = 1 next_test = ite_num + 0 visdom_tab_title = "StructArchWithoutStructImgs(WithHFlip)" ############ ############ ############ ############ tra_img_name_list = glob.glob(data_dir + tra_image_dir + '*' + image_ext) print("data_dir + tra_image_dir + '*' + image_ext: ", data_dir + tra_image_dir + '*' + image_ext) test_img_name_list = glob.glob(data_dir + test_image_dir + '*' + image_ext) print("data_dir + test_image_dir + '*' + image_ext: ", data_dir + test_image_dir + '*' + image_ext) tra_lbl_name_list = [] for img_path in tra_img_name_list: img_name = img_path.split("/")[-1] aaa = img_name.split(".") bbb = aaa[0:-1] imidx = bbb[0] for i in range(1, len(bbb)): imidx = imidx + "." + bbb[i] tra_lbl_name_list.append(data_dir + tra_label_dir + imidx + label_ext) test_lbl_name_list = [] for img_path in test_img_name_list: img_name = img_path.split("/")[-1] aaa = img_name.split(".") bbb = aaa[0:-1] imidx = bbb[0] for i in range(1, len(bbb)): imidx = imidx + "." + bbb[i] test_lbl_name_list.append(data_dir + test_label_dir + imidx + label_ext) print("---") print("train images: ", len(tra_img_name_list)) print("train labels: ", len(tra_lbl_name_list)) print("---") print("---") print("test images: ", len(test_img_name_list)) print("test labels: ", len(test_lbl_name_list)) print("---") train_num = len(tra_img_name_list) test_num = len(test_img_name_list) salobj_dataset = SalObjDataset(img_name_list=tra_img_name_list, lbl_name_list=tra_lbl_name_list, transform=transforms.Compose([ RescaleT(256), RandomCrop(224), ToTensorLab(flag=0) ]), category="train", enableInpaintAug=enableInpaintAug) salobj_dataset_test = SalObjDataset(img_name_list=test_img_name_list, lbl_name_list=test_lbl_name_list, transform=transforms.Compose([ RescaleT(256), RandomCrop(224), ToTensorLab(flag=0) ]), category="test", enableInpaintAug=enableInpaintAug) salobj_dataloader = DataLoader(salobj_dataset, batch_size=batch_size_train, shuffle=True, num_workers=1) salobj_dataloader_test = DataLoader(salobj_dataset_test, batch_size=batch_size_val, shuffle=True, num_workers=1) # ------- 3. define model -------- # define the net net = BASNet(3, 1) if resume_train: # print("resume_model_path:", resume_model_path) checkpoint = torch.load(resume_model_path) net.load_state_dict(checkpoint) if torch.cuda.is_available(): net.to(device) # ------- 4. define optimizer -------- print("---define optimizer...") optimizer = optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) plotter = VisdomLinePlotter(env_name=visdom_tab_title) best_ave_mae = 100000 best_max_fmeasure = 0 best_relaxed_fmeasure = 0 best_ave_maxf = 0 best_own_RelaxedFmeasure = 0 for epoch in range(last_epoch - 1, epoch_num): ### Train network train_loss0 = AverageMeter() train_loss1 = AverageMeter() train_loss2 = AverageMeter() train_loss3 = AverageMeter() train_loss4 = AverageMeter() train_loss5 = AverageMeter() train_loss6 = AverageMeter() train_loss7 = AverageMeter() train_struct_loss1 = AverageMeter() train_struct_loss2 = AverageMeter() train_struct_loss3 = AverageMeter() train_struct_loss4 = AverageMeter() train_struct_loss5 = AverageMeter() train_struct_loss6 = AverageMeter() train_struct_loss7 = AverageMeter() test_loss0 = AverageMeter() test_loss1 = AverageMeter() test_loss2 = AverageMeter() test_loss3 = AverageMeter() test_loss4 = AverageMeter() test_loss5 = AverageMeter() test_loss6 = AverageMeter() test_loss7 = AverageMeter() test_struct_loss1 = AverageMeter() test_struct_loss2 = AverageMeter() test_struct_loss3 = AverageMeter() test_struct_loss4 = AverageMeter() test_struct_loss5 = AverageMeter() test_struct_loss6 = AverageMeter() test_struct_loss7 = AverageMeter() average_mae = AverageMeter() average_maxf = AverageMeter() average_relaxedf = AverageMeter() average_own_RelaxedFMeasure = AverageMeter() net.train() for i, data in enumerate(salobj_dataloader): ite_num = ite_num + 1 ite_num4val = ite_num4val + 1 inputs, labels, labels_struct = data['image'], data['label'], data[ 'label2'] inputs = inputs.type(torch.FloatTensor) labels = labels.type(torch.FloatTensor) labels_struct = labels_struct.type(torch.FloatTensor) # wrap them in Variable if torch.cuda.is_available(): inputs_v, labels_v, labels_struct_v = Variable( inputs.to(device), requires_grad=False), Variable( labels.to(device), requires_grad=False), Variable( labels_struct.to(device), requires_grad=False) else: inputs_v, labels_v, labels_struct_v = Variable( inputs, requires_grad=False), Variable( labels, requires_grad=False), Variable(labels_struct, requires_grad=False) # y zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct = net( inputs_v) loss2, loss = muti_bce_loss_fusion( d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct, labels_v, train_loss0, train_loss1, train_loss2, train_loss3, train_loss4, train_loss5, train_loss6, train_loss7, train_struct_loss1, train_struct_loss2, train_struct_loss3, train_struct_loss4, train_struct_loss5, train_struct_loss6, train_struct_loss7) loss.backward() optimizer.step() # # print statistics running_loss += loss.data running_tar_loss += loss2.data # del temporary outputs and loss del d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct, loss2, loss print( "[train epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f " % (epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val)) plotter.plot('loss0', 'train', 'Main Loss 0', epoch + 1, float(train_loss0.avg)) plotter.plot('loss1', 'train', 'Main Loss 1', epoch + 1, float(train_loss1.avg)) plotter.plot('loss2', 'train', 'Main Loss 2', epoch + 1, float(train_loss2.avg)) plotter.plot('loss3', 'train', 'Main Loss 3', epoch + 1, float(train_loss3.avg)) plotter.plot('loss4', 'train', 'Main Loss 4', epoch + 1, float(train_loss4.avg)) plotter.plot('loss5', 'train', 'Main Loss 5', epoch + 1, float(train_loss5.avg)) plotter.plot('loss6', 'train', 'Main Loss 6', epoch + 1, float(train_loss6.avg)) plotter.plot('loss7', 'train', 'Main Loss 7', epoch + 1, float(train_loss7.avg)) plotter.plot('structloss1', 'train', 'Struct Loss 1', epoch + 1, float(train_struct_loss1.avg)) plotter.plot('structloss2', 'train', 'Struct Loss 2', epoch + 1, float(train_struct_loss2.avg)) plotter.plot('structloss3', 'train', 'Struct Loss 3', epoch + 1, float(train_struct_loss3.avg)) plotter.plot('structloss4', 'train', 'Struct Loss 4', epoch + 1, float(train_struct_loss4.avg)) plotter.plot('structloss5', 'train', 'Struct Loss 5', epoch + 1, float(train_struct_loss5.avg)) plotter.plot('structloss6', 'train', 'Struct Loss 6', epoch + 1, float(train_struct_loss6.avg)) plotter.plot('structloss7', 'train', 'Struct Loss 7', epoch + 1, float(train_struct_loss7.avg)) ### Validate model print("---Evaluate model---") if ite_num >= next_test: # test and save model 10000 iterations, due to very large DUTS-TE dataset next_test = ite_num + test_increments net.eval() max_epoch_fmeasure = 0 for i, data in enumerate(salobj_dataloader_test): inputs, labels = data['image'], data['label'] inputs = inputs.type(torch.FloatTensor) labels = labels.type(torch.FloatTensor) if torch.cuda.is_available(): inputs_v, labels_v = Variable( inputs.to(device), requires_grad=False), Variable(labels.to(device), requires_grad=False) else: inputs_v, labels_v = Variable( inputs, requires_grad=False), Variable(labels, requires_grad=False) d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct = net( inputs_v) pred = d0[:, 0, :, :] pred = normPRED(pred) pred = pred.squeeze() predict_np = pred.cpu().data.numpy() im = Image.fromarray(predict_np * 255).convert('RGB') img_name = test_img_name_list[i] image = cv2.imread(img_name) imo = im.resize((image.shape[1], image.shape[0]), resample=Image.BILINEAR) imo = imo.convert("L") ### Convert to grayscale 1-channel resizedImg_np = np.array( imo) ### Result is 2D numpy array predicted salient map img__lbl_name = test_lbl_name_list[i] gt_img = np.array(Image.open(img__lbl_name).convert( "L")) ### Ground truth salient map ### Compute metrics result_mae = getMAE(gt_img, resizedImg_np) average_mae.update(result_mae, 1) precision, recall = getPRCurve(gt_img, resizedImg_np) result_maxfmeasure = getMaxFMeasure(precision, recall) result_maxfmeasure = result_maxfmeasure.mean() average_maxf.update(result_maxfmeasure, 1) if (result_maxfmeasure > max_epoch_fmeasure): max_epoch_fmeasure = result_maxfmeasure result_relaxedfmeasure = getRelaxedFMeasure( gt_img, resizedImg_np) result_ownrelaxedfmeasure = own_RelaxedFMeasure( gt_img, resizedImg_np) average_relaxedf.update(result_relaxedfmeasure, 1) average_own_RelaxedFMeasure.update(result_ownrelaxedfmeasure, 1) loss2, loss = muti_bce_loss_fusion( d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct, labels_v, test_loss0, test_loss1, test_loss2, test_loss3, test_loss4, test_loss5, test_loss6, test_loss7, test_struct_loss1, test_struct_loss2, test_struct_loss3, test_struct_loss4, test_struct_loss5, test_struct_loss6, test_struct_loss7) del d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct, loss2, loss print( "[test epoch: %3d/%3d, batch: %5d/%5d, ite: %d] test loss: %3f, tar: %3f " % (epoch + 1, epoch_num, (i + 1) * batch_size_val, test_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val)) model_name = model_dir + "basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f.pth" % ( epoch + 1, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val) torch.save(net.state_dict(), model_name) running_loss = 0.0 running_tar_loss = 0.0 net.train() # resume train ite_num4val = 1 if (average_mae.avg < best_ave_mae): best_ave_mae = average_mae.avg newname = model_dir + "bestMAE/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_mae_%3f.pth" % ( epoch + 1, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, best_ave_mae) fold_dir = newname.rsplit("/", 1) if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0]) copyfile(model_name, newname) if (max_epoch_fmeasure > best_max_fmeasure): best_max_fmeasure = max_epoch_fmeasure newname = model_dir + "bestEpochMaxF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_maxfmeas_%3f.pth" % ( epoch + 1, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, best_max_fmeasure) fold_dir = newname.rsplit("/", 1) if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0]) copyfile(model_name, newname) if (average_maxf.avg > best_ave_maxf): best_ave_maxf = average_maxf.avg newname = model_dir + "bestAveMaxF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_avemfmeas_%3f.pth" % ( epoch + 1, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, best_ave_maxf) fold_dir = newname.rsplit("/", 1) if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0]) copyfile(model_name, newname) if (average_relaxedf.avg > best_relaxed_fmeasure): best_relaxed_fmeasure = average_relaxedf.avg newname = model_dir + "bestAveRelaxF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_averelaxfmeas_%3f.pth" % ( epoch + 1, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, best_relaxed_fmeasure) fold_dir = newname.rsplit("/", 1) if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0]) copyfile(model_name, newname) if (average_own_RelaxedFMeasure.avg > best_own_RelaxedFmeasure): best_own_RelaxedFmeasure = average_own_RelaxedFMeasure.avg newname = model_dir + "bestOwnRelaxedF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_averelaxfmeas_%3f.pth" % ( epoch + 1, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, best_own_RelaxedFmeasure) fold_dir = newname.rsplit("/", 1) if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0]) copyfile(model_name, newname) plotter.plot('loss0', 'test', 'Main Loss 0', epoch + 1, float(test_loss0.avg)) plotter.plot('loss1', 'test', 'Main Loss 1', epoch + 1, float(test_loss1.avg)) plotter.plot('loss2', 'test', 'Main Loss 2', epoch + 1, float(test_loss2.avg)) plotter.plot('loss3', 'test', 'Main Loss 3', epoch + 1, float(test_loss3.avg)) plotter.plot('loss4', 'test', 'Main Loss 4', epoch + 1, float(test_loss4.avg)) plotter.plot('loss5', 'test', 'Main Loss 5', epoch + 1, float(test_loss5.avg)) plotter.plot('loss6', 'test', 'Main Loss 6', epoch + 1, float(test_loss6.avg)) plotter.plot('loss7', 'test', 'Main Loss 7', epoch + 1, float(test_loss7.avg)) plotter.plot('structloss1', 'test', 'Struct Loss 1', epoch + 1, float(test_struct_loss1.avg)) plotter.plot('structloss2', 'test', 'Struct Loss 2', epoch + 1, float(test_struct_loss2.avg)) plotter.plot('structloss3', 'test', 'Struct Loss 3', epoch + 1, float(test_struct_loss3.avg)) plotter.plot('structloss4', 'test', 'Struct Loss 4', epoch + 1, float(test_struct_loss4.avg)) plotter.plot('structloss5', 'test', 'Struct Loss 5', epoch + 1, float(test_struct_loss5.avg)) plotter.plot('structloss6', 'test', 'Struct Loss 6', epoch + 1, float(test_struct_loss6.avg)) plotter.plot('structloss7', 'test', 'Struct Loss 7', epoch + 1, float(test_struct_loss7.avg)) plotter.plot('mae', 'test', 'Average Epoch MAE', epoch + 1, float(average_mae.avg)) plotter.plot('max_maxf', 'test', 'Max Max Epoch F-Measure', epoch + 1, float(max_epoch_fmeasure)) plotter.plot('ave_maxf', 'test', 'Average Max F-Measure', epoch + 1, float(average_maxf.avg)) plotter.plot('ave_relaxedf', 'test', 'Average Relaxed F-Measure', epoch + 1, float(average_relaxedf.avg)) plotter.plot('own_RelaxedFMeasure', 'test', 'Own Average Relaxed F-Measure', epoch + 1, float(average_own_RelaxedFMeasure.avg)) print('-------------Congratulations! Training Done!!!-------------')
RandomCrop(224), ToTensorLab(flag=0)])) #train_data, val_data = torch.utils.data.random_split(salobj_dataset, [train_size, valid_size]) train_loader = DataLoader(train_data, batch_size=batch_size_train, shuffle=True, num_workers=0) valid_loader = DataLoader(val_data, batch_size=batch_size_train, shuffle=True, num_workers=0) # ------- 3. define model -------- # define the net net = BASNet(3, 1) net.load_state_dict(torch.load(PATH)) #device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs!") net = nn.DataParallel(net) #net.to(device) if torch.cuda.is_available(): net.cuda() # ------- 4. define optimizer --------
def train(): data_dir = '/media/markytools/New Volume/Courses/EE298CompVis/finalproject/datasets/' test_image_dir = 'DUTS/DUTS-TE/DUTS-TE-Image/' test_label_dir = 'DUTS/DUTS-TE/DUTS-TE-Mask/' image_ext = '.jpg' label_ext = '.png' model_dir = "../saved_models/" resume_train = True resume_model_path = model_dir + "basnet-original.pth" last_epoch = 1 epoch_num = 100000 batch_size_train = 8 batch_size_val = 1 train_num = 0 val_num = 0 enableInpaintAug = False device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu") #set CPU to 0 # ------- 5. training process -------- print("---start training...") test_increments = 15000 ite_num = 0 running_loss = 0.0 running_tar_loss = 0.0 ite_num4val = 1 next_test = ite_num + 0 ############ ############ ############ ############ test_img_name_list = glob.glob(data_dir + test_image_dir + '*' + image_ext) print("data_dir + test_image_dir + '*' + image_ext: ", data_dir + test_image_dir + '*' + image_ext) test_lbl_name_list = [] for img_path in test_img_name_list: img_name = img_path.split("/")[-1] aaa = img_name.split(".") bbb = aaa[0:-1] imidx = bbb[0] for i in range(1,len(bbb)): imidx = imidx + "." + bbb[i] test_lbl_name_list.append(data_dir + test_label_dir + imidx + label_ext) print("---") print("test images: ", len(test_img_name_list)) print("test labels: ", len(test_lbl_name_list)) print("---") test_num = len(test_img_name_list) for test_lbl in test_lbl_name_list: test_jpg = test_lbl.replace("png", "jpg") test_jpg = test_jpg.replace("Mask", "Image") if test_jpg not in test_img_name_list: print("test_lbl not in label: ", test_lbl) salobj_dataset_test = SalObjDataset( img_name_list=test_img_name_list, lbl_name_list=test_lbl_name_list, transform=transforms.Compose([ RescaleT(256), RandomCrop(224), ToTensorLab(flag=0)]), category="test", enableInpaintAug=enableInpaintAug) salobj_dataloader_test = DataLoader(salobj_dataset_test, batch_size=batch_size_val, shuffle=True, num_workers=1) # ------- 3. define model -------- # define the net net = BASNet(3, 1) if resume_train: # print("resume_model_path:", resume_model_path) checkpoint = torch.load(resume_model_path) net.load_state_dict(checkpoint) if torch.cuda.is_available(): net.to(device) # ------- 4. define optimizer -------- print("---define optimizer...") optimizer = optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) plotter = VisdomLinePlotter(env_name='NewlyAddedRelaxedMeasureEnv1') best_ave_mae = 100000 best_max_fmeasure = 0 best_relaxed_fmeasure = 0 best_ave_maxf = 0 ### Train network train_loss0 = AverageMeter() train_loss1 = AverageMeter() train_loss2 = AverageMeter() train_loss3 = AverageMeter() train_loss4 = AverageMeter() train_loss5 = AverageMeter() train_loss6 = AverageMeter() train_loss7 = AverageMeter() test_loss0 = AverageMeter() test_loss1 = AverageMeter() test_loss2 = AverageMeter() test_loss3 = AverageMeter() test_loss4 = AverageMeter() test_loss5 = AverageMeter() test_loss6 = AverageMeter() test_loss7 = AverageMeter() average_mae = AverageMeter() average_maxf = AverageMeter() average_relaxedf = AverageMeter() ### Validate model print("---Evaluate model---") next_test = ite_num + test_increments net.eval() max_epoch_fmeasure = 0 for i, data in enumerate(salobj_dataloader_test): inputs, labels = data['image'], data['label'] inputs = inputs.type(torch.FloatTensor) labels = labels.type(torch.FloatTensor) if torch.cuda.is_available(): inputs_v, labels_v = Variable(inputs.to(device), requires_grad=False), Variable(labels.to(device), requires_grad=False) else: inputs_v, labels_v = Variable(inputs, requires_grad=False), Variable(labels, requires_grad=False) d0, d1, d2, d3, d4, d5, d6, d7 = net(inputs_v) pred = d0[:,0,:,:] pred = normPRED(pred) pred = pred.squeeze() predict_np = pred.cpu().data.numpy() im = Image.fromarray(predict_np*255).convert('RGB') img_name = test_img_name_list[i] image = cv2.imread(img_name) imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR) imo = imo.convert("L") ### Convert to grayscale 1-channel resizedImg_np = np.array(imo) ### Result is 2D numpy array predicted salient map img__lbl_name = test_lbl_name_list[i] gt_img = np.array(Image.open(img__lbl_name).convert("L")) ### Ground truth salient map ### Compute metrics img_name_png = result_mae = getMAE(gt_img, resizedImg_np) average_mae.update(result_mae, 1) precision, recall = getPRCurve(gt_img, resizedImg_np) result_maxfmeasure = getMaxFMeasure(precision, recall) result_maxfmeasure = result_maxfmeasure.mean() average_maxf.update(result_maxfmeasure, 1) if (result_maxfmeasure > max_epoch_fmeasure): max_epoch_fmeasure = result_maxfmeasure result_relaxedfmeasure = getRelaxedFMeasure(gt_img, resizedImg_np) average_relaxedf.update(result_relaxedfmeasure, 1) loss2, loss = muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, d7,labels_v, test_loss0, test_loss1, test_loss2, test_loss3, test_loss4, test_loss5, test_loss6, test_loss7) del d0, d1, d2, d3, d4, d5, d6, d7,loss2, loss print("Average Epoch MAE: ", average_mae.avg) print("Max Max Epoch F-Measure: ", average_maxf.avg) print("Average Max F-Measure: ", max_epoch_fmeasure) print("Average Relaxed F-Measure: ", average_relaxedf.avg) print('-------------Congratulations! Training Done!!!-------------')