--- a
+++ b/train.py
@@ -0,0 +1,103 @@
+"""
+Separated training for OmiEmbed
+"""
+import time
+import warnings
+from util import util
+from params.train_params import TrainParams
+from datasets import create_single_dataloader
+from models import create_model
+from util.visualizer import Visualizer
+
+
+if __name__ == "__main__":
+    warnings.filterwarnings('ignore')
+    # Get parameters
+    param = TrainParams().parse()
+    if param.deterministic:
+        util.setup_seed(param.seed)
+
+    # Dataset related
+    dataloader, sample_list = create_single_dataloader(param, enable_drop_last=True)
+    print('The size of training set is {}'.format(len(dataloader)))
+    # Get the dimension of input omics data
+    param.omics_dims = dataloader.get_omics_dims()
+    if param.downstream_task in ['classification', 'multitask', 'alltask']:
+        # Get the number of classes for the classification task
+        if param.class_num == 0:
+            param.class_num = dataloader.get_class_num()
+        if param.downstream_task != 'alltask':
+            print('The number of classes: {}'.format(param.class_num))
+    if param.downstream_task in ['regression', 'multitask', 'alltask']:
+        # Get the range of the target values
+        values_min = dataloader.get_values_min()
+        values_max = dataloader.get_values_max()
+        if param.regression_scale == 1:
+            param.regression_scale = values_max
+        print('The range of the target values is [{}, {}]'.format(values_min, values_max))
+    if param.downstream_task in ['survival', 'multitask', 'alltask']:
+        # Get the range of T
+        survival_T_min = dataloader.get_survival_T_min()
+        survival_T_max = dataloader.get_survival_T_max()
+        if param.survival_T_max == -1:
+            param.survival_T_max = survival_T_max
+        print('The range of survival T is [{}, {}]'.format(survival_T_min, survival_T_max))
+
+    # Model related
+    model = create_model(param)     # Create a model given param.model and other parameters
+    model.setup(param)              # Regular setup for the model: load and print networks, create schedulers
+    visualizer = Visualizer(param)  # Create a visualizer to print results
+
+    # Start the epoch loop
+    visualizer.print_phase(model.phase)
+    for epoch in range(param.epoch_count, param.epoch_num + 1):     # outer loop for different epochs
+        epoch_start_time = time.time()                              # Start time of this epoch
+        model.epoch = epoch
+        # TRAINING
+        model.set_train()                                           # Set train mode for training
+        iter_load_start_time = time.time()                          # Start time of data loading for this iteration
+        output_dict, losses_dict, metrics_dict = model.init_log_dict()          # Initialize the log dictionaries
+        if epoch == param.epoch_num_p1 + 1:
+            model.phase = 'p2'                                      # Change to supervised phase
+            visualizer.print_phase(model.phase)
+        if epoch == param.epoch_num_p1 + param.epoch_num_p2 + 1:
+            model.phase = 'p3'                                      # Change to supervised phase
+            visualizer.print_phase(model.phase)
+        if param.save_latent_space and epoch == param.epoch_num:
+            latent_dict = model.init_latent_dict()
+
+        # Start training loop
+        for i, data in enumerate(dataloader):                 # Inner loop for different iteration within one epoch
+            model.iter = i
+            dataset_size = len(dataloader)
+            actual_batch_size = len(data['index'])
+            iter_start_time = time.time()                           # Timer for computation per iteration
+            if i % param.print_freq == 0:
+                load_time = iter_start_time - iter_load_start_time  # Data loading time for this iteration
+            model.set_input(data)                                   # Unpack input data from the output dictionary of the dataloader
+            model.update()                                          # Calculate losses, gradients and update network parameters
+            model.update_log_dict(output_dict, losses_dict, metrics_dict, actual_batch_size)       # Update the log dictionaries
+            if param.save_latent_space and epoch == param.epoch_num:
+                latent_dict = model.update_latent_dict(latent_dict)  # Update the latent space array
+            if i % param.print_freq == 0:                           # Print training losses and save logging information to the disk
+                comp_time = time.time() - iter_start_time           # Computational time for this iteration
+                visualizer.print_train_log(epoch, i, losses_dict, metrics_dict, load_time, comp_time, param.batch_size, dataset_size)
+            iter_load_start_time = time.time()
+
+        # Model saving
+        if param.save_model:
+            if param.save_epoch_freq == -1:  # Only save networks during last epoch
+                if epoch == param.epoch_num:
+                    print('Saving the model at the end of epoch {:d}'.format(epoch))
+                    model.save_networks(str(epoch))
+            elif epoch % param.save_epoch_freq == 0:                # Save both the generator and the discriminator every <save_epoch_freq> epochs
+                print('Saving the model at the end of epoch {:d}'.format(epoch))
+                # model.save_networks('latest')
+                model.save_networks(str(epoch))
+
+        train_time = time.time() - epoch_start_time
+        current_lr = model.update_learning_rate()  # update learning rates at the end of each epoch
+        visualizer.print_train_summary(epoch, losses_dict, output_dict, train_time, current_lr)
+
+        if param.save_latent_space and epoch == param.epoch_num:
+            visualizer.save_latent_space(latent_dict, sample_list)