--- a
+++ b/test.py
@@ -0,0 +1,72 @@
+"""
+Separated testing for OmiEmbed
+"""
+import time
+from util import util
+from params.test_params import TestParams
+from datasets import create_single_dataloader
+from models import create_model
+from util.visualizer import Visualizer
+
+if __name__ == '__main__':
+    # Get testing parameter
+    param = TestParams().parse()
+    if param.deterministic:
+        util.setup_seed(param.seed)
+
+    # Dataset related
+    dataloader, sample_list = create_single_dataloader(param, shuffle=False)  # No shuffle for testing
+    print('The size of testing set is {}'.format(len(dataloader)))
+    # Get sample list for the dataset
+    param.sample_list = dataloader.get_sample_list()
+    # Get the dimension of input omics data
+    param.omics_dims = dataloader.get_omics_dims()
+    if param.downstream_task == 'classification' or param.downstream_task == 'multitask':
+        # Get the number of classes for the classification task
+        if param.class_num == 0:
+            param.class_num = dataloader.get_class_num()
+        print('The number of classes: {}'.format(param.class_num))
+    if param.downstream_task == 'regression' or param.downstream_task == 'multitask':
+        # 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 == 'survival' or param.downstream_task == 'multitask':
+        # 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
+
+    # TESTING
+    model.set_eval()
+    test_start_time = time.time()  # Start time of testing
+    output_dict, losses_dict, metrics_dict = model.init_log_dict()  # Initialize the log dictionaries
+    if param.save_latent_space:
+        latent_dict = model.init_latent_dict()
+
+    # Start testing loop
+    for i, data in enumerate(dataloader):
+        dataset_size = len(dataloader)
+        actual_batch_size = len(data['index'])
+        model.set_input(data)  # Unpack input data from the output dictionary of the dataloader
+        model.test()  # Run forward to get the output tensors
+        model.update_log_dict(output_dict, losses_dict, metrics_dict, actual_batch_size)  # Update the log dictionaries
+        if param.save_latent_space:
+            latent_dict = model.update_latent_dict(latent_dict)  # Update the latent space array
+        if i % param.print_freq == 0:  # Print testing log
+            visualizer.print_test_log(param.epoch_to_load, i, losses_dict, metrics_dict, param.batch_size, dataset_size)
+
+    test_time = time.time() - test_start_time
+    visualizer.print_test_summary(param.epoch_to_load, losses_dict, output_dict, test_time)
+    visualizer.save_output_dict(output_dict)
+    if param.save_latent_space:
+        visualizer.save_latent_space(latent_dict, sample_list)