Pytorch_CNN-RNN/test_models.py

print("--- INITIALIZING LIBRARIES ---")
from utils.training import train_model, test_model, initalize_dataloaders, plot_confusion_matrix, plot_roc_curve, plot_image_selection
import tomli as tl
import torch
import os
from utils.models import CNN_Net
 
print("--- LIBRARIES INITIALIZED ---")

#GET CONFIG SETTINGS
if os.getenv('ADL_CONFIG_PATH') is None:
    with open ('config.toml', 'rb') as f:
        config = tl.load(f)
else:
    with open(os.getenv('ADL_CONFIG_PATH'), 'rb') as f:
        config = tl.load(f)
        
device = torch.device(config['cuda']['device'])

#For each file in the model directory, run model tests and save results
plot_path = config['paths']['plot_output']
model_path = config['paths']['model_output']
test_output_path = config['paths']['testing_record_output']

#get all files in model directory
model_files = os.listdir(model_path)

#for each model in the model path, determine timestamp from file name and load the model, then test the model
print("--- TESTING MODELS ---")
for model_file in model_files:
    #get model name from file name
    model_name = model_file[:model_file.find("_")]
    
    #get timestamp from file name
    timestamp = model_file[(model_file.find("t-") + 2): model_file.find("_", model_file.find("t-"))]
    
    #get seed from file name
    seed = int(model_file[(model_file.find("s-") + 2): model_file.find("_", model_file.find("s-"))])
    
    print("  - Testing Model: " + timestamp + ", Seed: ", seed)
    print("    * Loading Dataset")
    
    _, _, test_loader, test_set = initalize_dataloaders(config['paths']['mri_data'], config['paths']['xls_data'], config['dataset']['validation_split'], seed, cuda_device=torch.device('cpu'), batch_size=config['training']['batch_size'])
    
    print("    * Loading Model")
    model = torch.load(model_path + model_file)
    model.eval()
    
    print("    * Testing Model")
    predicted, actual, correct, incorrect, max_preds, max_actuals = test_model(model, test_loader, cuda_device=device)
    print("    * Accuracy: " + str(correct / (correct + incorrect)))
        
    plot_confusion_matrix(max_preds, max_actuals, model_name, timestamp, plot_path)
    plot_roc_curve(predicted, actual, model_name, timestamp, plot_path)
    plot_image_selection(model, test_set, model_name, timestamp, plot_path, cuda_device=device)