mitosis training

src/napatrackmater/Trackvector.py

@@ -29,6 +29,7 @@
 import json
 from tqdm import tqdm 
 from torch.optim.lr_scheduler import MultiStepLR
+import matplotlib.pyplot as plt
 
 class TrackVector(TrackMate):
     def __init__(
@@ -1357,6 +1358,55 @@ def train_mitosis_neural_net(features_array, labels_array_class1, labels_array_c
          val_acc_class2=val_acc_class2_values)   
     torch.save(model.state_dict(), save_path + '_mitosis_track_model.pth')
 
+
+
+def plot_metrics_from_npz(npz_file):
+    data = np.load(npz_file)
+
+    train_loss_class1 = data['train_loss_class1']
+    train_loss_class2 = data['train_loss_class2']
+    val_loss_class1 = data['val_loss_class1']
+    val_loss_class2 = data['val_loss_class2']
+    train_acc_class1 = data['train_acc_class1']
+    train_acc_class2 = data['train_acc_class2']
+    val_acc_class1 = data['val_acc_class1']
+    val_acc_class2 = data['val_acc_class2']
+
+    epochs = len(train_loss_class1)
+
+    plt.figure(figsize=(12, 4))
+    plt.subplot(1, 2, 1)
+    plt.plot(range(epochs), train_loss_class1, label='Train Loss Class 1')
+    plt.plot(range(epochs), val_loss_class1, label='Validation Loss Class 1')
+    plt.legend()
+    plt.title('Loss for Class 1')
+
+    plt.subplot(1, 2, 2)
+    plt.plot(range(epochs), train_loss_class2, label='Train Loss Class 2')
+    plt.plot(range(epochs), val_loss_class2, label='Validation Loss Class 2')
+    plt.legend()
+    plt.title('Loss for Class 2')
+
+    plt.tight_layout()
+    plt.show()
+
+    plt.figure(figsize=(12, 4))
+    plt.subplot(1, 2, 1)
+    plt.plot(range(epochs), train_acc_class1, label='Train Acc Class 1')
+    plt.plot(range(epochs), val_acc_class1, label='Validation Acc Class 1')
+    plt.legend()
+    plt.title('Accuracy for Class 1')
+
+    plt.subplot(1, 2, 2)
+    plt.plot(range(epochs), train_acc_class2, label='Train Acc Class 2')
+    plt.plot(range(epochs), val_acc_class2, label='Validation Acc Class 2')
+    plt.legend()
+    plt.title('Accuracy for Class 2')
+
+    plt.tight_layout()
+    plt.show()
+
+
 def predict_with_model(saved_model_path, features_array):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")