added loading and testing the model again

Tune.py

@@ -8,7 +8,7 @@ from IdentitySubset import IdentitySubset
 from architectures.Model import Model, Architecture
 
 # numbre of classes 
-CLASS_SIZE = 50
+CLASS_SIZE = 20
 # batch 
 BATCH_SIZE = 16
 
@@ -21,7 +21,7 @@ TRAINING_SMPLE = 28
 
 # learning rate
 LR_RATE = 0.0001
-EPOCHS = 20
+EPOCHS = 16
 
 # depends on model architecture
 # ResNet, DenseNet = 224
@@ -36,7 +36,7 @@ RESOLUTION = 224
 #  - GOOGLENET
 #  - EFFICIENTNET
 #  - SHUFFLENET
-arch = Architecture.GOOGLENET
+arch = Architecture.RESNET50
 
 # DATA PREPARATION
 # load data set and prepare
@@ -88,19 +88,15 @@ model = Model.create(
     device = device, 
     size = CLASS_SIZE)
 
-# FINETUNING
+# we may need to load existing model or finetune
 model.train(
-    epochs = EPOCHS, 
-    loader = train_loader, 
-    rate = LR_RATE)
+        epochs = EPOCHS, 
+        loader = train_loader, 
+        rate = LR_RATE)
 
-# save.
+    # save.
 model.save(filename=arch.name.lower())
-'''
-torch.save(
-    model.get().state_dict(), 
-    f'trained/{arch.name.lower()}.pth'
-)'''
+
 
 # done tuning
 print('Model saved!')
@@ -125,3 +121,15 @@ print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")
 # Evaluate
 model.evaluate(
     loader = test_loader)
+
+# test again
+reloaded = Model.create(
+    arch=arch,
+    device = device,
+    size = CLASS_SIZE
+    )
+reloaded.load(arch = arch)
+print("Evaluating loaded")
+reloaded.evaluate(
+    loader = test_loader
+)