Initial commit

Tune.py

@@ -0,0 +1,112 @@
+import torch
+from torch.utils.data import DataLoader
+from sklearn.metrics import classification_report
+import SetUp
+from Data import *
+from IdentitySubset import IdentitySubset
+# models
+from architectures.Model import Model, Architecture
+
+# numbre of classes 
+CLASS_SIZE = 30
+# batch 
+BATCH_SIZE = 16
+
+# size of images per class trainset + testset
+# 30 works best, more than that and we dont have enough data
+SAMPLE_SIZE = 30
+
+# this is then full sample - test sample
+TRAINING_SMPLE = 28
+
+# learning rate
+LR_RATE = 0.0001
+EPOCHS = 20
+
+# depends on model architecture
+# ResNet, DenseNet = 224
+# Inception = 299
+RESOLUTION = 299
+
+# model architecture
+arch = Architecture.INCEPTION
+
+# DATA PREPARATION
+# load data set and prepare
+dataset = get_set(res = RESOLUTION)
+# select identities for experiment
+selected_identities = select_ids(
+    dataset = dataset,
+    sample_size = SAMPLE_SIZE,
+    class_size = CLASS_SIZE
+    )
+
+print(f'> Selected {CLASS_SIZE} random identity classes from CelebA dataset.')
+print(f'> A class has {TRAINING_SMPLE} train and {SAMPLE_SIZE-TRAINING_SMPLE} test samples')
+
+# split class images to train/test indices
+train_indices, test_indices = get_indices(
+    dataset = dataset, 
+    identities = selected_identities, 
+    split_at = TRAINING_SMPLE
+)
+
+# helps map class id to index
+id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}
+
+# we remap identities because crossEntropyLoss requires in indices 0 -> (n-1)
+# where n = class size.
+train_data = IdentitySubset(
+    dataset, 
+    train_indices, 
+    id_map)
+
+train_loader = DataLoader(
+    train_data, 
+    batch_size = BATCH_SIZE, 
+    shuffle = True)
+
+print(f"> Total training images: {len(train_data)}")
+
+print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}, epochs = {EPOCHS}')
+
+# MODEL PREPARATION
+# cuda if exists (it does here)
+device = SetUp.get_device()
+# Create model using Factory
+model = Model.create(
+    arch = arch, 
+    device = device, 
+    size = CLASS_SIZE)
+
+# FINETUNING
+model.train(
+    epochs = EPOCHS, 
+    loader = train_loader, 
+    rate = LR_RATE)
+
+# save.
+torch.save(
+    model.get().state_dict(), 
+    f'{arch.name}.pth')
+
+# done tuning
+print('Model saved!')
+
+# EVALUATE
+# Testing
+test_data = IdentitySubset(
+    dataset, 
+    test_indices, 
+    id_map)
+
+test_loader = DataLoader(
+    test_data, 
+    batch_size=BATCH_SIZE, 
+    shuffle=False)
+
+print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")
+
+# Evaluate
+model.evaluate(
+    loader = test_loader)