played around with CASIA-WEB-FACE

Tune.py

@@ -1,16 +1,21 @@
-import torch
+# Finetuning a selected model 
+# on a selected dataset 
+# using selected parameters
+
 from torch.utils.data import DataLoader
 from sklearn.metrics import classification_report
 import SetUp
 from Data import *
-from IdentitySubset import IdentitySubset
+#from datasets.Casia import *
+#from IdentitySubset import IdentitySubset
+from datasets.UniversalIdentitySubset import UniversalIdentitySubset as IdentitySubset
 # models
 from architectures.Model import Model, Architecture
 
 # numbre of classes 
 CLASS_SIZE = 20
 # batch 
-BATCH_SIZE = 16
+BATCH_SIZE = 32
 
 # size of images per class trainset + testset
 # 30 works best, more than that and we dont have enough data
@@ -21,7 +26,7 @@ TRAINING_SMPLE = 28
 
 # learning rate
 LR_RATE = 0.0001
-EPOCHS = 16
+EPOCHS = 20
 
 # depends on model architecture
 # ResNet, DenseNet = 224
@@ -42,11 +47,18 @@ arch = Architecture.RESNET50
 # load data set and prepare
 dataset = get_set()
 # select identities for experiment
-selected_identities = select_ids(
-    dataset = dataset,
-    sample_size = SAMPLE_SIZE,
-    class_size = CLASS_SIZE
-    )
+#selected_identities = select_ids(
+#    dataset = dataset,
+#    sample_size = SAMPLE_SIZE,
+#    class_size = CLASS_SIZE
+#    )
+
+# this selects the top 50 based on sample size
+# that way repeated calls return the same classes
+selected_identities = select_top_ids(
+    dataset=dataset,
+    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')