chore: rename map function

src/alexnet.py

@@ -11,7 +11,7 @@ from src.model.siamese import SiameseModel
 model_name = 'imagenette_alexnet'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=AlexNetModel.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=AlexNetModel.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 
 # create model

src/alexnet_cifar10.py

@@ -11,7 +11,7 @@ from src.model.siamese import SiameseModel
 model_name = 'cifar10_alexnet'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=AlexNetModel.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=AlexNetModel.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 
 # create model

src/data/cifar10.py

@@ -7,7 +7,7 @@ NUM_CLASSES = 10
 CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
 
 
-def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=None):
+def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, map_fn=None):
     train_ds = tf.keras.utils.image_dataset_from_directory(
         directory='../datasets/cifar10/train/',
         labels='inferred',
@@ -27,15 +27,15 @@ def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=Non
         interpolation='nearest'
     )
 
-    if preprocess_fn is not None:
-        train_ds = train_ds.map(preprocess_fn).prefetch(tf.data.AUTOTUNE)
-        test_ds = test_ds.map(preprocess_fn).prefetch(tf.data.AUTOTUNE)
+    if map_fn is not None:
+        train_ds = train_ds.map(map_fn).prefetch(tf.data.AUTOTUNE)
+        test_ds = test_ds.map(map_fn).prefetch(tf.data.AUTOTUNE)
 
     return train_ds, test_ds
 
 
-def load_dataset3(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=None):
-    train_ds, test_ds = load_dataset(image_size=image_size, batch_size=batch_size, preprocess_fn=preprocess_fn)
+def load_dataset3(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, map_fn=None):
+    train_ds, test_ds = load_dataset(image_size=image_size, batch_size=batch_size, map_fn=map_fn)
 
     train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()
     train_ds = train_ds.skip(train_ds_size / 10)

src/data/imagenette.py

@@ -7,7 +7,7 @@ NUM_CLASSES = 10
 CLASS_NAMES = ['fish', 'dog', 'player', 'saw', 'building', 'music', 'truck', 'gas', 'ball', 'parachute']
 
 
-def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=None):
+def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, map_fn=None):
     train_ds = tf.keras.utils.image_dataset_from_directory(
         directory='../datasets/imagenette2/train/',
         labels='inferred',
@@ -27,15 +27,15 @@ def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=Non
         interpolation='nearest'
     )
 
-    if preprocess_fn is not None:
-        train_ds = train_ds.map(preprocess_fn).prefetch(tf.data.AUTOTUNE)
-        test_ds = test_ds.map(preprocess_fn).prefetch(tf.data.AUTOTUNE)
+    if map_fn is not None:
+        train_ds = train_ds.map(map_fn).prefetch(tf.data.AUTOTUNE)
+        test_ds = test_ds.map(map_fn).prefetch(tf.data.AUTOTUNE)
 
     return train_ds, test_ds
 
 
-def load_dataset3(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=None):
-    train_ds, test_ds = load_dataset(image_size=image_size, batch_size=batch_size, preprocess_fn=preprocess_fn)
+def load_dataset3(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, map_fn=None):
+    train_ds, test_ds = load_dataset(image_size=image_size, batch_size=batch_size, map_fn=map_fn)
 
     test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()
     val_ds = test_ds.take(test_ds_size / 2)

src/data/simple3.py

@@ -7,7 +7,7 @@ NUM_CLASSES = 3
 CLASS_NAMES = ['building', 'dog', 'player']
 
 
-def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=None):
+def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, map_fn=None):
     ds = tf.keras.utils.image_dataset_from_directory(
         directory='../datasets/simple3/',
         labels='inferred',
@@ -17,14 +17,14 @@ def load_dataset(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=Non
         interpolation='nearest'
     )
 
-    if preprocess_fn is not None:
-        ds = ds.map(preprocess_fn).prefetch(tf.data.AUTOTUNE)
+    if map_fn is not None:
+        ds = ds.map(map_fn).prefetch(tf.data.AUTOTUNE)
 
     return ds
 
 
-def load_dataset3(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, preprocess_fn=None):
-    ds = load_dataset(image_size=image_size, batch_size=batch_size, preprocess_fn=preprocess_fn)
+def load_dataset3(image_size=IMAGE_SIZE, batch_size=BATCH_SIZE, map_fn=None):
+    ds = load_dataset(image_size=image_size, batch_size=batch_size, map_fn=map_fn)
 
     ds_size = tf.data.experimental.cardinality(ds).numpy()
     train_ds = ds.take(ds_size * 0.6)

src/efficientnet.py

@@ -10,7 +10,7 @@ from src.model.siamese import SiameseModel
 model_name = 'cifar10_efficientnet'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, batch_size=BATCH_SIZE, preprocess_fn=EfficientNetModel.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, batch_size=BATCH_SIZE, map_fn=EfficientNetModel.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 
 model = EfficientNetModel()

src/mobilenet.py

@@ -8,24 +8,23 @@ from src.utils.common import get_modeldir
 from src.model.mobilenet import MobileNetModel, PRETRAIN_EPOCHS, TARGET_SHAPE, EMBEDDING_VECTOR_DIMENSION
 from src.model.siamese import SiameseModel
 
-model_name = 'imagenet_mobilenet'
+model_name = 'imagenette_mobilenet'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=MobileNetModel.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=MobileNetModel.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 PRETRAIN_TOTAL_STEPS = PRETRAIN_EPOCHS * len(train_ds)
 
-# create model
 model = MobileNetModel()
 model.compile(optimizer=tf.keras.optimizers.RMSprop(tf.keras.optimizers.schedules.CosineDecay(1e-3, PRETRAIN_TOTAL_STEPS)))
 model.summary()
 
 # load weights
-model.load_weights(get_modeldir(model_name + '.h5'))
+# model.load_weights(get_modeldir(model_name + '.h5'))
 
 # train & save model
-# model.fit(train_ds, epochs=PRETRAIN_EPOCHS, validation_data=val_ds)
-# model.save_weights(get_modeldir(model_name + '.h5'))
+model.fit(train_ds, epochs=PRETRAIN_EPOCHS, validation_data=val_ds)
+model.save_weights(get_modeldir(model_name + '.h5'))
 
 # evaluate
 print('evaluating...')

src/mobilenet_cifar10.py

@@ -11,7 +11,7 @@ from src.model.siamese import SiameseModel
 model_name = 'cifar10_mobilenet'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=MobileNetModel.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=MobileNetModel.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 PRETRAIN_TOTAL_STEPS = PRETRAIN_EPOCHS * len(train_ds)
 
@@ -55,6 +55,6 @@ inference_model.save(get_modeldir(model_name + '_inference.tf'))
 
 
 print('visualization')
-# compute embeddings of the images and their labels, store them in a tsv file for visualization
+# compute vectors of the images and their labels, store them in a tsv file for visualization
 siamese_vectors, siamese_labels = calc_vectors(comb_ds, inference_model)
 project_embeddings(siamese_vectors, siamese_labels, model_name + '_siamese')

src/utils/common.py

+import time
 import tensorflow as tf
 from os import path, curdir
-import time
 
 
 def normalize_image(image):

src/vgg16.py

@@ -11,7 +11,7 @@ from src.model.siamese import SiameseModel
 model_name = 'imagenet_vgg16'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=VGG16Model.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=VGG16Model.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 PRETRAIN_TOTAL_STEPS = PRETRAIN_EPOCHS * len(train_ds)
 

src/vgg16_cifar10.py

@@ -13,7 +13,7 @@ embeddings_name = model_name + '_embeddings'
 
 TARGET_SHAPE = (32, 32)
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=VGG16Model.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=VGG16Model.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 PRETRAIN_TOTAL_STEPS = PRETRAIN_EPOCHS * len(train_ds)
 

src/vgg16_small.py

@@ -13,7 +13,7 @@ embeddings_name = model_name + '_embeddings'
 
 TARGET_SHAPE = (32, 32)
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, preprocess_fn=VGG16Model.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, map_fn=VGG16Model.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 PRETRAIN_TOTAL_STEPS = PRETRAIN_EPOCHS * len(train_ds)
 

src/vit.py

@@ -11,7 +11,7 @@ from src.model.siamese import SiameseModel
 model_name = 'cifar10_vit'
 embeddings_name = model_name + '_embeddings'
 
-train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, batch_size=BATCH_SIZE, preprocess_fn=VitModel.preprocess_input)
+train_ds, val_ds, test_ds = load_dataset3(image_size=TARGET_SHAPE, batch_size=BATCH_SIZE, map_fn=VitModel.preprocess_input)
 comb_ds = train_ds.concatenate(val_ds).concatenate(test_ds)
 
 model = VitModel()
@@ -24,7 +24,7 @@ save_embeddings(emb_vectors, emb_labels, embeddings_name)
 # emb_vectors, emb_labels = load_embeddings(embeddings_name)
 
 # siamese is the model we train
-siamese = SiameseModel(embedding_vector_dimension=384, image_vector_dimensions=512)
+siamese = SiameseModel(embedding_vector_dimension=384, image_vector_dimensions=3)
 siamese.compile(loss_margin=0.05, optimizer=tf.keras.optimizers.Adam(learning_rate=0.001))
 siamese.summary()