fix: paths for new models

src/efficientnet.py

@@ -23,10 +23,11 @@ embedding_model = tf.keras.models.Sequential([
 ])
 
 embedding_model.build(MODEL_INPUT_SIZE)
+embedding_model.summary()
 
 #DATASET_NAME = 'cats_vs_dogs'
 DATASET_NAME = 'cifar10'
-# DATASET_NAME = 'cars196'
+#DATASET_NAME = 'cars196'
 
 ds = tfds.load(DATASET_NAME, split='train')
 
@@ -50,7 +51,7 @@ batched_ds = ds.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
 # "label" has shape [BATCH_SIZE,1] and is an integer label (value between 0 and 9)
 
 # Naming schema: <dataset_name>-<dataset_split>.<model-name>.embeddings.pickle
-DST_FNAME = f'/content/drive/MyDrive/{DATASET_NAME}-train.efficientnet_v2_imagenet1k_s.embeddings.pickle'
+DST_FNAME = get_datadir('efficientnet_v2_imagenet1k_s.embeddings.pkl')
 
 if Path(DST_FNAME).exists():
   # When you need to use the embeddings, upload the file (or store it on Drive and mount your drive folder in Colab), then run:
@@ -79,8 +80,6 @@ embeddings_ds = tf.data.Dataset.zip((
     tf.data.Dataset.from_tensor_slices(embeddings),
     tf.data.Dataset.from_tensor_slices(labels)
 )).cache().shuffle(1000).repeat()
-# change for triplet loss implementation
-
 
 @tf.function
 def make_label_for_pair(embeddings, labels):
@@ -99,13 +98,11 @@ train_ds = train_ds.map(make_label_for_pair, num_parallel_calls=tf.data.AUTOTUNE
 
 ## Model hyperparters
 EMBEDDING_VECTOR_DIMENSION = 1280
-# EMBEDDING_VECTOR_DIMENSION = int(1280/2)
 IMAGE_VECTOR_DIMENSIONS = 128
 ACTIVATION_FN = 'tanh' # same as in paper
 MARGIN = 0.005
 
-DST_MODEL_FNAME = f'/content/drive/MyDrive/trained_model.margin-{MARGIN}.{Path(Path(DST_FNAME).stem).stem}'
-
+DST_MODEL_FNAME = get_modeldir('seamese_cifar10_' + str(Path(Path(DST_FNAME).stem).stem) + '_' + str(IMAGE_VECTOR_DIMENSIONS) + '.tf')
 
 ## These functions are straight from the Keras tutorial linked above
 
@@ -257,7 +254,7 @@ inference_model = tf.keras.models.load_model(DST_MODEL_FNAME, compile=False)
 
 # NUM_SAMPLES_TO_DISPLAY = 10000
 NUM_SAMPLES_TO_DISPLAY = 3000
-LOG_DIR=Path('logs')
+LOG_DIR=Path('logs_efficientnet')
 LOG_DIR.mkdir(exist_ok=True, parents=True)
 
 val_ds = (tfds.load(DATASET_NAME, split='test')

src/vit.py

 import sys
 sys.path.append("..")
 
+from src.data.embeddings import *
+from src.model.alexnet import AlexNetModel
+from src.utils.common import get_modeldir
+
 import tensorflow as tf
 import tensorflow_hub as hub
 import tensorflow_datasets as tfds
@@ -52,9 +56,8 @@ batched_ds = ds.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
 # "image" has shape [BATCH_SIZE,32,32,3] and is an RGB uint8 image
 # "label" has shape [BATCH_SIZE,1] and is an integer label (value between 0 and 9)
 
-
 # Naming schema: <dataset_name>-<dataset_split>.<model-name>.embeddings.pickle
-DST_FNAME = f'/content/drive/MyDrive/{DATASET_NAME}-train.vit_s16_fe.embeddings.pickle'
+DST_FNAME = get_datadir('vit_s16_fe.embeddings.pkl')
 
 if Path(DST_FNAME).exists():
   # When you need to use the embeddings, upload the file (or store it on Drive and mount your drive folder in Colab), then run:
@@ -100,13 +103,12 @@ train_ds = train_ds.map(make_label_for_pair, num_parallel_calls=tf.data.AUTOTUNE
 #train_ds = train_ds.map(lambda _, vals: vals) # discard the prepended "selected" class from the rejction resample, since we aleady have it available
 
 ## Model hyperparters
-EMBEDDING_VECTOR_DIMENSION = 1280
+EMBEDDING_VECTOR_DIMENSION = 384
 IMAGE_VECTOR_DIMENSIONS = 128
 ACTIVATION_FN = 'tanh' # same as in paper
 MARGIN = 0.005
 
-DST_MODEL_FNAME = f'/content/drive/MyDrive/trained_model.margin-{MARGIN}.{Path(Path(DST_FNAME).stem).stem}'
-
+DST_MODEL_FNAME = get_modeldir('seamese_cifar10_' + str(Path(Path(DST_FNAME).stem).stem) + '_' + str(IMAGE_VECTOR_DIMENSIONS) + '.tf')
 
 ## These functions are straight from the Keras tutorial linked above
 
@@ -257,7 +259,7 @@ def write_embeddings_for_tensorboard(image_vectors: list, labels: list, root_dir
 inference_model = tf.keras.models.load_model(DST_MODEL_FNAME, compile=False)
 
 NUM_SAMPLES_TO_DISPLAY = 10000
-LOG_DIR=Path('logs')
+LOG_DIR=Path('logs_vit')
 LOG_DIR.mkdir(exist_ok=True, parents=True)
 
 val_ds = (tfds.load(DATASET_NAME, split='test')