Merge pull request #9 from CornellDataScience/siamese-model

Model cleanup

src/model/Siamese_Network.py

@@ -1,14 +1,13 @@
 # https://keras.io/examples/vision/siamese_network/
 
-import matplotlib.pyplot as plt
-import numpy as np
 import os
 import random
 import tensorflow as tf
 from pathlib import Path
-from tensorflow.keras import applications
+
+from utils import preprocess_image, preprocess_triplets
+
 from tensorflow.keras import layers
-from tensorflow.keras import losses
 from tensorflow.keras import optimizers
 from tensorflow.keras import metrics
 from tensorflow.keras import Model
@@ -20,113 +19,6 @@
 anchor_images_path = cache_dir / "left"
 positive_images_path = cache_dir / "right"
 
-
-def preprocess_image(filename):
-    """
-    Load the specified file as a JPEG image, preprocess it and
-    resize it to the target shape.
-    """
-
-    image_string = tf.io.read_file(filename)
-    image = tf.image.decode_jpeg(image_string, channels=3)
-    image = tf.image.convert_image_dtype(image, tf.float32)
-    image = tf.image.resize(image, target_shape)
-    return image
-
-
-def preprocess_triplets(anchor, positive, negative):
-    """
-    Given the filenames corresponding to the three images, load and
-    preprocess them.
-    """
-
-    return (
-        preprocess_image(anchor),
-        preprocess_image(positive),
-        preprocess_image(negative),
-    )
-
-
-# We need to make sure both the anchor and positive images are loaded in
-# sorted order so we can match them together.
-anchor_images = sorted(
-    [str(anchor_images_path / f) for f in os.listdir(anchor_images_path)]
-)
-
-positive_images = sorted(
-    [str(positive_images_path / f) for f in os.listdir(positive_images_path)]
-)
-
-all_images = anchor_images + positive_images
-random.shuffle(all_images)
-print(all_images)
-
-image_count = len(anchor_images)
-print(image_count)
-
-anchor_dataset = tf.data.Dataset.from_tensor_slices(anchor_images)
-positive_dataset = tf.data.Dataset.from_tensor_slices(positive_images)
-negative_dataset = tf.data.Dataset.from_tensor_slices(all_images[:100])
-
-print(len(negative_dataset))
-print(len(anchor_dataset))
-
-dataset = tf.data.Dataset.zip((anchor_dataset, positive_dataset, negative_dataset))
-dataset = dataset.shuffle(buffer_size=1024)
-dataset = dataset.map(preprocess_triplets)
-
-# Let's now split our dataset in train and validation.
-train_dataset = dataset.take(round(image_count * 0.8))
-val_dataset = dataset.skip(round(image_count * 0.8))
-
-batch_size = 16
-
-train_dataset = train_dataset.batch(batch_size, drop_remainder=False)
-train_dataset = train_dataset.prefetch(8)
-
-val_dataset = val_dataset.batch(batch_size, drop_remainder=False)
-val_dataset = val_dataset.prefetch(8)
-
-
-def visualize(anchor, positive, negative):
-    """Visualize a few triplets from the supplied batches."""
-
-    def show(ax, image):
-        ax.imshow(image)
-        ax.get_xaxis().set_visible(False)
-        ax.get_yaxis().set_visible(False)
-
-    fig = plt.figure(figsize=(9, 9))
-
-    axs = fig.subplots(3, 3)
-    for i in range(3):
-        show(axs[i, 0], anchor[i])
-        show(axs[i, 1], positive[i])
-        show(axs[i, 2], negative[i])
-
-
-visualize(*list(train_dataset.take(1).as_numpy_iterator())[0])
-
-base_cnn = resnet.ResNet50(
-    weights="imagenet", input_shape=target_shape + (3,), include_top=False
-)
-
-flatten = layers.Flatten()(base_cnn.output)
-dense1 = layers.Dense(512, activation="relu")(flatten)
-dense1 = layers.BatchNormalization()(dense1)
-dense2 = layers.Dense(256, activation="relu")(dense1)
-dense2 = layers.BatchNormalization()(dense2)
-output = layers.Dense(256)(dense2)
-
-embedding = Model(base_cnn.input, output, name="Embedding")
-
-trainable = False
-for layer in base_cnn.layers:
-    if layer.name == "conv5_block1_out":
-        trainable = True
-    layer.trainable = trainable
-
-
 class DistanceLayer(layers.Layer):
     """
     This layer is responsible for computing the distance between the anchor
@@ -142,22 +34,6 @@ def call(self, anchor, positive, negative):
         an_distance = tf.reduce_sum(tf.square(anchor - negative), -1)
         return (ap_distance, an_distance)
 
-
-anchor_input = layers.Input(name="anchor", shape=target_shape + (3,))
-positive_input = layers.Input(name="positive", shape=target_shape + (3,))
-negative_input = layers.Input(name="negative", shape=target_shape + (3,))
-
-distances = DistanceLayer()(
-    embedding(resnet.preprocess_input(anchor_input)),
-    embedding(resnet.preprocess_input(positive_input)),
-    embedding(resnet.preprocess_input(negative_input)),
-)
-
-siamese_network = Model(
-    inputs=[anchor_input, positive_input, negative_input], outputs=distances
-)
-
-
 class SiameseModel(Model):
     """The Siamese Network model with a custom training and testing loops.
 
@@ -224,9 +100,72 @@ def metrics(self):
         return [self.loss_tracker]
 
 
-# Training
+# We need to make sure both the anchor and positive images are loaded in
+# sorted order so we can match them together.
+anchor_images = sorted(
+    [str(anchor_images_path / f) for f in os.listdir(anchor_images_path)]
+)
+
+positive_images = sorted(
+    [str(positive_images_path / f) for f in os.listdir(positive_images_path)]
+)
+
+all_images = anchor_images + positive_images
+random.shuffle(all_images)
+
+image_count = len(anchor_images)
+
+anchor_dataset = tf.data.Dataset.from_tensor_slices(anchor_images)
+positive_dataset = tf.data.Dataset.from_tensor_slices(positive_images)
+negative_dataset = tf.data.Dataset.from_tensor_slices(all_images[:100])
+
+dataset = tf.data.Dataset.zip((anchor_dataset, positive_dataset, negative_dataset))
+dataset = dataset.shuffle(buffer_size=1024)
+dataset = dataset.map(preprocess_triplets)
+
+# Let's now split our dataset in train and validation.
+train_dataset = dataset.take(round(image_count * 0.8))
+val_dataset = dataset.skip(round(image_count * 0.8))
+
+batch_size = 16
+
+train_dataset = train_dataset.batch(batch_size, drop_remainder=False).prefetch(8)
+val_dataset = val_dataset.batch(batch_size, drop_remainder=False).prefetch(8)
+
+base_cnn = resnet.ResNet50(
+    weights="imagenet", input_shape=target_shape + (3,), include_top=False
+)
+
+flatten = layers.Flatten()(base_cnn.output)
+dense1 = layers.BatchNormalization()(layers.Dense(512, activation="relu")(flatten))
+dense2 = layers.BatchNormalization()(layers.Dense(256, activation="relu")(dense1))
+output = layers.Dense(256)(dense2)
+
+embedding = Model(base_cnn.input, output, name="Embedding")
+
+trainable = False
+for layer in base_cnn.layers:
+    if layer.name == "conv5_block1_out":
+        trainable = True
+    layer.trainable = trainable
+
+anchor_input = layers.Input(name="anchor", shape=target_shape + (3,))
+positive_input = layers.Input(name="positive", shape=target_shape + (3,))
+negative_input = layers.Input(name="negative", shape=target_shape + (3,))
+
+distances = DistanceLayer()(
+    embedding(resnet.preprocess_input(anchor_input)),
+    embedding(resnet.preprocess_input(positive_input)),
+    embedding(resnet.preprocess_input(negative_input)),
+)
 
+siamese_network = Model(
+    inputs=[anchor_input, positive_input, negative_input], outputs=distances
+)
+
+# Training
 siamese_model = SiameseModel(siamese_network)
 siamese_model.compile(optimizer=optimizers.Adam(0.0001))  # 0..0001
 siamese_model.fit(train_dataset, epochs=7, validation_data=val_dataset)
-embedding.save("siamese_feature.h5")
+embedding.save_weights("weights/embedding_weights.h5")
+siamese_model.save_weights("weights/siamese_weights.h5")

src/model/Siamese_Predictor.py

@@ -1,28 +1,17 @@
-# from Siamese_Network import preprocess_image
+import threading
 import tensorflow as tf
+import time
+
+from utils import preprocess_image
+
 from tensorflow.keras.applications import resnet
-import argparse
 from tensorflow.keras import metrics
-import time
+
 from watchdog.observers import Observer
 from watchdog.events import PatternMatchingEventHandler
-import threading
-
-def preprocess_image(filename):
-    """
-    Load the specified file as a JPEG image, preprocess it and
-    resize it to the target shape.
-    """
-    target_shape = (200, 200)
-
-    image_string = tf.io.read_file(filename)
-    image = tf.image.decode_jpeg(image_string, channels=3)
-    image = tf.image.convert_image_dtype(image, tf.float32)
-    image = tf.image.resize(image, target_shape)
-    return image
 
 # load the model
-embedding = tf.keras.models.load_model("siamese_feature.h5", compile=False)
+embedding = tf.keras.models.load_model("siamese_weights.h5", compile=False)
 print("Model is done loading")
 
 # load david base

src/model/utils.py

@@ -0,0 +1,28 @@
+import tensorflow as tf
+
+target_shape = (200, 200)
+
+def preprocess_image(filename):
+    """
+    Load the specified file as a JPEG image, preprocess it and
+    resize it to the target shape.
+    """
+
+    image_string = tf.io.read_file(filename)
+    image = tf.image.decode_jpeg(image_string, channels=3)
+    image = tf.image.convert_image_dtype(image, tf.float32)
+    image = tf.image.resize(image, target_shape)
+    return image
+
+
+def preprocess_triplets(anchor, positive, negative):
+    """
+    Given the filenames corresponding to the three images, load and
+    preprocess them.
+    """
+
+    return (
+        preprocess_image(anchor),
+        preprocess_image(positive),
+        preprocess_image(negative),
+    )