holiday similarity update

1 files changed, 98 insertions, 0 deletions

diff --git a/cv/holiday_similarity/siamese-finetune.py b/cv/holiday_similarity/siamese-finetune.py
new file mode 100644
index 0000000..9cd3dbb
--- /dev/null
+++ b/cv/holiday_similarity/siamese-finetune.py
@@ -0,0 +1,98 @@
+from keras.applications import resnet50
+from PIL import Image
+from keras import models, callbacks
+from keras.applications import imagenet_utils
+from keras.applications import resnet50
+from keras.preprocessing.image import ImageDataGenerator
+from sklearn import model_selection
+
+from cv.holiday_similarity.data_utils import *
+from cv.holiday_similarity.vis_utils import *
+
+
+def e2e_network():
+    inception_1 = resnet50.ResNet50(weights="imagenet", include_top=True)
+    inception_2 = resnet50.ResNet50(weights="imagenet", include_top=True)
+
+    for layer in inception_1.layers:
+        layer.trainable = False
+        layer.name = layer.name + "_1"
+    for layer in inception_2.layers:
+        layer.trainable = False
+        layer.name = layer.name + "_2"
+
+    vector_1 = inception_1.get_layer("avg_pool_1").output
+    vector_2 = inception_2.get_layer("avg_pool_2").output
+
+    sim_head = models.load_model(os.path.join(DATA_DIR, "models", "resnet-dot-best.h5"))
+    for layer in sim_head.layers:
+        print(layer.name, layer.input_shape, layer.output_shape)
+
+    prediction = sim_head([vector_1, vector_2])
+
+    model = models.Model(inputs=[inception_1.input, inception_2.input], outputs=prediction)
+    model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+
+    return model
+
+
+def load_image_cache(image_cache, image_filename):
+    image = plt.imread(os.path.join(IMAGE_DIR, image_filename))
+    image = np.asarray(Image.fromarray(image).resize((224, 224)))
+    image = image.astype("float32")
+    image = imagenet_utils.preprocess_input(image)
+    image_cache[image_filename] = image
+
+
+def generate_image_cache(triples_data):
+    image_cache = {}
+    num_pairs = len(triples_data)
+    for i, (image_filename_l, image_filename_r, _) in enumerate(triples_data):
+        if i % 1000 == 0:
+            print("images from {:d}/{:d} pairs loaded to cache".format(i, num_pairs))
+        if image_filename_l not in image_cache:
+            load_image_cache(image_cache, image_filename_l)
+        if image_filename_r not in image_cache:
+            load_image_cache(image_cache, image_filename_r)
+    return image_cache
+
+
+if __name__ == '__main__':
+    DATA_DIR = './data'
+    IMAGE_DIR = '/Users/chunhuizhang/workspaces/00_datasets/images/INRIA Holidays dataset /jpg'
+
+    BATCH_SIZE = 32
+    NUM_EPOCHS = 5
+    BEST_MODEL_FILE = os.path.join(DATA_DIR, "models", "resnet-ft-best.h5")
+    FINAL_MODEL_FILE = os.path.join(DATA_DIR, "models", "resnet-ft-final.h5")
+
+    triples_data = create_triples(IMAGE_DIR)
+    triples_data_trainval, triples_data_test = model_selection.train_test_split(triples_data, train_size=0.8)
+    triples_data_train, triples_data_val = model_selection.train_test_split(triples_data_trainval, train_size=0.9)
+    print(len(triples_data_train), len(triples_data_val), len(triples_data_test))
+
+    datagen_args = dict(rotation_range=10,
+                        width_shift_range=0.2,
+                        height_shift_range=0.2,
+                        zoom_range=0.2)
+    datagens = [ImageDataGenerator(**datagen_args),
+                ImageDataGenerator(**datagen_args)]
+
+    image_cache = generate_image_cache(triples_data)
+
+    train_pair_gen = pair_generator(triples_data_train, image_cache, datagens, BATCH_SIZE)
+    val_pair_gen = pair_generator(triples_data_val, image_cache, None, BATCH_SIZE)
+
+    num_train_steps = len(triples_data_train) // BATCH_SIZE
+    num_val_steps = len(triples_data_val) // BATCH_SIZE
+
+    model = e2e_network()
+    checkpoint = callbacks.ModelCheckpoint(filepath=BEST_MODEL_FILE, save_best_only=True)
+    history = model.fit_generator(train_pair_gen,
+                                  steps_per_epoch=num_train_steps,
+                                  epochs=NUM_EPOCHS,
+                                  validation_data=val_pair_gen,
+                                  validation_steps=num_val_steps,
+                                  callbacks=[checkpoint])
+
+    plot_training_curve(history)