Add EfficientNet benchmark (#394)

* init

* refactor

* minor improvements

* Remove softmax activation

* small

* rename

* redo the tf change

---------

Co-authored-by: chenmoneygithub <chenmoney@chenmoney-gpu-scratch.us-west1-a.c.keras-team-gcp.internal>

benchmarks/model_benchmark/benchmark_utils.py

@@ -0,0 +1,24 @@
+import time
+
+import keras_core
+
+
+class BenchmarkMetricsCallback(keras_core.callbacks.Callback):
+    def __init__(self, start_batch=1, stop_batch=None):
+        self.start_batch = start_batch
+        self.stop_batch = stop_batch
+
+        # Store the throughput of each epoch.
+        self.state = {"throughput": []}
+
+    def on_train_batch_begin(self, batch, logs=None):
+        if batch == self.start_batch:
+            self.state["epoch_begin_time"] = time.time()
+
+    def on_train_batch_end(self, batch, logs=None):
+        if batch == self.stop_batch:
+            epoch_end_time = time.time()
+            throughput = (self.stop_batch - self.start_batch + 1) / (
+                epoch_end_time - self.state["epoch_begin_time"]
+            )
+            self.state["throughput"].append(throughput)

benchmarks/model_benchmark/efficient_net_image_classification_benchmark.py

@@ -0,0 +1,148 @@
+"""Image classification benchmark with EfficientNetV2B0.
+
+To run the benchmark, make sure you are in model_benchmark/ directory, and run
+the command below:
+
+python3 -m model_benchmark.resnet_image_classification_benchmark \
+    --epochs=2 \
+    --batch_size=32
+"""
+
+import time
+
+import keras_core as keras
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from absl import app
+from absl import flags
+from absl import logging
+
+
+from model_benchmark.benchmark_utils import BenchmarkMetricsCallback
+from keras_core.applications import EfficientNetV2B0
+
+flags.DEFINE_integer("epochs", 1, "The number of epochs.")
+flags.DEFINE_integer("batch_size", 4, "Batch Size.")
+flags.DEFINE_string(
+    "mixed_precision_policy",
+    "mixed_float16",
+    "The global precision policy to use, e.g., 'mixed_float16' or 'float32'.",
+)
+
+FLAGS = flags.FLAGS
+
+BATCH_SIZE = 32
+IMAGE_SIZE = (224, 224)
+CHANNELS = 3
+
+
+def load_data():
+    # Load cats vs dogs dataset, and split into train and validation sets.
+    train_dataset, val_dataset = tfds.load(
+        "cats_vs_dogs", split=["train[:90%]", "train[90%:]"], as_supervised=True
+    )
+
+    resizing = keras.layers.Resizing(
+        IMAGE_SIZE[0], IMAGE_SIZE[1], crop_to_aspect_ratio=True
+    )
+
+    def preprocess_inputs(image, label):
+        image = tf.cast(image, "float32")
+        return resizing(image), label
+
+    train_dataset = (
+        train_dataset.map(
+            preprocess_inputs, num_parallel_calls=tf.data.AUTOTUNE
+        )
+        .batch(FLAGS.batch_size)
+        .cache()
+        .prefetch(tf.data.AUTOTUNE)
+    )
+    val_dataset = (
+        val_dataset.map(preprocess_inputs, num_parallel_calls=tf.data.AUTOTUNE)
+        .batch(FLAGS.batch_size)
+        .cache()
+        .prefetch(tf.data.AUTOTUNE)
+    )
+    return train_dataset, val_dataset
+
+
+def load_model():
+    # Load the EfficientNetV2B0 model and add a classification head.
+    model = EfficientNetV2B0(include_top=False, weights="imagenet")
+    classifier = keras.models.Sequential(
+        [
+            keras.Input([IMAGE_SIZE[0], IMAGE_SIZE[1], CHANNELS]),
+            model,
+            keras.layers.GlobalAveragePooling2D(),
+            keras.layers.Dense(2),
+        ]
+    )
+    return classifier
+
+
+def main(_):
+    keras.mixed_precision.set_dtype_policy(FLAGS.mixed_precision_policy)
+
+    logging.info(
+        "Benchmarking configs...\n"
+        "=========================\n"
+        f"MODEL: EfficientNetV2B0\n"
+        f"TASK: image classification/dogs-vs-cats \n"
+        f"BATCH_SIZE: {FLAGS.batch_size}\n"
+        f"EPOCHS: {FLAGS.epochs}\n"
+        "=========================\n"
+    )
+
+    # Load datasets.
+    train_ds, validation_ds = load_data()
+
+    # Load the model.
+    classifier = load_model()
+
+    lr = keras.optimizers.schedules.PolynomialDecay(
+        5e-4,
+        decay_steps=train_ds.cardinality() * FLAGS.epochs,
+        end_learning_rate=0.0,
+    )
+    optimizer = keras.optimizers.Adam(lr)
+    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+
+    benchmark_metrics_callback = BenchmarkMetricsCallback(
+        start_batch=1,
+        stop_batch=train_ds.cardinality().numpy()-1,
+    )
+
+    classifier.compile(
+        optimizer=optimizer,
+        loss=loss,
+        metrics=["sparse_categorical_accuracy"],
+    )
+    # Start training.
+    logging.info("Starting Training...")
+
+    st = time.time()
+
+    history = classifier.fit(
+        train_ds,
+        validation_data=validation_ds,
+        epochs=FLAGS.epochs,
+        callbacks=[benchmark_metrics_callback],
+    )
+
+    wall_time = time.time() - st
+    validation_accuracy = history.history["val_sparse_categorical_accuracy"][-1]
+
+    examples_per_second = np.mean(
+        np.array(benchmark_metrics_callback.state["throughput"])
+    ) * FLAGS.batch_size
+
+    logging.info("Training Finished!")
+    logging.info(f"Wall Time: {wall_time:.4f} seconds.")
+    logging.info(f"Validation Accuracy: {validation_accuracy:.4f}")
+    logging.info(f"examples_per_second: {examples_per_second:.4f}")
+
+
+if __name__ == "__main__":
+    app.run(main)

keras_core/__init__.py

@@ -1,4 +1,5 @@
 from keras_core import activations
+from keras_core import applications
 from keras_core import backend
 from keras_core import constraints
 from keras_core import datasets

keras_core/backend/tensorflow/random.py

@@ -61,8 +61,22 @@ def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
     )
 
 
+def _get_concrete_noise_shape(inputs, noise_shape):
+    if noise_shape is None:
+        return tf.shape(inputs)
+
+    concrete_inputs_shape = tf.shape(inputs)
+    concrete_noise_shape = []
+    for i, value in enumerate(noise_shape):
+        concrete_noise_shape.append(
+            concrete_inputs_shape[i] if value is None else value
+        )
+    return concrete_noise_shape
+
+
 def dropout(inputs, rate, noise_shape=None, seed=None):
     seed = tf_draw_seed(seed)
+    noise_shape = _get_concrete_noise_shape(inputs, noise_shape)
     return tf.nn.experimental.stateless_dropout(
         inputs,
         rate=rate,