Create resnet50_keras_core.py

official/benchmark/resnet50_keras_core.py

@@ -0,0 +1,98 @@
+# Lint as: python3
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Resnet50 Keras core benchmark."""
+
+import tempfile
+import time
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from official.benchmark import perfzero_benchmark
+
+
+def _decode_and_center_crop(image_bytes):
+  """Crops to center of image with padding then scales image_size."""
+  shape = tf.image.extract_jpeg_shape(image_bytes)
+  image_height, image_width, image_size = shape[0], shape[1], 224
+
+  padded_center_crop_size = tf.cast(
+      ((image_size / (image_size + 32)) *
+       tf.cast(tf.minimum(image_height, image_width), tf.float32)),
+      tf.int32,
+  )
+
+  offset_height = ((image_height - padded_center_crop_size) + 1) // 2
+  offset_width = ((image_width - padded_center_crop_size) + 1) // 2
+  crop_window = tf.stack([
+      offset_height, offset_width, padded_center_crop_size,
+      padded_center_crop_size
+  ])
+  image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
+  return tf.image.resize(image, [image_size, image_size], method="bicubic")
+
+
+def _preprocessing(data):
+  return (
+      tf.cast(_decode_and_center_crop(data["image"]), tf.float32),
+      data["label"],
+  )
+
+
+def _run_benchmark():
+  """Runs a resnet50 compile/fit() call and returns the wall time."""
+  tmp_dir = tempfile.mkdtemp()
+  start_time = time.time()
+
+  batch_size = 64
+  dataset = tfds.load(
+      "imagenette",
+      decoders={"image": tfds.decode.SkipDecoding()},
+      split="train",
+  )
+
+  dataset = (
+      dataset.cache().repeat(
+          2
+      )  # Artificially increase time per epoch to make it easier to measure
+      .map(_preprocessing,
+           num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(
+               batch_size).prefetch(1))
+
+  with tf.distribute.MirroredStrategy().scope():
+    model = tf.keras.applications.ResNet50(weights=None)
+    model.compile(
+        optimizer=tf.train.experimental.enable_mixed_precision_graph_rewrite(
+            tf.keras.optimizers.Adam(), loss_scale="dynamic"),
+        loss="sparse_categorical_crossentropy",
+    )
+
+  tb_cbk = tf.keras.callbacks.TensorBoard(
+      f"{tmp_dir}/{tf.__version__}", profile_batch=300)
+  model.fit(dataset, verbose=2, epochs=3, callbacks=[tb_cbk])
+  end_time = time.time()
+  return end_time - start_time
+
+
+class Resnet50KerasCoreBenchmark(perfzero_benchmark.PerfZeroBenchmark):
+
+  def benchmark_1_gpu(self):
+    wall_time = _run_benchmark()
+    self.report_benchmark(iters=-1, wall_time=wall_time)
+
+
+if __name__ == "__main__":
+  tf.test.main()