From 2ee97ae55a3bd1b3373a3a79a774838a32b25b16 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 4 Mar 2020 14:58:41 -0800 Subject: [PATCH] Fix resnet50_test.py to be compatible with TF 2. PiperOrigin-RevId: 298948797 Change-Id: I422c1e3ae2758ee2b4eafcd1db865f77505885ea --- .../python/eager/benchmarks/resnet50/resnet50.py | 4 ++-- .../eager/benchmarks/resnet50/resnet50_test.py | 14 +++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/tensorflow/python/eager/benchmarks/resnet50/resnet50.py b/tensorflow/python/eager/benchmarks/resnet50/resnet50.py index 1237928b2d9..7e6a835bbcb 100644 --- a/tensorflow/python/eager/benchmarks/resnet50/resnet50.py +++ b/tensorflow/python/eager/benchmarks/resnet50/resnet50.py @@ -286,8 +286,8 @@ class ResNet50(tf.keras.Model): if pooling == 'avg': self.global_pooling = functools.partial( tf.reduce_mean, - reduction_indices=reduction_indices, - keep_dims=False) + axis=reduction_indices, + keepdims=False) elif pooling == 'max': self.global_pooling = functools.partial( tf.reduce_max, reduction_indices=reduction_indices, keep_dims=False) diff --git a/tensorflow/python/eager/benchmarks/resnet50/resnet50_test.py b/tensorflow/python/eager/benchmarks/resnet50/resnet50_test.py index 754a3e74219..5562b31fe95 100644 --- a/tensorflow/python/eager/benchmarks/resnet50/resnet50_test.py +++ b/tensorflow/python/eager/benchmarks/resnet50/resnet50_test.py @@ -63,10 +63,10 @@ def _events_from_file(filepath): Returns: A list of all tf.compat.v1.Event protos in the event file. """ - records = list(tf.python_io.tf_record_iterator(filepath)) + records = list(tf.compat.v1.python_io.tf_record_iterator(filepath)) result = [] for r in records: - event = tf.Event() + event = tf.compat.v1.Event() event.ParseFromString(r) result.append(event) return result @@ -193,13 +193,13 @@ class ResNet50Test(tf.test.TestCase): device, data_format = resnet50_test_util.device_and_data_format() model = resnet50.ResNet50(data_format) tf.compat.v2.summary.experimental.set_step( - tf.train.get_or_create_global_step()) + tf.compat.v1.train.get_or_create_global_step()) logdir = tempfile.mkdtemp() with tf.compat.v2.summary.create_file_writer( logdir, max_queue=0, name='t0').as_default(), tf.compat.v2.summary.record_if(True): with tf.device(device), context.execution_mode(execution_mode): - optimizer = tf.train.GradientDescentOptimizer(0.1) + optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1) images, labels = resnet50_test_util.random_batch(2, data_format) apply_gradients(model, optimizer, compute_gradients(model, images, labels)) @@ -218,7 +218,7 @@ class ResNet50Test(tf.test.TestCase): def test_no_garbage(self): device, data_format = resnet50_test_util.device_and_data_format() model = resnet50.ResNet50(data_format) - optimizer = tf.train.GradientDescentOptimizer(0.1) + optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1) with tf.device(device): images, labels = resnet50_test_util.random_batch(2, data_format) gc.disable() @@ -338,7 +338,7 @@ class ResNet50Benchmarks(tf.test.Benchmark): (images, labels) = resnet50_test_util.random_batch( batch_size, data_format) model = resnet50.ResNet50(data_format) - optimizer = tf.train.GradientDescentOptimizer(0.1) + optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1) apply_grads = apply_gradients if defun: model.call = tf.function(model.call) @@ -409,5 +409,5 @@ class ResNet50Benchmarks(tf.test.Benchmark): if __name__ == '__main__': - tf.enable_eager_execution() + tf.compat.v1.enable_eager_execution() tf.test.main()