Merge pull request tensorflow#5225 from tfboyd/resnet_synthetic_fix

tfboyd · web-flow · commit 481728dbf8e1 · 2018-09-04T16:56:33.000-07:00
ResNet synthetic data performance enhancement.
diff --git a/official/resnet/cifar10_main.py b/official/resnet/cifar10_main.py
@@ -135,9 +135,9 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1, num_gpus=None):
   )
 
 
-def get_synth_input_fn():
+def get_synth_input_fn(dtype):
   return resnet_run_loop.get_synth_input_fn(
-      _HEIGHT, _WIDTH, _NUM_CHANNELS, _NUM_CLASSES)
+      _HEIGHT, _WIDTH, _NUM_CHANNELS, _NUM_CLASSES, dtype=dtype)
 
 
 ###############################################################################
@@ -243,8 +243,9 @@ def run_cifar(flags_obj):
   Args:
     flags_obj: An object containing parsed flag values.
   """
-  input_function = (flags_obj.use_synthetic_data and get_synth_input_fn()
-                    or input_fn)
+  input_function = (flags_obj.use_synthetic_data and
+                    get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or
+                    input_fn)
   resnet_run_loop.resnet_main(
       flags_obj, cifar10_model_fn, input_function, DATASET_NAME,
       shape=[_HEIGHT, _WIDTH, _NUM_CHANNELS])
diff --git a/official/resnet/cifar10_test.py b/official/resnet/cifar10_test.py
@@ -77,9 +77,9 @@ def test_dataset_input_fn(self):
           self.assertAllClose(pixel, np.array([-1.225, 0., 1.225]), rtol=1e-3)
 
   def cifar10_model_fn_helper(self, mode, resnet_version, dtype):
-    input_fn = cifar10_main.get_synth_input_fn()
+    input_fn = cifar10_main.get_synth_input_fn(dtype)
     dataset = input_fn(True, '', _BATCH_SIZE)
-    iterator = dataset.make_one_shot_iterator()
+    iterator = dataset.make_initializable_iterator()
     features, labels = iterator.get_next()
     spec = cifar10_main.cifar10_model_fn(
         features, labels, mode, {
diff --git a/official/resnet/imagenet_main.py b/official/resnet/imagenet_main.py
@@ -196,9 +196,10 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1, num_gpus=None):
   )
 
 
-def get_synth_input_fn():
+def get_synth_input_fn(dtype):
   return resnet_run_loop.get_synth_input_fn(
-      _DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES)
+      _DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES,
+      dtype=dtype)
 
 
 ###############################################################################
@@ -331,8 +332,9 @@ def run_imagenet(flags_obj):
   Args:
     flags_obj: An object containing parsed flag values.
   """
-  input_function = (flags_obj.use_synthetic_data and get_synth_input_fn()
-                    or input_fn)
+  input_function = (flags_obj.use_synthetic_data and
+                    get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or
+                    input_fn)
 
   resnet_run_loop.resnet_main(
       flags_obj, imagenet_model_fn, input_function, DATASET_NAME,
diff --git a/official/resnet/imagenet_test.py b/official/resnet/imagenet_test.py
@@ -191,9 +191,9 @@ def resnet_model_fn_helper(self, mode, resnet_version, dtype):
     """Tests that the EstimatorSpec is given the appropriate arguments."""
     tf.train.create_global_step()
 
-    input_fn = imagenet_main.get_synth_input_fn()
+    input_fn = imagenet_main.get_synth_input_fn(dtype)
     dataset = input_fn(True, '', _BATCH_SIZE)
-    iterator = dataset.make_one_shot_iterator()
+    iterator = dataset.make_initializable_iterator()
     features, labels = iterator.get_next()
     spec = imagenet_main.imagenet_model_fn(
         features, labels, mode, {
diff --git a/official/resnet/resnet_run_loop.py b/official/resnet/resnet_run_loop.py
@@ -108,29 +108,47 @@ def process_record_dataset(dataset, is_training, batch_size, shuffle_buffer,
   return dataset
 
 
-def get_synth_input_fn(height, width, num_channels, num_classes):
-  """Returns an input function that returns a dataset with zeroes.
+def get_synth_input_fn(height, width, num_channels, num_classes,
+                       dtype=tf.float32):
+  """Returns an input function that returns a dataset with random data.
 
-  This is useful in debugging input pipeline performance, as it removes all
-  elements of file reading and image preprocessing.
+  This input_fn returns a data set that iterates over a set of random data and
+  bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
+  copy is still included. This used to find the upper throughput bound when
+  tunning the full input pipeline.
 
   Args:
     height: Integer height that will be used to create a fake image tensor.
     width: Integer width that will be used to create a fake image tensor.
     num_channels: Integer depth that will be used to create a fake image tensor.
     num_classes: Number of classes that should be represented in the fake labels
       tensor
+    dtype: Data type for features/images.
 
   Returns:
     An input_fn that can be used in place of a real one to return a dataset
     that can be used for iteration.
   """
-  def input_fn(is_training, data_dir, batch_size, *args, **kwargs):  # pylint: disable=unused-argument
-    return model_helpers.generate_synthetic_data(
-        input_shape=tf.TensorShape([batch_size, height, width, num_channels]),
-        input_dtype=tf.float32,
-        label_shape=tf.TensorShape([batch_size]),
-        label_dtype=tf.int32)
+  # pylint: disable=unused-argument
+  def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
+    """Returns dataset filled with random data."""
+    # Synthetic input should be within [0, 255].
+    inputs = tf.truncated_normal(
+        [batch_size] + [height, width, num_channels],
+        dtype=dtype,
+        mean=127,
+        stddev=60,
+        name='synthetic_inputs')
+
+    labels = tf.random_uniform(
+        [batch_size],
+        minval=0,
+        maxval=num_classes - 1,
+        dtype=tf.int32,
+        name='synthetic_labels')
+    data = tf.data.Dataset.from_tensors((inputs, labels)).repeat()
+    data = data.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
+    return data
 
   return input_fn
 
@@ -230,7 +248,7 @@ def resnet_model_fn(features, labels, mode, model_class,
 
   # Generate a summary node for the images
   tf.summary.image('images', features, max_outputs=6)
-
+  # TODO(tobyboyd): Add cast as part of input pipeline on cpu and remove.
   features = tf.cast(features, dtype)
 
   model = model_class(resnet_size, data_format, resnet_version=resnet_version,
