def _benchmark_eager_apply(self,
label,
device_and_format,
defun=False,
execution_mode=None):
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
model = resnet50.ResNet50(data_format)
if defun:
model.call = tfe.function(model.call)
batch_size = 64
num_burn = 5
num_iters = 30
with tf.device(device):
images, _ = random_batch(batch_size, data_format)
for _ in xrange(num_burn):
model(images, training=False).cpu()
if execution_mode:
tfe.async_wait()
gc.collect()
start = time.time()
for _ in xrange(num_iters):
model(images, training=False).cpu()
if execution_mode:
tfe.async_wait()
self._report(label, start, num_iters, device, batch_size,
data_format)
def _apply(self, defun=False, execution_mode=None):
device, data_format = device_and_data_format()
model = resnet50.ResNet50(data_format)
if defun:
model.call = tfe.function(model.call)
with tf.device(device), tfe.execution_mode(execution_mode):
images, _ = random_batch(2, data_format)
output = model(images, training=False)
tfe.async_wait()
self.assertEqual((2, 1000), output.shape)
def _benchmark_eager_train(self,
label,
make_iterator,
device_and_format,
defun=False,
execution_mode=None):
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
for batch_size in self._train_batch_sizes():
(images, labels) = random_batch(batch_size, data_format)
model = densenet.DenseNet(self.depth,
self.growth_rate,
self.num_blocks,
self.output_classes,
self.num_layers_in_each_block,
data_format,
bottleneck=True,
compression=0.5,
weight_decay=1e-4,
dropout_rate=0,
pool_initial=True,
include_top=True)
optimizer = tf.train.GradientDescentOptimizer(0.1)
apply_grads = apply_gradients
if defun:
model.call = tfe.defun(model.call)
apply_grads = tfe.defun(apply_gradients)
num_burn = 3
num_iters = 10
with tf.device(device):
iterator = make_iterator((images, labels))
for _ in xrange(num_burn):
(images, labels) = iterator.next()
apply_grads(model, optimizer,
compute_gradients(model, images, labels))
if execution_mode:
tfe.async_wait()
self._force_device_sync()
gc.collect()
start = time.time()
for _ in xrange(num_iters):
(images, labels) = iterator.next()
apply_grads(model, optimizer,
compute_gradients(model, images, labels))
if execution_mode:
tfe.async_wait()
self._force_device_sync()
self._report(label, start, num_iters, device, batch_size,
data_format)
def _test_train(self, execution_mode=None):
device, data_format = device_and_data_format()
model = resnet50.ResNet50(data_format)
tf.train.get_or_create_global_step()
logdir = tempfile.mkdtemp()
with tf.contrib.summary.create_file_writer(
logdir, max_queue=0, name='t0').as_default(
), tf.contrib.summary.always_record_summaries():
with tf.device(device), tfe.execution_mode(execution_mode):
optimizer = tf.train.GradientDescentOptimizer(0.1)
images, labels = random_batch(2, data_format)
apply_gradients(model, optimizer,
compute_gradients(model, images, labels))
self.assertEqual(320, len(model.variables))
tfe.async_wait()
events = summary_test_util.events_from_logdir(logdir)
self.assertEqual(len(events), 2)
self.assertEqual(events[1].summary.value[0].tag, 'loss')
def _benchmark_eager_train(self,
label,
make_iterator,
device_and_format,
defun=False,
execution_mode=None):
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
for batch_size in self._train_batch_sizes():
(images, labels) = random_batch(batch_size, data_format)
model = resnet50.ResNet50(data_format)
optimizer = tf.train.GradientDescentOptimizer(0.1)
apply_grads = apply_gradients
if defun:
model.call = tfe.function(model.call)
apply_grads = tfe.function(apply_gradients)
num_burn = 3
num_iters = 10
with tf.device(device):
iterator = make_iterator((images, labels))
for _ in xrange(num_burn):
(images, labels) = iterator.next()
apply_grads(model, optimizer,
compute_gradients(model, images, labels))
if execution_mode:
tfe.async_wait()
self._force_device_sync()
gc.collect()
start = time.time()
for _ in xrange(num_iters):
(images, labels) = iterator.next()
apply_grads(model, optimizer,
compute_gradients(model, images, labels))
if execution_mode:
tfe.async_wait()
self._force_device_sync()
self._report(label, start, num_iters, device, batch_size,
data_format)
def _benchmark_eager_apply(self,
label,
device_and_format,
defun=False,
execution_mode=None):
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
model = densenet.DenseNet(self.depth,
self.growth_rate,
self.num_blocks,
self.output_classes,
self.num_layers_in_each_block,
data_format,
bottleneck=True,
compression=0.5,
weight_decay=1e-4,
dropout_rate=0,
pool_initial=True,
include_top=True)
if defun:
# TODO(apassos) enable tfe.function here
model.call = tfe.defun(model.call)
batch_size = 64
num_burn = 5
num_iters = 30
with tf.device(device):
images, _ = random_batch(batch_size, data_format)
for _ in xrange(num_burn):
model(images, training=False).cpu()
if execution_mode:
tfe.async_wait()
gc.collect()
start = time.time()
for _ in xrange(num_iters):
model(images, training=False).cpu()
if execution_mode:
tfe.async_wait()
self._report(label, start, num_iters, device, batch_size,
data_format)