def export(
args,
save_dir,
checkpoint_path=None,
skip_samples=False,
num_classes=None,
opset=None,
):
assert not skip_samples or num_classes
# dataset creation
if not skip_samples:
val_dataset, num_classes = _create_dataset(args, train=False)
with tf_compat.Graph().as_default():
input_shape = ModelRegistry.input_shape(args.arch_key)
inputs = tf_compat.placeholder(tf_compat.float32,
[None] + list(input_shape),
name="inputs")
outputs = _create_model(args, num_classes, inputs)
with tf_compat.Session() as sess:
_load_model(args,
sess,
checkpoint_path=checkpoint_path
or args.checkpoint_path)
exporter = GraphExporter(save_dir)
if not skip_samples:
# Export a batch of samples and expected outputs
tf_dataset = val_dataset.build(args.num_samples,
repeat_count=1,
num_parallel_calls=1)
tf_iter = tf_compat.data.make_one_shot_iterator(tf_dataset)
features, _ = tf_iter.get_next()
inputs_val = sess.run(features)
exporter.export_samples([inputs], [inputs_val], [outputs],
sess)
# Export model to tensorflow checkpoint format
LOGGER.info("exporting tensorflow in {}".format(save_dir))
exporter.export_checkpoint(sess=sess)
# Export model to pb format
LOGGER.info("exporting pb in {}".format(exporter.pb_path))
exporter.export_pb(outputs=[outputs])
# Export model to onnx format
LOGGER.info("exporting onnx in {}".format(exporter.onnx_path))
exporter.export_onnx([inputs], [outputs], opset=opset or args.onnx_opset)
def test_resnets(key: str, pretrained: Union[bool, str], test_input: bool,
const: Callable):
input_shape = ModelRegistry.input_shape(key)
# test out the stand alone constructor
with tf_compat.Graph().as_default():
inputs = tf_compat.placeholder(tf_compat.float32, [None, *input_shape],
name="inputs")
logits = const(inputs, training=False)
if test_input:
with tf_compat.Session() as sess:
sess.run(tf_compat.global_variables_initializer())
out = sess.run(
logits,
feed_dict={inputs: numpy.random.random((1, *input_shape))})
assert out.sum() != 0
# test out the registry
with tf_compat.Graph().as_default():
inputs = tf_compat.placeholder(tf_compat.float32, [None, *input_shape],
name="inputs")
logits = ModelRegistry.create(key, inputs, training=False)
with tf_compat.Session() as sess:
if test_input:
sess.run(tf_compat.global_variables_initializer())
out = sess.run(
logits,
feed_dict={inputs: numpy.random.random((1, *input_shape))})
assert out.sum() != 0
if pretrained:
ModelRegistry.load_pretrained(key, pretrained)
if test_input:
out = sess.run(
logits,
feed_dict={
inputs: numpy.random.random((1, *input_shape))
},
)
assert out.sum() != 0
def pruning_loss_sensitivity(args, save_dir):
input_shape = ModelRegistry.input_shape(args.arch_key)
train_dataset, num_classes = _create_dataset(args,
train=True,
image_size=input_shape[1])
with tf_compat.Graph().as_default() as graph:
# create model graph
inputs = tf_compat.placeholder(tf_compat.float32,
[None] + list(input_shape),
name="inputs")
outputs = _create_model(args, num_classes, inputs)
with tf_compat.Session() as sess:
_load_model(args, sess, checkpoint_path=args.checkpoint_path)
if args.approximate:
LOGGER.info(
"Running weight magnitude loss sensitivity analysis...")
analysis = pruning_loss_sens_magnitude(graph, sess)
else:
op_vars = pruning_loss_sens_op_vars(graph)
train_steps = math.ceil(len(train_dataset) / args.batch_size)
train_dataset = _build_dataset(args, train_dataset,
args.batch_size)
handle, iterator, dataset_iter = create_split_iterators_handle(
[train_dataset])
dataset_iter = dataset_iter[0]
images, labels = iterator.get_next()
loss = batch_cross_entropy_loss(outputs, labels)
tensor_names = ["inputs:0", labels.name]
sess.run(dataset_iter.initializer)
def feed_dict_creator(
step: int) -> Dict[str, tf_compat.Tensor]:
assert step < train_steps
batch_data = [
tens.eval(session=sess)
for tens in dataset_iter.get_next()
]
return dict(zip(tensor_names, batch_data))
LOGGER.info("Running one shot loss sensitivity analysis...")
analysis = pruning_loss_sens_one_shot(
op_vars=op_vars,
loss_tensor=loss,
steps_per_measurement=args.steps_per_measurement,
feed_dict_creator=feed_dict_creator,
sess=sess,
)
# saving and printing results
LOGGER.info("completed...")
LOGGER.info("Saving results in {}".format(save_dir))
analysis.save_json(
os.path.join(
save_dir,
"ks_approx_sensitivity.json"
if args.approximate else "ks_one_shot_sensitivity.json",
))
analysis.plot(
os.path.join(
save_dir,
os.path.join(
save_dir,
"ks_approx_sensitivity.png"
if args.approximate else "ks_one_shot_sensitivity.png",
),
),
plot_integral=True,
)
analysis.print_res()