def perform_inference():
print('Performing inference')
FLAGS.resume = True # Get saved weights, not new ones
run_dir = get_run_dir(FLAGS.log_dir, FLAGS.model)
checkpoint_dir = os.path.join(run_dir, 'train')
fused_graph_file = os.path.join(checkpoint_dir, 'fused_graph.pb')
eval_data, eval_labels = data.numpy_eval_inputs(True, FLAGS.data_dir,
FLAGS.batch_size)
with gfile.FastGFile(fused_graph_file, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
assert len(graph.get_operations(
)) == 0, "Assuming an empty graph here to populate with fused graph"
tf.import_graph_def(graph_def, name='')
print('nodes', [n.name for n in graph_def.node])
XXX = graph.get_tensor_by_name('XXX:0')
YYY = graph.get_tensor_by_name('YYY:0')
print("Running model")
with tf.Session(graph=graph) as sess:
eval_batch_data = eval_data[0]
eval_batch_label = eval_labels[0]
YYY = sess.run(YYY, feed_dict={XXX: eval_batch_data})
report_accuracy(YYY, eval_batch_label)
def save_weights():
"""Saves CIFAR10 weights"""
FLAGS.resume = True # Get saved weights, not new ones
print(FLAGS.save_dir)
run_dir = get_run_dir(FLAGS.save_dir, FLAGS.model)
print('run_dir', run_dir)
checkpoint_dir = os.path.join(run_dir, 'train')
with tf.Graph().as_default() as g:
# Get images and labels for CIFAR-10.
images, labels = data.train_inputs(data_dir=FLAGS.data_dir)
model = select.by_name(FLAGS.model, FLAGS, training=True)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = model.inference(images)
print('Multiplicative depth', model.mult_depth())
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
else:
print('### ERROR No checkpoint file found###')
print('ckpt_dir', checkpoint_dir)
print('ckpt.model_checkpoint_path', ckpt.model_checkpoint_path)
print('ckpt', ckpt)
return
# Save variables
for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
weight = (sess.run([var]))[0].flatten().tolist()
filename = model._name_to_filename(var.name)
dir_name = filename.rsplit('/', 1)[0]
os.makedirs(dir_name, exist_ok=True)
print("saving", filename)
np.savetxt(str(filename), weight)
def perform_inference():
print('Performing inference')
FLAGS.resume = True # Get saved weights, not new ones
run_dir = get_run_dir(FLAGS.save_dir, FLAGS.model, model_number = FLAGS.model_number)
checkpoint_dir = os.path.join(run_dir, 'train')
fused_graph_file = os.path.join(checkpoint_dir, 'fused_graph.pb')
eval_data, eval_labels = data.numpy_eval_inputs(True, FLAGS.data_dir,
FLAGS.batch_size)
with gfile.FastGFile(fused_graph_file, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
assert len(graph.get_operations(
)) == 0, "Assuming an empty graph here to populate with fused graph"
tf.import_graph_def(graph_def, name='')
print('nodes', [n.name for n in graph_def.node])
XXX = graph.get_tensor_by_name('XXX:0')
YYY = graph.get_tensor_by_name('YYY:0')
print("GETTING CONFIGS")
print(XXX.name)
config = server_config_from_flags(FLAGS, XXX.name)
print("Running model")
with tf.Session(graph=graph, config=config) as sess:
start_time = time.time()
eval_batch_data = eval_data[0]
eval_batch_label = eval_labels[0]
YYY = sess.run(YYY, feed_dict={XXX: eval_batch_data})
elapsed_time = time.time() - start_time
print("total time(s)", np.round(elapsed_time, 6))
report_accuracy(YYY, eval_batch_label)
def optimize_model_for_inference():
"""Optimizes CIFAR-10 model for inference"""
FLAGS.resume = True # Get saved weights, not new ones
run_dir = get_run_dir(FLAGS.log_dir, FLAGS.model)
checkpoint_dir = os.path.join(run_dir, 'train')
print('run_dir', run_dir)
print('checkpoint dir', checkpoint_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
train_graph = os.path.join(checkpoint_dir, 'graph.pbtxt')
frozen_graph = os.path.join(checkpoint_dir, 'graph_constants.pb')
fused_graph = os.path.join(checkpoint_dir, 'fused_graph.pb')
with tf.Session() as sess:
# TODO this should be a placeholder, right?
# Build a new inference graph, with variables to be restored from
# training graph.
IMAGE_SIZE = 24 if FLAGS.data_aug else 32
if FLAGS.batch_norm:
images = tf.constant(1,
dtype=tf.float32,
shape=[1, IMAGE_SIZE, IMAGE_SIZE, 3])
else:
images = tf.constant(
1,
dtype=tf.float32,
shape=[FLAGS.batch_size, IMAGE_SIZE, IMAGE_SIZE, 3])
model = select.by_name(FLAGS.model, FLAGS, training=False)
# Create dummy input and output nodes
images = tf.identity(images, 'XXX')
logits = model.inference(images)
logits = tf.identity(logits, 'YYY')
if FLAGS.batch_norm:
# Restore values from the trained model into corresponding variables in the
# inference graph.
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
print('ckpt.model_checkpoint_path', ckpt.model_checkpoint_path)
assert ckpt and ckpt.model_checkpoint_path, "No checkpoint found in {}".format(
checkpoint_dir)
saver = tf.train.Saver()
saver.restore(sess, ckpt.model_checkpoint_path)
# Write fully-assembled inference graph to a file, so freeze_graph can use it
tf.io.write_graph(sess.graph,
checkpoint_dir,
'inference_graph.pbtxt',
as_text=True)
# Freeze graph, converting variables to inline-constants in pb file
constant_graph = os.path.join(checkpoint_dir, 'graph_constants.pb')
freeze_graph.freeze_graph(
input_graph=os.path.join(checkpoint_dir,
'inference_graph.pbtxt'),
input_saver="",
input_binary=False,
input_checkpoint=ckpt.model_checkpoint_path,
output_node_names='YYY',
restore_op_name='save/restore_all',
filename_tensor_name='save/Const:0',
initializer_nodes=[],
output_graph=os.path.join(checkpoint_dir,
'graph_constants.pb'),
clear_devices=True)
# Load frozen graph into a graph_def for optimize_lib to use
with gfile.FastGFile(constant_graph, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
# Optimize graph for inference, folding Batch Norm ops into conv/MM
fused_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def=graph_def,
input_node_names=['XXX'],
output_node_names=['YYY'],
placeholder_type_enum=dtypes.float32.as_datatype_enum,
toco_compatible=False)
print('Optimized for inference.')
tf.io.write_graph(fused_graph_def,
checkpoint_dir,
name='fused_graph.pb',
as_text=False)
else:
tf.io.write_graph(sess.graph,
checkpoint_dir,
'fused_graph.pb',
as_text=False)