view_pb.py

import sys
import tensorflow as tf

pb_file = sys.argv[1]
log_dir = 'logdir'

with open(pb_file, "rb") as f:
    g_def = tf.GraphDef()
    g_def.ParseFromString(f.read())
    tf.import_graph_def(g_def)

import tempfile
import os
with tempfile.TemporaryDirectory() as log_dir:
    tf.summary.FileWriter(log_dir, tf.get_default_graph())
    os.system('tensorboard --logdir=' + log_dir)


#timeline
from tensorflow.python.client import timeline
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
for loop in range(8):
    sess.run(output_node, feed_dict = {}, options=run_options, run_metadata=run_metadata)
    tl = timeline.Timeline(run_metadata.step_stats)
    ctf = tl.generate_chrome_trace_format()
    with open('timeline.json', 'w') as f:
        f.write(ctf)

#profile
from tensorflow.python.profiler import model_analyzer
from tensorflow.python.profiler import option_builder
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
profiler = model_analyzer.Profiler(graph=sess.graph)
for loop in range(8):
    sess.run(output_node, feed_dict = {}, options=run_options, run_metadata=run_metadata)
    profiler.add_step(step=loop, run_meta=run_metadata)
profile_op_opt_builder = option_builder.ProfileOptionBuilder()
profile_op_opt_builder.select(['micros','occurrence', 'float_ops', 'input_shapes'])
profile_op_opt_builder.order_by('micros')
profile_op_opt_builder.with_max_depth(50)
profiler.profile_operations(profile_op_opt_builder.build())
#profiler.profile_operations(option_builder.ProfileOptionBuilder().time_and_memory())