d2bc3da0a1
* added UNet for medical image segmentation * added TF-AMP support for RN50 * small updates for other models (READMEs, benchmark & testing scripts)
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
#
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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from absl import flags
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from time import time
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import tensorflow as tf
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from object_detection import model_hparams
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from object_detection import model_lib
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flags.DEFINE_string('checkpoint_dir', None, 'Path to directory holding a checkpoint. If '
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'`checkpoint_dir` is not provided, benchmark is running on random model')
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flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config file.')
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flags.DEFINE_integer('warmup_iters', 100, 'Number of iterations skipped during benchmark')
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flags.DEFINE_integer('benchmark_iters', 300, 'Number of iterations measured by benchmark')
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flags.DEFINE_integer('batch_size', 1, 'Number of inputs processed paralelly')
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FLAGS = flags.FLAGS
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flags.mark_flag_as_required('pipeline_config_path')
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def build_estimator():
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session_config = tf.ConfigProto()
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config = tf.estimator.RunConfig(session_config=session_config)
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train_and_eval_dict = model_lib.create_estimator_and_inputs(
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run_config=config,
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hparams=model_hparams.create_hparams(None),
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pipeline_config_path=FLAGS.pipeline_config_path)
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estimator = train_and_eval_dict['estimator']
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eval_input_fns = train_and_eval_dict['eval_input_fns']
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return estimator, eval_input_fns[0]
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def build_benchmark_input_fn(input_fn):
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def benchmark_input_fn(params={}):
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params['batch_size'] = FLAGS.batch_size
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return input_fn(params).repeat().take(FLAGS.warmup_iters + FLAGS.benchmark_iters)
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return benchmark_input_fn
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class TimingHook(tf.train.SessionRunHook):
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def __init__(self):
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super(TimingHook, self).__init__()
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self.times = []
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def before_run(self, *args, **kwargs):
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super(TimingHook, self).before_run(*args, **kwargs)
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self.start_time = time()
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def log_progress(self):
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if sys.stdout.isatty():
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print(len(self.times) - FLAGS.warmup_iters, '/', FLAGS.benchmark_iters, ' '*10, end='\r')
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def after_run(self, *args, **kwargs):
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super(TimingHook, self).after_run(*args, **kwargs)
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self.times.append(time() - self.start_time)
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self.log_progress()
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def collect_result(self):
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return FLAGS.batch_size * FLAGS.benchmark_iters / sum(self.times[FLAGS.benchmark_iters:])
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def end(self, *args, **kwargs):
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super(TimingHook, self).end(*args, **kwargs)
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print()
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print('Benchmark result:', self.collect_result(), 'img/s')
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def main(unused_argv):
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tf.logging.set_verbosity(tf.logging.INFO)
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estimator, eval_input_fn = build_estimator()
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checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) \
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if FLAGS.checkpoint_dir \
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else None
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results = estimator.predict(
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input_fn=build_benchmark_input_fn(eval_input_fn),
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checkpoint_path=checkpoint_path,
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hooks=[ TimingHook() ],
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yield_single_examples=False
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)
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list(results)
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if __name__ == '__main__':
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tf.app.run()
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