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How to use running_config method in autotest

Best Python code snippet using autotest_python

main.py

Source:main.py

1#!/usr/bin/env python2# -*- coding: utf-8 -*-3# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.4#5# Licensed under the Apache License, Version 2.0 (the "License");6# you may not use this file except in compliance with the License.7# You may obtain a copy of the License at8#9#     http://www.apache.org/licenses/LICENSE-2.010#11# Unless required by applicable law or agreed to in writing, software12# distributed under the License is distributed on an "AS IS" BASIS,13# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.14# See the License for the specific language governing permissions and15# limitations under the License.16import os17import warnings18warnings.simplefilter("ignore")19import tensorflow as tf20import horovod.tensorflow as hvd21from utils import hvd_utils22from runtime import Runner23from utils.cmdline_helper import parse_cmdline24if __name__ == "__main__":25    tf.logging.set_verbosity(tf.logging.ERROR)26    FLAGS = parse_cmdline()27    RUNNING_CONFIG = tf.contrib.training.HParams(28        mode=FLAGS.mode,29        # ======= Directory HParams ======= #30        log_dir=FLAGS.results_dir,31        model_dir=FLAGS.results_dir,32        summaries_dir=FLAGS.results_dir,33        data_dir=FLAGS.data_dir,34        data_idx_dir=FLAGS.data_idx_dir,35        # ========= Model HParams ========= #36        n_classes=1001,37        input_format='NHWC',38        compute_format=FLAGS.data_format,39        dtype=tf.float32 if FLAGS.precision == "fp32" else tf.float16,40        height=224,41        width=224,42        n_channels=3,43        # ======= Training HParams ======== #44        iter_unit=FLAGS.iter_unit,45        num_iter=FLAGS.num_iter,46        warmup_steps=FLAGS.warmup_steps,47        batch_size=FLAGS.batch_size,48        log_every_n_steps=FLAGS.display_every,49        lr_init=FLAGS.lr_init,50        lr_warmup_epochs=FLAGS.lr_warmup_epochs,51        weight_decay=FLAGS.weight_decay,52        momentum=FLAGS.momentum,53        loss_scale=FLAGS.loss_scale,54        label_smoothing=FLAGS.label_smoothing,55        use_cosine_lr=FLAGS.use_cosine_lr,56        use_static_loss_scaling=FLAGS.use_static_loss_scaling,57        distort_colors=False,58        59        # ======= Optimization HParams ======== #60        use_xla=FLAGS.use_xla,61        use_tf_amp=FLAGS.use_tf_amp,62        use_dali=FLAGS.use_dali,63        gpu_memory_fraction=FLAGS.gpu_memory_fraction,64        65        seed=FLAGS.seed,66    )67    # ===================================68    runner = Runner(69        # ========= Model HParams ========= #70        n_classes=RUNNING_CONFIG.n_classes,71        input_format=RUNNING_CONFIG.input_format,72        compute_format=RUNNING_CONFIG.compute_format,73        dtype=RUNNING_CONFIG.dtype,74        n_channels=RUNNING_CONFIG.n_channels,75        height=RUNNING_CONFIG.height,76        width=RUNNING_CONFIG.width,77        distort_colors=RUNNING_CONFIG.distort_colors,78        log_dir=RUNNING_CONFIG.log_dir,79        model_dir=RUNNING_CONFIG.model_dir,80        data_dir=RUNNING_CONFIG.data_dir,81        data_idx_dir=RUNNING_CONFIG.data_idx_dir,82        # ======= Optimization HParams ======== #83        use_xla=RUNNING_CONFIG.use_xla,84        use_tf_amp=RUNNING_CONFIG.use_tf_amp,85        use_dali=RUNNING_CONFIG.use_dali,86        gpu_memory_fraction=RUNNING_CONFIG.gpu_memory_fraction,87        seed=RUNNING_CONFIG.seed88    )89    if RUNNING_CONFIG.mode in ["train", "train_and_evaluate", "training_benchmark"]:90        runner.train(91            iter_unit=RUNNING_CONFIG.iter_unit,92            num_iter=RUNNING_CONFIG.num_iter,93            batch_size=RUNNING_CONFIG.batch_size,94            warmup_steps=RUNNING_CONFIG.warmup_steps,95            log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,96            weight_decay=RUNNING_CONFIG.weight_decay,97            lr_init=RUNNING_CONFIG.lr_init,98            lr_warmup_epochs=RUNNING_CONFIG.lr_warmup_epochs,99            momentum=RUNNING_CONFIG.momentum,100            loss_scale=RUNNING_CONFIG.loss_scale,       101            label_smoothing=RUNNING_CONFIG.label_smoothing,102            use_static_loss_scaling=RUNNING_CONFIG.use_static_loss_scaling,103            use_cosine_lr=RUNNING_CONFIG.use_cosine_lr,104            is_benchmark=RUNNING_CONFIG.mode == 'training_benchmark',105            106        )107    if RUNNING_CONFIG.mode in ["train_and_evaluate", 'evaluate', 'inference_benchmark']:108        if RUNNING_CONFIG.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():109            raise NotImplementedError("Only single GPU inference is implemented.")110        elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:111            runner.evaluate(112                iter_unit=RUNNING_CONFIG.iter_unit if RUNNING_CONFIG.mode != "train_and_evaluate" else "epoch",113                num_iter=RUNNING_CONFIG.num_iter if RUNNING_CONFIG.mode != "train_and_evaluate" else 1,114                warmup_steps=RUNNING_CONFIG.warmup_steps,115                batch_size=RUNNING_CONFIG.batch_size,116                log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,117                is_benchmark=RUNNING_CONFIG.mode == 'inference_benchmark'...

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