138 lines
4.9 KiB
Python
Executable file
138 lines
4.9 KiB
Python
Executable file
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright (c) 2018, 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 os
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import warnings
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warnings.simplefilter("ignore")
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import tensorflow as tf
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import horovod.tensorflow as hvd
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import dllogger
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from utils import hvd_utils
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from runtime import Runner
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from model.resnet import model_architectures
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from utils.cmdline_helper import parse_cmdline
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if __name__ == "__main__":
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tf.logging.set_verbosity(tf.logging.ERROR)
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FLAGS = parse_cmdline(model_architectures.keys())
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hvd.init()
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if hvd.rank() == 0:
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log_path = os.path.join(FLAGS.results_dir, FLAGS.log_filename)
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os.makedirs(FLAGS.results_dir, exist_ok=True)
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dllogger.init(
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backends=[
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dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE, filename=log_path),
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dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE)
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]
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)
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else:
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dllogger.init(backends=[])
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dllogger.log(data=vars(FLAGS), step='PARAMETER')
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runner = Runner(
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# ========= Model HParams ========= #
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n_classes=1001,
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architecture=FLAGS.arch,
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input_format='NHWC',
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compute_format=FLAGS.data_format,
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dtype=tf.float32 if FLAGS.precision == 'fp32' else tf.float16,
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n_channels=3,
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height=224,
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width=224,
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distort_colors=False,
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log_dir=FLAGS.results_dir,
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model_dir=FLAGS.model_dir if FLAGS.model_dir is not None else FLAGS.results_dir,
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data_dir=FLAGS.data_dir,
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data_idx_dir=FLAGS.data_idx_dir,
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weight_init=FLAGS.weight_init,
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use_xla=FLAGS.use_xla,
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use_tf_amp=FLAGS.use_tf_amp,
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use_dali=FLAGS.use_dali,
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gpu_memory_fraction=FLAGS.gpu_memory_fraction,
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gpu_id=FLAGS.gpu_id,
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seed=FLAGS.seed
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)
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if FLAGS.mode in ["train", "train_and_evaluate", "training_benchmark"]:
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runner.train(
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iter_unit=FLAGS.iter_unit,
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num_iter=FLAGS.num_iter,
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run_iter=FLAGS.run_iter,
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batch_size=FLAGS.batch_size,
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warmup_steps=FLAGS.warmup_steps,
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log_every_n_steps=FLAGS.display_every,
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weight_decay=FLAGS.weight_decay,
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lr_init=FLAGS.lr_init,
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lr_warmup_epochs=FLAGS.lr_warmup_epochs,
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momentum=FLAGS.momentum,
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loss_scale=FLAGS.loss_scale,
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label_smoothing=FLAGS.label_smoothing,
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mixup=FLAGS.mixup,
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use_static_loss_scaling=FLAGS.use_static_loss_scaling,
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use_cosine_lr=FLAGS.use_cosine_lr,
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is_benchmark=FLAGS.mode == 'training_benchmark',
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use_final_conv=FLAGS.use_final_conv,
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quantize=FLAGS.quantize,
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symmetric=FLAGS.symmetric,
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quant_delay = FLAGS.quant_delay,
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use_qdq = FLAGS.use_qdq,
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finetune_checkpoint = FLAGS.finetune_checkpoint,
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)
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if FLAGS.mode in ["train_and_evaluate", 'evaluate', 'inference_benchmark']:
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if FLAGS.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
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raise NotImplementedError("Only single GPU inference is implemented.")
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elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
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runner.evaluate(
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iter_unit=FLAGS.iter_unit if FLAGS.mode != "train_and_evaluate" else "epoch",
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num_iter=FLAGS.num_iter if FLAGS.mode != "train_and_evaluate" else 1,
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warmup_steps=FLAGS.warmup_steps,
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batch_size=FLAGS.batch_size,
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log_every_n_steps=FLAGS.display_every,
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is_benchmark=FLAGS.mode == 'inference_benchmark',
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export_dir=FLAGS.export_dir,
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quantize=FLAGS.quantize,
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symmetric=FLAGS.symmetric,
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use_final_conv=FLAGS.use_final_conv,
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use_qdq=FLAGS.use_qdq
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)
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if FLAGS.mode == 'predict':
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if FLAGS.to_predict is None:
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raise ValueError("No data to predict on.")
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if not os.path.isfile(FLAGS.to_predict):
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raise ValueError("Only prediction on single images is supported!")
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if hvd_utils.is_using_hvd():
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raise NotImplementedError("Only single GPU inference is implemented.")
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elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
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runner.predict(FLAGS.to_predict, quantize=FLAGS.quantize, symmetric=FLAGS.symmetric, use_qdq=FLAGS.use_qdq, use_final_conv=FLAGS.use_final_conv)
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