# Copyright (c) 2020 Sony Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from ..runner import Runner
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class Searcher(Runner):
r"""Searching the best architecture."""
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def run(self):
r"""Run the training process."""
self.callback_on_start()
if self.cur_epoch == 0:
# do not run warmup if start from checkpoint
self._start_warmup()
for self.cur_epoch in range(self.cur_epoch, self.hparams['epoch']):
self.monitor.reset()
lr = self.optimizer['train'].get_learning_rate()
self.monitor.info(f'Running epoch={self.cur_epoch}\tlr={lr:.5f}\n')
for i in range(self.one_epoch_train):
self.train_on_batch()
self.valid_on_batch()
if i % (self.args['print_frequency']) == 0:
self.monitor.display(i)
self.callback_on_epoch_end()
self.monitor.write(self.cur_epoch)
self.callback_on_finish()
self.monitor.close()
return self
def _start_warmup(self):
r"""Performs warmup for the model on training."""
for cur_epoch in range(self.hparams['warmup']):
self.monitor.reset()
lr = self.optimizer['warmup'].get_learning_rate()
self.monitor.info(f'warm-up epoch={cur_epoch}\tlr={lr:.5f}\n')
for i in range(self.one_epoch_train):
self.train_on_batch(key='warmup')
if i % (self.args['print_frequency']) == 0:
self.monitor.display(i)
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def callback_on_epoch_end(self):
r"""Calls this after one epoch."""
if self.comm.rank == 0:
if self.args['save_nnp']:
self.model.save_net_nnp(
self._abs_output_path,
self.placeholder['valid']['inputs'][0],
self.placeholder['valid']['outputs'][0],
save_params=self.args.get('save_params'))
else:
self.model.save_parameters(
path=os.path.join(self._abs_output_path, 'arch.h5'),
params=self.model.get_arch_parameters()
)
# checkpoint
self.save_checkpoint()
if self.args['no_visualize']: # action:store_false
self.model.visualize(self._abs_output_path)
self.monitor.info(self.model.summary() + '\n')
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def callback_on_finish(self):
r"""Calls this on finishing the training."""
if self.comm.rank == 0:
if self.args['save_nnp']:
self.model.save_net_nnp(
self._abs_output_path,
self.placeholder['valid']['inputs'][0],
self.placeholder['valid']['outputs'][0],
save_params=self.args.get('save_params'))
else:
self.model.save_parameters(
path=os.path.join(self._abs_output_path, 'weights.h5'),
params=self.model.get_net_parameters()
)
if self.args['no_visualize']: # action:store_false
self.model.visualize(self._abs_output_path)
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def callback_on_start(self):
r"""Calls this on starting the training."""
# load checkpoint if available
self.load_checkpoint()