!3508 modelzoo: support VGG network in GPU
Merge pull request !3508 from ms_yan/vgg_gpupull/3508/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
f15c6e2b77
@ -0,0 +1,39 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""define loss function for network"""
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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import mindspore.nn as nn
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class CrossEntropy(_Loss):
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"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
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def __init__(self, smooth_factor=0., num_classes=1001):
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super(CrossEntropy, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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def construct(self, logit, label):
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one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
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loss = self.ce(logit, one_hot_label)
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loss = self.mean(loss, 0)
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return loss
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@ -0,0 +1,82 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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get logger.
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"""
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import logging
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import os
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import sys
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from datetime import datetime
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class LOGGER(logging.Logger):
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"""
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set up logging file.
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Args:
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logger_name (string): logger name.
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log_dir (string): path of logger.
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Returns:
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string, logger path
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"""
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def __init__(self, logger_name, rank=0):
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super(LOGGER, self).__init__(logger_name)
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if rank % 8 == 0:
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console = logging.StreamHandler(sys.stdout)
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console.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
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console.setFormatter(formatter)
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self.addHandler(console)
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def setup_logging_file(self, log_dir, rank=0):
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"""set up log file"""
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self.rank = rank
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if not os.path.exists(log_dir):
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os.makedirs(log_dir, exist_ok=True)
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log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
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self.log_fn = os.path.join(log_dir, log_name)
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fh = logging.FileHandler(self.log_fn)
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fh.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
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fh.setFormatter(formatter)
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self.addHandler(fh)
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def info(self, msg, *args, **kwargs):
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if self.isEnabledFor(logging.INFO):
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self._log(logging.INFO, msg, args, **kwargs)
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def save_args(self, args):
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self.info('Args:')
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args_dict = vars(args)
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for key in args_dict.keys():
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self.info('--> %s: %s', key, args_dict[key])
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self.info('')
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def important_info(self, msg, *args, **kwargs):
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if self.isEnabledFor(logging.INFO) and self.rank == 0:
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line_width = 2
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important_msg = '\n'
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important_msg += ('*'*70 + '\n')*line_width
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important_msg += ('*'*line_width + '\n')*2
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important_msg += '*'*line_width + ' '*8 + msg + '\n'
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important_msg += ('*'*line_width + '\n')*2
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important_msg += ('*'*70 + '\n')*line_width
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self.info(important_msg, *args, **kwargs)
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def get_logger(path, rank):
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logger = LOGGER("mindversion", rank)
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logger.setup_logging_file(path, rank)
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return logger
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@ -0,0 +1,53 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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choose samples from the dataset
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"""
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import math
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import numpy as np
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class DistributedSampler():
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"""
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sampling the dataset.
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Args:
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Returns:
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num_samples, number of samples.
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"""
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def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
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self.dataset = dataset
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self.rank = rank
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self.group_size = group_size
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self.dataset_length = len(self.dataset)
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self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
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self.total_size = self.num_samples * self.group_size
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self.shuffle = shuffle
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self.seed = seed
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def __iter__(self):
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if self.shuffle:
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self.seed = (self.seed + 1) & 0xffffffff
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np.random.seed(self.seed)
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indices = np.random.permutation(self.dataset_length).tolist()
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else:
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indices = list(range(len(self.dataset_length)))
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indices += indices[:(self.total_size - len(indices))]
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indices = indices[self.rank::self.group_size]
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return iter(indices)
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def __len__(self):
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return self.num_samples
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@ -0,0 +1,36 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Util class or function."""
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def get_param_groups(network):
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"""Param groups for optimizer."""
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decay_params = []
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no_decay_params = []
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for x in network.trainable_params():
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parameter_name = x.name
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if parameter_name.endswith('.bias'):
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# all bias not using weight decay
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no_decay_params.append(x)
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elif parameter_name.endswith('.gamma'):
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# bn weight bias not using weight decay, be carefully for now x not include BN
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no_decay_params.append(x)
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elif parameter_name.endswith('.beta'):
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# bn weight bias not using weight decay, be carefully for now x not include BN
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no_decay_params.append(x)
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else:
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decay_params.append(x)
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return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
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@ -0,0 +1,213 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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Initialize.
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"""
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import math
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from functools import reduce
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common import initializer as init
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def _calculate_gain(nonlinearity, param=None):
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r"""
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Return the recommended gain value for the given nonlinearity function.
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The values are as follows:
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================= ====================================================
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nonlinearity gain
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================= ====================================================
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Linear / Identity :math:`1`
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Conv{1,2,3}D :math:`1`
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Sigmoid :math:`1`
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Tanh :math:`\frac{5}{3}`
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ReLU :math:`\sqrt{2}`
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Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
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================= ====================================================
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Args:
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nonlinearity: the non-linear function
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param: optional parameter for the non-linear function
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Examples:
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>>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
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"""
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linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
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if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
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return 1
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if nonlinearity == 'tanh':
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return 5.0 / 3
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if nonlinearity == 'relu':
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return math.sqrt(2.0)
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if nonlinearity == 'leaky_relu':
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if param is None:
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negative_slope = 0.01
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elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
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negative_slope = param
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else:
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raise ValueError("negative_slope {} not a valid number".format(param))
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return math.sqrt(2.0 / (1 + negative_slope ** 2))
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raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
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def _assignment(arr, num):
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"""Assign the value of `num` to `arr`."""
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if arr.shape == ():
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arr = arr.reshape((1))
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arr[:] = num
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arr = arr.reshape(())
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else:
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if isinstance(num, np.ndarray):
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arr[:] = num[:]
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else:
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arr[:] = num
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return arr
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def _calculate_in_and_out(arr):
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"""
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Calculate n_in and n_out.
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Args:
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arr (Array): Input array.
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Returns:
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Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
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"""
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dim = len(arr.shape)
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if dim < 2:
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raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
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n_in = arr.shape[1]
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n_out = arr.shape[0]
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if dim > 2:
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counter = reduce(lambda x, y: x * y, arr.shape[2:])
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n_in *= counter
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n_out *= counter
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return n_in, n_out
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def _select_fan(array, mode):
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mode = mode.lower()
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valid_modes = ['fan_in', 'fan_out']
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if mode not in valid_modes:
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raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
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fan_in, fan_out = _calculate_in_and_out(array)
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return fan_in if mode == 'fan_in' else fan_out
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class KaimingInit(init.Initializer):
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r"""
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Base Class. Initialize the array with He kaiming algorithm.
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Args:
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a: the negative slope of the rectifier used after this layer (only
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used with ``'leaky_relu'``)
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mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
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preserves the magnitude of the variance of the weights in the
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forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
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backwards pass.
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nonlinearity: the non-linear function, recommended to use only with
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``'relu'`` or ``'leaky_relu'`` (default).
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"""
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def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
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super(KaimingInit, self).__init__()
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self.mode = mode
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self.gain = _calculate_gain(nonlinearity, a)
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def _initialize(self, arr):
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pass
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class KaimingUniform(KaimingInit):
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r"""
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Initialize the array with He kaiming uniform algorithm. The resulting tensor will
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have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
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.. math::
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\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
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Input:
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arr (Array): The array to be assigned.
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Returns:
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Array, assigned array.
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Examples:
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>>> w = np.empty(3, 5)
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>>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
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"""
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def _initialize(self, arr):
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fan = _select_fan(arr, self.mode)
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bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
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np.random.seed(0)
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data = np.random.uniform(-bound, bound, arr.shape)
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_assignment(arr, data)
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class KaimingNormal(KaimingInit):
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r"""
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Initialize the array with He kaiming normal algorithm. The resulting tensor will
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have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
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.. math::
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\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
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Input:
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arr (Array): The array to be assigned.
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Returns:
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Array, assigned array.
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Examples:
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>>> w = np.empty(3, 5)
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>>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
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"""
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def _initialize(self, arr):
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fan = _select_fan(arr, self.mode)
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std = self.gain / math.sqrt(fan)
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np.random.seed(0)
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data = np.random.normal(0, std, arr.shape)
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_assignment(arr, data)
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def default_recurisive_init(custom_cell):
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"""default_recurisive_init"""
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for _, cell in custom_cell.cells_and_names():
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if isinstance(cell, nn.Conv2d):
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cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
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cell.weight.default_input.shape,
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cell.weight.default_input.dtype).to_tensor()
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if cell.bias is not None:
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fan_in, _ = _calculate_in_and_out(cell.weight.default_input.asnumpy())
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bound = 1 / math.sqrt(fan_in)
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np.random.seed(0)
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cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, cell.bias.default_input.shape),
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cell.bias.default_input.dtype)
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elif isinstance(cell, nn.Dense):
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cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
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cell.weight.default_input.shape,
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cell.weight.default_input.dtype).to_tensor()
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if cell.bias is not None:
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fan_in, _ = _calculate_in_and_out(cell.weight.default_input.asnumpy())
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bound = 1 / math.sqrt(fan_in)
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np.random.seed(0)
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cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, cell.bias.default_input.shape),
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cell.bias.default_input.dtype)
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elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
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pass
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@ -0,0 +1,40 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
warm up cosine annealing learning rate.
|
||||
"""
|
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import math
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import numpy as np
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from .linear_warmup import linear_warmup_lr
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def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
|
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"""warm up cosine annealing learning rate."""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
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||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
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||||
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
last_epoch = i // steps_per_epoch
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
@ -0,0 +1,84 @@
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
warm up step learning rate.
|
||||
"""
|
||||
from collections import Counter
|
||||
import numpy as np
|
||||
|
||||
from .linear_warmup import linear_warmup_lr
|
||||
|
||||
|
||||
def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
||||
"""Set learning rate."""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
if warmup_steps != 0:
|
||||
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
else:
|
||||
inc_each_step = 0
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr_value = float(lr_init) + inc_each_step * float(i)
|
||||
else:
|
||||
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
|
||||
lr_value = float(lr_max) * base * base
|
||||
if lr_value < 0.0:
|
||||
lr_value = 0.0
|
||||
lr_each_step.append(lr_value)
|
||||
|
||||
current_step = global_step
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
learning_rate = lr_each_step[current_step:]
|
||||
|
||||
return learning_rate
|
||||
|
||||
|
||||
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
|
||||
"""warmup_step_lr"""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
milestones = lr_epochs
|
||||
milestones_steps = []
|
||||
for milestone in milestones:
|
||||
milestones_step = milestone * steps_per_epoch
|
||||
milestones_steps.append(milestones_step)
|
||||
|
||||
lr_each_step = []
|
||||
lr = base_lr
|
||||
milestones_steps_counter = Counter(milestones_steps)
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = lr * gamma**milestones_steps_counter[i]
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
||||
|
||||
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
|
||||
|
||||
|
||||
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
lr_epochs = []
|
||||
for i in range(1, max_epoch):
|
||||
if i % epoch_size == 0:
|
||||
lr_epochs.append(i)
|
||||
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
|
||||
File diff suppressed because it is too large
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Reference in new issue