!14528 add ssd, resnet,unet evaluation while training process
From: @zhao_ting_v Reviewed-by: @c_34,@wuxuejian Signed-off-by: @c_34pull/14528/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
c9c8d5fe44
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# Copyright 2021 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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"""Evaluation callback when training"""
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import os
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import stat
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from mindspore import save_checkpoint
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from mindspore import log as logger
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from mindspore.train.callback import Callback
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class EvalCallBack(Callback):
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"""
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Evaluation callback when training.
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Args:
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eval_function (function): evaluation function.
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eval_param_dict (dict): evaluation parameters' configure dict.
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interval (int): run evaluation interval, default is 1.
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eval_start_epoch (int): evaluation start epoch, default is 1.
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save_best_ckpt (bool): Whether to save best checkpoint, default is True.
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besk_ckpt_name (str): bast checkpoint name, default is `best.ckpt`.
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metrics_name (str): evaluation metrics name, default is `acc`.
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Returns:
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None
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Examples:
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>>> EvalCallBack(eval_function, eval_param_dict)
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"""
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def __init__(self, eval_function, eval_param_dict, interval=1, eval_start_epoch=1, save_best_ckpt=True,
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ckpt_directory="./", besk_ckpt_name="best.ckpt", metrics_name="acc"):
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super(EvalCallBack, self).__init__()
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self.eval_param_dict = eval_param_dict
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self.eval_function = eval_function
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self.eval_start_epoch = eval_start_epoch
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if interval < 1:
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raise ValueError("interval should >= 1.")
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self.interval = interval
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self.save_best_ckpt = save_best_ckpt
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self.best_res = 0
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self.best_epoch = 0
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if not os.path.isdir(ckpt_directory):
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os.makedirs(ckpt_directory)
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self.bast_ckpt_path = os.path.join(ckpt_directory, besk_ckpt_name)
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self.metrics_name = metrics_name
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def remove_ckpoint_file(self, file_name):
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"""Remove the specified checkpoint file from this checkpoint manager and also from the directory."""
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try:
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os.chmod(file_name, stat.S_IWRITE)
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os.remove(file_name)
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except OSError:
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logger.warning("OSError, failed to remove the older ckpt file %s.", file_name)
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except ValueError:
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logger.warning("ValueError, failed to remove the older ckpt file %s.", file_name)
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def epoch_end(self, run_context):
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"""Callback when epoch end."""
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cb_params = run_context.original_args()
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cur_epoch = cb_params.cur_epoch_num
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if cur_epoch >= self.eval_start_epoch and (cur_epoch - self.eval_start_epoch) % self.interval == 0:
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res = self.eval_function(self.eval_param_dict)
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print("epoch: {}, {}: {}".format(cur_epoch, self.metrics_name, res), flush=True)
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if res >= self.best_res:
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self.best_res = res
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self.best_epoch = cur_epoch
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print("update best result: {}".format(res), flush=True)
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if self.save_best_ckpt:
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if os.path.exists(self.bast_ckpt_path):
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self.remove_ckpoint_file(self.bast_ckpt_path)
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save_checkpoint(cb_params.train_network, self.bast_ckpt_path)
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print("update best checkpoint at: {}".format(self.bast_ckpt_path), flush=True)
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def end(self, run_context):
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print("End training, the best {0} is: {1}, the best {0} epoch is {2}".format(self.metrics_name,
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self.best_res,
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self.best_epoch), flush=True)
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# Copyright 2021 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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"""evaluation metric."""
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from mindspore.communication.management import GlobalComm
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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class ClassifyCorrectCell(nn.Cell):
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r"""
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Cell that returns correct count of the prediction in classification network.
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This Cell accepts a network as arguments.
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It returns orrect count of the prediction to calculate the metrics.
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Args:
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network (Cell): The network Cell.
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Inputs:
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- **data** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
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- **label** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
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Outputs:
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Tuple, containing a scalar correct count of the prediction
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Examples:
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>>> # For a defined network Net without loss function
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>>> net = Net()
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>>> eval_net = nn.ClassifyCorrectCell(net)
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"""
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def __init__(self, network):
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super(ClassifyCorrectCell, self).__init__(auto_prefix=False)
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self._network = network
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self.argmax = P.Argmax()
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self.equal = P.Equal()
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self.cast = P.Cast()
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self.reduce_sum = P.ReduceSum()
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self.allreduce = P.AllReduce(P.ReduceOp.SUM, GlobalComm.WORLD_COMM_GROUP)
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def construct(self, data, label):
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outputs = self._network(data)
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y_pred = self.argmax(outputs)
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y_pred = self.cast(y_pred, mstype.int32)
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y_correct = self.equal(y_pred, label)
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y_correct = self.cast(y_correct, mstype.float32)
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y_correct = self.reduce_sum(y_correct)
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total_correct = self.allreduce(y_correct)
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return (total_correct,)
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class DistAccuracy(nn.Metric):
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r"""
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Calculates the accuracy for classification data in distributed mode.
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The accuracy class creates two local variables, correct number and total number that are used to compute the
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frequency with which predictions matches labels. This frequency is ultimately returned as the accuracy: an
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idempotent operation that simply divides correct number by total number.
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.. math::
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\text{accuracy} =\frac{\text{true_positive} + \text{true_negative}}
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{\text{true_positive} + \text{true_negative} + \text{false_positive} + \text{false_negative}}
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Args:
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eval_type (str): Metric to calculate the accuracy over a dataset, for classification (single-label).
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Examples:
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>>> y_correct = Tensor(np.array([20]))
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>>> metric = nn.DistAccuracy(batch_size=3, device_num=8)
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>>> metric.clear()
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>>> metric.update(y_correct)
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>>> accuracy = metric.eval()
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"""
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def __init__(self, batch_size, device_num):
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super(DistAccuracy, self).__init__()
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self.clear()
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self.batch_size = batch_size
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self.device_num = device_num
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def clear(self):
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"""Clears the internal evaluation result."""
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self._correct_num = 0
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self._total_num = 0
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def update(self, *inputs):
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"""
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Updates the internal evaluation result :math:`y_{pred}` and :math:`y`.
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Args:
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inputs: Input `y_correct`. `y_correct` is a `scalar Tensor`.
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`y_correct` is the right prediction count that gathered from all devices
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it's a scalar in float type
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Raises:
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ValueError: If the number of the input is not 1.
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"""
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if len(inputs) != 1:
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raise ValueError('Distribute accuracy needs 1 input (y_correct), but got {}'.format(len(inputs)))
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y_correct = self._convert_data(inputs[0])
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self._correct_num += y_correct
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self._total_num += self.batch_size * self.device_num
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def eval(self):
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"""
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Computes the accuracy.
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Returns:
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Float, the computed result.
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Raises:
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RuntimeError: If the sample size is 0.
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"""
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if self._total_num == 0:
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raise RuntimeError('Accuracy can not be calculated, because the number of samples is 0.')
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return self._correct_num / self._total_num
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@ -0,0 +1,90 @@
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# Copyright 2021 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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"""Evaluation callback when training"""
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import os
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import stat
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from mindspore import save_checkpoint
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from mindspore import log as logger
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from mindspore.train.callback import Callback
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class EvalCallBack(Callback):
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"""
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Evaluation callback when training.
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Args:
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eval_function (function): evaluation function.
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eval_param_dict (dict): evaluation parameters' configure dict.
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interval (int): run evaluation interval, default is 1.
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eval_start_epoch (int): evaluation start epoch, default is 1.
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save_best_ckpt (bool): Whether to save best checkpoint, default is True.
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besk_ckpt_name (str): bast checkpoint name, default is `best.ckpt`.
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metrics_name (str): evaluation metrics name, default is `acc`.
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Returns:
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None
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Examples:
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>>> EvalCallBack(eval_function, eval_param_dict)
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"""
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def __init__(self, eval_function, eval_param_dict, interval=1, eval_start_epoch=1, save_best_ckpt=True,
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ckpt_directory="./", besk_ckpt_name="best.ckpt", metrics_name="acc"):
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super(EvalCallBack, self).__init__()
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self.eval_param_dict = eval_param_dict
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self.eval_function = eval_function
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self.eval_start_epoch = eval_start_epoch
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if interval < 1:
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raise ValueError("interval should >= 1.")
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self.interval = interval
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self.save_best_ckpt = save_best_ckpt
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self.best_res = 0
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self.best_epoch = 0
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if not os.path.isdir(ckpt_directory):
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os.makedirs(ckpt_directory)
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self.bast_ckpt_path = os.path.join(ckpt_directory, besk_ckpt_name)
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self.metrics_name = metrics_name
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def remove_ckpoint_file(self, file_name):
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"""Remove the specified checkpoint file from this checkpoint manager and also from the directory."""
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try:
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os.chmod(file_name, stat.S_IWRITE)
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os.remove(file_name)
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except OSError:
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logger.warning("OSError, failed to remove the older ckpt file %s.", file_name)
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except ValueError:
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logger.warning("ValueError, failed to remove the older ckpt file %s.", file_name)
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def epoch_end(self, run_context):
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"""Callback when epoch end."""
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cb_params = run_context.original_args()
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cur_epoch = cb_params.cur_epoch_num
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if cur_epoch >= self.eval_start_epoch and (cur_epoch - self.eval_start_epoch) % self.interval == 0:
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res = self.eval_function(self.eval_param_dict)
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print("epoch: {}, {}: {}".format(cur_epoch, self.metrics_name, res), flush=True)
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if res >= self.best_res:
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self.best_res = res
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self.best_epoch = cur_epoch
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print("update best result: {}".format(res), flush=True)
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if self.save_best_ckpt:
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if os.path.exists(self.bast_ckpt_path):
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self.remove_ckpoint_file(self.bast_ckpt_path)
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save_checkpoint(cb_params.train_network, self.bast_ckpt_path)
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print("update best checkpoint at: {}".format(self.bast_ckpt_path), flush=True)
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def end(self, run_context):
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print("End training, the best {0} is: {1}, the best {0} epoch is {2}".format(self.metrics_name,
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self.best_res,
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self.best_epoch), flush=True)
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@ -0,0 +1,90 @@
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# Copyright 2021 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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"""Evaluation callback when training"""
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import os
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import stat
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from mindspore import save_checkpoint
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from mindspore import log as logger
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from mindspore.train.callback import Callback
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class EvalCallBack(Callback):
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"""
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Evaluation callback when training.
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Args:
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eval_function (function): evaluation function.
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eval_param_dict (dict): evaluation parameters' configure dict.
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interval (int): run evaluation interval, default is 1.
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eval_start_epoch (int): evaluation start epoch, default is 1.
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save_best_ckpt (bool): Whether to save best checkpoint, default is True.
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besk_ckpt_name (str): bast checkpoint name, default is `best.ckpt`.
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metrics_name (str): evaluation metrics name, default is `acc`.
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Returns:
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None
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Examples:
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>>> EvalCallBack(eval_function, eval_param_dict)
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"""
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def __init__(self, eval_function, eval_param_dict, interval=1, eval_start_epoch=1, save_best_ckpt=True,
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ckpt_directory="./", besk_ckpt_name="best.ckpt", metrics_name="acc"):
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super(EvalCallBack, self).__init__()
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self.eval_param_dict = eval_param_dict
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self.eval_function = eval_function
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self.eval_start_epoch = eval_start_epoch
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if interval < 1:
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raise ValueError("interval should >= 1.")
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self.interval = interval
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self.save_best_ckpt = save_best_ckpt
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self.best_res = 0
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self.best_epoch = 0
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if not os.path.isdir(ckpt_directory):
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os.makedirs(ckpt_directory)
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self.bast_ckpt_path = os.path.join(ckpt_directory, besk_ckpt_name)
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self.metrics_name = metrics_name
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def remove_ckpoint_file(self, file_name):
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"""Remove the specified checkpoint file from this checkpoint manager and also from the directory."""
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try:
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os.chmod(file_name, stat.S_IWRITE)
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os.remove(file_name)
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except OSError:
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logger.warning("OSError, failed to remove the older ckpt file %s.", file_name)
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except ValueError:
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logger.warning("ValueError, failed to remove the older ckpt file %s.", file_name)
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def epoch_end(self, run_context):
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"""Callback when epoch end."""
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cb_params = run_context.original_args()
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cur_epoch = cb_params.cur_epoch_num
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if cur_epoch >= self.eval_start_epoch and (cur_epoch - self.eval_start_epoch) % self.interval == 0:
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res = self.eval_function(self.eval_param_dict)
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print("epoch: {}, {}: {}".format(cur_epoch, self.metrics_name, res), flush=True)
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if res >= self.best_res:
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self.best_res = res
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self.best_epoch = cur_epoch
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print("update best result: {}".format(res), flush=True)
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if self.save_best_ckpt:
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if os.path.exists(self.bast_ckpt_path):
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self.remove_ckpoint_file(self.bast_ckpt_path)
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save_checkpoint(cb_params.train_network, self.bast_ckpt_path)
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print("update best checkpoint at: {}".format(self.bast_ckpt_path), flush=True)
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def end(self, run_context):
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print("End training, the best {0} is: {1}, the best {0} epoch is {2}".format(self.metrics_name,
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self.best_res,
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self.best_epoch), flush=True)
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