forked from mindspore-Ecosystem/mindspore
106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
# 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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"""Resnet50 utils"""
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import time
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import numpy as np
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from mindspore.train.callback import Callback
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from mindspore import Tensor
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from mindspore import nn
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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.common import dtype as mstype
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class Monitor(Callback):
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"""
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Monitor loss and time.
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Args:
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lr_init (numpy array): train lr
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Returns:
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None
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Examples:
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>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
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"""
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def __init__(self, lr_init=None, step_threshold=10):
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super(Monitor, self).__init__()
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self.lr_init = lr_init
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self.lr_init_len = len(lr_init)
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self.step_threshold = step_threshold
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def epoch_begin(self, run_context):
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self.losses = []
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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cb_params = run_context.original_args()
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / cb_params.batch_num
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print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:8.6f}".format(epoch_mseconds,
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per_step_mseconds,
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np.mean(self.losses)))
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self.epoch_mseconds = epoch_mseconds
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def step_begin(self, run_context):
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self.step_time = time.time()
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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step_mseconds = (time.time() - self.step_time) * 1000
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step_loss = cb_params.net_outputs
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if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
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step_loss = step_loss[0]
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if isinstance(step_loss, Tensor):
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step_loss = np.mean(step_loss.asnumpy())
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self.losses.append(step_loss)
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
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print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:8.6f}/{:8.6f}], time:[{:5.3f}], lr:[{:5.5f}]".format(
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cb_params.cur_epoch_num, cb_params.epoch_num, cur_step_in_epoch +
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1, cb_params.batch_num, step_loss,
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np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
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if cb_params.cur_step_num == self.step_threshold:
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run_context.request_stop()
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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 /
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(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(
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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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