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104 lines
3.6 KiB
104 lines
3.6 KiB
# 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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Defined callback for DeepFM.
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"""
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import time
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from mindspore.train.callback import Callback
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from mindspore import Tensor
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import numpy as np
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class TimeMonitor(Callback):
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"""
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Time monitor for calculating cost of each epoch.
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Args:
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data_size (int): step size of an epoch.
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"""
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def __init__(self, data_size):
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super(TimeMonitor, self).__init__()
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self.data_size = data_size
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def epoch_begin(self, run_context):
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / self.data_size
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print("epoch time: {0}, per step time: {1}".format(epoch_mseconds, per_step_mseconds), flush=True)
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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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step_mseconds = (time.time() - self.step_time) * 1000
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print(f"step time {step_mseconds}", flush=True)
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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):
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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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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)
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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: {:5.6f}".format(epoch_mseconds,
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per_step_mseconds,
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np.mean(self.losses)))
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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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"""step end"""
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cb_params = run_context.original_args()
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step_mseconds = (time.time() - self.step_time)
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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:[{:5.6f}/{:5.6f}], time:[{:5.3f}], lr:[{:.9f}]".format(
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cb_params.cur_epoch_num -
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1, cb_params.epoch_num, cur_step_in_epoch, 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].asnumpy()))
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