forked from mindspore-Ecosystem/mindspore
192 lines
6.6 KiB
Python
192 lines
6.6 KiB
Python
# Copyright 2022 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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""" test OnRequestExit Callback"""
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import os
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import shutil
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import signal
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import sys
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import time
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from multiprocessing import Process
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import numpy as np
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import pytest
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from mindspore import nn, context
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from mindspore import dataset as ds
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.train import Callback, OnRequestExit, LossMonitor, Model
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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"""
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LeNet5 network
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"""
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(channel, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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def generator_multi_column():
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i = 0
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while i < 1000:
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i += 1
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yield np.ones((1, 32, 32)).astype(np.float32) * 0.01, np.array(1).astype(np.int32)
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def send_signal(sleep_time):
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time.sleep(sleep_time)
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os.kill(os.getppid(), signal.SIGTERM)
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def construct_model():
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forward_net = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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optim = nn.Momentum(forward_net.trainable_params(), 0.01, 0.9)
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model = Model(forward_net, loss_fn=loss, optimizer=optim, metrics={'accuracy'})
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return model
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def construct_dataset():
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dataset = ds.GeneratorDataset(source=generator_multi_column, column_names=["data", "label"])
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dataset = dataset.batch(32, drop_remainder=True)
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return dataset
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class EpochAndStepRecord(Callback):
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"""Define EpochAndStepRecord Callback to record epoch and step"""
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def __init__(self):
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self.epoch = 0
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self.step = 0
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def on_train_end(self, run_context):
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cb_params = run_context.original_args()
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self.epoch = cb_params.cur_epoch_num
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self.step = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
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def on_eval_end(self, run_context):
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cb_params = run_context.original_args()
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self.step = cb_params.cur_step_num
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_on_request_exit_callback():
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"""
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Feature: OnRequestExit Callback.
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Description: test OnRequestExit Callback when a signal receive.
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Expectation: When a signal received,
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the train process should be stopped and save the ckpt and mindir should be saved.
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"""
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if sys.platform != 'linux':
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return
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context.set_context(mode=context.GRAPH_MODE)
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directory = "./data"
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if os.path.exists(directory):
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shutil.rmtree(directory)
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os.makedirs(directory)
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dataset = construct_dataset()
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model = construct_model()
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loss_monitor = LossMonitor()
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epoch_and_step_record = EpochAndStepRecord()
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on_request_exit = OnRequestExit(save_ckpt=True, file_name='LeNet5', directory=directory)
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epoch_num = 100
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step_num = dataset.get_dataset_size()
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send_signal_process = Process(target=send_signal, args=[1])
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send_signal_process.start()
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model.train(epoch_num, dataset, callbacks=[loss_monitor, epoch_and_step_record, on_request_exit])
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train_ckpt_file = f"{directory}/LeNet5_train.ckpt"
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train_mindir_file = f"{directory}/LeNet5_train.mindir"
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eval_ckpt_file = f"{directory}/LeNet5_eval.ckpt"
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eval_mindir_file = f"{directory}/LeNet5_eval.mindir"
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assert epoch_and_step_record.epoch != epoch_num or epoch_and_step_record.step != step_num
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assert os.path.isfile(train_ckpt_file)
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assert os.path.isfile(train_mindir_file)
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ckpt_ctime = os.path.getctime(train_ckpt_file)
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mindir_ctime = os.path.getctime(train_mindir_file)
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dataset = construct_dataset()
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model = construct_model()
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on_request_exit = OnRequestExit(save_ckpt=True, file_name='LeNet5', directory=directory)
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send_signal_process = Process(target=send_signal, args=[0.5])
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send_signal_process.start()
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model.train(epoch_num, dataset, callbacks=[loss_monitor, epoch_and_step_record, on_request_exit])
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assert epoch_and_step_record.epoch != epoch_num or epoch_and_step_record.step != step_num
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assert os.path.getctime(train_ckpt_file) > ckpt_ctime
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assert os.path.getctime(train_mindir_file) > mindir_ctime
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send_signal_process = Process(target=send_signal, args=[0.14])
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send_signal_process.start()
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on_request_exit = OnRequestExit(save_ckpt=True, file_name='LeNet5', directory=directory)
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model.eval(dataset, callbacks=[loss_monitor, epoch_and_step_record, on_request_exit])
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assert epoch_and_step_record.step != step_num
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assert os.path.isfile(eval_ckpt_file)
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assert os.path.isfile(eval_mindir_file)
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dataset = construct_dataset()
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model = construct_model()
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on_request_exit = OnRequestExit(save_ckpt=True, file_name='LeNet5', directory=directory)
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model.eval(dataset, callbacks=[loss_monitor, epoch_and_step_record, on_request_exit])
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assert epoch_and_step_record.step == step_num
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shutil.rmtree(directory)
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