forked from mindspore-Ecosystem/mindspore
372 lines
16 KiB
Python
372 lines
16 KiB
Python
# Copyright 2020-2022 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 SummaryCollector."""
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import os
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import re
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import shutil
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import tempfile
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import json
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from collections import Counter
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import numpy as np
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import pytest
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from mindspore.common import set_seed
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from mindspore import nn, Tensor, context
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from mindspore.common.initializer import Normal
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from mindspore.nn.metrics import Loss
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from mindspore.nn.optim import Momentum
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from mindspore.train.callback import SummaryCollector, SummaryLandscape
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from mindspore.train.summary.summary_record import SummaryRecord
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from tests.st.summary.dataset import create_mnist_dataset
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from tests.summary_utils import SummaryReader
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from tests.security_utils import security_off_wrap
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def callback_fn():
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"""A python function job"""
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network = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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metrics = {"Loss": Loss()}
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model = Model(network, loss, metrics=metrics)
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ds_train = create_mnist_dataset("train", num_samples=6)
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return model, network, ds_train, metrics
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class LeNet5(nn.Cell):
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"""
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Lenet network
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Args:
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num_class (int): Number of classes. Default: 10.
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num_channel (int): Number of channels. Default: 1.
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Returns:
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Tensor, output tensor
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Examples:
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>>> LeNet(num_class=10)
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"""
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def __init__(self, num_class=10, num_channel=1, include_top=True):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
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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.include_top = include_top
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if self.include_top:
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self.flatten = nn.Flatten()
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self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
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self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
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self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
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self.scalar_summary = P.ScalarSummary()
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self.image_summary = P.ImageSummary()
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self.histogram_summary = P.HistogramSummary()
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self.tensor_summary = P.TensorSummary()
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self.channel = Tensor(num_channel)
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def construct(self, x):
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"""construct."""
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self.image_summary('image', x)
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x = self.conv1(x)
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self.histogram_summary('histogram', x)
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x = self.relu(x)
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self.tensor_summary('tensor', x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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self.scalar_summary('scalar', self.channel)
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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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if not self.include_top:
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return x
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x = self.flatten(x)
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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class TestSummary:
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"""Test summary collector the basic function."""
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base_summary_dir = ''
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@classmethod
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def setup_class(cls):
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"""Run before test this class."""
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device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
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context.set_context(mode=context.GRAPH_MODE, device_id=device_id)
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cls.base_summary_dir = tempfile.mkdtemp(suffix='summary')
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@classmethod
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def teardown_class(cls):
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"""Run after test this class."""
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if os.path.exists(cls.base_summary_dir):
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shutil.rmtree(cls.base_summary_dir)
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def _run_network(self, dataset_sink_mode=False, num_samples=2, **kwargs):
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"""run network."""
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lenet = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9)
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model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'loss': Loss()})
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summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
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summary_collector = SummaryCollector(summary_dir=summary_dir, collect_freq=2, **kwargs)
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ds_train = create_mnist_dataset("train", num_samples=num_samples)
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model.train(1, ds_train, callbacks=[summary_collector], dataset_sink_mode=dataset_sink_mode)
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ds_eval = create_mnist_dataset("test")
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model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode, callbacks=[summary_collector])
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return summary_dir
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_summary_with_sink_mode_false(self):
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"""Test summary with sink mode false, and num samples is 64."""
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summary_dir = self._run_network(num_samples=10)
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tag_list = self._list_summary_tags(summary_dir)
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expected_tag_set = {'conv1.weight/auto', 'conv2.weight/auto', 'fc1.weight/auto', 'fc1.bias/auto',
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'fc2.weight/auto', 'input_data/auto', 'loss/auto',
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'histogram', 'image', 'scalar', 'tensor'}
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assert set(expected_tag_set) == set(tag_list)
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# num samples is 10, batch size is 2, so step is 5, collect freq is 2,
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# SummaryCollector will collect the first step and 2th, 4th step
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tag_count = 3
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for value in Counter(tag_list).values():
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assert value == tag_count
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_summary_with_sink_mode_true(self):
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"""Test summary with sink mode true, and num samples is 64."""
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summary_dir = self._run_network(dataset_sink_mode=True, num_samples=10)
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tag_list = self._list_summary_tags(summary_dir)
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# There will not record input data when dataset sink mode is True
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expected_tags = {'conv1.weight/auto', 'conv2.weight/auto', 'fc1.weight/auto', 'fc1.bias/auto',
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'fc2.weight/auto', 'loss/auto', 'histogram', 'image', 'scalar', 'tensor'}
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assert set(expected_tags) == set(tag_list)
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tag_count = 1
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for value in Counter(tag_list).values():
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assert value == tag_count
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_summarycollector_user_defind(self):
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"""Test SummaryCollector with user-defined."""
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summary_dir = self._run_network(dataset_sink_mode=True, num_samples=2,
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custom_lineage_data={'test': 'self test'},
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export_options={'tensor_format': 'npy'})
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tag_list = self._list_summary_tags(summary_dir)
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file_list = self._list_tensor_files(summary_dir)
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# There will not record input data when dataset sink mode is True
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expected_tags = {'conv1.weight/auto', 'conv2.weight/auto', 'fc1.weight/auto', 'fc1.bias/auto',
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'fc2.weight/auto', 'loss/auto', 'histogram', 'image', 'scalar', 'tensor'}
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assert set(expected_tags) == set(tag_list)
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expected_files = {'tensor_1.npy'}
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assert set(expected_files) == set(file_list)
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@staticmethod
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def _list_summary_tags(summary_dir):
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"""list summary tags."""
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summary_file_path = ''
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for file in os.listdir(summary_dir):
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if re.search("_MS", file):
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summary_file_path = os.path.join(summary_dir, file)
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break
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assert summary_file_path
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tags = list()
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with SummaryReader(summary_file_path) as summary_reader:
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while True:
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summary_event = summary_reader.read_event()
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if not summary_event:
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break
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for value in summary_event.summary.value:
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tags.append(value.tag)
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return tags
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@staticmethod
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def _list_tensor_files(summary_dir):
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"""list tensor tags."""
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export_file_path = ''
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for file in os.listdir(summary_dir):
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if re.search("export_", file):
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export_file_path = os.path.join(summary_dir, file)
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break
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assert export_file_path
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tensor_file_path = os.path.join(export_file_path, "tensor")
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assert tensor_file_path
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tensors = list()
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for file in os.listdir(tensor_file_path):
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tensors.append(file)
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return tensors
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def _train_network(self, epoch=3, dataset_sink_mode=False, num_samples=2, **kwargs):
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"""run network."""
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lenet = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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optim = Momentum(lenet.trainable_params(), learning_rate=0.01, momentum=0.9)
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model = Model(lenet, loss_fn=loss, optimizer=optim)
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summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
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summary_collector = SummaryCollector(summary_dir=summary_dir, collect_freq=2, **kwargs)
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ds_train = create_mnist_dataset("train", num_samples=num_samples)
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model.train(epoch, ds_train, callbacks=[summary_collector], dataset_sink_mode=dataset_sink_mode)
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return summary_dir
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@staticmethod
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def _list_summary_collect_landscape_tags(summary_dir):
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"""list summary landscape tags."""
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summary_dir_path = ''
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for file in os.listdir(summary_dir):
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if re.search("ckpt_dir", file):
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summary_dir_path = os.path.join(summary_dir, file)
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break
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assert summary_dir_path
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summary_file_path = ''
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for file in os.listdir(summary_dir_path):
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if re.search(".json", file):
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summary_file_path = os.path.join(summary_dir_path, file)
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break
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assert summary_file_path
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tags = list()
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with open(summary_file_path, 'r') as file:
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data = json.load(file)
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for key, value in data.items():
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tags.append(key)
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assert value
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return tags
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@staticmethod
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def _list_landscape_tags(summary_dir):
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"""list landscape tags."""
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expected_tags = {'landscape_[1, 3]', 'landscape_[3]'}
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summary_list = []
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for file in os.listdir(summary_dir):
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if re.search("_MS", file):
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summary_file_path = os.path.join(summary_dir, file)
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summary_list = summary_list + [summary_file_path]
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else:
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continue
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assert summary_list
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tags = []
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for summary_path in summary_list:
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with SummaryReader(summary_path) as summary_reader:
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while True:
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summary_event = summary_reader.read_event()
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if not summary_event:
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break
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for value in summary_event.summary.value:
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if value.tag in expected_tags:
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tags.append(value.loss_landscape.landscape.z.float_data)
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break
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return tags
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_summary_collector_landscape(self):
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"""Test summary collector with landscape."""
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set_seed(1)
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interval_1 = [1, 2, 3]
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num_samples = 6
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summary_dir = self._train_network(epoch=3, num_samples=num_samples,
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collect_specified_data={'collect_landscape':
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{'landscape_size': 4,
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'unit': 'epoch',
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'create_landscape': {'train': True,
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'result': True},
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'num_samples': num_samples,
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'intervals': [interval_1]}})
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tag_list = self._list_summary_collect_landscape_tags(summary_dir)
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expected_tags = {'epoch_group', 'model_params_file_map', 'step_per_epoch', 'unit', 'num_samples',
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'landscape_size', 'create_landscape'}
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assert set(expected_tags) == set(tag_list)
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device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
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summary_landscape = SummaryLandscape(summary_dir)
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summary_landscape.gen_landscapes_with_multi_process(callback_fn, device_ids=[device_id])
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expected_pca_value = np.array([2.2795451, 2.2795504, 2.2795559, 2.2795612, 2.2795450, 2.2795503, 2.2795557,
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2.2795612, 2.2795449, 2.2795503, 2.2795557, 2.2795610, 2.2795449, 2.2795502,
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2.2795555, 2.2795610])
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expe_pca_value_asc = np.array([2.2795452, 2.2795503, 2.2795557, 2.2795612, 2.2795450, 2.2795503, 2.2795557,
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2.2795612, 2.2795449, 2.2795502, 2.2795555, 2.2795609, 2.2795449, 2.2795502,
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2.2795554, 2.2795610])
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expected_random_value = np.array([2.2729474, 2.2777648, 2.2829195, 2.2884243, 2.2724223, 2.2771732, 2.2822458,
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2.2875971, 2.2725493, 2.2771329, 2.2819973, 2.2875895, 2.2730918, 2.2774068,
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2.2822349, 2.2881028])
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expe_random_value_asc = np.array([2.2729466, 2.2777647, 2.2829201, 2.2884242, 2.2724224, 2.2771732, 2.2822458,
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2.2875975, 2.2725484, 2.2771326, 2.2819972, 2.2875896, 2.2730910, 2.2774070,
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2.2822352, 2.2881035])
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tag_list_landscape = self._list_landscape_tags(summary_dir)
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assert np.all(abs(expected_pca_value - tag_list_landscape[0]) < 1.e-6) or \
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np.all(abs(expe_pca_value_asc - tag_list_landscape[0]) < 1.e-6)
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assert np.all(abs(expected_random_value - tag_list_landscape[1]) < 1.e-6) or \
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np.all(abs(expe_random_value_asc - tag_list_landscape[1]) < 1.e-6)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_summary_of_more_than_one_instance(self):
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"""
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Feature: Test the multi instances of SummaryRecord in a script.
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Description: Multi instances of SummaryRecord in a script.
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Expectation: Throw RuntimeError.
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"""
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with pytest.raises(RuntimeError) as errinfo:
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summary_dir1 = tempfile.mkdtemp(dir=self.base_summary_dir)
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summary_record1 = SummaryRecord(log_dir=summary_dir1)
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summary_dir2 = tempfile.mkdtemp(dir=self.base_summary_dir)
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_ = SummaryRecord(log_dir=summary_dir2)
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assert "only one instance is supported in a training process" in str(errinfo.value)
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summary_record1.close()
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