forked from mindspore-Ecosystem/mindspore
add summary st for ops
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# 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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"""dataset base."""
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import os
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from mindspore import dataset as ds
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from mindspore.common import dtype as mstype
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from mindspore.dataset.transforms import c_transforms as C
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from mindspore.dataset.vision import Inter
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from mindspore.dataset.vision import c_transforms as CV
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def create_mnist_dataset(mode='train', num_samples=2, batch_size=2):
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"""create dataset for train or test"""
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mnist_path = '/home/workspace/mindspore_dataset/mnist'
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num_parallel_workers = 1
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# define dataset
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mnist_ds = ds.MnistDataset(os.path.join(mnist_path, mode), num_samples=num_samples, shuffle=False)
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resize_height, resize_width = 32, 32
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081)
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rescale_op = CV.Rescale(1.0 / 255.0, shift=0.0)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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mnist_ds = mnist_ds.batch(batch_size=batch_size, drop_remainder=True)
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return mnist_ds
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@ -12,29 +12,23 @@
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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 model train """
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"""test SummaryCollector."""
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import os
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import re
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import tempfile
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import shutil
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import tempfile
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from collections import Counter
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import pytest
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from mindspore import dataset as ds
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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.dataset.transforms import c_transforms as C
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from mindspore.dataset.vision import c_transforms as CV
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from mindspore.dataset.vision import Inter
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from mindspore.common import dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.common.initializer import Normal
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from mindspore.train import Model
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from mindspore.train.callback import SummaryCollector
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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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@ -52,6 +46,7 @@ class LeNet5(nn.Cell):
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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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@ -72,6 +67,7 @@ class LeNet5(nn.Cell):
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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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@ -92,43 +88,9 @@ class LeNet5(nn.Cell):
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return x
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def create_dataset(data_path, num_samples=2):
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"""create dataset for train or test"""
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num_parallel_workers = 1
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# define dataset
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mnist_ds = ds.MnistDataset(data_path, num_samples=num_samples)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift=0.0)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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mnist_ds = mnist_ds.shuffle(buffer_size=10000) # 10000 as in LeNet train script
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mnist_ds = mnist_ds.batch(batch_size=2, drop_remainder=True)
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return mnist_ds
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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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mnist_path = '/home/workspace/mindspore_dataset/mnist'
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@classmethod
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def setup_class(cls):
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@ -144,6 +106,7 @@ class TestSummary:
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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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@ -151,10 +114,10 @@ class TestSummary:
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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_dataset(os.path.join(self.mnist_path, "train"), num_samples=num_samples)
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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_dataset(os.path.join(self.mnist_path, "test"))
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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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@ -202,10 +165,12 @@ class TestSummary:
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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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def test_summarycollector_user_defind(self):
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"""Test SummaryCollector with user defind."""
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summary_dir = self._run_network(dataset_sink_mode=True, num_samples=2, user_defind={'test': 'self test'})
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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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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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@ -213,9 +178,9 @@ class TestSummary:
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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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@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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@ -0,0 +1,115 @@
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# 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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""" test summary ops."""
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import os
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import shutil
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import tempfile
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import numpy as np
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import pytest
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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.summary.summary_record import _get_summary_tensor_data
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from tests.st.summary.dataset import create_mnist_dataset
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class LeNet5(nn.Cell):
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"""LeNet network"""
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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.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('x', x)
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self.tensor_summary('x', 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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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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self.scalar_summary('x_fc3', x[0][0])
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return x
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class TestSummaryOps:
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"""Test summary ops."""
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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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@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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def test_summary_ops(self):
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"""Test summary operators."""
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ds_train = create_mnist_dataset('train', num_samples=1, batch_size=1)
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ds_train_iter = ds_train.create_dict_iterator()
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expected_data = next(ds_train_iter)['image'].asnumpy()
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net = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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model = Model(net, loss_fn=loss, optimizer=optim, metrics={'loss': Loss()})
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model.train(1, ds_train, dataset_sink_mode=False)
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summary_data = _get_summary_tensor_data()
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image_data = summary_data['x[:Image]'].asnumpy()
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tensor_data = summary_data['x[:Tensor]'].asnumpy()
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x_fc3 = summary_data['x_fc3[:Scalar]'].asnumpy()
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assert np.allclose(expected_data, image_data)
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assert np.allclose(expected_data, tensor_data)
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assert not np.allclose(0, x_fc3)
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