!2743 add more ut and st for SummaryCollector
Merge pull request !2743 from ougongchang/summary_collector_utpull/2743/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
2fadbb1d04
@ -1,99 +0,0 @@
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# Copyright 2019 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 model train """
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import os
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import numpy as np
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from apply_momentum import ApplyMomentum
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore.nn import wrap
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from mindspore import Tensor, Model
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from mindspore.common.api import ms_function
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.ops import operations as P
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from mindspore.train.summary.summary_record import SummaryRecord
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CUR_DIR = os.getcwd()
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SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
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context.set_context(device_target="Ascend")
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class MsWrapper(nn.Cell):
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def __init__(self, network):
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super(MsWrapper, self).__init__(auto_prefix=False)
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self._network = network
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@ms_function
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def construct(self, *args):
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return self._network(*args)
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def me_train_tensor(net, input_np, label_np, epoch_size=2):
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context.set_context(mode=context.GRAPH_MODE)
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loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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opt = ApplyMomentum(Tensor(np.array([0.1])), Tensor(np.array([0.9])),
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filter(lambda x: x.requires_grad, net.get_parameters()))
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Model(net, loss, opt)
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_network = wrap.WithLossCell(net, loss)
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_train_net = MsWrapper(wrap.TrainOneStepCell(_network, opt))
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_train_net.set_train()
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with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=_train_net) as summary_writer:
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for epoch in range(0, epoch_size):
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print(f"epoch %d" % (epoch))
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output = _train_net(Tensor(input_np), Tensor(label_np))
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summary_writer.record(i)
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print("********output***********")
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print(output.asnumpy())
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def me_infer_tensor(net, input_np):
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net.set_train()
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net = MsWrapper(net)
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output = net(Tensor(input_np))
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return output
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def test_net():
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class Net(nn.Cell):
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def __init__(self, cin, cout):
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super(Net, self).__init__()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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self.conv = nn.Conv2d(cin, cin, kernel_size=1, stride=1, padding=0, has_bias=False, pad_mode="same")
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self.bn = nn.BatchNorm2d(cin, momentum=0.1, eps=0.0001)
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self.add = P.TensorAdd()
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self.relu = P.ReLU()
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self.mean = P.ReduceMean(keep_dims=True)
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self.reshape = P.Reshape()
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self.dense = nn.Dense(cin, cout)
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def construct(self, input_x):
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output = input_x
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output = self.maxpool(output)
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identity = output
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output = self.conv(output)
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output = self.bn(output)
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output = self.add(output, identity)
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output = self.relu(output)
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output = self.mean(output, (-2, -1))
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output = self.reshape(output, (32, -1))
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output = self.dense(output)
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return output
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net = Net(2048, 1001)
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input_np = np.ones([32, 2048, 14, 14]).astype(np.float32) * 0.01
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label_np = np.ones([32]).astype(np.int32)
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me_train_tensor(net, input_np, label_np)
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# me_infer_tensor(net, input_np)
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@ -1,89 +0,0 @@
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# Copyright 2019 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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"""Summary gpu st."""
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import os
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import random
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import tempfile
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import shutil
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.train.summary.summary_record import SummaryRecord
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class SummaryNet(nn.Cell):
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"""Summary net."""
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def __init__(self, tag_tuple=None, scalar=1):
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super(SummaryNet, self).__init__()
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self.summary_s = P.ScalarSummary()
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self.summary_i = P.ImageSummary()
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self.summary_t = P.TensorSummary()
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self.histogram_summary = P.HistogramSummary()
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self.add = P.TensorAdd()
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self.tag_tuple = tag_tuple
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self.scalar = scalar
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def construct(self, x, y, image):
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"""Run summary net."""
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self.summary_i("image", image)
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self.summary_s("x1", x)
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z = self.add(x, y)
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self.summary_t("z1", z)
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self.histogram_summary("histogram", z)
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return z
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def train_summary_record(test_writer, steps):
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"""Train and record summary."""
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net = SummaryNet()
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out_me_dict = {}
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for i in range(0, steps):
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x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
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y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
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image = Tensor(np.array([[[[1.2]]]]).astype(np.float32))
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out_put = net(x, y, image)
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test_writer.record(i)
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out_me_dict[i] = out_put.asnumpy()
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return out_me_dict
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class TestGpuSummary:
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"""Test Gpu summary."""
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summary_dir = tempfile.mkdtemp(suffix='_gpu_summary')
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def setup_method(self):
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"""Run before method."""
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if not os.path.exists(self.summary_dir):
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os.mkdir(self.summary_dir)
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def teardown_method(self):
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"""Run after method."""
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if os.path.exists(self.summary_dir):
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shutil.rmtree(self.summary_dir)
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@pytest.mark.level0
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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_step10_summaryrecord1(self):
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"""Test record 10 step summary."""
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with SummaryRecord(self.summary_dir) as test_writer:
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train_summary_record(test_writer, steps=10)
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@ -0,0 +1,194 @@
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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 model train """
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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 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.nn.metrics import Accuracy
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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.transforms.vision import c_transforms as CV
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from mindspore.dataset.transforms.vision import Inter
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import TruncatedNormal
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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
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from tests.summary_utils import SummaryReader
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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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"""Define LeNet5 network."""
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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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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(channel)
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def construct(self, data):
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"""define construct."""
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self.image_summary('image', data)
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output = self.conv1(data)
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self.histogram_summary('histogram', output)
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output = self.relu(output)
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self.tensor_summary('tensor', output)
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output = self.max_pool2d(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.max_pool2d(output)
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output = self.flatten(output)
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output = self.fc1(output)
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output = self.relu(output)
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output = self.fc2(output)
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output = self.relu(output)
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output = self.fc3(output)
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self.scalar_summary('scalar', self.channel)
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return output
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def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
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"""create dataset for train or test"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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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(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, 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, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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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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"""Run before test this class."""
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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.env_onecard
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def test_summary_ascend(self):
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"""Test summary ascend."""
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context.set_context(mode=context.GRAPH_MODE)
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self._run_network()
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def _run_network(self, dataset_sink_mode=True):
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lenet = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, 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={'acc': Accuracy()})
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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=1)
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ds_train = create_dataset(os.path.join(self.mnist_path, "train"))
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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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model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode, callbacks=[summary_collector])
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self._check_summary_result(summary_dir)
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@staticmethod
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def _check_summary_result(summary_dir):
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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 not summary_file_path
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with SummaryReader(summary_file_path) as summary_reader:
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tags = set()
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# Read the event that record by SummaryCollector.begin
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summary_reader.read_event()
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summary_event = summary_reader.read_event()
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for value in summary_event.summary.value:
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tags.add(value.tag)
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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', 'histogram', 'image', 'scalar', 'tensor']
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assert set(expected_tags) == tags
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