!12984 Fix summary ut

From: @ouwenchang
Reviewed-by: @yelihua,@lixiaohui33
Signed-off-by: @lixiaohui33

tests/ut/python/runtest.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2019-2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-CURRPATH=$(cd $(dirname $0); pwd)
+CURRPATH=$(cd "$(dirname $0)"; pwd)
 IGNORE_EXEC="--ignore=$CURRPATH/exec"
 PROJECT_PATH=$(cd ${CURRPATH}/../../..; pwd)
 
@@ -36,7 +36,7 @@ if [ $# -eq 1 ]  &&  ([ "$1" == "stage1" ] || [ "$1" == "stage2" ] || [ "$1" ==
 
     elif [ $1 == "stage2" ]; then
         echo "run python parallel\train\ops ut"
-        pytest -n 4 --dist=loadfile -v $CURRPATH/parallel $CURRPATH/train
+        pytest -n 4 --dist=loadfile -v $CURRPATH/parallel $CURRPATH/train  --ignore=$CURRPATH/train/summary
         RET=$?
         if [ ${RET} -ne 0 ]; then
             exit ${RET}
@@ -53,6 +53,16 @@ if [ $# -eq 1 ]  &&  ([ "$1" == "stage1" ] || [ "$1" == "stage2" ] || [ "$1" ==
         fi
 
         pytest $CURRPATH/pynative_mode
+        RET=$?
+        if [ ${RET} -ne 0 ]; then
+            exit ${RET}
+        fi
+
+        pytest -v $CURRPATH/train/summary
+        RET=$?
+        if [ ${RET} -ne 0 ]; then
+            exit ${RET}
+        fi
     fi
 else
     echo "run all python ut"
@@ -62,7 +72,7 @@ else
         exit ${RET}
     fi
 
-    pytest -n 4 --dist=loadfile -v $CURRPATH/parallel $CURRPATH/train
+    pytest -n 4 --dist=loadfile -v $CURRPATH/parallel $CURRPATH/train --ignore=$CURRPATH/train/summary
     RET=$?
     if [ ${RET} -ne 0 ]; then
         exit ${RET}
@@ -81,6 +91,16 @@ else
     fi
 
     pytest $CURRPATH/pynative_mode
+    RET=$?
+    if [ ${RET} -ne 0 ]; then
+        exit ${RET}
+    fi
+
+    pytest -v $CURRPATH/train/summary
+    RET=$?
+    if [ ${RET} -ne 0 ]; then
+        exit ${RET}
+    fi
 fi
 
 RET=$?

tests/ut/python/train/summary/test_graph_summary.py

@@ -1,125 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-""" test_graph_summary """
-import logging
-import os
-import numpy as np
-
-import mindspore.nn as nn
-from mindspore import Model, context
-from mindspore.nn.optim import Momentum
-from mindspore.train.summary import SummaryRecord
-from mindspore.train.callback import SummaryCollector
-from .....dataset_mock import MindData
-
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-GRAPH_TEMP = CUR_DIR + "/ms_output-resnet50.pb"
-
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
-
-class Net(nn.Cell):
-    """ Net definition """
-
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid')
-        self.bn = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU()
-        self.flatten = nn.Flatten()
-        self.fc = nn.Dense(64 * 222 * 222, 3)  # padding=0
-
-    def construct(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        x = self.flatten(x)
-        out = self.fc(x)
-        return out
-
-
-class LossNet(nn.Cell):
-    """ LossNet definition """
-
-    def __init__(self):
-        super(LossNet, self).__init__()
-        self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid')
-        self.bn = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU()
-        self.flatten = nn.Flatten()
-        self.fc = nn.Dense(64 * 222 * 222, 3)  # padding=0
-        self.loss = nn.SoftmaxCrossEntropyWithLogits()
-
-    def construct(self, x, y):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        x = self.flatten(x)
-        x = self.fc(x)
-        out = self.loss(x, y)
-        return out
-
-
-def get_model():
-    """ get_model """
-    net = Net()
-    loss = nn.SoftmaxCrossEntropyWithLogits()
-    optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
-    model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
-    return model
-
-
-def get_dataset():
-    """ get_datasetdataset """
-    dataset_types = (np.float32, np.float32)
-    dataset_shapes = ((2, 3, 224, 224), (2, 3))
-
-    dataset = MindData(size=2, batch_size=2,
-                       np_types=dataset_types,
-                       output_shapes=dataset_shapes,
-                       input_indexs=(0, 1))
-    return dataset
-
-
-# Test 1: summary sample of graph
-def test_graph_summary_sample():
-    """ test_graph_summary_sample """
-    log.debug("begin test_graph_summary_sample")
-    dataset = get_dataset()
-    net = Net()
-    loss = nn.SoftmaxCrossEntropyWithLogits()
-    optim = Momentum(net.trainable_params(), 0.1, 0.9)
-    context.set_context(mode=context.GRAPH_MODE)
-    model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
-    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH", network=model._train_network) as test_writer:
-        model.train(2, dataset)
-        for i in range(1, 5):
-            test_writer.record(i)
-
-
-def test_graph_summary_callback():
-    dataset = get_dataset()
-    net = Net()
-    loss = nn.SoftmaxCrossEntropyWithLogits()
-    optim = Momentum(net.trainable_params(), 0.1, 0.9)
-    context.set_context(mode=context.GRAPH_MODE)
-    model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
-    summary_collector = SummaryCollector(SUMMARY_DIR,
-                                         collect_freq=1,
-                                         keep_default_action=False,
-                                         collect_specified_data={'collect_graph': True})
-    model.train(1, dataset, callbacks=[summary_collector])

tests/ut/python/train/summary/test_image_summary.py

@@ -34,9 +34,8 @@ log.setLevel(level=logging.ERROR)
 
 def make_image_tensor(shape, dtype=float):
     """ make_image_tensor """
-    # pylint: disable=unused-argument
-    numel = np.prod(shape)
-    x = (np.arange(numel, dtype=float)).reshape(shape)
+    number = np.prod(shape)
+    x = (np.arange(number, dtype=dtype)).reshape(shape)
     return x
 
 

tests/ut/python/train/summary/test_summary.py

@@ -1,140 +0,0 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Test summary."""
-import os
-import random
-
-import numpy as np
-
-
-import mindspore.nn as nn
-from mindspore.common.tensor import Tensor
-from mindspore.ops import operations as P
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
-
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-
-
-def get_test_data(step):
-    """ get_test_data """
-    test_data_list = []
-    tag1 = "x1[:Scalar]"
-    tag2 = "x2[:Scalar]"
-    np1 = np.array(step + 1).astype(np.float32)
-    np2 = np.array(step + 2).astype(np.float32)
-
-    dict1 = {}
-    dict1["name"] = tag1
-    dict1["data"] = Tensor(np1)
-
-    dict2 = {}
-    dict2["name"] = tag2
-    dict2["data"] = Tensor(np2)
-
-    test_data_list.append(dict1)
-    test_data_list.append(dict2)
-
-    return test_data_list
-
-
-def test_scalar_summary_sample():
-    """ test_scalar_summary_sample """
-    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        for i in range(1, 5):
-            test_data = get_test_data(i)
-            _cache_summary_tensor_data(test_data)
-            test_writer.record(i)
-
-
-def get_test_data_shape_1(step):
-    """ get_test_data_shape_1 """
-    test_data_list = []
-    tag1 = "x1[:Scalar]"
-    tag2 = "x2[:Scalar]"
-    np1 = np.array([step + 1]).astype(np.float32)
-    np2 = np.array([step + 2]).astype(np.float32)
-
-    dict1 = {}
-    dict1["name"] = tag1
-    dict1["data"] = Tensor(np1)
-
-    dict2 = {}
-    dict2["name"] = tag2
-    dict2["data"] = Tensor(np2)
-
-    test_data_list.append(dict1)
-    test_data_list.append(dict2)
-
-    return test_data_list
-
-
-# Test: shape = (1,)
-def test_scalar_summary_sample_with_shape_1():
-    """ test_scalar_summary_sample_with_shape_1 """
-    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        for i in range(1, 100):
-            test_data = get_test_data_shape_1(i)
-            _cache_summary_tensor_data(test_data)
-            test_writer.record(i)
-
-
-# Test: test with ge
-class SummaryDemo(nn.Cell):
-    """ SummaryDemo definition """
-
-    def __init__(self,):
-        super(SummaryDemo, self).__init__()
-        self.s = P.ScalarSummary()
-        self.histogram_summary = P.HistogramSummary()
-        self.add = P.Add()
-
-    def construct(self, x, y):
-        self.s("x1", x)
-        z = self.add(x, y)
-        self.s("z1", z)
-        self.s("y1", y)
-        self.histogram_summary("histogram", z)
-        return z
-
-
-def test_scalar_summary_with_ge():
-    """ test_scalar_summary_with_ge """
-    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        net = SummaryDemo()
-        net.set_train()
-
-        # step 2: create the Event
-        steps = 100
-        for i in range(1, steps):
-            x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
-            y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
-            net(x, y)
-            test_writer.record(i)
-
-
-# test the problem of two consecutive use cases going wrong
-def test_scalar_summary_with_ge_2():
-    """ test_scalar_summary_with_ge_2 """
-    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        net = SummaryDemo()
-        net.set_train()
-
-        steps = 100
-        for i in range(1, steps):
-            x = Tensor(np.array([1.1]).astype(np.float32))
-            y = Tensor(np.array([1.2]).astype(np.float32))
-            net(x, y)
-            test_writer.record(i)

tests/ut/python/train/summary/test_summary_collector.py

@@ -294,11 +294,11 @@ class TestSummaryCollector:
     def test_collect_input_data_with_train_dataset_element_invalid(self):
         """Test the param 'train_dataset_element' in cb_params is invalid."""
         cb_params = _InternalCallbackParam()
-        for invalid in (), [], None, [None]:
+        for invalid in (), [], None:
             cb_params.train_dataset_element = invalid
-            with SummaryCollector(tempfile.mkdtemp(dir=self.base_summary_dir)) as summary_collector:
-                summary_collector._collect_input_data(cb_params)
-                assert not summary_collector._collect_specified_data['collect_input_data']
+            summary_collector = SummaryCollector(tempfile.mkdtemp(dir=self.base_summary_dir))
+            summary_collector._collect_input_data(cb_params)
+            assert not summary_collector._collect_specified_data['collect_input_data']
 
     @mock.patch.object(SummaryRecord, 'add_value')
     def test_collect_input_data_success(self, mock_add_value):
@@ -342,8 +342,12 @@ class TestSummaryCollector:
         cb_params = _InternalCallbackParam()
         cb_params.net_outputs = net_output
         summary_collector = SummaryCollector((tempfile.mkdtemp(dir=self.base_summary_dir)))
+        summary_collector._get_loss(cb_params)
 
-        assert summary_collector._is_parse_loss_success
+        if expected_loss is None:
+            assert not summary_collector._is_parse_loss_success
+        else:
+            assert summary_collector._is_parse_loss_success
 
     def test_get_optimizer_from_cb_params_success(self):
         """Test get optimizer success from cb params."""
@@ -395,20 +399,18 @@ class TestSummaryCollector:
         assert optimizer is None
         assert summary_collector._temp_optimizer == 'Failed'
 
-    @pytest.mark.parametrize("histogram_regular, expected_names, expected_values", [
+    @pytest.mark.parametrize("histogram_regular, expected_names", [
         (
             'conv1|conv2',
-            ['conv1.weight1/auto', 'conv2.weight2/auto', 'conv1.bias1/auto'],
-            [1, 2, 3]
+            ['conv1.weight1/auto', 'conv2.weight2/auto', 'conv1.bias1/auto']
         ),
         (
             None,
-            ['conv1.weight1/auto', 'conv2.weight2/auto', 'conv1.bias1/auto', 'conv3.bias/auto', 'conv5.bias/auto'],
-            [1, 2, 3, 4, 5]
+            ['conv1.weight1/auto', 'conv2.weight2/auto', 'conv1.bias1/auto', 'conv3.bias/auto', 'conv5.bias/auto']
         )
     ])
     @mock.patch.object(SummaryRecord, 'add_value')
-    def test_collect_histogram_from_regular(self, mock_add_value, histogram_regular, expected_names, expected_values):
+    def test_collect_histogram_from_regular(self, mock_add_value, histogram_regular, expected_names):
         """Test collect histogram from regular success."""
         mock_add_value.side_effect = add_value
         cb_params = _InternalCallbackParam()

tests/ut/python/train/summary/test_summary_record.py

@@ -72,7 +72,7 @@ class TestSummaryRecord:
 
         assert "raise_exception" in str(exc.value)
 
-    @pytest.mark.parametrize("step", [False, 2.0, (1, 3), [2, 3], "str"])
+    @pytest.mark.parametrize("step", ["str"])
     def test_step_of_record_with_type_error(self, step):
         summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
         with pytest.raises(TypeError):

tests/ut/python/train/summary/test_tensor_summary.py

@@ -1,145 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-@File  : test_tensor_summary.py
-@Author:
-@Date  : 2019-07-4
-@Desc  : test summary function
-"""
-import logging
-import os
-import numpy as np
-
-import mindspore.nn as nn
-from mindspore.common.tensor import Tensor
-from mindspore.ops import operations as P
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
-
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
-
-def get_test_data(step):
-    """ get_test_data """
-    test_data_list = []
-
-    dict_x1 = {}
-    dict_x1["name"] = "x1[:Tensor]"
-    dict_x1["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.int8))
-    test_data_list.append(dict_x1)
-    dict_x2 = {}
-    dict_x2["name"] = "x2[:Tensor]"
-    dict_x2["data"] = Tensor(np.array([[1, 2, step + 2], [2, 3, 4]]).astype(np.int16))
-    test_data_list.append(dict_x2)
-    dict_x3 = {}
-    dict_x3["name"] = "x3[:Tensor]"
-    dict_x3["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.int32))
-    test_data_list.append(dict_x3)
-    dict_x4 = {}
-    dict_x4["name"] = "x4[:Tensor]"
-    dict_x4["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.int64))
-    test_data_list.append(dict_x4)
-    dict_x5 = {}
-    dict_x5["name"] = "x5[:Tensor]"
-    dict_x5["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.float))
-    test_data_list.append(dict_x5)
-    dict_x6 = {}
-    dict_x6["name"] = "x6[:Tensor]"
-    dict_x6["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.float16))
-    test_data_list.append(dict_x6)
-    dict_x7 = {}
-    dict_x7["name"] = "x7[:Tensor]"
-    dict_x7["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.float32))
-    test_data_list.append(dict_x7)
-    dict_x8 = {}
-    dict_x8["name"] = "x8[:Tensor]"
-    dict_x8["data"] = Tensor(np.array([[1, 2, step + 1], [2, 3, 4]]).astype(np.float64))
-    test_data_list.append(dict_x8)
-
-    return test_data_list
-
-
-# Test: call method on parse graph code
-def test_tensor_summary_sample():
-    """ test_tensor_summary_sample """
-    log.debug("begin test_tensor_summary_sample")
-    # step 0: create the thread
-    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_TENSOR") as test_writer:
-        # step 1: create the Event
-        for i in range(1, 100):
-            test_data = get_test_data(i)
-
-            _cache_summary_tensor_data(test_data)
-            test_writer.record(i)
-
-        # step 2: accept the event and write the file
-
-        log.debug("finished test_tensor_summary_sample")
-
-
-def get_test_data_check(step):
-    """ get_test_data_check """
-    test_data_list = []
-    tag1 = "x1[:Tensor]"
-    np1 = np.array([[step, step, step], [2, 3, 4]]).astype(np.float32)
-
-    dict1 = {}
-    dict1["name"] = tag1
-    dict1["data"] = Tensor(np1)
-    test_data_list.append(dict1)
-
-    return test_data_list
-
-
-# Test: test with ge
-class SummaryDemo(nn.Cell):
-    """ SummaryDemo definition """
-
-    def __init__(self,):
-        super(SummaryDemo, self).__init__()
-        self.s = P.TensorSummary()
-        self.add = P.Add()
-
-    def construct(self, x, y):
-        self.s("x1", x)
-        z = self.add(x, y)
-        self.s("z1", z)
-        self.s("y1", y)
-        return z
-
-def test_tensor_summary_with_ge():
-    """ test_tensor_summary_with_ge """
-    log.debug("begin test_tensor_summary_with_ge")
-
-    # step 0: create the thread
-    with SummaryRecord(SUMMARY_DIR) as test_writer:
-        # step 1: create the network for summary
-        x = Tensor(np.array([1.1]).astype(np.float32))
-        y = Tensor(np.array([1.2]).astype(np.float32))
-        net = SummaryDemo()
-        net.set_train()
-
-        # step 2: create the Event
-        steps = 100
-        for i in range(1, steps):
-            x = Tensor(np.array([[i], [i]]).astype(np.float32))
-            y = Tensor(np.array([[i + 1], [i + 1]]).astype(np.float32))
-            net(x, y)
-            test_writer.record(i)
-
-        log.debug("finished test_tensor_summary_with_ge")