!9495 fix summary st, and support test dataset sink mode

From: @ouwenchang
Reviewed-by: 
Signed-off-by:

tests/st/summary/test_summary.py

@@ -18,88 +18,86 @@ import re
 import tempfile
 import shutil
 
+from collections import Counter
+
 import pytest
 
 from mindspore import dataset as ds
 from mindspore import nn, Tensor, context
-from mindspore.nn.metrics import Accuracy
+from mindspore.nn.metrics import Loss
 from mindspore.nn.optim import Momentum
 from mindspore.dataset.transforms import c_transforms as C
 from mindspore.dataset.vision import c_transforms as CV
 from mindspore.dataset.vision import Inter
 from mindspore.common import dtype as mstype
-from mindspore.common.initializer import TruncatedNormal
 from mindspore.ops import operations as P
+from mindspore.common.initializer import Normal
 from mindspore.train import Model
 from mindspore.train.callback import SummaryCollector
 
 from tests.summary_utils import SummaryReader
 
 
-def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-    """weight initial for conv layer"""
-    weight = weight_variable()
-    return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=kernel_size, stride=stride, padding=padding,
-                     weight_init=weight, has_bias=False, pad_mode="valid")
-
-
-def fc_with_initialize(input_channels, out_channels):
-    """weight initial for fc layer"""
-    weight = weight_variable()
-    bias = weight_variable()
-    return nn.Dense(input_channels, out_channels, weight, bias)
-
+class LeNet5(nn.Cell):
+    """
+    Lenet network
 
-def weight_variable():
-    """weight initial"""
-    return TruncatedNormal(0.02)
+    Args:
+        num_class (int): Number of classes. Default: 10.
+        num_channel (int): Number of channels. Default: 1.
 
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet(num_class=10)
 
-class LeNet5(nn.Cell):
-    """Define LeNet5 network."""
-    def __init__(self, num_class=10, channel=1):
+    """
+    def __init__(self, num_class=10, num_channel=1, include_top=True):
         super(LeNet5, self).__init__()
-        self.num_class = num_class
-        self.conv1 = conv(channel, 6, 5)
-        self.conv2 = conv(6, 16, 5)
-        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-        self.fc2 = fc_with_initialize(120, 84)
-        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.flatten = nn.Flatten()
+        self.include_top = include_top
+        if self.include_top:
+            self.flatten = nn.Flatten()
+            self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
+            self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
+            self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
+
         self.scalar_summary = P.ScalarSummary()
         self.image_summary = P.ImageSummary()
         self.histogram_summary = P.HistogramSummary()
         self.tensor_summary = P.TensorSummary()
-        self.channel = Tensor(channel)
-
-    def construct(self, data):
-        """define construct."""
-        self.image_summary('image', data)
-        output = self.conv1(data)
-        self.histogram_summary('histogram', output)
-        output = self.relu(output)
-        self.tensor_summary('tensor', output)
-        output = self.max_pool2d(output)
-        output = self.conv2(output)
-        output = self.relu(output)
-        output = self.max_pool2d(output)
-        output = self.flatten(output)
-        output = self.fc1(output)
-        output = self.relu(output)
-        output = self.fc2(output)
-        output = self.relu(output)
-        output = self.fc3(output)
+        self.channel = Tensor(num_channel)
+
+    def construct(self, x):
+        self.image_summary('image', x)
+        x = self.conv1(x)
+        self.histogram_summary('histogram', x)
+        x = self.relu(x)
+        self.tensor_summary('tensor', x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
         self.scalar_summary('scalar', self.channel)
-        return output
-
-
-def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        if not self.include_top:
+            return x
+        x = self.flatten(x)
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+def create_dataset(data_path, num_samples=2):
     """create dataset for train or test"""
+    num_parallel_workers = 1
+
     # define dataset
-    mnist_ds = ds.MnistDataset(data_path)
+    mnist_ds = ds.MnistDataset(data_path, num_samples=num_samples)
 
     resize_height, resize_width = 32, 32
     rescale = 1.0 / 255.0
@@ -122,8 +120,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers
 
     # apply DatasetOps
     mnist_ds = mnist_ds.shuffle(buffer_size=10000)  # 10000 as in LeNet train script
-    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
-    mnist_ds = mnist_ds.repeat(repeat_size)
+    mnist_ds = mnist_ds.batch(batch_size=2, drop_remainder=True)
 
     return mnist_ds
 
@@ -136,6 +133,8 @@ class TestSummary:
     @classmethod
     def setup_class(cls):
         """Run before test this class."""
+        device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
+        context.set_context(mode=context.GRAPH_MODE, device_id=device_id)
         cls.base_summary_dir = tempfile.mkdtemp(suffix='summary')
 
     @classmethod
@@ -144,51 +143,77 @@ class TestSummary:
         if os.path.exists(cls.base_summary_dir):
             shutil.rmtree(cls.base_summary_dir)
 
-    @pytest.mark.level0
-    @pytest.mark.platform_x86_ascend_training
-    @pytest.mark.env_onecard
-    def test_summary_ascend(self):
-        """Test summary ascend."""
-        context.set_context(mode=context.GRAPH_MODE)
-        self._run_network()
-
-    def _run_network(self, dataset_sink_mode=True):
+    def _run_network(self, dataset_sink_mode=False, num_samples=2):
         lenet = LeNet5()
         loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
         optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9)
-        model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()})
+        model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Loss()})
         summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
-        summary_collector = SummaryCollector(summary_dir=summary_dir, collect_freq=1)
+        summary_collector = SummaryCollector(summary_dir=summary_dir, collect_freq=2)
 
-        ds_train = create_dataset(os.path.join(self.mnist_path, "train"))
+        ds_train = create_dataset(os.path.join(self.mnist_path, "train"), num_samples=num_samples)
         model.train(1, ds_train, callbacks=[summary_collector], dataset_sink_mode=dataset_sink_mode)
 
         ds_eval = create_dataset(os.path.join(self.mnist_path, "test"))
         model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode, callbacks=[summary_collector])
+        return summary_dir
+
+    @pytest.mark.level0
+    @pytest.mark.platform_x86_ascend_training
+    @pytest.mark.platform_arm_ascend_training
+    @pytest.mark.env_onecard
+    def test_summary_with_sink_mode_false(self):
+        """Test summary with sink mode false, and num samples is 64."""
+        summary_dir = self._run_network(num_samples=10)
+
+        tag_list = self._list_summary_tags(summary_dir)
 
-        self._check_summary_result(summary_dir)
+        expected_tag_set = {'conv1.weight/auto', 'conv2.weight/auto', 'fc1.weight/auto', 'fc1.bias/auto',
+                            'fc2.weight/auto', 'input_data/auto', 'loss/auto',
+                            'histogram', 'image', 'scalar', 'tensor'}
+        assert set(expected_tag_set) == set(tag_list)
+
+        # num samples is 10, batch size is 2, so step is 5, collect freq is 2,
+        # SummaryCollector will collect the first step and 2th, 4th step
+        tag_count = 3
+        for value in Counter(tag_list).values():
+            assert value == tag_count
+
+    @pytest.mark.level0
+    @pytest.mark.platform_x86_ascend_training
+    @pytest.mark.platform_arm_ascend_training
+    @pytest.mark.env_onecard
+    def test_summary_with_sink_mode_true(self):
+        """Test summary with sink mode true, and num samples is 64."""
+        summary_dir = self._run_network(dataset_sink_mode=True, num_samples=10)
+
+        tag_list = self._list_summary_tags(summary_dir)
+
+        # There will not record input data when dataset sink mode is True
+        expected_tags = {'conv1.weight/auto', 'conv2.weight/auto', 'fc1.weight/auto', 'fc1.bias/auto',
+                         'fc2.weight/auto', 'loss/auto', 'histogram', 'image', 'scalar', 'tensor'}
+        assert set(expected_tags) == set(tag_list)
+
+        tag_count = 1
+        for value in Counter(tag_list).values():
+            assert value == tag_count
 
     @staticmethod
-    def _check_summary_result(summary_dir):
+    def _list_summary_tags(summary_dir):
         summary_file_path = ''
         for file in os.listdir(summary_dir):
             if re.search("_MS", file):
                 summary_file_path = os.path.join(summary_dir, file)
                 break
+        assert summary_file_path
 
-        assert not summary_file_path
-
+        tags = list()
         with SummaryReader(summary_file_path) as summary_reader:
-            tags = set()
-
-            # Read the event that record by SummaryCollector.begin
-            summary_reader.read_event()
-
-            summary_event = summary_reader.read_event()
-            for value in summary_event.summary.value:
-                tags.add(value.tag)
 
-            # There will not record input data when dataset sink mode is True
-            expected_tags = ['conv1.weight/auto', 'conv2.weight/auto', 'fc1.weight/auto', 'fc1.bias/auto',
-                             'fc2.weight/auto', 'histogram', 'image', 'scalar', 'tensor']
-            assert set(expected_tags) == tags
+            while True:
+                summary_event = summary_reader.read_event()
+                if not summary_event:
+                    break
+                for value in summary_event.summary.value:
+                    tags.append(value.tag)
+        return tags

tests/summary_utils.py

@@ -54,6 +54,8 @@ class SummaryReader:
         """Read next event."""
         file_handler = self._file_handler
         header = file_handler.read(_HEADER_SIZE)
+        if not header:
+            return None
         data_len = struct.unpack('Q', header)[0]
         # Ignore crc check.
         file_handler.read(_HEADER_CRC_SIZE)