fix summary_graph bug (#4712)

* fix summary_graph bug

* modify test_summary

Co-authored-by: Shenghang Tsai <jackalcooper@gmail.com>

oneflow/python/summary/summary_graph.py

@@ -55,7 +55,7 @@ class Graph(object):
             raise OSError("You must create only one structure graph log file!")
 
         self.structure_graph_filename_ = self.logdir_ + "/structure_graph.json"
-        struct_graph_str = oneflow._oneflow_internal.GetStructureGraph()
+        struct_graph_str = oneflow._oneflow_internal.GetSerializedStructureGraph()
         with open(self.structure_graph_filename_, "w", encoding="utf-8") as f:
             f.write(str(struct_graph_str))
             f.flush()

oneflow/python/test/ops/test_summary.py

@@ -19,158 +19,171 @@ import oneflow as flow
 from collections import OrderedDict
 import cv2
 import time
+import tempfile
+import unittest
 
 from test_util import GenArgList
 
 
-def _read_images_by_cv(image_files):
-    images = [
-        cv2.cvtColor(cv2.imread(image_file), cv2.COLOR_BGR2RGB).astype(np.uint8)
-        for image_file in image_files
-    ]
-    return [cv2.resize(image, (512, 512)) for image in images]
-
-
 def summary_demo():
     func_config = flow.FunctionConfig()
     func_config.default_data_type(flow.float)
     func_config.default_logical_view(flow.scope.mirrored_view())
-    logdir = "/oneflow/log"
-
-    @flow.global_function(function_config=func_config)
-    def CreateWriter():
-        flow.summary.create_summary_writer(logdir)
-
-    @flow.global_function(function_config=func_config)
-    def ScalarJob(
-        value: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.float),
-        step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
-        tag: flow.typing.ListNumpy.Placeholder((1000,), dtype=flow.int8),
-    ):
-        flow.summary.scalar(value, step, tag)
-
-    @flow.global_function(function_config=func_config)
-    def HistogramJob(
-        value: flow.typing.ListNumpy.Placeholder((200, 200, 200), dtype=flow.float),
-        step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
-        tag: flow.typing.ListNumpy.Placeholder((9,), dtype=flow.int8),
-    ):
-        flow.summary.histogram(value, step, tag)
-
-    @flow.global_function(function_config=func_config)
-    def PbJob(
-        value: flow.typing.ListNumpy.Placeholder((1500,), dtype=flow.int8),
-        step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
-    ):
-        flow.summary.pb(value, step=step)
-
-    @flow.global_function(function_config=func_config)
-    def ImageJob(
-        value: flow.typing.ListNumpy.Placeholder(
-            shape=(100, 2000, 2000, 4), dtype=flow.uint8
-        ),
-        step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
-        tag: flow.typing.ListNumpy.Placeholder((10,), dtype=flow.int8),
-    ):
-        flow.summary.image(value, step=step, tag=tag)
-
-    @flow.global_function(function_config=func_config)
-    def FlushJob():
-        flow.summary.flush_summary_writer()
-
-    CreateWriter()
-    projecotr = flow.summary.Projector(logdir)
-    projecotr.create_embedding_projector()
-    projecotr.create_exception_projector()
-
-    hparams = {
-        flow.summary.HParam("learning_rate", flow.summary.RealRange(1e-2, 1e-1)): 0.02,
-        flow.summary.HParam("dense_layers", flow.summary.IntegerRange(2, 7)): 5,
-        flow.summary.HParam(
-            "optimizer", flow.summary.ValueSet(["adam", "sgd"])
-        ): "adam",
-        flow.summary.HParam("accuracy", flow.summary.RealRange(1e-2, 1e-1)): 0.001,
-        flow.summary.HParam("magic", flow.summary.ValueSet([False, True])): True,
-        flow.summary.Metric("loss", float): 0.02,
-        "dropout": 0.6,
-    }
-
-    for i in range(200):
-        t = ["vgg16", "resnet50", "mask-rcnn", "yolov3"]
-        pb = flow.summary.text(t)
-        value = np.fromstring(str(pb), dtype=np.int8)
-        step = np.array([i], dtype=np.int64)
-        PbJob([value], [step])
-
-        pb2 = flow.summary.hparams(hparams)
-        value = np.fromstring(str(pb2), dtype=np.int8)
-        step = np.array([i], dtype=np.int64)
-        PbJob([value], [step])
-
-    for idx in range(10):
-        value = np.array([idx], dtype=np.float32)
-        step = np.array([idx], dtype=np.int64)
-        tag = np.fromstring("scalar", dtype=np.int8)
-        ScalarJob([value], [step], [tag])
-
-    value = np.array(
-        [
-            [[1, 2, 3, 0], [0, 2, 3, 1], [2, 3, 4, 1]],
-            [[1, 0, 2, 0], [2, 1, 2, 0], [2, 1, 1, 1]],
-        ],
-        dtype=np.float64,
-    )
-
-    for idx in range(1):
-        value = np.random.rand(100, 100, 100).astype(np.float32)
-        step = np.array([idx], dtype=np.int64)
-        tag = np.fromstring("histogram", dtype=np.int8)
-        HistogramJob([value], [step], [tag])
-
-    value_ = np.random.rand(10, 10, 10).astype(np.float32)
-    label = (np.random.rand(10) * 10).astype(np.int64)
-    x = (np.random.rand(10, 10, 10) * 255).astype(np.uint8)
-    sample_name = "sample"
-    sample_type = "image"
-    step = 1
-    tag_exception = "exception_projector"
-    tag_embedding = "embedding_projector"
-    projecotr.exception_projector(
-        value=value,
-        tag=tag_exception,
-        step=step,
-        sample_name=sample_name,
-        sample_type=sample_type,
-        x=x,
-    )
-    projecotr.embedding_projector(
-        value=value,
-        label=label,
-        tag=tag_embedding,
-        step=step,
-        sample_name=sample_name,
-        sample_type=sample_type,
-        x=x,
-    )
-
-    image1_path = "~/oneflow/image1"
-    image2_path = "~/oneflow/image2"
-
-    image_files = [
-        image1_path,
-        image2_path,
-    ]
-
-    images = _read_images_by_cv(image_files)
-    images = np.array(images, dtype=np.uint8)
-    imageRed = np.ones([512, 512, 3]).astype(np.uint8)
-    Red = np.array([0, 255, 255], dtype=np.uint8)
-    imageNew = np.multiply(imageRed, Red)
-    imageNew = np.expand_dims(imageNew, axis=0)
-    images = np.concatenate((images, imageNew), axis=0)
-    step = np.array([1], dtype=np.int64)
-    tag = np.fromstring("image", dtype=np.int8)
-    ImageJob([images], [step], [tag])
-
-    graph = flow.summary.Graph(logdir)
-    graph.write_structure_graph()
+    with tempfile.TemporaryDirectory() as logdir:
+
+        @flow.global_function(function_config=func_config)
+        def CreateWriter():
+            flow.summary.create_summary_writer(logdir)
+
+        @flow.global_function(function_config=func_config)
+        def ScalarJob(
+            value: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.float),
+            step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
+            tag: flow.typing.ListNumpy.Placeholder((1000,), dtype=flow.int8),
+        ):
+            flow.summary.scalar(value, step, tag)
+
+        @flow.global_function(function_config=func_config)
+        def HistogramJob(
+            value: flow.typing.ListNumpy.Placeholder((200, 200, 200), dtype=flow.float),
+            step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
+            tag: flow.typing.ListNumpy.Placeholder((9,), dtype=flow.int8),
+        ):
+            flow.summary.histogram(value, step, tag)
+
+        @flow.global_function(function_config=func_config)
+        def PbJob(
+            value: flow.typing.ListNumpy.Placeholder((1500,), dtype=flow.int8),
+            step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
+        ):
+            flow.summary.pb(value, step=step)
+
+        @flow.global_function(function_config=func_config)
+        def ImageJob(
+            value: flow.typing.ListNumpy.Placeholder(
+                shape=(100, 2000, 2000, 4), dtype=flow.uint8
+            ),
+            step: flow.typing.ListNumpy.Placeholder((1,), dtype=flow.int64),
+            tag: flow.typing.ListNumpy.Placeholder((10,), dtype=flow.int8),
+        ):
+            flow.summary.image(value, step=step, tag=tag)
+
+        @flow.global_function(function_config=func_config)
+        def FlushJob():
+            flow.summary.flush_summary_writer()
+
+        CreateWriter()
+        projecotr = flow.summary.Projector(logdir)
+        projecotr.create_embedding_projector()
+        projecotr.create_exception_projector()
+
+        hparams = {
+            flow.summary.HParam(
+                "learning_rate", flow.summary.RealRange(1e-2, 1e-1)
+            ): 0.02,
+            flow.summary.HParam("dense_layers", flow.summary.IntegerRange(2, 7)): 5,
+            flow.summary.HParam(
+                "optimizer", flow.summary.ValueSet(["adam", "sgd"])
+            ): "adam",
+            flow.summary.HParam("accuracy", flow.summary.RealRange(1e-2, 1e-1)): 0.001,
+            flow.summary.HParam("magic", flow.summary.ValueSet([False, True])): True,
+            flow.summary.Metric("loss", float): 0.02,
+            "dropout": 0.6,
+        }
+
+        for i in range(200):
+            t = ["vgg16", "resnet50", "mask-rcnn", "yolov3"]
+            pb = flow.summary.text(t)
+            value = np.fromstring(str(pb), dtype=np.int8)
+            step = np.array([i], dtype=np.int64)
+            PbJob([value], [step])
+
+            pb2 = flow.summary.hparams(hparams)
+            value = np.fromstring(str(pb2), dtype=np.int8)
+            step = np.array([i], dtype=np.int64)
+            PbJob([value], [step])
+
+        for idx in range(100):
+            value = np.array([idx], dtype=np.float32)
+            step = np.array([idx], dtype=np.int64)
+            tag = np.fromstring("scalar", dtype=np.int8)
+            ScalarJob([value], [step], [tag])
+
+        value = np.array(
+            [
+                [[1, 2, 3, 0], [0, 2, 3, 1], [2, 3, 4, 1]],
+                [[1, 0, 2, 0], [2, 1, 2, 0], [2, 1, 1, 1]],
+            ],
+            dtype=np.float64,
+        )
+
+        for idx in range(20):
+            value = np.random.rand(100, 100, 100).astype(np.float32)
+            step = np.array([idx], dtype=np.int64)
+            tag = np.fromstring("histogram", dtype=np.int8)
+            HistogramJob([value], [step], [tag])
+
+        value_ = np.random.rand(10, 10, 10).astype(np.float32)
+        label = (np.random.rand(10) * 10).astype(np.int64)
+        x = (np.random.rand(10, 10, 10) * 255).astype(np.uint8)
+        sample_name = "sample"
+        sample_type = "image"
+        step = 1
+        tag_exception = "exception_projector"
+        tag_embedding = "embedding_projector"
+        for i in range(20):
+            projecotr.exception_projector(
+                value=value,
+                tag=tag_exception,
+                step=step,
+                sample_name=sample_name,
+                sample_type=sample_type,
+                x=x,
+            )
+            projecotr.embedding_projector(
+                value=value,
+                label=label,
+                tag=tag_embedding,
+                step=step,
+                sample_name=sample_name,
+                sample_type=sample_type,
+                x=x,
+            )
+
+        images = [
+            cv2.cvtColor(np.ones([512, 512], np.uint8), cv2.COLOR_BGR2RGB).astype(
+                np.uint8
+            ),
+            cv2.cvtColor(np.ones([512, 512], np.uint8), cv2.COLOR_BGR2RGB).astype(
+                np.uint8
+            ),
+        ]
+
+        images = np.array(images, dtype=np.uint8)
+        imageRed = np.ones([512, 512, 3]).astype(np.uint8)
+        Red = np.array([0, 255, 255], dtype=np.uint8)
+        imageNew = np.multiply(imageRed, Red)
+        imageNew = np.expand_dims(imageNew, axis=0)
+        images = np.concatenate((images, imageNew), axis=0)
+        step = np.array([1], dtype=np.int64)
+        tag = np.fromstring("image", dtype=np.int8)
+        for i in range(20):
+            ImageJob([images], [step], [tag])
+
+        graph = flow.summary.Graph(logdir)
+        graph.write_structure_graph()
+
+        time.sleep(1)
+        FlushJob()
+        time.sleep(1)
+
+
+@flow.unittest.skip_unless_1n1d()
+class TestSummary(flow.unittest.TestCase):
+    @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
+    def test_summary(test_case):
+        summary_demo()
+
+
+if __name__ == "__main__":
+    unittest.main()