support multi-dimentional vectors api (#874)

README.md

@@ -211,48 +211,6 @@ After launching the panel by one of the above methods, developers can see the vi
   <img src="https://user-images.githubusercontent.com/48054808/90868674-ba321f00-e3c9-11ea-83c1-f03c6dd19187.png" width="70%"/>
 </p>
 
-
-### 3. Read data in log files using LogReader
-
-VisualDL also provide `LogReader` interface to read raw data from log files.
-
-```python
-class LogReader(logdir=None,
-                file_name='')
-```
-
-#### interface parameters
-
-| parameters | type   | meaning                              |
-| ---------- | ------ | ------------------------------------ |
-| logdir     | string | Path to the log directory. Required. |
-| file_name  | string | File name of the log file. Required. |
-
-#### Example
-
-Suppose there is a log file named `vdlrecords.1605533348.log` in directory `./log`. We can get scalar data in `loss` tag by
-
-```python
-from visualdl import LogReader
-
-reader = LogReader(logdir='./log', file_name='vdlrecords.1605533348.log')
-data = reader.get_data('scalar', 'loss')
-print(data)
-```
-
-The result is a list of
-
-```python
-...
-id: 5
-tag: "Metrics/Training(Step): loss"
-timestamp: 1605533356039
-value: 3.1297709941864014
-...
-```
-
-For more information of `LogReader`, please refer to [LogReader](./docs/io/LogReader.md).
-
 ## Function Preview
 
 ### Scalar

demo/components/high_dimensional_test.py

@@ -29,3 +29,22 @@ if __name__ == '__main__':
         writer.add_embeddings(tag='default',
                               labels=labels,
                               hot_vectors=hot_vectors)
+
+    """
+    # You can code as follow if use multi-dimensional labels.
+    hot_vectors = [
+        [1.3561076367500755, 1.3116267195134017, 1.6785401875616097],
+        [1.1039614644440658, 1.8891609992484688, 1.32030488587171],
+        [1.9924524852447711, 1.9358920727142739, 1.2124401279391606],
+        [1.4129542689796446, 1.7372166387197474, 1.7317806077076527],
+        [1.3913371800587777, 1.4684674577930312, 1.5214136352476377]]
+
+    labels = [["label_a_1", "label_a_2", "label_a_3", "label_a_4", "label_a_5"],
+              ["label_b_1", "label_b_2", "label_b_3", "label_b_4", "label_b_5"]]
+    labels_meta = ["label_a", "label_b"]
+    with LogWriter(logdir="./log/high_dimensional_test/train") as writer:
+        writer.add_embeddings(tag='default',
+                              labels=labels,
+                              labels_meta=labels_meta,
+                              hot_vectors=hot_vectors)
+    """

visualdl/component/base_component.py

@@ -159,12 +159,12 @@ def image(tag, image_array, step, walltime=None, dataformats="HWC"):
     ])
 
 
-def embedding(tag, labels, hot_vectors, step, walltime=None):
+def embedding(tag, labels, hot_vectors, step, labels_meta=None, walltime=None):
     """Package data to one embedding.
 
     Args:
         tag (string): Data identifier
-        labels (numpy.array or list): A list of labels.
+        labels (list): A list of labels.
         hot_vectors (numpy.array or list): A matrix which each row is
             feature of labels.
         step (int): Step of embeddings.
@@ -175,9 +175,18 @@ def embedding(tag, labels, hot_vectors, step, walltime=None):
     """
     embeddings = Record.Embeddings()
 
-    for index in range(len(hot_vectors)):
-        embeddings.embeddings.append(
-            Record.Embedding(label=labels[index], vectors=hot_vectors[index]))
+    if labels_meta:
+        embeddings.label_meta.extend(labels_meta)
+
+    if isinstance(labels[0], list):
+        temp = []
+        for index in range(len(labels[0])):
+            temp.append([label[index] for label in labels])
+        labels = temp
+    for label, hot_vector in zip(labels, hot_vectors):
+        if not isinstance(label, list):
+            label = [label]
+        embeddings.embeddings.append(Record.Embedding(label=label, vectors=hot_vector))
 
     return Record(values=[
         Record.Value(

visualdl/proto/record.proto

@@ -16,12 +16,13 @@ message Record {
   }
 
   message Embedding {
-    string label = 1;
+    repeated string label = 1;
     repeated float vectors = 2;
   }
 
   message Embeddings {
     repeated Embedding embeddings = 1;
+    repeated string label_meta = 2;
   }
 
   message bytes_embeddings {

visualdl/proto/record_pb2.py

@@ -18,7 +18,7 @@ DESCRIPTOR = _descriptor.FileDescriptor(
   package='visualdl',
   syntax='proto3',
   serialized_options=None,
-  serialized_pb=b'\n\x0crecord.proto\x12\x08visualdl\"\xb4\x07\n\x06Record\x12&\n\x06values\x18\x01 \x03(\x0b\x32\x16.visualdl.Record.Value\x1a%\n\x05Image\x12\x1c\n\x14\x65ncoded_image_string\x18\x04 \x01(\x0c\x1a}\n\x05\x41udio\x12\x13\n\x0bsample_rate\x18\x01 \x01(\x02\x12\x14\n\x0cnum_channels\x18\x02 \x01(\x03\x12\x15\n\rlength_frames\x18\x03 \x01(\x03\x12\x1c\n\x14\x65ncoded_audio_string\x18\x04 \x01(\x0c\x12\x14\n\x0c\x63ontent_type\x18\x05 \x01(\t\x1a+\n\tEmbedding\x12\r\n\x05label\x18\x01 \x01(\t\x12\x0f\n\x07vectors\x18\x02 \x03(\x02\x1a<\n\nEmbeddings\x12.\n\nembeddings\x18\x01 \x03(\x0b\x32\x1a.visualdl.Record.Embedding\x1a\x43\n\x10\x62ytes_embeddings\x12\x16\n\x0e\x65ncoded_labels\x18\x01 \x01(\x0c\x12\x17\n\x0f\x65ncoded_vectors\x18\x02 \x01(\x0c\x1a\x34\n\tHistogram\x12\x10\n\x04hist\x18\x01 \x03(\x01\x42\x02\x10\x01\x12\x15\n\tbin_edges\x18\x02 \x03(\x01\x42\x02\x10\x01\x1al\n\x07PRCurve\x12\x0e\n\x02TP\x18\x01 \x03(\x03\x42\x02\x10\x01\x12\x0e\n\x02\x46P\x18\x02 \x03(\x03\x42\x02\x10\x01\x12\x0e\n\x02TN\x18\x03 \x03(\x03\x42\x02\x10\x01\x12\x0e\n\x02\x46N\x18\x04 \x03(\x03\x42\x02\x10\x01\x12\x11\n\tprecision\x18\x05 \x03(\x01\x12\x0e\n\x06recall\x18\x06 \x03(\x01\x1a \n\x08MetaData\x12\x14\n\x0c\x64isplay_name\x18\x01 \x01(\t\x1a\xe5\x02\n\x05Value\x12\n\n\x02id\x18\x01 \x01(\x03\x12\x0b\n\x03tag\x18\x02 \x01(\t\x12\x11\n\ttimestamp\x18\x03 \x01(\x03\x12\x0f\n\x05value\x18\x04 \x01(\x02H\x00\x12\'\n\x05image\x18\x05 \x01(\x0b\x32\x16.visualdl.Record.ImageH\x00\x12\'\n\x05\x61udio\x18\x06 \x01(\x0b\x32\x16.visualdl.Record.AudioH\x00\x12\x31\n\nembeddings\x18\x07 \x01(\x0b\x32\x1b.visualdl.Record.EmbeddingsH\x00\x12/\n\thistogram\x18\x08 \x01(\x0b\x32\x1a.visualdl.Record.HistogramH\x00\x12,\n\x08pr_curve\x18\t \x01(\x0b\x32\x18.visualdl.Record.PRCurveH\x00\x12.\n\tmeta_data\x18\n \x01(\x0b\x32\x19.visualdl.Record.MetaDataH\x00\x42\x0b\n\tone_valueb\x06proto3'
+  serialized_pb=b'\n\x0crecord.proto\x12\x08visualdl\"\xc8\x07\n\x06Record\x12&\n\x06values\x18\x01 \x03(\x0b\x32\x16.visualdl.Record.Value\x1a%\n\x05Image\x12\x1c\n\x14\x65ncoded_image_string\x18\x04 \x01(\x0c\x1a}\n\x05\x41udio\x12\x13\n\x0bsample_rate\x18\x01 \x01(\x02\x12\x14\n\x0cnum_channels\x18\x02 \x01(\x03\x12\x15\n\rlength_frames\x18\x03 \x01(\x03\x12\x1c\n\x14\x65ncoded_audio_string\x18\x04 \x01(\x0c\x12\x14\n\x0c\x63ontent_type\x18\x05 \x01(\t\x1a+\n\tEmbedding\x12\r\n\x05label\x18\x01 \x03(\t\x12\x0f\n\x07vectors\x18\x02 \x03(\x02\x1aP\n\nEmbeddings\x12.\n\nembeddings\x18\x01 \x03(\x0b\x32\x1a.visualdl.Record.Embedding\x12\x12\n\nlabel_meta\x18\x02 \x03(\t\x1a\x43\n\x10\x62ytes_embeddings\x12\x16\n\x0e\x65ncoded_labels\x18\x01 \x01(\x0c\x12\x17\n\x0f\x65ncoded_vectors\x18\x02 \x01(\x0c\x1a\x34\n\tHistogram\x12\x10\n\x04hist\x18\x01 \x03(\x01\x42\x02\x10\x01\x12\x15\n\tbin_edges\x18\x02 \x03(\x01\x42\x02\x10\x01\x1al\n\x07PRCurve\x12\x0e\n\x02TP\x18\x01 \x03(\x03\x42\x02\x10\x01\x12\x0e\n\x02\x46P\x18\x02 \x03(\x03\x42\x02\x10\x01\x12\x0e\n\x02TN\x18\x03 \x03(\x03\x42\x02\x10\x01\x12\x0e\n\x02\x46N\x18\x04 \x03(\x03\x42\x02\x10\x01\x12\x11\n\tprecision\x18\x05 \x03(\x01\x12\x0e\n\x06recall\x18\x06 \x03(\x01\x1a \n\x08MetaData\x12\x14\n\x0c\x64isplay_name\x18\x01 \x01(\t\x1a\xe5\x02\n\x05Value\x12\n\n\x02id\x18\x01 \x01(\x03\x12\x0b\n\x03tag\x18\x02 \x01(\t\x12\x11\n\ttimestamp\x18\x03 \x01(\x03\x12\x0f\n\x05value\x18\x04 \x01(\x02H\x00\x12\'\n\x05image\x18\x05 \x01(\x0b\x32\x16.visualdl.Record.ImageH\x00\x12\'\n\x05\x61udio\x18\x06 \x01(\x0b\x32\x16.visualdl.Record.AudioH\x00\x12\x31\n\nembeddings\x18\x07 \x01(\x0b\x32\x1b.visualdl.Record.EmbeddingsH\x00\x12/\n\thistogram\x18\x08 \x01(\x0b\x32\x1a.visualdl.Record.HistogramH\x00\x12,\n\x08pr_curve\x18\t \x01(\x0b\x32\x18.visualdl.Record.PRCurveH\x00\x12.\n\tmeta_data\x18\n \x01(\x0b\x32\x19.visualdl.Record.MetaDataH\x00\x42\x0b\n\tone_valueb\x06proto3'
 )
 
 
@@ -121,8 +121,8 @@ _RECORD_EMBEDDING = _descriptor.Descriptor(
   fields=[
     _descriptor.FieldDescriptor(
       name='label', full_name='visualdl.Record.Embedding.label', index=0,
-      number=1, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=b"".decode('utf-8'),
+      number=1, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
@@ -163,6 +163,13 @@ _RECORD_EMBEDDINGS = _descriptor.Descriptor(
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='label_meta', full_name='visualdl.Record.Embeddings.label_meta', index=1,
+      number=2, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
@@ -176,7 +183,7 @@ _RECORD_EMBEDDINGS = _descriptor.Descriptor(
   oneofs=[
   ],
   serialized_start=288,
-  serialized_end=348,
+  serialized_end=368,
 )
 
 _RECORD_BYTES_EMBEDDINGS = _descriptor.Descriptor(
@@ -212,8 +219,8 @@ _RECORD_BYTES_EMBEDDINGS = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=350,
-  serialized_end=417,
+  serialized_start=370,
+  serialized_end=437,
 )
 
 _RECORD_HISTOGRAM = _descriptor.Descriptor(
@@ -249,8 +256,8 @@ _RECORD_HISTOGRAM = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=419,
-  serialized_end=471,
+  serialized_start=439,
+  serialized_end=491,
 )
 
 _RECORD_PRCURVE = _descriptor.Descriptor(
@@ -314,8 +321,8 @@ _RECORD_PRCURVE = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=473,
-  serialized_end=581,
+  serialized_start=493,
+  serialized_end=601,
 )
 
 _RECORD_METADATA = _descriptor.Descriptor(
@@ -344,8 +351,8 @@ _RECORD_METADATA = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=583,
-  serialized_end=615,
+  serialized_start=603,
+  serialized_end=635,
 )
 
 _RECORD_VALUE = _descriptor.Descriptor(
@@ -440,8 +447,8 @@ _RECORD_VALUE = _descriptor.Descriptor(
       name='one_value', full_name='visualdl.Record.Value.one_value',
       index=0, containing_type=None, fields=[]),
   ],
-  serialized_start=618,
-  serialized_end=975,
+  serialized_start=638,
+  serialized_end=995,
 )
 
 _RECORD = _descriptor.Descriptor(
@@ -471,7 +478,7 @@ _RECORD = _descriptor.Descriptor(
   oneofs=[
   ],
   serialized_start=27,
-  serialized_end=975,
+  serialized_end=995,
 )
 
 _RECORD_IMAGE.containing_type = _RECORD

visualdl/server/lib.py

@@ -226,7 +226,11 @@ def get_embedding_labels(log_reader, name):
         run, decode_tag(tag))
     labels = []
     for item in records[0].embeddings.embeddings:
-        labels.append([item.label])
+        labels.append(item.label)
+
+    label_meta = records[0].embeddings.label_meta
+    if label_meta:
+        labels = [label_meta] + labels
 
     with io.StringIO() as fp:
         csv_writer = csv.writer(fp, delimiter='\t')

visualdl/writer/writer.py

@@ -188,17 +188,18 @@ class LogWriter(object):
             image(tag=tag, image_array=img, step=step, walltime=walltime,
                   dataformats=dataformats))
 
-    def add_embeddings(self, tag, labels, hot_vectors, walltime=None):
+    def add_embeddings(self, tag, labels, hot_vectors, labels_meta=None, walltime=None):
         """Add embeddings to vdl record file.
 
         Args:
             tag (string): Data identifier
-            labels (numpy.array or list): A list of labels.
+            labels (numpy.array or list): A 1D or 2D matrix of labels
             hot_vectors (numpy.array or list): A matrix which each row is
                 feature of labels.
+            labels_meta (numpy.array or list): Meta data of labels.
             walltime (int): Wall time of embeddings.
 
-        Example:
+        Example 1:
             hot_vectors = [
             [1.3561076367500755, 1.3116267195134017, 1.6785401875616097],
             [1.1039614644440658, 1.8891609992484688, 1.32030488587171],
@@ -207,9 +208,32 @@ class LogWriter(object):
             [1.3913371800587777, 1.4684674577930312, 1.5214136352476377]]
 
             labels = ["label_1", "label_2", "label_3", "label_4", "label_5"]
+            # or like this
+            # labels = [["label_1", "label_2", "label_3", "label_4", "label_5"]]
+
+            writer.add_embeddings(tag='default',
+                                  labels=labels,
+                                  vectors=hot_vectors,
+                                  walltime=round(time.time() * 1000))
 
-            writer.add_embedding(labels=labels, vectors=hot_vectors,
-                                 walltime=round(time.time() * 1000))
+        Example 2:
+            hot_vectors = [
+            [1.3561076367500755, 1.3116267195134017, 1.6785401875616097],
+            [1.1039614644440658, 1.8891609992484688, 1.32030488587171],
+            [1.9924524852447711, 1.9358920727142739, 1.2124401279391606],
+            [1.4129542689796446, 1.7372166387197474, 1.7317806077076527],
+            [1.3913371800587777, 1.4684674577930312, 1.5214136352476377]]
+
+            labels = [["label_a_1", "label_a_2", "label_a_3", "label_a_4", "label_a_5"],
+                      ["label_b_1", "label_b_2", "label_b_3", "label_b_4", "label_b_5"]]
+
+            labels_meta = ["label_a", "label_2"]
+
+            writer.add_embeddings(tag='default',
+                                  labels=labels,
+                                  labels_meta=labels_meta,
+                                  vectors=hot_vectors,
+                                  walltime=round(time.time() * 1000))
         """
         if '%' in tag:
             raise RuntimeError("% can't appear in tag!")
@@ -217,12 +241,17 @@ class LogWriter(object):
             hot_vectors = hot_vectors.tolist()
         if isinstance(labels, np.ndarray):
             labels = labels.tolist()
+
+        if isinstance(labels[0], list) and not labels_meta:
+            labels_meta = ["label_%d" % i for i in range(len(labels))]
+
         step = 0
         walltime = round(time.time() * 1000) if walltime is None else walltime
         self._get_file_writer().add_record(
             embedding(
                 tag=tag,
                 labels=labels,
+                labels_meta=labels_meta,
                 hot_vectors=hot_vectors,
                 step=step,
                 walltime=walltime))