closes #24

demo.py

@@ -42,4 +42,5 @@
 label = dataset.test_labels[:100]
 features = images.view(100, 784)
 writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))        
+writer.add_embedding(features, global_step=1, tag='noMetadata')        
 writer.close()

demo_embedding.py

@@ -49,7 +49,7 @@ def get_data(value, shape):
 #settings for train and log
 num_epochs = 20
 embedding_log = 5
-writer = SummaryWriter()
+writer = SummaryWriter(comment='mnist_embedding_training')
 
 #TRAIN
 for epoch in range(num_epochs):

tensorboardX/embedding.py

@@ -28,11 +28,11 @@ def make_sprite(label_img, save_path):
     else:
         torchvision.utils.save_image(label_img, os.path.join(save_path, 'sprite.png'), nrow=nrow, padding=0)
 
-def append_pbtxt(metadata, label_img, save_path, global_step):
+def append_pbtxt(metadata, label_img, save_path, global_step, tag):
     with open(os.path.join(save_path, 'projector_config.pbtxt'), 'a') as f:
         #step = os.path.split(save_path)[-1]
         f.write('embeddings {\n')
-        f.write('tensor_name: "embedding:{}"\n'.format(global_step))
+        f.write('tensor_name: "{}:{}"\n'.format(tag, global_step))
         f.write('tensor_path: "{}"\n'.format(os.path.join(global_step,"tensors.tsv")))
         if metadata is not None:
             f.write('metadata_path: "{}"\n'.format(os.path.join(global_step,"metadata.tsv")))

tensorboardX/writer.py

@@ -376,14 +376,15 @@ def add_graph(self, model, lastVar):
             return
         self.file_writer.add_graph(graph(model, lastVar))
 
-    def add_embedding(self, mat, metadata=None, label_img=None, global_step=None):
+    def add_embedding(self, mat, metadata=None, label_img=None, global_step=None, tag='default'):
         """Add embedding projector data to summary.
 
         Args:
             mat (torch.Tensor): A matrix which each row is the feature vector of the data point
             metadata (list): A list of labels, each element will be convert to string
             label_img (torch.Tensor): Images correspond to each data point
             global_step (int): Global step value to record
+            tag (string): Name for the embedding
         Shape:
             mat: :math:`(N, D)`, where N is number of data and D is feature dimension
 
@@ -426,7 +427,7 @@ def add_embedding(self, mat, metadata=None, label_img=None, global_step=None):
         assert mat.dim() == 2, 'mat should be 2D, where mat.size(0) is the number of data points'
         make_mat(mat.tolist(), save_path)
         #new funcion to append to the config file a new embedding
-        append_pbtxt(metadata, label_img, self.file_writer.get_logdir(), str(global_step).zfill(5))
+        append_pbtxt(metadata, label_img, self.file_writer.get_logdir(), str(global_step).zfill(5), tag)
 
     def close(self):
         self.file_writer.flush()