Add dtype visualization to plot_network

python/mxnet/visualization.py

@@ -208,7 +208,7 @@ def print_layer_summary(node, out_shape):
     print('Total params: %s' % total_params)
     print('_' * line_length)
 
-def plot_network(symbol, title="plot", save_format='pdf', shape=None, node_attrs={},
+def plot_network(symbol, title="plot", save_format='pdf', shape=None, dtype=None, node_attrs={},
                  hide_weights=True):
     """Creates a visualization (Graphviz digraph object) of the given computation graph.
     Graphviz must be installed for this function to work.
@@ -224,6 +224,10 @@ def plot_network(symbol, title="plot", save_format='pdf', shape=None, node_attrs
         Specifies the shape of the input tensors. If specified, the visualization will include
         the shape of the tensors between the nodes. `shape` is a dictionary mapping
         input symbol names (str) to the corresponding tensor shape (tuple).
+    dtype: dict, optional
+        Specifies the type of the input tensors. If specified, the visualization will include
+        the type of the tensors between the nodes. `dtype` is a dictionary mapping
+        input symbol names (str) to the corresponding tensor type (e.g. `numpy.float32`).
     node_attrs: dict, optional
         Specifies the attributes for nodes in the generated visualization. `node_attrs` is
         a dictionary of Graphviz attribute names and values. For example::
@@ -274,11 +278,19 @@ def plot_network(symbol, title="plot", save_format='pdf', shape=None, node_attrs
     draw_shape = False
     if shape is not None:
         draw_shape = True
-        interals = symbol.get_internals()
-        _, out_shapes, _ = interals.infer_shape(**shape)
+        internals = symbol.get_internals()
+        _, out_shapes, _ = internals.infer_shape(**shape)
         if out_shapes is None:
             raise ValueError("Input shape is incomplete")
-        shape_dict = dict(zip(interals.list_outputs(), out_shapes))
+        shape_dict = dict(zip(internals.list_outputs(), out_shapes))
+    draw_type = False
+    if dtype is not None:
+        draw_type = True
+        internals = symbol.get_internals()
+        _, out_types, _ = internals.infer_type(**dtype)
+        if out_types is None:
+            raise ValueError("Input type is incomplete")
+        type_dict = dict(zip(internals.list_outputs(), out_types))
     conf = json.loads(symbol.tojson())
     nodes = conf["nodes"]
     # check if multiple nodes have the same name
@@ -370,7 +382,7 @@ def looks_like_weight(name):
                 input_node = nodes[item[0]]
                 input_name = input_node["name"]
                 if input_name not in hidden_nodes:
-                    attr = {"dir": "back", 'arrowtail':'open'}
+                    attr = {"dir": "back", 'arrowtail':'open', 'label': ''}
                     # add shapes
                     if draw_shape:
                         if input_node["op"] != "null":
@@ -387,6 +399,19 @@ def looks_like_weight(name):
                             shape = shape_dict[key][1:]
                             label = "x".join([str(x) for x in shape])
                             attr["label"] = label
+                    if draw_type:
+                        if input_node["op"] != "null":
+                            key = input_name + "_output"
+                            if "attrs" in input_node:
+                                params = input_node["attrs"]
+                                if "num_outputs" in params:
+                                    key += str(int(params["num_outputs"]) - 1)
+                            dtype = type_dict[key]
+                            attr["label"] += '(' + dtype.__name__ + ')'
+                        else:
+                            key = input_name
+                            dtype = type_dict[key]
+                            attr["label"] += '(' + dtype.__name__ + ')'
                     dot.edge(tail_name=name, head_name=input_name, **attr)
 
     return dot

tests/python/unittest/test_viz.py

@@ -19,6 +19,7 @@
 import warnings
 
 import mxnet as mx
+import numpy as np
 
 
 def test_print_summary():
@@ -55,6 +56,7 @@ def test_plot_network():
     net = mx.sym.SoftmaxOutput(data=net, name='out')
     with warnings.catch_warnings(record=True) as w:
         digraph = mx.viz.plot_network(net, shape={'data': (100, 200)},
+                                      dtype={'data': np.float32},
                                       node_attrs={"fixedsize": "false"})
     assert len(w) == 1
     assert "There are multiple variables with the same name in your graph" in str(w[-1].message)