save arch.json when save-nnp option is specified.

nnabla_nas/contrib/classification/darts/helper.py

@@ -15,7 +15,6 @@
 import json
 import os
 
-from graphviz import Digraph
 import imageio
 from nnabla.logger import logger
 import numpy as np
@@ -26,6 +25,7 @@
 
 
 def plot(choice, prob, filename):
+    from graphviz import Digraph
     g = Digraph(format='png',
                 edge_attr=dict(fontsize='14', fontname="times"),
                 node_attr=dict(style='filled', shape='rect', align='center',
@@ -109,4 +109,13 @@ def save_dart_arch(model, output_path):
     arch_file = os.path.join(output_path, 'arch.json')
     logger.info('Saving arch to {}'.format(arch_file))
     write_to_json_file(memo, arch_file)
+
+
+def visualize_dart_arch(output_path):
+    r"""Saves visualized DARTS architecture.
+
+    Args:
+        output_path (str): Where to save the architecture.
+    """
+    arch_file = os.path.join(output_path, 'arch.json')
     visualize(arch_file, output_path)

nnabla_nas/contrib/classification/darts/network.py

@@ -15,7 +15,6 @@
 from collections import Counter
 from collections import OrderedDict
 import json
-import os
 
 import nnabla.functions as F
 from nnabla.initializer import ConstantInitializer
@@ -25,7 +24,7 @@
 from .... import module as Mo
 from ..base import ClassificationModel as Model
 from ..misc import AuxiliaryHeadCIFAR
-from .helper import save_dart_arch
+from .helper import save_dart_arch, visualize_dart_arch
 
 
 class SearchNet(Model):
@@ -176,8 +175,21 @@ def save_parameters(self, path=None, params=None, grad_only=False):
         super().save_parameters(path, params=params, grad_only=grad_only)
         if self._shared:
             # save the architectures
-            output_path = os.path.dirname(path)
-            save_dart_arch(self, output_path)
+            save_dart_arch(self, path)
+
+    def save_net_nnp(self, path, inp, out, calc_latency=False,
+                     func_real_latency=None, func_accum_latency=None):
+        super().save_net_nnp(path, inp, out, calc_latency=False,
+                             func_real_latency=func_real_latency,
+                             func_accum_latency=func_accum_latency)
+        if self._shared:
+            # save the architectures
+            save_dart_arch(self, path)
+
+    def visualize(self, path):
+        if self._shared:
+            # save the architectures
+            visualize_dart_arch(path)
 
     def loss(self, outputs, targets, loss_weights=None):
         loss = F.mean(F.softmax_cross_entropy(outputs[0], targets[0]))

nnabla_nas/contrib/classification/fairnas/network.py

@@ -8,7 +8,7 @@
 from ..base import ClassificationModel as Model
 from .modules import ChoiceBlock
 from ..mobilenet.modules import ConvBNReLU, CANDIDATES
-from ..mobilenet.helper import plot_mobilenet
+from ..mobilenet.helper import visualize_mobilenet_arch
 from ..mobilenet.network import _make_divisible, label_smoothing_loss
 
 
@@ -146,12 +146,10 @@ def extra_repr(self):
                 f'candidates={self._candidates}, '
                 f'skip_connect={self._skip_connect}')
 
-    def save_parameters(self, path=None, params=None, grad_only=False):
-        super().save_parameters(path, params=params, grad_only=grad_only)
+    def visualize(self, path):
         # save the architectures
         if isinstance(self._features[2]._mixed, Mo.MixedOp):
-            output_path = os.path.dirname(path)
-            plot_mobilenet(self, os.path.join(output_path, 'arch'))
+            visualize_mobilenet_arch(self, os.path.join(path, 'arch'))
 
     def loss(self, outputs, targets, loss_weights=None):
         assert len(outputs) == 1 and len(targets) == 1

nnabla_nas/contrib/classification/mobilenet/helper.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import graphviz
 import imageio
 
 default_style = {
@@ -40,7 +39,8 @@ def get_width(label):
     return '2'
 
 
-def plot_mobilenet(model, filename):
+def visualize_mobilenet_arch(model, filename):
+    import graphviz
     r"""Plot the architecture of MobileNet V2
 
     Args:

nnabla_nas/contrib/classification/mobilenet/network.py

@@ -21,7 +21,7 @@
 
 from .... import module as Mo
 from ..base import ClassificationModel as Model
-from .helper import plot_mobilenet
+from .helper import visualize_mobilenet_arch
 from .modules import CANDIDATES
 from .modules import ChoiceBlock
 from .modules import ConvBNReLU
@@ -234,12 +234,10 @@ def print_arch(arch_idx, op_names):
         self._arch_idx = arch_idx
         return txt + ''.join(stats)
 
-    def save_parameters(self, path=None, params=None, grad_only=False):
-        super().save_parameters(path, params=params, grad_only=grad_only)
+    def visualize(self, path):
         # save the architectures
         if isinstance(self._features[2]._mixed, Mo.MixedOp):
-            output_path = os.path.dirname(path)
-            plot_mobilenet(self, os.path.join(output_path, 'arch'))
+            visualize_mobilenet_arch(self, os.path.join(path, 'arch'))
 
     def loss(self, outputs, targets, loss_weights=None):
         assert len(outputs) == 1 and len(targets) == 1

nnabla_nas/contrib/model.py

@@ -95,3 +95,11 @@ def metrics(self, outputs, targets):
             NotImplementedError: [description]
         """
         raise NotImplementedError
+
+    def visualize(self, path):
+        r"""Save visualized graph to a file.
+
+        Args:
+            path (str): Path to directory to save.
+        """
+        return

nnabla_nas/module/module.py

@@ -312,12 +312,12 @@ def save_net_nnp(self, path, inp, out, calc_latency=False,
             hasattr(self, 'name') and self.name) else 'empty'
         contents = {'networks': [{'name': name_for_nnp,
                                   'batch_size': batch_size,
-                                  'outputs': {'y': out},
+                                  'outputs': {"y'": out},
                                   'names': {'x': inp}}],
                     'executors': [{'name': 'runtime',
                                    'network': name_for_nnp,
                                    'data': ['x'],
-                                   'output': ['y']}]}
+                                   'output': ["y'"]}]}
 
         save(filename, contents, variable_batch_size=False)
 

nnabla_nas/module/static/static_module.py

@@ -26,8 +26,6 @@
 # import nnabla_nas.module as mo
 from ... import module as mo
 
-from graphviz import Digraph
-
 
 def _get_abs_string_index(obj, idx):
     """Get the absolute index for the list of modules"""
@@ -904,6 +902,7 @@ def get_gv_graph(self, active_only=True,
             color_map (dict): the mapping of class instance to
                 vertice color used to visualize the graph.
         """
+        from graphviz import Digraph
         graph = Digraph(name=self.name)
         # 1. get all the static modules in the graph
         if active_only:

nnabla_nas/runner/searcher/fairnas.py

@@ -166,6 +166,8 @@ def callback_on_epoch_end(self):
                 )
             # checkpoint
             self.save_checkpoint()
+            if self.args['no_visualize']:  # action:store_false
+                self.model.visualize(self.args['output_path'])
 
         # reset loss and metric
         self.loss.zero()

nnabla_nas/runner/searcher/search.py

@@ -73,6 +73,9 @@ def callback_on_epoch_end(self):
                 )
             # checkpoint
             self.save_checkpoint()
+            if self.args['no_visualize']:  # action:store_false
+                self.model.visualize(self.args['output_path'])
+
         self.monitor.info(self.model.summary() + '\n')
 
     def callback_on_finish(self):
@@ -88,6 +91,8 @@ def callback_on_finish(self):
                     path=os.path.join(self.args['output_path'], 'weights.h5'),
                     params=self.model.get_net_parameters()
                 )
+            if self.args['no_visualize']:  # action:store_false
+                self.model.visualize(self.args['output_path'])
 
     def callback_on_start(self):
         r"""Calls this on starting the training."""

nnabla_nas/runner/trainer/train.py

@@ -151,6 +151,8 @@ def callback_on_epoch_end(self):
                     self.model.save_parameters(path)
                 # checkpoint
                 self.save_checkpoint({'best_metric': self._best_metric})
+                if self.args['no_visualize']:  # action:store_false
+                    self.model.visualize(self.args['output_path'])
 
         # reset loss and metric
         self.loss.zero()

nnabla_nas/utils/cli/cli.py

@@ -62,6 +62,8 @@ def main():
                         help='Path to save the monitoring log files.')
     parser.add_argument('--save-nnp', action='store_true',
                         help='Store network and parameter with nnp format.')
+    parser.add_argument('--no-visualize', action='store_false',
+                        help='Disable visualization with graphviz.')
 
     options = parser.parse_args()