fix(mge/tools): improve `module_visualize` result's robustness and beauty

GitOrigin-RevId: ef7b57377619fabcf50d4a48235b8d196659f1d4

imperative/python/megengine/tools/network_visualize.py

@@ -7,11 +7,12 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import argparse
+import logging
 
 import numpy as np
 
 from megengine.core.tensor.dtype import is_quantize
-from megengine.logger import get_logger
+from megengine.logger import _imperative_rt_logger, get_logger, set_mgb_log_level
 from megengine.utils.module_stats import (
     print_flops_stats,
     print_params_stats,
@@ -58,6 +59,8 @@ def visualize(
             "TensorBoard and TensorboardX are required for visualize.", exc_info=True
         )
         return
+    # FIXME: remove this after resolving "span dist too large" warning
+    old_level = set_mgb_log_level(logging.ERROR)
 
     graph = Network.load(model_path)
     writer = SummaryWriter(log_path)
@@ -126,6 +129,9 @@ def visualize(
                     std="{:.2g}".format(node.numpy().std()),
                 )
             )
+        # FIXME(MGE-2165): nodes outside network module may lead to unknown display bug
+        if not len(node.name.split(".")) > 2 and not node in graph.input_vars:
+            continue
         node_list.append(
             NodeDef(
                 name=process_name(node.name), op=node.type, input=inp_list, attr=attr,
@@ -145,6 +151,10 @@ def visualize(
         step_stats=StepStats(dev_stats=[DeviceStepStats(device=device)])
     )
     writer._get_file_writer().add_graph((graph_def, stepstats))
+
+    # FIXME: remove this after resolving "span dist too large" warning
+    _imperative_rt_logger.set_log_level(old_level)
+
     return total_params, total_flops
 
 

imperative/python/megengine/utils/module_stats.py

@@ -135,7 +135,9 @@ def print_flops_stats(flops, bar_length_max=20):
     ]
 
     total_flops_str = sizeof_fmt(total_flops_num, suffix="OPs")
-    total_var_size = sum(sum(s[1] for s in i["output_shapes"]) for i in flops)
+    total_var_size = sum(
+        sum(s[1] if len(s) > 1 else 0 for s in i["output_shapes"]) for i in flops
+    )
     flops.append(
         dict(name="total", flops=total_flops_str, output_shapes=total_var_size)
     )