tabulated model summary

MANIFEST.in

+include requirements.txt
+include opt-requirements.txt

requirements.txt

 numpy
 six
 termcolor
+tabulate
 tqdm>4.11.1
 msgpack-python
 msgpack-numpy

setup.py

@@ -18,26 +18,11 @@ except ImportError:
     long_description = open('README.md').read()
 
 # configure requirements
-req = [
-    'numpy',
-    'six',
-    'termcolor',
-    'tqdm>4.11.1',
-    'msgpack-python',
-    'msgpack-numpy',
-    'pyzmq',
-    'subprocess32;python_version<"3.0"',
-    'functools32;python_version<"3.0"',
-]
-extra_req = [
-    'pillow',
-    'scipy',
-    'h5py',
-    'lmdb',
-    'matplotlib',
-    'scikit-learn',
-    'tornado;python_version<"3.0"',
-]
+reqfile = os.path.join(CURRENT_DIR, 'requirements.txt')
+req = [x.strip() for x in open(reqfile).readlines()]
+
+reqfile = os.path.join(CURRENT_DIR, 'opt-requirements.txt')
+extra_req = [x.strip() for x in open(reqfile).readlines()]
 
 # parse scripts
 scripts = ['scripts/plot-point.py', 'scripts/dump-model-params.py']

tensorpack/tfutils/modelutils.py

@@ -4,6 +4,7 @@
 
 import tensorflow as tf
 from termcolor import colored
+from tabulate import tabulate
 
 from ..utils import logger
 from .summary import add_moving_summary
@@ -18,18 +19,18 @@ def describe_model():
     if len(train_vars) == 0:
         logger.info("No trainable variables in the graph!")
         return
-    msg = [""]
     total = 0
+    data = []
     for v in train_vars:
         shape = v.get_shape()
         ele = shape.num_elements()
         total += ele
-        msg.append("{}: shape={}, dim={}".format(
-            v.name, shape.as_list(), ele))
+        data.append([v.name, shape.as_list(), ele])
+    table = tabulate(data, headers=['name', 'shape', 'dim'])
     size_mb = total * 4 / 1024.0**2
-    msg.append(colored(
-        "Total #param={} ({:.02f} MB assuming all float32)".format(total, size_mb), 'cyan'))
-    logger.info(colored("Model Parameters: ", 'cyan') + '\n'.join(msg))
+    summary_msg = colored(
+        "\nTotal #param={} ({:.02f} MB assuming all float32)".format(total, size_mb), 'cyan')
+    logger.info(colored("Model Parameters: \n", 'cyan') + table + summary_msg)
 
 
 def get_shape_str(tensors):

tensorpack/train/base.py

@@ -117,10 +117,8 @@ class Trainer(object):
         self._callbacks.setup_graph(weakref.proxy(self))
 
         # create session
-        sess_creator = self.config.session_creator
         logger.info("Finalize the graph, create the session ...")
-
-        self.sess = sess_creator.create_session()
+        self.sess = self.config.session_creator.create_session()
         self._monitored_sess = tf.train.MonitoredSession(
             session_creator=ReuseSessionCreator(self.sess), hooks=None)