Improved assembling of suggest_save_args report

Also runs slow tests for coverage again.

lucid/modelzoo/vision_base.py

@@ -204,14 +204,11 @@ def get_layer(self, name):
 
   @staticmethod
   def suggest_save_args(graph_def=None):
+    # TODO: Check with uint8 placeholders
     if graph_def is None:
       graph_def = tf.get_default_graph().as_graph_def()
 
-    inferred_info = {
-        "input_name" : None,
-        "image_shape" : None,
-        "output_names": None,
-    }
+    inferred_info = dict.fromkeys(("input_name", "image_shape", "output_names", "image_value_range"))
 
     nodes_of_op = lambda s: [n.name for n in graph_def.node if n.op == s]
     node_by_name = lambda s: [n for n in graph_def.node if n.name == s][0]
@@ -245,34 +242,25 @@ def suggest_save_args(graph_def=None):
     else:
       warnings.warn("Could not infer output_names.")
 
-    if all(inferred_info.values()):
-      return inferred_info
-    else:
-      raise AttributeError("Could not infer all arguments for saving.")
-
-
-    # print("")
-    # print("# Sanity check all inferred values before using this code!")
-    # print("save_model(")
-    # print("    save_path    = 'gs://save/model.pb', # TODO: replace")
+    report = []
+    report.append("# Please sanity check all inferred values before using this code!")
+    report.append("Model.save(")
 
-    # if inferred_info["input_name"] is not None:
-    #   print("    input_name   = %s," % repr(inferred_info["input_name"]))
-    # else:
-    #   print("    input_name   =   ,                   # TODO (eg. 'input' )")
-
-    # if inferred_info["output_names"] is not None:
-    #   print("    output_names = %s," % repr(inferred_info["output_names"]) )
-    # else:
-    #   print("    output_names = [ ],                  # TODO (eg. ['logits'] )")
-
-    # if inferred_info["image_shape"] is not None:
-    #   print("    image_shape  = %s,"% repr(inferred_info["image_shape"]) )
-    # else:
-    #   print("    image_shape  =   ,                   # TODO (eg. [224, 224, 3] )")
-
-    # print("    image_value_range =                  # TODO (eg. [0, 1], [0, 255], [-117, 138] )")
-    # print("  )")
+    suggestions = {
+        "input_name" : 'input',
+        "image_shape" : [224, 224, 3],
+        "output_names": ['logits'],
+        "image_value_range": "[-1, 1], [0, 1], [0, 255], or [-117, 138]"
+    }
+    for key, value in inferred_info.items():
+      if value is not None:
+        report.append("    {}={!r},".format(key, value))
+      else:
+        report.append("    {}=_,                   # TODO (eg. {!r})".format(key, suggestions[key]))
+    report.append("  )")
+
+    print("\n".join(report))
+    return inferred_info
 
 
   @staticmethod

tests/conftest.py

@@ -1,4 +1,18 @@
 import pytest
+import tensorflow as tf
+
+
+@pytest.fixture
+def minimodel():
+  def inner(input=None, shape=(16,16,3)):
+    """Constructs a tiny graph containing one each of a typical input
+    (tf.placegholder), variable and typical output (softmax) nodes."""
+    if input is None:
+      input = tf.placeholder(tf.float32, shape=shape, name="input")
+    w = tf.Variable(0.1, name="variable")
+    logits = tf.reduce_mean(w*input, name="output", axis=(0,1))
+    return tf.nn.softmax(logits)
+  return inner
 
 # Add support for a slow tests marker:
 

tests/modelzoo/test_saveload.py

@@ -7,12 +7,9 @@
 
 shape = (16,16,3)
 
-def test_Model_save():
+def test_Model_save(minimodel):
   with tf.Session().as_default() as sess:
-    x = tf.placeholder(tf.float32, shape=shape, name="input")
-    w = tf.Variable(0.1, name="variable")
-    logits = tf.reduce_mean(w*x, axis=(0,1))
-    y = tf.nn.softmax(logits, name="output")
+    _ = minimodel()
     sess.run(tf.global_variables_initializer())
     path = "./tests/fixtures/minigraph.pb"
     Model.save(path, "input", ["output"], shape, [0,1])

tests/modelzoo/test_vision_base.py

@@ -2,30 +2,26 @@
 
 from lucid.modelzoo.vision_base import Model
 
-shape = [16,16,3]
 
-def test_suggest_save_args(capsys):
+def test_suggest_save_args(capsys, minimodel):
   path = "./tests/fixtures/minigraph.pb"
 
   with tf.Graph().as_default() as graph, tf.Session() as sess:
-    x = tf.placeholder(tf.float32, shape=shape, name="input")
-    w = tf.Variable(0.1, name="variable")
-    logits = tf.reduce_mean(w*x, name="output", axis=(0,1))
-    y = tf.nn.softmax(logits)
+    _ = minimodel()
     sess.run(tf.global_variables_initializer())
 
     # ask for suggested arguments
     inferred = Model.suggest_save_args()
-
     # they should be both printed...
     captured = capsys.readouterr().out  # captures stdout
     names = ["input_name", "image_shape", "output_names"]
     assert all(name in captured for name in names)
     #...and returned
-    assert all(inferred.values())
 
     # check that these inferred values work
-    Model.save(path, image_value_range=(0,1), **inferred)
+    inferred.update(image_value_range=(0,1))
+    Model.save(path, **inferred)
     loaded_model = Model.load(path)
-    assert str(shape) in repr(loaded_model.graph_def)
+    assert "0.100" in repr(loaded_model.graph_def)
+
 

tox.ini

@@ -5,7 +5,7 @@ envlist = py{27,36}
 deps =
   tensorflow
   .[test]
-commands = coverage run --source lucid --omit lucid/scratch/*,lucid/recipes/*,lucid/misc/gl/* -m py.test {posargs}
+commands = coverage run --source lucid --omit lucid/scratch/*,lucid/recipes/*,lucid/misc/gl/* -m py.test --run-slow {posargs}
 
 [pytest]
 addopts = --verbose