Fixed improper HNASNet architecture (#6419)

research/deeplab/core/nas_network.py

@@ -156,8 +156,8 @@ def _build_nas_base(images,
           stride = 2
           filter_scaling *= hparams.filter_scaling_rate
         elif backbone[cell_num] == backbone[cell_num - 1] - 1:
-          scaled_height = scale_dimension(tf.shape(net)[1], 2)
-          scaled_width = scale_dimension(tf.shape(net)[2], 2)
+          scaled_height = scale_dimension(net.shape[1].value, 2)
+          scaled_width = scale_dimension(net.shape[2].value, 2)
           net = resize_bilinear(net, [scaled_height, scaled_width], net.dtype)
           filter_scaling /= hparams.filter_scaling_rate
       net = cell(

research/deeplab/core/nas_network_test.py

@@ -19,7 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-
 import numpy as np
 import tensorflow as tf
 
@@ -47,8 +46,9 @@ def create_test_input(batch, height, width, channels):
 class NASNetworkTest(tf.test.TestCase):
   """Tests with complete small NAS networks."""
 
-  def _pnasnet_small(self,
+  def _pnasnet(self,
                images,
+               backbone,
                num_classes,
                is_training=True,
                output_stride=16,
@@ -63,7 +63,6 @@ class NASNetworkTest(tf.test.TestCase):
     if not is_training:
       hparams.set_hparam('drop_path_keep_prob', 1.0)
 
-    backbone = [1, 2, 2]
     cell = nas_genotypes.PNASCell(hparams.num_conv_filters,
                                   hparams.drop_path_keep_prob,
                                   len(backbone),
@@ -81,16 +80,26 @@ class NASNetworkTest(tf.test.TestCase):
 
   def testFullyConvolutionalEndpointShapes(self):
     num_classes = 10
-    inputs = create_test_input(2, 321, 321, 3)
+    backbone = [0, 0, 0, 1, 2, 1, 2, 2, 3, 3, 2, 1]
+    inputs = create_test_input(None, 321, 321, 3)
     with slim.arg_scope(nas_network.nas_arg_scope()):
-      _, end_points = self._pnasnet_small(inputs,
-                                          num_classes)
+      _, end_points = self._pnasnet(inputs, backbone, num_classes)
       endpoint_to_shape = {
-          'Stem': [2, 81, 81, 128],
-          'Cell_0': [2, 41, 41, 100],
-          'Cell_1': [2, 21, 21, 200],
-          'Cell_2': [2, 21, 21, 200]}
-      for endpoint, shape in endpoint_to_shape.iteritems():
+          'Stem': [None, 81, 81, 128],
+          'Cell_0': [None, 81, 81, 50],
+          'Cell_1': [None, 81, 81, 50],
+          'Cell_2': [None, 81, 81, 50],
+          'Cell_3': [None, 41, 41, 100],
+          'Cell_4': [None, 21, 21, 200],
+          'Cell_5': [None, 41, 41, 100],
+          'Cell_6': [None, 21, 21, 200],
+          'Cell_7': [None, 21, 21, 200],
+          'Cell_8': [None, 11, 11, 400],
+          'Cell_9': [None, 11, 11, 400],
+          'Cell_10': [None, 21, 21, 200],
+          'Cell_11': [None, 41, 41, 100]
+      }
+      for endpoint, shape in endpoint_to_shape.items():
         self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)