Fix BatchNorm converter for CoreML when fix_gamma=True

tools/coreml/converter/_layers.py

@@ -472,18 +472,23 @@ def convert_batchnorm(net, node, module, builder):
     inputs = node['inputs']
 
 
-    eps = 1e-3 # Default value of eps for MXNet.
-    use_global_stats = False # Default value of use_global_stats for MXNet.
+    eps = 1e-3  # Default value of eps for MXNet.
+    use_global_stats = False  # Default value of use_global_stats for MXNet.
+    fix_gamma = True  # Default value of fix_gamma for MXNet.
     attrs = _get_attrs(node)
     if 'eps' in attrs:
         eps = literal_eval(attrs['eps'])
+    if 'fix_gamma' in attrs:
+        fix_gamma = literal_eval(attrs['fix_gamma'])
 
     args, aux = module.get_params()
     gamma = args[_get_node_name(net, inputs[1][0])].asnumpy()
     beta = args[_get_node_name(net, inputs[2][0])].asnumpy()
     mean = aux[_get_node_name(net, inputs[3][0])].asnumpy()
     variance = aux[_get_node_name(net, inputs[4][0])].asnumpy()
     nb_channels = gamma.shape[0]
+    if fix_gamma:
+        gamma.fill(1.)
     builder.add_batchnorm(
         name=name,
         channels=nb_channels,

tools/coreml/test/test_mxnet_converter.py

@@ -938,6 +938,40 @@ def test_batch_norm_no_global_stats(self):
             name='batch_norm_1')
         self._test_mxnet_model(net, input_shape=input_shape, mode='random', delta=1e-2)
 
+    def test_batch_norm_with_fix_gamma(self):
+        """ The gamma will always be an array of ones when fix_gamma=True. The values
+            of gamma may be changed accidentally if there have been fix_gamma=False before
+            the final trained model.
+        """
+        np.random.seed(1988)
+        input_shape = (1, 1, 2, 3)
+
+        net = mx.sym.Variable('data')
+        gamma = mx.sym.Variable('gamma')
+        beta = mx.sym.Variable('beta')
+        moving_mean = mx.sym.Variable('moving_mean')
+        moving_var = mx.sym.Variable('moving_var')
+        net = mx.symbol.BatchNorm(
+            data=net,
+            gamma=gamma,
+            beta=beta,
+            moving_mean=moving_mean,
+            moving_var=moving_var,
+            fix_gamma=True,
+            name='batch_norm_1')
+        self._test_mxnet_model(net, input_shape=input_shape, mode='random', delta=1e-2)
+
+        np.random.seed(1988)
+        net = mx.symbol.BatchNorm(
+            data=net,
+            gamma=gamma,
+            beta=beta,
+            moving_mean=moving_mean,
+            moving_var=moving_var,
+            fix_gamma=False,
+            name='batch_norm_2')
+        self._test_mxnet_model(net, input_shape=input_shape, mode='random', delta=1e-2)
+
     def test_pre_processing_args(self):
         np.random.seed(1988)
         input_shape = (1, 10)