TensorFlow -> MXNet mobilenet converts passed. correctness not tested.

mmdnn/conversion/examples/imagenet_test.py

@@ -25,7 +25,7 @@ class TestKit(object):
             'resnet'            : [(22, 11.756789), (147, 8.5718527), (24, 6.1751032), (88, 4.3121386), (141, 4.1778097)],
             'resnet_v1_101'     : [(21, 14.384739), (23, 14.262486), (144, 14.068737), (94, 12.17205), (134, 12.064575)],
             'inception_v3'      : [(22, 9.4921198), (24, 4.0932288), (25, 3.700398), (23, 3.3715961), (147, 3.3620636)],
-            'mobilenet'         : [(22, 16.223597), (24, 14.54775), (147, 13.173758), (145, 11.36431), (728, 11.083847)]
+            'mobilenet_v1_1.0'         : [(22, 16.223597), (24, 14.54775), (147, 13.173758), (145, 11.36431), (728, 11.083847)]
         },
         'keras' : {
             'vgg16'             : [(21, 0.81199354), (562, 0.019326132), (23, 0.018279659), (144, 0.012460723), (22, 0.012429929)],

mmdnn/conversion/mxnet/mxnet_emitter.py

@@ -383,12 +383,13 @@ def predict(model, labels, url):
         else:
             num_filter = IR_node.IR_layer.attr["kernel_shape"].list.i[-1]
 
-        no_bias = not IR_node.IR_layer.attr["use_bias"].b
-        if not no_bias and self.weight_loaded:
+        use_bias = IR_node.get_attr('use_bias', False)
+        if use_bias and self.weight_loaded:
             self.output_weights[IR_node.name + "_bias"] = weight_dict['bias']
 
         if pattern == "DepthwiseConv":
-            num_group = num_filter
+            num_group = IR_node.IR_layer.attr["kernel_shape"].list.i[-2]
+            num_filter = num_filter * num_group
             pattern = "Convolution"
         else:
             num_group = IR_node.get_attr('group', 1)
@@ -404,6 +405,8 @@ def predict(model, labels, url):
         if self.weight_loaded:
             # if layout not in MXNetEmitter.channels_last:
             weights = MXNetEmitter.transpose(weights, dim)
+            if num_group > 1:
+                weights = np.swapaxes(weights, 0, 1)
             self.output_weights[IR_node.name + "_weight"] = weights
 
         code = ""
@@ -418,7 +421,7 @@ def predict(model, labels, url):
                 tuple(pad),
                 num_filter,
                 num_group,
-                no_bias,
+                not use_bias,
                 layout,
                 IR_node.name)
         else:
@@ -432,7 +435,7 @@ def predict(model, labels, url):
                 dilate,
                 num_filter,
                 num_group,
-                no_bias,
+                not use_bias,
                 layout,
                 IR_node.name)
 

tests/test_conversion_imagenet.py

@@ -323,7 +323,7 @@ class TestModels(CorrectnessTest):
             'resnet_v2_50' : [TensorflowEmit, KerasEmit, PytorchEmit], # TODO: CntkEmit
             'resnet_v2_152' : [TensorflowEmit, KerasEmit, PytorchEmit], # TODO: CntkEmit
             'mobilenet_v1_1.0' : [TensorflowEmit, KerasEmit],
-            # 'inception_resnet_v2' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
+            # 'inception_resnet_v2' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit], # TODO
             # 'nasnet-a_large' : [TensorflowEmit, KerasEmit, PytorchEmit], # TODO
          },
     }