MXNet -> Cntk resnext test passed.

README.md

@@ -63,6 +63,7 @@ Models                                              | Caffe | Keras | Tensorflow
 [SqueezeNet](https://arxiv.org/pdf/1602.07360)      |   √   |   √   |     √      |   √  |   √   |    ×
 DenseNet                                            |       |   √   |     √      |   √  |       |            |
 [NASNet](https://arxiv.org/abs/1707.07012)          |       |   √   |     √      | × (no SeparableConv)
+[ResNext]                                           |       |   √   |     √      |   √  |   √   |
 
 #### On-going frameworks
 

mmdnn/conversion/cntk/README.md

@@ -29,9 +29,9 @@ CNTK model file is saved as [cntk_inception_v3.dnn], generated by [cntk_inceptio
 
 Ubuntu 16.04 with
 
-- CNTK gpu 2.3
+- CNTK CPU 2.4
 
-@ 2017/12/01
+@ 2018/02/08
 
 ## Limitation
 

mmdnn/conversion/mxnet/mxnet_parser.py

@@ -480,8 +480,17 @@ class MXNetParser(Parser):
             # print("Warning: Layer [{}] has changed model data format from [{}] to [{}]".format(source_node.name, self.data_format, layout))
             self.data_format = layout
 
+        # groups
+        group = int(layer_attr.get("num_group", "1"))
+        IR_node.attr["group"].i = group
         in_channel = self.IR_layer_map[IR_node.input[0]].attr["_output_shapes"].list.shape[0].dim[-1].size
 
+        if group == in_channel:
+            self._copy_and_reop(source_node, IR_node, "DepthwiseConv")
+        else:
+            self._copy_and_reop(source_node, IR_node, "Conv")
+        in_channel = in_channel // group
+
         assert "num_filter" in layer_attr
         out_channel = int(layer_attr.get("num_filter"))
 
@@ -511,14 +520,6 @@ class MXNetParser(Parser):
         # data_format
         assign_IRnode_values(IR_node, {'data_format' : layout})
 
-        # groups
-        group = int(layer_attr.get("num_group", "1"))
-        IR_node.attr["group"].i = group
-        if group == in_channel:
-            self._copy_and_reop(source_node, IR_node, "DepthwiseConv")
-        else:
-            self._copy_and_reop(source_node, IR_node, "Conv")
-
         # padding
         if "pad" in layer_attr:
             pad = MXNetParser.str2intList(layer_attr.get("pad"))

tests/test_conversion_imagenet.py

@@ -140,7 +140,7 @@ class TestModels(CorrectnessTest):
 
     @staticmethod
     def TensorflowEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
-        print("Testing {} from {} to Tensorflow.".format(architecture_name, original_framework))
+        print("Testing {} from {} to TensorFlow.".format(architecture_name, original_framework))
 
         # IR to code
         emitter = TensorflowEmitter((architecture_path, weight_path))
@@ -169,7 +169,7 @@ class TestModels(CorrectnessTest):
 
     @staticmethod
     def PytorchEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
-        print("Testing {} from {} to Pytorch.".format(architecture_name, original_framework))
+        print("Testing {} from {} to PyTorch.".format(architecture_name, original_framework))
 
         # IR to code
         emitter = PytorchEmitter((architecture_path, weight_path))
@@ -247,8 +247,8 @@ class TestModels(CorrectnessTest):
             'imagenet1k-inception-bn'   : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
             'imagenet1k-resnet-152'     : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
             'squeezenet_v1.1'           : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
-            'imagenet1k-resnext-101-64x4d' : [TensorflowEmit, PytorchEmit], # TODO: CntkEmit
-            'imagenet1k-resnext-50'        : [TensorflowEmit, KerasEmit, PytorchEmit], # TODO: CntkEmit
+            'imagenet1k-resnext-101-64x4d' : [CntkEmit, TensorflowEmit, PytorchEmit], # Keras is too slow
+            'imagenet1k-resnext-50'        : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
         }
     }