CoremlBugFixes:Input variable name can be something other than data; … (

#7746)

* CoremlBugFixes:Input variable name can be something other than data; making pad and stride optional.

Earlier, we were not providing data_names argument while creating the module which meant that the input data variable name was assumed to be "data". This is fixed. Also, added a unit test for it (due to which utils.load_model(..) had to be refactored).

The second bug was we missed assuming pad and stride parameters for convolutional layers are optional arguments. Added a unit test for this too.

Also, tested with mnist model from the tutorial (by changing the input variable name to something other than data)

* Minor rewording of a unit test.

tools/coreml/converter/_layers.py

@@ -290,7 +290,7 @@ def convert_convolution(net, node, module, builder):
     else:
         has_bias = True
 
-    if literal_eval(param['pad']) != (0, 0):
+    if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
         pad = literal_eval(param['pad'])
         builder.add_padding(
             name=name+"_pad",
@@ -314,7 +314,12 @@ def convert_convolution(net, node, module, builder):
         Wb = None
 
     channels = W.shape[1]
-    stride_height, stride_width = literal_eval(param['stride'])
+
+    stride_height = 1
+    stride_width = 1
+    if 'stride' in param.keys():
+        stride_height, stride_width = literal_eval(param['stride'])
+
     kernel_height, kernel_width = literal_eval(param['kernel'])
 
     W = W.transpose((2, 3, 1, 0))
@@ -367,7 +372,7 @@ def convert_pooling(net, node, module, builder):
         raise TypeError("Pooling type %s not supported" % layer_type_mx)
 
     # Add padding if there is any
-    if literal_eval(param['pad']) != (0, 0):
+    if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
         pad = literal_eval(param['pad'])
         builder.add_padding(
             name=name+"_pad",
@@ -380,7 +385,11 @@ def convert_pooling(net, node, module, builder):
             output_name=name+"_pad_output")
         input_name = name+"_pad_output"
 
-    stride_height, stride_width = literal_eval(param['stride'])
+    stride_height = 1
+    stride_width = 1
+    if 'stride' in param.keys():
+        stride_height, stride_width = literal_eval(param['stride'])
+
     kernel_width, kernel_height = literal_eval(param['kernel'])
 
     type_map = {'valid': 'VALID', 'full': 'INCLUDE_LAST_PIXEL'}

tools/coreml/converter/utils.py

@@ -19,7 +19,7 @@
 
 
 def load_model(model_name, epoch_num, data_shapes, label_shapes, label_names, gpus=''):
-    """Loads and returns a given MXNet model.
+    """Returns a module loaded with the provided model.
 
     Parameters
     ----------
@@ -53,12 +53,59 @@ def load_model(model_name, epoch_num, data_shapes, label_shapes, label_names, gp
     MXNet module
     """
     sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, epoch_num)
+
+    mod = create_module(sym, data_shapes, label_shapes, label_names, gpus)
+
+    mod.set_params(
+        arg_params=arg_params,
+        aux_params=aux_params,
+        allow_missing=True
+    )
+
+    return mod
+
+
+def create_module(sym, data_shapes, label_shapes, label_names, gpus=''):
+    """Creates a new MXNet module.
+
+    Parameters
+    ----------
+    sym : Symbol
+        An MXNet symbol.
+
+    input_shape: tuple
+        The shape of the input data in the form of (batch_size, channels, height, width)
+
+    files: list of strings
+        List of URLs pertaining to files that need to be downloaded in order to use the model.
+
+    data_shapes: list of tuples.
+        List of tuples where each tuple is a pair of input variable name and its shape.
+
+    label_shapes: list of (str, tuple)
+        Typically is ``data_iter.provide_label``.
+
+    label_names: list of str
+        Name of the output labels in the MXNet symbolic graph.
+
+    gpus: str
+        Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
+        If empty, we use CPU.
+
+    Returns
+    -------
+    MXNet module
+    """
     if gpus == '':
         devices = mx.cpu()
     else:
         devices = [mx.gpu(int(i)) for i in gpus.split(',')]
+
+    data_names = [data_shape[0] for data_shape in data_shapes]
+
     mod = mx.mod.Module(
         symbol=sym,
+        data_names=data_names,
         context=devices,
         label_names=label_names
     )
@@ -67,11 +114,5 @@ def load_model(model_name, epoch_num, data_shapes, label_shapes, label_names, gp
         data_shapes=data_shapes,
         label_shapes=label_shapes
     )
-    mod.set_params(
-        arg_params=arg_params,
-        aux_params=aux_params,
-        allow_missing=True
-    )
     return mod
 
-

tools/coreml/mxnet_coreml_converter.py

@@ -88,7 +88,7 @@
     input_shape = yaml.safe_load(args.input_shape)
     data_shapes = []
     for key in input_shape:
-        # We prepend 1 because the coreml model only accept 1 input data at a time.
+        # We prepend 1 because the coreml model only accept 1 input data at a time (=batch-size).
         shape = (1,)+literal_eval(input_shape[key])
         input_shape[key] = shape
         data_shapes.append((key, shape))

tools/coreml/test/test_mxnet_converter.py

@@ -25,30 +25,22 @@
 sys.path.append(current_working_directory + "/../converter/")
 import _mxnet_converter as mxnet_converter
 from collections import namedtuple
-
+from converter import utils
 
 def _mxnet_remove_batch(input_data):
     for blob in input_data:
         input_data[blob] = np.reshape(input_data[blob], input_data[blob].shape[1:])
     return input_data
 
 
-def _get_mxnet_module(net, input_shape, mode, label_names, input_names=None):
+def _get_mxnet_module(net, data_shapes, mode, label_names, input_names=None):
     """ Given a symbolic graph, input shape and the initialization mode,
         returns an MXNet module.
     """
     mx.random.seed(1993)
 
-    mod = mx.mod.Module(
-        symbol=net,
-        context=mx.cpu(),
-        label_names=label_names
-    )
-    mod.bind(
-        for_training=False,
-        data_shapes=[('data', input_shape)],
-        label_shapes=input_names
-    )
+    mod = utils.create_module(sym=net, data_shapes=data_shapes, label_shapes=input_names, label_names=label_names)
+
     if mode == 'random':
         mod.init_params(
             initializer=mx.init.Uniform(scale=.1)
@@ -73,7 +65,7 @@ class SingleLayerTest(unittest.TestCase):
     In order to do so, it converts model and generates preds on both CoreML and MXNet and check they are the same.
     """
     def _test_mxnet_model(self, net, input_shape, mode, class_labels=None, coreml_mode=None, label_names=None, delta=1e-3,
-                          pre_processing_args=None):
+                          pre_processing_args=None, input_name='data'):
         """ Helper method that convert the CoreML model into CoreML and compares the predictions over random data.
 
         Parameters
@@ -92,21 +84,27 @@ def _test_mxnet_model(self, net, input_shape, mode, class_labels=None, coreml_mo
 
         delta: float
             The maximum difference b/w predictions of MXNet and CoreML that is tolerable.
+
+        input_name: str
+            The name of the input variable to the symbolic graph.
         """
-        mod = _get_mxnet_module(net, input_shape, mode, label_names)
+
+        data_shapes=[(input_name, input_shape)]
+
+        mod = _get_mxnet_module(net, data_shapes, mode, label_names)
 
         # Generate some dummy data
-        input_data = {'data': np.random.uniform(-10., 10., input_shape)}
+        input_data = {input_name: np.random.uniform(-10., 10., input_shape)}
         Batch = namedtuple('Batch', ['data'])
-        mod.forward(Batch([mx.nd.array(input_data['data'])]))
+        mod.forward(Batch([mx.nd.array(input_data[input_name])]))
         mxnet_preds = mod.get_outputs()[0].asnumpy().flatten()
 
         # Get predictions from coreml
         coreml_model = mxnet_converter.convert(
             model=mod,
             class_labels=class_labels,
             mode=coreml_mode,
-            input_shape={'data': input_shape},
+            input_shape={input_name: input_shape},
             preprocessor_args=pre_processing_args
         )
         coreml_preds = coreml_model.predict(_mxnet_remove_batch(input_data)).values()[0].flatten()
@@ -512,7 +510,7 @@ def test_tiny_synset_random_input(self):
         net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
         net = mx.sym.SoftmaxOutput(net, name='softmax')
         mod = _get_mxnet_module(net,
-                                input_shape=input_shape,
+                                data_shapes=[('data', input_shape)],
                                 mode='random',
                                 label_names=['softmax_label'])
 
@@ -941,6 +939,35 @@ def test_pre_processing_args(self):
         self._test_mxnet_model(net, input_shape=input_shape, mode='random', label_names=['softmax_label'],
                                pre_processing_args={'red_bias':0, 'blue_bias':0, 'green_bias':0, 'image_scale':1})
 
+    def test_different_input_variables(self):
+        """
+        Verifying the behavior when input variable name is different than the standard name - 'data'.
+        """
+        np.random.seed(1988)
+        input_shape = (1, 10)
+        net = mx.sym.Variable('data1')
+        net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+        self._test_mxnet_model(net, input_shape=input_shape, mode='zeros', input_name='data1')
+
+    def test_really_tiny_conv_optional_params(self):
+        """
+        Verifying the behavior of a convolutional layer when stride and pad are not provided.
+        """
+        np.random.seed(1988)
+        input_shape = (1, 1, 10, 10)
+        num_filter = 1
+        kernel = (1 ,1)
+
+        # Define a model
+        net = mx.sym.Variable('data')
+        net = mx.symbol.Convolution(
+            data=net,
+            num_filter=num_filter,
+            kernel=kernel,
+            name='conv_1'
+        )
+        self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
     # TODO test_concat