ONNX export: Add Crop, Deconvolution and fix the default stride of Pooling to 1

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -219,6 +219,68 @@ def convert_convolution(node, **kwargs):
     return [conv_node]
 
 
+@mx_op.register("Deconvolution")
+def convert_deconvolution(node, **kwargs):
+    """Map MXNet's deconvolution operator attributes to onnx's ConvTranspose operator
+    and return the created node.
+    """
+    name, inputs, attrs = get_inputs(node, kwargs)
+
+    kernel_dims = list(parse_helper(attrs, "kernel"))
+    stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
+    pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
+    num_group = int(attrs.get("num_group", 1))
+    dilations = list(parse_helper(attrs, "dilate", [1, 1]))
+    adj_dims = list(parse_helper(attrs, "adj", [0, 0]))
+
+    pad_dims = pad_dims + pad_dims
+
+    deconv_node = onnx.helper.make_node(
+        "ConvTranspose",
+        inputs=inputs,
+        outputs=[name],
+        kernel_shape=kernel_dims,
+        strides=stride_dims,
+        dilations=dilations,
+        output_padding=adj_dims,
+        pads=pad_dims,
+        group=num_group,
+        name=name
+    )
+
+    return [deconv_node]
+
+
+@mx_op.register("Crop")
+def convert_crop(node, **kwargs):
+    """Map MXNet's crop operator attributes to onnx's Crop operator
+    and return the created node.
+    """
+    name, inputs, attrs = get_inputs(node, kwargs)
+    num_inputs = len(inputs)
+
+    y, x = list(parse_helper(attrs, "offset", [0, 0]))
+    h, w = list(parse_helper(attrs, "h_w", [0, 0]))
+    if num_inputs > 1:
+        h, w = kwargs["out_shape"][-2:]
+    border = [x, y, x + w, y + h]
+
+    crop_node = onnx.helper.make_node(
+        "Crop",
+        inputs=[inputs[0]],
+        outputs=[name],
+        border=border,
+        scale=[1, 1],
+        name=name
+    )
+
+    logging.warning(
+        "Using an experimental ONNX operator: Crop. " \
+        "Its definition can change.")
+
+    return [crop_node]
+
+
 @mx_op.register("FullyConnected")
 def convert_fully_connected(node, **kwargs):
     """Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
@@ -583,8 +645,8 @@ def convert_pooling(node, **kwargs):
     name, input_nodes, attrs = get_inputs(node, kwargs)
 
     kernel = eval(attrs["kernel"])
-    pool_type = attrs["pool_type"]
-    stride = eval(attrs["stride"]) if attrs.get("stride") else None
+    pool_type = attrs["pool_type"] if attrs.get("pool_type") else "max"
+    stride = eval(attrs["stride"]) if attrs.get("stride") else (1, 1)
     global_pool = get_boolean_attribute_value(attrs, "global_pool")
 
     pooling_convention = attrs.get('pooling_convention', 'valid')

tests/python-pytest/onnx/test_cases.py

@@ -115,7 +115,8 @@
              'test_Softmax',
              'test_softmax_functional',
              'test_softmax_lastdim',
-             ]
+             ],
+    'export': ['test_ConvTranspose2d']
 }
 
 STANDARD_MODEL = {