Merge branch 'feature/resize_4d_conversion' of https://github.com/klassen9/qonnx into klassen9-feature/resize_4d_conversion

Author: maltanar

src/qonnx/transformation/change_3d_tensors_to_4d.py

@@ -64,6 +64,7 @@ def _find_invalid_nodes(model):
         "Reshape",
         "MaxPool",
         "Upsample",
+        "Resize",
     ]
     invalid_nodes = []
     for n in model.graph.node:
@@ -194,6 +195,41 @@ def apply(self, model):
                 assert list(scales.shape) == [3]
                 scales = np.append(scales, np.asarray(1.0, dtype=np.float32))
                 model.set_initializer(n.input[1], scales)
+            elif node_op_type == "Resize":
+                assert ("axes" not in [x.name for x in n.attribute]), (
+                        "%s: Axes attribute is not supported." % n.name
+                        )  
+                assert (not (len(n.input) in (3, 4) and model.get_initializer(n.input[1]) is not None)), (
+                        "%s: ROI input is not supported." % n.name
+                        )    
+                if len(n.input) == 2:
+                    # Resize version 10
+                    scales = model.get_initializer(n.input[1])
+                    scales = np.append(scales, np.asarray(1.0, dtype=np.float32))
+                    model.set_initializer(n.input[1], scales)
+                elif len(n.input) == 3:
+                    # Resize version 11 and up (no size input)
+                    scales = model.get_initializer(n.input[2])
+                    scales = np.append(scales, np.asarray(1.0, dtype=np.float32))
+                    model.set_initializer(n.input[2], scales)
+                elif len(n.input) == 4:
+                    scales_exists = (model.get_initializer(n.input[2]) is not None) and (len(model.get_initializer(n.input[2])) != 0)
+                    sizes_exists = (model.get_initializer(n.input[3]) is not None) and (len(model.get_initializer(n.input[3])) != 0)
+                    assert (scales_exists ^ sizes_exists), (
+                        "%s: Either scales or the target output size must " 
+                        "be specified. Specifying both is prohibited." % n.name
+                        )  
+                    if (scales_exists): 
+                        # Scales parameter is a 1d list of upsampling factors along each axis
+                        scales = model.get_initializer(n.input[2])
+                        scales = np.append(scales, np.asarray(1.0, dtype=np.float32))
+                        model.set_initializer(n.input[2], scales)
+                    else:
+                        # Size parameter is a 1d list of the target size along each axis
+                        sizes = model.get_initializer(n.input[3])
+                        sizes = np.append(sizes, np.asarray(1.0, dtype=np.int64))
+                        model.set_initializer(n.input[3], sizes)
+                input_shape.append(1)
 
         # Change format of each input/value_info/output tensor
         for k, v in all_tensors.items():

tests/transformation/test_4d_conversion.py

@@ -327,8 +327,67 @@ def create_conv_upsample():
             model.set_initializer(tensor_name, gen_finn_dt_tensor(DataType["FLOAT32"], init_shape))
     return model
 
+def create_resize():
+    """
+    Creates an model for testing the 3D to 4D transform of the resize node.
+    """
+    resize_node1 = onnx.helper.make_node(
+        "Resize",
+        inputs=["in_resize1", "roi_resize1", "scales_resize1", "sizes_resize1"],
+        outputs=["out_resize1"],
+        name="Resize1",
+        mode="nearest",
+    )
+
+    resize_node2 = onnx.helper.make_node(
+        "Resize",
+        inputs=["out_resize1", "roi_resize2", "scales_resize2"],
+        outputs=["out_resize2"],
+        name="Resize2",
+        mode="nearest",
+    )
+    
+    in_resize1 = onnx.helper.make_tensor_value_info("in_resize1", onnx.TensorProto.FLOAT, [1, 32, 4])
+    out_resize1 = onnx.helper.make_tensor_value_info("out_resize1", onnx.TensorProto.FLOAT, [1, 32, 8]) 
+    out_resize2 = onnx.helper.make_tensor_value_info("out_resize2", onnx.TensorProto.FLOAT, [1, 32, 16]) 
+    
+    roi_resize1 = onnx.helper.make_tensor_value_info("roi_resize1", onnx.TensorProto.FLOAT, [4])
+    scales_resize1 = onnx.helper.make_tensor_value_info("scales_resize1", onnx.TensorProto.FLOAT, [])
+    sizes_resize1 = onnx.helper.make_tensor_value_info("sizes_resize1", onnx.TensorProto.INT64, [3])
+
+    roi_resize2 = onnx.helper.make_tensor_value_info("roi_resize2", onnx.TensorProto.FLOAT, [4])
+    scales_resize2 = onnx.helper.make_tensor_value_info("scales_resize2", onnx.TensorProto.FLOAT, [3])
+    
+    list_of_nodes = [
+        resize_node1,
+        resize_node2,
+    ]
+    list_of_value_infos = [
+        out_resize1,
+        roi_resize1,
+        sizes_resize1,
+        scales_resize1,
+        roi_resize2,
+        scales_resize2,
+    ]
+
+    graph = onnx.helper.make_graph(
+        nodes=list_of_nodes,
+        name="4d_conversion_resize_test_graph",
+        inputs=[in_resize1],
+        outputs=[out_resize2],
+        value_info=list_of_value_infos,
+    )
+
+    onnx_model = qonnx_make_model(graph, producer_name="4d_conversion_resize_test-model")
+    model = ModelWrapper(onnx_model)
+    model = model.transform(InferShapes())
+    model.set_initializer("sizes_resize1", np.array([1, 32, 8], dtype=np.int64))
+    model.set_initializer("scales_resize1", np.array([], dtype=np.float32))
+    model.set_initializer("scales_resize2", np.array([1., 1., 2.], dtype=np.float32))
+    return model
 
-@pytest.mark.parametrize("test_model", ["Quartz", "VGG", "ConvUpsample"])
+@pytest.mark.parametrize("test_model", ["Quartz", "VGG", "ConvUpsample", "Resize"])
 def test_4d_conversion(test_model):
     """
     Test for the 3D to 4D transformation with a valid graph.
@@ -340,6 +399,8 @@ def test_4d_conversion(test_model):
         model = create_arbitrary_model_vgg()
     elif test_model == "ConvUpsample":
         model = create_conv_upsample()
+    elif test_model == "Resize":
+        model = create_resize()
     else:
         raise Exception("Unknown test_model in test_4d_conversion")