Added support for SliceOp

validation/operations/gen_tests.py

@@ -1340,6 +1340,118 @@ def split_uneven_channel_axis_4d():
     save_model(model, "split/uneven_channel_axis_4d", "split_uneven_channel_axis_4d.onnx")
 
 
+# ---------------------------------------------------------------------------
+# Slice tests
+# ---------------------------------------------------------------------------
+
+def slice_2d_basic():
+    """Slice a 2D tensor with explicit axes and unit steps."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 6])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3])
+    starts = make_int64_initializer("starts", [1, 2])
+    ends = make_int64_initializer("ends", [3, 5])
+    axes = make_int64_initializer("axes", [0, 1])
+    steps = make_int64_initializer("steps", [1, 1])
+    node = helper.make_node("Slice", ["X", "starts", "ends", "axes", "steps"], ["Y"])
+    graph = helper.make_graph([node], "slice_2d_basic", [X], [Y], initializer=[starts, ends, axes, steps])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/2d_basic", "slice_2d_basic.onnx")
+
+
+def slice_default_axes():
+    """Slice with omitted axes and steps using default positional axes."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
+    starts = make_int64_initializer("starts", [0, 1, 0])
+    ends = make_int64_initializer("ends", [2, 3, 4])
+    node = helper.make_node("Slice", ["X", "starts", "ends"], ["Y"])
+    graph = helper.make_graph([node], "slice_default_axes", [X], [Y], initializer=[starts, ends])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/default_axes", "slice_default_axes.onnx")
+
+
+def slice_negative_axis():
+    """Slice using a negative axis."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 5])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 3])
+    starts = make_int64_initializer("starts", [1])
+    ends = make_int64_initializer("ends", [4])
+    axes = make_int64_initializer("axes", [-1])
+    node = helper.make_node("Slice", ["X", "starts", "ends", "axes"], ["Y"])
+    graph = helper.make_graph([node], "slice_negative_axis", [X], [Y], initializer=[starts, ends, axes])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/negative_axis", "slice_negative_axis.onnx")
+
+
+def slice_negative_indices():
+    """Slice with negative indices along one axis."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 5])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 3])
+    starts = make_int64_initializer("starts", [-4])
+    ends = make_int64_initializer("ends", [-1])
+    axes = make_int64_initializer("axes", [1])
+    node = helper.make_node("Slice", ["X", "starts", "ends", "axes"], ["Y"])
+    graph = helper.make_graph([node], "slice_negative_indices", [X], [Y], initializer=[starts, ends, axes])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/negative_indices", "slice_negative_indices.onnx")
+
+
+def slice_step2():
+    """Slice with a positive step greater than one."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 8])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4])
+    starts = make_int64_initializer("starts", [0])
+    ends = make_int64_initializer("ends", [8])
+    axes = make_int64_initializer("axes", [2])
+    steps = make_int64_initializer("steps", [2])
+    node = helper.make_node("Slice", ["X", "starts", "ends", "axes", "steps"], ["Y"])
+    graph = helper.make_graph([node], "slice_step2", [X], [Y], initializer=[starts, ends, axes, steps])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/step2", "slice_step2.onnx")
+
+
+def slice_nchw_spatial_crop():
+    """Slice an NCHW tensor across the spatial axes."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 8, 8])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 4, 6])
+    starts = make_int64_initializer("starts", [2, 1])
+    ends = make_int64_initializer("ends", [6, 7])
+    axes = make_int64_initializer("axes", [2, 3])
+    node = helper.make_node("Slice", ["X", "starts", "ends", "axes"], ["Y"])
+    graph = helper.make_graph([node], "slice_nchw_spatial_crop", [X], [Y], initializer=[starts, ends, axes])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/nchw_spatial_crop", "slice_nchw_spatial_crop.onnx")
+
+
+def slice_after_conv():
+    """Conv followed by a spatial crop using Slice."""
+    rng = np.random.default_rng(108)
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 8, 8])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 4, 4])
+    W = numpy_helper.from_array(rng.uniform(-1, 1, (4, 3, 3, 3)).astype(np.float32), name="W")
+    starts = make_int64_initializer("starts", [1, 1])
+    ends = make_int64_initializer("ends", [5, 5])
+    axes = make_int64_initializer("axes", [2, 3])
+    conv = helper.make_node("Conv", ["X", "W"], ["C"], kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
+    slice_node = helper.make_node("Slice", ["C", "starts", "ends", "axes"], ["Y"])
+    graph = helper.make_graph([conv, slice_node], "slice_after_conv", [X], [Y], initializer=[W, starts, ends, axes])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/after_conv", "slice_after_conv.onnx")
+
+
+def slice_large_channel_1024():
+    """Slice a large channel range out of a 1024-channel tensor."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 1, 1])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 512, 1, 1])
+    starts = make_int64_initializer("starts", [128])
+    ends = make_int64_initializer("ends", [640])
+    axes = make_int64_initializer("axes", [1])
+    node = helper.make_node("Slice", ["X", "starts", "ends", "axes"], ["Y"])
+    graph = helper.make_graph([node], "slice_large_channel_1024", [X], [Y], initializer=[starts, ends, axes])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "slice/large_channel_1024", "slice_large_channel_1024.onnx")
+
+
 # ---------------------------------------------------------------------------
 # Gather tests
 # ---------------------------------------------------------------------------
@@ -1880,6 +1992,16 @@ if __name__ == "__main__":
     split_negative_axis()
     split_uneven_channel_axis_4d()
 
+    print("\nGenerating Slice tests:")
+    slice_2d_basic()
+    slice_default_axes()
+    slice_negative_axis()
+    slice_negative_indices()
+    slice_step2()
+    slice_nchw_spatial_crop()
+    slice_after_conv()
+    slice_large_channel_1024()
+
     print("\nGenerating Softmax tests:")
     softmax_basic()
     softmax_3d_last_axis()