[Relay][Frontend][ONNX] operator support: DepthToSpace, SpaceToDepth (#4271)

diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 0c581c9..3d90d15 100644 (file)
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -472,6 +472,76 @@ class Reshape(OnnxOpConverter):
                 static_shape.asnumpy().astype('int32')))
         return out
 
+
+class DepthToSpace(OnnxOpConverter):
+    """ Operator converter for DepthToSpace.
+    """
+
+    @classmethod
+    def _impl_v11(cls, inputs, attr, params):
+
+        block_size = int(attr['blocksize'])
+        mode = attr.get("mode", "DCR")
+
+        # handle NCHW layout
+        indata = infer_value_simulated(inputs[0], params)
+        in_n, in_c, in_h, in_w = indata.shape
+
+        # reshape to proper output
+        new_c = int(in_c / (block_size * block_size))
+        new_h = in_h * block_size
+        new_w = in_w * block_size
+        newshape = (in_n, new_c, new_h, new_w)
+
+        if mode == "DCR":
+            # expand input to larger dimension.
+            expanded = _op.reshape(inputs[0],
+                                   newshape=(in_n, block_size, block_size, new_c, in_h, in_w))
+            # reorder to expand spatial blocks.
+            transposed = _op.transpose(expanded, axes=(0, 3, 4, 1, 5, 2))
+
+        else:  # CRD mode
+            # expand input to larger dimension.
+            expanded = _op.reshape(inputs[0],
+                                   newshape=(in_n, new_c, block_size, block_size, in_h, in_w))
+            # reorder to expand spatial blocks.
+            transposed = _op.transpose(expanded, axes=(0, 1, 4, 2, 5, 3))
+
+        return AttrCvt(op_name="reshape",
+                       extras={'newshape': newshape},
+                       ignores=['mode', 'blocksize'])([transposed], attr)
+
+
+class SpaceToDepth(OnnxOpConverter):
+    """ Operator converter for SpaceToDepth.
+    """
+
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+
+        block_size = int(attr['blocksize'])
+
+        # handle NCHW layout
+        indata = infer_value_simulated(inputs[0], params)
+        in_n, in_c, in_h, in_w = indata.shape
+
+        # reshape to proper output
+        new_c = in_c * (block_size * block_size)
+        new_h = int(in_h / block_size)
+        new_w = int(in_w / block_size)
+        newshape = (in_n, new_c, new_h, new_w)
+
+        # expand input to larger dimension.
+        expanded = _op.reshape(inputs[0],
+                               newshape=(in_n, in_c, new_h, block_size, new_w, block_size))
+        # reorder to expand spatial blocks.
+        transposed = _op.transpose(expanded, axes=(0, 3, 5, 1, 2, 4))
+
+        return AttrCvt(op_name="reshape",
+                       extras={'newshape': newshape},
+                       ignores=['blocksize'])([transposed], attr)
+
+
 class Concat(OnnxOpConverter):
     """ Operator converter for Concat.
     """
@@ -1121,6 +1191,8 @@ def _get_convert_map(opset):
         'Split': Split.get_converter(opset),
         'Slice': Slice.get_converter(opset),
         'Transpose': AttrCvt('transpose', {'perm': 'axes'}),
+        'DepthToSpace': DepthToSpace.get_converter(opset),
+        'SpaceToDepth': SpaceToDepth.get_converter(opset),
         'Gather': Gather.get_converter(opset),
         'Squeeze': AttrCvt('squeeze', {'axes': 'axis'}),
         'Unsqueeze': Unsqueeze.get_converter(opset),

diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
index 6391a1a..e074bac 100644 (file)
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -77,19 +77,19 @@ def get_tvm_output(graph_def, input_data, target, ctx, output_shape=None, output
         return tvm_output.asnumpy()
 
 
-def get_caffe2_output(model, x, dtype='float32'):
-    import caffe2.python.onnx.backend
-    prepared_backend = caffe2.python.onnx.backend.prepare(model)
-    W = {model.graph.input[0].name: x.astype(dtype)}
-    c2_out = prepared_backend.run(W)[0]
-    return c2_out
+def get_onnxruntime_output(model, x, dtype='float32'):
+    import onnxruntime.backend
+    rep = onnxruntime.backend.prepare(model, 'CPU')
+    x = x.astype(dtype)
+    ort_out = rep.run(x)[0]
+    return ort_out
 
 
 def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
     dtype = 'float32'
     x = np.random.uniform(size=data_shape)
     model = onnx.load_model(graph_file)
-    c2_out = get_caffe2_output(model, x, dtype)
+    c2_out = get_onnxruntime_output(model, x, dtype)
     for target, ctx in ctx_list():
         tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype)
         tvm.testing.assert_allclose(c2_out, tvm_out, rtol=1e-5, atol=1e-5)
@@ -142,6 +142,57 @@ def test_reshape():
     tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
 
 
+def verify_depth_to_space(inshape, outshape, mode, blockSize):
+    node = onnx.helper.make_node('DepthToSpace',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 blocksize=blockSize)
+
+    graph = helper.make_graph([node],
+                              "depth_to_space_test",
+                              inputs=[helper.make_tensor_value_info("x", TensorProto.FLOAT, list(inshape))],
+                              outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(outshape))])
+
+    model = helper.make_model(graph, producer_name='depth_to_space_test')
+
+    for target, ctx in ctx_list():
+        x = np.random.uniform(size=inshape).astype('float32')
+        tvm_out = get_tvm_output(model, x, target, ctx, outshape, 'float32')
+        onnx_out = get_onnxruntime_output(model, x, 'float32')
+        tvm.testing.assert_allclose(onnx_out, tvm_out)
+
+
+def test_depth_to_space():
+    # current onnx.checker use OpSet-1 version of DepthToSpace, which doesn't have a mode argument.
+    # TO-DO, we can add mode arguement to test CRD mode and DCR mode
+    # in the future when we update to a newer onnx version.
+    verify_depth_to_space((1, 8, 2, 3), (1, 2, 4, 6), mode="CRD", blockSize=2)
+
+
+def verify_space_to_depth(inshape, outshape, blockSize):
+    node = onnx.helper.make_node('SpaceToDepth',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 blocksize=blockSize)
+
+    graph = helper.make_graph([node],
+                              "space_to_depth_test",
+                              inputs=[helper.make_tensor_value_info("x", TensorProto.FLOAT, list(inshape))],
+                              outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(outshape))])
+
+    model = helper.make_model(graph, producer_name='space_to_depth_test')
+
+    for target, ctx in ctx_list():
+        x = np.random.uniform(size=inshape).astype('float32')
+        tvm_out = get_tvm_output(model, x, target, ctx, outshape, 'float32')
+        onnx_out = get_onnxruntime_output(model, x, 'float32')
+        tvm.testing.assert_allclose(onnx_out, tvm_out)
+
+
+def test_space_to_depth():
+    verify_space_to_depth((1, 1, 4, 6), (1, 4, 2, 3), 2)
+
+
 def test_shape():
     in_shape = (4, 3, 3, 4)
     ref_shape = (6, 2, 4, 3)
@@ -1372,7 +1423,7 @@ def check_torch_conversion(model, input_size):
     onnx_model = onnx.load(file_name)
     for target, ctx in ctx_list():
         input_data = np.random.uniform(size=input_size).astype('int32')
-        c2_out = get_caffe2_output(onnx_model, input_data)
+        c2_out = get_onnxruntime_output(onnx_model, input_data)
         tvm_out = get_tvm_output(onnx_model, input_data, target, ctx)
         tvm.testing.assert_allclose(c2_out, tvm_out)
 
@@ -1574,6 +1625,7 @@ def test_erf():
     z = scipy.special.erf(x)
     verify_erf(x, z)
 
+
 def verify_where(condition, x, y, dtype, outdata):
     node = helper.make_node('Where', inputs=['condition', 'x', 'y'], outputs=['out'])
     graph = helper.make_graph([node],
@@ -1588,6 +1640,7 @@ def verify_where(condition, x, y, dtype, outdata):
         tvm_out = get_tvm_output(model, [condition, x, y], target, ctx, outdata.shape)
         tvm.testing.assert_allclose(outdata, tvm_out)
 
+
 def test_where():
     condition = np.array([[1, 0], [1, 1]], dtype=np.bool)
     x = np.array([[1, 2], [3, 4]], dtype=np.int64)
@@ -1704,3 +1757,5 @@ if __name__ == '__main__':
     test_erf()
     test_where()
     test_or()
+    test_depth_to_space()
+    test_space_to_depth()