[ONNX]Pool3d & upsample3d op support (apache#5135)

* [ONNX]Pool3d and Upsample3d op updated

* Pool3d and Upsample3d testcase

* Review comments fixed

* Review comments

python/tvm/relay/frontend/onnx.py

@@ -137,8 +137,10 @@ def onnx_default_layout(dims):
         return 'NCW'
     if dims == 2:
         return 'NCHW'
+    if dims == 3:
+        return 'NCDHW'
 
-    msg = "Only 1d and 2d layouts are currently supported"
+    msg = "Only 1D, 2D and 3D layouts are currently supported"
     raise tvm.error.OpAttributeInvalid(msg.format(op_name))
 
 
@@ -151,8 +153,10 @@ def onnx_storage_order2layout(storage_order, dims=2):
         return 'NCW' if storage_order == 0 else 'NWC'
     if dims == 2:
         return 'NCHW' if storage_order == 0 else 'NHWC'
+    if dims == 3:
+        return 'NCDHW' if storage_order == 0 else 'NDHWC'
 
-    msg = "Only 1d and 2d layouts are currently supported"
+    msg = "Only 1D, 2D and 3D layouts are currently supported"
     raise tvm.error.OpAttributeInvalid(msg.format(op_name))
 
 
@@ -780,19 +784,31 @@ def _impl_v9(cls, inputs, attr, params):
             assert len(inputs) == 2, "Upsample op take 2 inputs, {} given".format(len(inputs))
             scales = params[inputs[1].name_hint].asnumpy()
             inputs = inputs[:1]
-        assert len(scales) == 4 and scales[0] == 1.0 and scales[1] == 1.0
+        assert scales[0] == 1.0 and scales[1] == 1.0
+        input_shape = infer_shape(inputs[0])
+        dims = len(input_shape)
         mode = attr.get('mode')
         if mode == b'nearest':
             method = "nearest_neighbor"
         elif mode == b'linear':
-            method = "bilinear"
+            method = "trilinear" if dims == 5 else "bilinear"
         else:
             raise tvm.error.OpAttributeInvalid(
                 'Value {} in attribute "mode" of operator Upsample is not valid.'.format(mode))
-        attr = {'scale_h': scales[-2], 'scale_w': scales[-1], 'method': method,
-                'layout': 'NCHW', 'align_corners': True}
-        return AttrCvt('upsampling')(inputs, attr)
-
+        attr = {'scale_h': scales[-2],
+                'scale_w': scales[-1],
+                'method': method}
+        if dims == 5:
+            assert len(scales) == 5
+            attr['scale_d'] = scales[-3]
+            attr['layout'] = 'NCDHW'
+            op_name = 'upsampling3d'
+        else:
+            assert len(scales) == 4
+            attr['layout'] = 'NCHW'
+            attr['align_corners'] = True
+            op_name = 'upsampling'
+        return AttrCvt(op_name)(inputs, attr)
 
 class Shape(OnnxOpConverter):
     """ Operator converter for Shape.

tests/python/frontend/onnx/test_forward.py

@@ -741,6 +741,30 @@ def _test_upsample_nearest():
         tvm.testing.assert_allclose(out_array, tvm_out)
 
 
+def _test_upsample3d_nearest():
+    scale = 2
+    in_shape = (1, 1, 3, 3, 3)
+    out_shape = (1, 1, 3*scale, 3*scale, 3*scale)
+    y = helper.make_node("Upsample", ['in'], [
+                         'out'], mode='nearest', scales=[1.0, 1.0, 2.0, 2.0, 2.0])
+
+    in_array = np.random.uniform(size=in_shape).astype(np.float32)
+    out_array = topi.testing.upsampling3d_python(
+        in_array, (scale, scale, scale), "NCDHW")
+
+    graph = helper.make_graph([y],
+                              'upsample_nearest_test',
+                              inputs=[helper.make_tensor_value_info(
+                                  "in", TensorProto.FLOAT, list(in_shape))],
+                              outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))])
+
+    model = helper.make_model(graph, producer_name='upsample_nearest_test')
+
+    for target, ctx in ctx_list():
+        tvm_out = get_tvm_output(
+            model, in_array, target, ctx, out_shape, 'float32')
+        tvm.testing.assert_allclose(out_array, tvm_out)
+
 def _test_upsample_bilinear():
     scale = 2
     in_shape = (1, 1, 3, 3)
@@ -800,11 +824,45 @@ def _test_upsample_bilinear_opset9():
         tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)
 
 
+def _test_upsample3d_trilinear():
+    scale = 2
+    in_shape = (1, 1, 3, 3, 3)
+    out_shape = (1, 1, 3*scale, 3*scale, 3*scale)
+    y = helper.make_node("Upsample", ['in', 'scales'], ['out'], mode='linear')
+    scales = [1.0, 1.0, 2.0, 2.0, 2.0]
+    in_array = np.random.uniform(size=in_shape).astype(np.float32)
+    out_array = topi.testing.trilinear_resize3d_python(
+        in_array, (3*scale, 3*scale, 3*scale), "NCDHW", coordinate_transformation_mode="half_pixel")
+
+    ref_array = np.array(scales)
+    ref_node = helper.make_node('Constant',
+                                inputs=[],
+                                outputs=['scales'],
+                                value=onnx.helper.make_tensor(name='const_tensor',
+                                                              data_type=TensorProto.FLOAT,
+                                                              dims=ref_array.shape,
+                                                              vals=ref_array.flatten().astype(float)))
+
+    graph = helper.make_graph([ref_node, y],
+                              'upsample_trilinear_test',
+                              inputs=[helper.make_tensor_value_info(
+                                  "in", TensorProto.FLOAT, list(in_shape))],
+                              outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))])
+
+    model = helper.make_model(
+        graph, producer_name='upsample_trilinear_test')
+
+    for target, ctx in ctx_list():
+        tvm_out = get_tvm_output(
+            model, in_array, target, ctx, out_shape, 'float32')
+        tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)
+
 def test_upsample():
     _test_upsample_nearest()
     _test_upsample_bilinear()
     _test_upsample_bilinear_opset9()
-
+    _test_upsample3d_nearest()
+    _test_upsample3d_trilinear()
 
 def _test_softmax(inshape, axis):
     opname = 'Softmax'
@@ -1999,6 +2057,23 @@ def test_pooling():
                        mode=mode,
                        auto_pad='SAME_UPPER')
 
+        # Pool3D with stride
+        verify_pooling(x_shape=[1, 1, 32, 32, 32],
+                       kernel_shape=[3, 3, 3],
+                       strides=[2, 2, 2],
+                       pads=[1, 1, 1, 1, 1, 1],
+                       out_shape=[1, 1, 16, 16, 16],
+                       mode=mode)
+
+        # Pool3D with stride and autopadding
+        verify_pooling(x_shape=[1, 1, 32, 32, 32],
+                       kernel_shape=[3, 3, 3],
+                       strides=[2, 2, 2],
+                       pads=None,
+                       out_shape=[1, 1, 16, 16, 16],
+                       mode=mode,
+                       auto_pad='SAME_UPPER')
+
 
 def verify_lstm(seq_length,
                 batch_size,