[TENSORFLOW]Sparse2Dense support

python/tvm/relay/frontend/tensorflow.py

@@ -1281,6 +1281,18 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _sparse_to_dense():
+    def _impl(inputs, attr, params, mod):
+        sparse_indices = inputs[0]
+        sparse_values = inputs[2]
+        default_value = inputs[3]
+        output_shape = attr["_output_shapes"][0]
+
+        return _op.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value)
+
+    return _impl
+
+
 def _bias_add():
     def _impl(inputs, attr, params, mod):
         # Must expand for proper broadcasting in NCHW.
@@ -2383,6 +2395,7 @@ def _impl(inputs, attr, params, mod):
     "Softplus": _softplus(),
     "SpaceToBatchND": _space_to_batch_nd(),
     "SpaceToDepth": _space_to_depth(),
+    "SparseToDense": _sparse_to_dense(),
     "Split": _split(False),
     "SplitV": _split(True),
     "Sqrt": AttrCvt("sqrt"),

tests/python/frontend/tensorflow/test_forward.py

@@ -3936,6 +3936,83 @@ def test_forward_dilation():
     _test_dilation2d([1, 3, 3, 1], [2, 2, 1], [1, 1, 1, 1], [1, 1, 2, 1], "VALID")
 
 
+#######################################################################
+# Sparse To Dense
+# ---------------
+def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
+    with tf.Graph().as_default():
+        indices = tf.placeholder(
+            shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
+        )
+        values = tf.placeholder(
+            shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
+        )
+        oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))
+
+        if default_value == None:
+            output = tf.sparse_to_dense(indices, oshape, values)
+            compare_tf_with_tvm(
+                [sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
+            )
+        else:
+            dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
+            output = tf.sparse_to_dense(indices, oshape, values, dv)
+            compare_tf_with_tvm(
+                [sparse_indices, sparse_values, default_value],
+                ["indices:0", "values:0", "default_value:0"],
+                output.name,
+            )
+
+
+def test_forward_sparse_to_dense():
+    # scalar
+    _test_sparse_to_dense(
+        sparse_indices=np.int32(1),
+        sparse_values=np.int32(3),
+        default_value=np.int32(0),
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+    # vector
+    _test_sparse_to_dense(
+        sparse_indices=np.array([0, 1, 4]).astype("int32"),
+        sparse_values=np.array([3, 3, 3]).astype("int32"),
+        default_value=np.int32(0),
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+    # vector nXd
+    _test_sparse_to_dense(
+        sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
+        sparse_values=np.array([1, 2]).astype("int32"),
+        default_value=np.int32(0),
+        output_shape=np.array([3, 4]).astype("int32"),
+    )
+
+    _test_sparse_to_dense(
+        sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
+        sparse_values=np.array([1, 2]).astype("int32"),
+        default_value=np.int32(4),
+        output_shape=np.array([2, 3, 4]).astype("int32"),
+    )
+
+    # floats
+    _test_sparse_to_dense(
+        sparse_indices=np.array([0, 1, 4]).astype("int32"),
+        sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+        default_value=np.float32(3.5),
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+    # default value not specified
+    _test_sparse_to_dense(
+        sparse_indices=np.array([0, 1, 4]).astype("int32"),
+        sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+        default_value=None,
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+
 #######################################################################
 # infinity ops
 # ------------