[Relay][Frontend] Implement SpaceToBatchND in Tensorflow frontend

python/tvm/relay/frontend/tensorflow.py

@@ -968,6 +968,91 @@ def _impl(inputs, attr, params):
         return AttrCvt(op_name=name)(inputs, attr)
     return _impl
 
+def _space_to_batch_nd():
+    def _impl(inputs, attr, params):
+        input_node = inputs[0]
+        input_shape = attr['_input_shapes'][input_node]
+        block_shape = params.pop(inputs[1].name_hint).asnumpy().tolist()
+        paddings = params.pop(inputs[2].name_hint).asnumpy().tolist()
+        N = len(input_shape)
+        M = len(block_shape)
+        batch = input_shape[0]
+        remaining_shape_length = N - M - 1
+        paddings = [(0, 0)] + paddings + [(0, 0)] * remaining_shape_length
+        # From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/space-to-batch-n-d:
+        # Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings
+        # to produce padded of shape padded_shape.
+        padded = tvm.relay.nn.pad(input_node, pad_width=paddings)
+        # Reshape padded to reshaped_padded of shape:
+        # [batch] + [padded_shape[1] / block_shape[0], block_shape[0], ...,
+        # padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape
+        shape1 = [batch] + [item for i in range(M) for item in [-4, -1, block_shape[i]]] + [-2]
+        reshaped_padded = tvm.relay.reshape(padded, newshape=shape1)
+        # Permute dimensions of reshaped_padded to produce permuted_reshaped_padded of shape:
+        # block_shape + [batch] + [padded_shape[1] / block_shape[0], ...,
+        # padded_shape[M] / block_shape[M-1]] + remaining_shape
+        axes = [2 * i + 2 for i in range(M)] + [0] + [2 * i + 1 for i in range(M)] + \
+               list(range(1 + 2 * M, 1 + 2 * M + remaining_shape_length))
+        permuted_reshaped_padded = tvm.relay.transpose(reshaped_padded, axes=axes)
+        permuted_reshaped_padded_shape = _infer_out_shapes(permuted_reshaped_padded, params)[0]
+        # Reshape permuted_reshaped_padded to flatten block_shape into the batch dimension,
+        # producing an output tensor of shape:
+        # [batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ...,
+        # padded_shape[M] / block_shape[M-1]] + remaining_shape
+        shape2 = [batch * np.prod(block_shape)] + list(permuted_reshaped_padded_shape)[M + 1:]
+        reshaped_permuted_reshaped_padded = tvm.relay.reshape(permuted_reshaped_padded,
+                                                              newshape=shape2)
+        return reshaped_permuted_reshaped_padded
+
+    return _impl
+
+
+def _batch_to_space_nd():
+    def _impl(inputs, attr, params):
+        input_node = inputs[0]
+        input_shape = attr['_input_shapes'][input_node]
+        block_shape = params.pop(inputs[1].name_hint).asnumpy().tolist()
+        crops = params.pop(inputs[2].name_hint).asnumpy().tolist()
+        M = len(block_shape)
+        batch = input_shape[0]
+        # From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/batch-to-space-n-d:
+        # Reshape input to reshaped of shape:
+        # [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape),
+        #  input_shape[1], ..., input_shape[N-1]]
+        shape1 = block_shape + [batch // np.prod(block_shape)] + input_shape[1:]
+        reshaped = tvm.relay.reshape(input_node, newshape=shape1)
+        # Permute dimensions of reshaped to produce permuted of shape
+        # [batch / prod(block_shape), input_shape[1], block_shape[0], ...,
+        # input_shape[M], block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]]
+        axes = [M] + [axis for i in range(M) for axis in [M + i + 1, i]] + \
+            list(range(2 * M + 1, len(shape1)))
+        permuted = tvm.relay.transpose(reshaped, axes=axes)
+        # Reshape permuted to produce reshaped_permuted of shape
+        # [batch / prod(block_shape), input_shape[1] * block_shape[0], ...,
+        #  input_shape[M] * block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]]
+        shape2 = [0] + [-3] * M + [-2]
+        reshaped_permuted = tvm.relay.reshape(permuted, newshape=shape2)
+        # Crop the start and end of dimensions [1, ..., M] of reshaped_permuted according to crops
+        # to produce the output of shape:
+        # [batch / prod(block_shape), input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
+        #  ..., input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
+        #  input_shape[M+1], ..., input_shape[N-1]]
+        reshaped_permuted_shape = _infer_out_shapes(reshaped_permuted, params)[0]
+        cropped = reshaped_permuted
+        for axis in range(1, M+1):
+            crop = crops[axis - 1]
+            if crop != [0, 0]:
+                indices = tvm.relay.arange(
+                    crop[0],
+                    reshaped_permuted_shape[axis] - crop[1],
+                    dtype='int32'
+                )
+                cropped = tvm.relay.take(cropped, indices=indices, axis=axis)
+
+        return cropped
+
+    return _impl
+
 # compatible operators that do NOT require any conversion.
 _identity_list = []
 
@@ -1044,6 +1129,8 @@ def _impl(inputs, attr, params):
     'Split'                             : _split(False),
     'SplitV'                            : _split(True),
     'Unpack'                            : _unpack(),
+    'SpaceToBatchND'                    : _space_to_batch_nd(),
+    'BatchToSpaceND'                    : _batch_to_space_nd(),
 }
 
 def _LSTMBlockCell():

tests/python/frontend/tensorflow/test_forward.py

@@ -145,6 +145,7 @@ def is_gpu_available():
     else:
         return False
 
+
 #######################################################################
 # Pooling
 # -------
@@ -205,6 +206,19 @@ def test_forward_pooling():
                          dilation_rate=[1, 1],
                          strides=[2, 1])
 
+            # Tests involving SpaceToBatchND
+            _test_pooling(input_shape=[1, 1, 2, 1],
+                         window_shape=[1, 1],
+                         padding='VALID',
+                         pooling_type=pool_type,
+                         dilation_rate=[1, 2])
+
+            _test_pooling(input_shape=[1, 2, 1],
+                         window_shape=[1],
+                         padding='VALID',
+                         pooling_type=pool_type,
+                         dilation_rate=[2])
+
 #######################################################################
 # Convolution
 # -----------
@@ -213,12 +227,8 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
                       dilations, strides, padding, data_format):
     """ One iteration of convolution with given shapes and attributes """
 
-    total_size_1 = 1
-    total_size_2 = 1
-    for s in tensor_in_sizes:
-        total_size_1 *= s
-    for s in filter_in_sizes:
-        total_size_2 *= s
+    total_size_1 = np.prod(tensor_in_sizes)
+    total_size_2 = np.prod(filter_in_sizes)
     # Initializes the input tensor with array containing incrementing
     # numbers from 1.
     data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
@@ -237,6 +247,7 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
         nn_ops.conv2d(in_data,
                       in_filter,
                       strides=strides,
+                      dilations=dilations,
                       padding=padding,
                       data_format=data_format)
 
@@ -255,6 +266,116 @@ def test_forward_convolution():
     _test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC')
     _test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC')
 
+#######################################################################
+# SpaceToBatchND
+# --------------
+def _test_space_to_batch_nd(input_shape, block_shape, paddings, dtype='int32'):
+    data = np.random.uniform(0, 5, size=input_shape).astype(dtype)
+
+    with tf.Graph().as_default():
+        in_data = tf.placeholder(shape=input_shape, dtype=dtype)
+        out = tf.space_to_batch_nd(in_data, block_shape, paddings)
+
+        compare_tf_with_tvm(data, in_data.name, out.name)
+
+def test_forward_space_to_batch_nd():
+    # test cases: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/space-to-batch-n-d
+    _test_space_to_batch_nd(
+        input_shape=[1, 2, 2, 1],
+        block_shape=[2, 2],
+        paddings=[[0, 0], [0, 0]]
+    )
+
+    _test_space_to_batch_nd(
+        input_shape=[1, 2, 2, 3],
+        block_shape=[2, 2],
+        paddings=[[0, 0], [0, 0]]
+    )
+
+    _test_space_to_batch_nd(
+        input_shape=[1, 4, 4, 1],
+        block_shape=[2, 2],
+        paddings=[[0, 0], [0, 0]]
+    )
+
+    _test_space_to_batch_nd(
+        input_shape=[2, 2, 4, 1],
+        block_shape=[2, 2],
+        paddings=[[0, 0], [2, 0]],
+        dtype='int64'
+    )
+
+    # pylint: disable=line-too-long
+    # https://github.com/tensorflow/tensorflow/blob/24f578/tensorflow/python/kernel_tests/spacetobatch_op_test.py
+    _test_space_to_batch_nd(
+        input_shape=[2, 3],
+        block_shape=[2],
+        paddings=[[1, 0]],
+        dtype='float32'
+    )
+
+    _test_space_to_batch_nd(
+        input_shape=[2, 3, 2],
+        block_shape=[2],
+        paddings=[[1, 0]],
+        dtype='float64'
+    )
+
+#######################################################################
+# BatchToSpaceND
+# --------------
+def _test_batch_to_space_nd(input_shape, block_shape, crops, dtype='int32'):
+    data = np.random.uniform(0, 5, size=input_shape).astype(dtype)
+
+    with tf.Graph().as_default():
+        in_data = tf.placeholder(shape=input_shape, dtype=dtype)
+        out = tf.batch_to_space_nd(in_data, block_shape, crops)
+
+        compare_tf_with_tvm(data, in_data.name, out.name)
+
+def test_forward_batch_to_space_nd():
+    # test cases: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/batch-to-space-n-d
+    _test_batch_to_space_nd(
+        input_shape=[4, 1, 1, 1],
+        block_shape=[2, 2],
+        crops=[[0, 0], [0, 0]]
+    )
+
+    _test_batch_to_space_nd(
+        input_shape=[4, 1, 1, 3],
+        block_shape=[2, 2],
+        crops=[[0, 0], [0, 0]]
+    )
+
+    _test_batch_to_space_nd(
+        input_shape=[4, 2, 2, 1],
+        block_shape=[2, 2],
+        crops=[[0, 0], [0, 0]]
+    )
+
+    _test_batch_to_space_nd(
+        input_shape=[8, 1, 3, 1],
+        block_shape=[2, 2],
+        crops=[[0, 0], [2, 0]],
+        dtype='int64'
+    )
+
+    # pylint: disable=line-too-long
+    # https://github.com/tensorflow/tensorflow/blob/24f578/tensorflow/python/kernel_tests/batchtospace_op_test.py
+    _test_batch_to_space_nd(
+        input_shape=[18, 2, 1, 2],
+        block_shape=[2, 3],
+        crops=[[1, 1], [0, 0]],
+        dtype='float32'
+    )
+
+    _test_batch_to_space_nd(
+        input_shape=[20, 5, 8, 7],
+        block_shape=[2, 2],
+        crops=[[1, 1], [1, 1]],
+        dtype='float64'
+    )
+
 #######################################################################
 # Reshape
 # -------
@@ -1296,6 +1417,8 @@ def test_forward_rel_ops():
         _test_forward_concat_v2()
     test_forward_lrn()
     test_forward_l2_normalize()
+    test_forward_space_to_batch_nd()
+    test_forward_batch_to_space_nd()
 
     # End to End
     test_forward_inception_v3()