diff options
| -rw-r--r-- | tensorflow/core/framework/shape_inference.h | 2 | ||||
| -rw-r--r-- | tensorflow/core/ops/array_ops.cc | 12 | ||||
| -rw-r--r-- | tensorflow/core/ops/array_ops_test.cc | 36 | ||||
| -rw-r--r-- | tensorflow/python/framework/common_shapes.py | 2 | ||||
| -rw-r--r-- | tensorflow/python/kernel_tests/spacetobatch_op_test.py | 8 | ||||
| -rw-r--r-- | tensorflow/python/kernel_tests/transpose_op_test.py | 2 | ||||
| -rw-r--r-- | tensorflow/python/ops/array_ops.py | 255 |
7 files changed, 54 insertions, 263 deletions
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h index c3dc5fec4e..5c456650ad 100644 --- a/tensorflow/core/framework/shape_inference.h +++ b/tensorflow/core/framework/shape_inference.h @@ -270,14 +270,12 @@ class InferenceContext { // Returns in <*out> a sub-shape of <s> with dimensions [start:]. // <start> can be negative to index from the end of the shape. If <start> > // rank of <s>, then an empty subshape is returned. - // Returns an error if the rank of <s> is < <start>. Status Subshape(ShapeHandle s, int64 start, ShapeHandle* out) TF_MUST_USE_RESULT; // Returns in <*out> a sub-shape of <s>, with dimensions [start:end]. // <start> and <end> can be negative, to index from the end of the shape. // <start> and <end> are set to the rank of <s> if > rank of <s>. - // Returns an error if the rank of <s> is insufficient. Status Subshape(ShapeHandle s, int64 start, int64 end, ShapeHandle* out) TF_MUST_USE_RESULT; diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index c208dc4327..1094215ed2 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -2281,10 +2281,12 @@ REGISTER_OP("ExpandDims") .Attr("Tdim: {int32, int64} = DT_INT32") .SetShapeFn([](InferenceContext* c) { ShapeHandle input = c->input(0); - ShapeHandle expand_dim; - TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &expand_dim)); const Tensor* dim_t = c->input_tensor(1); + if (dim_t != nullptr && dim_t->NumElements() != 1) { + return errors::InvalidArgument( + "'dim' input must be a tensor with a single value"); + } if (dim_t == nullptr || !c->RankKnown(input)) { c->set_output(0, c->UnknownShape()); return Status::OK(); @@ -2516,7 +2518,8 @@ REGISTER_OP("SpaceToBatch") DimensionHandle pad1_dim = c->Dim(paddings, 1); if (!c->ValueKnown(pad0_dim) || !c->ValueKnown(pad1_dim)) { - return shape_inference::UnknownShape(c); + c->set_output(0, c->UnknownShapeOfRank(4)); + return Status::OK(); } int64 pad0 = c->Value(pad0_dim); @@ -2694,7 +2697,8 @@ REGISTER_OP("BatchToSpace") DimensionHandle crops1_dim = c->Dim(crops, 1); if (!c->ValueKnown(crops0_dim) || !c->ValueKnown(crops1_dim)) { - return shape_inference::UnknownShape(c); + c->set_output(0, c->UnknownShapeOfRank(4)); + return Status::OK(); } int64 crops0 = c->Value(crops0_dim); diff --git a/tensorflow/core/ops/array_ops_test.cc b/tensorflow/core/ops/array_ops_test.cc index 80453af80d..cfeb060ca6 100644 --- a/tensorflow/core/ops/array_ops_test.cc +++ b/tensorflow/core/ops/array_ops_test.cc @@ -385,7 +385,6 @@ TEST(ArrayOpsTest, ExpandDims_ShapeFn) { // With unknown dim tensor value, output is unknown. INFER_OK(op, "?;?", "?"); - INFER_ERROR("Shape must be rank 0 but is rank 1", op, "?;[1]"); Tensor dim_t; op.input_tensors[1] = &dim_t; @@ -399,11 +398,21 @@ TEST(ArrayOpsTest, ExpandDims_ShapeFn) { dim_t = test::AsScalar<int32>(idx); INFER_OK(op, "?;?", "?"); INFER_OK(op, "[5,?,7];?", "[d0_0,1,d0_1,d0_2]"); + + // Repeat with int64. + dim_t = test::AsScalar<int64>(idx); + INFER_OK(op, "?;?", "?"); + INFER_OK(op, "[5,?,7];?", "[d0_0,1,d0_1,d0_2]"); } for (int32 idx : {2, -2}) { dim_t = test::AsScalar<int32>(idx); INFER_OK(op, "?;?", "?"); INFER_OK(op, "[5,?,7];?", "[d0_0,d0_1,1,d0_2]"); + + // Repeat with int64. + dim_t = test::AsScalar<int64>(idx); + INFER_OK(op, "?;?", "?"); + INFER_OK(op, "[5,?,7];?", "[d0_0,d0_1,1,d0_2]"); } for (int32 idx : {3, -1}) { @@ -411,7 +420,26 @@ TEST(ArrayOpsTest, ExpandDims_ShapeFn) { dim_t = test::AsScalar<int32>(idx); INFER_OK(op, "?;?", "?"); INFER_OK(op, "[5,?,7];?", "[d0_0,d0_1,d0_2,1]"); + + // Repeat with int64. + dim_t = test::AsScalar<int64>(idx); + INFER_OK(op, "?;?", "?"); + INFER_OK(op, "[5,?,7];?", "[d0_0,d0_1,d0_2,1]"); } + + // Expand using an input vector tensor. + std::vector<int32> dims; + dims.push_back(0); + dim_t = test::AsTensor<int32>(dims); + INFER_OK(op, "?;?", "?"); + INFER_OK(op, "[5,?,7];?", "[1,d0_0,d0_1,d0_2]"); + + // Expand using too many input elements. + dims.push_back(1); + dim_t = test::AsTensor<int32>(dims); + INFER_ERROR("'dim' input must be a tensor with a single", op, "?;?"); + INFER_ERROR("'dim' input must be a tensor with a single", op, "[5,6,7];?"); + // Examples from ExpandDims doc. dim_t = test::AsScalar<int32>(0); INFER_OK(op, "[2];[]", "[1,d0_0]"); @@ -928,8 +956,8 @@ TEST(ArrayOpsTest, SpaceToBatch_ShapeFn) { // Paddings not known, but batch size can be computed. INFER_OK(op, "[1,10,10,3];[2,2]", "[4,?,?,d0_3]"); - // Unknown paddings means unknown shape - INFER_OK(op, "[1,10,10,3];?", "?"); + // Unknown paddings means unknown shape of rank 4. + INFER_OK(op, "[1,10,10,3];?", "[?,?,?,?]"); // Paddings not correct shape INFER_ERROR("Shape must be rank 2 but is rank 1", op, "[1,10,10,3];[4]"); @@ -971,7 +999,7 @@ TEST(ArrayOpsTest, BatchToSpace_ShapeFn) { "[5,8,8,3];[2,2]"); // Unknown croppings means unknown shape - INFER_OK(op, "[4,8,8,3];?", "?"); + INFER_OK(op, "[4,8,8,3];?", "[?,?,?,?]"); // croppings not correct shape INFER_ERROR("Shape must be rank 2 but is rank 1", op, "[4,8,8,3];[4]"); diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py index ba366cbc13..8c5251bdf1 100644 --- a/tensorflow/python/framework/common_shapes.py +++ b/tensorflow/python/framework/common_shapes.py @@ -646,7 +646,7 @@ def call_cpp_shape_fn(op, input_tensors_needed=None, if str(result) != str(python_result): raise ValueError( ("Python vs CPP shape mismatch. " - "python: %s vs CPP: %s on node %s " + "CPP: %s vs python: %s on node %s " "with input shapes %s") % ( str(result), str(python_result), str(op.node_def), ",".join([str(i.get_shape()) for i in op.inputs]))) diff --git a/tensorflow/python/kernel_tests/spacetobatch_op_test.py b/tensorflow/python/kernel_tests/spacetobatch_op_test.py index f3ff2d517a..b340394017 100644 --- a/tensorflow/python/kernel_tests/spacetobatch_op_test.py +++ b/tensorflow/python/kernel_tests/spacetobatch_op_test.py @@ -166,7 +166,7 @@ class SpaceToBatchErrorHandlingTest(tf.test.TestCase): x_np = [[[[1], [2]], [[3], [4]]]] paddings = np.zeros((2, 2), dtype=np.int32) block_size = 10 - with self.assertRaises(IndexError): + with self.assertRaises(ValueError): out_tf = tf.space_to_batch(x_np, paddings, block_size) out_tf.eval() @@ -175,7 +175,7 @@ class SpaceToBatchErrorHandlingTest(tf.test.TestCase): x_np = [[[[1], [2], [3]], [[3], [4], [7]]]] paddings = np.zeros((2, 2), dtype=np.int32) block_size = 3 - with self.assertRaises(IndexError): + with self.assertRaises(ValueError): _ = tf.space_to_batch(x_np, paddings, block_size) def testBlockSizeNotDivisibleHeight(self): @@ -183,7 +183,7 @@ class SpaceToBatchErrorHandlingTest(tf.test.TestCase): x_np = [[[[1], [2]], [[3], [4]], [[5], [6]]]] paddings = np.zeros((2, 2), dtype=np.int32) block_size = 3 - with self.assertRaises(IndexError): + with self.assertRaises(ValueError): _ = tf.space_to_batch(x_np, paddings, block_size) def testBlockSizeNotDivisibleBoth(self): @@ -191,7 +191,7 @@ class SpaceToBatchErrorHandlingTest(tf.test.TestCase): x_np = [[[[1], [2]], [[3], [4]]]] paddings = np.zeros((2, 2), dtype=np.int32) block_size = 3 - with self.assertRaises(IndexError): + with self.assertRaises(ValueError): _ = tf.space_to_batch(x_np, paddings, block_size) def testUnknownShape(self): diff --git a/tensorflow/python/kernel_tests/transpose_op_test.py b/tensorflow/python/kernel_tests/transpose_op_test.py index ec8a41f59c..5bc3f5358a 100644 --- a/tensorflow/python/kernel_tests/transpose_op_test.py +++ b/tensorflow/python/kernel_tests/transpose_op_test.py @@ -226,7 +226,7 @@ class TransposeTest(tf.test.TestCase): self._testError(np.arange(0., 2 ** 11).reshape([2] * 11), np.arange(11), "not implemented") - with self.assertRaises(IndexError): + with self.assertRaises(ValueError): tf.transpose(np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 3]) self._testError(np.arange(0., 30).reshape([2, 3, 5]), [0, 1, 1], diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 54e2298e35..9141b873fd 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -807,39 +807,7 @@ ops.RegisterShape("Unpack")(common_shapes.call_cpp_shape_fn) @ops.RegisterShape("Concat") def _ConcatShape(op): - concat_dim = tensor_util.constant_value(op.inputs[0]) - if concat_dim is None: - # Return an unknown shape with the same rank as the inputs, or an - # unknown rank if no input's rank is known. - rank = None - for value in op.inputs[1:]: - if rank is not None: - value.get_shape().assert_has_rank(rank) - else: - rank = value.get_shape().ndims - if rank == 0: - raise ValueError("Can't concatenate scalars (use tf.pack instead)") - return [tensor_shape.unknown_shape(ndims=rank)] - - else: - # Merge all the non-concat dims, and sum the concat dim to make an - # output shape. - concat_dim = int(concat_dim) - if concat_dim < 0: - raise ValueError("Expected concat_dim >= 0, but got %d" % concat_dim) - - output_shape = op.inputs[1].get_shape() - for value in op.inputs[2:]: - value_shape = value.get_shape() - if value_shape.ndims is not None and concat_dim >= value_shape.ndims: - raise ValueError("Expected concat_dim in range [0, %d), but got %d" % - (value_shape.ndims, concat_dim)) - before = output_shape[:concat_dim].merge_with(value_shape[:concat_dim]) - at = output_shape[concat_dim] + value_shape[concat_dim] - after = output_shape[ - concat_dim + 1:].merge_with(value_shape[concat_dim + 1:]) - output_shape = before.concatenate(at).concatenate(after) - return [output_shape] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[0]) ops.RegisterShape("ConcatOffset")(common_shapes.call_cpp_shape_fn) @@ -1834,63 +1802,12 @@ ops.RegisterShape("ListDiff")(common_shapes.call_cpp_shape_fn) @ops.RegisterShape("Pad") @ops.RegisterShape("MirrorPad") def _PadShape(op): - """Shape function for the Pad op. - - This op has two inputs: - - * input: A rank-N tensor. - * paddings: An N-by-2 matrix, in which the i^th row contains the - number of padding elements to add before and after `input` in the - i^th dimension. - - It has one output, which has the same rank as input, and additional - elements according to the values in paddings. - - Args: - op: A Pad Operation. - - Returns: - A single-element list containing the shape of the output. - - Raises: - ValueError: If the input shapes are incompatible. - """ - paddings_shape = op.inputs[1].get_shape().with_rank(2) - input_shape = op.inputs[0].get_shape() - input_shape = input_shape.with_rank(paddings_shape[0].value) - paddings_shape = paddings_shape.merge_with( - tensor_shape.matrix(input_shape.ndims, 2)) - paddings = tensor_util.constant_value(op.inputs[1]) - if paddings is None: - return [tensor_shape.unknown_shape(ndims=input_shape.ndims)] - else: - output_dims = [] - for i, dim in enumerate(input_shape.dims): - if paddings[i, 0] < 0 or paddings[i, 1] < 0: - raise ValueError("paddings must be non-negative") - output_dims.append(dim + paddings[i, 0] + paddings[i, 1]) - return [tensor_shape.TensorShape(output_dims)] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[1]) @ops.RegisterShape("MirrorPadGrad") def _MirrorPadGradShape(op): - """Shape function for the MirrorPadGrad op.""" - paddings_shape = op.inputs[1].get_shape().with_rank(2) - input_shape = op.inputs[0].get_shape().with_rank(paddings_shape[0].value) - paddings_shape = paddings_shape.merge_with(tensor_shape.matrix( - input_shape.ndims, 2)) - paddings = tensor_util.constant_value(op.inputs[1]) - if paddings is None: - return [tensor_shape.unknown_shape(ndims=input_shape.ndims)] - - output_dims = [] - for i, dim in enumerate(input_shape.dims): - if paddings[i, 0] < 0 or paddings[i, 1] < 0: - raise ValueError("Paddings must be non-negative.") - if dim < paddings[i, 0] + paddings[i, 1]: - raise ValueError("Output dimension is negative.") - output_dims.append(dim - paddings[i, 0] - paddings[i, 1]) - return [tensor_shape.TensorShape(output_dims)] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[1]) ops.RegisterShape("ReverseSequence")(common_shapes.call_cpp_shape_fn) @@ -1900,58 +1817,12 @@ ops.RegisterShape("ShapeN")(common_shapes.call_cpp_shape_fn) @ops.RegisterShape("Transpose") def _TransposeShape(op): - """Shape function for the Transpose op. - - This op takes two inputs: - - * input: a rank-N tensor of arbitrary shape. - * shuffle: a length-N vector. - - Its output is the rank-N tensor computed by permuting the dimensions - of input according to shuffle. - - Args: - op: A Transpose op. - - Returns: - A single-element list containing the shape of the output. - - Raises: - ValueError: If the shapes of input and shuffle are incompatible. - IndexError: If shuffle contains an index that is >= the rank of input. - """ - input_shape = op.inputs[0].get_shape() - transpose_shape = op.inputs[1].get_shape().merge_with(tensor_shape.vector( - input_shape.ndims)) - transpose_vec = tensor_util.constant_value(op.inputs[1]) - if transpose_vec is None: - return [tensor_shape.unknown_shape(ndims=transpose_shape[0].value)] - else: - return [tensor_shape.TensorShape([input_shape[i] - for i in transpose_vec.tolist()])] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[1]) @ops.RegisterShape("Split") def _SplitShape(op): - """Shape function for the Split op.""" - split_dim = tensor_util.constant_value(op.inputs[0]) - num_split = len(op.outputs) - input_shape = op.inputs[1].get_shape() - if split_dim is None: - return [tensor_shape.unknown_shape(ndims=input_shape.ndims)] * num_split - else: - split_dim = int(split_dim) - input_shape = input_shape.with_rank_at_least(split_dim + 1) - if not (input_shape[split_dim] % num_split).is_compatible_with(0): - raise ValueError( - "Number of ways to split should evenly divide the split " - "dimension but got split_dim %d (size = %d) and num_split %d" % - (split_dim, input_shape[split_dim].value, num_split)) - prefix = input_shape[:split_dim] - size_in_split_dim = input_shape[split_dim] // num_split - suffix = input_shape[split_dim + 1:] - output_shape = prefix.concatenate(size_in_split_dim).concatenate(suffix) - return [output_shape] * num_split + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[0]) @ops.RegisterShape("Tile") @@ -2088,18 +1959,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"): @ops.RegisterShape("EditDistance") def _EditDistanceShape(op): - """Shape function for the EditDistance op.""" - hypothesis_shape = tensor_util.constant_value(op.inputs[2]) - truth_shape = tensor_util.constant_value(op.inputs[5]) - if hypothesis_shape is not None and truth_shape is not None: - if len(hypothesis_shape) != len(truth_shape): - raise ValueError( - "Inconsistent ranks in hypothesis and truth. Saw shapes: %s and %s" % - (str(hypothesis_shape), str(truth_shape))) - return [tensor_shape.TensorShape( - [max(h, t) for h, t in zip(hypothesis_shape[:-1], truth_shape[:-1])])] - - return [tensor_shape.unknown_shape()] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[2, 5]) # The remaining ops do not change the shape of their inputs. @@ -2164,80 +2024,7 @@ def _ExtractImagePatchesShape(op): @ops.RegisterShape("SpaceToBatch") def _SpaceToBatchShape(op): - """Shape function for the SpaceToBatch op. - - The output shape is determined by the following inputs/ attributes: - - * input: A rank-4 tensor with shape [B, H, W, D] - * paddings: A 2-by-2 matrix, specified as follows: - - paddings = [[pad_top, pad_bottom], [pad_left, pad_right]], - - implying effective padded spatial dimensions: - - Hp = pad_top + H + pad_bottom - Wp = pad_left + W + pad_right - - Both Hp and Wp must be multiples of block_size. - * block_size: an int. - - Its output is also a rank-4 tensor with shape: - - [B*block_size*block_size, Hp/block_size, Wp/block_size, D] - - Args: - op: A SpaceToBatch op. - - Returns: - A single-element list containing the shape of the output. - - Raises: - ValueError: If the shapes of inputs are not as expected. - IndexError: If block_size does not divide Wp or Hp. - """ - # Check that the input tensor is 4-D. - try: - input_shape = op.inputs[0].get_shape().with_rank(4) - except ValueError: - raise ValueError( - "tf.space_to_batch() requires 4-D input tensor.") - - # Check that the paddings tensor is a matrix with shape [2, 2]. - try: - paddings_shape = op.inputs[1].get_shape().with_rank(2) - except ValueError: - raise ValueError( - "tf.space_to_batch() requires 2-D paddings tensor.") - - if paddings_shape[0] != 2 or paddings_shape[1] != 2: - raise ValueError( - "tf.space_to_batch() requires input paddings with shape [2, 2].") - - block_size = op.get_attr("block_size") - if block_size <= 1: - raise ValueError("Attribute block_size has to be > 1.") - - paddings = tensor_util.constant_value(op.inputs[1]) - if paddings is not None: - if (paddings[0, 0] < 0 or paddings[0, 1] < 0 or - paddings[1, 0] < 0 or paddings[1, 1] < 0): - raise ValueError("paddings cannot be negative.") - - input_height = input_shape[1] + paddings[0, 0] + paddings[0, 1] - input_width = input_shape[2] + paddings[1, 0] + paddings[1, 1] - - if input_height % block_size > 0 or input_width % block_size > 0: - raise IndexError("block_size needs to divide both width and height.") - else: - input_height = tensor_shape.Dimension(None) - input_width = tensor_shape.Dimension(None) - - batch = input_shape[0] * block_size * block_size - height = input_height // block_size - width = input_width // block_size - depth = input_shape[3] - - return [tensor_shape.TensorShape([batch, height, width, depth])] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[1]) @ops.RegisterShape("BatchToSpace") @@ -2584,33 +2371,7 @@ def one_hot(indices, depth, on_value=None, off_value=None, @ops.RegisterShape("OneHot") def _OneHotShape(op): - """Shape function for the OneHot op. - - It closely follows the code in the .cc implementation. - - Args: - op: A OneHot Operation. - - Returns: - A single-element list containing the shape of the output. - - Raises: - ValueError: if axis < -1. - """ - indices_shape = op.inputs[0].get_shape() - indices_dims = indices_shape.ndims - depth = tensor_util.constant_value(op.inputs[1]) - axis = op.get_attr("axis") - - if axis < -1: - raise ValueError("axis must be >= -1") - - new_shape = None - if indices_dims is not None: - new_shape = indices_shape.as_list() - new_shape.insert(axis % (indices_dims + 1), depth) - - return [tensor_shape.TensorShape(new_shape)] + return common_shapes.call_cpp_shape_fn(op, input_tensors_needed=[1]) @ops.RegisterShape("PlaceholderWithDefault") |