From 670937e11dca65253c5693a0328284cd4003cb2f Mon Sep 17 00:00:00 2001 From: liym27 <33742067+liym27@users.noreply.github.com> Date: Thu, 10 Oct 2019 10:09:52 +0800 Subject: [PATCH] add input type and dtype check for reshape op. (#20099) enhance shape error messages for reshape op. test=develop --- paddle/fluid/operators/reshape_op.cc | 81 +++++++++++++------ python/paddle/fluid/layers/nn.py | 33 ++++++-- .../fluid/tests/unittests/test_reshape_op.py | 64 +++++++++++++++ 3 files changed, 148 insertions(+), 30 deletions(-) diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc index 0059921c04..ccf4333084 100644 --- a/paddle/fluid/operators/reshape_op.cc +++ b/paddle/fluid/operators/reshape_op.cc @@ -27,8 +27,12 @@ inline std::vector get_new_shape( std::vector vec_new_shape; for (size_t i = 0; i < list_new_shape_tensor.size(); ++i) { auto tensor = list_new_shape_tensor[i]; - PADDLE_ENFORCE_EQ(tensor->dims(), framework::make_ddim({1}), - "shape of dim tensor should be [1]"); + PADDLE_ENFORCE_EQ( + tensor->dims(), framework::make_ddim({1}), + "ShapeError: If the element type of 'shape' in ReshapeOp is Tensor, " + "the element's shape must be [1]. But received the element's shape " + "is [%s]", + tensor->dims()); if (platform::is_gpu_place(tensor->place())) { framework::Tensor temp; TensorCopySync(*tensor, platform::CPUPlace(), &temp); @@ -58,8 +62,12 @@ class ReshapeOp : public framework::OperatorWithKernel { if (ctx->HasInputs("ShapeTensor")) { // top prority shape auto ShapeTensor = ctx->Inputs("ShapeTensor"); - PADDLE_ENFORCE_GT(ShapeTensor.size(), 0, - "The size of Input(ShapeTensor) can't be zero"); + PADDLE_ENFORCE_GT( + ShapeTensor.size(), 0, + "ShapeError: When `shape` in ReshapeOp is a list or tuple " + "which contains Tensor, the shape's size can't be zero. " + "But received shape's size is %d.", + ShapeTensor.size()); auto infer_shape = ctx->Attrs().Get>("shape"); const int64_t copy_dim_val = 0; auto in_dims = ctx->GetInputDim("X"); @@ -67,8 +75,10 @@ class ReshapeOp : public framework::OperatorWithKernel { if (infer_shape[i] == copy_dim_val) { PADDLE_ENFORCE_LT( static_cast(i), in_dims.size(), - "The dimension of data to copy from input must be less " - "than the dimension of input."); + "ShapeError: The index of 0 in `shape` must be less than " + "the input tensor X's dimensions. But received shape[%d] " + "= 0, X's dimensions = %d, X's shape = [%s].", + i, in_dims.size(), in_dims); infer_shape[i] = in_dims[i]; } } @@ -98,8 +108,10 @@ class ReshapeOp : public framework::OperatorWithKernel { return; } - PADDLE_ENFORCE_EQ(!shape.empty(), true, - "The shape information must be set by Attr(shape)."); + PADDLE_ENFORCE_EQ( + !shape.empty(), true, + "ShapeError: The parameter 'shape' in ReshapeOp must be set. " + "But received 'shape' is empty."); auto x_dims = ctx->GetInputDim("X"); auto out_dims = ValidateShape(shape, x_dims); ctx->SetOutputDim("Out", out_dims); @@ -128,18 +140,25 @@ class ReshapeOp : public framework::OperatorWithKernel { if (shape[i] == unk_dim_val) { PADDLE_ENFORCE_EQ( unk_dim_idx, -1, - "Only one input dimension of Attr(shape) can be unknown."); + "ShapeError: Only one dimension value of 'shape' in ReshapeOp can " + "be -1. But received shape = [%s], shape[%d] is also -1.", + framework::make_ddim(shape), i); unk_dim_idx = i; } else if (shape[i] == copy_dim_val) { PADDLE_ENFORCE_LT( static_cast(i), in_dims.size(), - "The index of dimension to copy from input shape must be less " - "than the size of input shape."); + "ShapeError: The index of 0 in `shape` must be less than " + "the input tensor X's dimensions. " + "But received shape = [%s], shape[%d] = 0, X's shape = [%s], " + "X's dimensions = %d.", + framework::make_ddim(shape), i, in_dims, in_dims.size()); } else { PADDLE_ENFORCE_GT( shape[i], 0, - "Each input dimension of Attr(shape) must not be negtive except " - "one unknown dimension."); + "ShapeError: Each dimension value of 'shape' in ReshapeOp must not " + "be negtive except one unknown dimension. " + "But received shape = [%s], shape[%d] = %d.", + framework::make_ddim(shape), i, shape[i]); } capacity *= (shape[i] ? shape[i] : in_dims[i]); @@ -155,12 +174,25 @@ class ReshapeOp : public framework::OperatorWithKernel { // the following check will fail. output_shape[unk_dim_idx] = -in_size / capacity; PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size, - "Invalid shape is given."); + "ShapeError: The 'shape' in ReshapeOp is invalid. " + "The input tensor X'size must be divisible by known " + "capacity of 'shape'. " + "But received X's shape = [%s], X's size = %d, " + "'shape' is [%s], known " + "capacity of 'shape' is %d.", + in_dims, in_size, framework::make_ddim(shape), + capacity); } else { output_shape[unk_dim_idx] = -1; } } else { - PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given."); + PADDLE_ENFORCE_EQ( + capacity, in_size, + "ShapeError: The 'shape' in ReshapeOp is invalid. " + "The input tensor X'size must be equal to the capacity of 'shape'. " + "But received X's shape = [%s], X's size = %d, 'shape' is [%s], the " + "capacity of 'shape' is %d.", + in_dims, in_size, framework::make_ddim(shape), capacity); } return framework::make_ddim(output_shape); } @@ -188,22 +220,25 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker { void Make() override { AddInput("X", "(Tensor). The input tensor of reshape operator."); AddInput("Shape", - "(Tensor, optional). If provided, reshape according to " - "this given shape. That is to say it has a higher priority than " - "the shape attribute, while the shape attribute still should be " + "(Tensor, optional). Target shape of reshape operator. " + "It has a higher priority than Attr(shape) but a lower priority " + "than Input(ShapeTensor). The Attr(shape) still should be " "set correctly to gurantee shape inference in compile time.") .AsDispensable(); AddInput( "ShapeTensor", - "(vector>, optional). If provided, reshape will use this" - "The shape of the tensor in vector MUST BE [1]" - "it has the highest priority compare with Input(Shape) and " - "attr(shape).") + "(vector>, optional). Target shape of reshape operator. " + "It has the highest priority compare with Input(Shape) and " + "Attr(shape)." + "The shape of the element in vector must be [1].") .AsDuplicable() .AsDispensable(); AddOutput("Out", "(Tensor). The output tensor of reshape operator."); AddAttr>( - "shape", "(std::vector) Target shape of reshape operator.") + "shape", + "(std::vector) Target shape of reshape operator." + "It has the lowest priority compare with Input(Shape) and " + " Input(ShapeTensor).") .SetDefault({}); AddComment(R"DOC( Reshape Operator. diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 95e450bddc..761f6ec2ac 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -8208,13 +8208,25 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): dim = fluid.layers.fill_constant([1], "int32", 5) reshaped_2 = fluid.layers.reshape(data_2, shape=[dim, 10]) """ + if not isinstance(x, Variable): + raise TypeError( + "The type of 'x' in reshape must be Variable, but received %s." % + (type(x))) + + if convert_dtype(x.dtype) not in ['float32', 'float64', 'int32', 'int64']: + raise TypeError( + "The data type of 'x' in reshape must be float32, float64, int32 or int64, " + "but received %s." % (convert_dtype(x.dtype))) if not isinstance(shape, (list, tuple, Variable)): raise TypeError( - "Input shape must be an Variable or python list or tuple.") + "The type of 'shape' in reshape must be Variable, list or tuple, but " + "received %s." % (type(shape))) if not isinstance(actual_shape, Variable) and (actual_shape is not None): - raise TypeError("actual_shape should either be Variable or None.") + raise TypeError( + "The type of 'actual_shape' in reshape must be Variable " + "or None, but received %s." % (type(actual_shape))) helper = LayerHelper("reshape2", **locals()) inputs = {"X": x} @@ -8249,15 +8261,21 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): attrs_shape.append(dim_size) if dim_size == -1: assert unk_dim_idx == -1, ( - "Only one dimension in shape can be unknown.") + "Only one dimension value of 'shape' in reshape can " + "be -1. But received shape[%d] is also -1." % dim_idx) unk_dim_idx = dim_idx elif dim_size == 0: assert dim_idx < len(x.shape), ( - "The indice of 0s in shape can not exceed Rank(X).") + "The index of 0 in `shape` must be less than " + "the input tensor X's dimensions. " + "But received shape[%d] = 0, X's dimensions = %d." % + (dim_idx, len(x.shape))) else: assert dim_size > 0, ( - "Each dimension size given in shape must not be negtive " - "except one unknown dimension.") + "Each dimension value of 'shape' in reshape must not " + "be negtive except one unknown dimension. " + "But received shape[%d] = %s." % + (dim_idx, str(dim_size))) return attrs_shape if in_dygraph_mode(): @@ -8269,7 +8287,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): inputs["Shape"] = shape elif isinstance(shape, (list, tuple)): assert len(shape) > 0, ( - "The size of argument(shape) can't be zero.") + "The size of 'shape' in reshape can't be zero, " + "but received %s." % len(shape)) attrs["shape"] = get_attr_shape(shape) if contain_var(shape): inputs['ShapeTensor'] = get_new_shape_tensor(shape) diff --git a/python/paddle/fluid/tests/unittests/test_reshape_op.py b/python/paddle/fluid/tests/unittests/test_reshape_op.py index beaffd055c..ea43b6b603 100644 --- a/python/paddle/fluid/tests/unittests/test_reshape_op.py +++ b/python/paddle/fluid/tests/unittests/test_reshape_op.py @@ -19,6 +19,7 @@ import numpy as np from op_test import OpTest import paddle.fluid as fluid +from paddle.fluid import compiler, Program, program_guard # situation 1: have shape( list, no tensor), no actual shape(Tensor) @@ -202,10 +203,13 @@ class TestReshapeAPI(OpTest): # situation 1: have shape( list, no tensor), no actual shape(Tensor) out_1 = fluid.layers.reshape(x, shape) + # situation 2: have shape(list, no tensor), have actual shape(Tensor) out_2 = fluid.layers.reshape(x, shape=shape, actual_shape=actual_shape) + # Situation 3: have shape(list, have tensor), no actual shape(Tensor) out_3 = fluid.layers.reshape(x, shape=[positive_five, 10]) + # Situation 4: have shape(Tensor), no actual shape(Tensor) out_4 = fluid.layers.reshape(x, shape=actual_shape) @@ -222,5 +226,65 @@ class TestReshapeAPI(OpTest): assert np.array_equal(res_4, input.reshape(shape)) +# Test Input Error +class TestReshapeOpError(OpTest): + def test_errors(self): + with program_guard(Program(), Program()): + # The x type of reshape_op must be Variable. + def test_x_type(): + x1 = fluid.create_lod_tensor( + np.array([[-1]]), [[1]], fluid.CPUPlace()) + fluid.layers.reshape(x1, shape=[1]) + + self.assertRaises(TypeError, test_x_type) + + # The x dtype of reshape_op must be float32, float64, int32 or int64. + def test_x_dtype(): + x2 = fluid.layers.data( + name="x2", + shape=[2, 25], + append_batch_size=False, + dtype="float16") + fluid.layers.reshape(x2, shape=[2, 5, 5]) + + self.assertRaises(TypeError, test_x_dtype) + + x3 = fluid.layers.data( + name="x3", + shape=[2, 25], + append_batch_size=False, + dtype="float32") + + # The argument shape's type of reshape_op must be list, tuple or Variable. + def test_shape_type(): + fluid.layers.reshape(x3, shape=1) + + self.assertRaises(TypeError, test_shape_type) + + # The argument actual_shape's type of reshape_op must be Variable or None. + def test_actual_shape_type(): + fluid.layers.reshape(x3, shape=[25, 2], actual_shape=1) + + self.assertRaises(TypeError, test_actual_shape_type) + + # The argument shape have more than one -1. + def test_shape_1(): + fluid.layers.reshape(x3, shape=[-1, -1, 5]) + + self.assertRaises(AssertionError, test_shape_1) + + # The argument shape have element 0 whose index exceed the input dimension. + def test_shape_2(): + fluid.layers.reshape(x3, [2, 5, 5, 0]) + + self.assertRaises(AssertionError, test_shape_2) + + # The argument shape have more than one negtive value. + def test_shape_3(): + fluid.layers.reshape(x3, [-1, -2, 5]) + + self.assertRaises(AssertionError, test_shape_3) + + if __name__ == "__main__": unittest.main() -- GitLab