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add input type and dtype check for reshape op. (#20099)

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enhance shape error messages for reshape op.
test=develop
revert-20712-fix_depthwise_conv
liym27 6 years ago committed by Tao Luo
parent d77e770353
commit 670937e11d
3 changed files with 148 additions and 30 deletions
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81
paddle/fluid/operators/reshape_op.cc
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@ -27,8 +27,12 @@ inline std::vector<int> get_new_shape(
std::vector<int> 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<std::vector<int>>("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<int>(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<int>(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<int32>, 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<int32>, 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<Tensor<int32>>, 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<Tensor<int32>>, 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<std::vector<int>>(
"shape", "(std::vector<int>) Target shape of reshape operator.")
"shape",
"(std::vector<int>) Target shape of reshape operator."
"It has the lowest priority compare with Input(Shape) and "
" Input(ShapeTensor).")
.SetDefault({});
AddComment(R"DOC(
Reshape Operator.

33
python/paddle/fluid/layers/nn.py
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@ -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)

64
python/paddle/fluid/tests/unittests/test_reshape_op.py
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@ -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()

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