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modify roll test=develop (#25321)

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fix_copy_if_different
yaoxuefeng 5 years ago committed by GitHub
parent bdc2c2dba8
commit c42d662e2a
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 70 additions and 47 deletions
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4
paddle/fluid/operators/roll_op.cc
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@ -33,7 +33,7 @@ class RollOp : public framework::OperatorWithKernel {
platform::errors::InvalidArgument(
"Output(Out) of RollOp should not be null."));
auto dims = ctx->Attrs().Get<std::vector<int64_t>>("dims");
auto dims = ctx->Attrs().Get<std::vector<int64_t>>("axis");
auto shifts = ctx->Attrs().Get<std::vector<int64_t>>("shifts");
PADDLE_ENFORCE_EQ(dims.size(), shifts.size(),
@ -92,7 +92,7 @@ class RollOpMaker : public framework::OpProtoAndCheckerMaker {
"of the tensor are shifted.")
.SetDefault({});
AddAttr<std::vector<int64_t>>(
"dims",
"axis",
"Axis along which to roll. It must have the same size "
"with shifts.")
.SetDefault({});

8
paddle/fluid/operators/roll_op.h
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@ -82,7 +82,7 @@ class RollKernel : public framework::OpKernel<T> {
auto& input = input_var->Get<LoDTensor>();
auto* output = output_var->GetMutable<LoDTensor>();
std::vector<int64_t> shifts = context.Attr<std::vector<int64_t>>("shifts");
std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("dims");
std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
std::vector<T> out_vec;
TensorToVector(input, context.device_context(), &out_vec);
@ -94,8 +94,8 @@ class RollKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(
dims[i] < input_dim.size() && dims[i] >= (0 - input_dim.size()), true,
platform::errors::OutOfRange(
"Attr(dims[%d]) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(dims[%d]) = %d.",
"Attr(axis[%d]) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(axis[%d]) = %d.",
i, input_dim.size(), input_dim.size() - 1, i, dims[i]));
shift_along_dim(out_vec.data(), input_dim, dims[i], shifts[i]);
}
@ -114,7 +114,7 @@ class RollGradKernel : public framework::OpKernel<T> {
auto& input = input_var->Get<LoDTensor>();
auto* output = output_var->GetMutable<LoDTensor>();
std::vector<int64_t> shifts = context.Attr<std::vector<int64_t>>("shifts");
std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("dims");
std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
std::vector<T> out_vec;
TensorToVector(input, context.device_context(), &out_vec);

26
python/paddle/fluid/tests/unittests/test_roll_op.py
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@ -28,17 +28,17 @@ class TestRollOp(OpTest):
self.op_type = "roll"
self.init_dtype_type()
self.inputs = {'X': np.random.random(self.x_shape).astype(self.dtype)}
self.attrs = {'shifts': self.shifts, 'dims': self.dims}
self.attrs = {'shifts': self.shifts, 'axis': self.axis}
self.outputs = {
'Out': np.roll(self.inputs['X'], self.attrs['shifts'],
self.attrs['dims'])
self.attrs['axis'])
}
def init_dtype_type(self):
self.dtype = np.float64
self.x_shape = (100, 4, 5)
self.shifts = [101, -1]
self.dims = [0, -2]
self.axis = [0, -2]
def test_check_output(self):
self.check_output()
@ -52,7 +52,7 @@ class TestRollOpCase2(TestRollOp):
self.dtype = np.float32
self.x_shape = (100, 10, 5)
self.shifts = [8, -1]
self.dims = [-1, -2]
self.axis = [-1, -2]
class TestRollAPI(unittest.TestCase):
@ -78,7 +78,7 @@ class TestRollAPI(unittest.TestCase):
# case 2:
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 3])
z = paddle.roll(x, shifts=1, dims=0)
z = paddle.roll(x, shifts=1, axis=0)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': self.data_x},
fetch_list=[z.name],
@ -101,12 +101,26 @@ class TestRollAPI(unittest.TestCase):
# case 2:
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(self.data_x)
z = paddle.roll(x, shifts=1, dims=0)
z = paddle.roll(x, shifts=1, axis=0)
np_z = z.numpy()
expect_out = np.array([[7.0, 8.0, 9.0], [1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]])
self.assertTrue(np.allclose(expect_out, np_z))
def test_roll_op_false(self):
self.input_data()
def test_axis_out_range():
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 3])
z = paddle.roll(x, shifts=1, axis=10)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': self.data_x},
fetch_list=[z.name],
return_numpy=False)
self.assertRaises(ValueError, test_axis_out_range)
if __name__ == "__main__":
unittest.main()

79
python/paddle/tensor/manipulation.py
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@ -104,23 +104,24 @@ def flip(input, dims, name=None):
return out
def roll(input, shifts, dims=None):
def roll(x, shifts, axis=None, name=None):
"""
:alias_main: paddle.roll
:alias: paddle.roll,paddle.tensor.roll,paddle.tensor.manipulation.roll
Roll the `input` tensor along the given dimension(s). Elements that are shifted beyond
the last position are re-introduced at the first position. If a dimension is not specified,
Roll the `x` tensor along the given axis(axes). With specific 'shifts', Elements that
roll beyond the last position are re-introduced at the first according to 'shifts'.
If a axis is not specified,
the tensor will be flattened before rolling and then restored to the original shape.
Args:
input (Variable): The input tensor variable.
x (Variable): The x tensor variable as input.
shifts (int|list|tuple): The number of places by which the elements
of the `input` tensor are shifted.
dims (int|list|tuple|None): Dimentions along which to roll.
of the `x` tensor are shifted.
axis (int|list|tuple|None): axis(axes) along which to roll.
Returns:
Variable: A Tensor with same data type as `input`.
Variable: A Tensor with same data type as `x`.
Examples:
.. code-block:: python
@ -131,48 +132,56 @@ def roll(input, shifts, dims=None):
data = np.array([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]])
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(data)
out_z1 = paddle.roll(x, shifts=1)
print(out_z1.numpy())
#[[9. 1. 2.]
# [3. 4. 5.]
# [6. 7. 8.]]
out_z2 = paddle.roll(x, shifts=1, dims=0)
print(out_z2.numpy())
#[[7. 8. 9.]
# [1. 2. 3.]
# [4. 5. 6.]]
paddle.enable_imperative()
x = paddle.imperative.to_variable(data)
out_z1 = paddle.roll(x, shifts=1)
print(out_z1.numpy())
#[[9. 1. 2.]
# [3. 4. 5.]
# [6. 7. 8.]]
out_z2 = paddle.roll(x, shifts=1, axis=0)
print(out_z2.numpy())
#[[7. 8. 9.]
# [1. 2. 3.]
# [4. 5. 6.]]
"""
helper = LayerHelper("roll", **locals())
origin_shape = input.shape
origin_shape = x.shape
if type(shifts) == int:
shifts = [shifts]
if type(dims) == int:
dims = [dims]
if dims:
check_type(dims, 'dims', (list, tuple), 'roll')
if type(axis) == int:
axis = [axis]
len_origin_shape = len(origin_shape)
if axis:
for i in range(len(axis)):
if axis[i] >= len_origin_shape or axis[i] < -len_origin_shape:
raise ValueError(
"axis is out of range, it should be in range [{}, {}), but received {}".
format(-len_origin_shape, len_origin_shape, axis))
if axis:
check_type(axis, 'axis', (list, tuple), 'roll')
check_type(shifts, 'shifts', (list, tuple), 'roll')
if in_dygraph_mode():
if dims is None:
input = core.ops.reshape(input, 'shape', [-1, 1])
dims = [0]
out = core.ops.roll(input, 'dims', dims, 'shifts', shifts)
if axis is None:
x = core.ops.reshape(x, 'shape', [-1, 1])
axis = [0]
out = core.ops.roll(x, 'axis', axis, 'shifts', shifts)
return core.ops.reshape(out, 'shape', origin_shape)
out = helper.create_variable_for_type_inference(input.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
if dims is None:
input = reshape(input, shape=[-1, 1])
dims = [0]
if axis is None:
x = reshape(x, shape=[-1, 1])
axis = [0]
helper.append_op(
type='roll',
inputs={'X': input},
inputs={'X': x},
outputs={'Out': out},
attrs={'dims': dims,
attrs={'axis': axis,
'shifts': shifts})
out = reshape(out, shape=origin_shape, inplace=True)
return out

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