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未验证 提交 4ec1251a 编写于 作者: L Leo Chen 提交者： GitHub
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Refine squeeze, test=develop (#25281) 

* refine squeeze, test=develop

* update squeeze, test=develop

* refine compile-time infershape, test=develop

* add more unittest, test=develop

* follow comments, test=develop

* add update_api, test=develop

* follow comments, test=develop
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paddle/fluid/operators/squeeze_op.cc
浏览文件 @ 4ec1251a
......@@ -13,61 +13,35 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/squeeze_op.h"
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class SqueezeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Squeeze");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Squeeze");
const auto &x_dims = ctx->GetInputDim("X");
// Check input tensor dims (<6) Eigen limit.
PADDLE_ENFORCE_LE(x_dims.size(), 6,
platform::errors::InvalidArgument(
"The dimensions of Input(X) "
"should be in the range of [1, 6] (Eigen limit)."
"But received X's dimensions = %d, X's shape=[%s].",
x_dims.size(), x_dims));
const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
auto out_dims = GetOutputShape(axes, x_dims);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx->ShareLoD("X", "Out");
}
}
static framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
const framework::DDim &in_dims) {
framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
const framework::DDim &in_dims,
bool is_runtime) {
size_t num_squeeze_dims = squeeze_dims.size();
int cnt_squeezed_dims = 0;
bool should_squeeze[9] = {false};
std::vector<bool> should_squeeze(in_dims.size(), false);
// Determines number of dimensions of output tensor after squeeze.
// Mark and count the dimensions need to be squeezed
// Mark dimensions need to be squeezed.
if (num_squeeze_dims == 0) {
for (int idx = 0; idx < in_dims.size(); ++idx) {
if (in_dims[idx] == 1) {
should_squeeze[idx] = true;
++cnt_squeezed_dims;
for (int i = 0; i < in_dims.size(); ++i) {
if (in_dims[i] == 1) {
should_squeeze[i] = true;
}
}
} else {
for (size_t idx = 0; idx < num_squeeze_dims; ++idx) {
int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + in_dims.size()
: squeeze_dims[idx];
for (size_t i = 0; i < num_squeeze_dims; ++i) {
int current = squeeze_dims[i] < 0 ? squeeze_dims[i] + in_dims.size()
: squeeze_dims[i];
PADDLE_ENFORCE_GE(
current, 0,
platform::errors::InvalidArgument(
......@@ -81,22 +55,56 @@ class SqueezeOp : public framework::OperatorWithKernel {
"But current axis is:%d, input tensor's shape = [%s].",
-in_dims.size(), in_dims.size() - 1, current, in_dims));
if (!(should_squeeze[current])) {
++cnt_squeezed_dims;
if (!should_squeeze[current]) {
if (is_runtime) {
// At run time, dim of 1 is allowed to squeeze
if (in_dims[current] == 1) {
should_squeeze[current] = true;
}
} else {
// At compile time, dim of -1 or 1 is allowed to squeeze
if (in_dims[current] == 1 || in_dims[current] == -1) {
should_squeeze[current] = true;
}
}
}
}
}
// Make output dimensions
std::vector<int64_t> output_shape(in_dims.size() - cnt_squeezed_dims, 0);
for (int in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
if (!should_squeeze[in_idx]) {
output_shape[out_idx++] = in_dims[in_idx];
std::vector<int64_t> output_shape;
for (int i = 0; i < in_dims.size(); ++i) {
if (!should_squeeze[i]) {
output_shape.push_back(in_dims[i]);
}
}
return framework::make_ddim(output_shape);
}
class SqueezeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Squeeze");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Squeeze");
const auto &x_dims = ctx->GetInputDim("X");
// Check input tensor dims (<6) Eigen limit.
PADDLE_ENFORCE_LE(x_dims.size(), 6,
platform::errors::InvalidArgument(
"The dimensions of Input(X) "
"should be in the range of [1, 6] (Eigen limit)."
"But received X's dimensions = %d, X's shape=[%s].",
x_dims.size(), x_dims));
const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
auto out_dims = GetOutputShape(axes, x_dims, false);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx->ShareLoD("X", "Out");
}
}
protected:
......@@ -183,7 +191,7 @@ class Squeeze2Op : public framework::OperatorWithKernel {
const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
auto out_dims = SqueezeOp::GetOutputShape(axes, x_dims);
auto out_dims = GetOutputShape(axes, x_dims, false);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
......
paddle/fluid/operators/squeeze_op.h
浏览文件 @ 4ec1251a
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -24,6 +25,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
const framework::DDim &in_dims, bool is_runtime);
template <typename DeviceContext, typename T>
class SqueezeKernel : public framework::OpKernel<T> {
public:
......@@ -33,7 +37,7 @@ class SqueezeKernel : public framework::OpKernel<T> {
auto &axes = context.Attr<std::vector<int>>("axes");
auto x_dims = in->dims();
auto out_dims = GetOutputShape(axes, x_dims);
auto out_dims = GetOutputShape(axes, x_dims, true);
out->mutable_data(context.GetPlace(), in->type());
framework::TensorCopy(
......@@ -41,64 +45,6 @@ class SqueezeKernel : public framework::OpKernel<T> {
context.template device_context<platform::DeviceContext>(), out);
out->Resize(out_dims);
}
static framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
const framework::DDim &in_dims) {
size_t num_squeeze_dims = squeeze_dims.size();
int cnt_squeezed_dims = 0;
bool should_squeeze[9] = {false};
// Determines number of dimensions of output tensor after squeeze.
// Mark and count the dimensions need to be squeezed
if (num_squeeze_dims == 0) {
for (int idx = 0; idx < in_dims.size(); ++idx) {
if (in_dims[idx] == 1) {
should_squeeze[idx] = true;
++cnt_squeezed_dims;
}
}
} else {
for (size_t idx = 0; idx < num_squeeze_dims; ++idx) {
int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + in_dims.size()
: squeeze_dims[idx];
PADDLE_ENFORCE_GE(
current, 0,
platform::errors::InvalidArgument(
"Each axis in Attr(axes) should be in the range of [%d, %d]"
"But current axis is:%d, input tensor's shape = [%s].",
-in_dims.size(), in_dims.size() - 1, current, in_dims));
PADDLE_ENFORCE_LT(
current, in_dims.size(),
platform::errors::InvalidArgument(
"Each axis in Attr(axes) should be in the range of [%d, %d]"
"But current axis is:%d, input tensor's shape = [%s].",
-in_dims.size(), in_dims.size() - 1, current, in_dims));
PADDLE_ENFORCE_EQ(in_dims[current], 1,
platform::errors::InvalidArgument(
"The size of axis that will be squeezed "
"should be equal to 1. But current axis = %d,"
"input tensor's shape = [%s].",
in_dims[current], in_dims));
if (!(should_squeeze[current])) {
++cnt_squeezed_dims;
}
should_squeeze[current] = true;
}
}
// Make output dimensions
std::vector<int64_t> output_shape(in_dims.size() - cnt_squeezed_dims, 0);
for (int in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
if (!should_squeeze[in_idx]) {
output_shape[out_idx++] = in_dims[in_idx];
}
}
return framework::make_ddim(output_shape);
}
};
template <typename DeviceContext, typename T>
......@@ -126,8 +72,7 @@ class Squeeze2Kernel : public framework::OpKernel<T> {
auto &axes = context.Attr<std::vector<int>>("axes");
auto x_dims = in->dims();
auto out_dims =
SqueezeKernel<DeviceContext, T>::GetOutputShape(axes, x_dims);
auto out_dims = GetOutputShape(axes, x_dims, true);
out->mutable_data(context.GetPlace(), in->type());
framework::TensorCopy(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 4ec1251a
......@@ -6203,6 +6203,10 @@ def squeeze(input, axes, name=None):
y = layers.squeeze(input=x, axes=[2]) # y.shape=[None, 5, 10]
"""
if in_dygraph_mode():
out, _ = core.ops.squeeze2(input, 'axes', axes)
return out
helper = LayerHelper("squeeze", **locals())
check_variable_and_dtype(
input, 'input',
......
python/paddle/fluid/tests/unittests/test_squeeze_op.py
浏览文件 @ 4ec1251a
......@@ -70,6 +70,14 @@ class TestSqueezeOp3(TestSqueezeOp):
self.new_shape = (6, 5, 1, 4)
# Correct: The demension of axis is not of size 1 remains unchanged.
class TestSqueezeOp4(TestSqueezeOp):
def init_test_case(self):
self.ori_shape = (6, 1, 5, 1, 4, 1)
self.axes = (1, 2)
self.new_shape = (6, 5, 1, 4, 1)
class TestSqueezeOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
......@@ -90,7 +98,7 @@ class API_TestSqueeze(unittest.TestCase):
with fluid.program_guard(fluid.Program(), fluid.Program()):
data1 = fluid.layers.data(
'data1', shape=[-1, 1, 10], dtype='float64')
result_squeeze = paddle.squeeze(data1, axes=[1])
result_squeeze = paddle.squeeze(data1, axis=[1])
place = fluid.CPUPlace()
exe = fluid.Executor(place)
input1 = np.random.random([5, 1, 10]).astype('float64')
......@@ -105,7 +113,25 @@ class API_TestDygraphSqueeze(unittest.TestCase):
with fluid.dygraph.guard():
input_1 = np.random.random([5, 1, 10]).astype("int32")
input = fluid.dygraph.to_variable(input_1)
output = paddle.squeeze(input, axes=[1])
output = paddle.squeeze(input, axis=[1])
out_np = output.numpy()
expected_out = np.squeeze(input_1, axis=1)
self.assertTrue(np.allclose(expected_out, out_np))
def test_axis_not_list(self):
with fluid.dygraph.guard():
input_1 = np.random.random([5, 1, 10]).astype("int32")
input = fluid.dygraph.to_variable(input_1)
output = paddle.squeeze(input, axis=1)
out_np = output.numpy()
expected_out = np.squeeze(input_1, axis=1)
self.assertTrue(np.allclose(expected_out, out_np))
def test_dimension_not_1(self):
with fluid.dygraph.guard():
input_1 = np.random.random([5, 1, 10]).astype("int32")
input = fluid.dygraph.to_variable(input_1)
output = paddle.squeeze(input, axis=(1, 2))
out_np = output.numpy()
expected_out = np.squeeze(input_1, axis=1)
self.assertTrue(np.allclose(expected_out, out_np))
......
python/paddle/tensor/manipulation.py
浏览文件 @ 4ec1251a
......@@ -40,6 +40,7 @@ from ..fluid.layers import scatter_nd_add #DEFINE_ALIAS
from ..fluid.layers import scatter_nd #DEFINE_ALIAS
from ..fluid.layers import shard_index #DEFINE_ALIAS
from ..fluid.layers import unique_with_counts #DEFINE_ALIAS
from ..fluid import layers
__all__ = [
'cast', 'concat', 'expand', 'expand_as', 'flatten', 'gather', 'gather_nd',
......@@ -442,83 +443,81 @@ def split(input, num_or_sections, dim=-1, name=None):
return outs
def squeeze(input, axes, out=None, name=None):
def squeeze(x, axis=None, name=None):
"""
:alias_main: paddle.squeeze
:alias: paddle.squeeze,paddle.tensor.squeeze,paddle.tensor.manipulation.squeeze
:alias: paddle.squeeze, paddle.tensor.squeeze, paddle.tensor.manipulation.squeeze
This OP will squeeze single-dimensional entries of input tensor's shape. If axes is provided, will
remove the dims by axes, the dims selected by axes should be one. If not provide axes, all dims equal
to one will be deleted.
This OP will squeeze the dimension(s) of size 1 of input tensor x's shape.
If axis is provided, it will remove the dimension(s) by given axis that of size 1.
If the dimension of given axis is not of size 1, the dimension remain unchanged.
If axis is not provided, all dims equal of size 1 will be removed.
.. code-block:: text
Case1:
Input:
X.shape = (1, 3, 1, 5)
axes = [0]
x.shape = [1, 3, 1, 5] # If axis is not provided, all dims equal of size 1 will be removed.
axis = None
Output:
Out.shape = (3, 1, 5)
out.shape = [3, 5]
Case2:
Input:
X.shape = (1, 3, 1, 5)
axes = []
x.shape = [1, 3, 1, 5] # If axis is provided, it will remove the dimension(s) by given axis that of size 1.
axis = 0
Output:
out.shape = [3, 1, 5]
Case4:
Input:
x.shape = [1, 3, 1, 5] # If the dimension of one given axis (3) is not of size 1, the dimension remain unchanged.
axis = [0, 2, 3]
Output:
Out.shape = (3, 5)
out.shape = [3, 5]
Case3:
Case4:
Input:
X.shape = [1,3,1,5]
axes = [-2]
x.shape = [1, 3, 1, 5] # If axis is negative, axis = axis + ndim (number of dimensions in x).
axis = [-2]
Output:
Out.shape = [1,3,5]
out.shape = [1, 3, 5]
Args:
input (Variable): The input Tensor. Support data type: float32, float64, int8, int32, int64.
axes (list): One integer or List of integers, indicating the dimensions to be squeezed.
Axes range is :math:`[-rank(input), rank(input))`.
If axes is negative, :math:`axes=axes+rank(input)`.
input (Tensor): The input Tensor. Support data type: float32, float64, int8, int32, int64.
axis (int|list|tuple, optional): An integer or list of integers, indicating the dimensions to be squeezed. Default is None.
The range of axis is :math:`[-ndim(input), ndim(input))`.
If axis is negative, :math:`axis = axis + ndim(input)`.
If axis is None, all the dimensions of input of size 1 will be removed.
name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
Returns:
Variable: Output squeezed Tensor. Data type is same as input Tensor.
Tensor: Output squeezed Tensor. Data type is same as input Tensor.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid as fluid
with fluid.dygraph.guard():
input_1 = np.random.random([5, 1, 10]).astype("int32")
# input is a variable which shape is [5, 1, 10]
input = fluid.dygraph.to_variable(input_1)
paddle.enable_imperative()
output = paddle.squeeze(input, axes=[1])
x = paddle.rand([5, 1, 10])
output = paddle.squeeze(x, axis=1)
# output.shape [5, 10]
"""
if axis is None:
axis = []
elif isinstance(axis, int):
axis = [axis]
elif isinstance(axis, tuple):
axis = list(axis)
helper = LayerHelper("squeeze", **locals())
check_variable_and_dtype(input, 'input',
['float32', 'float64', 'int8', 'int32', 'int64'],
'squeeze')
check_type(axes, 'axes', list, 'squeeze')
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="squeeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out,
"XShape": x_shape})
return out
return layers.squeeze(x, axis, name)
def unsqueeze(input, axes, out=None, name=None):
......
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