Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle
未验证 提交 b9421dc1 编写于 作者: H heyanru 提交者： GitHub
浏览文件
操作

[Fluid Clean] remove pixel_shuffle, fsp_matrix, where, sign, unique, unique_with_counts (#48441)

上级 a365024c
显示空白变更内容
内联 并排
Showing with 19 addition and 486 deletion
python/paddle/fluid/layers/nn.py
浏览文件 @ b9421dc1
......@@ -96,8 +96,6 @@ __all__ = [
'resize_trilinear',
'resize_nearest',
'relu',
'unique',
'unique_with_counts',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
......@@ -117,11 +115,7 @@ __all__ = [
'get_tensor_from_selected_rows',
'temporal_shift',
'py_func',
'pixel_shuffle',
'fsp_matrix',
'continuous_value_model',
'where',
'sign',
'unfold',
'deformable_roi_pooling',
'shard_index',
......@@ -7020,121 +7014,6 @@ py_func.registered_func = PyFuncRegistry.registered_func
py_func.registered_func_num = PyFuncRegistry.registered_func_num
def pixel_shuffle(x, upscale_factor):
"""
This op rearranges elements in a tensor of shape [N, C, H, W]
to a tensor of shape [N, C/r**2, H*r, W*r].
This is useful for implementing efficient sub-pixel convolution
with a stride of 1/r.
Please refer to the paper: `Real-Time Single Image and Video Super-Resolution
Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_ .
by Shi et. al (2016) for more details.
Parameters:
x(Variable): 4-D tensor, the data type should be float32 or float64.
upscale_factor(int): factor to increase spatial resolution.
Returns:
Out(Variable): Reshaped tensor according to the new dimension.
Raises:
ValueError: If the square of upscale_factor cannot divide the channels of input.
Examples:
.. code-block:: python
# declarative mode
import paddle.fluid as fluid
import numpy as np
input = fluid.data(name="input", shape=[2,9,4,4])
output = fluid.layers.pixel_shuffle(x=input, upscale_factor=3)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,9,4,4).astype("float32")
output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data},
fetch_list=[output],
return_numpy=True)
# print(output.shape)
# (2L, 1L, 12L, 12L)
"""
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pixel_shuffle')
helper = LayerHelper("pixel_shuffle", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
if not isinstance(upscale_factor, int):
raise TypeError("upscale factor must be int type")
helper.append_op(
type="pixel_shuffle",
inputs={"X": x},
outputs={"Out": out},
attrs={"upscale_factor": upscale_factor},
)
return out
def fsp_matrix(x, y):
"""
**FSP matrix op**
This op is used to calculate the flow of solution procedure (FSP) matrix of two 4-D Tensor feature maps.
Given feature map x with shape [x_channel, h, w] and feature map y with shape
[y_channel, h, w], we can get the fsp matrix of x and y in two steps:
1. reshape x into matrix with shape [x_channel, h * w] and reshape and
transpose y into matrix with shape [h * w, y_channel].
2. multiply x and y to get fsp matrix with shape [x_channel, y_channel].
The output is a batch of fsp matrices.
Args:
x (Variable): A 4-D Tensor feature map with shape [batch_size, x_channel, height, width].
A Tensor with type float32, float64.
y (Variable): A 4-D Tensor feature map with shape [batch_size, y_channel, height, width].
The y_channel can be different with the x_channel of Input(X)
while the other dimensions must be the same with Input(X)'s. A Tensor with
type float32, float64.
Returns:
fsp matrix (Variable): The output of FSP op with shape [batch_size, x_channel, y_channel].
The x_channel is the channel of x and the y_channel is the channel of y. A Tensor with
type float32, float64.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.data(name='data', shape=[None, 3, 32, 32])
feature_map_0 = fluid.layers.conv2d(data, num_filters=2,
filter_size=3)
feature_map_1 = fluid.layers.conv2d(feature_map_0, num_filters=2,
filter_size=1)
loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1)
"""
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'fsp_matrix')
check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'fsp_matrix')
helper = LayerHelper('fsp_matrix', **locals())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype(input_param_name='x')
)
helper.append_op(type='fsp', inputs={'X': x, 'Y': y}, outputs={'Out': out})
return out
def continuous_value_model(input, cvm, use_cvm=True):
r"""
......@@ -7192,192 +7071,6 @@ def continuous_value_model(input, cvm, use_cvm=True):
return out
def where(condition):
"""
Return an int64 tensor with rank 2, specifying the coordinate of true element in `condition`.
Args:
condition(Variable): A bool tensor with rank at least 1, the data type is bool.
Returns:
Variable, the output data type is int64. : The tensor variable storing a 2-D tensor, which involves all coordinate.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
# condition is a tensor [True, False, True]
condition = layers.assign(np.array([1, 0, 1], dtype='int32'))
condition = layers.cast(condition, 'bool')
out = layers.where(condition) # [[0], [2]]
# condition is a tensor [[True, False], [False, True]]
condition = layers.assign(np.array([[1, 0], [0, 1]], dtype='int32'))
condition = layers.cast(condition, 'bool')
out = layers.where(condition) # [[0, 0], [1, 1]]
# condition is a tensor [False, False, False]
condition = layers.assign(np.array([0, 0, 0], dtype='int32'))
condition = layers.cast(condition, 'bool')
out = layers.where(condition) # [[]]
"""
if in_dygraph_mode():
return _C_ops.nonzero(condition)
if _in_legacy_dygraph():
return _legacy_C_ops.where_index(condition)
helper = LayerHelper("where_index", **locals())
out = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.INT64
)
helper.append_op(
type='where_index',
inputs={'Condition': condition},
outputs={'Out': [out]},
)
return out
@deprecated(since="2.0.0", update_to="paddle.sign")
def sign(x):
r"""
This OP returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero.
Args:
x(Variable|numpy.ndarray): The input variable could be N-D tensor or N-D numpy array, \
the input data type is float32 or float64.
Returns:
Variable, the output data type is the same as input data type. : The output sign tensor with identical shape to input :attr:`x`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
# [1.0, 0.0, -1.0]
data = fluid.layers.sign(np.array([3.0, 0.0, -2.0], dtype='float32'))
"""
helper = LayerHelper("sign", **locals())
check_type(x, 'x', (Variable, np.ndarray), 'sign')
if isinstance(x, np.ndarray):
x = assign(x)
check_dtype(x.dtype, 'x', ['float16', 'float32', 'float64'], 'sign')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='sign', inputs={'X': [x]}, outputs={'Out': [out]})
return out
def unique(x, dtype='int32'):
r"""
Return a unique tensor for `x` and an index tensor pointing to this unique tensor.
Args:
x(Tensor): A 1-D input tensor, it's data type should be float32, float64, int32, int64.
dtype(np.dtype|str, optional): The type of index tensor: int32, int64. Default: int32.
Returns:
tuple: (out, index). `out` is the unique tensor for `x`, with identical dtype to `x`, and \
`index` is an index tensor pointing to `out`, by which user can recover the original `x` tensor.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
x = fluid.layers.assign(np.array([2, 3, 3, 1, 5, 3], dtype='int32'))
out, index = fluid.layers.unique(x) # out is [2, 3, 1, 5]; index is [0, 1, 1, 2, 3, 1]
"""
check_variable_and_dtype(
x, "x", ['float32', 'float64', 'int32', 'int64'], "unique"
)
helper = LayerHelper("unique", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
index = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='unique',
inputs={'X': x},
attrs={'dtype': convert_np_dtype_to_dtype_(dtype)},
outputs={'Out': [out], 'Index': [index]},
)
return out, index
def unique_with_counts(x, dtype='int32'):
r"""
This OP return a unique tensor for `x` , and count tensor that the count of unique result in raw input, \
and an index tensor pointing to this unique tensor.
**NOTICE**: This op support the variable type of Tensor only.
Args:
x(Variable): A 1-D input tensor with input shape of :math:`[N]` , the input data type is float32, float64, int32, int64.
dtype(np.dtype|core.VarDesc.VarType|str): The type of count and index tensor, it could be int32, int64. Default value is int32.
Returns:
tuple, the variable type in tuple is Tensor, the output :attr:`out` data type is the same as input :attr:`x`, \
and data type of output :attr:`index` and :attr:`count` will be int32 or int64.: The :attr:`out` is unique tensor for input :attr:`x`,\
the data shape is :math:`[K]`, the `K` may be different to the `N` in shape of :attr:`x`. :attr:`index` is an index tensor pointing\
to :attr:`out`, the data shape is :math:`[N]` , the data shape is the same as input :attr:`x`. :attr:`count` is count of unique element in\
the :attr:`x`, the data shape is :math:`[K]`, the data shape is the same as output :attr:`out`.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
x = fluid.layers.assign(np.array([2, 3, 3, 1, 5, 3], dtype='int32'))
out, index, count = fluid.layers.unique_with_counts(x) # out is [2, 3, 1, 5]; index is [0, 1, 1, 2, 3, 1]
# count is [1, 3, 1, 1]
# x.shape=(6,) out.shape=(4,), index.shape=(6,), count.shape=(4,)
"""
check_variable_and_dtype(
x, "x", ['float32', 'float64', 'int32', 'int64'], "unique_with_counts"
)
if not (dtype == 'int32' or dtype == 'int64'):
raise TypeError(
"Op unique_with_counts, index dtype must be int32 or int64"
)
if x is None or len(x.shape) != 1:
raise ValueError(
"Op unique_with_counts, x must not be null and size of dim must be 1"
)
helper = LayerHelper("unique_with_counts", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
index = helper.create_variable_for_type_inference(dtype)
count = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='unique_with_counts',
inputs={'X': x},
attrs={'dtype': convert_np_dtype_to_dtype_(dtype)},
outputs={'Out': [out], 'Index': [index], 'Count': [count]},
)
return out, index, count
def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
r"""
......
python/paddle/fluid/tests/unittests/mlu/test_where_index_op_mlu.py
浏览文件 @ b9421dc1
......@@ -108,7 +108,7 @@ class TestWhereOpError(unittest.TestCase):
def test_api(self):
with program_guard(Program(), Program()):
cond = fluid.layers.data(name='cond', shape=[4], dtype='bool')
result = fluid.layers.where(cond)
result = paddle.nonzero(cond)
exe = fluid.Executor(paddle.device.MLUPlace(0))
exe.run(fluid.default_startup_program())
......@@ -119,7 +119,7 @@ class TestWhereOpError(unittest.TestCase):
class TestWhereRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.where([10])
paddle.nonzero([10])
self.assertRaises(TypeError, test_type)
......
python/paddle/fluid/tests/unittests/npu/test_where_index_npu.py
浏览文件 @ b9421dc1
......@@ -98,7 +98,7 @@ class TestWhereOpError(unittest.TestCase):
def test_api(self):
with program_guard(Program(), Program()):
cond = fluid.layers.data(name='cond', shape=[4], dtype='bool')
result = fluid.layers.where(cond)
result = paddle.nonzero(cond)
exe = fluid.Executor(paddle.NPUPlace(0))
exe.run(fluid.default_startup_program())
......@@ -109,7 +109,7 @@ class TestWhereOpError(unittest.TestCase):
class TestWhereRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.where([10])
paddle.nonzero([10])
self.assertRaises(TypeError, test_type)
......
python/paddle/fluid/tests/unittests/test_fsp_op.py
浏览文件 @ b9421dc1
......@@ -17,8 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
def fsp_matrix(a, b):
batch = a.shape[0]
......@@ -62,30 +60,5 @@ class TestFSPOp(OpTest):
self.check_grad(['X', 'Y'], 'Out')
class BadInputTest(unittest.TestCase):
def test_error(self):
with fluid.program_guard(fluid.Program()):
def test_bad_x():
data = fluid.layers.data(name='data', shape=[3, 32, 32])
feature_map_0 = [1, 2, 3]
feature_map_1 = fluid.layers.conv2d(
data, num_filters=2, filter_size=3
)
loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1)
self.assertRaises(TypeError, test_bad_x)
def test_bad_y():
data = fluid.layers.data(name='data', shape=[3, 32, 32])
feature_map_0 = fluid.layers.conv2d(
data, num_filters=2, filter_size=3
)
feature_map_1 = [1, 2, 3]
loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1)
self.assertRaises(TypeError, test_bad_y)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ b9421dc1
......@@ -3677,21 +3677,12 @@ class TestBook(LayerTest):
out = layers.temporal_shift(x, seg_num=2, shift_ratio=0.2)
return out
def make_fsp_matrix(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32")
y = self._get_data(name="Y", shape=[8, 4, 4], dtype="float32")
out = layers.fsp_matrix(x, y)
return out
def make_pixel_shuffle(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name="X", shape=[9, 4, 4], dtype="float32")
out = layers.pixel_shuffle(x, upscale_factor=3)
out = paddle.nn.functional.pixel_shuffle(x, upscale_factor=3)
return out
def make_mse_loss(self):
......
python/paddle/fluid/tests/unittests/test_sign_op.py
浏览文件 @ b9421dc1
......@@ -46,7 +46,7 @@ class TestSignOpError(unittest.TestCase):
with program_guard(Program(), Program()):
# The input type of sign_op must be Variable or numpy.ndarray.
input1 = 12
self.assertRaises(TypeError, fluid.layers.sign, input1)
self.assertRaises(TypeError, paddle.sign, input1)
# The input dtype of sign_op must be float16, float32, float64.
input2 = fluid.layers.data(
name='input2', shape=[12, 10], dtype="int32"
......@@ -54,12 +54,12 @@ class TestSignOpError(unittest.TestCase):
input3 = fluid.layers.data(
name='input3', shape=[12, 10], dtype="int64"
)
self.assertRaises(TypeError, fluid.layers.sign, input2)
self.assertRaises(TypeError, fluid.layers.sign, input3)
self.assertRaises(TypeError, paddle.sign, input2)
self.assertRaises(TypeError, paddle.sign, input3)
input4 = fluid.layers.data(
name='input4', shape=[4], dtype="float16"
)
fluid.layers.sign(input4)
paddle.sign(input4)
class TestSignAPI(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_unique.py
浏览文件 @ b9421dc1
......@@ -74,13 +74,13 @@ class TestRandom(TestUniqueOp):
class TestUniqueRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.unique([10])
paddle.unique([10])
self.assertRaises(TypeError, test_type)
def test_dtype():
data = fluid.data(shape=[10], dtype="float16", name="input")
fluid.layers.unique(data)
paddle.unique(data)
self.assertRaises(TypeError, test_dtype)
......
python/paddle/fluid/tests/unittests/test_unique_with_counts.py
浏览文件 @ b9421dc1
......@@ -17,6 +17,7 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
......@@ -82,13 +83,13 @@ class TestRandom(TestUniqueWithCountsOp):
class TestUniqueWithCountsRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.unique_with_counts([10])
paddle.unique([10])
self.assertRaises(TypeError, test_type)
def test_dtype():
data = fluid.data(shape=[10], dtype="float16", name="input")
fluid.layers.unique_with_counts(data)
paddle.unique(data)
self.assertRaises(TypeError, test_dtype)
......
python/paddle/fluid/tests/unittests/test_var_base.py
浏览文件 @ b9421dc1
......@@ -1289,7 +1289,7 @@ class TestVarBase(unittest.TestCase):
def func_test_tensor_str_shape_with_zero(self):
paddle.disable_static(paddle.CPUPlace())
x = paddle.ones((10, 10))
y = paddle.fluid.layers.where(x == 0)
y = paddle.nonzero(x == 0)
a_str = str(y)
expected = '''Tensor(shape=[0, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
......
python/paddle/fluid/tests/unittests/test_where_index.py 已删除 100644 → 0
浏览文件 @ a365024c
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
from paddle.fluid.op import Operator
class TestWhereIndexOp(OpTest):
def setUp(self):
self.op_type = "where_index"
self.init_config()
def test_check_output(self):
self.check_output()
def init_config(self):
self.inputs = {
'Condition': np.array([True, False, True]),
}
self.outputs = {'Out': np.array([[0], [2]], dtype='int64')}
class TestAllFalse(unittest.TestCase):
def setUp(self):
self.op_type = "where_index"
self.init_config()
def check_with_place(self, place):
scope = core.Scope()
condition = scope.var('Condition').get_tensor()
condition.set(self.cond_data, place)
out = scope.var("Out").get_tensor()
out.set(np.full(self.shape, 0).astype('int64'), place)
op = Operator("where_index", Condition="Condition", Out="Out")
op.run(scope, place)
out_array = np.array(out)
self.assertTrue((out_array == self.out_data).all())
def init_config(self):
self.cond_data = np.array([False, False, False])
self.shape = (3, 1)
self.out_data = np.array([], dtype='int64')
def test_all_false(self):
self.check_with_place(core.CPUPlace())
if core.is_compiled_with_cuda():
self.check_with_place(core.CUDAPlace(0))
class TestRank2(TestWhereIndexOp):
def init_config(self):
self.inputs = {
'Condition': np.array([[True, False], [False, True]]),
}
self.outputs = {'Out': np.array([[0, 0], [1, 1]], dtype='int64')}
class TestRank3(TestWhereIndexOp):
def init_config(self):
self.inputs = {
'Condition': np.array(
[
[[True, False], [False, True]],
[[False, True], [True, False]],
[[False, False], [False, True]],
]
),
}
self.outputs = {
'Out': np.array(
[[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0], [2, 1, 1]],
dtype='int64',
)
}
class TestWhereOpError(unittest.TestCase):
def test_api(self):
with program_guard(Program(), Program()):
cond = fluid.layers.data(name='cond', shape=[4], dtype='bool')
result = fluid.layers.where(cond)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
cond_i = np.array([True, False, False, False]).astype("bool")
out = exe.run(fluid.default_main_program(), feed={'cond': cond_i})
class TestWhereRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.where([10])
self.assertRaises(TypeError, test_type)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_where_index_xpu.py
浏览文件 @ b9421dc1
......@@ -102,7 +102,7 @@ class TestWhereOpError(unittest.TestCase):
def test_api(self):
with program_guard(Program(), Program()):
cond = fluid.layers.data(name='cond', shape=[4], dtype='bool')
result = fluid.layers.where(cond)
result = paddle.nonzero(cond)
exe = fluid.Executor(paddle.XPUPlace(0))
exe.run(fluid.default_startup_program())
......@@ -113,7 +113,7 @@ class TestWhereOpError(unittest.TestCase):
class TestWhereRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.where([10])
paddle.nonzero([10])
self.assertRaises(TypeError, test_type)
......
python/paddle/fluid/variable_index.py
浏览文件 @ b9421dc1
......@@ -331,10 +331,9 @@ def get_value_for_bool_tensor(var, item):
)
def idx_not_empty(var, item):
from .layers.nn import where
from ..tensor import gather_nd
bool_2_idx = where(item == True)
bool_2_idx = paddle.nonzero(item == True)
return gather_nd(var, bool_2_idx)
def idx_empty(var):
......@@ -864,13 +863,12 @@ def set_value_for_bool_tensor(var, item, value):
def idx_not_empty(var, item, value):
from .framework import Variable
from .layers import assign
from .layers.nn import where
from ..tensor import gather_nd, scatter_nd_add
if not isinstance(value, Variable):
value = assign(value).cast(var.dtype)
idx = where(item)
idx = paddle.nonzero(item)
gather_val = gather_nd(var, idx)
gather_val_new = value - gather_val
out = scatter_nd_add(var, idx, gather_val_new)
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或