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未验证 提交 aa669c74 编写于 作者: J JYChen 提交者： GitHub
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[Fluid Clean] move fluid.layers.deformable_conv to static.nn (#47435) 

* move fluid.layers.deformable_conv to static.nn

* modify example code

* fix doc

* Update common.py
Co-authored-by: NLigoml <39876205+Ligoml@users.noreply.github.com>
上级 f778c170
隐藏空白更改
内联 并排
Showing with 268 addition and 421 deletion
python/paddle/fluid/layers/nn.py
浏览文件 @ aa669c74
......@@ -202,7 +202,6 @@ __all__ = [
'continuous_value_model',
'where',
'sign',
'deformable_conv',
'unfold',
'deformable_roi_pooling',
'filter_by_instag',
......@@ -15607,250 +15606,6 @@ def unique_with_counts(x, dtype='int32'):
return out, index, count
def deformable_conv(
input,
offset,
mask,
num_filters,
filter_size,
stride=1,
padding=0,
dilation=1,
groups=None,
deformable_groups=None,
im2col_step=None,
param_attr=None,
bias_attr=None,
modulated=True,
name=None,
):
r"""
:api_attr: Static Graph
**Deformable Convolution op**
Compute 2-D deformable convolution on 4-D input.
Given input image x, output feature map y, the deformable convolution operation can be expressed as follow:
Deformable Convolution v2:
.. math::
y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k}
Deformable Convolution v1:
.. math::
y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k)}
Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location,
Which :math:`\Delta m_k` is one in deformable convolution v1. Please refer to `Deformable ConvNets v2: More Deformable, Better Results
<https://arxiv.org/abs/1811.11168v2>`_ and `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_.
Example:
- Input:
Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
Offset shape: :math:`(N, 2 * deformable\_groups * H_f * H_w, H_{in}, W_{in})`
Mask shape: :math:`(N, deformable\_groups * H_f * H_w, H_{in}, W_{in})`
- Output:
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
.. math::
H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args:
input (Variable): The input image with [N, C, H, W] format. A Tensor with type
float32, float64.
offset (Variable): The input coordinate offset of deformable convolution layer.
A Tensor with type float32, float64.
Mask (Variable, Optional): The input mask of deformable convolution layer.
A Tensor with type float32, float64. It should be None when you use
deformable convolution v1.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the deformable conv layer. According to
grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1.
deformable_groups (int): The number of deformable group partitions.
Default: deformable_groups = 1.
im2col_step (int): Maximum number of images per im2col computation;
The total batch size should be devisable by this value or smaller
than this value; if you face out of memory problem, you can try
to use a smaller value here.
Default: im2col_step = 64.
param_attr (ParamAttr, Optional): The parameter attribute for learnable parameters/weights
of deformable conv. If it is set to None or one attribute of ParamAttr,
deformable conv will create ParamAttr as param_attr.
If the Initializer of the param_attr is not set, the parameter is
initialized with :math:`Normal(0.0, std)`, and the
:math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
bias_attr (ParamAttr|bool, Optional): The parameter attribute for the bias of
deformable conv layer. If it is set to False, no bias will be added
to the output units. If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
modulated (bool): Make sure which version should be used between v1 and v2, where v2 is \
used while True. Default: True.
name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
Generally, no setting is required. Default: None.
Returns:
Variable: The tensor variable storing the deformable convolution \
result. A Tensor with type float32, float64.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
#deformable conv v2:
import paddle.fluid as fluid
import paddle
paddle.enable_static()
C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1
data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
offset = fluid.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
mask = fluid.data(name='mask', shape=[None, deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
out = fluid.layers.deformable_conv(input=data, offset=offset, mask=mask,
num_filters=2, filter_size=filter_size, padding=1, modulated=True)
#deformable conv v1:
import paddle.fluid as fluid
C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1
data = fluid.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
offset = fluid.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
out = fluid.layers.deformable_conv(input=data, offset=offset, mask=None,
num_filters=2, filter_size=filter_size, padding=1, modulated=False)
"""
check_variable_and_dtype(
input, "input", ['float32', 'float64'], 'deformable_conv'
)
check_variable_and_dtype(
offset, "offset", ['float32', 'float64'], 'deformable_conv'
)
check_type(mask, 'mask', (Variable, type(None)), 'deformable_conv')
num_channels = input.shape[1]
assert param_attr is not False, "param_attr should not be False here."
helper = LayerHelper('deformable_conv', **locals())
dtype = helper.input_dtype()
if not isinstance(input, Variable):
raise TypeError("Input of deformable_conv must be Variable")
if not isinstance(offset, Variable):
raise TypeError("Input Offset of deformable_conv must be Variable")
if groups is None:
num_filter_channels = num_channels
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
input_shape = input.shape
filter_shape = [num_filters, int(num_filter_channels)] + filter_size
def _get_default_param_initializer():
filter_elem_num = filter_size[0] * filter_size[1] * num_channels
if filter_elem_num <= 0:
raise ValueError(
"Invalid filter number, excepted number is larger than 0, but"
" received {}, please check the input shape and "
"filter size.".format(filter_elem_num)
)
std = (2.0 / filter_elem_num) ** 0.5
return Normal(0.0, std, 0)
filter_param = helper.create_parameter(
attr=helper.param_attr,
shape=filter_shape,
dtype=dtype,
default_initializer=_get_default_param_initializer(),
)
pre_bias = helper.create_variable_for_type_inference(dtype)
if modulated:
helper.append_op(
type='deformable_conv',
inputs={
'Input': input,
'Filter': filter_param,
'Offset': offset,
'Mask': mask,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'deformable_groups': deformable_groups,
'im2col_step': im2col_step,
},
)
else:
helper.append_op(
type='deformable_conv_v1',
inputs={
'Input': input,
'Filter': filter_param,
'Offset': offset,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'deformable_groups': deformable_groups,
'im2col_step': im2col_step,
},
)
output = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
return output
def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
r"""
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_deformable_conv.py
浏览文件 @ aa669c74
......@@ -16,6 +16,7 @@ import os
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import PassVersionChecker
......@@ -38,7 +39,7 @@ class TRTDeformableConvTest(InferencePassTest):
name='mask', shape=self.mask_size, dtype=self.dtype
)
output = fluid.layers.deformable_conv(
output = paddle.static.nn.common.deformable_conv(
input,
offset,
mask,
......
python/paddle/fluid/tests/unittests/test_deform_conv2d.py
浏览文件 @ aa669c74
......@@ -141,7 +141,7 @@ class TestDeformConv2D(TestCase):
dtype=self.dtype,
)
y_v1 = paddle.fluid.layers.deformable_conv(
y_v1 = paddle.static.nn.common.deformable_conv(
input=x,
offset=offset,
mask=None,
......@@ -158,7 +158,7 @@ class TestDeformConv2D(TestCase):
modulated=False,
)
y_v2 = paddle.fluid.layers.deformable_conv(
y_v2 = paddle.static.nn.common.deformable_conv(
input=x,
offset=offset,
mask=mask,
......@@ -357,7 +357,7 @@ class TestDeformConv2DFunctional(TestCase):
dtype=self.dtype,
)
y_v1 = paddle.fluid.layers.deformable_conv(
y_v1 = paddle.static.nn.common.deformable_conv(
input=x,
offset=offset,
mask=None,
......@@ -374,7 +374,7 @@ class TestDeformConv2DFunctional(TestCase):
modulated=False,
)
y_v2 = paddle.fluid.layers.deformable_conv(
y_v2 = paddle.static.nn.common.deformable_conv(
input=x,
offset=offset,
mask=mask,
......
python/paddle/fluid/tests/unittests/test_deformable_conv_op.py
浏览文件 @ aa669c74
......@@ -15,7 +15,6 @@
import paddle
import unittest
import numpy as np
import paddle.fluid as fluid
from op_test import OpTest
from paddle.fluid.framework import _test_eager_guard
......@@ -395,13 +394,13 @@ class TestModulatedDeformableConvInvalidInput(unittest.TestCase):
def test_invalid_input():
paddle.enable_static()
input = [1, 3, 32, 32]
offset = fluid.data(
offset = paddle.static.data(
name='offset', shape=[None, 3, 32, 32], dtype='float32'
)
mask = fluid.data(
mask = paddle.static.data(
name='mask', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
loss = paddle.static.nn.common.deformable_conv(
input, offset, mask, num_filters=4, filter_size=1
)
......@@ -409,16 +408,16 @@ class TestModulatedDeformableConvInvalidInput(unittest.TestCase):
def test_invalid_offset():
paddle.enable_static()
input = fluid.data(
input = paddle.static.data(
name='input', shape=[None, 3, 32, 32], dtype='int32'
)
offset = fluid.data(
offset = paddle.static.data(
name='offset', shape=[None, 3, 32, 32], dtype='float32'
)
mask = fluid.data(
mask = paddle.static.data(
name='mask', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
loss = paddle.static.nn.common.deformable_conv(
input, offset, mask, num_filters=4, filter_size=1
)
......@@ -426,16 +425,16 @@ class TestModulatedDeformableConvInvalidInput(unittest.TestCase):
def test_invalid_filter():
paddle.enable_static()
input = fluid.data(
input = paddle.static.data(
name='input_filter', shape=[None, 3, 32, 32], dtype='float32'
)
offset = fluid.data(
offset = paddle.static.data(
name='offset_filter', shape=[None, 3, 32, 32], dtype='float32'
)
mask = fluid.data(
mask = paddle.static.data(
name='mask_filter', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
loss = paddle.static.nn.common.deformable_conv(
input, offset, mask, num_filters=4, filter_size=0
)
......
python/paddle/fluid/tests/unittests/test_deformable_conv_v1_op.py
浏览文件 @ aa669c74
......@@ -15,9 +15,7 @@
import paddle
import unittest
import numpy as np
import paddle.fluid as fluid
from op_test import OpTest
from paddle.fluid.framework import _test_eager_guard
def dmc_bilinear(data_im, height, width, h, w):
......@@ -348,46 +346,5 @@ class TestWithDouble(TestModulatedDeformableConvOp):
self.dtype = np.float64
class TestModulatedDeformableConvV1InvalidInput(unittest.TestCase):
def test_error(self):
def test_invalid_input():
input = [1, 3, 32, 32]
offset = fluid.data(
name='offset', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
input,
offset,
mask=None,
num_filters=4,
filter_size=1,
modulated=False,
)
self.assertRaises(TypeError, test_invalid_input)
def test_invalid_offset():
input = fluid.data(
name='input', shape=[None, 3, 32, 32], dtype='int32'
)
offset = fluid.data(
name='offset', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
input,
offset,
mask=None,
num_filters=4,
filter_size=1,
modulated=False,
)
self.assertRaises(TypeError, test_invalid_offset)
def test_error_with_eager_guard(self):
with _test_eager_guard():
self.test_error()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ aa669c74
......@@ -4797,57 +4797,6 @@ class TestBook(LayerTest):
self.assertIsNotNone(out)
print(str(program))
def test_deformable_conv(self):
with self.static_graph():
input = layers.data(
name='input',
append_batch_size=False,
shape=[2, 3, 32, 32],
dtype="float32",
)
offset = layers.data(
name='offset',
append_batch_size=False,
shape=[2, 18, 32, 32],
dtype="float32",
)
mask = layers.data(
name='mask',
append_batch_size=False,
shape=[2, 9, 32, 32],
dtype="float32",
)
out = layers.deformable_conv(
input=input,
offset=offset,
mask=mask,
num_filters=2,
filter_size=3,
padding=1,
)
return out
def test_deformable_conv2(self):
with self.static_graph():
input = fluid.data(
name='input', shape=[None, 3, None, None], dtype="float32"
)
offset = fluid.data(
name='offset', shape=[None, 18, None, None], dtype="float32"
)
mask = fluid.data(
name='mask', shape=[None, 9, None, None], dtype="float32"
)
out = layers.deformable_conv(
input=input,
offset=offset,
mask=mask,
num_filters=2,
filter_size=3,
padding=1,
)
return out
def test_unfold(self):
with self.static_graph():
x = layers.data(name='x', shape=[3, 20, 20], dtype='float32')
......@@ -4900,33 +4849,6 @@ class TestBook(LayerTest):
)
return out
def test_deformable_conv_v1(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = layers.data(
name='input',
append_batch_size=False,
shape=[2, 3, 32, 32],
dtype="float32",
)
offset = layers.data(
name='offset',
append_batch_size=False,
shape=[2, 18, 32, 32],
dtype="float32",
)
out = layers.deformable_conv(
input=input,
offset=offset,
mask=None,
num_filters=2,
filter_size=3,
padding=1,
modulated=False,
)
return out
def test_retinanet_target_assign(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......
python/paddle/fluid/tests/unittests/xpu/test_deformable_conv_op_xpu.py
浏览文件 @ aa669c74
......@@ -19,7 +19,6 @@ import unittest
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test_xpu import OpTest, XPUOpTest
import paddle
from xpu.get_test_cover_info import (
......@@ -304,40 +303,6 @@ class XPUTestModulatedDeformableConvOp(XPUOpTestWrapper):
self.input_size[3],
]
class TestModulatedDeformableConvInvalidInput(unittest.TestCase):
def test_error(self):
def test_invalid_input():
paddle.enable_static()
input = [1, 3, 32, 32]
offset = fluid.data(
name='offset', shape=[None, 3, 32, 32], dtype='float32'
)
mask = fluid.data(
name='mask', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
input, offset, mask, num_filters=4, filter_size=1
)
self.assertRaises(TypeError, test_invalid_input)
def test_invalid_offset():
paddle.enable_static()
input = fluid.data(
name='input', shape=[None, 3, 32, 32], dtype='int32'
)
offset = fluid.data(
name='offset', shape=[None, 3, 32, 32], dtype='float32'
)
mask = fluid.data(
name='mask', shape=[None, 3, 32, 32], dtype='float32'
)
loss = fluid.layers.deformable_conv(
input, offset, mask, num_filters=4, filter_size=1
)
self.assertRaises(TypeError, test_invalid_offset)
support_types = get_xpu_op_support_types('deformable_conv')
for stype in support_types:
......
python/paddle/static/nn/common.py
浏览文件 @ aa669c74
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 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.
......@@ -15,6 +15,13 @@
import paddle
from paddle.fluid.framework import static_only
from paddle.common_ops_import import (
check_type,
check_variable_and_dtype,
utils,
LayerHelper,
)
__all__ = []
......@@ -167,6 +174,247 @@ def fc(
)
def deformable_conv(
input,
offset,
mask,
num_filters,
filter_size,
stride=1,
padding=0,
dilation=1,
groups=None,
deformable_groups=None,
im2col_step=None,
param_attr=None,
bias_attr=None,
modulated=True,
name=None,
):
r"""
**Deformable Convolution op**
Compute 2-D deformable convolution on 4-D input.
Given input image x, output feature map y, the deformable convolution operation can be expressed as follow:
Deformable Convolution v2:
.. math::
y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k}
Deformable Convolution v1:
.. math::
y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k)}
Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location,
Which :math:`\Delta m_k` is one in deformable convolution v1. Please refer to `Deformable ConvNets v2: More Deformable, Better Results
<https://arxiv.org/abs/1811.11168v2>`_ and `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_.
Example:
- Input:
Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
Offset shape: :math:`(N, 2 * deformable\_groups * H_f * H_w, H_{in}, W_{in})`
Mask shape: :math:`(N, deformable\_groups * H_f * H_w, H_{in}, W_{in})`
- Output:
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
.. math::
H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args:
input (Tensor): The input image with [N, C, H, W] format. A Tensor with type
float32, float64.
offset (Tensor): The input coordinate offset of deformable convolution layer.
A Tensor with type float32, float64.
Mask (Variable, Optional): The input mask of deformable convolution layer.
A Tensor with type float32, float64. It should be None when you use
deformable convolution v1.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the deformable conv layer. According to
grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1.
deformable_groups (int): The number of deformable group partitions.
Default: deformable_groups = 1.
im2col_step (int): Maximum number of images per im2col computation;
The total batch size should be devisable by this value or smaller
than this value; if you face out of memory problem, you can try
to use a smaller value here.
Default: im2col_step = 64.
param_attr (ParamAttr, Optional): The parameter attribute for learnable parameters/weights
of deformable conv. If it is set to None or one attribute of ParamAttr,
deformable conv will create ParamAttr as param_attr.
If the Initializer of the param_attr is not set, the parameter is
initialized with :math:`Normal(0.0, std)`, and the
:math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
bias_attr (ParamAttr|bool, Optional): The parameter attribute for the bias of
deformable conv layer. If it is set to False, no bias will be added
to the output units. If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
modulated (bool): Make sure which version should be used between v1 and v2, where v2 is \
used while True. Default: True.
name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
Generally, no setting is required. Default: None.
Returns:
Tensor: The tensor variable storing the deformable convolution \
result. A Tensor with type float32, float64.
Examples:
.. code-block:: python
#deformable conv v2:
import paddle
paddle.enable_static()
C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1
data = paddle.static.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
offset = paddle.static.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
mask = paddle.static.data(name='mask', shape=[None, deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
out = paddle.static.layers.common.deformable_conv(input=data, offset=offset, mask=mask,
num_filters=2, filter_size=filter_size, padding=1, modulated=True)
#deformable conv v1:
import paddle
C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1
data = paddle.static.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
offset = paddle.static.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
out = paddle.static.layers.common.deformable_conv(input=data, offset=offset, mask=None,
num_filters=2, filter_size=filter_size, padding=1, modulated=False)
"""
check_variable_and_dtype(
input, "input", ['float32', 'float64'], 'deformable_conv'
)
check_variable_and_dtype(
offset, "offset", ['float32', 'float64'], 'deformable_conv'
)
check_type(
mask, 'mask', (paddle.static.Variable, type(None)), 'deformable_conv'
)
num_channels = input.shape[1]
assert param_attr is not False, "param_attr should not be False here."
helper = LayerHelper('deformable_conv', **locals())
dtype = helper.input_dtype()
if not isinstance(input, paddle.static.Variable):
raise TypeError("Input of deformable_conv must be Variable")
if not isinstance(offset, paddle.static.Variable):
raise TypeError("Input Offset of deformable_conv must be Variable")
if groups is None:
num_filter_channels = num_channels
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
input_shape = input.shape
filter_shape = [num_filters, int(num_filter_channels)] + filter_size
def _get_default_param_initializer():
filter_elem_num = filter_size[0] * filter_size[1] * num_channels
if filter_elem_num <= 0:
raise ValueError(
"Invalid filter number, excepted number is larger than 0, but"
" received {}, please check the input shape and "
"filter size.".format(filter_elem_num)
)
std = (2.0 / filter_elem_num) ** 0.5
return paddle.nn.initializer.normal.NormalInitializer(0.0, std, 0)
filter_param = helper.create_parameter(
attr=helper.param_attr,
shape=filter_shape,
dtype=dtype,
default_initializer=_get_default_param_initializer(),
)
pre_bias = helper.create_variable_for_type_inference(dtype)
if modulated:
helper.append_op(
type='deformable_conv',
inputs={
'Input': input,
'Filter': filter_param,
'Offset': offset,
'Mask': mask,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'deformable_groups': deformable_groups,
'im2col_step': im2col_step,
},
)
else:
helper.append_op(
type='deformable_conv_v1',
inputs={
'Input': input,
'Filter': filter_param,
'Offset': offset,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'deformable_groups': deformable_groups,
'im2col_step': im2col_step,
},
)
output = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
return output
@static_only
def deform_conv2d(
x,
......@@ -309,7 +557,7 @@ def deform_conv2d(
"""
if mask is None:
return paddle.fluid.layers.deformable_conv(
return deformable_conv(
input=x,
offset=offset,
mask=mask,
......@@ -327,7 +575,7 @@ def deform_conv2d(
name=name,
)
else:
return paddle.fluid.layers.deformable_conv(
return deformable_conv(
input=x,
offset=offset,
mask=mask,
......
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