# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.vision.ops import DeformConv2D
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Normal, Constant, XavierUniform
__all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"]
class DeformableConvV2(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
weight_attr=None,
bias_attr=None,
lr_scale=1,
regularizer=None,
skip_quant=False,
dcn_bias_regularizer=L2Decay(0.),
dcn_bias_lr_scale=2.):
super(DeformableConvV2, self).__init__()
self.offset_channel = 2 * kernel_size**2 * groups
self.mask_channel = kernel_size**2 * groups
if bias_attr:
# in FCOS-DCN head, specifically need learning_rate and regularizer
dcn_bias_attr = ParamAttr(
initializer=Constant(value=0),
regularizer=dcn_bias_regularizer,
learning_rate=dcn_bias_lr_scale)
else:
# in ResNet backbone, do not need bias
dcn_bias_attr = False
self.conv_dcn = DeformConv2D(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2 * dilation,
dilation=dilation,
deformable_groups=groups,
weight_attr=weight_attr,
bias_attr=dcn_bias_attr)
if lr_scale == 1 and regularizer is None:
offset_bias_attr = ParamAttr(initializer=Constant(0.))
else:
offset_bias_attr = ParamAttr(
initializer=Constant(0.),
learning_rate=lr_scale,
regularizer=regularizer)
self.conv_offset = nn.Conv2D(
in_channels,
groups * 3 * kernel_size**2,
kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
weight_attr=ParamAttr(initializer=Constant(0.0)),
bias_attr=offset_bias_attr)
if skip_quant:
self.conv_offset.skip_quant = True
def forward(self, x):
offset_mask = self.conv_offset(x)
offset, mask = paddle.split(
offset_mask,
num_or_sections=[self.offset_channel, self.mask_channel],
axis=1)
mask = F.sigmoid(mask)
y = self.conv_dcn(x, offset, mask=mask)
return y
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
dcn_groups=1,
is_vd_mode=False,
act=None,
is_dcn=False):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
if not is_dcn:
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
bias_attr=False)
else:
self._conv = DeformableConvV2(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=dcn_groups, #groups,
bias_attr=False)
self._batch_norm = nn.BatchNorm(out_channels, act=act)
def forward(self, inputs):
if self.is_vd_mode:
inputs = self._pool2d_avg(inputs)
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(
self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
is_dcn=False, ):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
act='relu')
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
is_dcn=is_dcn,
dcn_groups=2)
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * 4,
kernel_size=1,
act=None)
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * 4,
kernel_size=1,
stride=1,
is_vd_mode=False if if_first else True)
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(
self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False, ):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu')
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
act=None)
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
is_vd_mode=False if if_first else True)
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
class ResNet_vd(nn.Layer):
def __init__(self,
in_channels=3,
layers=50,
dcn_stage=None,
out_indices=None,
**kwargs):
super(ResNet_vd, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512,
1024] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.dcn_stage = dcn_stage if dcn_stage is not None else [
False, False, False, False
]
self.out_indices = out_indices if out_indices is not None else [
0, 1, 2, 3
]
self.conv1_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=32,
kernel_size=3,
stride=2,
act='relu')
self.conv1_2 = ConvBNLayer(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu')
self.conv1_3 = ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu')
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.stages = []
self.out_channels = []
if layers >= 50:
for block in range(len(depth)):
block_list = []
shortcut = False
is_dcn = self.dcn_stage[block]
for i in range(depth[block]):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
is_dcn=is_dcn))
shortcut = True
block_list.append(bottleneck_block)
if block in self.out_indices:
self.out_channels.append(num_filters[block] * 4)
self.stages.append(nn.Sequential(*block_list))
else:
for block in range(len(depth)):
block_list = []
shortcut = False
for i in range(depth[block]):
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BasicBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block],
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0))
shortcut = True
block_list.append(basic_block)
if block in self.out_indices:
self.out_channels.append(num_filters[block])
self.stages.append(nn.Sequential(*block_list))
def forward(self, inputs):
y = self.conv1_1(inputs)
y = self.conv1_2(y)
y = self.conv1_3(y)
y = self.pool2d_max(y)
out = []
for i, block in enumerate(self.stages):
y = block(y)
if i in self.out_indices:
out.append(y)
return out