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# copyright (c) 2021 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
from paddleseg.models import layers
from paddleseg.utils import utils
__all__ = [
"ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
]
class ConvBNLayer(nn.Layer):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1,
is_vd_mode=False,
act=None,
):
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)
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2 if dilation == 1 else 0,
dilation=dilation,
groups=groups,
bias_attr=False)
self._batch_norm = layers.SyncBatchNorm(out_channels)
self._act_op = layers.Activation(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)
y = self._act_op(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
dilation=1):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
act='relu')
self.dilation = dilation
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
dilation=dilation)
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 or stride == 1 else True)
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
####################################################################
# If given dilation rate > 1, using corresponding padding.
# The performance drops down without the follow padding.
if self.dilation > 1:
padding = self.dilation
y = F.pad(y, [padding, padding, padding, padding])
#####################################################################
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):
"""
The ResNet_vd implementation based on PaddlePaddle.
The original article refers to Jingdong
Tong He, et, al. "Bag of Tricks for Image Classification with Convolutional Neural Networks"
(https://arxiv.org/pdf/1812.01187.pdf).
Args:
layers (int, optional): The layers of ResNet_vd. The supported layers are (18, 34, 50, 101, 152, 200). Default: 50.
output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 8.
multi_grid (tuple|list, optional): The grid of stage4. Defult: (1, 1, 1).
pretrained (str, optional): The path of pretrained model.
"""
def __init__(self,
input_channels=3,
layers=50,
output_stride=32,
multi_grid=(1, 1, 1),
pretrained=None):
super(ResNet_vd, self).__init__()
self.conv1_logit = None # for gscnn shape stream
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]
# for channels of four returned stages
self.feat_channels = [c * 4 for c in num_filters
] if layers >= 50 else num_filters
self.feat_channels = [64] + self.feat_channels
dilation_dict = None
if output_stride == 8:
dilation_dict = {2: 2, 3: 4}
elif output_stride == 16:
dilation_dict = {3: 2}
self.conv1_1 = ConvBNLayer(
in_channels=input_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.block_list = []
self.stage_list = []
if layers >= 50:
for block in range(len(depth)):
shortcut = False
block_list = []
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
###############################################################################
# Add dilation rate for some segmentation tasks, if dilation_dict is not None.
dilation_rate = dilation_dict[
block] if dilation_dict and block in dilation_dict else 1
# Actually block here is 'stage', and i is 'block' in 'stage'
# At the stage 4, expand the the dilation_rate if given multi_grid
if block == 3:
dilation_rate = dilation_rate * multi_grid[i]
###############################################################################
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
and dilation_rate == 1 else 1,
shortcut=shortcut,
if_first=block == i == 0,
dilation=dilation_rate))
block_list.append(bottleneck_block)
shortcut = True
self.stage_list.append(block_list)
else:
for block in range(len(depth)):
shortcut = False
block_list = []
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
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))
block_list.append(basic_block)
shortcut = True
self.stage_list.append(block_list)
self.pretrained = pretrained
self.init_weight()
def forward(self, inputs):
feat_list = []
y = self.conv1_1(inputs)
y = self.conv1_2(y)
y = self.conv1_3(y)
feat_list.append(y)
y = self.pool2d_max(y)
# A feature list saves the output feature map of each stage.
for stage in self.stage_list:
for block in stage:
y = block(y)
feat_list.append(y)
return feat_list
def init_weight(self):
utils.load_pretrained_model(self, self.pretrained)
@manager.BACKBONES.add_component
def ResNet18_vd(**args):
model = ResNet_vd(layers=18, **args)
return model
def ResNet34_vd(**args):
model = ResNet_vd(layers=34, **args)
return model
@manager.BACKBONES.add_component
def ResNet50_vd(**args):
model = ResNet_vd(layers=50, **args)
return model
@manager.BACKBONES.add_component
def ResNet101_vd(**args):
model = ResNet_vd(layers=101, **args)
return model
def ResNet152_vd(**args):
model = ResNet_vd(layers=152, **args)
return model
def ResNet200_vd(**args):
model = ResNet_vd(layers=200, **args)
return model
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