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# Copyright (c) 2021 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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg import utils
from paddleseg.models import layers
from paddleseg.cvlibs import manager, param_init
__all__ = ['ENet']
@manager.MODELS.add_component
class ENet(nn.Layer):
"""
The ENet implementation based on PaddlePaddle.
The original article refers to
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello, et al."ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation"
(https://arxiv.org/abs/1606.02147).
Args:
num_classes (int): The unique number of target classes.
pretrained (str, optional): The path or url of pretrained model. Default: None.
encoder_relu (bool, optional): When ``True`` ReLU is used as the activation
function; otherwise, PReLU is used. Default: False.
decoder_relu (bool, optional): When ``True`` ReLU is used as the activation
function; otherwise, PReLU is used. Default: True.
"""
def __init__(self,
num_classes,
pretrained=None,
encoder_relu=False,
decoder_relu=True):
super(ENet, self).__init__()
self.numclasses = num_classes
self.initial_block = InitialBlock(3, 16, relu=encoder_relu)
self.downsample1_0 = DownsamplingBottleneck(
16, 64, return_indices=True, dropout_prob=0.01, relu=encoder_relu)
self.regular1_1 = RegularBottleneck(
64, padding=1, dropout_prob=0.01, relu=encoder_relu)
self.regular1_2 = RegularBottleneck(
64, padding=1, dropout_prob=0.01, relu=encoder_relu)
self.regular1_3 = RegularBottleneck(
64, padding=1, dropout_prob=0.01, relu=encoder_relu)
self.regular1_4 = RegularBottleneck(
64, padding=1, dropout_prob=0.01, relu=encoder_relu)
self.downsample2_0 = DownsamplingBottleneck(
64, 128, return_indices=True, dropout_prob=0.1, relu=encoder_relu)
self.regular2_1 = RegularBottleneck(
128, padding=1, dropout_prob=0.1, relu=encoder_relu)
self.dilated2_2 = RegularBottleneck(
128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
self.asymmetric2_3 = RegularBottleneck(
128,
kernel_size=5,
padding=2,
asymmetric=True,
dropout_prob=0.1,
relu=encoder_relu)
self.dilated2_4 = RegularBottleneck(
128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
self.regular2_5 = RegularBottleneck(
128, padding=1, dropout_prob=0.1, relu=encoder_relu)
self.dilated2_6 = RegularBottleneck(
128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
self.asymmetric2_7 = RegularBottleneck(
128,
kernel_size=5,
asymmetric=True,
padding=2,
dropout_prob=0.1,
relu=encoder_relu)
self.dilated2_8 = RegularBottleneck(
128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
self.regular3_0 = RegularBottleneck(
128, padding=1, dropout_prob=0.1, relu=encoder_relu)
self.dilated3_1 = RegularBottleneck(
128, dilation=2, padding=2, dropout_prob=0.1, relu=encoder_relu)
self.asymmetric3_2 = RegularBottleneck(
128,
kernel_size=5,
padding=2,
asymmetric=True,
dropout_prob=0.1,
relu=encoder_relu)
self.dilated3_3 = RegularBottleneck(
128, dilation=4, padding=4, dropout_prob=0.1, relu=encoder_relu)
self.regular3_4 = RegularBottleneck(
128, padding=1, dropout_prob=0.1, relu=encoder_relu)
self.dilated3_5 = RegularBottleneck(
128, dilation=8, padding=8, dropout_prob=0.1, relu=encoder_relu)
self.asymmetric3_6 = RegularBottleneck(
128,
kernel_size=5,
asymmetric=True,
padding=2,
dropout_prob=0.1,
relu=encoder_relu)
self.dilated3_7 = RegularBottleneck(
128, dilation=16, padding=16, dropout_prob=0.1, relu=encoder_relu)
self.upsample4_0 = UpsamplingBottleneck(
128, 64, dropout_prob=0.1, relu=decoder_relu)
self.regular4_1 = RegularBottleneck(
64, padding=1, dropout_prob=0.1, relu=decoder_relu)
self.regular4_2 = RegularBottleneck(
64, padding=1, dropout_prob=0.1, relu=decoder_relu)
self.upsample5_0 = UpsamplingBottleneck(
64, 16, dropout_prob=0.1, relu=decoder_relu)
self.regular5_1 = RegularBottleneck(
16, padding=1, dropout_prob=0.1, relu=decoder_relu)
self.transposed_conv = nn.Conv2DTranspose(
16,
num_classes,
kernel_size=3,
stride=2,
padding=1,
bias_attr=False)
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
input_size = x.shape
x = self.initial_block(x)
stage1_input_size = x.shape
x, max_indices1_0 = self.downsample1_0(x)
x = self.regular1_1(x)
x = self.regular1_2(x)
x = self.regular1_3(x)
x = self.regular1_4(x)
stage2_input_size = x.shape
x, max_indices2_0 = self.downsample2_0(x)
x = self.regular2_1(x)
x = self.dilated2_2(x)
x = self.asymmetric2_3(x)
x = self.dilated2_4(x)
x = self.regular2_5(x)
x = self.dilated2_6(x)
x = self.asymmetric2_7(x)
x = self.dilated2_8(x)
x = self.regular3_0(x)
x = self.dilated3_1(x)
x = self.asymmetric3_2(x)
x = self.dilated3_3(x)
x = self.regular3_4(x)
x = self.dilated3_5(x)
x = self.asymmetric3_6(x)
x = self.dilated3_7(x)
x = self.upsample4_0(x, max_indices2_0, output_size=stage2_input_size)
x = self.regular4_1(x)
x = self.regular4_2(x)
x = self.upsample5_0(x, max_indices1_0, output_size=stage1_input_size)
x = self.regular5_1(x)
x = self.transposed_conv(x, output_size=input_size[2:])
return [x]
def init_weight(self):
if self.pretrained is not None:
utils.load_pretrained_model(self, self.pretrained)
class InitialBlock(nn.Layer):
"""
The initial block is composed of two branches:
1. a main branch which performs a regular convolution with stride 2;
2. an extension branch which performs max-pooling.
Doing both operations in parallel and concatenating their results
allows for efficient downsampling and expansion. The main branch
outputs 13 feature maps while the extension branch outputs 3, for a
total of 16 feature maps after concatenation.
Args:
in_channels (int): the number of input channels.
out_channels (int): the number output channels.
kernel_size (int, optional): the kernel size of the filters used in
the convolution layer. Default: 3.
padding (int, optional): zero-padding added to both sides of the
input. Default: 0.
bias (bool, optional): Adds a learnable bias to the output if
``True``. Default: False.
relu (bool, optional): When ``True`` ReLU is used as the activation
function; otherwise, PReLU is used. Default: True.
"""
def __init__(self, in_channels, out_channels, bias=False, relu=True):
super(InitialBlock, self).__init__()
if relu:
activation = nn.ReLU
else:
activation = nn.PReLU
self.main_branch = nn.Conv2D(
in_channels,
out_channels - 3,
kernel_size=3,
stride=2,
padding=1,
bias_attr=bias)
self.ext_branch = nn.MaxPool2D(3, stride=2, padding=1)
self.batch_norm = layers.SyncBatchNorm(out_channels)
self.out_activation = activation()
def forward(self, x):
main = self.main_branch(x)
ext = self.ext_branch(x)
out = paddle.concat((main, ext), 1)
out = self.batch_norm(out)
return self.out_activation(out)
class RegularBottleneck(nn.Layer):
"""
Regular bottlenecks are the main building block of ENet.
Main branch:
1. Shortcut connection.
Extension branch:
1. 1x1 convolution which decreases the number of channels by
``internal_ratio``, also called a projection;
2. regular, dilated or asymmetric convolution;
3. 1x1 convolution which increases the number of channels back to
``channels``, also called an expansion;
4. dropout as a regularizer.
Args:
channels (int): the number of input and output channels.
internal_ratio (int, optional): a scale factor applied to
``channels`` used to compute the number of
channels after the projection. eg. given ``channels`` equal to 128 and
internal_ratio equal to 2 the number of channels after the projection
is 64. Default: 4.
kernel_size (int, optional): the kernel size of the filters used in
the convolution layer described above in item 2 of the extension
branch. Default: 3.
padding (int, optional): zero-padding added to both sides of the
input. Default: 0.
dilation (int, optional): spacing between kernel elements for the
convolution described in item 2 of the extension branch. Default: 1.
asymmetric (bool, optional): flags if the convolution described in
item 2 of the extension branch is asymmetric or not. Default: False.
dropout_prob (float, optional): probability of an element to be
zeroed. Default: 0 (no dropout).
bias (bool, optional): Adds a learnable bias to the output if
``True``. Default: False.
relu (bool, optional): When ``True`` ReLU is used as the activation
function; otherwise, PReLU is used. Default: True.
"""
def __init__(self,
channels,
internal_ratio=4,
kernel_size=3,
padding=0,
dilation=1,
asymmetric=False,
dropout_prob=0,
bias=False,
relu=True):
super(RegularBottleneck, self).__init__()
if internal_ratio <= 1 or internal_ratio > channels:
raise RuntimeError("Value out of range. Expected value in the "
"interval [1, {0}], got internal_scale={1}.".
format(channels, internal_ratio))
internal_channels = channels // internal_ratio
if relu:
activation = nn.ReLU
else:
activation = nn.PReLU
self.ext_conv1 = nn.Sequential(
nn.Conv2D(
channels,
internal_channels,
kernel_size=1,
stride=1,
bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation())
if asymmetric:
self.ext_conv2 = nn.Sequential(
nn.Conv2D(
internal_channels,
internal_channels,
kernel_size=(kernel_size, 1),
stride=1,
padding=(padding, 0),
dilation=dilation,
bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation(),
nn.Conv2D(
internal_channels,
internal_channels,
kernel_size=(1, kernel_size),
stride=1,
padding=(0, padding),
dilation=dilation,
bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation())
else:
self.ext_conv2 = nn.Sequential(
nn.Conv2D(
internal_channels,
internal_channels,
kernel_size=kernel_size,
stride=1,
padding=padding,
dilation=dilation,
bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation())
self.ext_conv3 = nn.Sequential(
nn.Conv2D(
internal_channels,
channels,
kernel_size=1,
stride=1,
bias_attr=bias),
layers.SyncBatchNorm(channels),
activation())
self.ext_regul = nn.Dropout2D(p=dropout_prob)
self.out_activation = activation()
def forward(self, x):
main = x
ext = self.ext_conv1(x)
ext = self.ext_conv2(ext)
ext = self.ext_conv3(ext)
ext = self.ext_regul(ext)
out = main + ext
return self.out_activation(out)
class DownsamplingBottleneck(nn.Layer):
"""
Downsampling bottlenecks further downsample the feature map size.
Main branch:
1. max pooling with stride 2; indices are saved to be used for
unpooling later.
Extension branch:
1. 2x2 convolution with stride 2 that decreases the number of channels
by ``internal_ratio``, also called a projection;
2. regular convolution (by default, 3x3);
3. 1x1 convolution which increases the number of channels to
``out_channels``, also called an expansion;
4. dropout as a regularizer.
Args:
in_channels (int): the number of input channels.
out_channels (int): the number of output channels.
internal_ratio (int, optional): a scale factor applied to ``channels``
used to compute the number of channels after the projection. eg. given
``channels`` equal to 128 and internal_ratio equal to 2 the number of
channels after the projection is 64. Default: 4.
return_indices (bool, optional): if ``True``, will return the max
indices along with the outputs. Useful when unpooling later.
dropout_prob (float, optional): probability of an element to be
zeroed. Default: 0 (no dropout).
bias (bool, optional): Adds a learnable bias to the output if
``True``. Default: False.
relu (bool, optional): When ``True`` ReLU is used as the activation
function; otherwise, PReLU is used. Default: True.
"""
def __init__(self,
in_channels,
out_channels,
internal_ratio=4,
return_indices=False,
dropout_prob=0,
bias=False,
relu=True):
super(DownsamplingBottleneck, self).__init__()
self.return_indices = return_indices
if internal_ratio <= 1 or internal_ratio > in_channels:
raise RuntimeError("Value out of range. Expected value in the "
"interval [1, {0}], got internal_scale={1}. ".
format(in_channels, internal_ratio))
internal_channels = in_channels // internal_ratio
if relu:
activation = nn.ReLU
else:
activation = nn.PReLU
self.main_max1 = nn.MaxPool2D(2, stride=2, return_mask=return_indices)
self.ext_conv1 = nn.Sequential(
nn.Conv2D(
in_channels,
internal_channels,
kernel_size=2,
stride=2,
bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation())
self.ext_conv2 = nn.Sequential(
nn.Conv2D(
internal_channels,
internal_channels,
kernel_size=3,
stride=1,
padding=1,
bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation())
self.ext_conv3 = nn.Sequential(
nn.Conv2D(
internal_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=bias),
layers.SyncBatchNorm(out_channels),
activation())
self.ext_regul = nn.Dropout2D(p=dropout_prob)
self.out_activation = activation()
def forward(self, x):
if self.return_indices:
main, max_indices = self.main_max1(x)
else:
main = self.main_max1(x)
ext = self.ext_conv1(x)
ext = self.ext_conv2(ext)
ext = self.ext_conv3(ext)
ext = self.ext_regul(ext)
n, ch_ext, h, w = ext.shape
ch_main = main.shape[1]
padding = paddle.zeros((n, ch_ext - ch_main, h, w))
main = paddle.concat((main, padding), 1)
out = main + ext
return self.out_activation(out), max_indices
class UpsamplingBottleneck(nn.Layer):
"""
The upsampling bottlenecks upsample the feature map resolution using max
pooling indices stored from the corresponding downsampling bottleneck.
Main branch:
1. 1x1 convolution with stride 1 that decreases the number of channels by
``internal_ratio``, also called a projection;
2. max unpool layer using the max pool indices from the corresponding
downsampling max pool layer.
Extension branch:
1. 1x1 convolution with stride 1 that decreases the number of channels by
``internal_ratio``, also called a projection;
2. transposed convolution (by default, 3x3);
3. 1x1 convolution which increases the number of channels to
``out_channels``, also called an expansion;
4. dropout as a regularizer.
Args:
in_channels (int): the number of input channels.
out_channels (int): the number of output channels.
internal_ratio (int, optional): a scale factor applied to ``in_channels``
used to compute the number of channels after the projection. eg. given
``in_channels`` equal to 128 and ``internal_ratio`` equal to 2 the number
of channels after the projection is 64. Default: 4.
dropout_prob (float, optional): probability of an element to be zeroed.
Default: 0 (no dropout).
bias (bool, optional): Adds a learnable bias to the output if ``True``.
Default: False.
relu (bool, optional): When ``True`` ReLU is used as the activation
function; otherwise, PReLU is used. Default: True.
"""
def __init__(self,
in_channels,
out_channels,
internal_ratio=4,
dropout_prob=0,
bias=False,
relu=True):
super(UpsamplingBottleneck, self).__init__()
if internal_ratio <= 1 or internal_ratio > in_channels:
raise RuntimeError("Value out of range. Expected value in the "
"interval [1, {0}], got internal_scale={1}. ".
format(in_channels, internal_ratio))
internal_channels = in_channels // internal_ratio
if relu:
activation = nn.ReLU
else:
activation = nn.PReLU
self.main_conv1 = nn.Sequential(
nn.Conv2D(
in_channels, out_channels, kernel_size=1, bias_attr=bias),
layers.SyncBatchNorm(out_channels))
self.ext_conv1 = nn.Sequential(
nn.Conv2D(
in_channels, internal_channels, kernel_size=1, bias_attr=bias),
layers.SyncBatchNorm(internal_channels),
activation())
self.ext_tconv1 = nn.Conv2DTranspose(
internal_channels,
internal_channels,
kernel_size=2,
stride=2,
bias_attr=bias)
self.ext_tconv1_bnorm = layers.SyncBatchNorm(internal_channels)
self.ext_tconv1_activation = activation()
self.ext_conv2 = nn.Sequential(
nn.Conv2D(
internal_channels, out_channels, kernel_size=1, bias_attr=bias),
layers.SyncBatchNorm(out_channels))
self.ext_regul = nn.Dropout2D(p=dropout_prob)
self.out_activation = activation()
def forward(self, x, max_indices, output_size):
main = self.main_conv1(x)
main = F.max_unpool2d(
main, max_indices, kernel_size=2, output_size=output_size)
ext = self.ext_conv1(x)
ext = self.ext_tconv1(ext, output_size=output_size[2:])
ext = self.ext_tconv1_bnorm(ext)
ext = self.ext_tconv1_activation(ext)
ext = self.ext_conv2(ext)
ext = self.ext_regul(ext)
out = main + ext
return self.out_activation(out)
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