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# copyright (c) 2022 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.
# The gca code was heavily based on https://github.com/Yaoyi-Li/GCA-Matting
# and https://github.com/open-mmlab/mmediting
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.models import layers
from paddleseg import utils
from paddleseg.cvlibs import manager, param_init
from ppmatting.models.layers import GuidedCxtAtten
@manager.MODELS.add_component
class GCABaseline(nn.Layer):
def __init__(self, backbone, pretrained=None):
super().__init__()
self.encoder = backbone
self.decoder = ResShortCut_D_Dec([2, 3, 3, 2])
def forward(self, inputs):
x = paddle.concat([inputs['img'], inputs['trimap'] / 255], axis=1)
embedding, mid_fea = self.encoder(x)
alpha_pred = self.decoder(embedding, mid_fea)
if self.training:
logit_dict = {'alpha_pred': alpha_pred, }
loss_dict = {}
alpha_gt = inputs['alpha']
loss_dict["alpha"] = F.l1_loss(alpha_pred, alpha_gt)
loss_dict["all"] = loss_dict["alpha"]
return logit_dict, loss_dict
return alpha_pred
@manager.MODELS.add_component
class GCA(GCABaseline):
def __init__(self, backbone, pretrained=None):
super().__init__(backbone, pretrained)
self.decoder = ResGuidedCxtAtten_Dec([2, 3, 3, 2])
def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""5x5 convolution with padding"""
return nn.Conv2D(
in_planes,
out_planes,
kernel_size=5,
stride=stride,
padding=2,
groups=groups,
bias_attr=False,
dilation=dilation)
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2D(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias_attr=False,
dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2D(
in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False)
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
upsample=None,
norm_layer=None,
large_kernel=False):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm
self.stride = stride
conv = conv5x5 if large_kernel else conv3x3
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
if self.stride > 1:
self.conv1 = nn.utils.spectral_norm(
nn.Conv2DTranspose(
inplanes,
inplanes,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False))
else:
self.conv1 = nn.utils.spectral_norm(conv(inplanes, inplanes))
self.bn1 = norm_layer(inplanes)
self.activation = nn.LeakyReLU(0.2)
self.conv2 = nn.utils.spectral_norm(conv(inplanes, planes))
self.bn2 = norm_layer(planes)
self.upsample = upsample
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
out = self.bn2(out)
if self.upsample is not None:
identity = self.upsample(x)
out += identity
out = self.activation(out)
return out
class ResNet_D_Dec(nn.Layer):
def __init__(self,
layers=[3, 4, 4, 2],
norm_layer=None,
large_kernel=False,
late_downsample=False):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm
self._norm_layer = norm_layer
self.large_kernel = large_kernel
self.kernel_size = 5 if self.large_kernel else 3
self.inplanes = 512 if layers[0] > 0 else 256
self.late_downsample = late_downsample
self.midplanes = 64 if late_downsample else 32
self.conv1 = nn.utils.spectral_norm(
nn.Conv2DTranspose(
self.midplanes,
32,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False))
self.bn1 = norm_layer(32)
self.leaky_relu = nn.LeakyReLU(0.2)
self.conv2 = nn.Conv2D(
32,
1,
kernel_size=self.kernel_size,
stride=1,
padding=self.kernel_size // 2)
self.upsample = nn.UpsamplingNearest2D(scale_factor=2)
self.tanh = nn.Tanh()
self.layer1 = self._make_layer(BasicBlock, 256, layers[0], stride=2)
self.layer2 = self._make_layer(BasicBlock, 128, layers[1], stride=2)
self.layer3 = self._make_layer(BasicBlock, 64, layers[2], stride=2)
self.layer4 = self._make_layer(
BasicBlock, self.midplanes, layers[3], stride=2)
self.init_weight()
def _make_layer(self, block, planes, blocks, stride=1):
if blocks == 0:
return nn.Sequential(nn.Identity())
norm_layer = self._norm_layer
upsample = None
if stride != 1:
upsample = nn.Sequential(
nn.UpsamplingNearest2D(scale_factor=2),
nn.utils.spectral_norm(
conv1x1(self.inplanes, planes * block.expansion)),
norm_layer(planes * block.expansion), )
elif self.inplanes != planes * block.expansion:
upsample = nn.Sequential(
nn.utils.spectral_norm(
conv1x1(self.inplanes, planes * block.expansion)),
norm_layer(planes * block.expansion), )
layers = [
block(self.inplanes, planes, stride, upsample, norm_layer,
self.large_kernel)
]
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
norm_layer=norm_layer,
large_kernel=self.large_kernel))
return nn.Sequential(*layers)
def forward(self, x, mid_fea):
x = self.layer1(x) # N x 256 x 32 x 32
print(x.shape)
x = self.layer2(x) # N x 128 x 64 x 64
print(x.shape)
x = self.layer3(x) # N x 64 x 128 x 128
print(x.shape)
x = self.layer4(x) # N x 32 x 256 x 256
print(x.shape)
x = self.conv1(x)
x = self.bn1(x)
x = self.leaky_relu(x)
x = self.conv2(x)
alpha = (self.tanh(x) + 1.0) / 2.0
return alpha
def init_weight(self):
for layer in self.sublayers():
if isinstance(layer, nn.Conv2D):
if hasattr(layer, "weight_orig"):
param = layer.weight_orig
else:
param = layer.weight
param_init.xavier_uniform(param)
elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
param_init.constant_init(layer.weight, value=1.0)
param_init.constant_init(layer.bias, value=0.0)
elif isinstance(layer, BasicBlock):
param_init.constant_init(layer.bn2.weight, value=0.0)
class ResShortCut_D_Dec(ResNet_D_Dec):
def __init__(self,
layers,
norm_layer=None,
large_kernel=False,
late_downsample=False):
super().__init__(
layers, norm_layer, large_kernel, late_downsample=late_downsample)
def forward(self, x, mid_fea):
fea1, fea2, fea3, fea4, fea5 = mid_fea['shortcut']
x = self.layer1(x) + fea5
x = self.layer2(x) + fea4
x = self.layer3(x) + fea3
x = self.layer4(x) + fea2
x = self.conv1(x)
x = self.bn1(x)
x = self.leaky_relu(x) + fea1
x = self.conv2(x)
alpha = (self.tanh(x) + 1.0) / 2.0
return alpha
class ResGuidedCxtAtten_Dec(ResNet_D_Dec):
def __init__(self,
layers,
norm_layer=None,
large_kernel=False,
late_downsample=False):
super().__init__(
layers, norm_layer, large_kernel, late_downsample=late_downsample)
self.gca = GuidedCxtAtten(128, 128)
def forward(self, x, mid_fea):
fea1, fea2, fea3, fea4, fea5 = mid_fea['shortcut']
im = mid_fea['image_fea']
x = self.layer1(x) + fea5 # N x 256 x 32 x 32
x = self.layer2(x) + fea4 # N x 128 x 64 x 64
x = self.gca(im, x, mid_fea['unknown']) # contextual attention
x = self.layer3(x) + fea3 # N x 64 x 128 x 128
x = self.layer4(x) + fea2 # N x 32 x 256 x 256
x = self.conv1(x)
x = self.bn1(x)
x = self.leaky_relu(x) + fea1
x = self.conv2(x)
alpha = (self.tanh(x) + 1.0) / 2.0
return alpha
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