Spaces:
Configuration error
Configuration error
File size: 11,412 Bytes
1ab1a09 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 |
# 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.
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
@manager.MODELS.add_component
class MobileSeg(nn.Layer):
"""
The semantic segmentation models for mobile devices.
Args:
num_classes (int): The number of target classes.
backbone(nn.Layer): Backbone network, such as stdc1net and resnet18. The backbone must
has feat_channels, of which the length is 5.
backbone_indices (List(int), optional): The values indicate the indices of output of backbone.
Default: [2, 3, 4].
cm_bin_sizes (List(int), optional): The bin size of context module. Default: [1,2,4].
cm_out_ch (int, optional): The output channel of the last context module. Default: 128.
arm_type (str, optional): The type of attention refinement module. Default: ARM_Add_SpAttenAdd3.
arm_out_chs (List(int), optional): The out channels of each arm module. Default: [64, 96, 128].
seg_head_inter_chs (List(int), optional): The intermediate channels of segmentation head.
Default: [64, 64, 64].
resize_mode (str, optional): The resize mode for the upsampling operation in decoder.
Default: bilinear.
use_last_fuse (bool, optional): Whether use fusion in the last. Default: False.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
backbone_indices=[1, 2, 3],
cm_bin_sizes=[1, 2],
cm_out_ch=64,
arm_type='UAFMMobile',
arm_out_chs=[32, 48, 64],
seg_head_inter_chs=[32, 32, 32],
resize_mode='bilinear',
use_last_fuse=False,
pretrained=None):
super().__init__()
# backbone
assert hasattr(backbone, 'feat_channels'), \
"The backbone should has feat_channels."
assert len(backbone.feat_channels) >= len(backbone_indices), \
f"The length of input backbone_indices ({len(backbone_indices)}) should not be" \
f"greater than the length of feat_channels ({len(backbone.feat_channels)})."
assert len(backbone.feat_channels) > max(backbone_indices), \
f"The max value ({max(backbone_indices)}) of backbone_indices should be " \
f"less than the length of feat_channels ({len(backbone.feat_channels)})."
self.backbone = backbone
assert len(backbone_indices) >= 1, "The lenght of backbone_indices " \
"should not be lesser than 1"
self.backbone_indices = backbone_indices # [..., x16_id, x32_id]
backbone_out_chs = [backbone.feat_channels[i] for i in backbone_indices]
# head
if len(arm_out_chs) == 1:
arm_out_chs = arm_out_chs * len(backbone_indices)
assert len(arm_out_chs) == len(backbone_indices), "The length of " \
"arm_out_chs and backbone_indices should be equal"
self.ppseg_head = MobileSegHead(backbone_out_chs, arm_out_chs,
cm_bin_sizes, cm_out_ch, arm_type,
resize_mode, use_last_fuse)
if len(seg_head_inter_chs) == 1:
seg_head_inter_chs = seg_head_inter_chs * len(backbone_indices)
assert len(seg_head_inter_chs) == len(backbone_indices), "The length of " \
"seg_head_inter_chs and backbone_indices should be equal"
self.seg_heads = nn.LayerList() # [..., head_16, head32]
for in_ch, mid_ch in zip(arm_out_chs, seg_head_inter_chs):
self.seg_heads.append(SegHead(in_ch, mid_ch, num_classes))
# pretrained
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
x_hw = paddle.shape(x)[2:]
feats_backbone = self.backbone(x) # [x4, x8, x16, x32]
assert len(feats_backbone) >= len(self.backbone_indices), \
f"The nums of backbone feats ({len(feats_backbone)}) should be greater or " \
f"equal than the nums of backbone_indices ({len(self.backbone_indices)})"
feats_selected = [feats_backbone[i] for i in self.backbone_indices]
feats_head = self.ppseg_head(feats_selected) # [..., x8, x16, x32]
if self.training:
logit_list = []
for x, seg_head in zip(feats_head, self.seg_heads):
x = seg_head(x)
logit_list.append(x)
logit_list = [
F.interpolate(
x, x_hw, mode='bilinear', align_corners=False)
for x in logit_list
]
else:
x = self.seg_heads[0](feats_head[0])
x = F.interpolate(x, x_hw, mode='bilinear', align_corners=False)
logit_list = [x]
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class MobileSegHead(nn.Layer):
"""
The head of MobileSeg.
Args:
backbone_out_chs (List(Tensor)): The channels of output tensors in the backbone.
arm_out_chs (List(int)): The out channels of each arm module.
cm_bin_sizes (List(int)): The bin size of context module.
cm_out_ch (int): The output channel of the last context module.
arm_type (str): The type of attention refinement module.
resize_mode (str): The resize mode for the upsampling operation in decoder.
"""
def __init__(self, backbone_out_chs, arm_out_chs, cm_bin_sizes, cm_out_ch,
arm_type, resize_mode, use_last_fuse):
super().__init__()
self.cm = MobileContextModule(backbone_out_chs[-1], cm_out_ch,
cm_out_ch, cm_bin_sizes)
assert hasattr(layers,arm_type), \
"Not support arm_type ({})".format(arm_type)
arm_class = eval("layers." + arm_type)
self.arm_list = nn.LayerList() # [..., arm8, arm16, arm32]
for i in range(len(backbone_out_chs)):
low_chs = backbone_out_chs[i]
high_ch = cm_out_ch if i == len(
backbone_out_chs) - 1 else arm_out_chs[i + 1]
out_ch = arm_out_chs[i]
arm = arm_class(
low_chs, high_ch, out_ch, ksize=3, resize_mode=resize_mode)
self.arm_list.append(arm)
self.use_last_fuse = use_last_fuse
if self.use_last_fuse:
self.fuse_convs = nn.LayerList()
for i in range(1, len(arm_out_chs)):
conv = layers.SeparableConvBNReLU(
arm_out_chs[i],
arm_out_chs[0],
kernel_size=3,
bias_attr=False)
self.fuse_convs.append(conv)
self.last_conv = layers.SeparableConvBNReLU(
len(arm_out_chs) * arm_out_chs[0],
arm_out_chs[0],
kernel_size=3,
bias_attr=False)
def forward(self, in_feat_list):
"""
Args:
in_feat_list (List(Tensor)): Such as [x2, x4, x8, x16, x32].
x2, x4 and x8 are optional.
Returns:
out_feat_list (List(Tensor)): Such as [x2, x4, x8, x16, x32].
x2, x4 and x8 are optional.
The length of in_feat_list and out_feat_list are the same.
"""
high_feat = self.cm(in_feat_list[-1])
out_feat_list = []
for i in reversed(range(len(in_feat_list))):
low_feat = in_feat_list[i]
arm = self.arm_list[i]
high_feat = arm(low_feat, high_feat)
out_feat_list.insert(0, high_feat)
if self.use_last_fuse:
x_list = [out_feat_list[0]]
size = paddle.shape(out_feat_list[0])[2:]
for i, (x, conv
) in enumerate(zip(out_feat_list[1:], self.fuse_convs)):
x = conv(x)
x = F.interpolate(
x, size=size, mode='bilinear', align_corners=False)
x_list.append(x)
x = paddle.concat(x_list, axis=1)
x = self.last_conv(x)
out_feat_list[0] = x
return out_feat_list
class MobileContextModule(nn.Layer):
"""
Context Module for Mobile Model.
Args:
in_channels (int): The number of input channels to pyramid pooling module.
inter_channels (int): The number of inter channels to pyramid pooling module.
out_channels (int): The number of output channels after pyramid pooling module.
bin_sizes (tuple, optional): The out size of pooled feature maps. Default: (1, 3).
align_corners (bool): An argument of F.interpolate. It should be set to False
when the output size of feature is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
"""
def __init__(self,
in_channels,
inter_channels,
out_channels,
bin_sizes,
align_corners=False):
super().__init__()
self.stages = nn.LayerList([
self._make_stage(in_channels, inter_channels, size)
for size in bin_sizes
])
self.conv_out = layers.SeparableConvBNReLU(
in_channels=inter_channels,
out_channels=out_channels,
kernel_size=3,
bias_attr=False)
self.align_corners = align_corners
def _make_stage(self, in_channels, out_channels, size):
prior = nn.AdaptiveAvgPool2D(output_size=size)
conv = layers.ConvBNReLU(
in_channels=in_channels, out_channels=out_channels, kernel_size=1)
return nn.Sequential(prior, conv)
def forward(self, input):
out = None
input_shape = paddle.shape(input)[2:]
for stage in self.stages:
x = stage(input)
x = F.interpolate(
x,
input_shape,
mode='bilinear',
align_corners=self.align_corners)
if out is None:
out = x
else:
out += x
out = self.conv_out(out)
return out
class SegHead(nn.Layer):
def __init__(self, in_chan, mid_chan, n_classes):
super().__init__()
self.conv = layers.SeparableConvBNReLU(
in_chan, mid_chan, kernel_size=3, bias_attr=False)
self.conv_out = nn.Conv2D(
mid_chan, n_classes, kernel_size=1, bias_attr=False)
def forward(self, x):
x = self.conv(x)
x = self.conv_out(x)
return x
|