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# Copyright (c) 2020 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.cvlibs import manager, param_init
from paddleseg.models import layers
@manager.MODELS.add_component
class OCRNet(nn.Layer):
"""
The OCRNet implementation based on PaddlePaddle.
The original article refers to
Yuan, Yuhui, et al. "Object-Contextual Representations for Semantic Segmentation"
(https://arxiv.org/pdf/1909.11065.pdf)
Args:
num_classes (int): The unique number of target classes.
backbone (Paddle.nn.Layer): Backbone network.
backbone_indices (tuple): A tuple indicates the indices of output of backbone.
It can be either one or two values, if two values, the first index will be taken as
a deep-supervision feature in auxiliary layer; the second one will be taken as
input of pixel representation. If one value, it is taken by both above.
ocr_mid_channels (int, optional): The number of middle channels in OCRHead. Default: 512.
ocr_key_channels (int, optional): The number of key channels in ObjectAttentionBlock. Default: 256.
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. Default: False.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
backbone_indices,
ocr_mid_channels=512,
ocr_key_channels=256,
align_corners=False,
pretrained=None):
super().__init__()
self.backbone = backbone
self.backbone_indices = backbone_indices
in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
self.head = OCRHead(
num_classes=num_classes,
in_channels=in_channels,
ocr_mid_channels=ocr_mid_channels,
ocr_key_channels=ocr_key_channels)
self.align_corners = align_corners
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
feats = self.backbone(x)
feats = [feats[i] for i in self.backbone_indices]
logit_list = self.head(feats)
if not self.training:
logit_list = [logit_list[0]]
logit_list = [
F.interpolate(
logit,
paddle.shape(x)[2:],
mode='bilinear',
align_corners=self.align_corners) for logit in logit_list
]
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class OCRHead(nn.Layer):
"""
The Object contextual representation head.
Args:
num_classes(int): The unique number of target classes.
in_channels(tuple): The number of input channels.
ocr_mid_channels(int, optional): The number of middle channels in OCRHead. Default: 512.
ocr_key_channels(int, optional): The number of key channels in ObjectAttentionBlock. Default: 256.
"""
def __init__(self,
num_classes,
in_channels,
ocr_mid_channels=512,
ocr_key_channels=256):
super().__init__()
self.num_classes = num_classes
self.spatial_gather = SpatialGatherBlock(ocr_mid_channels, num_classes)
self.spatial_ocr = SpatialOCRModule(ocr_mid_channels, ocr_key_channels,
ocr_mid_channels)
self.indices = [-2, -1] if len(in_channels) > 1 else [-1, -1]
self.conv3x3_ocr = layers.ConvBNReLU(
in_channels[self.indices[1]], ocr_mid_channels, 3, padding=1)
self.cls_head = nn.Conv2D(ocr_mid_channels, self.num_classes, 1)
self.aux_head = nn.Sequential(
layers.ConvBNReLU(in_channels[self.indices[0]],
in_channels[self.indices[0]], 1),
nn.Conv2D(in_channels[self.indices[0]], self.num_classes, 1))
self.init_weight()
def forward(self, feat_list):
feat_shallow, feat_deep = feat_list[self.indices[0]], feat_list[
self.indices[1]]
soft_regions = self.aux_head(feat_shallow)
pixels = self.conv3x3_ocr(feat_deep)
object_regions = self.spatial_gather(pixels, soft_regions)
ocr = self.spatial_ocr(pixels, object_regions)
logit = self.cls_head(ocr)
return [logit, soft_regions]
def init_weight(self):
"""Initialize the parameters of model parts."""
for sublayer in self.sublayers():
if isinstance(sublayer, nn.Conv2D):
param_init.normal_init(sublayer.weight, std=0.001)
elif isinstance(sublayer, (nn.BatchNorm, nn.SyncBatchNorm)):
param_init.constant_init(sublayer.weight, value=1.0)
param_init.constant_init(sublayer.bias, value=0.0)
class SpatialGatherBlock(nn.Layer):
"""Aggregation layer to compute the pixel-region representation."""
def __init__(self, pixels_channels, regions_channels):
super().__init__()
self.pixels_channels = pixels_channels
self.regions_channels = regions_channels
def forward(self, pixels, regions):
# pixels: from (n, c, h, w) to (n, h*w, c)
pixels = paddle.reshape(pixels, (0, self.pixels_channels, -1))
pixels = paddle.transpose(pixels, (0, 2, 1))
# regions: from (n, k, h, w) to (n, k, h*w)
regions = paddle.reshape(regions, (0, self.regions_channels, -1))
regions = F.softmax(regions, axis=2)
# feats: from (n, k, c) to (n, c, k, 1)
feats = paddle.bmm(regions, pixels)
feats = paddle.transpose(feats, (0, 2, 1))
feats = paddle.unsqueeze(feats, axis=-1)
return feats
class SpatialOCRModule(nn.Layer):
"""Aggregate the global object representation to update the representation for each pixel."""
def __init__(self,
in_channels,
key_channels,
out_channels,
dropout_rate=0.1):
super().__init__()
self.attention_block = ObjectAttentionBlock(in_channels, key_channels)
self.conv1x1 = nn.Sequential(
layers.ConvBNReLU(2 * in_channels, out_channels, 1),
nn.Dropout2D(dropout_rate))
def forward(self, pixels, regions):
context = self.attention_block(pixels, regions)
feats = paddle.concat([context, pixels], axis=1)
feats = self.conv1x1(feats)
return feats
class ObjectAttentionBlock(nn.Layer):
"""A self-attention module."""
def __init__(self, in_channels, key_channels):
super().__init__()
self.in_channels = in_channels
self.key_channels = key_channels
self.f_pixel = nn.Sequential(
layers.ConvBNReLU(in_channels, key_channels, 1),
layers.ConvBNReLU(key_channels, key_channels, 1))
self.f_object = nn.Sequential(
layers.ConvBNReLU(in_channels, key_channels, 1),
layers.ConvBNReLU(key_channels, key_channels, 1))
self.f_down = layers.ConvBNReLU(in_channels, key_channels, 1)
self.f_up = layers.ConvBNReLU(key_channels, in_channels, 1)
def forward(self, x, proxy):
x_shape = paddle.shape(x)
# query : from (n, c1, h1, w1) to (n, h1*w1, key_channels)
query = self.f_pixel(x)
query = paddle.reshape(query, (0, self.key_channels, -1))
query = paddle.transpose(query, (0, 2, 1))
# key : from (n, c2, h2, w2) to (n, key_channels, h2*w2)
key = self.f_object(proxy)
key = paddle.reshape(key, (0, self.key_channels, -1))
# value : from (n, c2, h2, w2) to (n, h2*w2, key_channels)
value = self.f_down(proxy)
value = paddle.reshape(value, (0, self.key_channels, -1))
value = paddle.transpose(value, (0, 2, 1))
# sim_map (n, h1*w1, h2*w2)
sim_map = paddle.bmm(query, key)
sim_map = (self.key_channels**-.5) * sim_map
sim_map = F.softmax(sim_map, axis=-1)
# context from (n, h1*w1, key_channels) to (n , out_channels, h1, w1)
context = paddle.bmm(sim_map, value)
context = paddle.transpose(context, (0, 2, 1))
context = paddle.reshape(context,
(0, self.key_channels, x_shape[2], x_shape[3]))
context = self.f_up(context)
return context
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