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- # copyright (c) 2020 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,
- data_format='NCHW'):
- super(ConvBNLayer, self).__init__()
- if dilation != 1 and kernel_size != 3:
- raise RuntimeError("When the dilation isn't 1," \
- "the kernel_size should be 3.")
-
- self.is_vd_mode = is_vd_mode
- self._pool2d_avg = nn.AvgPool2D(
- kernel_size=2,
- stride=2,
- padding=0,
- ceil_mode=True,
- data_format=data_format)
- 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 dilation,
- dilation=dilation,
- groups=groups,
- bias_attr=False,
- data_format=data_format)
-
- self._batch_norm = layers.SyncBatchNorm(
- out_channels, data_format=data_format)
- 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,
- data_format='NCHW'):
- super(BottleneckBlock, self).__init__()
-
- self.data_format = data_format
- self.conv0 = ConvBNLayer(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=1,
- act='relu',
- data_format=data_format)
-
- self.dilation = dilation
-
- self.conv1 = ConvBNLayer(
- in_channels=out_channels,
- out_channels=out_channels,
- kernel_size=3,
- stride=stride,
- act='relu',
- dilation=dilation,
- data_format=data_format)
- self.conv2 = ConvBNLayer(
- in_channels=out_channels,
- out_channels=out_channels * 4,
- kernel_size=1,
- act=None,
- data_format=data_format)
-
- 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,
- data_format=data_format)
-
- self.shortcut = shortcut
- # NOTE: Use the wrap layer for quantization training
- self.add = layers.Add()
- self.relu = layers.Activation(act="relu")
-
- def forward(self, inputs):
- y = self.conv0(inputs)
- conv1 = self.conv1(y)
- conv2 = self.conv2(conv1)
-
- if self.shortcut:
- short = inputs
- else:
- short = self.short(inputs)
-
- y = self.add(short, conv2)
- y = self.relu(y)
- return y
-
-
- class BasicBlock(nn.Layer):
- def __init__(self,
- in_channels,
- out_channels,
- stride,
- dilation=1,
- shortcut=True,
- if_first=False,
- data_format='NCHW'):
- super(BasicBlock, self).__init__()
- self.conv0 = ConvBNLayer(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=3,
- stride=stride,
- dilation=dilation,
- act='relu',
- data_format=data_format)
- self.conv1 = ConvBNLayer(
- in_channels=out_channels,
- out_channels=out_channels,
- kernel_size=3,
- dilation=dilation,
- act=None,
- data_format=data_format)
-
- 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 or stride == 1 else True,
- data_format=data_format)
-
- self.shortcut = shortcut
- self.dilation = dilation
- self.data_format = data_format
- self.add = layers.Add()
- self.relu = layers.Activation(act="relu")
-
- def forward(self, inputs):
- y = self.conv0(inputs)
- conv1 = self.conv1(y)
-
- if self.shortcut:
- short = inputs
- else:
- short = self.short(inputs)
- y = self.add(short, conv1)
- y = self.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).
- in_channels (int, optional): The channels of input image. Default: 3.
- pretrained (str, optional): The path of pretrained model.
-
- """
-
- def __init__(self,
- layers=50,
- output_stride=8,
- multi_grid=(1, 1, 1),
- in_channels=3,
- pretrained=None,
- data_format='NCHW'):
- super(ResNet_vd, self).__init__()
-
- self.data_format = data_format
- 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
-
- 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=in_channels,
- out_channels=32,
- kernel_size=3,
- stride=2,
- act='relu',
- data_format=data_format)
- self.conv1_2 = ConvBNLayer(
- in_channels=32,
- out_channels=32,
- kernel_size=3,
- stride=1,
- act='relu',
- data_format=data_format)
- self.conv1_3 = ConvBNLayer(
- in_channels=32,
- out_channels=64,
- kernel_size=3,
- stride=1,
- act='relu',
- data_format=data_format)
- self.pool2d_max = nn.MaxPool2D(
- kernel_size=3, stride=2, padding=1, data_format=data_format)
-
- # 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,
- data_format=data_format))
-
- 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]):
- dilation_rate = dilation_dict[block] \
- if dilation_dict and block in dilation_dict else 1
- if block == 3:
- dilation_rate = dilation_rate * multi_grid[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 \
- and dilation_rate == 1 else 1,
- dilation=dilation_rate,
- shortcut=shortcut,
- if_first=block == i == 0,
- data_format=data_format))
- block_list.append(basic_block)
- shortcut = True
- self.stage_list.append(block_list)
-
- self.pretrained = pretrained
- self.init_weight()
-
- def forward(self, inputs):
- y = self.conv1_1(inputs)
- y = self.conv1_2(y)
- y = self.conv1_3(y)
- self.conv1_logit = y.clone()
- y = self.pool2d_max(y)
-
- # A feature list saves the output feature map of each stage.
- feat_list = []
- 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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