530 lines
16 KiB
Python
530 lines
16 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Code was based on https://github.com/ucbdrive/dla
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# reference: https://arxiv.org/abs/1707.06484
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import math
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn.initializer import Normal, Constant
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from ..base.theseus_layer import Identity
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"DLA34":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams",
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"DLA46_c":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams",
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"DLA46x_c":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46x_c_pretrained.pdparams",
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"DLA60":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams",
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"DLA60x":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams",
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"DLA60x_c":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams",
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"DLA102":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams",
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"DLA102x":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams",
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"DLA102x2":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams",
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"DLA169":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams"
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}
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__all__ = MODEL_URLS.keys()
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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class DlaBasic(nn.Layer):
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def __init__(self, inplanes, planes, stride=1, dilation=1, **cargs):
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super(DlaBasic, self).__init__()
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self.conv1 = nn.Conv2D(
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inplanes,
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planes,
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kernel_size=3,
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stride=stride,
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padding=dilation,
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bias_attr=False,
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dilation=dilation)
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self.bn1 = nn.BatchNorm2D(planes)
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self.relu = nn.ReLU()
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self.conv2 = nn.Conv2D(
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planes,
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planes,
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kernel_size=3,
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stride=1,
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padding=dilation,
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bias_attr=False,
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dilation=dilation)
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self.bn2 = nn.BatchNorm2D(planes)
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self.stride = stride
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def forward(self, x, residual=None):
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if residual is None:
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out += residual
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out = self.relu(out)
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return out
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class DlaBottleneck(nn.Layer):
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expansion = 2
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def __init__(self,
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inplanes,
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outplanes,
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stride=1,
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dilation=1,
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cardinality=1,
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base_width=64):
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super(DlaBottleneck, self).__init__()
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self.stride = stride
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mid_planes = int(
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math.floor(outplanes * (base_width / 64)) * cardinality)
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mid_planes = mid_planes // self.expansion
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self.conv1 = nn.Conv2D(
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inplanes, mid_planes, kernel_size=1, bias_attr=False)
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self.bn1 = nn.BatchNorm2D(mid_planes)
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self.conv2 = nn.Conv2D(
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mid_planes,
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mid_planes,
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kernel_size=3,
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stride=stride,
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padding=dilation,
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bias_attr=False,
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dilation=dilation,
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groups=cardinality)
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self.bn2 = nn.BatchNorm2D(mid_planes)
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self.conv3 = nn.Conv2D(
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mid_planes, outplanes, kernel_size=1, bias_attr=False)
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self.bn3 = nn.BatchNorm2D(outplanes)
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self.relu = nn.ReLU()
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def forward(self, x, residual=None):
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if residual is None:
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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out += residual
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out = self.relu(out)
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return out
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class DlaRoot(nn.Layer):
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def __init__(self, in_channels, out_channels, kernel_size, residual):
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super(DlaRoot, self).__init__()
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self.conv = nn.Conv2D(
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in_channels,
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out_channels,
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1,
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stride=1,
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bias_attr=False,
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padding=(kernel_size - 1) // 2)
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self.bn = nn.BatchNorm2D(out_channels)
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self.relu = nn.ReLU()
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self.residual = residual
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def forward(self, *x):
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children = x
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x = self.conv(paddle.concat(x, 1))
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x = self.bn(x)
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if self.residual:
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x += children[0]
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x = self.relu(x)
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return x
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class DlaTree(nn.Layer):
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def __init__(self,
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levels,
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block,
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in_channels,
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out_channels,
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stride=1,
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dilation=1,
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cardinality=1,
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base_width=64,
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level_root=False,
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root_dim=0,
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root_kernel_size=1,
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root_residual=False):
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super(DlaTree, self).__init__()
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if root_dim == 0:
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root_dim = 2 * out_channels
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if level_root:
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root_dim += in_channels
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self.downsample = nn.MaxPool2D(
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stride, stride=stride) if stride > 1 else Identity()
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self.project = Identity()
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cargs = dict(
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dilation=dilation, cardinality=cardinality, base_width=base_width)
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if levels == 1:
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self.tree1 = block(in_channels, out_channels, stride, **cargs)
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self.tree2 = block(out_channels, out_channels, 1, **cargs)
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if in_channels != out_channels:
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self.project = nn.Sequential(
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nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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bias_attr=False),
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nn.BatchNorm2D(out_channels))
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else:
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cargs.update(
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dict(
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root_kernel_size=root_kernel_size,
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root_residual=root_residual))
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self.tree1 = DlaTree(
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levels - 1,
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block,
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in_channels,
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out_channels,
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stride,
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root_dim=0,
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**cargs)
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self.tree2 = DlaTree(
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levels - 1,
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block,
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out_channels,
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out_channels,
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root_dim=root_dim + out_channels,
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**cargs)
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if levels == 1:
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self.root = DlaRoot(root_dim, out_channels, root_kernel_size,
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root_residual)
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self.level_root = level_root
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self.root_dim = root_dim
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self.levels = levels
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def forward(self, x, residual=None, children=None):
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children = [] if children is None else children
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bottom = self.downsample(x)
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residual = self.project(bottom)
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if self.level_root:
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children.append(bottom)
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x1 = self.tree1(x, residual)
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if self.levels == 1:
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x2 = self.tree2(x1)
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x = self.root(x2, x1, *children)
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else:
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children.append(x1)
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x = self.tree2(x1, children=children)
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return x
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class DLA(nn.Layer):
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def __init__(self,
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levels,
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channels,
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in_chans=3,
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cardinality=1,
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base_width=64,
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block=DlaBottleneck,
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residual_root=False,
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drop_rate=0.0,
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class_num=1000,
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with_pool=True):
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super(DLA, self).__init__()
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self.channels = channels
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self.class_num = class_num
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self.with_pool = with_pool
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self.cardinality = cardinality
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self.base_width = base_width
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self.drop_rate = drop_rate
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self.base_layer = nn.Sequential(
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nn.Conv2D(
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in_chans,
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channels[0],
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kernel_size=7,
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stride=1,
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padding=3,
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bias_attr=False),
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nn.BatchNorm2D(channels[0]),
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nn.ReLU())
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self.level0 = self._make_conv_level(channels[0], channels[0],
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levels[0])
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self.level1 = self._make_conv_level(
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channels[0], channels[1], levels[1], stride=2)
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cargs = dict(
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cardinality=cardinality,
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base_width=base_width,
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root_residual=residual_root)
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self.level2 = DlaTree(
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levels[2],
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block,
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channels[1],
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channels[2],
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2,
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level_root=False,
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**cargs)
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self.level3 = DlaTree(
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levels[3],
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block,
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channels[2],
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channels[3],
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2,
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level_root=True,
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**cargs)
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self.level4 = DlaTree(
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levels[4],
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block,
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channels[3],
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channels[4],
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2,
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level_root=True,
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**cargs)
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self.level5 = DlaTree(
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levels[5],
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block,
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channels[4],
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channels[5],
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2,
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level_root=True,
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**cargs)
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self.feature_info = [
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# rare to have a meaningful stride 1 level
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dict(
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num_chs=channels[0], reduction=1, module='level0'),
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dict(
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num_chs=channels[1], reduction=2, module='level1'),
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dict(
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num_chs=channels[2], reduction=4, module='level2'),
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dict(
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num_chs=channels[3], reduction=8, module='level3'),
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dict(
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num_chs=channels[4], reduction=16, module='level4'),
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dict(
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num_chs=channels[5], reduction=32, module='level5'),
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]
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self.num_features = channels[-1]
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if with_pool:
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self.global_pool = nn.AdaptiveAvgPool2D(1)
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if class_num > 0:
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self.fc = nn.Conv2D(self.num_features, class_num, 1)
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
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normal_ = Normal(mean=0.0, std=math.sqrt(2. / n))
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normal_(m.weight)
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elif isinstance(m, nn.BatchNorm2D):
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ones_(m.weight)
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zeros_(m.bias)
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def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
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modules = []
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for i in range(convs):
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modules.extend([
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nn.Conv2D(
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inplanes,
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planes,
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kernel_size=3,
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stride=stride if i == 0 else 1,
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padding=dilation,
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bias_attr=False,
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dilation=dilation), nn.BatchNorm2D(planes), nn.ReLU()
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])
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inplanes = planes
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return nn.Sequential(*modules)
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def forward_features(self, x):
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x = self.base_layer(x)
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x = self.level0(x)
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x = self.level1(x)
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x = self.level2(x)
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x = self.level3(x)
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x = self.level4(x)
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x = self.level5(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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if self.with_pool:
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x = self.global_pool(x)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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if self.class_num > 0:
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x = self.fc(x)
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x = x.flatten(1)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def DLA34(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 2, 2, 1),
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channels=(16, 32, 64, 128, 256, 512),
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block=DlaBasic,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA34"])
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return model
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def DLA46_c(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 2, 2, 1),
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channels=(16, 32, 64, 64, 128, 256),
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block=DlaBottleneck,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA46_c"])
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return model
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def DLA46x_c(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 2, 2, 1),
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channels=(16, 32, 64, 64, 128, 256),
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block=DlaBottleneck,
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cardinality=32,
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base_width=4,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA46x_c"])
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return model
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def DLA60(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 2, 3, 1),
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channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottleneck,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA60"])
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return model
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def DLA60x(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 2, 3, 1),
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channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottleneck,
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cardinality=32,
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base_width=4,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA60x"])
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return model
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def DLA60x_c(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 2, 3, 1),
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channels=(16, 32, 64, 64, 128, 256),
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block=DlaBottleneck,
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cardinality=32,
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base_width=4,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA60x_c"])
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return model
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def DLA102(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 3, 4, 1),
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channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottleneck,
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residual_root=True,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA102"])
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return model
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def DLA102x(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 3, 4, 1),
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channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottleneck,
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cardinality=32,
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base_width=4,
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residual_root=True,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA102x"])
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return model
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def DLA102x2(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 1, 3, 4, 1),
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channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottleneck,
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cardinality=64,
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base_width=4,
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residual_root=True,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA102x2"])
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return model
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def DLA169(pretrained=False, **kwargs):
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model = DLA(levels=(1, 1, 2, 3, 5, 1),
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channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottleneck,
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residual_root=True,
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["DLA169"])
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return model
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