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242 lines
8.8 KiB
242 lines
8.8 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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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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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 import ParamAttr |
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from paddle.nn.initializer import Constant, Uniform, Normal, XavierUniform |
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from paddlers.models.ppdet.core.workspace import register, serializable |
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from paddle.regularizer import L2Decay |
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from paddlers.models.ppdet.modeling.layers import DeformableConvV2, ConvNormLayer, LiteConv |
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import math |
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from paddlers.models.ppdet.modeling.ops import batch_norm |
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from ..shape_spec import ShapeSpec |
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__all__ = ['TTFFPN'] |
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class Upsample(nn.Layer): |
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def __init__(self, ch_in, ch_out, norm_type='bn'): |
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super(Upsample, self).__init__() |
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fan_in = ch_in * 3 * 3 |
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stdv = 1. / math.sqrt(fan_in) |
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self.dcn = DeformableConvV2( |
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ch_in, |
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ch_out, |
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kernel_size=3, |
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weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), |
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bias_attr=ParamAttr( |
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initializer=Constant(0), |
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regularizer=L2Decay(0.), |
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learning_rate=2.), |
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lr_scale=2., |
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regularizer=L2Decay(0.)) |
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self.bn = batch_norm( |
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ch_out, norm_type=norm_type, initializer=Constant(1.)) |
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def forward(self, feat): |
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dcn = self.dcn(feat) |
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bn = self.bn(dcn) |
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relu = F.relu(bn) |
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out = F.interpolate(relu, scale_factor=2., mode='bilinear') |
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return out |
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class DeConv(nn.Layer): |
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def __init__(self, ch_in, ch_out, norm_type='bn'): |
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super(DeConv, self).__init__() |
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self.deconv = nn.Sequential() |
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conv1 = ConvNormLayer( |
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ch_in=ch_in, |
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ch_out=ch_out, |
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stride=1, |
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filter_size=1, |
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norm_type=norm_type, |
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initializer=XavierUniform()) |
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conv2 = nn.Conv2DTranspose( |
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in_channels=ch_out, |
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out_channels=ch_out, |
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kernel_size=4, |
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padding=1, |
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stride=2, |
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groups=ch_out, |
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weight_attr=ParamAttr(initializer=XavierUniform()), |
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bias_attr=False) |
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bn = batch_norm(ch_out, norm_type=norm_type, norm_decay=0.) |
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conv3 = ConvNormLayer( |
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ch_in=ch_out, |
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ch_out=ch_out, |
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stride=1, |
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filter_size=1, |
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norm_type=norm_type, |
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initializer=XavierUniform()) |
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self.deconv.add_sublayer('conv1', conv1) |
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self.deconv.add_sublayer('relu6_1', nn.ReLU6()) |
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self.deconv.add_sublayer('conv2', conv2) |
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self.deconv.add_sublayer('bn', bn) |
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self.deconv.add_sublayer('relu6_2', nn.ReLU6()) |
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self.deconv.add_sublayer('conv3', conv3) |
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self.deconv.add_sublayer('relu6_3', nn.ReLU6()) |
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def forward(self, inputs): |
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return self.deconv(inputs) |
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class LiteUpsample(nn.Layer): |
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def __init__(self, ch_in, ch_out, norm_type='bn'): |
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super(LiteUpsample, self).__init__() |
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self.deconv = DeConv(ch_in, ch_out, norm_type=norm_type) |
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self.conv = LiteConv(ch_in, ch_out, norm_type=norm_type) |
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def forward(self, inputs): |
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deconv_up = self.deconv(inputs) |
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conv = self.conv(inputs) |
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interp_up = F.interpolate(conv, scale_factor=2., mode='bilinear') |
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return deconv_up + interp_up |
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class ShortCut(nn.Layer): |
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def __init__(self, |
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layer_num, |
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ch_in, |
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ch_out, |
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norm_type='bn', |
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lite_neck=False, |
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name=None): |
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super(ShortCut, self).__init__() |
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shortcut_conv = nn.Sequential() |
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for i in range(layer_num): |
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fan_out = 3 * 3 * ch_out |
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std = math.sqrt(2. / fan_out) |
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in_channels = ch_in if i == 0 else ch_out |
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shortcut_name = name + '.conv.{}'.format(i) |
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if lite_neck: |
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shortcut_conv.add_sublayer( |
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shortcut_name, |
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LiteConv( |
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in_channels=in_channels, |
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out_channels=ch_out, |
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with_act=i < layer_num - 1, |
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norm_type=norm_type)) |
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else: |
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shortcut_conv.add_sublayer( |
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shortcut_name, |
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nn.Conv2D( |
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in_channels=in_channels, |
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out_channels=ch_out, |
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kernel_size=3, |
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padding=1, |
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weight_attr=ParamAttr(initializer=Normal(0, std)), |
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bias_attr=ParamAttr( |
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learning_rate=2., regularizer=L2Decay(0.)))) |
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if i < layer_num - 1: |
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shortcut_conv.add_sublayer(shortcut_name + '.act', |
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nn.ReLU()) |
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self.shortcut = self.add_sublayer('shortcut', shortcut_conv) |
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def forward(self, feat): |
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out = self.shortcut(feat) |
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return out |
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@register |
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@serializable |
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class TTFFPN(nn.Layer): |
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""" |
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Args: |
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in_channels (list): number of input feature channels from backbone. |
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[128,256,512,1024] by default, means the channels of DarkNet53 |
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backbone return_idx [1,2,3,4]. |
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planes (list): the number of output feature channels of FPN. |
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[256, 128, 64] by default |
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shortcut_num (list): the number of convolution layers in each shortcut. |
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[3,2,1] by default, means DarkNet53 backbone return_idx_1 has 3 convs |
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in its shortcut, return_idx_2 has 2 convs and return_idx_3 has 1 conv. |
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norm_type (string): norm type, 'sync_bn', 'bn', 'gn' are optional. |
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bn by default |
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lite_neck (bool): whether to use lite conv in TTFNet FPN, |
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False by default |
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fusion_method (string): the method to fusion upsample and lateral layer. |
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'add' and 'concat' are optional, add by default |
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""" |
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__shared__ = ['norm_type'] |
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def __init__(self, |
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in_channels, |
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planes=[256, 128, 64], |
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shortcut_num=[3, 2, 1], |
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norm_type='bn', |
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lite_neck=False, |
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fusion_method='add'): |
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super(TTFFPN, self).__init__() |
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self.planes = planes |
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self.shortcut_num = shortcut_num[::-1] |
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self.shortcut_len = len(shortcut_num) |
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self.ch_in = in_channels[::-1] |
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self.fusion_method = fusion_method |
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self.upsample_list = [] |
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self.shortcut_list = [] |
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self.upper_list = [] |
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for i, out_c in enumerate(self.planes): |
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in_c = self.ch_in[i] if i == 0 else self.upper_list[-1] |
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upsample_module = LiteUpsample if lite_neck else Upsample |
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upsample = self.add_sublayer( |
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'upsample.' + str(i), |
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upsample_module( |
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in_c, out_c, norm_type=norm_type)) |
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self.upsample_list.append(upsample) |
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if i < self.shortcut_len: |
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shortcut = self.add_sublayer( |
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'shortcut.' + str(i), |
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ShortCut( |
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self.shortcut_num[i], |
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self.ch_in[i + 1], |
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out_c, |
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norm_type=norm_type, |
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lite_neck=lite_neck, |
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name='shortcut.' + str(i))) |
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self.shortcut_list.append(shortcut) |
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if self.fusion_method == 'add': |
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upper_c = out_c |
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elif self.fusion_method == 'concat': |
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upper_c = out_c * 2 |
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else: |
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raise ValueError('Illegal fusion method. Expected add or\ |
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concat, but received {}'.format(self.fusion_method)) |
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self.upper_list.append(upper_c) |
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def forward(self, inputs): |
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feat = inputs[-1] |
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for i, out_c in enumerate(self.planes): |
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feat = self.upsample_list[i](feat) |
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if i < self.shortcut_len: |
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shortcut = self.shortcut_list[i](inputs[-i - 2]) |
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if self.fusion_method == 'add': |
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feat = feat + shortcut |
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else: |
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feat = paddle.concat([feat, shortcut], axis=1) |
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return feat |
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@classmethod |
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def from_config(cls, cfg, input_shape): |
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return {'in_channels': [i.channels for i in input_shape], } |
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@property |
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def out_shape(self): |
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return [ShapeSpec(channels=self.upper_list[-1], )]
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