212 lines
7.9 KiB
Python
212 lines
7.9 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import math
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import torch.nn as nn
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from scepter.modules.model.backbone.video.bricks.base_branch import BaseBranch
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from scepter.modules.model.registry import BRICKS
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from scepter.modules.utils.config import Config, dict_to_yaml
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@BRICKS.register_class()
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class R2Plus1DBranch(BaseBranch):
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para_dict = {
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'DIM_IN': {
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'value': 64,
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'description': "the branch's dim in!"
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},
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'NUM_FILTERS': {
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'value': 64,
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'description': 'the num of filter!'
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},
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'DOWNSAMPLING': {
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'value': True,
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'description': 'downsample spatial data or not!'
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},
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'DOWNSAMPLING_TEMPORAL': {
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'value': True,
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'description': 'downsample temporal data or not!'
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},
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'EXPANISION_RATIO': {
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'value': 2,
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'description': 'expanision ratio for this branch!'
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},
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'BN_PARAMS': {
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'value':
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None,
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'description':
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'bn params data, key/value align with torch.BatchNorm3d/2d/1d!'
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}
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}
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para_dict.update(BaseBranch.para_dict)
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def __init__(self, cfg, logger=None):
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self.dim_in = cfg.DIM_IN
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self.num_filters = cfg.NUM_FILTERS
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self.kernel_size = cfg.KERNEL_SIZE
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self.downsampling = cfg.get('DOWNSAMPLING', True)
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self.downsampling_temporal = cfg.get('DOWNSAMPLING_TEMPORAL', True)
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self.expansion_ratio = cfg.get('EXPANISION_RATIO', 2)
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# bn_params or {}
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self.bn_params = cfg.get('BN_PARAMS', None) or dict()
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if isinstance(self.bn_params, Config):
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self.bn_params = self.bn_params.__dict__
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if self.downsampling:
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if self.downsampling_temporal:
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self.stride = (2, 2, 2)
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else:
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self.stride = (1, 2, 2)
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else:
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self.stride = (1, 1, 1)
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super(R2Plus1DBranch, self).__init__(cfg, logger=logger)
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def _construct_simple_block(self):
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mid_dim = int(
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math.floor(
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(self.kernel_size[0] * self.kernel_size[1] *
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self.kernel_size[2] * self.dim_in * self.num_filters) /
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(self.kernel_size[1] * self.kernel_size[2] * self.dim_in +
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self.kernel_size[0] * self.num_filters)))
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self.a1 = nn.Conv3d(in_channels=self.dim_in,
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out_channels=mid_dim,
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kernel_size=(1, self.kernel_size[1],
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self.kernel_size[2]),
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stride=(1, self.stride[1], self.stride[2]),
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padding=(0, self.kernel_size[1] // 2,
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self.kernel_size[2] // 2),
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bias=False)
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self.a1_bn = nn.BatchNorm3d(mid_dim, **self.bn_params)
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self.a1_relu = nn.ReLU(inplace=True)
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self.a2 = nn.Conv3d(in_channels=mid_dim,
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out_channels=self.num_filters,
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kernel_size=(self.kernel_size[0], 1, 1),
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stride=(self.stride[0], 1, 1),
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padding=(self.kernel_size[0] // 2, 0, 0),
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bias=False)
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self.a2_bn = nn.BatchNorm3d(self.num_filters, **self.bn_params)
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self.a2_relu = nn.ReLU(inplace=True)
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mid_dim = int(
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math.floor(
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(self.kernel_size[0] * self.kernel_size[1] *
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self.kernel_size[2] * self.num_filters * self.num_filters) /
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(self.kernel_size[1] * self.kernel_size[2] * self.num_filters +
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self.kernel_size[0] * self.num_filters)))
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self.b1 = nn.Conv3d(in_channels=self.num_filters,
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out_channels=mid_dim,
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kernel_size=(1, self.kernel_size[1],
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self.kernel_size[2]),
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stride=(1, 1, 1),
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padding=(0, self.kernel_size[1] // 2,
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self.kernel_size[2] // 2),
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bias=False)
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self.b1_bn = nn.BatchNorm3d(mid_dim, **self.bn_params)
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self.b1_relu = nn.ReLU(inplace=True)
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self.b2 = nn.Conv3d(in_channels=mid_dim,
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out_channels=self.num_filters,
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kernel_size=(self.kernel_size[0], 1, 1),
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stride=(1, 1, 1),
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padding=(self.kernel_size[0] // 2, 0, 0),
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bias=False)
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self.b2_bn = nn.BatchNorm3d(self.num_filters, **self.bn_params)
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def _construct_bottleneck(self):
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self.a = nn.Conv3d(in_channels=self.dim_in,
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out_channels=self.num_filters //
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self.expansion_ratio,
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kernel_size=(1, 1, 1),
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stride=(1, 1, 1),
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padding=0,
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bias=False)
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self.a_bn = nn.BatchNorm3d(self.num_filters // self.expansion_ratio,
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**self.bn_params)
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self.a_relu = nn.ReLU(inplace=True)
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self.b1 = nn.Conv3d(
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in_channels=self.num_filters // self.expansion_ratio,
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out_channels=self.num_filters // self.expansion_ratio,
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kernel_size=(1, self.kernel_size[1], self.kernel_size[2]),
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stride=(1, self.stride[1], self.stride[2]),
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padding=(0, self.kernel_size[1] // 2, self.kernel_size[2] // 2),
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bias=False)
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self.b1_bn = nn.BatchNorm3d(self.num_filters // self.expansion_ratio,
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**self.bn_params)
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self.b1_relu = nn.ReLU(inplace=True)
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self.b2 = nn.Conv3d(
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in_channels=self.num_filters // self.expansion_ratio,
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out_channels=self.num_filters // self.expansion_ratio,
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kernel_size=(self.kernel_size[0], 1, 1),
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stride=(self.stride[0], 1, 1),
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padding=(self.kernel_size[0] // 2, 0, 0),
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bias=False)
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self.b2_bn = nn.BatchNorm3d(self.num_filters // self.expansion_ratio,
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**self.bn_params)
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self.b2_relu = nn.ReLU(inplace=True)
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self.c = nn.Conv3d(in_channels=self.num_filters //
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self.expansion_ratio,
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out_channels=self.num_filters,
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kernel_size=(1, 1, 1),
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stride=(1, 1, 1),
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padding=0,
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bias=False)
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self.c_bn = nn.BatchNorm3d(self.num_filters, **self.bn_params)
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def forward(self, x):
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if self.branch_style == 'simple_block':
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x = self.a1(x)
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x = self.a1_bn(x)
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x = self.a1_relu(x)
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x = self.a2(x)
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x = self.a2_bn(x)
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x = self.a2_relu(x)
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x = self.b1(x)
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x = self.b1_bn(x)
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x = self.b1_relu(x)
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x = self.b2(x)
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x = self.b2_bn(x)
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return x
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elif self.branch_style == 'bottleneck':
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x = self.a(x)
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x = self.a_bn(x)
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x = self.a_relu(x)
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x = self.b1(x)
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x = self.b1_bn(x)
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x = self.b1_relu(x)
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x = self.b2(x)
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x = self.b2_bn(x)
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x = self.b2_relu(x)
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x = self.c(x)
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x = self.c_bn(x)
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return x
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@staticmethod
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def get_config_template():
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'''
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{ "ENV" :
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{ "description" : "",
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"A" : {
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"value": 1.0,
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"description": ""
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}
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}
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}
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:return:
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'''
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return dict_to_yaml('BRANCH',
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__class__.__name__,
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R2Plus1DBranch.para_dict,
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set_name=True)
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