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502 lines
21 KiB
502 lines
21 KiB
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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import warnings
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from yolov6.layers.dbb_transforms import *
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class SiLU(nn.Module):
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'''Activation of SiLU'''
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@staticmethod
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def forward(x):
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return x * torch.sigmoid(x)
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class Conv(nn.Module):
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'''Normal Conv with SiLU activation'''
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def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
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super().__init__()
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padding = kernel_size // 2
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self.conv = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias=bias,
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)
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self.bn = nn.BatchNorm2d(out_channels)
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self.act = nn.SiLU()
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def forward(self, x):
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return self.act(self.bn(self.conv(x)))
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def forward_fuse(self, x):
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return self.act(self.conv(x))
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class SimConv(nn.Module):
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'''Normal Conv with ReLU activation'''
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def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
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super().__init__()
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padding = kernel_size // 2
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self.conv = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias=bias,
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)
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self.bn = nn.BatchNorm2d(out_channels)
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self.act = nn.ReLU()
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def forward(self, x):
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return self.act(self.bn(self.conv(x)))
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def forward_fuse(self, x):
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return self.act(self.conv(x))
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class SimSPPF(nn.Module):
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'''Simplified SPPF with ReLU activation'''
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def __init__(self, in_channels, out_channels, kernel_size=5):
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super().__init__()
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c_ = in_channels // 2 # hidden channels
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self.cv1 = SimConv(in_channels, c_, 1, 1)
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self.cv2 = SimConv(c_ * 4, out_channels, 1, 1)
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self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)
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def forward(self, x):
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x = self.cv1(x)
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with warnings.catch_warnings():
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warnings.simplefilter('ignore')
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y1 = self.m(x)
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y2 = self.m(y1)
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return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
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class Transpose(nn.Module):
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'''Normal Transpose, default for upsampling'''
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def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
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super().__init__()
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self.upsample_transpose = torch.nn.ConvTranspose2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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bias=True
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)
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def forward(self, x):
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return self.upsample_transpose(x)
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class Concat(nn.Module):
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def __init__(self, dimension=1):
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super().__init__()
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self.d = dimension
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def forward(self, x):
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return torch.cat(x, self.d)
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def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
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'''Basic cell for rep-style block, including conv and bn'''
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result = nn.Sequential()
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result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False))
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result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
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return result
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class RepBlock(nn.Module):
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'''
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RepBlock is a stage block with rep-style basic block
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'''
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def __init__(self, in_channels, out_channels, n=1):
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super().__init__()
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self.conv1 = RepVGGBlock(in_channels, out_channels)
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self.block = nn.Sequential(*(RepVGGBlock(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
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def forward(self, x):
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x = self.conv1(x)
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if self.block is not None:
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x = self.block(x)
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return x
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class RepVGGBlock(nn.Module):
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'''RepVGGBlock is a basic rep-style block, including training and deploy status
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This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
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'''
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def __init__(self, in_channels, out_channels, kernel_size=3,
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stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
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super(RepVGGBlock, self).__init__()
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""" Initialization of the class.
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Args:
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in_channels (int): Number of channels in the input image
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out_channels (int): Number of channels produced by the convolution
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kernel_size (int or tuple): Size of the convolving kernel
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stride (int or tuple, optional): Stride of the convolution. Default: 1
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padding (int or tuple, optional): Zero-padding added to both sides of
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the input. Default: 1
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dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
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groups (int, optional): Number of blocked connections from input
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channels to output channels. Default: 1
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padding_mode (string, optional): Default: 'zeros'
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deploy: Whether to be deploy status or training status. Default: False
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use_se: Whether to use se. Default: False
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"""
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self.deploy = deploy
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self.groups = groups
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self.in_channels = in_channels
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self.out_channels = out_channels
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assert kernel_size == 3
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assert padding == 1
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padding_11 = padding - kernel_size // 2
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self.nonlinearity = nn.ReLU()
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if use_se:
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raise NotImplementedError("se block not supported yet")
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else:
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self.se = nn.Identity()
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if deploy:
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self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
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else:
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self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
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self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
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self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding_11, groups=groups)
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def forward(self, inputs):
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'''Forward process'''
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if hasattr(self, 'rbr_reparam'):
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return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
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if self.rbr_identity is None:
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id_out = 0
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else:
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id_out = self.rbr_identity(inputs)
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return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))
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def get_equivalent_kernel_bias(self):
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kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
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kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
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kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
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return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
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def _pad_1x1_to_3x3_tensor(self, kernel1x1):
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if kernel1x1 is None:
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return 0
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else:
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return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
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def _fuse_bn_tensor(self, branch):
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if branch is None:
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return 0, 0
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if isinstance(branch, nn.Sequential):
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kernel = branch.conv.weight
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running_mean = branch.bn.running_mean
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running_var = branch.bn.running_var
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gamma = branch.bn.weight
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beta = branch.bn.bias
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eps = branch.bn.eps
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else:
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assert isinstance(branch, nn.BatchNorm2d)
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if not hasattr(self, 'id_tensor'):
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input_dim = self.in_channels // self.groups
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kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
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for i in range(self.in_channels):
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kernel_value[i, i % input_dim, 1, 1] = 1
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self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
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kernel = self.id_tensor
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running_mean = branch.running_mean
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running_var = branch.running_var
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gamma = branch.weight
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beta = branch.bias
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eps = branch.eps
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape(-1, 1, 1, 1)
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return kernel * t, beta - running_mean * gamma / std
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def switch_to_deploy(self):
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if hasattr(self, 'rbr_reparam'):
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return
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kernel, bias = self.get_equivalent_kernel_bias()
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self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels, out_channels=self.rbr_dense.conv.out_channels,
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kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,
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padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation, groups=self.rbr_dense.conv.groups, bias=True)
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self.rbr_reparam.weight.data = kernel
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self.rbr_reparam.bias.data = bias
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for para in self.parameters():
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para.detach_()
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self.__delattr__('rbr_dense')
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self.__delattr__('rbr_1x1')
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if hasattr(self, 'rbr_identity'):
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self.__delattr__('rbr_identity')
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if hasattr(self, 'id_tensor'):
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self.__delattr__('id_tensor')
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self.deploy = True
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def conv_bn_v2(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1,
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padding_mode='zeros'):
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conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
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stride=stride, padding=padding, dilation=dilation, groups=groups,
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bias=False, padding_mode=padding_mode)
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bn_layer = nn.BatchNorm2d(num_features=out_channels, affine=True)
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se = nn.Sequential()
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se.add_module('conv', conv_layer)
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se.add_module('bn', bn_layer)
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return se
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class IdentityBasedConv1x1(nn.Conv2d):
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def __init__(self, channels, groups=1):
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super(IdentityBasedConv1x1, self).__init__(in_channels=channels, out_channels=channels, kernel_size=1, stride=1, padding=0, groups=groups, bias=False)
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assert channels % groups == 0
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input_dim = channels // groups
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id_value = np.zeros((channels, input_dim, 1, 1))
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for i in range(channels):
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id_value[i, i % input_dim, 0, 0] = 1
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self.id_tensor = torch.from_numpy(id_value).type_as(self.weight)
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nn.init.zeros_(self.weight)
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def forward(self, input):
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kernel = self.weight + self.id_tensor.to(self.weight.device)
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result = F.conv2d(input, kernel, None, stride=1, padding=0, dilation=self.dilation, groups=self.groups)
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return result
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def get_actual_kernel(self):
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return self.weight + self.id_tensor.to(self.weight.device)
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class BNAndPadLayer(nn.Module):
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def __init__(self,
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pad_pixels,
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num_features,
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eps=1e-5,
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momentum=0.1,
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affine=True,
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track_running_stats=True):
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super(BNAndPadLayer, self).__init__()
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self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
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self.pad_pixels = pad_pixels
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def forward(self, input):
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output = self.bn(input)
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if self.pad_pixels > 0:
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if self.bn.affine:
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pad_values = self.bn.bias.detach() - self.bn.running_mean * self.bn.weight.detach() / torch.sqrt(self.bn.running_var + self.bn.eps)
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else:
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pad_values = - self.bn.running_mean / torch.sqrt(self.bn.running_var + self.bn.eps)
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output = F.pad(output, [self.pad_pixels] * 4)
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pad_values = pad_values.view(1, -1, 1, 1)
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output[:, :, 0:self.pad_pixels, :] = pad_values
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output[:, :, -self.pad_pixels:, :] = pad_values
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output[:, :, :, 0:self.pad_pixels] = pad_values
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output[:, :, :, -self.pad_pixels:] = pad_values
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return output
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@property
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def bn_weight(self):
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return self.bn.weight
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@property
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def bn_bias(self):
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return self.bn.bias
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@property
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def running_mean(self):
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return self.bn.running_mean
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@property
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def running_var(self):
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return self.bn.running_var
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@property
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def eps(self):
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return self.bn.eps
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class DBBBlock(nn.Module):
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'''
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RepBlock is a stage block with rep-style basic block
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'''
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def __init__(self, in_channels, out_channels, n=1):
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super().__init__()
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self.conv1 = DiverseBranchBlock(in_channels, out_channels)
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self.block = nn.Sequential(*(DiverseBranchBlock(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
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def forward(self, x):
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x = self.conv1(x)
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if self.block is not None:
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x = self.block(x)
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return x
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class DiverseBranchBlock(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=3,
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stride=1, padding=1, dilation=1, groups=1,
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internal_channels_1x1_3x3=None,
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deploy=False, nonlinear=nn.ReLU(), single_init=False):
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super(DiverseBranchBlock, self).__init__()
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self.deploy = deploy
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if nonlinear is None:
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self.nonlinear = nn.Identity()
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else:
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self.nonlinear = nonlinear
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self.kernel_size = kernel_size
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self.out_channels = out_channels
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self.groups = groups
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assert padding == kernel_size // 2
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if deploy:
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self.dbb_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=True)
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else:
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self.dbb_origin = conv_bn_v2(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups)
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self.dbb_avg = nn.Sequential()
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if groups < out_channels:
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self.dbb_avg.add_module('conv',
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nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
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stride=1, padding=0, groups=groups, bias=False))
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self.dbb_avg.add_module('bn', BNAndPadLayer(pad_pixels=padding, num_features=out_channels))
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self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))
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self.dbb_1x1 = conv_bn_v2(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
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padding=0, groups=groups)
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else:
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self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding))
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self.dbb_avg.add_module('avgbn', nn.BatchNorm2d(out_channels))
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if internal_channels_1x1_3x3 is None:
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internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels
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self.dbb_1x1_kxk = nn.Sequential()
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if internal_channels_1x1_3x3 == in_channels:
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self.dbb_1x1_kxk.add_module('idconv1', IdentityBasedConv1x1(channels=in_channels, groups=groups))
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else:
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self.dbb_1x1_kxk.add_module('conv1', nn.Conv2d(in_channels=in_channels, out_channels=internal_channels_1x1_3x3,
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kernel_size=1, stride=1, padding=0, groups=groups, bias=False))
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self.dbb_1x1_kxk.add_module('bn1', BNAndPadLayer(pad_pixels=padding, num_features=internal_channels_1x1_3x3, affine=True))
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self.dbb_1x1_kxk.add_module('conv2', nn.Conv2d(in_channels=internal_channels_1x1_3x3, out_channels=out_channels,
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kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=False))
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self.dbb_1x1_kxk.add_module('bn2', nn.BatchNorm2d(out_channels))
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# The experiments reported in the paper used the default initialization of bn.weight (all as 1). But changing the initialization may be useful in some cases.
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if single_init:
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# Initialize the bn.weight of dbb_origin as 1 and others as 0. This is not the default setting.
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self.single_init()
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def get_equivalent_kernel_bias(self):
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k_origin, b_origin = transI_fusebn(self.dbb_origin.conv.weight, self.dbb_origin.bn)
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if hasattr(self, 'dbb_1x1'):
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k_1x1, b_1x1 = transI_fusebn(self.dbb_1x1.conv.weight, self.dbb_1x1.bn)
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k_1x1 = transVI_multiscale(k_1x1, self.kernel_size)
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else:
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k_1x1, b_1x1 = 0, 0
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if hasattr(self.dbb_1x1_kxk, 'idconv1'):
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k_1x1_kxk_first = self.dbb_1x1_kxk.idconv1.get_actual_kernel()
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else:
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k_1x1_kxk_first = self.dbb_1x1_kxk.conv1.weight
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k_1x1_kxk_first, b_1x1_kxk_first = transI_fusebn(k_1x1_kxk_first, self.dbb_1x1_kxk.bn1)
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k_1x1_kxk_second, b_1x1_kxk_second = transI_fusebn(self.dbb_1x1_kxk.conv2.weight, self.dbb_1x1_kxk.bn2)
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k_1x1_kxk_merged, b_1x1_kxk_merged = transIII_1x1_kxk(k_1x1_kxk_first, b_1x1_kxk_first, k_1x1_kxk_second, b_1x1_kxk_second, groups=self.groups)
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k_avg = transV_avg(self.out_channels, self.kernel_size, self.groups)
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k_1x1_avg_second, b_1x1_avg_second = transI_fusebn(k_avg.to(self.dbb_avg.avgbn.weight.device), self.dbb_avg.avgbn)
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if hasattr(self.dbb_avg, 'conv'):
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k_1x1_avg_first, b_1x1_avg_first = transI_fusebn(self.dbb_avg.conv.weight, self.dbb_avg.bn)
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k_1x1_avg_merged, b_1x1_avg_merged = transIII_1x1_kxk(k_1x1_avg_first, b_1x1_avg_first, k_1x1_avg_second, b_1x1_avg_second, groups=self.groups)
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else:
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k_1x1_avg_merged, b_1x1_avg_merged = k_1x1_avg_second, b_1x1_avg_second
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|
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return transII_addbranch((k_origin, k_1x1, k_1x1_kxk_merged, k_1x1_avg_merged), (b_origin, b_1x1, b_1x1_kxk_merged, b_1x1_avg_merged))
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|
|
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def switch_to_deploy(self):
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if hasattr(self, 'dbb_reparam'):
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|
return
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|
kernel, bias = self.get_equivalent_kernel_bias()
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self.dbb_reparam = nn.Conv2d(in_channels=self.dbb_origin.conv.in_channels, out_channels=self.dbb_origin.conv.out_channels,
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|
kernel_size=self.dbb_origin.conv.kernel_size, stride=self.dbb_origin.conv.stride,
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padding=self.dbb_origin.conv.padding, dilation=self.dbb_origin.conv.dilation, groups=self.dbb_origin.conv.groups, bias=True)
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self.dbb_reparam.weight.data = kernel
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|
self.dbb_reparam.bias.data = bias
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|
for para in self.parameters():
|
|
para.detach_()
|
|
self.__delattr__('dbb_origin')
|
|
self.__delattr__('dbb_avg')
|
|
if hasattr(self, 'dbb_1x1'):
|
|
self.__delattr__('dbb_1x1')
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|
self.__delattr__('dbb_1x1_kxk')
|
|
|
|
def forward(self, inputs):
|
|
|
|
if hasattr(self, 'dbb_reparam'):
|
|
return self.nonlinear(self.dbb_reparam(inputs))
|
|
|
|
out = self.dbb_origin(inputs)
|
|
if hasattr(self, 'dbb_1x1'):
|
|
out += self.dbb_1x1(inputs)
|
|
out += self.dbb_avg(inputs)
|
|
out += self.dbb_1x1_kxk(inputs)
|
|
return self.nonlinear(out)
|
|
|
|
def init_gamma(self, gamma_value):
|
|
if hasattr(self, "dbb_origin"):
|
|
torch.nn.init.constant_(self.dbb_origin.bn.weight, gamma_value)
|
|
if hasattr(self, "dbb_1x1"):
|
|
torch.nn.init.constant_(self.dbb_1x1.bn.weight, gamma_value)
|
|
if hasattr(self, "dbb_avg"):
|
|
torch.nn.init.constant_(self.dbb_avg.avgbn.weight, gamma_value)
|
|
if hasattr(self, "dbb_1x1_kxk"):
|
|
torch.nn.init.constant_(self.dbb_1x1_kxk.bn2.weight, gamma_value)
|
|
|
|
def single_init(self):
|
|
self.init_gamma(0.0)
|
|
if hasattr(self, "dbb_origin"):
|
|
torch.nn.init.constant_(self.dbb_origin.bn.weight, 1.0)
|
|
|
|
|
|
class DetectBackend(nn.Module):
|
|
def __init__(self, weights='yolov6s.pt', device=None, dnn=True):
|
|
|
|
super().__init__()
|
|
assert isinstance(weights, str) and Path(weights).suffix == '.pt', f'{Path(weights).suffix} format is not supported.'
|
|
from yolov6.utils.checkpoint import load_checkpoint
|
|
model = load_checkpoint(weights, map_location=device)
|
|
stride = int(model.stride.max())
|
|
self.__dict__.update(locals()) # assign all variables to self
|
|
|
|
def forward(self, im, val=False):
|
|
y = self.model(im)
|
|
if isinstance(y, np.ndarray):
|
|
y = torch.tensor(y, device=self.device)
|
|
return y
|