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177
network/CSPdarknet.py
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177
network/CSPdarknet.py
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import torch
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import torch.nn as nn
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class SiLU(nn.Module):
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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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def autopad(k, p=None):
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if p is None:
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p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
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return p
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class Focus(nn.Module):
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Focus, self).__init__()
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
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def forward(self, x):
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# 320, 320, 12 => 320, 320, 64
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return self.conv(
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# 640, 640, 3 => 320, 320, 12
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torch.cat(
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[
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x[..., ::2, ::2],
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x[..., 1::2, ::2],
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x[..., ::2, 1::2],
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x[..., 1::2, 1::2]
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], 1
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)
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)
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class Conv(nn.Module):
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
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super(Conv, self).__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
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self.bn = nn.BatchNorm2d(c2, eps=0.001, momentum=0.03)
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self.act = SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
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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 fuseforward(self, x):
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return self.act(self.conv(x))
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class Bottleneck(nn.Module):
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# Standard bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super(Bottleneck, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c2, 3, 1, g=g)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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class C3(nn.Module):
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# CSP Bottleneck with 3 convolutions
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(C3, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
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def forward(self, x):
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return self.cv3(torch.cat(
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(
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self.m(self.cv1(x)),
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self.cv2(x)
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)
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, dim=1))
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class SPP(nn.Module):
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# Spatial pyramid pooling layer used in YOLOv3-SPP
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def __init__(self, c1, c2, k=(5, 9, 13)):
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super(SPP, self).__init__()
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c_ = c1 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
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def forward(self, x):
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x = self.cv1(x)
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return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
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class CSPDarknet(nn.Module):
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def __init__(self, base_channels, base_depth, phi, pretrained):
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super().__init__()
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#-----------------------------------------------#
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# 输入图片是640, 640, 3
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# 初始的基本通道base_channels是64
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#-----------------------------------------------#
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#-----------------------------------------------#
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# 利用focus网络结构进行特征提取
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# 640, 640, 3 -> 320, 320, 12 -> 320, 320, 64
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#-----------------------------------------------#
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self.stem = Focus(3, base_channels, k=3)
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#-----------------------------------------------#
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# 完成卷积之后，320, 320, 64 -> 160, 160, 128
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# 完成CSPlayer之后，160, 160, 128 -> 160, 160, 128
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#-----------------------------------------------#
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self.dark2 = nn.Sequential(
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# 320, 320, 64 -> 160, 160, 128
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Conv(base_channels, base_channels * 2, 3, 2),
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# 160, 160, 128 -> 160, 160, 128
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C3(base_channels * 2, base_channels * 2, base_depth),
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)
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#-----------------------------------------------#
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# 完成卷积之后，160, 160, 128 -> 80, 80, 256
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# 完成CSPlayer之后，80, 80, 256 -> 80, 80, 256
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# 在这里引出有效特征层80, 80, 256
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# 进行加强特征提取网络FPN的构建
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#-----------------------------------------------#
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self.dark3 = nn.Sequential(
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Conv(base_channels * 2, base_channels * 4, 3, 2),
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C3(base_channels * 4, base_channels * 4, base_depth * 3),
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)
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#-----------------------------------------------#
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# 完成卷积之后，80, 80, 256 -> 40, 40, 512
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# 完成CSPlayer之后，40, 40, 512 -> 40, 40, 512
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# 在这里引出有效特征层40, 40, 512
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# 进行加强特征提取网络FPN的构建
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#-----------------------------------------------#
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self.dark4 = nn.Sequential(
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Conv(base_channels * 4, base_channels * 8, 3, 2),
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C3(base_channels * 8, base_channels * 8, base_depth * 3),
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)
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#-----------------------------------------------#
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# 完成卷积之后，40, 40, 512 -> 20, 20, 1024
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# 完成SPP之后，20, 20, 1024 -> 20, 20, 1024
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# 完成CSPlayer之后，20, 20, 1024 -> 20, 20, 1024
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#-----------------------------------------------#
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self.dark5 = nn.Sequential(
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Conv(base_channels * 8, base_channels * 16, 3, 2),
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SPP(base_channels * 16, base_channels * 16),
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C3(base_channels * 16, base_channels * 16, base_depth, shortcut=False),
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)
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if pretrained:
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url = {
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's' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_s_backbone.pth',
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'm' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_m_backbone.pth',
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'l' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_l_backbone.pth',
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'x' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_x_backbone.pth',
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}[phi]
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
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self.load_state_dict(checkpoint, strict=False)
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print("Load weights from ", url.split('/')[-1])
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def forward(self, x):
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x = self.stem(x)
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x = self.dark2(x)
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#-----------------------------------------------#
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# dark3的输出为80, 80, 256，是一个有效特征层
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#-----------------------------------------------#
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x = self.dark3(x)
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feat1 = x
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#-----------------------------------------------#
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# dark4的输出为40, 40, 512，是一个有效特征层
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#-----------------------------------------------#
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x = self.dark4(x)
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feat2 = x
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#-----------------------------------------------#
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# dark5的输出为20, 20, 1024，是一个有效特征层
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#-----------------------------------------------#
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x = self.dark5(x)
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feat3 = x
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return feat1, feat2, feat3
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249
network/ConvNext.py
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249
network/ConvNext.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import math
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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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def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
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if drop_prob == 0. or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1)
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random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
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if keep_prob > 0.0 and scale_by_keep:
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random_tensor.div_(keep_prob)
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return x * random_tensor
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class DropPath(nn.Module):
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None, scale_by_keep=True):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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self.scale_by_keep = scale_by_keep
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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def norm_cdf(x):
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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with torch.no_grad():
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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tensor.erfinv_()
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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tensor.clamp_(min=a, max=b)
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return tensor
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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#--------------------------------------#
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# Gelu激活函数的实现
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# 利用近似的数学公式
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#--------------------------------------#
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class GELU(nn.Module):
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def __init__(self):
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super(GELU, self).__init__()
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def forward(self, x):
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return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x,3))))
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#---------------------------------------------------------------------------------#
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# LayerNorm 支持两种形式channels_last (default) or channels_first.
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# channels_last 对应具有形状的输入(batch_size, height, width, channels)
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# channels_first 对应具有形状的输入(batch_size, channels, height, width).
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#---------------------------------------------------------------------------------#
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class LayerNorm(nn.Module):
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def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.eps = eps
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self.data_format = data_format
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if self.data_format not in ["channels_last", "channels_first"]:
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raise NotImplementedError
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self.normalized_shape = (normalized_shape, )
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def forward(self, x):
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if self.data_format == "channels_last":
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return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
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elif self.data_format == "channels_first":
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u = x.mean(1, keepdim=True)
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s = (x - u).pow(2).mean(1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.eps)
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x = self.weight[:, None, None] * x + self.bias[:, None, None]
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return x
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#--------------------------------------------------------------------------------------------------------------#
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# ConvNeXt Block有两种等效的实现:
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# (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
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# (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
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# 代码中使用（2），因为这个在PyTorch中稍微快一点
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#--------------------------------------------------------------------------------------------------------------#
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class Block(nn.Module):
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def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
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super().__init__()
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#--------------------------#
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# 7x7的逐层卷积
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#--------------------------#
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self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
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self.norm = LayerNorm(dim, eps=1e-6)
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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self.pwconv1 = nn.Linear(dim, 4 * dim)
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self.act = GELU()
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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self.pwconv2 = nn.Linear(4 * dim, dim)
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#--------------------------#
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# 加入缩放系数
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#--------------------------#
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self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) if layer_scale_init_value > 0 else None
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#--------------------------#
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# 加入Drop_path正则化
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#--------------------------#
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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input = x
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#--------------------------#
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# 7x7的逐层卷积
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#--------------------------#
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x = self.dwconv(x)
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x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
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x = self.norm(x)
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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x = self.pwconv1(x)
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x = self.act(x)
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#--------------------------#
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# 利用全连接层代替1x1卷积
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#--------------------------#
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x = self.pwconv2(x)
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#--------------------------#
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# 加入缩放系数
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#--------------------------#
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if self.gamma is not None:
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x = self.gamma * x
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x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
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#--------------------------#
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# 加入Drop_path正则化
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#--------------------------#
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x = input + self.drop_path(x)
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return x
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#-----------------------------------------------------#
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# ConvNeXt
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# A PyTorch impl of : `A ConvNet for the 2020s`
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# https://arxiv.org/pdf/2201.03545.pdf
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#-----------------------------------------------------#
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class ConvNeXt(nn.Module):
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def __init__(
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self, in_chans=3, num_classes=1000, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., **kwargs
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):
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super().__init__()
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self.downsample_layers = nn.ModuleList()
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#--------------------------------------------------#
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# bs, 3, 224, 224 -> bs, 96, 56, 56
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#--------------------------------------------------#
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stem = nn.Sequential(
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nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
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LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
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)
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self.downsample_layers.append(stem)
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#--------------------------------------------------#
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# 定义三次下采样的过程
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# 利用步长为2x2，卷积核大小为2x2的卷积进行下采样
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#--------------------------------------------------#
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for i in range(3):
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downsample_layer = nn.Sequential(
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LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
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nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
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)
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self.downsample_layers.append(downsample_layer)
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#--------------------------------------------------#
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# 根据深度的不同，定义不同的drop率
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#--------------------------------------------------#
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self.stages = nn.ModuleList()
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dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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#--------------------------------------------------#
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# 整个ConvNeXt除了Stem外，存在四个Stage
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# 每个Stage里面是多个ConvNeXt Block的堆叠。
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#--------------------------------------------------#
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for i in range(4):
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stage = nn.Sequential(
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*[Block(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2d, nn.Linear)):
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trunc_normal_(m.weight, std=.02)
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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outs = []
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for i in range(4):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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if i != 0:
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outs.append(x)
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return outs
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model_urls = {
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"convnext_tiny_1k" : "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/convnext_tiny_1k_224_ema_no_jit.pth",
|
||||
"convnext_small_1k" : "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/convnext_small_1k_224_ema_no_jit.pth",
|
||||
}
|
||||
|
||||
#------------------------------------------------------#
|
||||
# Tiny约等于Cspdarknet-L的尺寸
|
||||
#------------------------------------------------------#
|
||||
def ConvNeXt_Tiny(pretrained=False, **kwargs):
|
||||
model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
|
||||
if pretrained:
|
||||
url = model_urls['convnext_tiny_1k']
|
||||
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
|
||||
model.load_state_dict(checkpoint, strict=False)
|
||||
print("Load weights from ", url.split('/')[-1])
|
||||
return model
|
||||
|
||||
#------------------------------------------------------#
|
||||
# Tiny约等于Cspdarknet-X的尺寸
|
||||
#------------------------------------------------------#
|
||||
def ConvNeXt_Small(pretrained=False, **kwargs):
|
||||
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
|
||||
if pretrained:
|
||||
url = model_urls['convnext_small_1k']
|
||||
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
|
||||
model.load_state_dict(checkpoint, strict=False)
|
||||
print("Load weights from ", url.split('/')[-1])
|
||||
return model
|
||||
638
network/Swin_transformer.py
Normal file
638
network/Swin_transformer.py
Normal file
@@ -0,0 +1,638 @@
|
||||
# --------------------------------------------------------
|
||||
# Swin Transformer
|
||||
# Copyright (c) 2021 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Written by Ze Liu
|
||||
# --------------------------------------------------------
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
||||
def norm_cdf(x):
|
||||
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||
|
||||
with torch.no_grad():
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
tensor.erfinv_()
|
||||
|
||||
tensor.mul_(std * math.sqrt(2.))
|
||||
tensor.add_(mean)
|
||||
|
||||
tensor.clamp_(min=a, max=b)
|
||||
return tensor
|
||||
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||
|
||||
#--------------------------------------#
|
||||
# Gelu激活函数的实现
|
||||
# 利用近似的数学公式
|
||||
#--------------------------------------#
|
||||
class GELU(nn.Module):
|
||||
def __init__(self):
|
||||
super(GELU, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x,3))))
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# 对输入进来的图片进行高和宽的压缩
|
||||
# 并且进行通道的扩张。
|
||||
#-------------------------------------------------------#
|
||||
class PatchEmbed(nn.Module):
|
||||
def __init__(self, img_size=[224, 224], patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
# [224, 224]
|
||||
self.img_size = img_size
|
||||
# [4, 4]
|
||||
self.patch_size = [patch_size, patch_size]
|
||||
# [56, 56]
|
||||
self.patches_resolution = [self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1]]
|
||||
|
||||
# 3136
|
||||
self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
|
||||
# 3
|
||||
self.in_chans = in_chans
|
||||
# 96
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# bs, 224, 224, 3 -> bs, 56, 56, 96
|
||||
#-------------------------------------------------------#
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
B, C, H, W = x.shape
|
||||
# FIXME look at relaxing size constraints
|
||||
assert H == self.img_size[0] and W == self.img_size[1], \
|
||||
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]} * {self.img_size[1]})."
|
||||
#-------------------------------------------------------#
|
||||
# bs, 224, 224, 3 -> bs, 56, 56, 96 -> bs, 3136, 96
|
||||
#-------------------------------------------------------#
|
||||
x = self.proj(x).flatten(2).transpose(1, 2)
|
||||
if self.norm is not None:
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
def window_partition(x, window_size):
|
||||
B, H, W, C = x.shape
|
||||
#------------------------------------------------------------------#
|
||||
# bs, 56, 56, 96 -> bs, 8, 7, 8, 7, 96 -> bs * 64, 7, 7, 96
|
||||
#------------------------------------------------------------------#
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
#------------------------------------------------------------------#
|
||||
# bs * 64, 7, 7, 96 -> bs, 8, 8, 7, 7, 96 -> bs, 56, 56, 96
|
||||
#------------------------------------------------------------------#
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
#--------------------------------------------------------------------------#
|
||||
# 相对坐标矩阵，用于表示每个窗口内，其它点相对于自己的坐标
|
||||
# 由于相对坐标取值范围为-6 ~ +6。中间共13个值，因此需要13 * 13
|
||||
# 13 * 13, num_heads
|
||||
#--------------------------------------------------------------------------#
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
|
||||
)
|
||||
|
||||
#--------------------------------------------------------------------------#
|
||||
# 该部分用于获取7x7的矩阵内部，其它特征点相对于自身相对坐标
|
||||
#--------------------------------------------------------------------------#
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
#--------------------------------------------------------------------------#
|
||||
# 乘积获得q、k、v，用于计算多头注意力机制
|
||||
#--------------------------------------------------------------------------#
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
B_, N, C = x.shape
|
||||
#--------------------------------------------------------------------------#
|
||||
# bs * 64, 49, 96 -> bs * 64, 49, 96 * 3 ->
|
||||
# bs * 64, 49, 3, num_heads, 32 -> 3, bs * 64, num_head, 49, 32
|
||||
#--------------------------------------------------------------------------#
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
#--------------------------------------------------------------------------#
|
||||
# bs * 64, num_head, 49, 32
|
||||
#--------------------------------------------------------------------------#
|
||||
q, k, v = qkv[0], qkv[1], qkv[2]
|
||||
|
||||
#--------------------------------------------------------------------------#
|
||||
# bs * 64, num_head, 49, 49
|
||||
#--------------------------------------------------------------------------#
|
||||
q = q * self.scale
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
#--------------------------------------------------------------------------#
|
||||
# 这一步是根据已经求得的注意力，加上相对坐标的偏执量
|
||||
# 形成最后的注意力
|
||||
#--------------------------------------------------------------------------#
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
#--------------------------------------------------------------------------#
|
||||
# 加上mask，保证分区。
|
||||
# bs * 64, num_head, 49, 49
|
||||
#--------------------------------------------------------------------------#
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
#---------------------------------------------------------------------------------------#
|
||||
# bs * 64, num_head, 49, 49 @ bs * 64, num_head, 49, 32 -> bs * 64, num_head, 49, 32
|
||||
#
|
||||
# bs * 64, num_head, 49, 32 -> bs * 64, 49, 96
|
||||
#---------------------------------------------------------------------------------------#
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
|
||||
"""
|
||||
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
|
||||
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
|
||||
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
|
||||
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
|
||||
'survival rate' as the argument.
|
||||
"""
|
||||
if drop_prob == 0. or not training:
|
||||
return x
|
||||
keep_prob = 1 - drop_prob
|
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
||||
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
||||
if keep_prob > 0.0 and scale_by_keep:
|
||||
random_tensor.div_(keep_prob)
|
||||
return x * random_tensor
|
||||
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""
|
||||
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
"""
|
||||
def __init__(self, drop_prob=None, scale_by_keep=True):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
self.scale_by_keep = scale_by_keep
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
|
||||
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# 两次全连接
|
||||
#-------------------------------------------------------#
|
||||
class Mlp(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# 每个阶段重复的基础模块
|
||||
# 在这其中会使用WindowAttention进行特征提取
|
||||
#-------------------------------------------------------#
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
|
||||
self.mlp_ratio = mlp_ratio
|
||||
if min(self.input_resolution) <= self.window_size:
|
||||
self.shift_size = 0
|
||||
self.window_size = min(self.input_resolution)
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim,
|
||||
window_size = [self.window_size, self.window_size],
|
||||
num_heads = num_heads,
|
||||
qkv_bias = qkv_bias,
|
||||
qk_scale = qk_scale,
|
||||
attn_drop = attn_drop,
|
||||
proj_drop = drop
|
||||
)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
if self.shift_size > 0:
|
||||
#----------------------------------------------------------------#
|
||||
# 由于进行特征提取时，会对输入的特征层进行的平移
|
||||
# 如：
|
||||
# [ [
|
||||
# [1, 2, 3], [5, 6, 4],
|
||||
# [4, 5, 6], --> [8, 9, 7],
|
||||
# [7, 8, 9], [1, 2, 3],
|
||||
# ] ]
|
||||
# 这一步的作用就是使得平移后的区域块只计算自己部分的注意力机制
|
||||
#----------------------------------------------------------------#
|
||||
H, W = self.input_resolution
|
||||
_H, _W = _make_divisible(H, self.window_size), _make_divisible(W, self.window_size),
|
||||
img_mask = torch.zeros((1, _H, _W, 1)) # 1 H W 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
self.attn_mask = attn_mask.cpu().numpy()
|
||||
else:
|
||||
self.attn_mask = None
|
||||
|
||||
def forward(self, x):
|
||||
H, W = self.input_resolution
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
#-----------------------------------------------#
|
||||
# bs, 3136, 96 -> bs, 56, 56, 96
|
||||
#-----------------------------------------------#
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
_H, _W = _make_divisible(H, self.window_size), _make_divisible(W, self.window_size),
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
x = F.interpolate(x, [_H, _W], mode='bicubic', align_corners=False).permute(0, 2, 3, 1)
|
||||
|
||||
#-----------------------------------------------#
|
||||
# 进行特征层的平移
|
||||
#-----------------------------------------------#
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
shifted_x = x
|
||||
#------------------------------------------------------------------------------------------#
|
||||
# bs, 56, 56, 96 -> bs * 64, 7, 7, 96 -> bs * 64, 49, 96
|
||||
#------------------------------------------------------------------------------------------#
|
||||
x_windows = window_partition(shifted_x, self.window_size) # num_windows * B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
#-----------------------------------------------#
|
||||
# bs * 64, 49, 97 -> bs * 64, 49, 97
|
||||
#-----------------------------------------------#
|
||||
if type(self.attn_mask) != type(None):
|
||||
attn_mask = torch.tensor(self.attn_mask).cuda() if x.is_cuda else torch.tensor(self.attn_mask)
|
||||
else:
|
||||
attn_mask = None
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
#-----------------------------------------------#
|
||||
# bs * 64, 49, 97 -> bs, 56, 56, 96
|
||||
#-----------------------------------------------#
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, _H, _W) # B H' W' C
|
||||
|
||||
#-----------------------------------------------#
|
||||
# 将特征层平移回来
|
||||
#-----------------------------------------------#
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
x = F.interpolate(x, [H, W], mode='bicubic', align_corners=False).permute(0, 2, 3, 1)
|
||||
#-----------------------------------------------#
|
||||
# bs, 3136, 96
|
||||
#-----------------------------------------------#
|
||||
x = x.view(B, H * W, C)
|
||||
#-----------------------------------------------#
|
||||
# FFN
|
||||
# bs, 3136, 96
|
||||
#-----------------------------------------------#
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# 对输入进来的特征层进行高和宽的压缩
|
||||
# 进行跨特征点的特征提取，提取完成后进行堆叠。
|
||||
#-------------------------------------------------------#
|
||||
class PatchMerging(nn.Module):
|
||||
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.input_resolution = input_resolution
|
||||
self.dim = dim
|
||||
|
||||
self.norm = norm_layer(4 * dim)
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
H, W = self.input_resolution
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# bs, 3136, 96 -> bs, 56, 56, 96
|
||||
#-------------------------------------------------------#
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# x0 ~ x3 bs, 56, 56, 96 -> bs, 28, 28, 96
|
||||
#-------------------------------------------------------#
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# 4 X bs, 28, 28, 96 -> bs, 28, 28, 384
|
||||
#-------------------------------------------------------#
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
#-------------------------------------------------------#
|
||||
# bs, 28, 28, 384 -> bs, 784, 384
|
||||
#-------------------------------------------------------#
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# bs, 784, 384 -> bs, 784, 192
|
||||
#-------------------------------------------------------#
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
return x
|
||||
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# Swin-Transformer的基础模块。
|
||||
# 使用窗口多头注意力机制进行特征提取。
|
||||
# 使用PatchMerging进行高和宽的压缩。
|
||||
#-------------------------------------------------------#
|
||||
class BasicLayer(nn.Module):
|
||||
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
|
||||
super().__init__()
|
||||
#-------------------------------------------------------#
|
||||
# 四个阶段对应不同的dim
|
||||
# [96, 192, 384, 768]
|
||||
#-------------------------------------------------------#
|
||||
self.dim = dim
|
||||
#-------------------------------------------------------#
|
||||
# 四个阶段对应不同的输入分辨率
|
||||
# [[56, 56], [28, 28], [14, 14], [7, 7]]
|
||||
#-------------------------------------------------------#
|
||||
self.input_resolution = input_resolution
|
||||
#-------------------------------------------------------#
|
||||
# 四个阶段对应不同的多头注意力机制重复次数
|
||||
# [2, 2, 6, 2]
|
||||
#-------------------------------------------------------#
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
#-------------------------------------------------------#
|
||||
# 根据depth的次数利用窗口多头注意力机制进行特征提取。
|
||||
#-------------------------------------------------------#
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
SwinTransformerBlock(
|
||||
dim = dim,
|
||||
input_resolution = input_resolution,
|
||||
num_heads = num_heads,
|
||||
window_size = window_size,
|
||||
shift_size = 0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio = mlp_ratio,
|
||||
qkv_bias = qkv_bias,
|
||||
qk_scale = qk_scale,
|
||||
drop = drop,
|
||||
attn_drop = attn_drop,
|
||||
drop_path = drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer = norm_layer
|
||||
)
|
||||
for i in range(depth)
|
||||
]
|
||||
)
|
||||
|
||||
if downsample is not None:
|
||||
#-------------------------------------------------------#
|
||||
# 判断是否要进行下采样，即：高宽压缩
|
||||
#-------------------------------------------------------#
|
||||
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x):
|
||||
for blk in self.blocks:
|
||||
if self.use_checkpoint:
|
||||
x_ = checkpoint.checkpoint(blk, x)
|
||||
else:
|
||||
x_ = blk(x)
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x_)
|
||||
else:
|
||||
x = x_
|
||||
return x_, x
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
def __init__(self, img_size=[640, 640], patch_size=4, in_chans=3, num_classes=1000,
|
||||
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
|
||||
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||||
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||||
use_checkpoint=False, **kwargs):
|
||||
super().__init__()
|
||||
self.num_classes = num_classes
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
|
||||
self.mlp_ratio = mlp_ratio
|
||||
|
||||
#--------------------------------------------------#
|
||||
# bs, 224, 224, 3 -> bs, 3136, 96
|
||||
#--------------------------------------------------#
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size = img_size,
|
||||
patch_size = patch_size,
|
||||
in_chans = in_chans,
|
||||
embed_dim = embed_dim,
|
||||
norm_layer = norm_layer if self.patch_norm else None
|
||||
)
|
||||
|
||||
#--------------------------------------------------#
|
||||
# PatchEmbed之后的图像序列长度 3136
|
||||
# PatchEmbed之后的图像对应的分辨率 [56, 56]
|
||||
#--------------------------------------------------#
|
||||
num_patches = self.patch_embed.num_patches
|
||||
patches_resolution = self.patch_embed.patches_resolution
|
||||
self.patches_resolution = patches_resolution
|
||||
|
||||
if self.ape:
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
#--------------------------------------------------#
|
||||
# stochastic depth
|
||||
#--------------------------------------------------#
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
#---------------------------------------------------------------#
|
||||
# 构建swin-transform的每个阶段
|
||||
# bs, 3136, 96 -> bs, 784, 192 -> bs, 196, 384 -> bs, 49, 768
|
||||
#---------------------------------------------------------------#
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim = int(embed_dim * 2 ** i_layer),
|
||||
input_resolution = (patches_resolution[0] // (2 ** i_layer), patches_resolution[1] // (2 ** i_layer)),
|
||||
depth = depths[i_layer],
|
||||
num_heads = num_heads[i_layer],
|
||||
window_size = window_size,
|
||||
mlp_ratio = self.mlp_ratio,
|
||||
qkv_bias = qkv_bias,
|
||||
qk_scale = qk_scale,
|
||||
drop = drop_rate,
|
||||
attn_drop = attn_drop_rate,
|
||||
drop_path = dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer = norm_layer,
|
||||
downsample = PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint = use_checkpoint
|
||||
)
|
||||
self.layers.append(layer)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'absolute_pos_embed'}
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay_keywords(self):
|
||||
return {'relative_position_bias_table'}
|
||||
|
||||
def forward(self, x):
|
||||
x = self.patch_embed(x)
|
||||
if self.ape:
|
||||
x = x + self.absolute_pos_embed
|
||||
x = self.pos_drop(x)
|
||||
|
||||
inverval_outs = []
|
||||
for i, layer in enumerate(self.layers):
|
||||
x_, x = layer(x)
|
||||
if i != 0:
|
||||
inverval_outs.append(x_)
|
||||
|
||||
outs = []
|
||||
for i, layer in enumerate(inverval_outs):
|
||||
H, W = (self.patches_resolution[0] // (2 ** (i + 1)), self.patches_resolution[1] // (2 ** (i + 1)))
|
||||
B, L, C = layer.shape
|
||||
layer = layer.view([B, H, W, C]).permute([0, 3, 1, 2])
|
||||
outs.append(layer)
|
||||
|
||||
return outs
|
||||
|
||||
def Swin_transformer_Tiny(pretrained = False, input_shape = [640, 640], **kwargs):
|
||||
model = SwinTransformer(input_shape, depths=[2, 2, 6, 2], **kwargs)
|
||||
if pretrained:
|
||||
url = "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/swin_tiny_patch4_window7.pth"
|
||||
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
|
||||
model.load_state_dict(checkpoint, strict=False)
|
||||
print("Load weights from ", url.split('/')[-1])
|
||||
|
||||
return model
|
||||
1
network/__init__.py
Normal file
1
network/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
#
|
||||
BIN
network/__pycache__/CSPdarknet.cpython-310.pyc
Normal file
BIN
network/__pycache__/CSPdarknet.cpython-310.pyc
Normal file
Binary file not shown.
BIN
network/__pycache__/ConvNext.cpython-310.pyc
Normal file
BIN
network/__pycache__/ConvNext.cpython-310.pyc
Normal file
Binary file not shown.
BIN
network/__pycache__/Swin_transformer.cpython-310.pyc
Normal file
BIN
network/__pycache__/Swin_transformer.cpython-310.pyc
Normal file
Binary file not shown.
BIN
network/__pycache__/__init__.cpython-310.pyc
Normal file
BIN
network/__pycache__/__init__.cpython-310.pyc
Normal file
Binary file not shown.
BIN
network/__pycache__/yolo.cpython-310.pyc
Normal file
BIN
network/__pycache__/yolo.cpython-310.pyc
Normal file
Binary file not shown.
BIN
network/__pycache__/yolo_training.cpython-310.pyc
Normal file
BIN
network/__pycache__/yolo_training.cpython-310.pyc
Normal file
Binary file not shown.
132
network/yolo.py
Normal file
132
network/yolo.py
Normal file
@@ -0,0 +1,132 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from .ConvNext import ConvNeXt_Small, ConvNeXt_Tiny
|
||||
from .CSPdarknet import C3, Conv, CSPDarknet
|
||||
from .Swin_transformer import Swin_transformer_Tiny
|
||||
|
||||
|
||||
#---------------------------------------------------#
|
||||
# yolo_body
|
||||
#---------------------------------------------------#
|
||||
class YoloBody(nn.Module):
|
||||
def __init__(self, anchors_mask, num_classes, phi, backbone='cspdarknet', pretrained=False, input_shape=[640, 640]):
|
||||
super(YoloBody, self).__init__()
|
||||
depth_dict = {'s' : 0.33, 'm' : 0.67, 'l' : 1.00, 'x' : 1.33,}
|
||||
width_dict = {'s' : 0.50, 'm' : 0.75, 'l' : 1.00, 'x' : 1.25,}
|
||||
dep_mul, wid_mul = depth_dict[phi], width_dict[phi]
|
||||
|
||||
base_channels = int(wid_mul * 64) # 64
|
||||
base_depth = max(round(dep_mul * 3), 1) # 3
|
||||
#-----------------------------------------------#
|
||||
# 输入图片是640, 640, 3
|
||||
# 初始的基本通道是64
|
||||
#-----------------------------------------------#
|
||||
self.backbone_name = backbone
|
||||
if backbone == "cspdarknet":
|
||||
#---------------------------------------------------#
|
||||
# 生成CSPdarknet53的主干模型
|
||||
# 获得三个有效特征层，他们的shape分别是：
|
||||
# 80,80,256
|
||||
# 40,40,512
|
||||
# 20,20,1024
|
||||
#---------------------------------------------------#
|
||||
self.backbone = CSPDarknet(base_channels, base_depth, phi, pretrained)
|
||||
else:
|
||||
#---------------------------------------------------#
|
||||
# 如果输入不为cspdarknet，则调整通道数
|
||||
# 使其符合YoloV5的格式
|
||||
#---------------------------------------------------#
|
||||
self.backbone = {
|
||||
'convnext_tiny' : ConvNeXt_Tiny,
|
||||
'convnext_small' : ConvNeXt_Small,
|
||||
'swin_transfomer_tiny' : Swin_transformer_Tiny,
|
||||
}[backbone](pretrained=pretrained, input_shape=input_shape)
|
||||
in_channels = {
|
||||
'convnext_tiny' : [192, 384, 768],
|
||||
'convnext_small' : [192, 384, 768],
|
||||
'swin_transfomer_tiny' : [192, 384, 768],
|
||||
}[backbone]
|
||||
feat1_c, feat2_c, feat3_c = in_channels
|
||||
self.conv_1x1_feat1 = Conv(feat1_c, base_channels * 4, 1, 1)
|
||||
self.conv_1x1_feat2 = Conv(feat2_c, base_channels * 8, 1, 1)
|
||||
self.conv_1x1_feat3 = Conv(feat3_c, base_channels * 16, 1, 1)
|
||||
|
||||
self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
|
||||
|
||||
self.conv_for_feat3 = Conv(base_channels * 16, base_channels * 8, 1, 1)
|
||||
self.conv3_for_upsample1 = C3(base_channels * 16, base_channels * 8, base_depth, shortcut=False)
|
||||
|
||||
self.conv_for_feat2 = Conv(base_channels * 8, base_channels * 4, 1, 1)
|
||||
self.conv3_for_upsample2 = C3(base_channels * 8, base_channels * 4, base_depth, shortcut=False)
|
||||
|
||||
self.down_sample1 = Conv(base_channels * 4, base_channels * 4, 3, 2)
|
||||
self.conv3_for_downsample1 = C3(base_channels * 8, base_channels * 8, base_depth, shortcut=False)
|
||||
|
||||
self.down_sample2 = Conv(base_channels * 8, base_channels * 8, 3, 2)
|
||||
self.conv3_for_downsample2 = C3(base_channels * 16, base_channels * 16, base_depth, shortcut=False)
|
||||
|
||||
# 80, 80, 256 => 80, 80, 3 * (5 + num_classes) => 80, 80, 3 * (4 + 1 + num_classes)
|
||||
self.yolo_head_P3 = nn.Conv2d(base_channels * 4, len(anchors_mask[2]) * (5 + num_classes), 1)
|
||||
# 40, 40, 512 => 40, 40, 3 * (5 + num_classes) => 40, 40, 3 * (4 + 1 + num_classes)
|
||||
self.yolo_head_P4 = nn.Conv2d(base_channels * 8, len(anchors_mask[1]) * (5 + num_classes), 1)
|
||||
# 20, 20, 1024 => 20, 20, 3 * (5 + num_classes) => 20, 20, 3 * (4 + 1 + num_classes)
|
||||
self.yolo_head_P5 = nn.Conv2d(base_channels * 16, len(anchors_mask[0]) * (5 + num_classes), 1)
|
||||
|
||||
def forward(self, x):
|
||||
# backbone
|
||||
feat1, feat2, feat3 = self.backbone(x)
|
||||
if self.backbone_name != "cspdarknet":
|
||||
feat1 = self.conv_1x1_feat1(feat1)
|
||||
feat2 = self.conv_1x1_feat2(feat2)
|
||||
feat3 = self.conv_1x1_feat3(feat3)
|
||||
|
||||
# 20, 20, 1024 -> 20, 20, 512
|
||||
P5 = self.conv_for_feat3(feat3)
|
||||
# 20, 20, 512 -> 40, 40, 512
|
||||
P5_upsample = self.upsample(P5)
|
||||
# 40, 40, 512 -> 40, 40, 1024
|
||||
P4 = torch.cat([P5_upsample, feat2], 1)
|
||||
# 40, 40, 1024 -> 40, 40, 512
|
||||
P4 = self.conv3_for_upsample1(P4)
|
||||
|
||||
# 40, 40, 512 -> 40, 40, 256
|
||||
P4 = self.conv_for_feat2(P4)
|
||||
# 40, 40, 256 -> 80, 80, 256
|
||||
P4_upsample = self.upsample(P4)
|
||||
# 80, 80, 256 cat 80, 80, 256 -> 80, 80, 512
|
||||
P3 = torch.cat([P4_upsample, feat1], 1)
|
||||
# 80, 80, 512 -> 80, 80, 256
|
||||
P3 = self.conv3_for_upsample2(P3)
|
||||
|
||||
# 80, 80, 256 -> 40, 40, 256
|
||||
P3_downsample = self.down_sample1(P3)
|
||||
# 40, 40, 256 cat 40, 40, 256 -> 40, 40, 512
|
||||
P4 = torch.cat([P3_downsample, P4], 1)
|
||||
# 40, 40, 512 -> 40, 40, 512
|
||||
P4 = self.conv3_for_downsample1(P4)
|
||||
|
||||
# 40, 40, 512 -> 20, 20, 512
|
||||
P4_downsample = self.down_sample2(P4)
|
||||
# 20, 20, 512 cat 20, 20, 512 -> 20, 20, 1024
|
||||
P5 = torch.cat([P4_downsample, P5], 1)
|
||||
# 20, 20, 1024 -> 20, 20, 1024
|
||||
P5 = self.conv3_for_downsample2(P5)
|
||||
|
||||
#---------------------------------------------------#
|
||||
# 第三个特征层
|
||||
# y3=(batch_size,75,80,80)
|
||||
#---------------------------------------------------#
|
||||
out2 = self.yolo_head_P3(P3)
|
||||
#---------------------------------------------------#
|
||||
# 第二个特征层
|
||||
# y2=(batch_size,75,40,40)
|
||||
#---------------------------------------------------#
|
||||
out1 = self.yolo_head_P4(P4)
|
||||
#---------------------------------------------------#
|
||||
# 第一个特征层
|
||||
# y1=(batch_size,75,20,20)
|
||||
#---------------------------------------------------#
|
||||
out0 = self.yolo_head_P5(P5)
|
||||
return out0, out1, out2
|
||||
|
||||
465
network/yolo_training.py
Normal file
465
network/yolo_training.py
Normal file
@@ -0,0 +1,465 @@
|
||||
import math
|
||||
from copy import deepcopy
|
||||
from functools import partial
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
class YOLOLoss(nn.Module):
|
||||
def __init__(self, anchors, num_classes, input_shape, cuda, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]], label_smoothing = 0):
|
||||
super(YOLOLoss, self).__init__()
|
||||
#-----------------------------------------------------------#
|
||||
# 20x20的特征层对应的anchor是[116,90],[156,198],[373,326]
|
||||
# 40x40的特征层对应的anchor是[30,61],[62,45],[59,119]
|
||||
# 80x80的特征层对应的anchor是[10,13],[16,30],[33,23]
|
||||
#-----------------------------------------------------------#
|
||||
self.anchors = anchors
|
||||
self.num_classes = num_classes
|
||||
self.bbox_attrs = 5 + num_classes
|
||||
self.input_shape = input_shape
|
||||
self.anchors_mask = anchors_mask
|
||||
self.label_smoothing = label_smoothing
|
||||
|
||||
self.threshold = 4
|
||||
|
||||
self.balance = [0.4, 1.0, 4]
|
||||
self.box_ratio = 0.05
|
||||
self.obj_ratio = 1 * (input_shape[0] * input_shape[1]) / (640 ** 2)
|
||||
self.cls_ratio = 0.5 * (num_classes / 80)
|
||||
self.cuda = cuda
|
||||
|
||||
def clip_by_tensor(self, t, t_min, t_max):
|
||||
t = t.float()
|
||||
result = (t >= t_min).float() * t + (t < t_min).float() * t_min
|
||||
result = (result <= t_max).float() * result + (result > t_max).float() * t_max
|
||||
return result
|
||||
|
||||
def MSELoss(self, pred, target):
|
||||
return torch.pow(pred - target, 2)
|
||||
|
||||
def BCELoss(self, pred, target):
|
||||
epsilon = 1e-7
|
||||
pred = self.clip_by_tensor(pred, epsilon, 1.0 - epsilon)
|
||||
output = - target * torch.log(pred) - (1.0 - target) * torch.log(1.0 - pred)
|
||||
return output
|
||||
|
||||
def box_giou(self, b1, b2):
|
||||
"""
|
||||
输入为：
|
||||
----------
|
||||
b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh
|
||||
b2: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh
|
||||
|
||||
返回为：
|
||||
-------
|
||||
giou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
|
||||
"""
|
||||
#----------------------------------------------------#
|
||||
# 求出预测框左上角右下角
|
||||
#----------------------------------------------------#
|
||||
b1_xy = b1[..., :2]
|
||||
b1_wh = b1[..., 2:4]
|
||||
b1_wh_half = b1_wh/2.
|
||||
b1_mins = b1_xy - b1_wh_half
|
||||
b1_maxes = b1_xy + b1_wh_half
|
||||
#----------------------------------------------------#
|
||||
# 求出真实框左上角右下角
|
||||
#----------------------------------------------------#
|
||||
b2_xy = b2[..., :2]
|
||||
b2_wh = b2[..., 2:4]
|
||||
b2_wh_half = b2_wh/2.
|
||||
b2_mins = b2_xy - b2_wh_half
|
||||
b2_maxes = b2_xy + b2_wh_half
|
||||
|
||||
#----------------------------------------------------#
|
||||
# 求真实框和预测框所有的iou
|
||||
#----------------------------------------------------#
|
||||
intersect_mins = torch.max(b1_mins, b2_mins)
|
||||
intersect_maxes = torch.min(b1_maxes, b2_maxes)
|
||||
intersect_wh = torch.max(intersect_maxes - intersect_mins, torch.zeros_like(intersect_maxes))
|
||||
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
|
||||
b1_area = b1_wh[..., 0] * b1_wh[..., 1]
|
||||
b2_area = b2_wh[..., 0] * b2_wh[..., 1]
|
||||
union_area = b1_area + b2_area - intersect_area
|
||||
iou = intersect_area / union_area
|
||||
|
||||
#----------------------------------------------------#
|
||||
# 找到包裹两个框的最小框的左上角和右下角
|
||||
#----------------------------------------------------#
|
||||
enclose_mins = torch.min(b1_mins, b2_mins)
|
||||
enclose_maxes = torch.max(b1_maxes, b2_maxes)
|
||||
enclose_wh = torch.max(enclose_maxes - enclose_mins, torch.zeros_like(intersect_maxes))
|
||||
#----------------------------------------------------#
|
||||
# 计算对角线距离
|
||||
#----------------------------------------------------#
|
||||
enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
|
||||
giou = iou - (enclose_area - union_area) / enclose_area
|
||||
|
||||
return giou
|
||||
|
||||
#---------------------------------------------------#
|
||||
# 平滑标签
|
||||
#---------------------------------------------------#
|
||||
def smooth_labels(self, y_true, label_smoothing, num_classes):
|
||||
return y_true * (1.0 - label_smoothing) + label_smoothing / num_classes
|
||||
|
||||
def forward(self, l, input, targets=None, y_true=None):
|
||||
#----------------------------------------------------#
|
||||
# l 代表使用的是第几个有效特征层
|
||||
# input的shape为 bs, 3*(5+num_classes), 20, 20
|
||||
# bs, 3*(5+num_classes), 40, 40
|
||||
# bs, 3*(5+num_classes), 80, 80
|
||||
# targets 真实框的标签情况 [batch_size, num_gt, 5]
|
||||
#----------------------------------------------------#
|
||||
#--------------------------------#
|
||||
# 获得图片数量，特征层的高和宽
|
||||
# 20, 20
|
||||
#--------------------------------#
|
||||
bs = input.size(0)
|
||||
in_h = input.size(2)
|
||||
in_w = input.size(3)
|
||||
#-----------------------------------------------------------------------#
|
||||
# 计算步长
|
||||
# 每一个特征点对应原来的图片上多少个像素点
|
||||
# [640, 640] 高的步长为640 / 20 = 32，宽的步长为640 / 20 = 32
|
||||
# 如果特征层为20x20的话，一个特征点就对应原来的图片上的32个像素点
|
||||
# 如果特征层为40x40的话，一个特征点就对应原来的图片上的16个像素点
|
||||
# 如果特征层为80x80的话，一个特征点就对应原来的图片上的8个像素点
|
||||
# stride_h = stride_w = 32、16、8
|
||||
#-----------------------------------------------------------------------#
|
||||
stride_h = self.input_shape[0] / in_h
|
||||
stride_w = self.input_shape[1] / in_w
|
||||
#-------------------------------------------------#
|
||||
# 此时获得的scaled_anchors大小是相对于特征层的
|
||||
#-------------------------------------------------#
|
||||
scaled_anchors = [(a_w / stride_w, a_h / stride_h) for a_w, a_h in self.anchors]
|
||||
#-----------------------------------------------#
|
||||
# 输入的input一共有三个，他们的shape分别是
|
||||
# bs, 3 * (5+num_classes), 20, 20 => bs, 3, 5 + num_classes, 20, 20 => batch_size, 3, 20, 20, 5 + num_classes
|
||||
|
||||
# batch_size, 3, 20, 20, 5 + num_classes
|
||||
# batch_size, 3, 40, 40, 5 + num_classes
|
||||
# batch_size, 3, 80, 80, 5 + num_classes
|
||||
#-----------------------------------------------#
|
||||
prediction = input.view(bs, len(self.anchors_mask[l]), self.bbox_attrs, in_h, in_w).permute(0, 1, 3, 4, 2).contiguous()
|
||||
|
||||
#-----------------------------------------------#
|
||||
# 先验框的中心位置的调整参数
|
||||
#-----------------------------------------------#
|
||||
x = torch.sigmoid(prediction[..., 0])
|
||||
y = torch.sigmoid(prediction[..., 1])
|
||||
#-----------------------------------------------#
|
||||
# 先验框的宽高调整参数
|
||||
#-----------------------------------------------#
|
||||
w = torch.sigmoid(prediction[..., 2])
|
||||
h = torch.sigmoid(prediction[..., 3])
|
||||
#-----------------------------------------------#
|
||||
# 获得置信度，是否有物体
|
||||
#-----------------------------------------------#
|
||||
conf = torch.sigmoid(prediction[..., 4])
|
||||
#-----------------------------------------------#
|
||||
# 种类置信度
|
||||
#-----------------------------------------------#
|
||||
pred_cls = torch.sigmoid(prediction[..., 5:])
|
||||
#-----------------------------------------------#
|
||||
# self.get_target已经合并到dataloader中
|
||||
# 原因是在这里执行过慢，会大大延长训练时间
|
||||
#-----------------------------------------------#
|
||||
# y_true, noobj_mask = self.get_target(l, targets, scaled_anchors, in_h, in_w)
|
||||
|
||||
#---------------------------------------------------------------#
|
||||
# 将预测结果进行解码，判断预测结果和真实值的重合程度
|
||||
# 如果重合程度过大则忽略，因为这些特征点属于预测比较准确的特征点
|
||||
# 作为负样本不合适
|
||||
#----------------------------------------------------------------#
|
||||
pred_boxes = self.get_pred_boxes(l, x, y, h, w, targets, scaled_anchors, in_h, in_w)
|
||||
|
||||
if self.cuda:
|
||||
y_true = y_true.type_as(x)
|
||||
|
||||
loss = 0
|
||||
n = torch.sum(y_true[..., 4] == 1)
|
||||
if n != 0:
|
||||
#---------------------------------------------------------------#
|
||||
# 计算预测结果和真实结果的giou，计算对应有真实框的先验框的giou损失
|
||||
# loss_cls计算对应有真实框的先验框的分类损失
|
||||
#----------------------------------------------------------------#
|
||||
giou = self.box_giou(pred_boxes, y_true[..., :4]).type_as(x)
|
||||
loss_loc = torch.mean((1 - giou)[y_true[..., 4] == 1])
|
||||
loss_cls = torch.mean(self.BCELoss(pred_cls[y_true[..., 4] == 1], self.smooth_labels(y_true[..., 5:][y_true[..., 4] == 1], self.label_smoothing, self.num_classes)))
|
||||
loss += loss_loc * self.box_ratio + loss_cls * self.cls_ratio
|
||||
#-----------------------------------------------------------#
|
||||
# 计算置信度的loss
|
||||
# 也就意味着先验框对应的预测框预测的更准确
|
||||
# 它才是用来预测这个物体的。
|
||||
#-----------------------------------------------------------#
|
||||
tobj = torch.where(y_true[..., 4] == 1, giou.detach().clamp(0), torch.zeros_like(y_true[..., 4]))
|
||||
else:
|
||||
tobj = torch.zeros_like(y_true[..., 4])
|
||||
loss_conf = torch.mean(self.BCELoss(conf, tobj))
|
||||
|
||||
loss += loss_conf * self.balance[l] * self.obj_ratio
|
||||
# if n != 0:
|
||||
# print(loss_loc * self.box_ratio, loss_cls * self.cls_ratio, loss_conf * self.balance[l] * self.obj_ratio)
|
||||
return loss
|
||||
|
||||
def get_near_points(self, x, y, i, j):
|
||||
sub_x = x - i
|
||||
sub_y = y - j
|
||||
if sub_x > 0.5 and sub_y > 0.5:
|
||||
return [[0, 0], [1, 0], [0, 1]]
|
||||
elif sub_x < 0.5 and sub_y > 0.5:
|
||||
return [[0, 0], [-1, 0], [0, 1]]
|
||||
elif sub_x < 0.5 and sub_y < 0.5:
|
||||
return [[0, 0], [-1, 0], [0, -1]]
|
||||
else:
|
||||
return [[0, 0], [1, 0], [0, -1]]
|
||||
|
||||
def get_target(self, l, targets, anchors, in_h, in_w):
|
||||
#-----------------------------------------------------#
|
||||
# 计算一共有多少张图片
|
||||
#-----------------------------------------------------#
|
||||
bs = len(targets)
|
||||
#-----------------------------------------------------#
|
||||
# 用于选取哪些先验框不包含物体
|
||||
# bs, 3, 20, 20
|
||||
#-----------------------------------------------------#
|
||||
noobj_mask = torch.ones(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False)
|
||||
#-----------------------------------------------------#
|
||||
# 帮助找到每一个先验框最对应的真实框
|
||||
#-----------------------------------------------------#
|
||||
box_best_ratio = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False)
|
||||
#-----------------------------------------------------#
|
||||
# batch_size, 3, 20, 20, 5 + num_classes
|
||||
#-----------------------------------------------------#
|
||||
y_true = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, self.bbox_attrs, requires_grad = False)
|
||||
for b in range(bs):
|
||||
if len(targets[b])==0:
|
||||
continue
|
||||
batch_target = torch.zeros_like(targets[b])
|
||||
#-------------------------------------------------------#
|
||||
# 计算出正样本在特征层上的中心点
|
||||
# 获得真实框相对于特征层的大小
|
||||
#-------------------------------------------------------#
|
||||
batch_target[:, [0,2]] = targets[b][:, [0,2]] * in_w
|
||||
batch_target[:, [1,3]] = targets[b][:, [1,3]] * in_h
|
||||
batch_target[:, 4] = targets[b][:, 4]
|
||||
batch_target = batch_target.cpu()
|
||||
|
||||
#-----------------------------------------------------------------------------#
|
||||
# batch_target : num_true_box, 5
|
||||
# batch_target[:, 2:4] : num_true_box, 2
|
||||
# torch.unsqueeze(batch_target[:, 2:4], 1) : num_true_box, 1, 2
|
||||
# anchors : 9, 2
|
||||
# torch.unsqueeze(torch.FloatTensor(anchors), 0) : 1, 9, 2
|
||||
# ratios_of_gt_anchors : num_true_box, 9, 2
|
||||
# ratios_of_anchors_gt : num_true_box, 9, 2
|
||||
#
|
||||
# ratios : num_true_box, 9, 4
|
||||
# max_ratios : num_true_box, 9
|
||||
# max_ratios每一个真实框和每一个先验框的最大宽高比！
|
||||
#------------------------------------------------------------------------------#
|
||||
ratios_of_gt_anchors = torch.unsqueeze(batch_target[:, 2:4], 1) / torch.unsqueeze(torch.FloatTensor(anchors), 0)
|
||||
ratios_of_anchors_gt = torch.unsqueeze(torch.FloatTensor(anchors), 0) / torch.unsqueeze(batch_target[:, 2:4], 1)
|
||||
ratios = torch.cat([ratios_of_gt_anchors, ratios_of_anchors_gt], dim = -1)
|
||||
max_ratios, _ = torch.max(ratios, dim = -1)
|
||||
|
||||
for t, ratio in enumerate(max_ratios):
|
||||
#-------------------------------------------------------#
|
||||
# ratio : 9
|
||||
#-------------------------------------------------------#
|
||||
over_threshold = ratio < self.threshold
|
||||
over_threshold[torch.argmin(ratio)] = True
|
||||
for k, mask in enumerate(self.anchors_mask[l]):
|
||||
if not over_threshold[mask]:
|
||||
continue
|
||||
#----------------------------------------#
|
||||
# 获得真实框属于哪个网格点
|
||||
# x 1.25 => 1
|
||||
# y 3.75 => 3
|
||||
#----------------------------------------#
|
||||
i = torch.floor(batch_target[t, 0]).long()
|
||||
j = torch.floor(batch_target[t, 1]).long()
|
||||
|
||||
offsets = self.get_near_points(batch_target[t, 0], batch_target[t, 1], i, j)
|
||||
for offset in offsets:
|
||||
local_i = i + offset[0]
|
||||
local_j = j + offset[1]
|
||||
|
||||
if local_i >= in_w or local_i < 0 or local_j >= in_h or local_j < 0:
|
||||
continue
|
||||
|
||||
if box_best_ratio[b, k, local_j, local_i] != 0:
|
||||
if box_best_ratio[b, k, local_j, local_i] > ratio[mask]:
|
||||
y_true[b, k, local_j, local_i, :] = 0
|
||||
else:
|
||||
continue
|
||||
|
||||
#----------------------------------------#
|
||||
# 取出真实框的种类
|
||||
#----------------------------------------#
|
||||
c = batch_target[t, 4].long()
|
||||
|
||||
#----------------------------------------#
|
||||
# noobj_mask代表无目标的特征点
|
||||
#----------------------------------------#
|
||||
noobj_mask[b, k, local_j, local_i] = 0
|
||||
#----------------------------------------#
|
||||
# tx、ty代表中心调整参数的真实值
|
||||
#----------------------------------------#
|
||||
y_true[b, k, local_j, local_i, 0] = batch_target[t, 0]
|
||||
y_true[b, k, local_j, local_i, 1] = batch_target[t, 1]
|
||||
y_true[b, k, local_j, local_i, 2] = batch_target[t, 2]
|
||||
y_true[b, k, local_j, local_i, 3] = batch_target[t, 3]
|
||||
y_true[b, k, local_j, local_i, 4] = 1
|
||||
y_true[b, k, local_j, local_i, c + 5] = 1
|
||||
#----------------------------------------#
|
||||
# 获得当前先验框最好的比例
|
||||
#----------------------------------------#
|
||||
box_best_ratio[b, k, local_j, local_i] = ratio[mask]
|
||||
|
||||
return y_true, noobj_mask
|
||||
|
||||
def get_pred_boxes(self, l, x, y, h, w, targets, scaled_anchors, in_h, in_w):
|
||||
#-----------------------------------------------------#
|
||||
# 计算一共有多少张图片
|
||||
#-----------------------------------------------------#
|
||||
bs = len(targets)
|
||||
|
||||
#-----------------------------------------------------#
|
||||
# 生成网格，先验框中心，网格左上角
|
||||
#-----------------------------------------------------#
|
||||
grid_x = torch.linspace(0, in_w - 1, in_w).repeat(in_h, 1).repeat(
|
||||
int(bs * len(self.anchors_mask[l])), 1, 1).view(x.shape).type_as(x)
|
||||
grid_y = torch.linspace(0, in_h - 1, in_h).repeat(in_w, 1).t().repeat(
|
||||
int(bs * len(self.anchors_mask[l])), 1, 1).view(y.shape).type_as(x)
|
||||
|
||||
# 生成先验框的宽高
|
||||
scaled_anchors_l = np.array(scaled_anchors)[self.anchors_mask[l]]
|
||||
anchor_w = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([0])).type_as(x)
|
||||
anchor_h = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([1])).type_as(x)
|
||||
|
||||
anchor_w = anchor_w.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(w.shape)
|
||||
anchor_h = anchor_h.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(h.shape)
|
||||
#-------------------------------------------------------#
|
||||
# 计算调整后的先验框中心与宽高
|
||||
#-------------------------------------------------------#
|
||||
pred_boxes_x = torch.unsqueeze(x * 2. - 0.5 + grid_x, -1)
|
||||
pred_boxes_y = torch.unsqueeze(y * 2. - 0.5 + grid_y, -1)
|
||||
pred_boxes_w = torch.unsqueeze((w * 2) ** 2 * anchor_w, -1)
|
||||
pred_boxes_h = torch.unsqueeze((h * 2) ** 2 * anchor_h, -1)
|
||||
pred_boxes = torch.cat([pred_boxes_x, pred_boxes_y, pred_boxes_w, pred_boxes_h], dim = -1)
|
||||
return pred_boxes
|
||||
|
||||
def is_parallel(model):
|
||||
# Returns True if model is of type DP or DDP
|
||||
return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
|
||||
|
||||
def de_parallel(model):
|
||||
# De-parallelize a model: returns single-GPU model if model is of type DP or DDP
|
||||
return model.module if is_parallel(model) else model
|
||||
|
||||
def copy_attr(a, b, include=(), exclude=()):
|
||||
# Copy attributes from b to a, options to only include [...] and to exclude [...]
|
||||
for k, v in b.__dict__.items():
|
||||
if (len(include) and k not in include) or k.startswith('_') or k in exclude:
|
||||
continue
|
||||
else:
|
||||
setattr(a, k, v)
|
||||
|
||||
class ModelEMA:
|
||||
""" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
|
||||
Keeps a moving average of everything in the model state_dict (parameters and buffers)
|
||||
For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
|
||||
"""
|
||||
|
||||
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
|
||||
# Create EMA
|
||||
self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
|
||||
# if next(model.parameters()).device.type != 'cpu':
|
||||
# self.ema.half() # FP16 EMA
|
||||
self.updates = updates # number of EMA updates
|
||||
self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
|
||||
for p in self.ema.parameters():
|
||||
p.requires_grad_(False)
|
||||
|
||||
def update(self, model):
|
||||
# Update EMA parameters
|
||||
with torch.no_grad():
|
||||
self.updates += 1
|
||||
d = self.decay(self.updates)
|
||||
|
||||
msd = de_parallel(model).state_dict() # model state_dict
|
||||
for k, v in self.ema.state_dict().items():
|
||||
if v.dtype.is_floating_point:
|
||||
v *= d
|
||||
v += (1 - d) * msd[k].detach()
|
||||
|
||||
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
|
||||
# Update EMA attributes
|
||||
copy_attr(self.ema, model, include, exclude)
|
||||
|
||||
def weights_init(net, init_type='normal', init_gain = 0.02):
|
||||
def init_func(m):
|
||||
classname = m.__class__.__name__
|
||||
if hasattr(m, 'weight') and classname.find('Conv') != -1:
|
||||
if init_type == 'normal':
|
||||
torch.nn.init.normal_(m.weight.data, 0.0, init_gain)
|
||||
elif init_type == 'xavier':
|
||||
torch.nn.init.xavier_normal_(m.weight.data, gain=init_gain)
|
||||
elif init_type == 'kaiming':
|
||||
torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
|
||||
elif init_type == 'orthogonal':
|
||||
torch.nn.init.orthogonal_(m.weight.data, gain=init_gain)
|
||||
else:
|
||||
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
|
||||
elif classname.find('BatchNorm2d') != -1:
|
||||
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
|
||||
torch.nn.init.constant_(m.bias.data, 0.0)
|
||||
print('initialize network with %s type' % init_type)
|
||||
net.apply(init_func)
|
||||
|
||||
def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10):
|
||||
def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):
|
||||
if iters <= warmup_total_iters:
|
||||
# lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
|
||||
lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2
|
||||
) + warmup_lr_start
|
||||
elif iters >= total_iters - no_aug_iter:
|
||||
lr = min_lr
|
||||
else:
|
||||
lr = min_lr + 0.5 * (lr - min_lr) * (
|
||||
1.0
|
||||
+ math.cos(
|
||||
math.pi
|
||||
* (iters - warmup_total_iters)
|
||||
/ (total_iters - warmup_total_iters - no_aug_iter)
|
||||
)
|
||||
)
|
||||
return lr
|
||||
|
||||
def step_lr(lr, decay_rate, step_size, iters):
|
||||
if step_size < 1:
|
||||
raise ValueError("step_size must above 1.")
|
||||
n = iters // step_size
|
||||
out_lr = lr * decay_rate ** n
|
||||
return out_lr
|
||||
|
||||
if lr_decay_type == "cos":
|
||||
warmup_total_iters = min(max(warmup_iters_ratio * total_iters, 1), 3)
|
||||
warmup_lr_start = max(warmup_lr_ratio * lr, 1e-6)
|
||||
no_aug_iter = min(max(no_aug_iter_ratio * total_iters, 1), 15)
|
||||
func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)
|
||||
else:
|
||||
decay_rate = (min_lr / lr) ** (1 / (step_num - 1))
|
||||
step_size = total_iters / step_num
|
||||
func = partial(step_lr, lr, decay_rate, step_size)
|
||||
|
||||
return func
|
||||
|
||||
def set_optimizer_lr(optimizer, lr_scheduler_func, epoch):
|
||||
lr = lr_scheduler_func(epoch)
|
||||
for param_group in optimizer.param_groups:
|
||||
param_group['lr'] = lr
|
||||
Reference in New Issue
Block a user