414 lines
18 KiB
Python
414 lines
18 KiB
Python
# YOLOv5 common modules
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import math
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from copy import copy
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import requests
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import torch
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import torch.nn as nn
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from PIL import Image
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from torch.cuda import amp
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from utils.datasets import letterbox
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from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
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from utils.plots import color_list, plot_one_box
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from utils.torch_utils import time_synchronized
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import warnings
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def autopad(k, p=None): # kernel, padding
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# Pad to 'same'
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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] # auto-pad
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return p
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def DWConv(c1, c2, k=1, s=1, act=True):
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# Depthwise convolution
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return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) #math.gcd 函数（计算最大公约数） 深度卷积 不够灵活 仅在一个通道上 没有通道融合 但是计算量小
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class Conv(nn.Module):
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# Standard convolution
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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(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) #批归一化 使其均值接近0，方差接近1
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self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) #nn.Identity() 它的作用是直接返回输入，不对输入做任何改变。
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def forward(self, x):
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return self.act(self.bn(self.conv(x))) #将批归一化放在激活函数之前有助于规范化激活值
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def fuseforward(self, x):
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return self.act(self.conv(x))
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class TransformerLayer(nn.Module):
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# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
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def __init__(self, c, num_heads):
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super().__init__()
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self.q = nn.Linear(c, c, bias=False)
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self.k = nn.Linear(c, c, bias=False)
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self.v = nn.Linear(c, c, bias=False)
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self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
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self.fc1 = nn.Linear(c, c, bias=False)
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self.fc2 = nn.Linear(c, c, bias=False)
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def forward(self, x):
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x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
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x = self.fc2(self.fc1(x)) + x
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return x
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class TransformerBlock(nn.Module):
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# Vision Transformer https://arxiv.org/abs/2010.11929
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def __init__(self, c1, c2, num_heads, num_layers):
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super().__init__()
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self.conv = None
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if c1 != c2:
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self.conv = Conv(c1, c2)
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self.linear = nn.Linear(c2, c2) # learnable position embedding
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self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
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self.c2 = c2
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def forward(self, x):
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if self.conv is not None:
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x = self.conv(x)
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b, _, w, h = x.shape
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p = x.flatten(2)
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p = p.unsqueeze(0)
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p = p.transpose(0, 3)
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p = p.squeeze(3)
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e = self.linear(p)
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x = p + e
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x = self.tr(x)
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x = x.unsqueeze(3)
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x = x.transpose(0, 3)
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x = x.reshape(b, self.c2, w, h)
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return x
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class Bottleneck(nn.Module): #残差网络 ResNet
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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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# x + self.cv2(self.cv1(x)) 逐元素相加 要求两个特征图的 通道数（channel） 和 空间尺寸（height 和 width） 完全一致。
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class BottleneckCSP(nn.Module): #CSPNet 的核心思想是将网络的部分层通过并行连接，减少计算量并提高梯度流动性。
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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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(BottleneckCSP, 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 = nn.Conv2d(c1, c_, 1, 1, bias=False)
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self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
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self.cv4 = Conv(2 * c_, c2, 1, 1)
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self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
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self.act = nn.LeakyReLU(0.1, inplace=True)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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def forward(self, x):
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y1 = self.cv3(self.m(self.cv1(x)))
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y2 = self.cv2(x)
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return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
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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((self.m(self.cv1(x)), self.cv2(x)), dim=1))
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class C3TR(C3):
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# C3 module with TransformerBlock()
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e)
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self.m = TransformerBlock(c_, c_, 4, n)
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#空间金字塔池化 这是 YOLOv3 中的一部分，用于在不同尺度上进行池化，提取图像的多尺度特征。SPP 的目标是将不同大小的感受野用于捕捉图像中的多种信息，并结合这些信息来增强模型的表达能力。
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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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#等价于下面这段 （PS: nn.ModuleList 是 PyTorch 提供的一个容器，用于存储多个子模块。）
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# self.m = nn.ModuleList([
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# nn.MaxPool2d(kernel_size=5, stride=1, padding=5 // 2),
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# nn.MaxPool2d(kernel_size=9, stride=1, padding=9 // 2),
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# nn.MaxPool2d(kernel_size=13, stride=1, padding=13 // 2)
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# ])
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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)) #每个 m(x) 代表 x 经过一个不同池化核的最大池化操作的结果。
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class SPPF(nn.Module):
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# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
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def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
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super().__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_ * 4, c2, 1, 1)
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self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 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') # suppress torch 1.9.0 max_pool2d() warning
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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)) #0维度是 bs(batch-size) 1维度是通道
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#Focus:把宽度w和高度h的信息整合到c空间中
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class Focus(nn.Module):
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# Focus wh information into c-space
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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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# self.contract = Contract(gain=2)
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def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
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return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
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# return self.conv(self.contract(x))
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class Contract(nn.Module):
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# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
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def __init__(self, gain=2):
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super().__init__()
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self.gain = gain
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def forward(self, x):
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N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
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s = self.gain
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x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
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x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
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return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
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class Expand(nn.Module):
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# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
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def __init__(self, gain=2):
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super().__init__()
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self.gain = gain
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def forward(self, x):
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N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
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s = self.gain
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x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
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x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
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return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
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class Concat(nn.Module):
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# Concatenate a list of tensors along dimension
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def __init__(self, dimension=1):
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super(Concat, self).__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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class NMS(nn.Module):
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# Non-Maximum Suppression (NMS) module
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conf = 0.25 # confidence threshold
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iou = 0.45 # IoU threshold
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classes = None # (optional list) filter by class
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def __init__(self):
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super(NMS, self).__init__()
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def forward(self, x):
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return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
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class autoShape(nn.Module):
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# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
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conf = 0.25 # NMS confidence threshold
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iou = 0.45 # NMS IoU threshold
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classes = None # (optional list) filter by class
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def __init__(self, model):
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super(autoShape, self).__init__()
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self.model = model.eval()
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def autoshape(self):
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print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
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return self
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@torch.no_grad()
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def forward(self, imgs, size=640, augment=False, profile=False):
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# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
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# filename: imgs = 'data/samples/zidane.jpg'
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# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
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# PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
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# numpy: = np.zeros((640,1280,3)) # HWC
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# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
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# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
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t = [time_synchronized()]
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p = next(self.model.parameters()) # for device and type
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if isinstance(imgs, torch.Tensor): # torch
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with amp.autocast(enabled=p.device.type != 'cpu'):
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return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
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# Pre-process
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n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
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shape0, shape1, files = [], [], [] # image and inference shapes, filenames
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for i, im in enumerate(imgs):
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f = f'image{i}' # filename
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if isinstance(im, str): # filename or uri
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im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im
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elif isinstance(im, Image.Image): # PIL Image
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im, f = np.asarray(im), getattr(im, 'filename', f) or f
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files.append(Path(f).with_suffix('.jpg').name)
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if im.shape[0] < 5: # image in CHW
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im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
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im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
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s = im.shape[:2] # HWC
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shape0.append(s) # image shape
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g = (size / max(s)) # gain
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shape1.append([y * g for y in s])
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imgs[i] = im # update
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shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
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x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
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x = np.stack(x, 0) if n > 1 else x[0][None] # stack
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x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
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x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
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t.append(time_synchronized())
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with amp.autocast(enabled=p.device.type != 'cpu'):
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# Inference
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y = self.model(x, augment, profile)[0] # forward
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t.append(time_synchronized())
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# Post-process
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y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
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for i in range(n):
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scale_coords(shape1, y[i][:, :4], shape0[i])
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t.append(time_synchronized())
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return Detections(imgs, y, files, t, self.names, x.shape)
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class Detections:
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# detections class for YOLOv5 inference results
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def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
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super(Detections, self).__init__()
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d = pred[0].device # device
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gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
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self.imgs = imgs # list of images as numpy arrays
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
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self.names = names # class names
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self.files = files # image filenames
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self.xyxy = pred # xyxy pixels
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
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self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
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self.n = len(self.pred) # number of images (batch size)
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self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
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self.s = shape # inference BCHW shape
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def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
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colors = color_list()
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for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
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str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
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if pred is not None:
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for c in pred[:, -1].unique():
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n = (pred[:, -1] == c).sum() # detections per class
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str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
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if show or save or render:
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for *box, conf, cls in pred: # xyxy, confidence, class
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label = f'{self.names[int(cls)]} {conf:.2f}'
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plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
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img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
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if pprint:
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print(str.rstrip(', '))
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if show:
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img.show(self.files[i]) # show
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if save:
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f = self.files[i]
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img.save(Path(save_dir) / f) # save
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print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
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if render:
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self.imgs[i] = np.asarray(img)
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def print(self):
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self.display(pprint=True) # print results
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print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
|
||
|
||
def show(self):
|
||
self.display(show=True) # show results
|
||
|
||
def save(self, save_dir='runs/hub/exp'):
|
||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir
|
||
Path(save_dir).mkdir(parents=True, exist_ok=True)
|
||
self.display(save=True, save_dir=save_dir) # save results
|
||
|
||
def render(self):
|
||
self.display(render=True) # render results
|
||
return self.imgs
|
||
|
||
def pandas(self):
|
||
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
|
||
new = copy(self) # return copy
|
||
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
|
||
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
|
||
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
|
||
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
|
||
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
|
||
return new
|
||
|
||
def tolist(self):
|
||
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
|
||
for d in x:
|
||
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||
return x
|
||
|
||
def __len__(self):
|
||
return self.n
|
||
|
||
|
||
class Classify(nn.Module):
|
||
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||
super(Classify, self).__init__()
|
||
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
||
self.flat = nn.Flatten()
|
||
|
||
def forward(self, x):
|
||
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||
return self.flat(self.conv(z)) # flatten to x(b,c2)
|