434 lines
19 KiB
Python
434 lines
19 KiB
Python
# YOLOv5 common modules
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import logging
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import warnings
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from copy import copy
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from pathlib import Path
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import math
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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 exif_transpose, letterbox
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from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
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from utils.plots import colors, plot_one_box
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from utils.torch_utils import time_sync
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LOGGER = logging.getLogger(__name__)
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#为卷积或池化后特征图大小不变，在输入特征图上做零填充。填充多少，由此函数计算。
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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 如果k是int型的数，那就整除2，否则。。‘x // 2是x除2之后，取整数商。’
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return p
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class Conv(nn.Module):#Conv类继承于nn.Module 他做了标准卷积+bn层+hardswish
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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 #默认act为true
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super().__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)#bias为False因为，卷积2D和BN做完后，下面特征图融合时，偏置还会消掉。
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self.bn = nn.BatchNorm2d(c2)
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self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) #如果act为true时，，
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def forward(self, x):#正向传播函数 网络执行的顺序是由forward来决定的，先输入x，得到conv，再得到bn，再得到act是激活函数
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return self.act(self.bn(self.conv(x)))
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def forward_fuse(self, x): #此处没有BN，只有卷积和激活
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return self.act(self.conv(x))
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class DWConv(Conv): #dw卷积需要哪些参数，如下：输入、输出、卷积核大小、步长。将参数传给上面的Conv！
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# Depth-wise convolution class
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#在yolov5中没有真正使用，k=1是卷积核kenel，s=1是步长 #g是最大公约数，用于分组。这个缺失return返回conv。
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def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
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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).unsqueeze(0).transpose(0, 3).squeeze(3)
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return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h)
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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 #shortcut默认True即为有短接。
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super().__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 #输入与输出维度相同才能做相加运算
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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)) #x+两次卷积的值，否则只有两个卷积运算！！
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class BottleneckCSP(nn.Module):
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks#一个分支是标准bottleneck堆叠，另一个分支是普通卷积层
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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().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1) #Conv模块
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self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) #卷积运算
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self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)#卷积运算
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self.cv4 = Conv(2 * c_, c2, 1, 1) #Conv模块 做拼接后，inchannel维度变大了
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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)]) #*是解包，将list拆成很多独立元素
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#用了n次的Bottleneck操作，得到后解包送入Sequential，给m
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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)))) #cv4是Conv模块
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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().__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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class C3SPP(C3):
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# C3 module with SPP()
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def __init__(self, c1, c2, k=(5, 9, 13), 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 = SPP(c_, c_, k)
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#空间金字塔池化
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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)):#k是元祖
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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) #Conv模块
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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) #Conv模块
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])#最大池化 5,9,13都要做最大池化
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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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return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) #叠加没做最大池化的输入+最大池化的
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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().__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) 特征图宽高都会减半
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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))# 这里slice
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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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b, 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(b, 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(b, 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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b, 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(b, 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(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
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class Concat(nn.Module):#定义拼接的类
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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().__init__()
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self.d = dimension #定义沿着哪个维度进行拼接
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def forward(self, x):
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return torch.cat(x, self.d)
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# def nms(self, mode=True): # add or remove NMS module 是不是和AutoShape是一个作用？？？？？？？不是！！！
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# present = type(self.model[-1]) is NMS # last layer is NMS
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# if mode and not present:
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# print('Adding NMS... ')
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# m = NMS() # module
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# m.f = -1 # from
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# m.i = self.model[-1].i + 1 # index
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# self.model.add_module(name='%s' % m.i, module=m) # add
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# self.eval()
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# elif not mode and present:
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# print('Removing NMS... ')
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# self.model = self.model[:-1] # remove
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# return self
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class NMS(nn.Module):
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#非极大值抑制模块
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conf=0.25;
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iou=0.45
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classes=None
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def __in__(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): #图像来自不同文件，做一个预处理 在预处理、推理和非极大值抑制时要调整#在yolov5基本没有用？？
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# YOLOv5 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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max_det = 1000 # maximum number of detections per image
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def __init__(self, model):
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super().__init__()
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self.model = model.eval()
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def autoshape(self):
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LOGGER.info('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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# file: imgs = 'data/images/zidane.jpg' # str or PosixPath
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# URI: = 'https://ultralytics.com/images/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') or ImageGrab.grab() # 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_sync()]
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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, Path)): # filename or uri
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im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
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im = np.asarray(exif_transpose(im))
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elif isinstance(im, Image.Image): # PIL Image
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im, f = np.asarray(exif_transpose(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 if im.data.contiguous else np.ascontiguousarray(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_sync())
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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_sync())
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# Post-process
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y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) # 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_sync())
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return Detections(imgs, y, files, t, self.names, x.shape)
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class Flatten(nn.Module):
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#U展平
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@staticmethod
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def forward(x):
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return x.view(x.size(0),-1)
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class Detections:
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# YOLOv5 detections class for inference results
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def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
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super().__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, crop=False, render=False, save_dir=Path('')):
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for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
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str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
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if pred.shape[0]:
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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 or crop:
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for *box, conf, cls in reversed(pred): # xyxy, confidence, class
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label = f'{self.names[int(cls)]} {conf:.2f}'
|
||
if crop:
|
||
save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
|
||
else: # all others
|
||
plot_one_box(box, im, label=label, color=colors(cls))
|
||
else:
|
||
str += '(no detections)'
|
||
|
||
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
|
||
if pprint:
|
||
LOGGER.info(str.rstrip(', '))
|
||
if show:
|
||
im.show(self.files[i]) # show
|
||
if save:
|
||
f = self.files[i]
|
||
im.save(save_dir / f) # save
|
||
if i == self.n - 1:
|
||
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
|
||
if render:
|
||
self.imgs[i] = np.asarray(im)
|
||
|
||
def print(self):
|
||
self.display(pprint=True) # print results
|
||
LOGGER.info(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/detect/exp'):
|
||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
|
||
self.display(save=True, save_dir=save_dir) # save results
|
||
|
||
def crop(self, save_dir='runs/detect/exp'):
|
||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
|
||
self.display(crop=True, save_dir=save_dir) # crop results
|
||
LOGGER.info(f'Saved results to {save_dir}\n')
|
||
|
||
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):#用于第二级分类
|
||
# 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().__init__()
|
||
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) 自适应平均池化
|
||
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)
|