297 lines
14 KiB
Python
297 lines
14 KiB
Python
# YOLOv5 YOLO-specific modules
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import argparse
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import logging
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import sys
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from copy import deepcopy
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sys.path.append('./') # to run '$ python *.py' files in subdirectories
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logger = logging.getLogger(__name__)
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from models.common import *
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from models.experimental import *
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from utils.autoanchor import check_anchor_order
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from utils.general import make_divisible, check_file, set_logging
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from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
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select_device, copy_attr
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try:
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import thop # for FLOPS computation
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except ImportError:
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thop = None
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class Detect(nn.Module):
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stride = None # strides computed during build
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export = False # onnx export
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def __init__(self, nc=80, anchors=(), ch=()): # detection layer
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super(Detect, self).__init__()
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self.nc = nc # number of classes 一共几个类别
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self.no = nc + 5 # number of outputs per anchor
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self.nl = len(anchors) # number of detection layers 检测层的数量，即 anchors 列表的长度。3
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self.na = len(anchors[0]) // 2 # number of anchors na：每个检测层的锚框数量（anchors[0] 的宽高对数）。6/2
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self.grid = [torch.zeros(1)] * self.nl # init grid
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a = torch.tensor(anchors).float().view(self.nl, -1, 2)
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self.register_buffer('anchors', a) # shape(nl,na,2)
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self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
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self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv ch是通道数 3个特征图 128,256,512
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def forward(self, x):
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# x = x.copy() # for profiling
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# ny, nx: 特征图的高度和宽度。 no:类别+5 na:每个层检测锚框数量（6/2）
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z = [] # inference output
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self.training |= self.export #如果 export=True，则将模型状态设置为 training=True（某些推理操作需要适配 ONNX 导出）。
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for i in range(self.nl):
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x[i] = self.m[i](x[i]) # conv [b,c,h,w]->[b,no*na,h,w](3个特征图对应的通道数c=128,256,512)
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bs, _, ny, nx = x[i].shape #获取特征图形状 (以20×20为例) x(bs,255（no*na）,20,20) to x(bs,3,20,20,85)
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x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
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#view 的作用：将通道展开为 (na, no) 两个维度 1 1 1 1 1 80
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# permute 的作用：调整维度顺序，变换为： (B,na,H,W,no) no：每个锚框的输出维度 (x, y, w, h, confidence, classes)。
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#由于 permute 操作可能导致内存不连续，调用 contiguous 保证内存布局是连续的，便于后续计算。
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if not self.training: # inference
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if self.grid[i].shape[2:4] != x[i].shape[2:4]: #nx ny.第 2 和第 3 维度的形状。 x[i].shape[2] 是 height ; x[i].shape[3] 是 width。
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self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
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y = x[i].sigmoid() #将输入值压缩到 (0,1) 范围
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#print("original:",y[..., 0:2])
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#print("grid:",self.grid[i])
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y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
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#print("past:", y[..., 0:2])
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#print("--------------")
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y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
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z.append(y.view(bs, -1, self.no)) #预测结果 y 添加到结果列表 z 中
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#对特征图P3 P4 P5 [(bs,3×nx3×ny3,no),(bs,3×nx4×ny4,no),(bs,3×nx5×ny5,no)] (我认为维度和cat拼接之后一样，只不过拼接之前是 3个变量 在列表中 分别放置)
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# ->cat(z,1) (bs,3×nx3×ny3+3×nx4×ny4+3×nx5×ny5,no) (0维度，1维度，2维度，3维度)
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return x if self.training else (torch.cat(z, 1), x) # x(bs,3,20,20,85)
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# 计算xy wh 不是 反向复原
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#通过∗2，将范围扩展为[0, 2]：允许偏移点超出当前网格（超出范围的部分可能跨网格预测）。
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# 通过−0.5，将范围进一步调整为[−0.5,1.5]：引入负值，允许偏移点向左上角移动（跨网格预测）。 同时保留正值，允许点向右下角偏移（覆盖更大区域）。
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@staticmethod
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def _make_grid(nx=20, ny=20): #它的主要作用是创建一个 S×S 的二维网格（在每个网格中，存储该网格左上角的绝对坐标），供网络输出的偏移量加以利用。
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yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
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return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
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class Model(nn.Module):
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def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
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super(Model, self).__init__()
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if isinstance(cfg, dict):
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self.yaml = cfg # model dict
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else: # is *.yaml
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import yaml # for torch hub
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self.yaml_file = Path(cfg).name
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with open(cfg) as f:
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self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict
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# Define model
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ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
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if nc and nc != self.yaml['nc']:
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logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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self.yaml['nc'] = nc # override yaml value
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if anchors:
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logger.info(f'Overriding model.yaml anchors with anchors={anchors}')
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self.yaml['anchors'] = round(anchors) # override yaml value
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self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
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self.names = [str(i) for i in range(self.yaml['nc'])] # default names
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# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
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# Build strides, anchors
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m = self.model[-1] # Detect()
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if isinstance(m, Detect):
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s = 256 # 2x min stride
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m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
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m.anchors /= m.stride.view(-1, 1, 1)
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check_anchor_order(m)
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self.stride = m.stride
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self._initialize_biases() # only run once
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# print('Strides: %s' % m.stride.tolist())
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# Init weights, biases
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initialize_weights(self)
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self.info()
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logger.info('')
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def forward(self, x, augment=False, profile=False):
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if augment:
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img_size = x.shape[-2:] # height, width
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s = [1, 0.83, 0.67] # scales
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f = [None, 3, None] # flips (2-ud, 3-lr)
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y = [] # outputs
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for si, fi in zip(s, f):
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xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
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yi = self.forward_once(xi)[0] # forward
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# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
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yi[..., :4] /= si # de-scale
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if fi == 2:
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yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud
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elif fi == 3:
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yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr
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y.append(yi)
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return torch.cat(y, 1), None # augmented inference, train
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else:
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return self.forward_once(x, profile) # single-scale inference, train
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def forward_once(self, x, profile=False):
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y, dt = [], [] # outputs
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for m in self.model:
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if m.f != -1: # if not from previous layer
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x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
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if profile:
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o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS
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t = time_synchronized()
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for _ in range(10):
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_ = m(x)
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dt.append((time_synchronized() - t) * 100)
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print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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if profile:
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print('%.1fms total' % sum(dt))
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return x
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def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
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# https://arxiv.org/abs/1708.02002 section 3.3
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# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
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m = self.model[-1] # Detect() module
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for mi, s in zip(m.m, m.stride): # from
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b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
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b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
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b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
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mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
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def _print_biases(self):
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m = self.model[-1] # Detect() module
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for mi in m.m: # from
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b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
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print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
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# def _print_weights(self):
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# for m in self.model.modules():
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# if type(m) is Bottleneck:
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# print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
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def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
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print('Fusing layers... ')
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for m in self.model.modules():
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if type(m) is Conv and hasattr(m, 'bn'):
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m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
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delattr(m, 'bn') # remove batchnorm
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m.forward = m.fuseforward # update forward
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self.info()
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return self
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def nms(self, mode=True): # add or remove NMS module
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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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def autoshape(self): # add autoShape module
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print('Adding autoShape... ')
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m = autoShape(self) # wrap model
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copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
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return m
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def info(self, verbose=False, img_size=640): # print model information
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model_info(self, verbose, img_size)
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def parse_model(d, ch): # model_dict, input_channels(3)
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logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
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anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
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na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
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no = na * (nc + 5) # number of outputs = anchors * (classes + 5)(5=4(x,y,widyh,heigh)+1(置信度))
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layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
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for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
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m = eval(m) if isinstance(m, str) else m # eval strings
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for j, a in enumerate(args):
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try:
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args[j] = eval(a) if isinstance(a, str) else a # eval strings
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except:
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pass
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n = max(round(n * gd), 1) if n > 1 else n # depth gain
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if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
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C3, C3TR]:
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c1, c2 = ch[f], args[0]
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if c2 != no: # if not output
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c2 = make_divisible(c2 * gw, 8)
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args = [c1, c2, *args[1:]]
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if m in [BottleneckCSP, C3, C3TR]:
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args.insert(2, n) # number of repeats
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n = 1
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elif m is nn.BatchNorm2d:
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args = [ch[f]]
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elif m is Concat:
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c2 = sum([ch[x] for x in f])
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elif m is Detect:
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args.append([ch[x] for x in f])
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if isinstance(args[1], int): # number of anchors
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args[1] = [list(range(args[1] * 2))] * len(f)
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elif m is Contract:
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c2 = ch[f] * args[0] ** 2
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elif m is Expand:
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c2 = ch[f] // args[0] ** 2
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else:
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c2 = ch[f]
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m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
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t = str(m)[8:-2].replace('__main__.', '') # module type
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np = sum([x.numel() for x in m_.parameters()]) # number params
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m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
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logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
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save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
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layers.append(m_)
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if i == 0:
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ch = []
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ch.append(c2)
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return nn.Sequential(*layers), sorted(save)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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opt = parser.parse_args()
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opt.cfg = check_file(opt.cfg) # check file
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set_logging()
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device = select_device(opt.device)
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# Create model
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model = Model(opt.cfg).to(device)
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model.train()
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# Profile
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# img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
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# y = model(img, profile=True)
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# Tensorboard
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# from torch.utils.tensorboard import SummaryWriter
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# tb_writer = SummaryWriter()
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# print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
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# tb_writer.add_graph(model.model, img) # add model to tensorboard
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# tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard
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