为了适配C++工程代码，我在导出onnx模型时，会把models/yolo.py里面的forward函数改成下面这样，
python
#转模型
def forward(self, x):
z = [] # inference output
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if not self.training: # inference
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
y = x[i].sigmoid()
if self.inplace:
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
z.append(y.view(bs, -1, self.no))
else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
anchor, conf, prob = torch.split(y, [4, 1, self.nc], dim=4)
# add a idx (label ids before prob)
# ori
idxs = torch.argmax(prob, dim=-1).unsqueeze(axis=-1).type(x[i].dtype)
# new
#idxs = torch.max(prob, dim=-1)[1].data.unsqueeze(axis=-1).type(x[i].dtype)
y = torch.cat((xy, wh, conf, idxs, prob), -1)
z.append(y.view(bs, -1, self.no + 1))
return x if self.training else (torch.cat(z, 1))也就是把后面类别得分中最大的那个计算出来赋值给idxs，
原来的yolov5输出是x y w h box_score label1_confidence label2_confidence .... labeln_confidence.
我改完之后，输出变成x y w h box_score idxs label1_confidence label2_confidence .... labeln_confidence.
然后之前我都是在转onnx之前手动的去改代码，然后转完模型再改回来因为train和detect也要用到这个yolo.py中的forward函数，但是后来某项目中，要实现一个自动训练、自动检测、自动转模型，这就不能我手动改了，所以我第一个方法是我复制一份yolo.py复制成yolo_onnx.py，然后export.py中from models.yolo_onnx import Detect，这种方法不可行，因为其他还有还有很多地方也是用的from models.yolo import Detect，最后用的方法如下：
首先在yolo.py中的Detect类中增加一个成员export
python
class Detect(nn.Module):
stride = None # strides computed during build
onnx_dynamic = False # ONNX export parameter
export = False #增加的成员
......然后我在export.py的run函数中给这个值赋值为true
python
@torch.no_grad()
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
weights=ROOT / 'yolov5s.pt', # weights path
imgsz=(640, 640), # image (height, width)
batch_size=1, # batch size
device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
include=('torchscript', 'onnx'), # include formats
half=False, # FP16 half-precision export
inplace=False, # set YOLOv5 Detect() inplace=True
train=False, # model.train() mode
optimize=False, # TorchScript: optimize for mobile
int8=False, # CoreML/TF INT8 quantization
dynamic=False, # ONNX/TF: dynamic axes
simplify=False, # ONNX: simplify model
opset=12, # ONNX: opset version
verbose=False, # TensorRT: verbose log
workspace=4, # TensorRT: workspace size (GB)
nms=False, # TF: add NMS to model
agnostic_nms=False, # TF: add agnostic NMS to model
topk_per_class=100, # TF.js NMS: topk per class to keep
topk_all=100, # TF.js NMS: topk for all classes to keep
iou_thres=0.45, # TF.js NMS: IoU threshold
conf_thres=0.25 # TF.js NMS: confidence threshold
):
t = time.time()
include = [x.lower() for x in include] # to lowercase
formats = tuple(export_formats()['Argument'][1:]) # --include arguments
flags = [x in include for x in formats]
assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {formats}'
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags # export booleans
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights
# Load PyTorch model
device = select_device(device)
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
nc, names = model.nc, model.names # number of classes, class names
model.model[-1].export = True
# Checks
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12
assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'
# Input
gs = int(max(model.stride)) # grid size (max stride)
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
# Update model
if half:
im, model = im.half(), model.half() # to FP16
model.train() if train else model.eval() # training mode = no Detect() layer grid construction
for k, m in model.named_modules():
if isinstance(m, Conv): # assign export-friendly activations
if isinstance(m.act, nn.SiLU):
m.act = SiLU()
elif isinstance(m, Detect):
m.inplace = inplace
m.onnx_dynamic = dynamic
if hasattr(m, 'forward_export'):
m.forward = m.forward_export # assign custom forward (optional)
for _ in range(2):
y = model(im) # dry runs
shape = tuple(y[0].shape) # model output shape
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
# Exports
f = [''] * 10 # exported filenames
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
if jit:
f[0] = export_torchscript(model, im, file, optimize)
if engine: # TensorRT required before ONNX
f[1] = export_engine(model, im, file, train, half, simplify, workspace, verbose)
if onnx or xml: # OpenVINO requires ONNX
f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
if xml: # OpenVINO
f[3] = export_openvino(model, im, file)
if coreml:
_, f[4] = export_coreml(model, im, file)
# TensorFlow Exports
if any((saved_model, pb, tflite, edgetpu, tfjs)):
if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
model, f[5] = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class,
topk_all=topk_all, conf_thres=conf_thres, iou_thres=iou_thres) # keras model
if pb or tfjs: # pb prerequisite to tfjs
f[6] = export_pb(model, im, file)
if tflite or edgetpu:
f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100)
if edgetpu:
f[8] = export_edgetpu(model, im, file)
if tfjs:
f[9] = export_tfjs(model, im, file)
# Finish
f = [str(x) for x in f if x] # filter out '' and None
if any(f):
LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f"\nDetect: python detect.py --weights {f[-1]}"
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')"
f"\nValidate: python val.py --weights {f[-1]}"
f"\nVisualize: https://netron.app")
return f # return list of exported files/dirs然后修改yolo.py中的forward函数，增加分支判断
python
def forward(self, x):
if self.export:
print("self.export===============",self.export)
z = [] # inference output
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if not self.training: # inference
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
y = x[i].sigmoid()
if self.inplace:
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
z.append(y.view(bs, -1, self.no))
else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
anchor, conf, prob = torch.split(y, [4, 1, self.nc], dim=4)
# add a idx (label ids before prob)
# ori
idxs = torch.argmax(prob, dim=-1).unsqueeze(axis=-1).type(x[i].dtype)
# new
#idxs = torch.max(prob, dim=-1)[1].data.unsqueeze(axis=-1).type(x[i].dtype)
y = torch.cat((xy, wh, conf, idxs, prob), -1)
z.append(y.view(bs, -1, self.no + 1))
return x if self.training else (torch.cat(z, 1))
else:
print("self.export===============",self.export)
z = [] # inference output
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if not self.training: # inference
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
y = x[i].sigmoid()
if self.inplace:
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
y = torch.cat((xy, wh, y[..., 4:]), -1)
z.append(y.view(bs, -1, self.no))
return x if self.training else (torch.cat(z, 1), x)这样就可以实现train和export分别跑不同的代码了。