Closed Nr-rN closed 2 years ago
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Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt
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You don't need to modify any code to use detect.py, you just train a model and then use it directly:
python train.py
python detect.py --weights runs/train/exp/weights/best.pt
@glenn-jocher
Thank you for answering my question, but I failed to express my question clearly.
i want use ckpt = {.... ,'model':model.state_dict(), .....}
to save model
and load by
model = Model()
model.load_state_dict(torch.load(ckpt))
because in detect.py
line 82 attempt_load(weights, map_location=device)
if save model with ckpt = {.... ,'model': deepcopy(de_parallel(model)).half(), .....}
weight will contain the serialized data is bound to the specific classes and the exact directory structure used
I get Erroe:
File "/opt/det.py", line 3, in model = torch.load('./weights/best.pt',map_location='cpu') File "/home/tian/.conda/envs/jiqi/lib/python3.6/site-packages/torch/serialization.py", line 368, in load return _load(f, map_location, pickle_module) File "/home/tian/.conda/envs/jiqi/lib/python3.6/site-packages/torch/serialization.py", line 542, in _load result = unpickler.load() ModuleNotFoundError: No module named 'models'
When I see the same problem:
https://github.com/pytorch/pytorch/issues/18325
i user torch.jit.trace(model,torch.tensor(torch.rand(size=(1,3,640,640)))
but it not work
ERROR with:
TracingCheckError: Tracing failed sanity checks! ERROR: Graphs differed across invocations! Graph diff: graph(%self.1 : torch.models.yolo.Model, %x : Tensor): %2 : torch.torch.nn.modules.container.Sequential = prim::GetAttrname="model" %3 : torch.models.yolo.Detect = prim::GetAttrname="24" %4 : torch.torch.nn.modules.container.Sequential = prim::GetAttrname="model" %5 : torch.models.common.C3 = prim::GetAttrname="23" ... ... ... Comparison exception: expected tensor shape torch.Size([1, 1, 40, 40, 2]) doesn't match with actual tensor shape torch.Size([])!
now,i use this method ,like:
m = MyModule() m.state_dict()
OrderedDict([('l0.weight', tensor([[ 0.1400, 0.4563, -0.0271, -0.4406], [-0.3289, 0.2827, 0.4588, 0.2031]])), ('l0.bias', tensor([ 0.0300, -0.1316])), ('l1.weight', tensor([[0.6533, 0.3413]])), ('l1.bias', tensor([-0.1112]))])
torch.save(m.state_dict(), 'mymodule.pt') m_state_dict = torch.load('mymodule.pt') new_m = MyModule() new_m.load_state_dict(m_state_dict)
When I use the new loading method for reasoning, the result is worse than the original loading method
@Nr-rN we don't assist in debugging custom code. Inference examples are shown for detect.py and PyTorch Hub in the README.
If you find a reproducible bug in the unmodified repository code please raise a separate bug report.
π Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
Access additional YOLOv5 π resources:
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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLOv5 π and Vision AI β!
@glenn-jocher Thank you for answering my question, but I failed to express my question clearly. i want use
ckpt = {.... ,'model':model.state_dict(), .....}
to save model and load bymodel = Model()
model.load_state_dict(torch.load(ckpt))
because in
detect.py
line 82attempt_load(weights, map_location=device)
if save model withckpt = {.... ,'model': deepcopy(de_parallel(model)).half(), .....}
weight will contain the serialized data is bound to the specific classes and the exact directory structure usedI get Erroe:
File "/opt/det.py", line 3, in model = torch.load('./weights/best.pt',map_location='cpu') File "/home/tian/.conda/envs/jiqi/lib/python3.6/site-packages/torch/serialization.py", line 368, in load return _load(f, map_location, pickle_module) File "/home/tian/.conda/envs/jiqi/lib/python3.6/site-packages/torch/serialization.py", line 542, in _load result = unpickler.load() ModuleNotFoundError: No module named 'models'
When I see the same problem: https://github.com/pytorch/pytorch/issues/18325 i user
torch.jit.trace(model,torch.tensor(torch.rand(size=(1,3,640,640)))
but it not work ERROR with:TracingCheckError: Tracing failed sanity checks! ERROR: Graphs differed across invocations! Graph diff: graph(%self.1 : torch.models.yolo.Model, %x : Tensor): %2 : torch.torch.nn.modules.container.Sequential = prim::GetAttrname="model" %3 : torch.models.yolo.Detect = prim::GetAttrname="24" %4 : torch.torch.nn.modules.container.Sequential = prim::GetAttrname="model" %5 : torch.models.common.C3 = prim::GetAttrname="23" ... ... ... Comparison exception: expected tensor shape torch.Size([1, 1, 40, 40, 2]) doesn't match with actual tensor shape torch.Size([])!
now,i use this method ,like:
m = MyModule() m.state_dict()
OrderedDict([('l0.weight', tensor([[ 0.1400, 0.4563, -0.0271, -0.4406], [-0.3289, 0.2827, 0.4588, 0.2031]])), ('l0.bias', tensor([ 0.0300, -0.1316])), ('l1.weight', tensor([[0.6533, 0.3413]])), ('l1.bias', tensor([-0.1112]))])
torch.save(m.state_dict(), 'mymodule.pt') m_state_dict = torch.load('mymodule.pt') new_m = MyModule() new_m.load_state_dict(m_state_dict)
When I use the new loading method for reasoning, the result is worse than the original loading method
hello, how about your problem? solve it?
π Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
Access additional YOLOv5 π resources:
Access additional Ultralytics β‘ resources:
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLOv5 π and Vision AI β!
@zcunyi how about your problem? solve it?
βQuestion
In train.py line 373:
ckpt = {'epoch': epoch, 'best_fitness': best_fitness, 'model': deepcopy(de_parallel(model)).half(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None}
# Save last, best and delete
torch.save(ckpt, last)
change:
ckpt = {'epoch': epoch, 'best_fitness': best_fitness, 'model': model.state_dict(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None}
# Save last, best and delete
torch.save(ckpt, last)
`Use the code to inference(detect):
from models.yolo import Model
......
#loading model
model = Model().to(device).eval()
ckpt = torch.load('xxxxxx.pt')
model.load_state_dict(ckpt['model'], strict=False)
....
pred = model(img)
**I can't get rigtht results 'pred' by dectct.py, I don't know where the problem is, I guess: