RuntimeError: The size of tensor a (1024) must match the size of tensor b (128) at non-singleton dimension 1

[mizhitian-xiaomi]

I use train_dual.py with models/detect/yolov9-e.yaml cfg file to train a model.
when i use tools/reparameterization.ipynb sample, it occurred a runtime error: RuntimeError: The size of tensor a (1024) must match the size of tensor b (128) at non-singleton dimension 1 .
Please help me. How to solve it? What did I do wrong?

My code is follow :

import torch
import os, sys
sys.path.insert(0, os.getcwd())
from models.yolo import Model


cfg = "./models/detect/yolov9-e.yaml"
model = Model(cfg, ch=3, nc=18, anchors=3)

#model = model.half()
device = torch.device("cpu")
model = model.to(device)
_ = model.eval()
ckpt = torch.load('runs/train/yolov9-e_0417/weights/best.pt', map_location='cpu')

model.names = ckpt['model'].names
model.nc = ckpt['model'].yaml["nc"]


idx = 0
for k, v in model.state_dict().items():
    if "model.{}.".format(idx) in k:
        if idx < 29:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif idx < 42:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
    else:
        while True:
            idx += 1
            if "model.{}.".format(idx) in k:
                break
        if idx < 29:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif idx < 42:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
_ = model.eval()


m_ckpt = {'model': model.half(),
          'optimizer': None,
          'best_fitness': None,
          'ema': None,
          'updates': None,
          'opt': None,
          'git': None,
          'date': None,
          'epoch': -1}
torch.save(m_ckpt, "runs/train/yolov9-e_0417/weights/best_converted.pt")

the error:

[tuansunday05]

Did you solve this issue @mizhitian-xiaomi ?

[tuansunday05]

I use train_dual.py with models/detect/yolov9-e.yaml cfg file to train a model. when i use tools/reparameterization.ipynb sample, it occurred a runtime error: RuntimeError: The size of tensor a (1024) must match the size of tensor b (128) at non-singleton dimension 1 . Please help me. How to solve it? What did I do wrong?

My code is follow :

import torch
import os, sys
sys.path.insert(0, os.getcwd())
from models.yolo import Model


cfg = "./models/detect/yolov9-e.yaml"
model = Model(cfg, ch=3, nc=18, anchors=3)

#model = model.half()
device = torch.device("cpu")
model = model.to(device)
_ = model.eval()
ckpt = torch.load('runs/train/yolov9-e_0417/weights/best.pt', map_location='cpu')

model.names = ckpt['model'].names
model.nc = ckpt['model'].yaml["nc"]


idx = 0
for k, v in model.state_dict().items():
    if "model.{}.".format(idx) in k:
        if idx < 29:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif idx < 42:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
    else:
        while True:
            idx += 1
            if "model.{}.".format(idx) in k:
                break
        if idx < 29:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif idx < 42:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
_ = model.eval()


m_ckpt = {'model': model.half(),
          'optimizer': None,
          'best_fitness': None,
          'ema': None,
          'updates': None,
          'opt': None,
          'git': None,
          'date': None,
          'epoch': -1}
torch.save(m_ckpt, "runs/train/yolov9-e_0417/weights/best_converted.pt")

the error:

You should use the gelan-e.yaml for the configuration of converting yolov9-e. So it's must be cfg = "./models/detect/gelan-e.yaml"