Hello!
I’ve already asked this question on yolov7 repository
but I’m hoping to get more answers here 
In short, I’m trying to convert a yolo model trained on pytorch to the original darknet format.
Is anyone already attempted the conversion?
My script is below
import numpy as np
import torch
from models.yolo import Detect, Model
def load_cfg_pt(
cfg_path: str,
pt_path: str,
fuse: bool = False,
) -> tuple[dict, Model]:
with open(cfg_path, "r") as f:
cfg_lines = f.readlines()
# skip batch norm layers
if fuse:
layers = [
line.strip()
for line in cfg_lines
if line.strip() in ("[convolutional]", "[yolo]")
]
else:
layers = []
for i, line in enumerate(cfg_lines):
line = line.strip()
if line == "[yolo]":
layers.append(line)
elif line == "[convolutional]":
if cfg_lines[i + 1].strip() == "batch_normalize=1":
layers.append("[bn]")
else:
layers.append(line)
ckpt = torch.load(pt_path, map_location=torch.device('cpu'))
model = ckpt.get('model').float()
if fuse:
model.fuse()
model.eval()
return layers, model
def numpize(t: torch.Tensor) -> np.ndarray:
return t.data.cpu().detach().numpy()
if __name__ == "__main__":
cfg_path = "tinyv7.cfg"
pt_path = "yolov7-tiny.pt"
fuse = False
if not fuse:
print("Keep batch_norm layers!")
arch, model = load_cfg_pt(cfg_path, pt_path, fuse)
conv_b, conv_w = [], []
bn_b, bn_w, bn_m, bn_v = [], [], [], []
yolo_b, yolo_w = [], []
for i, (_, module) in enumerate(model.named_modules()):
# skip Model and Sequential
if i < 2:
continue
# parse layer-by-layer
isconv = isinstance(module, torch.nn.modules.conv.Conv2d)
isyolo = isinstance(module, Detect)
isbn = isinstance(module, torch.nn.modules.batchnorm.BatchNorm2d)
if not isconv and not isyolo and not isbn:
continue
if isyolo:
for j, (_, yolo_layer) in enumerate(module.named_modules()):
# skip Detect and ModuleList
if j < 2:
continue
# parse layer-by-layer
yolo_b.append(numpize(yolo_layer.bias))
yolo_w.append(numpize(yolo_layer.weight))
elif isconv:
if isinstance(module.bias, torch.Tensor):
conv_b.append(numpize(module.bias))
conv_w.append(numpize(module.weight))
else: # isbn
bn_b.append(numpize(module.bias))
bn_w.append(numpize(module.weight))
bn_m.append(numpize(module.running_mean))
bn_v.append(numpize(module.running_var))
# to fifo with pop()
conv_w.reverse()
conv_b.reverse()
bn_w.reverse()
bn_b.reverse()
bn_m.reverse()
bn_v.reverse()
yolo_w.reverse()
yolo_b.reverse()
with open(f'darknet{"_nobn"*fuse}.weights', "wb") as f:
# header
np.array([0, 2, 5, 0, 0], dtype=np.int32).tofile(f)
# https://github.com/AlexeyAB/darknet/issues/2587#issuecomment-500895636
for v in arch:
print(v)
if v == "[yolo]":
yolo_b.pop().tofile(f)
yolo_w.pop().tofile(f)
elif v == "[bn]":
bn_b.pop().tofile(f)
bn_w.pop().tofile(f)
bn_m.pop().tofile(f)
bn_v.pop().tofile(f)
conv_w.pop().tofile(f)
else:
conv_b.pop().tofile(f)
conv_w.pop().tofile(f)
this doesn’t give the right weights as the final binary size is larger than what it should be.
any idea?