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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters for low-augmentation COCO training from scratch
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials


lr0: 0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
# lr0: 0.0002 # initial learning rate (SGD=1E-2, Adam=1E-3)
# lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)

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# ... some modules and imports is not showing here
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# REPAIR implementations
def is_batchnorm(module): # 
    return isinstance(module, nn.modules.batchnorm._BatchNorm)

# if we need to do with rotation?
# basic implementation

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import math
from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F

from torchviz import make_dot

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