# """
classes = ["person"]
preddataPath = r"D:\practice\datas\PennFudanPed\PNGImages"
testdataPath = None
traindataPath = r"D:\practice\datas\PennFudanPed"
typeOfData = "PennFudanDataset"
"""
classes = ["bicycle", "bus", "car", "motorbike", "person"]
testdataPath = "/home/wucong/practise/datas/valid/PNGImages/"
traindataPath = "/home/wucong/practise/datas/VOCdevkit/"
typeOfData = "PascalVOCDataset"
# """
basePath = "./models/"
resize = (416,416)
cfg = config.get_cfg()
cfg["work"]["dataset"]["trainDataPath"] = traindataPath
cfg["work"]["dataset"]["testDataPath"] = testdataPath
cfg["work"]["dataset"]["predDataPath"] = preddataPath
cfg["work"]["dataset"]["typeOfData"] = typeOfData
cfg["work"]["save"]["basePath"] = basePath
cfg["network"]["backbone"]["model_name"] = "resnet18"
cfg["network"]["backbone"]["pretrained"] = True
cfg["work"]["train"]["resize"] = resize
cfg["work"]["train"]["epochs"] = 50
cfg["work"]["train"]["classes"] = classes
cfg["work"]["train"]["useImgaug"] = True
cfg["work"]["train"]["version"] = "v2"
cfg["work"]["train"]["method"] = 1
cfg["network"]["backbone"]["freeze_at"] = "res2"
cfg["network"]["RPN"]["num_boxes"] = 6 # 2
def main():
cfg = get_cfg()
args = get_args()
tetris = Tetris(cfg)
window = Window(tetris, cfg, args)
window.mainloop()
from dqn.train import Train
from config.config import get_cfg
if __name__ == '__main__':
cfg = get_cfg()
dqn = Train(cfg)
dqn.run()
torch.save(flappy_env.agent.brain.model.state_dict(), 'weight.pth')