102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
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"""
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将yopo模型转换为Tensorrt
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prepare:
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1 pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
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2 git clone https://github.com/NVIDIA-AI-IOT/torch2trt
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cd torch2trt
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python setup.py install
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"""
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import argparse
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import os
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import numpy as np
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import torch
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from torch2trt import torch2trt
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from flightgym import QuadrotorEnv_v1
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from ruamel.yaml import YAML, RoundTripDumper, dump
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from flightpolicy.envs import vec_env_wrapper as wrapper
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from flightpolicy.yopo.yopo_algorithm import YopoAlgorithm
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def prapare_input_observation(obs, lattice_space, lattice_primitive):
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obs_return = np.ones(
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(obs.shape[0], lattice_space.vertical_num, lattice_space.horizon_num, obs.shape[1]),
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dtype=np.float32)
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id = 0
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v_b = obs[:, 0:3]
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a_b = obs[:, 3:6]
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g_b = obs[:, 6:9]
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for i in range(lattice_space.vertical_num - 1, -1, -1):
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for j in range(lattice_space.horizon_num - 1, -1, -1):
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Rbp = lattice_primitive.getRotation(id)
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v_p = np.dot(Rbp.T, v_b.T).T
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a_p = np.dot(Rbp.T, a_b.T).T
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g_p = np.dot(Rbp.T, g_b.T).T
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obs_return[:, i, j, 0:3] = v_p
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obs_return[:, i, j, 3:6] = a_p
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obs_return[:, i, j, 6:9] = g_p
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id = id + 1
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obs_return = np.transpose(obs_return, [0, 3, 1, 2])
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return obs_return
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def parser():
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parser = argparse.ArgumentParser()
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parser.add_argument("--trial", type=int, default=1, help="trial number")
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parser.add_argument("--epoch", type=int, default=0, help="epoch number")
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parser.add_argument("--iter", type=int, default=0, help="iter number")
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parser.add_argument("--dir", type=str, default='yopo_trt.pth', help="output file name")
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return parser
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def main():
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args = parser().parse_args()
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# load configurations
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cfg = YAML().load(open(os.environ["FLIGHTMARE_PATH"] + "/flightlib/configs/vec_env.yaml", 'r'))
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cfg["env"]["num_envs"] = 1
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cfg["env"]["supervised"] = False
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cfg["env"]["imitation"] = False
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cfg["env"]["render"] = False
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# create environment
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train_env = QuadrotorEnv_v1(dump(cfg, Dumper=RoundTripDumper), False)
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train_env = wrapper.FlightEnvVec(train_env)
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model = YopoAlgorithm(env=train_env,
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policy_kwargs=dict(
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activation_fn=torch.nn.ReLU,
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net_arch=[256, 256],
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hidden_state=64
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))
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rsg_root = os.path.dirname(os.path.abspath(__file__))
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weight = rsg_root + "/saved/YOPO_{}/Policy/epoch{}_iter{}.pth".format(args.trial, args.epoch, args.iter)
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device = torch.device("cuda")
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saved_variables = torch.load(weight, map_location=device)
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model.policy.load_state_dict(saved_variables["state_dict"], strict=False)
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model.policy.set_training_mode(False)
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lattice_space = saved_variables["data"]["lattice_space"]
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lattice_primitive = saved_variables["data"]["lattice_primitive"]
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# The inputs should be consistent with training
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depth = np.zeros(shape=[1, 1, 96, 160], dtype=np.float32)
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obs = np.zeros(shape=[1, 9], dtype=np.float32)
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obs_input = prapare_input_observation(obs, lattice_space, lattice_primitive)
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depth_in = torch.from_numpy(depth).cuda()
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obs_in = torch.from_numpy(obs_input).cuda()
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model_trt = torch2trt(model.policy, [depth_in, obs_in])
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torch.save(model_trt.state_dict(), args.dir)
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# from torch2trt import TRTModule
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# model_trt = TRTModule()
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# model_trt.load_state_dict(torch.load('yopo_trt.pth'))
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y_trt = model_trt(depth_in, obs_in)
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y = model.policy(depth_in, obs_in)
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error = torch.mean(torch.abs(y - y_trt))
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print("transfer error: ", error)
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if __name__ == "__main__":
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main()
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