├── .gitignore ├── LICENCE ├── README.md ├── build.py ├── cost_model └── .gitignore ├── m_files ├── dla_f.m ├── dpt_f.m ├── eye_2_f.m ├── eye_f.m └── shi_f.m ├── my_stable_baselines ├── README.md ├── __init__.py ├── a2c │ ├── __init__.py │ ├── a2c.py │ ├── run_atari.py │ └── utils.py ├── acer │ ├── __init__.py │ ├── acer_simple.py │ ├── buffer.py │ └── run_atari.py ├── acktr │ ├── __init__.py │ ├── acktr.py │ ├── kfac.py │ ├── kfac_utils.py │ └── run_atari.py ├── bench │ ├── __init__.py │ └── monitor.py ├── common │ ├── __init__.py │ ├── atari_wrappers.py │ ├── base_class.py │ ├── bit_flipping_env.py │ ├── buffers.py │ ├── callbacks.py │ ├── cg.py │ ├── cmd_util.py │ ├── console_util.py │ ├── dataset.py │ ├── distributions.py │ ├── env_checker.py │ ├── evaluation.py │ ├── identity_env.py │ ├── input.py │ ├── math_util.py │ ├── misc_util.py │ ├── mpi_adam.py │ ├── mpi_moments.py │ ├── mpi_running_mean_std.py │ ├── noise.py │ ├── policies.py │ ├── runners.py │ ├── running_mean_std.py │ ├── save_util.py │ ├── schedules.py │ ├── segment_tree.py │ ├── tf_layers.py │ ├── tf_util.py │ ├── tile_images.py │ └── vec_env │ │ ├── __init__.py │ │ ├── base_vec_env.py │ │ ├── dummy_vec_env.py │ │ ├── subproc_vec_env.py │ │ ├── util.py │ │ ├── vec_check_nan.py │ │ ├── vec_frame_stack.py │ │ ├── vec_normalize.py │ │ └── vec_video_recorder.py ├── ddpg │ ├── __init__.py │ ├── ddpg.py │ ├── main.py │ ├── noise.py │ └── policies.py ├── deepq │ ├── __init__.py │ ├── build_graph.py │ ├── dqn.py │ ├── experiments │ │ ├── __init__.py │ │ ├── custom_cartpole.py │ │ ├── enjoy_cartpole.py │ │ ├── enjoy_mountaincar.py │ │ ├── run_atari.py │ │ ├── train_cartpole.py │ │ └── train_mountaincar.py │ ├── policies.py │ └── replay_buffer.py ├── gail │ ├── __init__.py │ ├── adversary.py │ ├── dataset │ │ ├── __init__.py │ │ ├── dataset.py │ │ └── record_expert.py │ └── model.py ├── her │ ├── __init__.py │ ├── her.py │ ├── replay_buffer.py │ └── utils.py ├── logger.py ├── ppo1 │ ├── __init__.py │ ├── pposgd_simple.py │ ├── run_atari.py │ ├── run_mujoco.py │ └── run_robotics.py ├── ppo2 │ ├── __init__.py │ ├── ppo2.py │ ├── run_atari.py │ └── run_mujoco.py ├── py.typed ├── results_plotter.py ├── sac │ ├── __init__.py │ ├── policies.py │ └── sac.py ├── td3 │ ├── __init__.py │ ├── policies.py │ └── td3.py └── trpo_mpi │ ├── __init__.py │ ├── run_atari.py │ ├── run_mujoco.py │ ├── trpo_mpi.py │ └── utils.py ├── requirements.txt ├── run ├── runGA.sh ├── runRL.sh └── run_blackbox.sh ├── src ├── cost_model_env.py ├── main_ga.py ├── main_ng.py ├── main_rl.py ├── scheuler_GA.py ├── scheuler_rl_AB.py ├── setting.py └── utils.py ├── traffic ├── batch1 │ ├── traffic0_m.csv │ ├── traffic1_m.csv │ ├── traffic2_m.csv │ ├── traffic3_m.csv │ ├── traffic4_m.csv │ ├── traffic5_m.csv │ ├── traffic6_m.csv │ ├── traffic7_m.csv │ ├── traffic8_m.csv │ └── traffic9_m.csv ├── batch2 │ ├── traffic0_m.csv │ ├── traffic1_m.csv │ ├── traffic2_m.csv │ ├── traffic3_m.csv │ ├── traffic4_m.csv │ ├── traffic5_m.csv │ ├── traffic6_m.csv │ ├── traffic7_m.csv │ ├── traffic8_m.csv │ └── traffic9_m.csv ├── batch3 │ ├── CONV │ │ ├── traffic0_m.csv │ │ ├── traffic1_m.csv │ │ ├── traffic2_m.csv │ │ ├── traffic3_m.csv │ │ ├── traffic4_m.csv │ │ ├── traffic5_m.csv │ │ ├── traffic6_m.csv │ │ ├── traffic7_m.csv │ │ ├── traffic8_m.csv │ │ └── traffic9_m.csv │ ├── FNN │ │ ├── traffic0_m.csv │ │ ├── traffic1_m.csv │ │ ├── traffic2_m.csv │ │ ├── traffic3_m.csv │ │ ├── traffic4_m.csv │ │ ├── traffic5_m.csv │ │ ├── traffic6_m.csv │ │ ├── traffic7_m.csv │ │ ├── traffic8_m.csv │ │ └── traffic9_m.csv │ └── GEMM │ │ ├── traffic0_m.csv │ │ ├── traffic1_m.csv │ │ ├── traffic2_m.csv │ │ ├── traffic3_m.csv │ │ ├── traffic4_m.csv │ │ ├── traffic5_m.csv │ │ ├── traffic6_m.csv │ │ ├── traffic7_m.csv │ │ ├── traffic8_m.csv │ │ └── traffic9_m.csv └── gen_traffic_v1.py └── traffic_insts ├── batch1 ├── description │ ├── insts0.csv │ ├── insts1.csv │ ├── insts10.csv │ ├── insts2.csv │ ├── insts3.csv │ ├── insts4.csv │ ├── insts5.csv │ ├── insts6.csv │ ├── insts7.csv │ ├── insts8.csv │ └── insts9.csv ├── insts1.csv ├── insts10.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── batch2 ├── description │ ├── insts0.csv │ ├── insts1.csv │ ├── insts2.csv │ ├── insts3.csv │ ├── insts4.csv │ ├── insts5.csv │ ├── insts6.csv │ ├── insts7.csv │ ├── insts8.csv │ └── insts9.csv ├── insts0.csv ├── insts1.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── batch3 ├── description │ ├── insts0.csv │ ├── insts1.csv │ ├── insts2.csv │ ├── insts3.csv │ ├── insts4.csv │ ├── insts5.csv │ ├── insts6.csv │ ├── insts7.csv │ ├── insts8.csv │ └── insts9.csv ├── insts0.csv ├── insts1.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── batch_conv ├── insts0.csv ├── insts1.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── batch_lang ├── insts0.csv ├── insts1.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── batch_mix ├── insts0.csv ├── insts1.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── batch_mix_combine ├── insts0.csv └── insts1.csv ├── batch_random ├── insts0.csv └── insts1.csv ├── batch_recom ├── insts0.csv ├── insts1.csv ├── insts2.csv ├── insts3.csv ├── insts4.csv ├── insts5.csv ├── insts6.csv ├── insts7.csv ├── insts8.csv └── insts9.csv ├── create_insts.py └── try_insts0.csv /.gitignore: -------------------------------------------------------------------------------- 1 | # These are some examples of commonly ignored file patterns. 2 | # You should customize this list as applicable to your project. 3 | # Learn more about .gitignore: 4 | # https://www.atlassian.com/git/tutorials/saving-changes/gitignore 5 | 6 | # Node artifact files 7 | node_modules/ 8 | dist/ 9 | 10 | # Compiled Java class files 11 | *.class 12 | 13 | # Compiled Python bytecode 14 | *.py[cod] 15 | 16 | # Log files 17 | *.log 18 | 19 | # Package files 20 | *.jar 21 | 22 | # Maven 23 | target/ 24 | dist/ 25 | 26 | # JetBrains IDE 27 | .idea/ 28 | 29 | # Unit test reports 30 | TEST*.xml 31 | 32 | # Generated by MacOS 33 | .DS_Store 34 | 35 | # Generated by Windows 36 | Thumbs.db 37 | 38 | # Applications 39 | *.app 40 | *.exe 41 | *.war 42 | 43 | # Large media files 44 | *.mp4 45 | *.tiff 46 | *.avi 47 | *.flv 48 | *.mov 49 | *.wmv 50 | 51 | # others 52 | *.plt 53 | 54 | -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Sheng-Chun Kao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MAGMA # 2 | image 3 | 4 | This is the implementation of the paper [MAGMA](https://arxiv.org/abs/2104.13997), [youtube](https://www.youtube.com/watch?v=8ZwTBlAswGA&t=4s). 5 | 6 | ### Installation ### 7 | * Install requirement 8 | ``` 9 | pip install -r requirements.txt 10 | ``` 11 | * Download cost model and build symbolic link 12 | ``` 13 | python build.py 14 | ``` 15 | ### Example Usage ### 16 | * Run MAGMA: ``sh run/runGA.sh`` 17 | * Run RL: ``sh run/runRL.sh`` 18 | * Available RLs: A2C, ACKTR, PPO2, DQN, TRPO, ACER, SAC DDPG 19 | * Run Blackbox: ``sh run/run_blackbox.sh`` 20 | * Avaliable Blackbox: PSO, Portfolio, OnePlusOne,CMA, DE, NaiveTBPSA, cGA, CauchyLHSSearch, HaltonSearch, HammersleySearch, MetaRecentering 21 | 22 | ### Contributor ### 23 | * Sheng-Chun (Felix) Kao 24 | * Tushar Krishna 25 | 26 | ### Citation ### 27 | ``` 28 | @inproceedings{kao2022magma, 29 | title={MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores}, 30 | author={Kao, Sheng-Chun and Krishna, Tushar}, 31 | booktitle={2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)}, 32 | pages={814--830}, 33 | year={2022}, 34 | organization={IEEE} 35 | } 36 | 37 | ``` 38 | -------------------------------------------------------------------------------- /build.py: -------------------------------------------------------------------------------- 1 | import os, sys 2 | commit_id = 'e1d8efd8e5469cf865a9db60007a70e3f0cb8778' 3 | dst_path = "cost_model/maestro" 4 | maestro_dir = "../temp/maestro" 5 | working_path = os.getcwd() 6 | dst_path = os.path.join(working_path, dst_path) 7 | maestro = os.path.join(maestro_dir, "maestro") 8 | maestro = os.path.abspath(maestro) 9 | if os.path.exists(maestro_dir) is False: 10 | os.system("git clone https://github.com/maestro-project/maestro.git {}".format(maestro_dir)) 11 | os.chdir(maestro_dir) 12 | os.system(f"git checkout {commit_id}") 13 | try: 14 | os.system("scons") 15 | except: 16 | "Something wring when building maestro, please check maestro repository installation step" 17 | if os.path.exists(maestro) is False: 18 | os.chdir(maestro_dir) 19 | try: 20 | os.system("scons") 21 | except: 22 | "Something wring when building maestro, please check maestro repository installation step" 23 | os.chdir(working_path) 24 | if os.path.exists(dst_path) is False: 25 | os.symlink(maestro, dst_path) 26 | -------------------------------------------------------------------------------- /cost_model/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore -------------------------------------------------------------------------------- /m_files/dla_f.m: -------------------------------------------------------------------------------- 1 | Dataflow { 2 | SpatialMap(1,1) K; 3 | TemporalMap(ClusterSz,ClusterSz) C; 4 | TemporalMap(Sz(R),Sz(R)) R; 5 | TemporalMap(Sz(S),Sz(S)) S; 6 | TemporalMap(Sz(R),1) Y; 7 | TemporalMap(Sz(S),1) X; 8 | Cluster(ClusterSz, P); 9 | SpatialMap(1,1) C; 10 | } 11 | -------------------------------------------------------------------------------- /m_files/dpt_f.m: -------------------------------------------------------------------------------- 1 | Dataflow { 2 | SpatialMap(ClusterSz,ClusterSz) C; 3 | TemporalMap(Sz(R),1) Y; 4 | TemporalMap(Sz(S),1) X; 5 | TemporalMap(Sz(R),Sz(R)) R; 6 | TemporalMap(Sz(S),Sz(S)) S; 7 | } 8 | -------------------------------------------------------------------------------- /m_files/eye_2_f.m: -------------------------------------------------------------------------------- 1 | Dataflow { 2 | TemporalMap(1,1) C; 3 | TemporalMap(1,1) K; 4 | SpatialMap(Sz(R),1) Y; 5 | TemporalMap(Sz(S),1) X; 6 | TemporalMap(Sz(R),Sz(R)) R; 7 | TemporalMap(Sz(S),Sz(S)) S; 8 | Cluster(Sz(R), P); 9 | SpatialMap(1,1) Y; 10 | SpatialMap(1,1) R; 11 | 12 | } 13 | 14 | -------------------------------------------------------------------------------- /m_files/eye_f.m: -------------------------------------------------------------------------------- 1 | Dataflow { 2 | TemporalMap(1,1) C; 3 | TemporalMap(1,1) K; 4 | SpatialMap(ClusterSz,ClusterSz) Y'; 5 | TemporalMap(Sz(S),Sz(S)) X'; 6 | TemporalMap(Sz(R),Sz(R)) R; 7 | TemporalMap(Sz(S),Sz(S)) S; 8 | Cluster(ClusterSz, P); 9 | TemporalMap(1,1) C; 10 | SpatialMap(1,1) Y'; 11 | SpatialMap(1,1) X'; 12 | TemporalMap(Sz(R),Sz(R)) R; 13 | TemporalMap(Sz(S),Sz(S)) S; 14 | 15 | } 16 | 17 | -------------------------------------------------------------------------------- /m_files/shi_f.m: -------------------------------------------------------------------------------- 1 | Dataflow { 2 | 3 | TemporalMap(1,1) K; 4 | SpatialMap(Sz(R),Sz(R)) Y'; 5 | TemporalMap(ClusterSz,ClusterSz) X'; 6 | TemporalMap(1,1) C; 7 | TemporalMap(Sz(R),Sz(R)) R; 8 | TemporalMap(Sz(S),Sz(S)) S; 9 | Cluster(ClusterSz, P); 10 | TemporalMap(1,1) C; 11 | TemporalMap(1,1) Y'; 12 | SpatialMap(1,1) X'; 13 | TemporalMap(Sz(R),Sz(R)) R; 14 | TemporalMap(Sz(S),Sz(S)) S; 15 | } 16 | 17 | -------------------------------------------------------------------------------- /my_stable_baselines/README.md: -------------------------------------------------------------------------------- 1 | #### These files are modified from [stable-baseline](https://stable-baselines.readthedocs.io/en/master/) code base -------------------------------------------------------------------------------- /my_stable_baselines/__init__.py: -------------------------------------------------------------------------------- 1 | from my_stable_baselines.a2c import A2C 2 | from my_stable_baselines.acer import ACER 3 | from my_stable_baselines.acktr import ACKTR 4 | from my_stable_baselines.deepq import DQN 5 | from my_stable_baselines.her import HER 6 | from my_stable_baselines.ppo2 import PPO2 7 | from my_stable_baselines.td3 import TD3 8 | from my_stable_baselines.sac import SAC 9 | 10 | # Load mpi4py-dependent algorithms only if mpi is installed. 11 | try: 12 | import mpi4py 13 | except ImportError: 14 | mpi4py = None 15 | 16 | if mpi4py is not None: 17 | from my_stable_baselines.ddpg import DDPG 18 | from my_stable_baselines.gail import GAIL 19 | from my_stable_baselines.ppo1 import PPO1 20 | from my_stable_baselines.trpo_mpi import TRPO 21 | del mpi4py 22 | 23 | __version__ = "2.10.0" 24 | -------------------------------------------------------------------------------- /my_stable_baselines/a2c/__init__.py: -------------------------------------------------------------------------------- 1 | from my_stable_baselines.a2c.a2c import A2C 2 | -------------------------------------------------------------------------------- /my_stable_baselines/a2c/run_atari.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | from stable_baselines import logger, A2C 4 | from stable_baselines.common.cmd_util import make_atari_env, atari_arg_parser 5 | from stable_baselines.common.vec_env import VecFrameStack 6 | from stable_baselines.common.policies import CnnPolicy, CnnLstmPolicy, CnnLnLstmPolicy 7 | 8 | 9 | def train(env_id, num_timesteps, seed, policy, lr_schedule, num_env): 10 | """ 11 | Train A2C model for atari environment, for testing purposes 12 | 13 | :param env_id: (str) Environment ID 14 | :param num_timesteps: (int) The total number of samples 15 | :param seed: (int) The initial seed for training 16 | :param policy: (A2CPolicy) The policy model to use (MLP, CNN, LSTM, ...) 17 | :param lr_schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant', 18 | 'double_linear_con', 'middle_drop' or 'double_middle_drop') 19 | :param num_env: (int) The number of environments 20 | """ 21 | policy_fn = None 22 | if policy == 'cnn': 23 | policy_fn = CnnPolicy 24 | elif policy == 'lstm': 25 | policy_fn = CnnLstmPolicy 26 | elif policy == 'lnlstm': 27 | policy_fn = CnnLnLstmPolicy 28 | if policy_fn is None: 29 | raise ValueError("Error: policy {} not implemented".format(policy)) 30 | 31 | env = VecFrameStack(make_atari_env(env_id, num_env, seed), 4) 32 | 33 | model = A2C(policy_fn, env, lr_schedule=lr_schedule, seed=seed) 34 | model.learn(total_timesteps=int(num_timesteps * 1.1)) 35 | env.close() 36 | 37 | 38 | def main(): 39 | """ 40 | Runs the test 41 | """ 42 | parser = atari_arg_parser() 43 | parser.add_argument('--policy', choices=['cnn', 'lstm', 'lnlstm'], default='cnn', help='Policy architecture') 44 | parser.add_argument('--lr_schedule', choices=['constant', 'linear'], default='constant', 45 | help='Learning rate schedule') 46 | args = parser.parse_args() 47 | logger.configure() 48 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, policy=args.policy, lr_schedule=args.lr_schedule, 49 | num_env=16) 50 | 51 | 52 | if __name__ == '__main__': 53 | main() 54 | -------------------------------------------------------------------------------- /my_stable_baselines/acer/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.acer.acer_simple import ACER 2 | -------------------------------------------------------------------------------- /my_stable_baselines/acer/run_atari.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import warnings 3 | 4 | from stable_baselines import logger, ACER 5 | from stable_baselines.common.policies import CnnPolicy, CnnLstmPolicy 6 | from stable_baselines.common.cmd_util import make_atari_env, atari_arg_parser 7 | from stable_baselines.common.vec_env import VecFrameStack 8 | 9 | 10 | def train(env_id, num_timesteps, seed, policy, lr_schedule, num_cpu): 11 | """ 12 | train an ACER model on atari 13 | 14 | :param env_id: (str) Environment ID 15 | :param num_timesteps: (int) The total number of samples 16 | :param seed: (int) The initial seed for training 17 | :param policy: (A2CPolicy) The policy model to use (MLP, CNN, LSTM, ...) 18 | :param lr_schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant', 19 | 'double_linear_con', 'middle_drop' or 'double_middle_drop') 20 | :param num_cpu: (int) The number of cpu to train on 21 | """ 22 | env = VecFrameStack(make_atari_env(env_id, num_cpu, seed), 4) 23 | if policy == 'cnn': 24 | policy_fn = CnnPolicy 25 | elif policy == 'lstm': 26 | policy_fn = CnnLstmPolicy 27 | else: 28 | warnings.warn("Policy {} not implemented".format(policy)) 29 | return 30 | 31 | model = ACER(policy_fn, env, lr_schedule=lr_schedule, buffer_size=5000, seed=seed) 32 | model.learn(total_timesteps=int(num_timesteps * 1.1)) 33 | env.close() 34 | # Free memory 35 | del model 36 | 37 | 38 | def main(): 39 | """ 40 | Runs the test 41 | """ 42 | parser = atari_arg_parser() 43 | parser.add_argument('--policy', choices=['cnn', 'lstm', 'lnlstm'], default='cnn', help='Policy architecture') 44 | parser.add_argument('--lr_schedule', choices=['constant', 'linear'], default='constant', 45 | help='Learning rate schedule') 46 | parser.add_argument('--logdir', help='Directory for logging') 47 | args = parser.parse_args() 48 | logger.configure(args.logdir) 49 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, 50 | policy=args.policy, lr_schedule=args.lr_schedule, num_cpu=16) 51 | 52 | 53 | if __name__ == '__main__': 54 | main() 55 | -------------------------------------------------------------------------------- /my_stable_baselines/acktr/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.acktr.acktr import ACKTR 2 | -------------------------------------------------------------------------------- /my_stable_baselines/acktr/run_atari.py: -------------------------------------------------------------------------------- 1 | from stable_baselines import logger, ACKTR 2 | from stable_baselines.common.cmd_util import make_atari_env, atari_arg_parser 3 | from stable_baselines.common.vec_env.vec_frame_stack import VecFrameStack 4 | from stable_baselines.common.policies import CnnPolicy 5 | 6 | 7 | def train(env_id, num_timesteps, seed, num_cpu): 8 | """ 9 | train an ACKTR model on atari 10 | 11 | :param env_id: (str) Environment ID 12 | :param num_timesteps: (int) The total number of samples 13 | :param seed: (int) The initial seed for training 14 | :param num_cpu: (int) The number of cpu to train on 15 | """ 16 | env = VecFrameStack(make_atari_env(env_id, num_cpu, seed), 4) 17 | model = ACKTR(CnnPolicy, env, nprocs=num_cpu, seed=seed) 18 | model.learn(total_timesteps=int(num_timesteps * 1.1)) 19 | env.close() 20 | 21 | 22 | def main(): 23 | """ 24 | Runs the test 25 | """ 26 | args = atari_arg_parser().parse_args() 27 | logger.configure() 28 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, num_cpu=32) 29 | 30 | 31 | if __name__ == '__main__': 32 | main() 33 | -------------------------------------------------------------------------------- /my_stable_baselines/bench/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.bench.monitor import Monitor, load_results 2 | -------------------------------------------------------------------------------- /my_stable_baselines/common/__init__.py: -------------------------------------------------------------------------------- 1 | # flake8: noqa F403 2 | from stable_baselines.common.console_util import fmt_row, fmt_item, colorize 3 | from stable_baselines.common.dataset import Dataset 4 | from stable_baselines.common.math_util import discount, discount_with_boundaries, explained_variance, \ 5 | explained_variance_2d, flatten_arrays, unflatten_vector 6 | from stable_baselines.common.misc_util import zipsame, set_global_seeds, boolean_flag 7 | from stable_baselines.common.base_class import BaseRLModel, ActorCriticRLModel, OffPolicyRLModel, SetVerbosity, \ 8 | TensorboardWriter 9 | from stable_baselines.common.cmd_util import make_vec_env 10 | -------------------------------------------------------------------------------- /my_stable_baselines/common/cg.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def conjugate_gradient(f_ax, b_vec, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10): 5 | """ 6 | conjugate gradient calculation (Ax = b), bases on 7 | https://epubs.siam.org/doi/book/10.1137/1.9781611971446 Demmel p 312 8 | 9 | :param f_ax: (function) The function describing the Matrix A dot the vector x 10 | (x being the input parameter of the function) 11 | :param b_vec: (numpy float) vector b, where Ax = b 12 | :param cg_iters: (int) the maximum number of iterations for converging 13 | :param callback: (function) callback the values of x while converging 14 | :param verbose: (bool) print extra information 15 | :param residual_tol: (float) the break point if the residual is below this value 16 | :return: (numpy float) vector x, where Ax = b 17 | """ 18 | first_basis_vect = b_vec.copy() # the first basis vector 19 | residual = b_vec.copy() # the residual 20 | x_var = np.zeros_like(b_vec) # vector x, where Ax = b 21 | residual_dot_residual = residual.dot(residual) # L2 norm of the residual 22 | 23 | fmt_str = "%10i %10.3g %10.3g" 24 | title_str = "%10s %10s %10s" 25 | if verbose: 26 | print(title_str % ("iter", "residual norm", "soln norm")) 27 | 28 | for i in range(cg_iters): 29 | if callback is not None: 30 | callback(x_var) 31 | if verbose: 32 | print(fmt_str % (i, residual_dot_residual, np.linalg.norm(x_var))) 33 | z_var = f_ax(first_basis_vect) 34 | v_var = residual_dot_residual / first_basis_vect.dot(z_var) 35 | x_var += v_var * first_basis_vect 36 | residual -= v_var * z_var 37 | new_residual_dot_residual = residual.dot(residual) 38 | mu_val = new_residual_dot_residual / residual_dot_residual 39 | first_basis_vect = residual + mu_val * first_basis_vect 40 | 41 | residual_dot_residual = new_residual_dot_residual 42 | if residual_dot_residual < residual_tol: 43 | break 44 | 45 | if callback is not None: 46 | callback(x_var) 47 | if verbose: 48 | print(fmt_str % (i + 1, residual_dot_residual, np.linalg.norm(x_var))) 49 | return x_var 50 | -------------------------------------------------------------------------------- /my_stable_baselines/common/console_util.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | import numpy as np 4 | 5 | 6 | # ================================================================ 7 | # Misc 8 | # ================================================================ 9 | 10 | 11 | def fmt_row(width, row, header=False): 12 | """ 13 | fits a list of items to at least a certain length 14 | 15 | :param width: (int) the minimum width of the string 16 | :param row: ([Any]) a list of object you wish to get the string representation 17 | :param header: (bool) whether or not to return the string as a header 18 | :return: (str) the string representation of all the elements in 'row', of length >= 'width' 19 | """ 20 | out = " | ".join(fmt_item(x, width) for x in row) 21 | if header: 22 | out = out + "\n" + "-" * len(out) 23 | return out 24 | 25 | 26 | def fmt_item(item, min_width): 27 | """ 28 | fits items to a given string length 29 | 30 | :param item: (Any) the item you wish to get the string representation 31 | :param min_width: (int) the minimum width of the string 32 | :return: (str) the string representation of 'x' of length >= 'l' 33 | """ 34 | if isinstance(item, np.ndarray): 35 | assert item.ndim == 0 36 | item = item.item() 37 | if isinstance(item, (float, np.float32, np.float64)): 38 | value = abs(item) 39 | if (value < 1e-4 or value > 1e+4) and value > 0: 40 | rep = "%7.2e" % item 41 | else: 42 | rep = "%7.5f" % item 43 | else: 44 | rep = str(item) 45 | return " " * (min_width - len(rep)) + rep 46 | 47 | 48 | COLOR_TO_NUM = dict( 49 | gray=30, 50 | red=31, 51 | green=32, 52 | yellow=33, 53 | blue=34, 54 | magenta=35, 55 | cyan=36, 56 | white=37, 57 | crimson=38 58 | ) 59 | 60 | 61 | def colorize(string, color, bold=False, highlight=False): 62 | """ 63 | Colorize, bold and/or highlight a string for terminal print 64 | 65 | :param string: (str) input string 66 | :param color: (str) the color, the lookup table is the dict at console_util.color2num 67 | :param bold: (bool) if the string should be bold or not 68 | :param highlight: (bool) if the string should be highlighted or not 69 | :return: (str) the stylized output string 70 | """ 71 | attr = [] 72 | num = COLOR_TO_NUM[color] 73 | if highlight: 74 | num += 10 75 | attr.append(str(num)) 76 | if bold: 77 | attr.append('1') 78 | return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) 79 | -------------------------------------------------------------------------------- /my_stable_baselines/common/evaluation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from stable_baselines.common.vec_env import VecEnv 4 | 5 | 6 | def evaluate_policy(model, env, n_eval_episodes=10, deterministic=True, 7 | render=False, callback=None, reward_threshold=None, 8 | return_episode_rewards=False): 9 | """ 10 | Runs policy for `n_eval_episodes` episodes and returns average reward. 11 | This is made to work only with one env. 12 | 13 | :param model: (BaseRLModel) The RL agent you want to evaluate. 14 | :param env: (gym.Env or VecEnv) The gym environment. In the case of a `VecEnv` 15 | this must contain only one environment. 16 | :param n_eval_episodes: (int) Number of episode to evaluate the agent 17 | :param deterministic: (bool) Whether to use deterministic or stochastic actions 18 | :param render: (bool) Whether to render the environment or not 19 | :param callback: (callable) callback function to do additional checks, 20 | called after each step. 21 | :param reward_threshold: (float) Minimum expected reward per episode, 22 | this will raise an error if the performance is not met 23 | :param return_episode_rewards: (bool) If True, a list of reward per episode 24 | will be returned instead of the mean. 25 | :return: (float, float) Mean reward per episode, std of reward per episode 26 | returns ([float], [int]) when `return_episode_rewards` is True 27 | """ 28 | if isinstance(env, VecEnv): 29 | assert env.num_envs == 1, "You must pass only one environment when using this function" 30 | 31 | episode_rewards, episode_lengths = [], [] 32 | for _ in range(n_eval_episodes): 33 | obs = env.reset() 34 | done, state = False, None 35 | episode_reward = 0.0 36 | episode_length = 0 37 | while not done: 38 | action, state = model.predict(obs, state=state, deterministic=deterministic) 39 | obs, reward, done, _info = env.step(action) 40 | episode_reward += reward 41 | if callback is not None: 42 | callback(locals(), globals()) 43 | episode_length += 1 44 | if render: 45 | env.render() 46 | episode_rewards.append(episode_reward) 47 | episode_lengths.append(episode_length) 48 | 49 | mean_reward = np.mean(episode_rewards) 50 | std_reward = np.std(episode_rewards) 51 | 52 | if reward_threshold is not None: 53 | assert mean_reward > reward_threshold, 'Mean reward below threshold: '\ 54 | '{:.2f} < {:.2f}'.format(mean_reward, reward_threshold) 55 | if return_episode_rewards: 56 | return episode_rewards, episode_lengths 57 | return mean_reward, std_reward 58 | -------------------------------------------------------------------------------- /my_stable_baselines/common/misc_util.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | import gym 4 | import numpy as np 5 | import tensorflow as tf 6 | 7 | 8 | def zipsame(*seqs): 9 | """ 10 | Performes a zip function, but asserts that all zipped elements are of the same size 11 | 12 | :param seqs: a list of arrays that are zipped together 13 | :return: the zipped arguments 14 | """ 15 | length = len(seqs[0]) 16 | assert all(len(seq) == length for seq in seqs[1:]) 17 | return zip(*seqs) 18 | 19 | 20 | def set_global_seeds(seed): 21 | """ 22 | set the seed for python random, tensorflow, numpy and gym spaces 23 | 24 | :param seed: (int) the seed 25 | """ 26 | tf.set_random_seed(seed) 27 | np.random.seed(seed) 28 | random.seed(seed) 29 | # prng was removed in latest gym version 30 | if hasattr(gym.spaces, 'prng'): 31 | gym.spaces.prng.seed(seed) 32 | 33 | 34 | def boolean_flag(parser, name, default=False, help_msg=None): 35 | """ 36 | Add a boolean flag to argparse parser. 37 | 38 | :param parser: (argparse.Parser) parser to add the flag to 39 | :param name: (str) -- will enable the flag, while --no- will disable it 40 | :param default: (bool) default value of the flag 41 | :param help_msg: (str) help string for the flag 42 | """ 43 | dest = name.replace('-', '_') 44 | parser.add_argument("--" + name, action="store_true", default=default, dest=dest, help=help_msg) 45 | parser.add_argument("--no-" + name, action="store_false", dest=dest) 46 | 47 | 48 | def mpi_rank_or_zero(): 49 | """ 50 | Return the MPI rank if mpi is installed. Otherwise, return 0. 51 | :return: (int) 52 | """ 53 | try: 54 | import mpi4py 55 | return mpi4py.MPI.COMM_WORLD.Get_rank() 56 | except ImportError: 57 | return 0 58 | 59 | 60 | def flatten_lists(listoflists): 61 | """ 62 | Flatten a python list of list 63 | 64 | :param listoflists: (list(list)) 65 | :return: (list) 66 | """ 67 | return [el for list_ in listoflists for el in list_] 68 | -------------------------------------------------------------------------------- /my_stable_baselines/common/mpi_moments.py: -------------------------------------------------------------------------------- 1 | from mpi4py import MPI 2 | import numpy as np 3 | 4 | from stable_baselines.common.misc_util import zipsame 5 | 6 | 7 | def mpi_mean(arr, axis=0, comm=None, keepdims=False): 8 | """ 9 | calculates the mean of an array, using MPI 10 | 11 | :param arr: (np.ndarray) 12 | :param axis: (int or tuple or list) the axis to run the means over 13 | :param comm: (MPI Communicators) if None, MPI.COMM_WORLD 14 | :param keepdims: (bool) keep the other dimensions intact 15 | :return: (np.ndarray or Number) the result of the sum 16 | """ 17 | arr = np.asarray(arr) 18 | assert arr.ndim > 0 19 | if comm is None: 20 | comm = MPI.COMM_WORLD 21 | xsum = arr.sum(axis=axis, keepdims=keepdims) 22 | size = xsum.size 23 | localsum = np.zeros(size + 1, arr.dtype) 24 | localsum[:size] = xsum.ravel() 25 | localsum[size] = arr.shape[axis] 26 | globalsum = np.zeros_like(localsum) 27 | comm.Allreduce(localsum, globalsum, op=MPI.SUM) 28 | return globalsum[:size].reshape(xsum.shape) / globalsum[size], globalsum[size] 29 | 30 | 31 | def mpi_moments(arr, axis=0, comm=None, keepdims=False): 32 | """ 33 | calculates the mean and std of an array, using MPI 34 | 35 | :param arr: (np.ndarray) 36 | :param axis: (int or tuple or list) the axis to run the moments over 37 | :param comm: (MPI Communicators) if None, MPI.COMM_WORLD 38 | :param keepdims: (bool) keep the other dimensions intact 39 | :return: (np.ndarray or Number) the result of the moments 40 | """ 41 | arr = np.asarray(arr) 42 | assert arr.ndim > 0 43 | mean, count = mpi_mean(arr, axis=axis, comm=comm, keepdims=True) 44 | sqdiffs = np.square(arr - mean) 45 | meansqdiff, count1 = mpi_mean(sqdiffs, axis=axis, comm=comm, keepdims=True) 46 | assert count1 == count 47 | std = np.sqrt(meansqdiff) 48 | if not keepdims: 49 | newshape = mean.shape[:axis] + mean.shape[axis+1:] 50 | mean = mean.reshape(newshape) 51 | std = std.reshape(newshape) 52 | return mean, std, count 53 | 54 | 55 | def _helper_runningmeanstd(): 56 | comm = MPI.COMM_WORLD 57 | np.random.seed(0) 58 | for (triple, axis) in [ 59 | ((np.random.randn(3), np.random.randn(4), np.random.randn(5)), 0), 60 | ((np.random.randn(3, 2), np.random.randn(4, 2), np.random.randn(5, 2)), 0), 61 | ((np.random.randn(2, 3), np.random.randn(2, 4), np.random.randn(2, 4)), 1)]: 62 | 63 | arr = np.concatenate(triple, axis=axis) 64 | ms1 = [arr.mean(axis=axis), arr.std(axis=axis), arr.shape[axis]] 65 | 66 | ms2 = mpi_moments(triple[comm.Get_rank()], axis=axis) 67 | 68 | for (res_1, res_2) in zipsame(ms1, ms2): 69 | print(res_1, res_2) 70 | assert np.allclose(res_1, res_2) 71 | print("ok!") 72 | -------------------------------------------------------------------------------- /my_stable_baselines/common/running_mean_std.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class RunningMeanStd(object): 5 | def __init__(self, epsilon=1e-4, shape=()): 6 | """ 7 | calulates the running mean and std of a data stream 8 | https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm 9 | 10 | :param epsilon: (float) helps with arithmetic issues 11 | :param shape: (tuple) the shape of the data stream's output 12 | """ 13 | self.mean = np.zeros(shape, 'float64') 14 | self.var = np.ones(shape, 'float64') 15 | self.count = epsilon 16 | 17 | def update(self, arr): 18 | batch_mean = np.mean(arr, axis=0) 19 | batch_var = np.var(arr, axis=0) 20 | batch_count = arr.shape[0] 21 | self.update_from_moments(batch_mean, batch_var, batch_count) 22 | 23 | def update_from_moments(self, batch_mean, batch_var, batch_count): 24 | delta = batch_mean - self.mean 25 | tot_count = self.count + batch_count 26 | 27 | new_mean = self.mean + delta * batch_count / tot_count 28 | m_a = self.var * self.count 29 | m_b = batch_var * batch_count 30 | m_2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count) 31 | new_var = m_2 / (self.count + batch_count) 32 | 33 | new_count = batch_count + self.count 34 | 35 | self.mean = new_mean 36 | self.var = new_var 37 | self.count = new_count 38 | -------------------------------------------------------------------------------- /my_stable_baselines/common/tile_images.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def tile_images(img_nhwc): 5 | """ 6 | Tile N images into one big PxQ image 7 | (P,Q) are chosen to be as close as possible, and if N 8 | is square, then P=Q. 9 | 10 | :param img_nhwc: (list) list or array of images, ndim=4 once turned into array. img nhwc 11 | n = batch index, h = height, w = width, c = channel 12 | :return: (numpy float) img_HWc, ndim=3 13 | """ 14 | img_nhwc = np.asarray(img_nhwc) 15 | n_images, height, width, n_channels = img_nhwc.shape 16 | # new_height was named H before 17 | new_height = int(np.ceil(np.sqrt(n_images))) 18 | # new_width was named W before 19 | new_width = int(np.ceil(float(n_images) / new_height)) 20 | img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0] * 0 for _ in range(n_images, new_height * new_width)]) 21 | # img_HWhwc 22 | out_image = img_nhwc.reshape(new_height, new_width, height, width, n_channels) 23 | # img_HhWwc 24 | out_image = out_image.transpose(0, 2, 1, 3, 4) 25 | # img_Hh_Ww_c 26 | out_image = out_image.reshape(new_height * height, new_width * width, n_channels) 27 | return out_image 28 | 29 | -------------------------------------------------------------------------------- /my_stable_baselines/common/vec_env/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import Union 2 | from copy import deepcopy 3 | 4 | import gym 5 | 6 | # flake8: noqa F401 7 | from stable_baselines.common.vec_env.base_vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, \ 8 | CloudpickleWrapper 9 | from stable_baselines.common.vec_env.dummy_vec_env import DummyVecEnv 10 | from stable_baselines.common.vec_env.subproc_vec_env import SubprocVecEnv 11 | from stable_baselines.common.vec_env.vec_frame_stack import VecFrameStack 12 | from stable_baselines.common.vec_env.vec_normalize import VecNormalize 13 | from stable_baselines.common.vec_env.vec_video_recorder import VecVideoRecorder 14 | from stable_baselines.common.vec_env.vec_check_nan import VecCheckNan 15 | 16 | 17 | def unwrap_vec_normalize(env: Union[gym.Env, VecEnv]) -> Union[VecNormalize, None]: 18 | """ 19 | :param env: (Union[gym.Env, VecEnv]) 20 | :return: (VecNormalize) 21 | """ 22 | env_tmp = env 23 | while isinstance(env_tmp, VecEnvWrapper): 24 | if isinstance(env_tmp, VecNormalize): 25 | return env_tmp 26 | env_tmp = env_tmp.venv 27 | return None 28 | 29 | 30 | # Define here to avoid circular import 31 | def sync_envs_normalization(env: Union[gym.Env, VecEnv], eval_env: Union[gym.Env, VecEnv]) -> None: 32 | """ 33 | Sync eval and train environments when using VecNormalize 34 | 35 | :param env: (Union[gym.Env, VecEnv])) 36 | :param eval_env: (Union[gym.Env, VecEnv])) 37 | """ 38 | env_tmp, eval_env_tmp = env, eval_env 39 | # Special case for the _UnvecWrapper 40 | # Avoid circular import 41 | from stable_baselines.common.base_class import _UnvecWrapper 42 | if isinstance(env_tmp, _UnvecWrapper): 43 | return 44 | while isinstance(env_tmp, VecEnvWrapper): 45 | if isinstance(env_tmp, VecNormalize): 46 | # sync reward and observation scaling 47 | eval_env_tmp.obs_rms = deepcopy(env_tmp.obs_rms) 48 | eval_env_tmp.ret_rms = deepcopy(env_tmp.ret_rms) 49 | env_tmp = env_tmp.venv 50 | # Make pytype happy, in theory env and eval_env have the same type 51 | assert isinstance(eval_env_tmp, VecEnvWrapper), "the second env differs from the first env" 52 | eval_env_tmp = eval_env_tmp.venv 53 | -------------------------------------------------------------------------------- /my_stable_baselines/common/vec_env/vec_frame_stack.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | import numpy as np 4 | from gym import spaces 5 | 6 | from stable_baselines.common.vec_env.base_vec_env import VecEnvWrapper 7 | 8 | 9 | class VecFrameStack(VecEnvWrapper): 10 | """ 11 | Frame stacking wrapper for vectorized environment 12 | 13 | :param venv: (VecEnv) the vectorized environment to wrap 14 | :param n_stack: (int) Number of frames to stack 15 | """ 16 | 17 | def __init__(self, venv, n_stack): 18 | self.venv = venv 19 | self.n_stack = n_stack 20 | wrapped_obs_space = venv.observation_space 21 | low = np.repeat(wrapped_obs_space.low, self.n_stack, axis=-1) 22 | high = np.repeat(wrapped_obs_space.high, self.n_stack, axis=-1) 23 | self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype) 24 | observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype) 25 | VecEnvWrapper.__init__(self, venv, observation_space=observation_space) 26 | 27 | def step_wait(self): 28 | observations, rewards, dones, infos = self.venv.step_wait() 29 | last_ax_size = observations.shape[-1] 30 | self.stackedobs = np.roll(self.stackedobs, shift=-last_ax_size, axis=-1) 31 | for i, done in enumerate(dones): 32 | if done: 33 | if 'terminal_observation' in infos[i]: 34 | old_terminal = infos[i]['terminal_observation'] 35 | new_terminal = np.concatenate( 36 | (self.stackedobs[i, ..., :-last_ax_size], old_terminal), axis=-1) 37 | infos[i]['terminal_observation'] = new_terminal 38 | else: 39 | warnings.warn( 40 | "VecFrameStack wrapping a VecEnv without terminal_observation info") 41 | self.stackedobs[i] = 0 42 | self.stackedobs[..., -observations.shape[-1]:] = observations 43 | return self.stackedobs, rewards, dones, infos 44 | 45 | def reset(self): 46 | """ 47 | Reset all environments 48 | """ 49 | obs = self.venv.reset() 50 | self.stackedobs[...] = 0 51 | self.stackedobs[..., -obs.shape[-1]:] = obs 52 | return self.stackedobs 53 | 54 | def close(self): 55 | self.venv.close() 56 | -------------------------------------------------------------------------------- /my_stable_baselines/ddpg/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.common.noise import AdaptiveParamNoiseSpec, NormalActionNoise, OrnsteinUhlenbeckActionNoise 2 | from stable_baselines.ddpg.ddpg import DDPG 3 | from stable_baselines.ddpg.policies import MlpPolicy, CnnPolicy, LnMlpPolicy, LnCnnPolicy 4 | -------------------------------------------------------------------------------- /my_stable_baselines/ddpg/noise.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.common.noise import NormalActionNoise, AdaptiveParamNoiseSpec, OrnsteinUhlenbeckActionNoise # pylint: disable=unused-import 2 | -------------------------------------------------------------------------------- /my_stable_baselines/deepq/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.deepq.policies import MlpPolicy, CnnPolicy, LnMlpPolicy, LnCnnPolicy 2 | from stable_baselines.deepq.build_graph import build_act, build_train # noqa 3 | from stable_baselines.deepq.dqn import DQN 4 | from stable_baselines.common.buffers import ReplayBuffer, PrioritizedReplayBuffer # noqa 5 | 6 | 7 | def wrap_atari_dqn(env): 8 | """ 9 | wrap the environment in atari wrappers for DQN 10 | 11 | :param env: (Gym Environment) the environment 12 | :return: (Gym Environment) the wrapped environment 13 | """ 14 | from stable_baselines.common.atari_wrappers import wrap_deepmind 15 | return wrap_deepmind(env, frame_stack=True, scale=False) 16 | -------------------------------------------------------------------------------- /my_stable_baselines/deepq/experiments/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maestro-project/magma/f25ef66508f6bb67009a4a280b654a8ce88d2505/my_stable_baselines/deepq/experiments/__init__.py -------------------------------------------------------------------------------- /my_stable_baselines/deepq/experiments/enjoy_cartpole.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import gym 4 | 5 | from stable_baselines.deepq import DQN 6 | 7 | 8 | def main(args): 9 | """ 10 | Run a trained model for the cartpole problem 11 | 12 | :param args: (ArgumentParser) the input arguments 13 | """ 14 | env = gym.make("CartPole-v0") 15 | model = DQN.load("cartpole_model.zip", env) 16 | 17 | while True: 18 | obs, done = env.reset(), False 19 | episode_rew = 0 20 | while not done: 21 | if not args.no_render: 22 | env.render() 23 | action, _ = model.predict(obs) 24 | obs, rew, done, _ = env.step(action) 25 | episode_rew += rew 26 | print("Episode reward", episode_rew) 27 | # No render is only used for automatic testing 28 | if args.no_render: 29 | break 30 | 31 | 32 | if __name__ == '__main__': 33 | parser = argparse.ArgumentParser(description="Enjoy trained DQN on cartpole") 34 | parser.add_argument('--no-render', default=False, action="store_true", help="Disable rendering") 35 | args = parser.parse_args() 36 | main(args) 37 | -------------------------------------------------------------------------------- /my_stable_baselines/deepq/experiments/enjoy_mountaincar.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import gym 4 | import numpy as np 5 | 6 | from stable_baselines.deepq import DQN 7 | 8 | 9 | def main(args): 10 | """ 11 | Run a trained model for the mountain car problem 12 | 13 | :param args: (ArgumentParser) the input arguments 14 | """ 15 | env = gym.make("MountainCar-v0") 16 | model = DQN.load("mountaincar_model.zip", env) 17 | 18 | while True: 19 | obs, done = env.reset(), False 20 | episode_rew = 0 21 | while not done: 22 | if not args.no_render: 23 | env.render() 24 | # Epsilon-greedy 25 | if np.random.random() < 0.02: 26 | action = env.action_space.sample() 27 | else: 28 | action, _ = model.predict(obs, deterministic=True) 29 | obs, rew, done, _ = env.step(action) 30 | episode_rew += rew 31 | print("Episode reward", episode_rew) 32 | # No render is only used for automatic testing 33 | if args.no_render: 34 | break 35 | 36 | 37 | if __name__ == '__main__': 38 | parser = argparse.ArgumentParser(description="Enjoy trained DQN on MountainCar") 39 | parser.add_argument('--no-render', default=False, action="store_true", help="Disable rendering") 40 | args = parser.parse_args() 41 | main(args) 42 | -------------------------------------------------------------------------------- /my_stable_baselines/deepq/experiments/run_atari.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from functools import partial 3 | 4 | from stable_baselines import bench, logger 5 | from stable_baselines.common import set_global_seeds 6 | from stable_baselines.common.atari_wrappers import make_atari 7 | from stable_baselines.deepq import DQN, wrap_atari_dqn, CnnPolicy 8 | 9 | 10 | def main(): 11 | """ 12 | Run the atari test 13 | """ 14 | parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) 15 | parser.add_argument('--env', help='environment ID', default='BreakoutNoFrameskip-v4') 16 | parser.add_argument('--seed', help='RNG seed', type=int, default=0) 17 | parser.add_argument('--prioritized', type=int, default=1) 18 | parser.add_argument('--dueling', type=int, default=1) 19 | parser.add_argument('--prioritized-replay-alpha', type=float, default=0.6) 20 | parser.add_argument('--num-timesteps', type=int, default=int(10e6)) 21 | 22 | args = parser.parse_args() 23 | logger.configure() 24 | set_global_seeds(args.seed) 25 | env = make_atari(args.env) 26 | env = bench.Monitor(env, logger.get_dir()) 27 | env = wrap_atari_dqn(env) 28 | policy = partial(CnnPolicy, dueling=args.dueling == 1) 29 | 30 | model = DQN( 31 | env=env, 32 | policy=policy, 33 | learning_rate=1e-4, 34 | buffer_size=10000, 35 | exploration_fraction=0.1, 36 | exploration_final_eps=0.01, 37 | train_freq=4, 38 | learning_starts=10000, 39 | target_network_update_freq=1000, 40 | gamma=0.99, 41 | prioritized_replay=bool(args.prioritized), 42 | prioritized_replay_alpha=args.prioritized_replay_alpha, 43 | ) 44 | model.learn(total_timesteps=args.num_timesteps) 45 | 46 | env.close() 47 | 48 | 49 | if __name__ == '__main__': 50 | main() 51 | -------------------------------------------------------------------------------- /my_stable_baselines/deepq/experiments/train_cartpole.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import gym 4 | import numpy as np 5 | 6 | from stable_baselines.deepq import DQN, MlpPolicy 7 | 8 | 9 | def callback(lcl, _glb): 10 | """ 11 | The callback function for logging and saving 12 | 13 | :param lcl: (dict) the local variables 14 | :param _glb: (dict) the global variables 15 | :return: (bool) is solved 16 | """ 17 | # stop training if reward exceeds 199 18 | if len(lcl['episode_rewards'][-101:-1]) == 0: 19 | mean_100ep_reward = -np.inf 20 | else: 21 | mean_100ep_reward = round(float(np.mean(lcl['episode_rewards'][-101:-1])), 1) 22 | is_solved = lcl['self'].num_timesteps > 100 and mean_100ep_reward >= 199 23 | return not is_solved 24 | 25 | 26 | def main(args): 27 | """ 28 | Train and save the DQN model, for the cartpole problem 29 | 30 | :param args: (ArgumentParser) the input arguments 31 | """ 32 | env = gym.make("CartPole-v0") 33 | model = DQN( 34 | env=env, 35 | policy=MlpPolicy, 36 | learning_rate=1e-3, 37 | buffer_size=50000, 38 | exploration_fraction=0.1, 39 | exploration_final_eps=0.02, 40 | ) 41 | model.learn(total_timesteps=args.max_timesteps, callback=callback) 42 | 43 | print("Saving model to cartpole_model.zip") 44 | model.save("cartpole_model.zip") 45 | 46 | 47 | if __name__ == '__main__': 48 | parser = argparse.ArgumentParser(description="Train DQN on cartpole") 49 | parser.add_argument('--max-timesteps', default=100000, type=int, help="Maximum number of timesteps") 50 | args = parser.parse_args() 51 | main(args) 52 | -------------------------------------------------------------------------------- /my_stable_baselines/deepq/experiments/train_mountaincar.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import gym 4 | 5 | from stable_baselines.deepq import DQN 6 | 7 | 8 | def main(args): 9 | """ 10 | Train and save the DQN model, for the mountain car problem 11 | 12 | :param args: (ArgumentParser) the input arguments 13 | """ 14 | env = gym.make("MountainCar-v0") 15 | 16 | # using layer norm policy here is important for parameter space noise! 17 | model = DQN( 18 | policy="LnMlpPolicy", 19 | env=env, 20 | learning_rate=1e-3, 21 | buffer_size=50000, 22 | exploration_fraction=0.1, 23 | exploration_final_eps=0.1, 24 | param_noise=True, 25 | policy_kwargs=dict(layers=[64]) 26 | ) 27 | model.learn(total_timesteps=args.max_timesteps) 28 | 29 | print("Saving model to mountaincar_model.zip") 30 | model.save("mountaincar_model") 31 | 32 | 33 | if __name__ == '__main__': 34 | parser = argparse.ArgumentParser(description="Train DQN on cartpole") 35 | parser.add_argument('--max-timesteps', default=100000, type=int, help="Maximum number of timesteps") 36 | args = parser.parse_args() 37 | main(args) 38 | -------------------------------------------------------------------------------- /my_stable_baselines/gail/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.gail.model import GAIL 2 | from stable_baselines.gail.dataset.dataset import ExpertDataset, DataLoader 3 | from stable_baselines.gail.dataset.record_expert import generate_expert_traj 4 | -------------------------------------------------------------------------------- /my_stable_baselines/gail/dataset/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maestro-project/magma/f25ef66508f6bb67009a4a280b654a8ce88d2505/my_stable_baselines/gail/dataset/__init__.py -------------------------------------------------------------------------------- /my_stable_baselines/her/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.her.her import HER 2 | from stable_baselines.her.replay_buffer import GoalSelectionStrategy, HindsightExperienceReplayWrapper 3 | from stable_baselines.her.utils import HERGoalEnvWrapper 4 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo1/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.ppo1.pposgd_simple import PPO1 2 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo1/run_atari.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import os 3 | 4 | from mpi4py import MPI 5 | 6 | from stable_baselines.common import set_global_seeds 7 | from stable_baselines import bench, logger, PPO1 8 | from stable_baselines.common.atari_wrappers import make_atari, wrap_deepmind 9 | from stable_baselines.common.cmd_util import atari_arg_parser 10 | from stable_baselines.common.policies import CnnPolicy 11 | 12 | 13 | def train(env_id, num_timesteps, seed): 14 | """ 15 | Train PPO1 model for Atari environments, for testing purposes 16 | 17 | :param env_id: (str) Environment ID 18 | :param num_timesteps: (int) The total number of samples 19 | :param seed: (int) The initial seed for training 20 | """ 21 | rank = MPI.COMM_WORLD.Get_rank() 22 | 23 | if rank == 0: 24 | logger.configure() 25 | else: 26 | logger.configure(format_strs=[]) 27 | workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() 28 | set_global_seeds(workerseed) 29 | env = make_atari(env_id) 30 | 31 | env = bench.Monitor(env, logger.get_dir() and 32 | os.path.join(logger.get_dir(), str(rank))) 33 | env.seed(workerseed) 34 | 35 | env = wrap_deepmind(env) 36 | env.seed(workerseed) 37 | 38 | model = PPO1(CnnPolicy, env, timesteps_per_actorbatch=256, clip_param=0.2, entcoeff=0.01, optim_epochs=4, 39 | optim_stepsize=1e-3, optim_batchsize=64, gamma=0.99, lam=0.95, schedule='linear', verbose=2) 40 | model.learn(total_timesteps=num_timesteps) 41 | env.close() 42 | del env 43 | 44 | 45 | def main(): 46 | """ 47 | Runs the test 48 | """ 49 | args = atari_arg_parser().parse_args() 50 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) 51 | 52 | 53 | if __name__ == '__main__': 54 | main() 55 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo1/run_mujoco.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | from stable_baselines.ppo1 import PPO1 4 | from stable_baselines.common.policies import MlpPolicy 5 | from stable_baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser 6 | from stable_baselines import logger 7 | 8 | 9 | def train(env_id, num_timesteps, seed): 10 | """ 11 | Train PPO1 model for the Mujoco environment, for testing purposes 12 | 13 | :param env_id: (str) Environment ID 14 | :param num_timesteps: (int) The total number of samples 15 | :param seed: (int) The initial seed for training 16 | """ 17 | env = make_mujoco_env(env_id, seed) 18 | model = PPO1(MlpPolicy, env, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=10, 19 | optim_stepsize=3e-4, optim_batchsize=64, gamma=0.99, lam=0.95, schedule='linear') 20 | model.learn(total_timesteps=num_timesteps) 21 | env.close() 22 | 23 | 24 | def main(): 25 | """ 26 | Runs the test 27 | """ 28 | args = mujoco_arg_parser().parse_args() 29 | logger.configure() 30 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) 31 | 32 | 33 | if __name__ == '__main__': 34 | main() 35 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo1/run_robotics.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | from mpi4py import MPI 4 | import mujoco_py # pytype:disable=import-error 5 | 6 | from stable_baselines.common import set_global_seeds 7 | from stable_baselines.common.policies import MlpPolicy 8 | from stable_baselines.common.cmd_util import make_robotics_env, robotics_arg_parser 9 | from stable_baselines.ppo1 import PPO1 10 | 11 | 12 | def train(env_id, num_timesteps, seed): 13 | """ 14 | Train PPO1 model for Robotics environment, for testing purposes 15 | 16 | :param env_id: (str) Environment ID 17 | :param num_timesteps: (int) The total number of samples 18 | :param seed: (int) The initial seed for training 19 | """ 20 | 21 | rank = MPI.COMM_WORLD.Get_rank() 22 | with mujoco_py.ignore_mujoco_warnings(): 23 | workerseed = seed + 10000 * rank 24 | set_global_seeds(workerseed) 25 | env = make_robotics_env(env_id, workerseed, rank=rank) 26 | 27 | model = PPO1(MlpPolicy, env, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=5, 28 | optim_stepsize=3e-4, optim_batchsize=256, gamma=0.99, lam=0.95, schedule='linear') 29 | model.learn(total_timesteps=num_timesteps) 30 | env.close() 31 | 32 | 33 | def main(): 34 | """ 35 | Runs the test 36 | """ 37 | args = robotics_arg_parser().parse_args() 38 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) 39 | 40 | 41 | if __name__ == '__main__': 42 | main() 43 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo2/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.ppo2.ppo2 import PPO2 2 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo2/run_atari.py: -------------------------------------------------------------------------------- 1 | from stable_baselines import PPO2, logger 2 | from stable_baselines.common.cmd_util import make_atari_env, atari_arg_parser 3 | from stable_baselines.common.vec_env import VecFrameStack 4 | from stable_baselines.common.policies import CnnPolicy, CnnLstmPolicy, CnnLnLstmPolicy, MlpPolicy 5 | 6 | 7 | def train(env_id, num_timesteps, seed, policy, 8 | n_envs=8, nminibatches=4, n_steps=128): 9 | """ 10 | Train PPO2 model for atari environment, for testing purposes 11 | 12 | :param env_id: (str) the environment id string 13 | :param num_timesteps: (int) the number of timesteps to run 14 | :param seed: (int) Used to seed the random generator. 15 | :param policy: (Object) The policy model to use (MLP, CNN, LSTM, ...) 16 | :param n_envs: (int) Number of parallel environments 17 | :param nminibatches: (int) Number of training minibatches per update. For recurrent policies, 18 | the number of environments run in parallel should be a multiple of nminibatches. 19 | :param n_steps: (int) The number of steps to run for each environment per update 20 | (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel) 21 | """ 22 | 23 | env = VecFrameStack(make_atari_env(env_id, n_envs, seed), 4) 24 | policy = {'cnn': CnnPolicy, 'lstm': CnnLstmPolicy, 'lnlstm': CnnLnLstmPolicy, 'mlp': MlpPolicy}[policy] 25 | model = PPO2(policy=policy, env=env, n_steps=n_steps, nminibatches=nminibatches, 26 | lam=0.95, gamma=0.99, noptepochs=4, ent_coef=.01, 27 | learning_rate=lambda f: f * 2.5e-4, cliprange=lambda f: f * 0.1, verbose=1) 28 | model.learn(total_timesteps=num_timesteps) 29 | 30 | env.close() 31 | # Free memory 32 | del model 33 | 34 | def main(): 35 | """ 36 | Runs the test 37 | """ 38 | parser = atari_arg_parser() 39 | parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm', 'mlp'], default='cnn') 40 | args = parser.parse_args() 41 | logger.configure() 42 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, 43 | policy=args.policy) 44 | 45 | 46 | if __name__ == '__main__': 47 | main() 48 | -------------------------------------------------------------------------------- /my_stable_baselines/ppo2/run_mujoco.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import numpy as np 3 | import gym 4 | 5 | from stable_baselines.common.cmd_util import mujoco_arg_parser 6 | from stable_baselines import bench, logger 7 | from stable_baselines.common import set_global_seeds 8 | from stable_baselines.common.vec_env.vec_normalize import VecNormalize 9 | from stable_baselines.ppo2 import PPO2 10 | from stable_baselines.common.policies import MlpPolicy 11 | from stable_baselines.common.vec_env.dummy_vec_env import DummyVecEnv 12 | 13 | 14 | def train(env_id, num_timesteps, seed): 15 | """ 16 | Train PPO2 model for Mujoco environment, for testing purposes 17 | 18 | :param env_id: (str) the environment id string 19 | :param num_timesteps: (int) the number of timesteps to run 20 | :param seed: (int) Used to seed the random generator. 21 | """ 22 | def make_env(): 23 | env_out = gym.make(env_id) 24 | env_out = bench.Monitor(env_out, logger.get_dir(), allow_early_resets=True) 25 | return env_out 26 | 27 | env = DummyVecEnv([make_env]) 28 | env = VecNormalize(env) 29 | 30 | set_global_seeds(seed) 31 | policy = MlpPolicy 32 | model = PPO2(policy=policy, env=env, n_steps=2048, nminibatches=32, lam=0.95, gamma=0.99, noptepochs=10, 33 | ent_coef=0.0, learning_rate=3e-4, cliprange=0.2) 34 | model.learn(total_timesteps=num_timesteps) 35 | 36 | return model, env 37 | 38 | 39 | def main(): 40 | """ 41 | Runs the test 42 | """ 43 | args = mujoco_arg_parser().parse_args() 44 | logger.configure() 45 | model, env = train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) 46 | 47 | if args.play: 48 | logger.log("Running trained model") 49 | obs = np.zeros((env.num_envs,) + env.observation_space.shape) 50 | obs[:] = env.reset() 51 | while True: 52 | actions = model.step(obs)[0] 53 | obs[:] = env.step(actions)[0] 54 | env.render() 55 | 56 | 57 | if __name__ == '__main__': 58 | main() 59 | -------------------------------------------------------------------------------- /my_stable_baselines/py.typed: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maestro-project/magma/f25ef66508f6bb67009a4a280b654a8ce88d2505/my_stable_baselines/py.typed -------------------------------------------------------------------------------- /my_stable_baselines/sac/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.sac.sac import SAC 2 | from stable_baselines.sac.policies import MlpPolicy, CnnPolicy, LnMlpPolicy, LnCnnPolicy 3 | -------------------------------------------------------------------------------- /my_stable_baselines/td3/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise 2 | from stable_baselines.td3.td3 import TD3 3 | from stable_baselines.td3.policies import MlpPolicy, CnnPolicy, LnMlpPolicy, LnCnnPolicy 4 | -------------------------------------------------------------------------------- /my_stable_baselines/trpo_mpi/__init__.py: -------------------------------------------------------------------------------- 1 | from stable_baselines.trpo_mpi.trpo_mpi import TRPO 2 | -------------------------------------------------------------------------------- /my_stable_baselines/trpo_mpi/run_atari.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import os 3 | 4 | from mpi4py import MPI 5 | 6 | from stable_baselines.common import set_global_seeds 7 | from stable_baselines import bench, logger, TRPO 8 | from stable_baselines.common.atari_wrappers import make_atari, wrap_deepmind 9 | from stable_baselines.common.cmd_util import atari_arg_parser 10 | from stable_baselines.common.policies import CnnPolicy 11 | 12 | 13 | def train(env_id, num_timesteps, seed): 14 | """ 15 | Train TRPO model for the atari environment, for testing purposes 16 | 17 | :param env_id: (str) Environment ID 18 | :param num_timesteps: (int) The total number of samples 19 | :param seed: (int) The initial seed for training 20 | """ 21 | rank = MPI.COMM_WORLD.Get_rank() 22 | 23 | if rank == 0: 24 | logger.configure() 25 | else: 26 | logger.configure(format_strs=[]) 27 | 28 | workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() 29 | set_global_seeds(workerseed) 30 | env = make_atari(env_id) 31 | 32 | env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank))) 33 | env.seed(workerseed) 34 | 35 | env = wrap_deepmind(env) 36 | env.seed(workerseed) 37 | 38 | model = TRPO(CnnPolicy, env, timesteps_per_batch=512, max_kl=0.001, cg_iters=10, cg_damping=1e-3, entcoeff=0.0, 39 | gamma=0.98, lam=1, vf_iters=3, vf_stepsize=1e-4) 40 | model.learn(total_timesteps=int(num_timesteps * 1.1)) 41 | env.close() 42 | # Free memory 43 | del env 44 | 45 | 46 | def main(): 47 | """ 48 | Runs the test 49 | """ 50 | args = atari_arg_parser().parse_args() 51 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) 52 | 53 | 54 | if __name__ == "__main__": 55 | main() 56 | -------------------------------------------------------------------------------- /my_stable_baselines/trpo_mpi/run_mujoco.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # noinspection PyUnresolvedReferences 3 | from mpi4py import MPI 4 | 5 | from stable_baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser 6 | from stable_baselines.common.policies import MlpPolicy 7 | from stable_baselines import logger 8 | from stable_baselines.trpo_mpi import TRPO 9 | import stable_baselines.common.tf_util as tf_util 10 | 11 | 12 | def train(env_id, num_timesteps, seed): 13 | """ 14 | Train TRPO model for the mujoco environment, for testing purposes 15 | 16 | :param env_id: (str) Environment ID 17 | :param num_timesteps: (int) The total number of samples 18 | :param seed: (int) The initial seed for training 19 | """ 20 | with tf_util.single_threaded_session(): 21 | rank = MPI.COMM_WORLD.Get_rank() 22 | if rank == 0: 23 | logger.configure() 24 | else: 25 | logger.configure(format_strs=[]) 26 | logger.set_level(logger.DISABLED) 27 | workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() 28 | 29 | env = make_mujoco_env(env_id, workerseed) 30 | model = TRPO(MlpPolicy, env, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1, entcoeff=0.0, 31 | gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3) 32 | model.learn(total_timesteps=num_timesteps) 33 | env.close() 34 | 35 | 36 | def main(): 37 | """ 38 | Runs the test 39 | """ 40 | args = mujoco_arg_parser().parse_args() 41 | train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) 42 | 43 | 44 | if __name__ == '__main__': 45 | main() 46 | -------------------------------------------------------------------------------- /my_stable_baselines/trpo_mpi/utils.py: -------------------------------------------------------------------------------- 1 | import gym 2 | import numpy as np 3 | 4 | 5 | def add_vtarg_and_adv(seg, gamma, lam): 6 | """ 7 | Compute target value using TD(lambda) estimator, and advantage with GAE(lambda) 8 | 9 | :param seg: (dict) the current segment of the trajectory (see traj_segment_generator return for more information) 10 | :param gamma: (float) Discount factor 11 | :param lam: (float) GAE factor 12 | """ 13 | # last element is only used for last vtarg, but we already zeroed it if last new = 1 14 | episode_starts = np.append(seg["episode_starts"], False) 15 | vpred = np.append(seg["vpred"], seg["nextvpred"]) 16 | rew_len = len(seg["rewards"]) 17 | seg["adv"] = np.empty(rew_len, 'float32') 18 | rewards = seg["rewards"] 19 | lastgaelam = 0 20 | for step in reversed(range(rew_len)): 21 | nonterminal = 1 - float(episode_starts[step + 1]) 22 | delta = rewards[step] + gamma * vpred[step + 1] * nonterminal - vpred[step] 23 | seg["adv"][step] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam 24 | seg["tdlamret"] = seg["adv"] + seg["vpred"] 25 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | matplotlib==3.1.0 2 | numpy==1.16.4 3 | opencv_python==4.2.0.34 4 | scipy==1.3.0 5 | pandas==0.24.2 6 | gym==0.17.2 7 | stable_baselines==2.10.0 8 | cloudpickle==1.2.1 9 | joblib==0.13.2 10 | tensorflow==2.4.1 11 | mpi4py==3.1.3 12 | mujoco_py==2.1.2.14 13 | nevergrad==0.5.0 14 | tensorboardX==2.5.1 15 | -------------------------------------------------------------------------------- /run/runGA.sh: -------------------------------------------------------------------------------- 1 | 2 | 3 | python ../src/main_ga.py --epoch 10 --alg GA --dram_bw 16 --outdir output_ga --setting 2 --instsdir batch_conv --num_microBatch 1 --instsfile insts4 -------------------------------------------------------------------------------- /run/runRL.sh: -------------------------------------------------------------------------------- 1 | 2 | 3 | python ../src/main_rl.py --alg PPO2 --dram_bw 16 --outdir output --setting 3 --epochs 10 --instsdir batch_mix --instsfile insts0 --save_all_records -------------------------------------------------------------------------------- /run/run_blackbox.sh: -------------------------------------------------------------------------------- 1 | 2 | python ../src/main_ng.py --epoch 10 --alg PSO --dram_bw 1 --outdir output_ng --setting 2 --instsdir batch_conv --instsfile insts4 3 | -------------------------------------------------------------------------------- /src/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | 5 | 6 | 7 | 8 | 9 | def CONV_traffic(): 10 | dim_range = [[1, 256], [1, 256], [3, 225], [3, 225], [1,3], [1, 3], [1]] 11 | dim = [random.randint(*dim_range[i]) for i in range(2)] 12 | r = random.randint(*dim_range[2]) 13 | dim += [r, r] 14 | r = random.choice(dim_range[4]) 15 | dim += [r, r] 16 | t = random.choice(dim_range[6]) 17 | dim += [t] 18 | return dim 19 | 20 | def GEMM_traffic(): 21 | dim_range = [[8, 128], [8, 128], [8, 128]] 22 | dim = [random.randint(*dim_range[i]) for i in range(3)] 23 | dim += [1,1,1,3] 24 | return dim 25 | 26 | 27 | def FNN_traffic(): 28 | dim_range = [[8, 512], [8, 812]] 29 | dim = [random.randint(*dim_range[i]) for i in range(2)] 30 | dim += [1,1,1,1,0] 31 | return dim 32 | 33 | 34 | def single_traffic(CONVtype): 35 | if CONVtype =="CONV": 36 | return CONV_traffic() 37 | elif CONVtype =="GEMM": 38 | return GEMM_traffic() 39 | elif CONVtype == "FFN": 40 | return FNN_traffic() 41 | else: 42 | print("Not supported ConvType") 43 | return None 44 | def get_CONVtype_choices(convtype="CONV"): 45 | if convtype == "mix": 46 | return ["CONV", "GEMM", "FFN"] 47 | else: 48 | return ["{}".format(convtype)] 49 | 50 | 51 | 52 | -------------------------------------------------------------------------------- /traffic/batch1/traffic0_m.csv: -------------------------------------------------------------------------------- 1 | 129,87,16,202,1,1,1 2 | 94,92,133,163,1,1,1 3 | 130,211,158,134,3,3,1 4 | 135,100,115,89,3,3,1 5 | 19,185,129,68,1,1,1 6 | 49,66,43,114,1,1,1 7 | 140,162,7,223,1,1,1 8 | 207,81,211,1,1,1,1 9 | 141,251,32,60,1,1,1 10 | 181,200,171,190,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic1_m.csv: -------------------------------------------------------------------------------- 1 | 106,85,68,107,3,3,1 2 | 130,109,1,190,3,3,1 3 | 216,219,96,60,1,1,1 4 | 24,50,106,16,3,3,1 5 | 21,188,101,75,1,1,1 6 | 147,222,64,201,3,3,1 7 | 113,140,47,31,1,1,1 8 | 52,101,165,158,3,3,1 9 | 83,251,17,116,3,3,1 10 | 194,66,173,12,3,3,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic2_m.csv: -------------------------------------------------------------------------------- 1 | 161,137,26,84,1,1,1 2 | 64,16,87,202,3,3,1 3 | 219,255,220,5,1,1,1 4 | 252,186,166,7,3,3,1 5 | 36,111,196,157,1,1,1 6 | 215,91,84,5,1,1,1 7 | 19,119,54,148,3,3,1 8 | 151,69,218,135,3,3,1 9 | 4,221,156,147,1,1,1 10 | 203,54,151,43,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic3_m.csv: -------------------------------------------------------------------------------- 1 | 54,9,5,151,1,1,1 2 | 48,232,123,146,3,3,1 3 | 133,7,50,208,3,3,1 4 | 139,139,18,134,1,1,1 5 | 56,254,201,160,3,3,1 6 | 178,97,14,36,1,1,1 7 | 176,152,196,19,3,3,1 8 | 40,132,211,213,3,3,1 9 | 218,5,218,10,3,3,1 10 | 150,143,19,130,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic4_m.csv: -------------------------------------------------------------------------------- 1 | 24,139,131,154,3,3,1 2 | 216,69,40,166,1,1,1 3 | 26,126,176,201,3,3,1 4 | 53,239,141,205,3,3,1 5 | 231,186,96,82,1,1,1 6 | 144,7,83,93,3,3,1 7 | 192,89,132,84,1,1,1 8 | 209,141,207,178,1,1,1 9 | 5,11,18,164,1,1,1 10 | 53,140,138,35,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic5_m.csv: -------------------------------------------------------------------------------- 1 | 111,147,96,118,3,3,1 2 | 156,187,32,27,1,1,1 3 | 213,8,203,55,1,1,1 4 | 149,103,169,95,3,3,1 5 | 102,114,50,225,3,3,1 6 | 202,172,70,10,3,3,1 7 | 11,161,161,214,3,3,1 8 | 15,183,191,139,1,1,1 9 | 147,237,143,61,1,1,1 10 | 156,8,111,221,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic6_m.csv: -------------------------------------------------------------------------------- 1 | 164,38,158,71,1,1,1 2 | 156,50,49,77,1,1,1 3 | 249,6,30,196,3,3,1 4 | 79,31,48,91,1,1,1 5 | 5,99,66,97,1,1,1 6 | 3,213,70,54,3,3,1 7 | 152,84,26,143,1,1,1 8 | 81,137,178,84,1,1,1 9 | 19,256,19,194,3,3,1 10 | 11,8,33,126,3,3,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic7_m.csv: -------------------------------------------------------------------------------- 1 | 14,124,68,20,3,3,1 2 | 245,10,209,88,3,3,1 3 | 71,136,85,78,1,1,1 4 | 29,252,224,212,1,1,1 5 | 181,16,86,57,3,3,1 6 | 102,110,221,146,3,3,1 7 | 251,51,4,65,3,3,1 8 | 154,171,58,123,1,1,1 9 | 126,250,223,49,1,1,1 10 | 222,232,9,178,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic8_m.csv: -------------------------------------------------------------------------------- 1 | 230,201,44,77,1,1,1 2 | 208,110,132,125,1,1,1 3 | 199,175,116,212,3,3,1 4 | 234,143,192,74,1,1,1 5 | 128,144,85,24,1,1,1 6 | 249,184,50,186,1,1,1 7 | 127,67,4,152,3,3,1 8 | 119,48,161,194,3,3,1 9 | 154,65,176,137,3,3,1 10 | 83,59,15,142,3,3,1 11 | -------------------------------------------------------------------------------- /traffic/batch1/traffic9_m.csv: -------------------------------------------------------------------------------- 1 | 16,178,63,128,1,1,1 2 | 230,108,6,26,3,3,1 3 | 157,60,33,209,1,1,1 4 | 216,87,130,169,1,1,1 5 | 84,29,5,198,1,1,1 6 | 163,16,107,129,1,1,1 7 | 28,50,178,144,1,1,1 8 | 183,86,144,14,1,1,1 9 | 58,209,175,10,3,3,1 10 | 208,14,100,101,1,1,1 11 | -------------------------------------------------------------------------------- /traffic/batch2/traffic0_m.csv: -------------------------------------------------------------------------------- 1 | 240,19,129,129,3,3,1 2 | 242,170,131,131,3,3,1 3 | 98,153,204,204,3,3,1 4 | 5,122,26,26,3,3,1 5 | 160,18,28,28,3,3,1 6 | 170,224,173,173,3,3,1 7 | 92,129,219,219,3,3,1 8 | 117,118,197,197,3,3,1 9 | 105,155,75,75,3,3,1 10 | 174,91,138,138,1,1,1 11 | 245,134,152,152,1,1,1 12 | 182,216,160,160,3,3,1 13 | 52,109,196,196,3,3,1 14 | 89,241,79,79,3,3,1 15 | 256,176,140,140,1,1,1 16 | 71,110,59,59,1,1,1 17 | 103,192,93,93,1,1,1 18 | 191,242,123,123,3,3,1 19 | 40,133,107,107,3,3,1 20 | 11,18,87,87,1,1,1 21 | 42,105,67,67,1,1,1 22 | 256,114,41,41,3,3,1 23 | 20,187,81,81,3,3,1 24 | 27,237,223,223,3,3,1 25 | 14,29,91,91,1,1,1 26 | 134,88,116,116,3,3,1 27 | 198,133,58,58,1,1,1 28 | 223,98,165,165,1,1,1 29 | 63,162,43,43,1,1,1 30 | 118,37,174,174,1,1,1 31 | 238,171,106,106,1,1,1 32 | 148,205,32,32,1,1,1 33 | 249,86,188,188,3,3,1 34 | 47,108,196,196,3,3,1 35 | 83,123,139,139,1,1,1 36 | 181,247,174,174,3,3,1 37 | 64,140,49,49,1,1,1 38 | 108,51,5,5,3,3,1 39 | 104,232,101,101,1,1,1 40 | 2,114,95,95,3,3,1 41 | 174,215,133,133,1,1,1 42 | 54,110,6,6,1,1,1 43 | 78,233,88,88,1,1,1 44 | 169,129,153,153,1,1,1 45 | 100,214,201,201,1,1,1 46 | 34,74,86,86,3,3,1 47 | 13,241,54,54,3,3,1 48 | 122,30,88,88,1,1,1 49 | 202,137,40,40,1,1,1 50 | 98,140,175,175,1,1,1 51 | 137,158,111,111,3,3,1 52 | 211,201,55,55,3,3,1 53 | 7,145,192,192,1,1,1 54 | 160,12,215,215,1,1,1 55 | 30,206,179,179,1,1,1 56 | 168,31,155,155,1,1,1 57 | 122,134,199,199,3,3,1 58 | 223,151,35,35,3,3,1 59 | 191,8,147,147,1,1,1 60 | 243,67,65,65,3,3,1 61 | 62,26,152,152,3,3,1 62 | 61,8,36,36,1,1,1 63 | 79,36,156,156,1,1,1 64 | 77,221,49,49,1,1,1 65 | 91,127,22,22,1,1,1 66 | 35,174,189,189,3,3,1 67 | 147,57,27,27,1,1,1 68 | 81,255,117,117,1,1,1 69 | 178,17,4,4,3,3,1 70 | 163,29,67,67,1,1,1 71 | 201,203,222,222,1,1,1 72 | 65,110,172,172,3,3,1 73 | 77,169,2,2,1,1,1 74 | 47,42,1,1,1,1,1 75 | 184,28,209,209,3,3,1 76 | 158,153,166,166,3,3,1 77 | 179,116,209,209,1,1,1 78 | 45,207,83,83,1,1,1 79 | 130,173,60,60,3,3,1 80 | 100,131,124,124,1,1,1 81 | 103,195,112,112,3,3,1 82 | 253,7,92,92,3,3,1 83 | 155,144,129,129,3,3,1 84 | 194,202,212,212,1,1,1 85 | 27,85,48,48,1,1,1 86 | 22,175,85,85,1,1,1 87 | 207,70,67,67,3,3,1 88 | 138,113,216,216,1,1,1 89 | 240,65,218,218,3,3,1 90 | 169,39,127,127,1,1,1 91 | 197,10,44,44,1,1,1 92 | 113,69,28,28,3,3,1 93 | 69,101,220,220,1,1,1 94 | 123,189,2,2,3,3,1 95 | 59,217,133,133,3,3,1 96 | 217,125,167,167,1,1,1 97 | 253,193,166,166,3,3,1 98 | 50,147,144,144,1,1,1 99 | 192,85,1,1,3,3,1 100 | 193,23,85,85,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic1_m.csv: -------------------------------------------------------------------------------- 1 | 40,110,4,4,3,3,1 2 | 129,101,144,144,1,1,1 3 | 10,171,224,224,3,3,1 4 | 38,59,99,99,3,3,1 5 | 5,177,40,40,3,3,1 6 | 167,13,220,220,3,3,1 7 | 97,122,56,56,3,3,1 8 | 121,243,184,184,1,1,1 9 | 166,30,162,162,1,1,1 10 | 94,92,81,81,3,3,1 11 | 61,74,45,45,3,3,1 12 | 207,245,174,174,1,1,1 13 | 48,166,10,10,1,1,1 14 | 110,98,186,186,3,3,1 15 | 138,126,87,87,1,1,1 16 | 78,10,184,184,1,1,1 17 | 170,152,128,128,1,1,1 18 | 146,184,220,220,1,1,1 19 | 235,188,47,47,1,1,1 20 | 195,4,175,175,1,1,1 21 | 126,102,155,155,3,3,1 22 | 240,79,60,60,1,1,1 23 | 29,135,194,194,3,3,1 24 | 72,70,166,166,3,3,1 25 | 92,148,152,152,3,3,1 26 | 201,9,58,58,3,3,1 27 | 6,168,185,185,1,1,1 28 | 31,154,36,36,1,1,1 29 | 120,30,51,51,3,3,1 30 | 164,112,212,212,1,1,1 31 | 219,173,47,47,3,3,1 32 | 140,239,165,165,1,1,1 33 | 110,239,132,132,3,3,1 34 | 168,131,201,201,3,3,1 35 | 56,136,44,44,1,1,1 36 | 181,94,77,77,1,1,1 37 | 117,201,27,27,1,1,1 38 | 62,71,1,1,1,1,1 39 | 234,22,48,48,1,1,1 40 | 71,193,223,223,3,3,1 41 | 235,243,48,48,3,3,1 42 | 197,190,64,64,1,1,1 43 | 196,1,170,170,3,3,1 44 | 109,225,153,153,3,3,1 45 | 222,136,143,143,1,1,1 46 | 86,6,158,158,1,1,1 47 | 8,110,189,189,1,1,1 48 | 236,23,84,84,1,1,1 49 | 161,179,22,22,1,1,1 50 | 146,61,33,33,3,3,1 51 | 175,223,121,121,1,1,1 52 | 113,69,150,150,1,1,1 53 | 126,143,64,64,3,3,1 54 | 103,161,17,17,1,1,1 55 | 14,163,64,64,1,1,1 56 | 100,93,36,36,1,1,1 57 | 179,77,196,196,3,3,1 58 | 203,8,116,116,3,3,1 59 | 48,194,177,177,1,1,1 60 | 104,19,175,175,3,3,1 61 | 210,161,88,88,1,1,1 62 | 144,11,195,195,3,3,1 63 | 239,196,43,43,3,3,1 64 | 112,205,49,49,1,1,1 65 | 139,208,113,113,3,3,1 66 | 154,214,18,18,3,3,1 67 | 77,191,41,41,3,3,1 68 | 145,24,84,84,1,1,1 69 | 256,116,128,128,1,1,1 70 | 51,232,123,123,1,1,1 71 | 80,78,29,29,1,1,1 72 | 203,185,134,134,3,3,1 73 | 2,182,119,119,3,3,1 74 | 144,31,135,135,3,3,1 75 | 178,151,194,194,3,3,1 76 | 193,93,18,18,1,1,1 77 | 233,152,153,153,1,1,1 78 | 118,163,188,188,1,1,1 79 | 6,123,214,214,1,1,1 80 | 31,156,15,15,1,1,1 81 | 136,250,224,224,1,1,1 82 | 115,39,36,36,1,1,1 83 | 54,207,50,50,1,1,1 84 | 138,145,76,76,1,1,1 85 | 16,226,112,112,1,1,1 86 | 137,72,223,223,1,1,1 87 | 185,122,154,154,3,3,1 88 | 152,99,124,124,1,1,1 89 | 119,107,153,153,3,3,1 90 | 219,129,64,64,3,3,1 91 | 183,187,162,162,1,1,1 92 | 219,197,34,34,3,3,1 93 | 196,179,99,99,3,3,1 94 | 50,202,179,179,3,3,1 95 | 28,81,108,108,3,3,1 96 | 65,43,197,197,1,1,1 97 | 213,220,14,14,3,3,1 98 | 3,72,139,139,3,3,1 99 | 67,48,119,119,3,3,1 100 | 210,246,7,7,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic2_m.csv: -------------------------------------------------------------------------------- 1 | 8,53,13,13,1,1,1 2 | 71,212,124,124,3,3,1 3 | 133,49,29,29,1,1,1 4 | 185,35,208,208,1,1,1 5 | 30,233,33,33,3,3,1 6 | 155,56,102,102,1,1,1 7 | 102,39,78,78,3,3,1 8 | 97,72,219,219,3,3,1 9 | 33,182,60,60,3,3,1 10 | 122,121,17,17,3,3,1 11 | 182,246,21,21,1,1,1 12 | 89,1,106,106,1,1,1 13 | 208,226,144,144,3,3,1 14 | 246,122,102,102,1,1,1 15 | 213,85,126,126,3,3,1 16 | 2,243,39,39,1,1,1 17 | 23,133,72,72,3,3,1 18 | 182,201,110,110,1,1,1 19 | 192,199,40,40,3,3,1 20 | 18,118,99,99,3,3,1 21 | 219,247,210,210,1,1,1 22 | 115,217,109,109,1,1,1 23 | 67,97,108,108,1,1,1 24 | 185,256,209,209,3,3,1 25 | 207,117,199,199,1,1,1 26 | 134,62,30,30,1,1,1 27 | 223,130,146,146,3,3,1 28 | 199,11,77,77,3,3,1 29 | 78,227,6,6,3,3,1 30 | 33,215,123,123,1,1,1 31 | 84,208,109,109,1,1,1 32 | 130,99,137,137,3,3,1 33 | 253,157,2,2,3,3,1 34 | 162,123,82,82,3,3,1 35 | 212,166,6,6,1,1,1 36 | 196,83,136,136,1,1,1 37 | 127,31,208,208,1,1,1 38 | 1,218,35,35,1,1,1 39 | 237,190,70,70,1,1,1 40 | 31,155,47,47,1,1,1 41 | 104,143,17,17,1,1,1 42 | 240,238,179,179,1,1,1 43 | 170,189,107,107,1,1,1 44 | 200,5,103,103,1,1,1 45 | 64,250,82,82,3,3,1 46 | 41,54,190,190,3,3,1 47 | 2,73,117,117,1,1,1 48 | 178,115,49,49,1,1,1 49 | 246,159,163,163,3,3,1 50 | 238,206,145,145,1,1,1 51 | 24,187,188,188,3,3,1 52 | 231,236,144,144,3,3,1 53 | 194,248,4,4,1,1,1 54 | 69,38,19,19,1,1,1 55 | 91,232,224,224,1,1,1 56 | 199,46,50,50,3,3,1 57 | 174,138,201,201,1,1,1 58 | 221,86,219,219,3,3,1 59 | 28,58,10,10,1,1,1 60 | 45,204,121,121,3,3,1 61 | 202,56,161,161,3,3,1 62 | 63,226,76,76,1,1,1 63 | 158,23,36,36,3,3,1 64 | 131,245,176,176,3,3,1 65 | 224,99,59,59,3,3,1 66 | 231,102,21,21,1,1,1 67 | 178,40,19,19,3,3,1 68 | 127,172,50,50,3,3,1 69 | 219,212,35,35,3,3,1 70 | 134,149,151,151,3,3,1 71 | 191,46,163,163,1,1,1 72 | 125,161,218,218,1,1,1 73 | 1,236,48,48,1,1,1 74 | 78,78,115,115,3,3,1 75 | 118,6,142,142,3,3,1 76 | 67,145,75,75,1,1,1 77 | 8,81,87,87,3,3,1 78 | 192,183,85,85,1,1,1 79 | 193,56,121,121,3,3,1 80 | 33,193,165,165,3,3,1 81 | 153,208,23,23,1,1,1 82 | 134,160,163,163,3,3,1 83 | 172,95,182,182,3,3,1 84 | 56,91,118,118,3,3,1 85 | 142,116,19,19,1,1,1 86 | 139,184,185,185,1,1,1 87 | 227,143,219,219,1,1,1 88 | 177,72,75,75,3,3,1 89 | 207,74,132,132,3,3,1 90 | 69,61,91,91,3,3,1 91 | 112,170,106,106,1,1,1 92 | 196,40,76,76,1,1,1 93 | 241,149,66,66,1,1,1 94 | 55,227,46,46,3,3,1 95 | 138,173,109,109,3,3,1 96 | 1,106,35,35,3,3,1 97 | 31,105,21,21,3,3,1 98 | 21,7,184,184,3,3,1 99 | 51,229,68,68,1,1,1 100 | 112,66,45,45,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic3_m.csv: -------------------------------------------------------------------------------- 1 | 71,222,173,173,3,3,1 2 | 194,33,125,125,1,1,1 3 | 176,235,166,166,1,1,1 4 | 179,191,221,221,1,1,1 5 | 159,141,122,122,1,1,1 6 | 57,14,82,82,3,3,1 7 | 84,101,170,170,3,3,1 8 | 41,74,59,59,3,3,1 9 | 120,153,187,187,1,1,1 10 | 150,88,115,115,1,1,1 11 | 98,145,177,177,3,3,1 12 | 216,61,143,143,3,3,1 13 | 217,17,136,136,3,3,1 14 | 41,43,85,85,1,1,1 15 | 71,154,36,36,3,3,1 16 | 98,52,35,35,1,1,1 17 | 85,242,133,133,3,3,1 18 | 94,219,10,10,1,1,1 19 | 100,54,164,164,1,1,1 20 | 171,12,159,159,3,3,1 21 | 164,244,150,150,3,3,1 22 | 81,223,31,31,3,3,1 23 | 76,234,76,76,3,3,1 24 | 251,98,225,225,3,3,1 25 | 228,47,68,68,3,3,1 26 | 5,5,139,139,1,1,1 27 | 160,139,193,193,3,3,1 28 | 133,207,3,3,3,3,1 29 | 16,95,55,55,1,1,1 30 | 20,45,114,114,3,3,1 31 | 129,255,200,200,3,3,1 32 | 98,180,207,207,1,1,1 33 | 197,195,38,38,1,1,1 34 | 215,249,210,210,1,1,1 35 | 183,65,210,210,1,1,1 36 | 85,16,2,2,3,3,1 37 | 184,210,60,60,3,3,1 38 | 124,169,79,79,1,1,1 39 | 216,228,89,89,3,3,1 40 | 159,145,68,68,1,1,1 41 | 252,101,134,134,1,1,1 42 | 150,127,27,27,3,3,1 43 | 90,57,96,96,3,3,1 44 | 115,134,58,58,1,1,1 45 | 115,239,65,65,1,1,1 46 | 164,9,159,159,1,1,1 47 | 88,9,69,69,3,3,1 48 | 51,167,136,136,1,1,1 49 | 221,220,58,58,3,3,1 50 | 31,10,3,3,3,3,1 51 | 44,236,28,28,3,3,1 52 | 200,114,80,80,3,3,1 53 | 75,231,40,40,3,3,1 54 | 138,66,159,159,1,1,1 55 | 173,41,168,168,1,1,1 56 | 2,143,133,133,3,3,1 57 | 215,245,153,153,3,3,1 58 | 101,161,137,137,1,1,1 59 | 34,35,51,51,1,1,1 60 | 161,121,156,156,1,1,1 61 | 107,88,119,119,1,1,1 62 | 65,43,176,176,3,3,1 63 | 6,59,99,99,3,3,1 64 | 18,62,14,14,1,1,1 65 | 38,78,93,93,1,1,1 66 | 98,74,212,212,1,1,1 67 | 125,59,115,115,1,1,1 68 | 157,143,2,2,1,1,1 69 | 21,190,122,122,3,3,1 70 | 256,126,112,112,1,1,1 71 | 64,193,109,109,3,3,1 72 | 93,59,135,135,3,3,1 73 | 160,81,2,2,1,1,1 74 | 145,191,67,67,3,3,1 75 | 113,185,214,214,1,1,1 76 | 17,236,59,59,3,3,1 77 | 42,199,86,86,3,3,1 78 | 136,143,210,210,1,1,1 79 | 78,161,82,82,1,1,1 80 | 122,195,53,53,3,3,1 81 | 59,202,198,198,3,3,1 82 | 79,71,11,11,3,3,1 83 | 30,207,148,148,1,1,1 84 | 187,128,150,150,1,1,1 85 | 151,16,168,168,1,1,1 86 | 29,67,156,156,1,1,1 87 | 230,228,101,101,1,1,1 88 | 180,151,127,127,1,1,1 89 | 253,189,99,99,3,3,1 90 | 196,74,183,183,1,1,1 91 | 210,148,24,24,3,3,1 92 | 9,20,199,199,3,3,1 93 | 195,107,38,38,1,1,1 94 | 250,59,71,71,3,3,1 95 | 55,223,141,141,3,3,1 96 | 36,4,27,27,3,3,1 97 | 222,30,43,43,1,1,1 98 | 251,84,92,92,3,3,1 99 | 136,27,21,21,3,3,1 100 | 101,99,114,114,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic4_m.csv: -------------------------------------------------------------------------------- 1 | 176,123,144,144,3,3,1 2 | 90,35,127,127,1,1,1 3 | 115,195,90,90,3,3,1 4 | 195,69,7,7,3,3,1 5 | 36,236,175,175,1,1,1 6 | 84,100,154,154,1,1,1 7 | 133,47,2,2,1,1,1 8 | 158,253,167,167,1,1,1 9 | 72,16,172,172,1,1,1 10 | 117,147,81,81,1,1,1 11 | 138,148,112,112,3,3,1 12 | 31,158,34,34,3,3,1 13 | 210,116,183,183,3,3,1 14 | 150,229,22,22,3,3,1 15 | 145,90,196,196,3,3,1 16 | 148,188,222,222,3,3,1 17 | 212,214,59,59,3,3,1 18 | 54,49,160,160,1,1,1 19 | 45,7,47,47,3,3,1 20 | 109,38,60,60,3,3,1 21 | 202,144,83,83,3,3,1 22 | 12,140,129,129,3,3,1 23 | 234,8,224,224,1,1,1 24 | 218,216,2,2,3,3,1 25 | 205,206,195,195,3,3,1 26 | 61,134,224,224,3,3,1 27 | 173,224,197,197,1,1,1 28 | 149,193,122,122,1,1,1 29 | 89,34,103,103,3,3,1 30 | 195,221,95,95,1,1,1 31 | 86,147,99,99,1,1,1 32 | 217,226,149,149,1,1,1 33 | 22,208,131,131,1,1,1 34 | 145,120,45,45,3,3,1 35 | 189,64,13,13,1,1,1 36 | 185,83,30,30,1,1,1 37 | 239,176,162,162,3,3,1 38 | 38,100,81,81,3,3,1 39 | 7,65,219,219,1,1,1 40 | 69,101,174,174,1,1,1 41 | 76,75,56,56,1,1,1 42 | 238,56,33,33,1,1,1 43 | 136,91,204,204,3,3,1 44 | 223,160,59,59,1,1,1 45 | 19,140,87,87,3,3,1 46 | 21,198,205,205,3,3,1 47 | 142,11,29,29,3,3,1 48 | 74,5,16,16,1,1,1 49 | 36,161,98,98,3,3,1 50 | 209,155,213,213,1,1,1 51 | 197,83,78,78,1,1,1 52 | 219,232,161,161,3,3,1 53 | 150,108,134,134,3,3,1 54 | 181,141,96,96,3,3,1 55 | 231,225,144,144,1,1,1 56 | 169,155,96,96,3,3,1 57 | 121,41,157,157,1,1,1 58 | 65,227,162,162,1,1,1 59 | 6,221,174,174,3,3,1 60 | 106,60,31,31,1,1,1 61 | 39,179,47,47,3,3,1 62 | 226,256,187,187,3,3,1 63 | 212,174,126,126,1,1,1 64 | 210,82,177,177,3,3,1 65 | 58,49,21,21,3,3,1 66 | 41,10,58,58,3,3,1 67 | 130,5,221,221,1,1,1 68 | 78,251,211,211,1,1,1 69 | 194,197,180,180,3,3,1 70 | 78,69,215,215,1,1,1 71 | 252,173,90,90,1,1,1 72 | 172,87,45,45,1,1,1 73 | 18,206,103,103,3,3,1 74 | 114,114,206,206,1,1,1 75 | 191,96,111,111,1,1,1 76 | 139,55,194,194,1,1,1 77 | 228,212,182,182,1,1,1 78 | 28,247,59,59,1,1,1 79 | 79,74,57,57,1,1,1 80 | 63,24,98,98,1,1,1 81 | 240,1,203,203,3,3,1 82 | 174,56,53,53,1,1,1 83 | 36,222,32,32,3,3,1 84 | 204,121,121,121,3,3,1 85 | 126,235,103,103,3,3,1 86 | 148,159,74,74,1,1,1 87 | 96,195,57,57,3,3,1 88 | 35,94,113,113,1,1,1 89 | 209,173,191,191,3,3,1 90 | 144,115,211,211,1,1,1 91 | 190,140,213,213,3,3,1 92 | 67,240,167,167,1,1,1 93 | 161,126,104,104,1,1,1 94 | 89,131,70,70,3,3,1 95 | 232,244,198,198,3,3,1 96 | 50,238,170,170,1,1,1 97 | 250,51,147,147,1,1,1 98 | 27,134,158,158,3,3,1 99 | 114,57,199,199,1,1,1 100 | 238,186,37,37,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic5_m.csv: -------------------------------------------------------------------------------- 1 | 192,229,56,56,3,3,1 2 | 135,220,149,149,1,1,1 3 | 200,96,162,162,1,1,1 4 | 65,6,101,101,3,3,1 5 | 13,192,71,71,3,3,1 6 | 85,88,53,53,3,3,1 7 | 34,41,135,135,3,3,1 8 | 228,43,152,152,3,3,1 9 | 247,45,97,97,1,1,1 10 | 98,180,14,14,1,1,1 11 | 110,161,161,161,3,3,1 12 | 124,91,24,24,3,3,1 13 | 24,45,26,26,1,1,1 14 | 233,215,5,5,1,1,1 15 | 42,60,204,204,1,1,1 16 | 132,127,198,198,3,3,1 17 | 3,184,70,70,1,1,1 18 | 55,91,190,190,1,1,1 19 | 208,172,137,137,1,1,1 20 | 136,125,5,5,3,3,1 21 | 37,24,153,153,3,3,1 22 | 148,179,180,180,3,3,1 23 | 63,138,78,78,3,3,1 24 | 235,20,194,194,3,3,1 25 | 93,89,171,171,1,1,1 26 | 189,113,30,30,3,3,1 27 | 212,95,71,71,3,3,1 28 | 134,70,68,68,1,1,1 29 | 157,130,153,153,3,3,1 30 | 219,45,158,158,3,3,1 31 | 219,198,128,128,3,3,1 32 | 134,226,42,42,3,3,1 33 | 7,190,178,178,1,1,1 34 | 203,61,166,166,1,1,1 35 | 206,186,84,84,1,1,1 36 | 61,14,24,24,1,1,1 37 | 12,56,168,168,1,1,1 38 | 111,18,112,112,1,1,1 39 | 163,103,26,26,3,3,1 40 | 224,141,205,205,3,3,1 41 | 85,122,80,80,3,3,1 42 | 67,240,203,203,1,1,1 43 | 137,222,48,48,3,3,1 44 | 188,158,26,26,1,1,1 45 | 187,181,135,135,1,1,1 46 | 145,3,122,122,1,1,1 47 | 255,148,45,45,1,1,1 48 | 71,189,219,219,1,1,1 49 | 37,45,171,171,1,1,1 50 | 33,172,41,41,1,1,1 51 | 229,110,40,40,1,1,1 52 | 11,174,73,73,3,3,1 53 | 207,181,103,103,3,3,1 54 | 115,113,127,127,1,1,1 55 | 140,143,102,102,3,3,1 56 | 84,205,148,148,1,1,1 57 | 142,91,132,132,1,1,1 58 | 238,130,2,2,3,3,1 59 | 127,171,148,148,1,1,1 60 | 147,99,220,220,1,1,1 61 | 219,74,178,178,1,1,1 62 | 119,167,22,22,3,3,1 63 | 88,249,166,166,3,3,1 64 | 21,140,145,145,1,1,1 65 | 55,181,46,46,3,3,1 66 | 204,105,141,141,1,1,1 67 | 65,143,188,188,3,3,1 68 | 142,92,13,13,1,1,1 69 | 17,248,23,23,3,3,1 70 | 202,39,74,74,1,1,1 71 | 256,148,87,87,3,3,1 72 | 184,64,176,176,1,1,1 73 | 13,126,13,13,1,1,1 74 | 120,249,138,138,1,1,1 75 | 1,25,102,102,3,3,1 76 | 38,75,193,193,1,1,1 77 | 98,126,215,215,1,1,1 78 | 103,95,132,132,3,3,1 79 | 201,234,23,23,1,1,1 80 | 253,145,142,142,3,3,1 81 | 193,78,84,84,3,3,1 82 | 55,201,87,87,3,3,1 83 | 65,15,162,162,3,3,1 84 | 251,75,196,196,3,3,1 85 | 48,33,142,142,1,1,1 86 | 97,180,48,48,3,3,1 87 | 66,245,162,162,1,1,1 88 | 14,218,116,116,1,1,1 89 | 166,244,190,190,1,1,1 90 | 208,246,52,52,1,1,1 91 | 142,167,182,182,1,1,1 92 | 21,118,198,198,1,1,1 93 | 220,179,210,210,1,1,1 94 | 93,10,34,34,3,3,1 95 | 56,226,35,35,3,3,1 96 | 249,248,72,72,1,1,1 97 | 19,141,39,39,1,1,1 98 | 124,148,179,179,3,3,1 99 | 195,87,203,203,1,1,1 100 | 140,110,139,139,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic6_m.csv: -------------------------------------------------------------------------------- 1 | 149,245,52,52,3,3,1 2 | 106,148,89,89,1,1,1 3 | 26,197,150,150,1,1,1 4 | 8,96,148,148,1,1,1 5 | 163,248,87,87,3,3,1 6 | 18,91,120,120,1,1,1 7 | 209,131,53,53,3,3,1 8 | 180,122,67,67,3,3,1 9 | 178,175,146,146,1,1,1 10 | 173,239,64,64,3,3,1 11 | 235,50,111,111,3,3,1 12 | 46,10,190,190,1,1,1 13 | 53,239,150,150,3,3,1 14 | 205,199,99,99,3,3,1 15 | 165,135,154,154,1,1,1 16 | 58,107,146,146,1,1,1 17 | 231,64,35,35,3,3,1 18 | 120,246,95,95,1,1,1 19 | 206,36,97,97,3,3,1 20 | 116,176,197,197,3,3,1 21 | 98,1,201,201,3,3,1 22 | 149,251,26,26,1,1,1 23 | 218,6,143,143,1,1,1 24 | 43,116,93,93,1,1,1 25 | 151,220,192,192,1,1,1 26 | 67,64,201,201,1,1,1 27 | 118,197,103,103,3,3,1 28 | 81,122,163,163,1,1,1 29 | 248,1,175,175,1,1,1 30 | 189,172,148,148,1,1,1 31 | 156,228,162,162,1,1,1 32 | 124,200,210,210,1,1,1 33 | 82,165,104,104,3,3,1 34 | 62,117,21,21,1,1,1 35 | 50,12,21,21,1,1,1 36 | 10,64,217,217,3,3,1 37 | 15,160,196,196,1,1,1 38 | 190,1,31,31,1,1,1 39 | 101,66,14,14,3,3,1 40 | 166,195,132,132,3,3,1 41 | 200,11,96,96,1,1,1 42 | 242,253,36,36,1,1,1 43 | 167,220,210,210,1,1,1 44 | 241,168,79,79,3,3,1 45 | 49,246,115,115,1,1,1 46 | 151,72,84,84,1,1,1 47 | 6,14,8,8,1,1,1 48 | 11,116,219,219,1,1,1 49 | 57,123,96,96,3,3,1 50 | 103,55,71,71,3,3,1 51 | 251,73,189,189,3,3,1 52 | 70,175,171,171,3,3,1 53 | 59,57,65,65,1,1,1 54 | 201,44,50,50,3,3,1 55 | 62,84,27,27,3,3,1 56 | 204,27,150,150,1,1,1 57 | 187,98,35,35,1,1,1 58 | 206,30,36,36,3,3,1 59 | 96,213,179,179,3,3,1 60 | 221,65,173,173,1,1,1 61 | 108,177,49,49,3,3,1 62 | 49,5,121,121,3,3,1 63 | 181,116,213,213,1,1,1 64 | 176,53,213,213,1,1,1 65 | 15,216,175,175,1,1,1 66 | 35,195,196,196,1,1,1 67 | 48,140,9,9,3,3,1 68 | 221,64,61,61,1,1,1 69 | 135,17,88,88,1,1,1 70 | 18,105,40,40,1,1,1 71 | 20,11,68,68,1,1,1 72 | 42,127,113,113,3,3,1 73 | 199,98,189,189,3,3,1 74 | 120,64,198,198,1,1,1 75 | 235,50,204,204,3,3,1 76 | 5,204,208,208,3,3,1 77 | 88,162,165,165,1,1,1 78 | 12,185,37,37,1,1,1 79 | 17,182,66,66,3,3,1 80 | 206,235,221,221,1,1,1 81 | 48,32,181,181,1,1,1 82 | 37,77,100,100,3,3,1 83 | 200,71,35,35,3,3,1 84 | 123,197,4,4,3,3,1 85 | 227,10,123,123,3,3,1 86 | 88,7,113,113,3,3,1 87 | 222,244,122,122,3,3,1 88 | 243,232,124,124,3,3,1 89 | 44,20,181,181,3,3,1 90 | 88,236,34,34,3,3,1 91 | 76,209,176,176,1,1,1 92 | 116,176,12,12,3,3,1 93 | 35,148,217,217,1,1,1 94 | 156,218,77,77,1,1,1 95 | 56,1,87,87,3,3,1 96 | 120,170,119,119,3,3,1 97 | 209,251,203,203,3,3,1 98 | 176,230,170,170,1,1,1 99 | 30,60,64,64,3,3,1 100 | 250,239,80,80,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic7_m.csv: -------------------------------------------------------------------------------- 1 | 111,183,82,82,3,3,1 2 | 41,147,89,89,1,1,1 3 | 81,84,104,104,3,3,1 4 | 194,86,17,17,1,1,1 5 | 116,11,64,64,1,1,1 6 | 54,38,198,198,3,3,1 7 | 120,65,53,53,3,3,1 8 | 23,7,137,137,1,1,1 9 | 246,53,79,79,1,1,1 10 | 181,219,42,42,1,1,1 11 | 19,188,30,30,1,1,1 12 | 10,9,66,66,1,1,1 13 | 187,1,150,150,1,1,1 14 | 101,41,101,101,1,1,1 15 | 235,235,13,13,1,1,1 16 | 195,204,208,208,3,3,1 17 | 29,80,203,203,1,1,1 18 | 36,58,204,204,3,3,1 19 | 18,47,174,174,3,3,1 20 | 92,205,179,179,1,1,1 21 | 205,92,102,102,1,1,1 22 | 100,92,111,111,3,3,1 23 | 29,95,59,59,3,3,1 24 | 183,91,188,188,1,1,1 25 | 225,90,18,18,3,3,1 26 | 213,160,146,146,3,3,1 27 | 209,50,10,10,3,3,1 28 | 20,46,7,7,3,3,1 29 | 181,210,85,85,3,3,1 30 | 246,26,143,143,1,1,1 31 | 233,93,169,169,1,1,1 32 | 124,24,136,136,1,1,1 33 | 229,202,200,200,3,3,1 34 | 83,85,182,182,1,1,1 35 | 215,204,111,111,1,1,1 36 | 245,88,45,45,1,1,1 37 | 131,120,81,81,1,1,1 38 | 206,211,210,210,1,1,1 39 | 26,68,4,4,1,1,1 40 | 69,124,221,221,3,3,1 41 | 225,126,208,208,1,1,1 42 | 148,27,180,180,3,3,1 43 | 116,16,55,55,1,1,1 44 | 213,209,167,167,3,3,1 45 | 151,136,47,47,1,1,1 46 | 85,42,33,33,3,3,1 47 | 229,120,100,100,1,1,1 48 | 237,81,192,192,3,3,1 49 | 159,120,215,215,3,3,1 50 | 159,116,35,35,1,1,1 51 | 20,30,61,61,3,3,1 52 | 168,51,70,70,1,1,1 53 | 162,251,162,162,1,1,1 54 | 195,173,29,29,1,1,1 55 | 199,249,182,182,3,3,1 56 | 242,58,196,196,3,3,1 57 | 83,204,116,116,1,1,1 58 | 80,64,18,18,3,3,1 59 | 2,17,24,24,3,3,1 60 | 49,251,92,92,1,1,1 61 | 127,171,42,42,3,3,1 62 | 196,128,62,62,1,1,1 63 | 168,209,84,84,3,3,1 64 | 201,85,84,84,1,1,1 65 | 72,9,205,205,3,3,1 66 | 39,190,35,35,3,3,1 67 | 212,139,203,203,1,1,1 68 | 198,25,200,200,1,1,1 69 | 199,84,177,177,3,3,1 70 | 232,120,100,100,3,3,1 71 | 102,110,171,171,3,3,1 72 | 181,93,45,45,3,3,1 73 | 83,46,213,213,3,3,1 74 | 12,32,8,8,3,3,1 75 | 143,149,86,86,1,1,1 76 | 16,128,21,21,1,1,1 77 | 165,73,212,212,3,3,1 78 | 141,117,26,26,3,3,1 79 | 175,95,175,175,3,3,1 80 | 249,244,144,144,1,1,1 81 | 18,151,66,66,1,1,1 82 | 78,174,102,102,3,3,1 83 | 21,54,31,31,1,1,1 84 | 187,208,15,15,3,3,1 85 | 221,193,123,123,3,3,1 86 | 151,212,146,146,3,3,1 87 | 46,254,97,97,1,1,1 88 | 87,93,5,5,3,3,1 89 | 59,220,103,103,3,3,1 90 | 96,64,214,214,1,1,1 91 | 145,125,44,44,1,1,1 92 | 75,82,4,4,1,1,1 93 | 124,175,56,56,3,3,1 94 | 140,216,112,112,1,1,1 95 | 119,166,73,73,3,3,1 96 | 69,83,188,188,3,3,1 97 | 73,190,49,49,1,1,1 98 | 84,84,47,47,3,3,1 99 | 136,252,194,194,1,1,1 100 | 39,172,156,156,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic8_m.csv: -------------------------------------------------------------------------------- 1 | 39,48,156,156,3,3,1 2 | 114,193,94,94,3,3,1 3 | 157,50,116,116,3,3,1 4 | 8,175,188,188,1,1,1 5 | 155,27,213,213,1,1,1 6 | 112,80,76,76,1,1,1 7 | 72,126,215,215,1,1,1 8 | 107,125,120,120,3,3,1 9 | 20,39,78,78,1,1,1 10 | 245,239,222,222,3,3,1 11 | 231,83,159,159,1,1,1 12 | 189,42,84,84,3,3,1 13 | 9,227,103,103,1,1,1 14 | 121,6,172,172,1,1,1 15 | 82,38,159,159,3,3,1 16 | 101,157,160,160,3,3,1 17 | 230,205,102,102,3,3,1 18 | 251,24,77,77,1,1,1 19 | 215,78,158,158,1,1,1 20 | 157,160,135,135,3,3,1 21 | 94,44,197,197,3,3,1 22 | 84,81,5,5,1,1,1 23 | 53,3,63,63,1,1,1 24 | 44,42,50,50,1,1,1 25 | 126,58,12,12,3,3,1 26 | 114,175,42,42,3,3,1 27 | 231,41,126,126,1,1,1 28 | 211,125,89,89,3,3,1 29 | 139,225,142,142,3,3,1 30 | 236,54,87,87,1,1,1 31 | 239,209,198,198,1,1,1 32 | 170,182,68,68,1,1,1 33 | 41,56,38,38,1,1,1 34 | 31,199,131,131,3,3,1 35 | 38,104,213,213,1,1,1 36 | 216,213,170,170,3,3,1 37 | 35,94,20,20,1,1,1 38 | 152,241,180,180,1,1,1 39 | 114,24,124,124,3,3,1 40 | 41,50,23,23,1,1,1 41 | 181,221,140,140,3,3,1 42 | 201,6,83,83,3,3,1 43 | 93,14,136,136,1,1,1 44 | 132,97,61,61,1,1,1 45 | 125,208,18,18,3,3,1 46 | 31,12,73,73,3,3,1 47 | 111,72,44,44,3,3,1 48 | 8,75,153,153,1,1,1 49 | 169,128,139,139,3,3,1 50 | 186,136,142,142,3,3,1 51 | 98,98,47,47,3,3,1 52 | 85,204,223,223,3,3,1 53 | 69,222,151,151,3,3,1 54 | 176,61,17,17,1,1,1 55 | 40,82,52,52,1,1,1 56 | 62,98,79,79,1,1,1 57 | 155,242,64,64,1,1,1 58 | 43,24,216,216,1,1,1 59 | 227,153,102,102,1,1,1 60 | 202,75,29,29,3,3,1 61 | 22,44,63,63,3,3,1 62 | 199,248,202,202,1,1,1 63 | 78,173,36,36,3,3,1 64 | 162,128,80,80,1,1,1 65 | 219,147,157,157,3,3,1 66 | 169,66,31,31,1,1,1 67 | 231,18,133,133,3,3,1 68 | 253,187,68,68,3,3,1 69 | 175,146,103,103,3,3,1 70 | 226,210,137,137,1,1,1 71 | 63,152,217,217,3,3,1 72 | 203,70,93,93,1,1,1 73 | 107,220,112,112,1,1,1 74 | 43,83,219,219,3,3,1 75 | 191,105,82,82,1,1,1 76 | 201,245,113,113,1,1,1 77 | 240,9,107,107,1,1,1 78 | 129,37,27,27,1,1,1 79 | 195,238,106,106,1,1,1 80 | 172,129,168,168,1,1,1 81 | 130,242,166,166,3,3,1 82 | 7,102,76,76,3,3,1 83 | 36,104,213,213,1,1,1 84 | 41,68,221,221,1,1,1 85 | 87,155,200,200,3,3,1 86 | 164,183,135,135,1,1,1 87 | 241,37,225,225,3,3,1 88 | 4,218,25,25,3,3,1 89 | 185,23,12,12,1,1,1 90 | 226,172,77,77,1,1,1 91 | 223,103,16,16,1,1,1 92 | 126,11,81,81,3,3,1 93 | 66,206,57,57,3,3,1 94 | 127,24,165,165,1,1,1 95 | 218,38,164,164,1,1,1 96 | 162,36,224,224,1,1,1 97 | 19,39,189,189,3,3,1 98 | 113,86,20,20,1,1,1 99 | 131,180,80,80,1,1,1 100 | 164,63,155,155,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch2/traffic9_m.csv: -------------------------------------------------------------------------------- 1 | 70,124,51,51,3,3,1 2 | 109,208,203,203,3,3,1 3 | 92,15,156,156,1,1,1 4 | 101,35,117,117,3,3,1 5 | 63,145,92,92,1,1,1 6 | 31,210,13,13,3,3,1 7 | 144,6,131,131,1,1,1 8 | 180,58,133,133,3,3,1 9 | 35,12,189,189,1,1,1 10 | 16,45,57,57,1,1,1 11 | 34,48,196,196,3,3,1 12 | 182,126,38,38,3,3,1 13 | 213,17,111,111,3,3,1 14 | 164,17,140,140,1,1,1 15 | 23,9,190,190,3,3,1 16 | 117,243,224,224,3,3,1 17 | 176,238,157,157,3,3,1 18 | 132,129,1,1,1,1,1 19 | 76,219,38,38,3,3,1 20 | 152,146,147,147,3,3,1 21 | 149,173,90,90,3,3,1 22 | 26,19,2,2,1,1,1 23 | 131,135,203,203,1,1,1 24 | 162,231,81,81,3,3,1 25 | 204,165,68,68,3,3,1 26 | 155,5,123,123,1,1,1 27 | 29,225,60,60,3,3,1 28 | 49,176,168,168,1,1,1 29 | 253,148,197,197,3,3,1 30 | 197,227,118,118,1,1,1 31 | 5,131,130,130,3,3,1 32 | 165,202,218,218,3,3,1 33 | 228,243,6,6,3,3,1 34 | 18,15,3,3,1,1,1 35 | 248,31,138,138,3,3,1 36 | 227,255,17,17,1,1,1 37 | 181,68,143,143,1,1,1 38 | 116,176,146,146,1,1,1 39 | 37,248,106,106,3,3,1 40 | 174,76,52,52,3,3,1 41 | 237,114,188,188,1,1,1 42 | 77,237,189,189,1,1,1 43 | 248,124,73,73,1,1,1 44 | 116,154,123,123,1,1,1 45 | 72,253,207,207,3,3,1 46 | 245,28,182,182,3,3,1 47 | 70,14,150,150,1,1,1 48 | 105,133,29,29,1,1,1 49 | 140,156,55,55,3,3,1 50 | 192,173,214,214,3,3,1 51 | 46,121,213,213,1,1,1 52 | 127,228,89,89,3,3,1 53 | 1,97,187,187,1,1,1 54 | 62,241,95,95,3,3,1 55 | 239,58,7,7,3,3,1 56 | 238,230,157,157,3,3,1 57 | 143,234,187,187,1,1,1 58 | 106,48,60,60,1,1,1 59 | 88,7,22,22,1,1,1 60 | 31,58,180,180,3,3,1 61 | 5,159,76,76,1,1,1 62 | 29,138,82,82,1,1,1 63 | 118,36,109,109,1,1,1 64 | 168,221,13,13,3,3,1 65 | 225,175,65,65,3,3,1 66 | 237,202,31,31,3,3,1 67 | 16,90,218,218,3,3,1 68 | 158,173,16,16,3,3,1 69 | 121,211,171,171,3,3,1 70 | 242,227,76,76,1,1,1 71 | 220,217,26,26,3,3,1 72 | 2,245,117,117,3,3,1 73 | 188,63,164,164,1,1,1 74 | 222,56,83,83,3,3,1 75 | 53,115,49,49,3,3,1 76 | 86,60,127,127,1,1,1 77 | 139,139,74,74,1,1,1 78 | 169,181,185,185,1,1,1 79 | 245,131,49,49,3,3,1 80 | 235,111,72,72,1,1,1 81 | 170,113,53,53,3,3,1 82 | 253,111,202,202,3,3,1 83 | 230,35,34,34,3,3,1 84 | 115,71,8,8,1,1,1 85 | 153,23,62,62,1,1,1 86 | 67,243,122,122,3,3,1 87 | 213,94,15,15,3,3,1 88 | 183,135,73,73,3,3,1 89 | 37,102,168,168,3,3,1 90 | 52,18,87,87,3,3,1 91 | 190,32,133,133,1,1,1 92 | 256,71,105,105,3,3,1 93 | 113,154,178,178,1,1,1 94 | 121,94,163,163,1,1,1 95 | 162,176,190,190,1,1,1 96 | 140,93,10,10,3,3,1 97 | 5,5,78,78,1,1,1 98 | 248,117,61,61,1,1,1 99 | 78,253,169,169,1,1,1 100 | 26,18,144,144,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic0_m.csv: -------------------------------------------------------------------------------- 1 | 221,196,11,11,1,1,1 2 | 243,95,225,225,1,1,1 3 | 1,89,198,198,1,1,1 4 | 21,52,20,20,3,3,1 5 | 132,129,158,158,1,1,1 6 | 79,101,96,96,1,1,1 7 | 22,208,184,184,1,1,1 8 | 125,239,75,75,3,3,1 9 | 119,250,56,56,1,1,1 10 | 229,143,8,8,3,3,1 11 | 238,30,99,99,3,3,1 12 | 241,37,26,26,3,3,1 13 | 198,50,100,100,3,3,1 14 | 232,1,223,223,3,3,1 15 | 8,90,185,185,3,3,1 16 | 241,179,94,94,3,3,1 17 | 253,66,78,78,3,3,1 18 | 181,120,173,173,3,3,1 19 | 102,32,79,79,1,1,1 20 | 39,129,158,158,3,3,1 21 | 228,96,11,11,3,3,1 22 | 229,39,146,146,1,1,1 23 | 34,209,122,122,1,1,1 24 | 247,42,101,101,1,1,1 25 | 38,8,11,11,1,1,1 26 | 67,118,3,3,3,3,1 27 | 222,32,59,59,3,3,1 28 | 177,145,114,114,3,3,1 29 | 230,113,179,179,1,1,1 30 | 6,254,12,12,3,3,1 31 | 229,1,208,208,3,3,1 32 | 122,213,100,100,3,3,1 33 | 193,59,48,48,1,1,1 34 | 248,114,153,153,3,3,1 35 | 36,46,12,12,1,1,1 36 | 162,103,20,20,1,1,1 37 | 203,72,67,67,1,1,1 38 | 44,48,155,155,3,3,1 39 | 70,4,77,77,3,3,1 40 | 200,158,167,167,1,1,1 41 | 109,141,8,8,1,1,1 42 | 230,145,143,143,1,1,1 43 | 246,15,169,169,3,3,1 44 | 1,180,87,87,3,3,1 45 | 174,51,96,96,3,3,1 46 | 79,58,48,48,1,1,1 47 | 234,44,127,127,1,1,1 48 | 35,256,96,96,1,1,1 49 | 73,236,215,215,1,1,1 50 | 57,166,129,129,3,3,1 51 | 95,155,174,174,3,3,1 52 | 36,197,116,116,1,1,1 53 | 206,173,206,206,3,3,1 54 | 116,16,59,59,1,1,1 55 | 121,38,167,167,3,3,1 56 | 176,86,115,115,1,1,1 57 | 247,64,61,61,1,1,1 58 | 28,185,93,93,1,1,1 59 | 51,128,192,192,1,1,1 60 | 236,2,213,213,1,1,1 61 | 211,45,48,48,1,1,1 62 | 199,189,113,113,1,1,1 63 | 63,61,192,192,1,1,1 64 | 248,136,65,65,3,3,1 65 | 205,148,207,207,3,3,1 66 | 62,66,21,21,1,1,1 67 | 182,222,45,45,1,1,1 68 | 97,115,110,110,1,1,1 69 | 182,138,31,31,3,3,1 70 | 49,34,112,112,1,1,1 71 | 223,179,107,107,1,1,1 72 | 55,76,53,53,1,1,1 73 | 122,128,225,225,1,1,1 74 | 118,101,200,200,3,3,1 75 | 201,86,90,90,1,1,1 76 | 191,155,137,137,1,1,1 77 | 143,95,199,199,1,1,1 78 | 186,75,144,144,3,3,1 79 | 250,24,190,190,3,3,1 80 | 176,200,20,20,3,3,1 81 | 11,218,99,99,1,1,1 82 | 156,209,84,84,3,3,1 83 | 143,8,154,154,1,1,1 84 | 188,108,177,177,3,3,1 85 | 109,144,33,33,1,1,1 86 | 138,201,157,157,3,3,1 87 | 75,51,117,117,1,1,1 88 | 234,74,6,6,3,3,1 89 | 33,199,112,112,1,1,1 90 | 113,194,104,104,3,3,1 91 | 75,101,123,123,3,3,1 92 | 170,132,119,119,3,3,1 93 | 169,171,79,79,1,1,1 94 | 220,203,36,36,3,3,1 95 | 94,112,151,151,1,1,1 96 | 51,96,12,12,3,3,1 97 | 134,233,97,97,3,3,1 98 | 88,53,58,58,3,3,1 99 | 10,171,178,178,3,3,1 100 | 136,40,9,9,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic1_m.csv: -------------------------------------------------------------------------------- 1 | 149,171,202,202,1,1,1 2 | 152,252,207,207,3,3,1 3 | 62,193,50,50,3,3,1 4 | 139,218,50,50,3,3,1 5 | 13,252,57,57,3,3,1 6 | 56,176,151,151,1,1,1 7 | 93,92,67,67,1,1,1 8 | 56,172,126,126,1,1,1 9 | 208,5,184,184,3,3,1 10 | 132,13,208,208,3,3,1 11 | 229,198,166,166,3,3,1 12 | 141,203,140,140,3,3,1 13 | 79,41,75,75,3,3,1 14 | 55,44,93,93,1,1,1 15 | 223,206,36,36,1,1,1 16 | 188,10,34,34,3,3,1 17 | 81,157,46,46,3,3,1 18 | 182,47,155,155,1,1,1 19 | 51,191,111,111,3,3,1 20 | 106,139,122,122,1,1,1 21 | 216,194,182,182,3,3,1 22 | 134,235,55,55,1,1,1 23 | 78,176,32,32,3,3,1 24 | 166,99,14,14,3,3,1 25 | 207,74,130,130,3,3,1 26 | 214,58,34,34,1,1,1 27 | 59,34,154,154,3,3,1 28 | 150,248,135,135,1,1,1 29 | 30,144,208,208,3,3,1 30 | 40,121,108,108,3,3,1 31 | 139,53,85,85,3,3,1 32 | 252,96,180,180,1,1,1 33 | 153,179,213,213,3,3,1 34 | 65,170,36,36,1,1,1 35 | 85,118,97,97,3,3,1 36 | 93,3,18,18,3,3,1 37 | 155,158,84,84,3,3,1 38 | 118,14,146,146,3,3,1 39 | 235,158,129,129,3,3,1 40 | 217,60,186,186,1,1,1 41 | 1,114,30,30,3,3,1 42 | 102,226,133,133,1,1,1 43 | 148,86,4,4,3,3,1 44 | 171,75,96,96,1,1,1 45 | 179,198,6,6,1,1,1 46 | 18,22,197,197,1,1,1 47 | 73,95,48,48,3,3,1 48 | 148,64,139,139,1,1,1 49 | 41,196,125,125,1,1,1 50 | 231,119,152,152,1,1,1 51 | 8,11,36,36,3,3,1 52 | 98,211,221,221,3,3,1 53 | 113,49,132,132,1,1,1 54 | 14,7,183,183,3,3,1 55 | 180,183,139,139,1,1,1 56 | 89,149,209,209,3,3,1 57 | 182,139,156,156,1,1,1 58 | 162,67,68,68,3,3,1 59 | 179,61,68,68,1,1,1 60 | 216,188,168,168,3,3,1 61 | 107,159,174,174,3,3,1 62 | 24,247,93,93,3,3,1 63 | 203,43,84,84,1,1,1 64 | 228,91,20,20,3,3,1 65 | 166,98,92,92,1,1,1 66 | 89,229,42,42,1,1,1 67 | 120,69,90,90,3,3,1 68 | 37,239,221,221,3,3,1 69 | 48,148,159,159,3,3,1 70 | 217,204,3,3,3,3,1 71 | 240,144,171,171,1,1,1 72 | 256,129,222,222,1,1,1 73 | 4,165,167,167,3,3,1 74 | 143,9,212,212,3,3,1 75 | 7,102,44,44,1,1,1 76 | 89,92,73,73,1,1,1 77 | 28,33,168,168,3,3,1 78 | 238,7,206,206,1,1,1 79 | 144,236,202,202,3,3,1 80 | 46,105,97,97,3,3,1 81 | 141,171,53,53,1,1,1 82 | 70,190,24,24,3,3,1 83 | 252,81,188,188,1,1,1 84 | 96,241,58,58,1,1,1 85 | 39,79,91,91,3,3,1 86 | 134,238,27,27,1,1,1 87 | 154,154,118,118,3,3,1 88 | 85,7,84,84,3,3,1 89 | 180,240,31,31,3,3,1 90 | 105,69,3,3,1,1,1 91 | 204,42,3,3,1,1,1 92 | 138,243,145,145,3,3,1 93 | 231,197,208,208,1,1,1 94 | 38,180,138,138,1,1,1 95 | 215,225,171,171,3,3,1 96 | 41,253,11,11,3,3,1 97 | 62,96,103,103,3,3,1 98 | 170,153,85,85,3,3,1 99 | 106,180,19,19,3,3,1 100 | 1,147,130,130,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic2_m.csv: -------------------------------------------------------------------------------- 1 | 205,28,165,165,1,1,1 2 | 186,1,32,32,3,3,1 3 | 234,130,55,55,3,3,1 4 | 6,250,192,192,3,3,1 5 | 19,224,85,85,1,1,1 6 | 188,72,24,24,1,1,1 7 | 182,24,5,5,3,3,1 8 | 235,32,57,57,1,1,1 9 | 74,190,133,133,1,1,1 10 | 183,91,104,104,3,3,1 11 | 106,110,53,53,3,3,1 12 | 214,114,168,168,1,1,1 13 | 40,70,78,78,1,1,1 14 | 186,250,11,11,1,1,1 15 | 88,112,185,185,1,1,1 16 | 254,37,133,133,3,3,1 17 | 101,197,5,5,1,1,1 18 | 227,140,109,109,1,1,1 19 | 78,106,186,186,3,3,1 20 | 31,137,147,147,1,1,1 21 | 35,15,53,53,1,1,1 22 | 149,93,43,43,3,3,1 23 | 38,104,164,164,3,3,1 24 | 97,175,51,51,3,3,1 25 | 175,28,9,9,1,1,1 26 | 26,183,4,4,1,1,1 27 | 250,221,113,113,3,3,1 28 | 56,115,27,27,1,1,1 29 | 160,228,109,109,1,1,1 30 | 18,157,109,109,1,1,1 31 | 39,220,99,99,3,3,1 32 | 50,45,81,81,3,3,1 33 | 12,215,159,159,1,1,1 34 | 157,161,223,223,3,3,1 35 | 256,206,187,187,1,1,1 36 | 243,189,112,112,1,1,1 37 | 79,112,141,141,1,1,1 38 | 164,155,150,150,3,3,1 39 | 35,128,141,141,1,1,1 40 | 47,133,178,178,1,1,1 41 | 185,148,222,222,1,1,1 42 | 83,78,107,107,1,1,1 43 | 183,224,147,147,1,1,1 44 | 65,59,6,6,3,3,1 45 | 203,114,55,55,1,1,1 46 | 33,216,220,220,1,1,1 47 | 37,151,131,131,3,3,1 48 | 237,201,114,114,1,1,1 49 | 81,93,164,164,3,3,1 50 | 29,101,95,95,3,3,1 51 | 202,141,101,101,3,3,1 52 | 220,71,155,155,1,1,1 53 | 52,5,212,212,1,1,1 54 | 122,148,217,217,1,1,1 55 | 81,171,133,133,3,3,1 56 | 67,14,16,16,1,1,1 57 | 72,218,182,182,1,1,1 58 | 28,236,33,33,1,1,1 59 | 32,146,200,200,1,1,1 60 | 244,78,21,21,3,3,1 61 | 31,256,67,67,1,1,1 62 | 252,148,111,111,1,1,1 63 | 56,121,214,214,3,3,1 64 | 67,1,72,72,1,1,1 65 | 104,184,19,19,1,1,1 66 | 2,75,72,72,3,3,1 67 | 122,79,163,163,1,1,1 68 | 146,98,215,215,3,3,1 69 | 70,175,74,74,3,3,1 70 | 215,245,119,119,3,3,1 71 | 98,229,124,124,1,1,1 72 | 11,242,63,63,1,1,1 73 | 207,243,99,99,1,1,1 74 | 207,245,64,64,3,3,1 75 | 211,28,195,195,1,1,1 76 | 250,246,51,51,3,3,1 77 | 60,132,147,147,1,1,1 78 | 75,5,116,116,3,3,1 79 | 251,212,198,198,3,3,1 80 | 175,178,23,23,3,3,1 81 | 117,244,188,188,3,3,1 82 | 133,157,219,219,3,3,1 83 | 63,55,73,73,3,3,1 84 | 116,73,33,33,1,1,1 85 | 194,32,134,134,1,1,1 86 | 15,13,47,47,3,3,1 87 | 54,155,206,206,1,1,1 88 | 132,75,113,113,1,1,1 89 | 31,26,26,26,3,3,1 90 | 136,10,64,64,1,1,1 91 | 94,172,150,150,3,3,1 92 | 82,104,202,202,3,3,1 93 | 91,114,105,105,1,1,1 94 | 151,117,25,25,3,3,1 95 | 144,73,26,26,1,1,1 96 | 164,86,143,143,1,1,1 97 | 183,44,32,32,3,3,1 98 | 53,30,107,107,1,1,1 99 | 143,204,51,51,3,3,1 100 | 231,182,70,70,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic3_m.csv: -------------------------------------------------------------------------------- 1 | 255,231,117,117,1,1,1 2 | 244,94,47,47,1,1,1 3 | 193,40,104,104,3,3,1 4 | 28,20,103,103,3,3,1 5 | 118,79,197,197,3,3,1 6 | 150,84,108,108,3,3,1 7 | 244,122,129,129,1,1,1 8 | 66,167,201,201,3,3,1 9 | 165,122,167,167,3,3,1 10 | 13,205,77,77,3,3,1 11 | 86,3,103,103,1,1,1 12 | 169,47,76,76,3,3,1 13 | 39,177,66,66,1,1,1 14 | 59,80,108,108,1,1,1 15 | 118,25,35,35,3,3,1 16 | 185,116,124,124,3,3,1 17 | 233,33,118,118,3,3,1 18 | 135,2,140,140,1,1,1 19 | 180,147,125,125,1,1,1 20 | 208,148,111,111,1,1,1 21 | 125,255,82,82,3,3,1 22 | 96,154,87,87,1,1,1 23 | 64,67,39,39,3,3,1 24 | 126,28,22,22,3,3,1 25 | 16,112,84,84,1,1,1 26 | 111,199,162,162,3,3,1 27 | 218,1,27,27,3,3,1 28 | 47,36,102,102,3,3,1 29 | 193,20,154,154,3,3,1 30 | 91,130,214,214,3,3,1 31 | 198,218,186,186,1,1,1 32 | 231,226,106,106,1,1,1 33 | 105,171,206,206,1,1,1 34 | 69,133,151,151,3,3,1 35 | 43,168,225,225,1,1,1 36 | 139,145,96,96,3,3,1 37 | 108,207,98,98,1,1,1 38 | 19,26,71,71,3,3,1 39 | 63,82,65,65,3,3,1 40 | 59,138,162,162,3,3,1 41 | 156,18,175,175,3,3,1 42 | 42,51,46,46,1,1,1 43 | 116,118,221,221,1,1,1 44 | 91,249,8,8,3,3,1 45 | 190,112,152,152,1,1,1 46 | 51,36,191,191,3,3,1 47 | 238,245,80,80,3,3,1 48 | 129,152,214,214,1,1,1 49 | 50,210,127,127,1,1,1 50 | 65,123,139,139,3,3,1 51 | 256,114,208,208,3,3,1 52 | 169,93,13,13,1,1,1 53 | 167,106,102,102,3,3,1 54 | 113,90,196,196,1,1,1 55 | 117,63,75,75,3,3,1 56 | 182,239,211,211,1,1,1 57 | 53,222,33,33,1,1,1 58 | 159,80,33,33,3,3,1 59 | 14,218,41,41,1,1,1 60 | 154,67,108,108,3,3,1 61 | 65,51,80,80,3,3,1 62 | 100,194,125,125,3,3,1 63 | 90,23,195,195,1,1,1 64 | 146,86,134,134,3,3,1 65 | 117,28,154,154,1,1,1 66 | 159,7,123,123,3,3,1 67 | 121,93,211,211,3,3,1 68 | 58,183,7,7,3,3,1 69 | 162,67,155,155,1,1,1 70 | 12,75,194,194,3,3,1 71 | 3,32,223,223,3,3,1 72 | 51,95,132,132,3,3,1 73 | 16,206,168,168,3,3,1 74 | 145,43,86,86,3,3,1 75 | 215,124,16,16,3,3,1 76 | 162,109,225,225,1,1,1 77 | 44,62,201,201,3,3,1 78 | 147,203,81,81,1,1,1 79 | 120,218,46,46,1,1,1 80 | 240,107,18,18,1,1,1 81 | 120,238,165,165,3,3,1 82 | 238,189,37,37,3,3,1 83 | 142,197,207,207,1,1,1 84 | 96,173,14,14,1,1,1 85 | 157,148,24,24,1,1,1 86 | 95,249,154,154,1,1,1 87 | 109,36,225,225,1,1,1 88 | 71,123,95,95,3,3,1 89 | 231,29,165,165,3,3,1 90 | 115,238,134,134,1,1,1 91 | 135,204,3,3,1,1,1 92 | 182,46,168,168,1,1,1 93 | 154,107,83,83,1,1,1 94 | 122,99,60,60,3,3,1 95 | 127,141,85,85,1,1,1 96 | 236,233,36,36,3,3,1 97 | 78,54,31,31,1,1,1 98 | 241,162,60,60,3,3,1 99 | 253,201,168,168,1,1,1 100 | 214,244,192,192,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic4_m.csv: -------------------------------------------------------------------------------- 1 | 202,116,210,210,3,3,1 2 | 154,81,96,96,1,1,1 3 | 249,72,74,74,1,1,1 4 | 227,67,182,182,1,1,1 5 | 187,123,63,63,3,3,1 6 | 103,41,132,132,1,1,1 7 | 90,34,144,144,3,3,1 8 | 213,114,220,220,3,3,1 9 | 84,129,215,215,3,3,1 10 | 49,8,154,154,3,3,1 11 | 32,118,84,84,1,1,1 12 | 236,39,220,220,3,3,1 13 | 235,38,79,79,1,1,1 14 | 128,148,140,140,1,1,1 15 | 98,184,4,4,3,3,1 16 | 132,63,160,160,3,3,1 17 | 137,143,51,51,3,3,1 18 | 151,163,27,27,1,1,1 19 | 127,118,26,26,3,3,1 20 | 186,72,108,108,1,1,1 21 | 213,93,116,116,1,1,1 22 | 96,103,109,109,1,1,1 23 | 147,110,21,21,1,1,1 24 | 59,89,103,103,3,3,1 25 | 185,182,113,113,3,3,1 26 | 165,59,163,163,1,1,1 27 | 175,139,9,9,1,1,1 28 | 160,209,115,115,3,3,1 29 | 122,93,28,28,1,1,1 30 | 202,194,14,14,3,3,1 31 | 70,138,225,225,3,3,1 32 | 189,255,39,39,3,3,1 33 | 159,129,43,43,1,1,1 34 | 127,82,17,17,1,1,1 35 | 71,239,212,212,3,3,1 36 | 113,215,115,115,1,1,1 37 | 112,161,47,47,1,1,1 38 | 85,198,24,24,1,1,1 39 | 89,54,137,137,1,1,1 40 | 46,214,196,196,3,3,1 41 | 160,251,173,173,3,3,1 42 | 50,167,174,174,1,1,1 43 | 162,214,152,152,1,1,1 44 | 195,249,130,130,1,1,1 45 | 242,85,86,86,1,1,1 46 | 113,100,49,49,1,1,1 47 | 150,199,129,129,1,1,1 48 | 82,251,46,46,1,1,1 49 | 191,28,109,109,3,3,1 50 | 192,223,93,93,1,1,1 51 | 209,106,48,48,1,1,1 52 | 170,21,16,16,1,1,1 53 | 141,236,12,12,1,1,1 54 | 85,16,76,76,1,1,1 55 | 248,171,108,108,3,3,1 56 | 111,130,43,43,3,3,1 57 | 7,243,116,116,1,1,1 58 | 156,187,61,61,1,1,1 59 | 240,127,122,122,1,1,1 60 | 189,118,184,184,1,1,1 61 | 54,191,26,26,3,3,1 62 | 128,246,169,169,1,1,1 63 | 249,217,152,152,3,3,1 64 | 39,238,123,123,3,3,1 65 | 94,168,141,141,1,1,1 66 | 204,48,6,6,3,3,1 67 | 183,227,147,147,3,3,1 68 | 236,132,71,71,3,3,1 69 | 92,184,143,143,1,1,1 70 | 166,27,49,49,1,1,1 71 | 33,207,77,77,1,1,1 72 | 247,210,24,24,3,3,1 73 | 12,1,3,3,3,3,1 74 | 65,74,29,29,1,1,1 75 | 239,23,73,73,3,3,1 76 | 49,126,194,194,3,3,1 77 | 181,93,203,203,3,3,1 78 | 245,39,29,29,1,1,1 79 | 134,5,81,81,3,3,1 80 | 117,97,86,86,1,1,1 81 | 107,183,68,68,1,1,1 82 | 115,153,184,184,3,3,1 83 | 63,116,49,49,1,1,1 84 | 246,207,149,149,3,3,1 85 | 100,203,178,178,1,1,1 86 | 27,194,124,124,1,1,1 87 | 202,145,8,8,1,1,1 88 | 91,182,51,51,3,3,1 89 | 255,75,156,156,1,1,1 90 | 237,70,90,90,1,1,1 91 | 177,80,19,19,1,1,1 92 | 199,237,4,4,3,3,1 93 | 103,57,15,15,3,3,1 94 | 91,193,27,27,3,3,1 95 | 74,54,208,208,3,3,1 96 | 118,131,58,58,3,3,1 97 | 226,80,149,149,3,3,1 98 | 184,184,54,54,1,1,1 99 | 10,136,101,101,3,3,1 100 | 27,193,95,95,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic5_m.csv: -------------------------------------------------------------------------------- 1 | 3,211,193,193,3,3,1 2 | 160,60,58,58,3,3,1 3 | 24,119,15,15,3,3,1 4 | 221,209,148,148,1,1,1 5 | 200,88,179,179,1,1,1 6 | 111,146,26,26,3,3,1 7 | 77,17,114,114,1,1,1 8 | 175,83,74,74,3,3,1 9 | 23,92,171,171,1,1,1 10 | 190,22,125,125,3,3,1 11 | 51,241,68,68,3,3,1 12 | 85,95,137,137,1,1,1 13 | 211,169,19,19,3,3,1 14 | 222,178,217,217,1,1,1 15 | 207,2,164,164,3,3,1 16 | 36,141,212,212,1,1,1 17 | 91,218,30,30,1,1,1 18 | 133,91,115,115,3,3,1 19 | 33,169,204,204,3,3,1 20 | 76,98,137,137,1,1,1 21 | 17,12,107,107,3,3,1 22 | 91,120,194,194,1,1,1 23 | 46,194,134,134,3,3,1 24 | 196,56,165,165,1,1,1 25 | 122,233,186,186,3,3,1 26 | 216,57,27,27,3,3,1 27 | 229,139,219,219,1,1,1 28 | 253,72,119,119,3,3,1 29 | 198,217,108,108,3,3,1 30 | 80,198,162,162,3,3,1 31 | 148,18,155,155,1,1,1 32 | 162,153,208,208,3,3,1 33 | 113,111,3,3,3,3,1 34 | 223,190,109,109,3,3,1 35 | 247,152,64,64,1,1,1 36 | 23,143,82,82,3,3,1 37 | 219,46,165,165,3,3,1 38 | 211,53,113,113,1,1,1 39 | 20,78,191,191,1,1,1 40 | 106,172,173,173,1,1,1 41 | 210,208,71,71,1,1,1 42 | 227,166,139,139,1,1,1 43 | 126,121,151,151,3,3,1 44 | 162,20,68,68,1,1,1 45 | 78,56,129,129,3,3,1 46 | 145,115,67,67,3,3,1 47 | 119,165,68,68,3,3,1 48 | 135,110,38,38,3,3,1 49 | 208,238,160,160,1,1,1 50 | 67,236,126,126,1,1,1 51 | 101,213,87,87,3,3,1 52 | 124,21,104,104,3,3,1 53 | 80,165,114,114,1,1,1 54 | 69,129,114,114,1,1,1 55 | 200,109,61,61,1,1,1 56 | 138,201,146,146,1,1,1 57 | 35,218,116,116,3,3,1 58 | 219,200,62,62,3,3,1 59 | 205,180,197,197,1,1,1 60 | 150,100,11,11,1,1,1 61 | 147,255,212,212,3,3,1 62 | 240,139,23,23,1,1,1 63 | 192,210,146,146,3,3,1 64 | 13,97,121,121,3,3,1 65 | 177,38,23,23,1,1,1 66 | 134,13,155,155,3,3,1 67 | 220,236,183,183,3,3,1 68 | 101,112,205,205,3,3,1 69 | 227,6,217,217,1,1,1 70 | 224,91,125,125,1,1,1 71 | 244,114,56,56,3,3,1 72 | 37,55,123,123,1,1,1 73 | 130,29,114,114,1,1,1 74 | 75,56,55,55,3,3,1 75 | 114,38,175,175,3,3,1 76 | 71,136,110,110,3,3,1 77 | 165,169,211,211,3,3,1 78 | 215,45,95,95,1,1,1 79 | 24,39,136,136,3,3,1 80 | 79,179,128,128,1,1,1 81 | 34,52,173,173,3,3,1 82 | 148,102,24,24,3,3,1 83 | 30,196,64,64,3,3,1 84 | 121,203,115,115,1,1,1 85 | 183,143,122,122,3,3,1 86 | 126,103,102,102,1,1,1 87 | 155,249,90,90,1,1,1 88 | 99,255,17,17,3,3,1 89 | 131,162,25,25,1,1,1 90 | 127,221,106,106,3,3,1 91 | 179,154,134,134,3,3,1 92 | 18,69,175,175,3,3,1 93 | 136,185,52,52,1,1,1 94 | 57,163,136,136,3,3,1 95 | 25,199,222,222,3,3,1 96 | 45,46,71,71,3,3,1 97 | 155,219,198,198,1,1,1 98 | 228,247,132,132,3,3,1 99 | 35,64,113,113,1,1,1 100 | 252,217,154,154,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic6_m.csv: -------------------------------------------------------------------------------- 1 | 27,96,87,87,3,3,1 2 | 29,256,115,115,3,3,1 3 | 27,57,48,48,3,3,1 4 | 17,81,198,198,3,3,1 5 | 132,240,111,111,3,3,1 6 | 4,148,149,149,1,1,1 7 | 115,83,54,54,3,3,1 8 | 22,171,149,149,3,3,1 9 | 124,81,147,147,1,1,1 10 | 230,143,90,90,3,3,1 11 | 123,178,68,68,3,3,1 12 | 161,201,182,182,3,3,1 13 | 30,12,88,88,3,3,1 14 | 96,146,182,182,3,3,1 15 | 166,188,9,9,3,3,1 16 | 143,234,8,8,1,1,1 17 | 190,108,126,126,3,3,1 18 | 239,20,96,96,1,1,1 19 | 160,89,218,218,3,3,1 20 | 223,102,13,13,3,3,1 21 | 209,67,213,213,3,3,1 22 | 56,211,62,62,3,3,1 23 | 136,26,151,151,3,3,1 24 | 57,240,207,207,1,1,1 25 | 214,21,56,56,3,3,1 26 | 168,251,217,217,3,3,1 27 | 121,204,28,28,1,1,1 28 | 128,56,149,149,1,1,1 29 | 222,12,194,194,3,3,1 30 | 51,137,58,58,1,1,1 31 | 160,34,103,103,1,1,1 32 | 82,14,72,72,1,1,1 33 | 226,110,160,160,1,1,1 34 | 170,227,79,79,1,1,1 35 | 42,162,27,27,1,1,1 36 | 243,128,202,202,1,1,1 37 | 220,47,6,6,1,1,1 38 | 191,85,164,164,3,3,1 39 | 2,175,85,85,1,1,1 40 | 134,218,79,79,1,1,1 41 | 47,172,180,180,1,1,1 42 | 135,208,175,175,1,1,1 43 | 171,29,3,3,3,3,1 44 | 85,199,133,133,1,1,1 45 | 244,126,74,74,3,3,1 46 | 162,99,45,45,3,3,1 47 | 33,139,10,10,3,3,1 48 | 33,43,190,190,1,1,1 49 | 95,3,31,31,1,1,1 50 | 26,41,196,196,3,3,1 51 | 168,180,120,120,3,3,1 52 | 256,1,152,152,3,3,1 53 | 11,188,178,178,3,3,1 54 | 150,67,210,210,3,3,1 55 | 125,102,120,120,3,3,1 56 | 200,217,70,70,1,1,1 57 | 208,215,211,211,1,1,1 58 | 60,45,133,133,1,1,1 59 | 118,233,162,162,3,3,1 60 | 41,58,93,93,3,3,1 61 | 52,20,121,121,1,1,1 62 | 215,146,176,176,1,1,1 63 | 131,171,127,127,3,3,1 64 | 74,238,43,43,3,3,1 65 | 30,192,74,74,3,3,1 66 | 23,232,77,77,1,1,1 67 | 218,193,103,103,3,3,1 68 | 218,79,206,206,1,1,1 69 | 171,199,212,212,3,3,1 70 | 97,199,84,84,3,3,1 71 | 144,5,27,27,1,1,1 72 | 77,93,180,180,1,1,1 73 | 180,241,172,172,1,1,1 74 | 182,198,198,198,1,1,1 75 | 238,190,147,147,1,1,1 76 | 172,163,188,188,3,3,1 77 | 139,86,104,104,1,1,1 78 | 180,164,195,195,1,1,1 79 | 241,88,49,49,1,1,1 80 | 164,232,97,97,1,1,1 81 | 93,192,175,175,3,3,1 82 | 238,183,20,20,3,3,1 83 | 133,101,147,147,3,3,1 84 | 228,4,111,111,3,3,1 85 | 194,202,85,85,1,1,1 86 | 209,215,64,64,1,1,1 87 | 137,200,195,195,3,3,1 88 | 205,161,172,172,3,3,1 89 | 158,64,93,93,3,3,1 90 | 240,237,224,224,3,3,1 91 | 61,139,146,146,1,1,1 92 | 119,221,60,60,3,3,1 93 | 97,206,11,11,1,1,1 94 | 123,174,27,27,1,1,1 95 | 148,223,163,163,3,3,1 96 | 170,229,41,41,3,3,1 97 | 190,111,184,184,3,3,1 98 | 146,181,114,114,3,3,1 99 | 27,188,206,206,3,3,1 100 | 208,51,14,14,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic7_m.csv: -------------------------------------------------------------------------------- 1 | 138,144,215,215,3,3,1 2 | 173,115,104,104,1,1,1 3 | 45,104,120,120,1,1,1 4 | 96,204,72,72,1,1,1 5 | 172,46,157,157,1,1,1 6 | 69,133,148,148,1,1,1 7 | 43,51,201,201,3,3,1 8 | 71,242,5,5,1,1,1 9 | 47,145,109,109,1,1,1 10 | 194,163,155,155,1,1,1 11 | 123,116,186,186,1,1,1 12 | 21,123,147,147,3,3,1 13 | 108,78,42,42,3,3,1 14 | 159,71,177,177,3,3,1 15 | 124,250,25,25,1,1,1 16 | 234,56,173,173,3,3,1 17 | 81,20,191,191,1,1,1 18 | 164,100,181,181,1,1,1 19 | 237,251,160,160,3,3,1 20 | 35,192,91,91,3,3,1 21 | 209,233,83,83,3,3,1 22 | 169,205,13,13,1,1,1 23 | 178,213,127,127,3,3,1 24 | 87,99,224,224,1,1,1 25 | 160,180,11,11,1,1,1 26 | 16,77,94,94,3,3,1 27 | 230,184,139,139,1,1,1 28 | 34,3,171,171,1,1,1 29 | 166,174,61,61,1,1,1 30 | 77,235,138,138,1,1,1 31 | 160,45,213,213,1,1,1 32 | 168,98,8,8,3,3,1 33 | 107,80,76,76,1,1,1 34 | 98,99,20,20,3,3,1 35 | 96,89,199,199,3,3,1 36 | 115,184,60,60,1,1,1 37 | 12,183,219,219,3,3,1 38 | 191,234,95,95,1,1,1 39 | 195,221,140,140,1,1,1 40 | 104,95,216,216,1,1,1 41 | 122,239,156,156,3,3,1 42 | 246,86,140,140,1,1,1 43 | 56,146,15,15,3,3,1 44 | 170,8,201,201,3,3,1 45 | 39,220,107,107,3,3,1 46 | 156,197,120,120,3,3,1 47 | 183,141,69,69,1,1,1 48 | 227,83,103,103,3,3,1 49 | 145,170,115,115,1,1,1 50 | 64,36,35,35,3,3,1 51 | 50,214,47,47,1,1,1 52 | 177,61,111,111,1,1,1 53 | 211,51,83,83,1,1,1 54 | 223,78,28,28,3,3,1 55 | 123,110,122,122,1,1,1 56 | 37,25,143,143,1,1,1 57 | 172,157,4,4,1,1,1 58 | 185,91,20,20,3,3,1 59 | 107,180,67,67,1,1,1 60 | 167,119,212,212,1,1,1 61 | 244,40,23,23,1,1,1 62 | 87,97,157,157,3,3,1 63 | 189,57,185,185,1,1,1 64 | 59,145,172,172,3,3,1 65 | 86,205,3,3,1,1,1 66 | 246,186,21,21,3,3,1 67 | 72,193,135,135,3,3,1 68 | 229,105,88,88,1,1,1 69 | 64,35,46,46,1,1,1 70 | 241,252,154,154,3,3,1 71 | 150,164,95,95,1,1,1 72 | 87,40,192,192,3,3,1 73 | 177,178,73,73,3,3,1 74 | 10,129,13,13,3,3,1 75 | 83,226,134,134,1,1,1 76 | 111,146,194,194,3,3,1 77 | 248,178,223,223,1,1,1 78 | 44,137,210,210,1,1,1 79 | 35,147,55,55,3,3,1 80 | 97,59,145,145,3,3,1 81 | 225,192,29,29,3,3,1 82 | 218,95,105,105,3,3,1 83 | 236,165,220,220,3,3,1 84 | 45,244,199,199,3,3,1 85 | 101,195,139,139,3,3,1 86 | 54,124,36,36,1,1,1 87 | 216,76,174,174,3,3,1 88 | 21,149,161,161,3,3,1 89 | 38,183,36,36,3,3,1 90 | 89,173,124,124,3,3,1 91 | 168,53,55,55,1,1,1 92 | 219,37,92,92,3,3,1 93 | 242,99,76,76,3,3,1 94 | 17,65,193,193,1,1,1 95 | 219,82,82,82,1,1,1 96 | 196,47,207,207,3,3,1 97 | 227,60,39,39,3,3,1 98 | 232,204,97,97,3,3,1 99 | 30,150,169,169,1,1,1 100 | 247,114,59,59,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic8_m.csv: -------------------------------------------------------------------------------- 1 | 63,182,218,218,3,3,1 2 | 50,254,179,179,3,3,1 3 | 42,152,118,118,3,3,1 4 | 149,61,135,135,3,3,1 5 | 24,71,196,196,3,3,1 6 | 210,96,102,102,3,3,1 7 | 250,150,26,26,3,3,1 8 | 168,90,6,6,3,3,1 9 | 56,234,155,155,3,3,1 10 | 129,164,124,124,3,3,1 11 | 151,243,163,163,3,3,1 12 | 236,78,4,4,1,1,1 13 | 210,244,200,200,1,1,1 14 | 123,208,55,55,3,3,1 15 | 52,137,130,130,1,1,1 16 | 224,141,160,160,1,1,1 17 | 51,215,53,53,3,3,1 18 | 207,194,160,160,3,3,1 19 | 6,145,31,31,1,1,1 20 | 106,160,51,51,3,3,1 21 | 75,167,206,206,3,3,1 22 | 254,21,25,25,1,1,1 23 | 20,256,112,112,3,3,1 24 | 192,117,224,224,1,1,1 25 | 73,75,75,75,1,1,1 26 | 233,104,89,89,3,3,1 27 | 156,54,192,192,1,1,1 28 | 139,184,80,80,1,1,1 29 | 135,78,123,123,1,1,1 30 | 84,119,48,48,1,1,1 31 | 207,147,157,157,1,1,1 32 | 58,173,213,213,1,1,1 33 | 57,130,198,198,3,3,1 34 | 183,82,79,79,1,1,1 35 | 118,223,6,6,1,1,1 36 | 128,219,95,95,3,3,1 37 | 55,197,97,97,1,1,1 38 | 181,80,195,195,3,3,1 39 | 93,191,65,65,1,1,1 40 | 119,94,40,40,3,3,1 41 | 170,149,95,95,3,3,1 42 | 246,14,115,115,3,3,1 43 | 204,68,135,135,3,3,1 44 | 155,229,154,154,1,1,1 45 | 142,140,179,179,3,3,1 46 | 209,102,34,34,1,1,1 47 | 79,24,88,88,3,3,1 48 | 195,91,80,80,1,1,1 49 | 195,104,71,71,1,1,1 50 | 175,94,201,201,1,1,1 51 | 146,204,62,62,1,1,1 52 | 150,86,15,15,1,1,1 53 | 12,13,89,89,3,3,1 54 | 211,118,144,144,3,3,1 55 | 105,96,159,159,3,3,1 56 | 109,56,76,76,3,3,1 57 | 24,64,9,9,3,3,1 58 | 224,56,143,143,1,1,1 59 | 80,175,165,165,1,1,1 60 | 11,237,35,35,3,3,1 61 | 230,235,9,9,3,3,1 62 | 69,193,127,127,1,1,1 63 | 40,73,58,58,1,1,1 64 | 110,233,60,60,1,1,1 65 | 219,60,36,36,1,1,1 66 | 219,234,218,218,1,1,1 67 | 9,177,197,197,3,3,1 68 | 66,175,85,85,3,3,1 69 | 224,154,162,162,1,1,1 70 | 138,66,57,57,3,3,1 71 | 71,163,86,86,1,1,1 72 | 86,249,84,84,3,3,1 73 | 88,133,216,216,1,1,1 74 | 51,19,171,171,1,1,1 75 | 166,195,59,59,3,3,1 76 | 241,46,59,59,1,1,1 77 | 40,144,70,70,3,3,1 78 | 56,24,35,35,1,1,1 79 | 123,36,181,181,3,3,1 80 | 141,59,72,72,3,3,1 81 | 138,254,81,81,1,1,1 82 | 125,155,16,16,1,1,1 83 | 123,124,174,174,1,1,1 84 | 74,17,169,169,3,3,1 85 | 5,6,155,155,3,3,1 86 | 150,179,46,46,1,1,1 87 | 121,173,30,30,1,1,1 88 | 16,53,35,35,1,1,1 89 | 65,162,187,187,1,1,1 90 | 118,128,197,197,1,1,1 91 | 222,158,131,131,3,3,1 92 | 149,37,102,102,1,1,1 93 | 149,140,162,162,3,3,1 94 | 86,9,105,105,3,3,1 95 | 155,89,209,209,1,1,1 96 | 214,9,9,9,3,3,1 97 | 182,9,11,11,1,1,1 98 | 210,207,110,110,3,3,1 99 | 69,124,216,216,1,1,1 100 | 153,217,133,133,3,3,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/CONV/traffic9_m.csv: -------------------------------------------------------------------------------- 1 | 256,122,221,221,3,3,1 2 | 210,124,46,46,3,3,1 3 | 194,67,70,70,1,1,1 4 | 235,122,214,214,3,3,1 5 | 60,149,21,21,1,1,1 6 | 87,161,170,170,1,1,1 7 | 95,82,113,113,1,1,1 8 | 242,4,176,176,1,1,1 9 | 120,184,9,9,1,1,1 10 | 39,256,211,211,1,1,1 11 | 72,173,125,125,1,1,1 12 | 226,195,91,91,1,1,1 13 | 29,114,107,107,1,1,1 14 | 58,253,6,6,3,3,1 15 | 153,87,201,201,1,1,1 16 | 166,190,127,127,1,1,1 17 | 211,219,81,81,1,1,1 18 | 51,44,55,55,3,3,1 19 | 140,60,213,213,3,3,1 20 | 36,156,13,13,1,1,1 21 | 24,251,157,157,3,3,1 22 | 247,240,140,140,1,1,1 23 | 79,16,216,216,1,1,1 24 | 65,86,132,132,3,3,1 25 | 134,84,67,67,3,3,1 26 | 55,120,46,46,1,1,1 27 | 18,76,62,62,1,1,1 28 | 157,87,84,84,3,3,1 29 | 134,88,158,158,1,1,1 30 | 200,106,35,35,1,1,1 31 | 84,78,177,177,3,3,1 32 | 46,137,130,130,1,1,1 33 | 231,5,162,162,1,1,1 34 | 245,16,82,82,3,3,1 35 | 162,93,30,30,3,3,1 36 | 133,19,33,33,1,1,1 37 | 118,13,197,197,1,1,1 38 | 142,120,19,19,3,3,1 39 | 230,206,182,182,1,1,1 40 | 255,33,224,224,1,1,1 41 | 70,55,143,143,1,1,1 42 | 210,253,75,75,3,3,1 43 | 165,231,218,218,1,1,1 44 | 243,256,35,35,1,1,1 45 | 55,17,29,29,1,1,1 46 | 61,60,224,224,1,1,1 47 | 228,206,16,16,3,3,1 48 | 9,106,22,22,1,1,1 49 | 2,116,194,194,3,3,1 50 | 242,176,209,209,1,1,1 51 | 236,181,152,152,1,1,1 52 | 44,39,33,33,3,3,1 53 | 194,79,94,94,3,3,1 54 | 146,96,117,117,1,1,1 55 | 170,10,96,96,3,3,1 56 | 175,226,8,8,3,3,1 57 | 185,192,170,170,1,1,1 58 | 55,69,110,110,3,3,1 59 | 153,153,184,184,3,3,1 60 | 75,42,135,135,1,1,1 61 | 95,20,9,9,1,1,1 62 | 59,218,11,11,3,3,1 63 | 221,210,81,81,3,3,1 64 | 253,96,8,8,1,1,1 65 | 66,192,97,97,1,1,1 66 | 219,87,98,98,1,1,1 67 | 76,168,59,59,3,3,1 68 | 233,80,180,180,1,1,1 69 | 160,93,48,48,1,1,1 70 | 219,237,62,62,1,1,1 71 | 222,187,75,75,3,3,1 72 | 146,248,33,33,1,1,1 73 | 191,251,212,212,1,1,1 74 | 103,121,38,38,3,3,1 75 | 126,228,211,211,3,3,1 76 | 127,32,42,42,3,3,1 77 | 69,210,147,147,1,1,1 78 | 215,91,26,26,1,1,1 79 | 211,163,97,97,1,1,1 80 | 90,166,94,94,3,3,1 81 | 21,29,219,219,3,3,1 82 | 216,182,18,18,1,1,1 83 | 157,114,110,110,3,3,1 84 | 234,226,21,21,3,3,1 85 | 35,207,174,174,3,3,1 86 | 204,10,10,10,3,3,1 87 | 59,162,10,10,1,1,1 88 | 11,167,193,193,3,3,1 89 | 167,32,178,178,3,3,1 90 | 114,127,114,114,3,3,1 91 | 192,23,109,109,3,3,1 92 | 223,178,192,192,3,3,1 93 | 223,76,174,174,1,1,1 94 | 117,136,133,133,3,3,1 95 | 145,127,69,69,3,3,1 96 | 196,116,77,77,3,3,1 97 | 109,136,137,137,3,3,1 98 | 44,55,185,185,1,1,1 99 | 223,130,221,221,3,3,1 100 | 245,125,183,183,1,1,1 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic0_m.csv: -------------------------------------------------------------------------------- 1 | 177,479,1,1,1,1,0 2 | 305,19,1,1,1,1,0 3 | 247,630,1,1,1,1,0 4 | 234,616,1,1,1,1,0 5 | 341,18,1,1,1,1,0 6 | 224,642,1,1,1,1,0 7 | 14,361,1,1,1,1,0 8 | 376,13,1,1,1,1,0 9 | 313,133,1,1,1,1,0 10 | 226,405,1,1,1,1,0 11 | 142,492,1,1,1,1,0 12 | 205,79,1,1,1,1,0 13 | 65,60,1,1,1,1,0 14 | 25,612,1,1,1,1,0 15 | 337,226,1,1,1,1,0 16 | 503,520,1,1,1,1,0 17 | 242,724,1,1,1,1,0 18 | 449,271,1,1,1,1,0 19 | 446,286,1,1,1,1,0 20 | 133,281,1,1,1,1,0 21 | 365,512,1,1,1,1,0 22 | 159,765,1,1,1,1,0 23 | 203,713,1,1,1,1,0 24 | 77,561,1,1,1,1,0 25 | 329,41,1,1,1,1,0 26 | 283,240,1,1,1,1,0 27 | 420,446,1,1,1,1,0 28 | 431,350,1,1,1,1,0 29 | 429,467,1,1,1,1,0 30 | 246,276,1,1,1,1,0 31 | 38,718,1,1,1,1,0 32 | 176,259,1,1,1,1,0 33 | 186,397,1,1,1,1,0 34 | 497,50,1,1,1,1,0 35 | 272,99,1,1,1,1,0 36 | 124,627,1,1,1,1,0 37 | 172,777,1,1,1,1,0 38 | 286,811,1,1,1,1,0 39 | 256,772,1,1,1,1,0 40 | 475,537,1,1,1,1,0 41 | 222,679,1,1,1,1,0 42 | 346,11,1,1,1,1,0 43 | 464,100,1,1,1,1,0 44 | 345,30,1,1,1,1,0 45 | 97,702,1,1,1,1,0 46 | 225,806,1,1,1,1,0 47 | 255,430,1,1,1,1,0 48 | 50,637,1,1,1,1,0 49 | 178,124,1,1,1,1,0 50 | 426,617,1,1,1,1,0 51 | 191,559,1,1,1,1,0 52 | 302,712,1,1,1,1,0 53 | 43,186,1,1,1,1,0 54 | 134,357,1,1,1,1,0 55 | 104,140,1,1,1,1,0 56 | 199,791,1,1,1,1,0 57 | 196,39,1,1,1,1,0 58 | 144,638,1,1,1,1,0 59 | 131,798,1,1,1,1,0 60 | 39,393,1,1,1,1,0 61 | 287,73,1,1,1,1,0 62 | 265,494,1,1,1,1,0 63 | 401,767,1,1,1,1,0 64 | 55,606,1,1,1,1,0 65 | 482,149,1,1,1,1,0 66 | 206,667,1,1,1,1,0 67 | 197,180,1,1,1,1,0 68 | 442,325,1,1,1,1,0 69 | 403,312,1,1,1,1,0 70 | 98,307,1,1,1,1,0 71 | 35,651,1,1,1,1,0 72 | 48,511,1,1,1,1,0 73 | 305,111,1,1,1,1,0 74 | 137,199,1,1,1,1,0 75 | 288,714,1,1,1,1,0 76 | 70,125,1,1,1,1,0 77 | 113,670,1,1,1,1,0 78 | 421,610,1,1,1,1,0 79 | 136,108,1,1,1,1,0 80 | 356,556,1,1,1,1,0 81 | 9,428,1,1,1,1,0 82 | 249,301,1,1,1,1,0 83 | 471,459,1,1,1,1,0 84 | 507,322,1,1,1,1,0 85 | 123,11,1,1,1,1,0 86 | 40,678,1,1,1,1,0 87 | 16,132,1,1,1,1,0 88 | 377,758,1,1,1,1,0 89 | 242,58,1,1,1,1,0 90 | 456,186,1,1,1,1,0 91 | 300,567,1,1,1,1,0 92 | 155,316,1,1,1,1,0 93 | 74,644,1,1,1,1,0 94 | 94,631,1,1,1,1,0 95 | 261,501,1,1,1,1,0 96 | 254,414,1,1,1,1,0 97 | 273,18,1,1,1,1,0 98 | 37,565,1,1,1,1,0 99 | 119,742,1,1,1,1,0 100 | 399,603,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic1_m.csv: -------------------------------------------------------------------------------- 1 | 248,352,1,1,1,1,0 2 | 486,282,1,1,1,1,0 3 | 114,14,1,1,1,1,0 4 | 475,35,1,1,1,1,0 5 | 350,251,1,1,1,1,0 6 | 404,257,1,1,1,1,0 7 | 142,713,1,1,1,1,0 8 | 444,795,1,1,1,1,0 9 | 322,356,1,1,1,1,0 10 | 233,614,1,1,1,1,0 11 | 399,764,1,1,1,1,0 12 | 82,56,1,1,1,1,0 13 | 225,335,1,1,1,1,0 14 | 279,342,1,1,1,1,0 15 | 98,175,1,1,1,1,0 16 | 201,207,1,1,1,1,0 17 | 252,451,1,1,1,1,0 18 | 74,659,1,1,1,1,0 19 | 248,313,1,1,1,1,0 20 | 434,391,1,1,1,1,0 21 | 366,404,1,1,1,1,0 22 | 76,75,1,1,1,1,0 23 | 146,694,1,1,1,1,0 24 | 373,279,1,1,1,1,0 25 | 481,479,1,1,1,1,0 26 | 182,195,1,1,1,1,0 27 | 153,243,1,1,1,1,0 28 | 116,543,1,1,1,1,0 29 | 489,741,1,1,1,1,0 30 | 337,468,1,1,1,1,0 31 | 467,200,1,1,1,1,0 32 | 450,88,1,1,1,1,0 33 | 73,536,1,1,1,1,0 34 | 476,731,1,1,1,1,0 35 | 369,249,1,1,1,1,0 36 | 178,731,1,1,1,1,0 37 | 51,188,1,1,1,1,0 38 | 33,114,1,1,1,1,0 39 | 151,732,1,1,1,1,0 40 | 167,40,1,1,1,1,0 41 | 419,529,1,1,1,1,0 42 | 11,489,1,1,1,1,0 43 | 104,221,1,1,1,1,0 44 | 34,240,1,1,1,1,0 45 | 511,244,1,1,1,1,0 46 | 192,400,1,1,1,1,0 47 | 251,731,1,1,1,1,0 48 | 461,427,1,1,1,1,0 49 | 39,688,1,1,1,1,0 50 | 289,89,1,1,1,1,0 51 | 165,453,1,1,1,1,0 52 | 71,795,1,1,1,1,0 53 | 466,514,1,1,1,1,0 54 | 84,98,1,1,1,1,0 55 | 9,489,1,1,1,1,0 56 | 273,179,1,1,1,1,0 57 | 229,411,1,1,1,1,0 58 | 355,745,1,1,1,1,0 59 | 490,193,1,1,1,1,0 60 | 466,794,1,1,1,1,0 61 | 497,84,1,1,1,1,0 62 | 457,275,1,1,1,1,0 63 | 33,242,1,1,1,1,0 64 | 323,348,1,1,1,1,0 65 | 331,85,1,1,1,1,0 66 | 158,195,1,1,1,1,0 67 | 246,310,1,1,1,1,0 68 | 150,678,1,1,1,1,0 69 | 485,734,1,1,1,1,0 70 | 220,245,1,1,1,1,0 71 | 171,308,1,1,1,1,0 72 | 399,748,1,1,1,1,0 73 | 193,213,1,1,1,1,0 74 | 179,131,1,1,1,1,0 75 | 29,51,1,1,1,1,0 76 | 269,80,1,1,1,1,0 77 | 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100 | 49,491,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic3_m.csv: -------------------------------------------------------------------------------- 1 | 492,341,1,1,1,1,0 2 | 197,560,1,1,1,1,0 3 | 182,457,1,1,1,1,0 4 | 469,225,1,1,1,1,0 5 | 140,234,1,1,1,1,0 6 | 291,529,1,1,1,1,0 7 | 367,262,1,1,1,1,0 8 | 220,413,1,1,1,1,0 9 | 482,261,1,1,1,1,0 10 | 257,382,1,1,1,1,0 11 | 71,720,1,1,1,1,0 12 | 444,442,1,1,1,1,0 13 | 227,230,1,1,1,1,0 14 | 338,110,1,1,1,1,0 15 | 444,372,1,1,1,1,0 16 | 349,236,1,1,1,1,0 17 | 312,93,1,1,1,1,0 18 | 270,506,1,1,1,1,0 19 | 411,358,1,1,1,1,0 20 | 89,567,1,1,1,1,0 21 | 230,494,1,1,1,1,0 22 | 249,474,1,1,1,1,0 23 | 139,121,1,1,1,1,0 24 | 139,62,1,1,1,1,0 25 | 330,39,1,1,1,1,0 26 | 432,533,1,1,1,1,0 27 | 382,455,1,1,1,1,0 28 | 222,121,1,1,1,1,0 29 | 151,675,1,1,1,1,0 30 | 452,73,1,1,1,1,0 31 | 462,292,1,1,1,1,0 32 | 137,655,1,1,1,1,0 33 | 254,483,1,1,1,1,0 34 | 122,15,1,1,1,1,0 35 | 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23,559,1,1,1,1,0 80 | 446,260,1,1,1,1,0 81 | 346,405,1,1,1,1,0 82 | 268,410,1,1,1,1,0 83 | 66,300,1,1,1,1,0 84 | 195,685,1,1,1,1,0 85 | 108,165,1,1,1,1,0 86 | 492,268,1,1,1,1,0 87 | 348,408,1,1,1,1,0 88 | 261,48,1,1,1,1,0 89 | 70,11,1,1,1,1,0 90 | 157,684,1,1,1,1,0 91 | 23,144,1,1,1,1,0 92 | 330,559,1,1,1,1,0 93 | 416,542,1,1,1,1,0 94 | 319,734,1,1,1,1,0 95 | 177,493,1,1,1,1,0 96 | 186,282,1,1,1,1,0 97 | 424,701,1,1,1,1,0 98 | 324,351,1,1,1,1,0 99 | 495,796,1,1,1,1,0 100 | 273,491,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic4_m.csv: -------------------------------------------------------------------------------- 1 | 166,274,1,1,1,1,0 2 | 56,209,1,1,1,1,0 3 | 98,743,1,1,1,1,0 4 | 345,487,1,1,1,1,0 5 | 472,669,1,1,1,1,0 6 | 327,210,1,1,1,1,0 7 | 484,624,1,1,1,1,0 8 | 225,760,1,1,1,1,0 9 | 429,221,1,1,1,1,0 10 | 217,445,1,1,1,1,0 11 | 327,321,1,1,1,1,0 12 | 362,681,1,1,1,1,0 13 | 490,674,1,1,1,1,0 14 | 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-------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic5_m.csv: -------------------------------------------------------------------------------- 1 | 74,782,1,1,1,1,0 2 | 410,375,1,1,1,1,0 3 | 108,505,1,1,1,1,0 4 | 286,32,1,1,1,1,0 5 | 241,231,1,1,1,1,0 6 | 14,768,1,1,1,1,0 7 | 473,807,1,1,1,1,0 8 | 124,55,1,1,1,1,0 9 | 218,473,1,1,1,1,0 10 | 41,415,1,1,1,1,0 11 | 24,123,1,1,1,1,0 12 | 264,446,1,1,1,1,0 13 | 261,619,1,1,1,1,0 14 | 89,11,1,1,1,1,0 15 | 363,639,1,1,1,1,0 16 | 503,275,1,1,1,1,0 17 | 186,719,1,1,1,1,0 18 | 150,508,1,1,1,1,0 19 | 328,739,1,1,1,1,0 20 | 146,453,1,1,1,1,0 21 | 170,220,1,1,1,1,0 22 | 378,611,1,1,1,1,0 23 | 254,173,1,1,1,1,0 24 | 293,584,1,1,1,1,0 25 | 485,421,1,1,1,1,0 26 | 259,370,1,1,1,1,0 27 | 336,13,1,1,1,1,0 28 | 470,366,1,1,1,1,0 29 | 210,688,1,1,1,1,0 30 | 349,199,1,1,1,1,0 31 | 328,661,1,1,1,1,0 32 | 152,478,1,1,1,1,0 33 | 177,539,1,1,1,1,0 34 | 387,91,1,1,1,1,0 35 | 270,501,1,1,1,1,0 36 | 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82,625,1,1,1,1,0 81 | 271,566,1,1,1,1,0 82 | 374,50,1,1,1,1,0 83 | 443,211,1,1,1,1,0 84 | 50,713,1,1,1,1,0 85 | 293,20,1,1,1,1,0 86 | 442,290,1,1,1,1,0 87 | 285,669,1,1,1,1,0 88 | 264,74,1,1,1,1,0 89 | 62,505,1,1,1,1,0 90 | 280,229,1,1,1,1,0 91 | 172,224,1,1,1,1,0 92 | 498,523,1,1,1,1,0 93 | 255,760,1,1,1,1,0 94 | 473,626,1,1,1,1,0 95 | 110,592,1,1,1,1,0 96 | 368,768,1,1,1,1,0 97 | 34,780,1,1,1,1,0 98 | 265,622,1,1,1,1,0 99 | 492,459,1,1,1,1,0 100 | 185,337,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic6_m.csv: -------------------------------------------------------------------------------- 1 | 133,461,1,1,1,1,0 2 | 397,630,1,1,1,1,0 3 | 187,657,1,1,1,1,0 4 | 60,644,1,1,1,1,0 5 | 432,428,1,1,1,1,0 6 | 96,210,1,1,1,1,0 7 | 129,222,1,1,1,1,0 8 | 38,791,1,1,1,1,0 9 | 48,446,1,1,1,1,0 10 | 389,771,1,1,1,1,0 11 | 8,166,1,1,1,1,0 12 | 420,550,1,1,1,1,0 13 | 228,535,1,1,1,1,0 14 | 363,712,1,1,1,1,0 15 | 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-------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic7_m.csv: -------------------------------------------------------------------------------- 1 | 39,705,1,1,1,1,0 2 | 75,541,1,1,1,1,0 3 | 87,642,1,1,1,1,0 4 | 360,504,1,1,1,1,0 5 | 389,781,1,1,1,1,0 6 | 287,283,1,1,1,1,0 7 | 434,124,1,1,1,1,0 8 | 86,501,1,1,1,1,0 9 | 120,373,1,1,1,1,0 10 | 323,737,1,1,1,1,0 11 | 198,361,1,1,1,1,0 12 | 196,605,1,1,1,1,0 13 | 420,477,1,1,1,1,0 14 | 274,466,1,1,1,1,0 15 | 122,167,1,1,1,1,0 16 | 377,79,1,1,1,1,0 17 | 308,447,1,1,1,1,0 18 | 450,164,1,1,1,1,0 19 | 395,540,1,1,1,1,0 20 | 92,512,1,1,1,1,0 21 | 450,294,1,1,1,1,0 22 | 369,574,1,1,1,1,0 23 | 460,414,1,1,1,1,0 24 | 397,798,1,1,1,1,0 25 | 128,147,1,1,1,1,0 26 | 157,770,1,1,1,1,0 27 | 282,71,1,1,1,1,0 28 | 89,180,1,1,1,1,0 29 | 81,703,1,1,1,1,0 30 | 478,331,1,1,1,1,0 31 | 453,262,1,1,1,1,0 32 | 229,766,1,1,1,1,0 33 | 462,653,1,1,1,1,0 34 | 10,20,1,1,1,1,0 35 | 300,600,1,1,1,1,0 36 | 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407,9,1,1,1,1,0 81 | 205,334,1,1,1,1,0 82 | 131,81,1,1,1,1,0 83 | 371,268,1,1,1,1,0 84 | 289,498,1,1,1,1,0 85 | 145,213,1,1,1,1,0 86 | 372,783,1,1,1,1,0 87 | 243,634,1,1,1,1,0 88 | 134,462,1,1,1,1,0 89 | 259,447,1,1,1,1,0 90 | 256,551,1,1,1,1,0 91 | 141,79,1,1,1,1,0 92 | 301,599,1,1,1,1,0 93 | 470,303,1,1,1,1,0 94 | 255,209,1,1,1,1,0 95 | 486,258,1,1,1,1,0 96 | 308,167,1,1,1,1,0 97 | 132,551,1,1,1,1,0 98 | 485,695,1,1,1,1,0 99 | 395,561,1,1,1,1,0 100 | 266,592,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic8_m.csv: -------------------------------------------------------------------------------- 1 | 295,625,1,1,1,1,0 2 | 376,151,1,1,1,1,0 3 | 262,734,1,1,1,1,0 4 | 38,462,1,1,1,1,0 5 | 61,323,1,1,1,1,0 6 | 392,35,1,1,1,1,0 7 | 235,343,1,1,1,1,0 8 | 461,654,1,1,1,1,0 9 | 472,219,1,1,1,1,0 10 | 273,204,1,1,1,1,0 11 | 413,167,1,1,1,1,0 12 | 203,786,1,1,1,1,0 13 | 202,541,1,1,1,1,0 14 | 326,490,1,1,1,1,0 15 | 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| 280,454,1,1,1,1,0 60 | 286,744,1,1,1,1,0 61 | 123,369,1,1,1,1,0 62 | 385,268,1,1,1,1,0 63 | 303,673,1,1,1,1,0 64 | 490,350,1,1,1,1,0 65 | 276,367,1,1,1,1,0 66 | 426,471,1,1,1,1,0 67 | 159,289,1,1,1,1,0 68 | 309,459,1,1,1,1,0 69 | 204,449,1,1,1,1,0 70 | 137,154,1,1,1,1,0 71 | 62,652,1,1,1,1,0 72 | 40,714,1,1,1,1,0 73 | 398,597,1,1,1,1,0 74 | 381,379,1,1,1,1,0 75 | 86,218,1,1,1,1,0 76 | 250,168,1,1,1,1,0 77 | 52,333,1,1,1,1,0 78 | 76,117,1,1,1,1,0 79 | 290,532,1,1,1,1,0 80 | 489,154,1,1,1,1,0 81 | 228,333,1,1,1,1,0 82 | 373,704,1,1,1,1,0 83 | 483,367,1,1,1,1,0 84 | 456,715,1,1,1,1,0 85 | 105,298,1,1,1,1,0 86 | 279,50,1,1,1,1,0 87 | 436,428,1,1,1,1,0 88 | 50,473,1,1,1,1,0 89 | 480,263,1,1,1,1,0 90 | 147,127,1,1,1,1,0 91 | 148,159,1,1,1,1,0 92 | 75,15,1,1,1,1,0 93 | 227,83,1,1,1,1,0 94 | 301,437,1,1,1,1,0 95 | 114,326,1,1,1,1,0 96 | 345,149,1,1,1,1,0 97 | 85,660,1,1,1,1,0 98 | 97,324,1,1,1,1,0 99 | 25,474,1,1,1,1,0 100 | 474,293,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/FNN/traffic9_m.csv: -------------------------------------------------------------------------------- 1 | 171,774,1,1,1,1,0 2 | 392,469,1,1,1,1,0 3 | 410,168,1,1,1,1,0 4 | 408,258,1,1,1,1,0 5 | 503,536,1,1,1,1,0 6 | 336,628,1,1,1,1,0 7 | 75,464,1,1,1,1,0 8 | 113,84,1,1,1,1,0 9 | 478,461,1,1,1,1,0 10 | 9,44,1,1,1,1,0 11 | 282,247,1,1,1,1,0 12 | 425,632,1,1,1,1,0 13 | 302,8,1,1,1,1,0 14 | 233,219,1,1,1,1,0 15 | 373,60,1,1,1,1,0 16 | 418,374,1,1,1,1,0 17 | 225,238,1,1,1,1,0 18 | 288,177,1,1,1,1,0 19 | 53,222,1,1,1,1,0 20 | 496,384,1,1,1,1,0 21 | 78,418,1,1,1,1,0 22 | 166,201,1,1,1,1,0 23 | 504,707,1,1,1,1,0 24 | 214,396,1,1,1,1,0 25 | 327,480,1,1,1,1,0 26 | 31,697,1,1,1,1,0 27 | 199,330,1,1,1,1,0 28 | 461,548,1,1,1,1,0 29 | 267,323,1,1,1,1,0 30 | 182,295,1,1,1,1,0 31 | 172,168,1,1,1,1,0 32 | 384,431,1,1,1,1,0 33 | 277,375,1,1,1,1,0 34 | 315,138,1,1,1,1,0 35 | 366,791,1,1,1,1,0 36 | 320,538,1,1,1,1,0 37 | 11,258,1,1,1,1,0 38 | 479,705,1,1,1,1,0 39 | 165,170,1,1,1,1,0 40 | 430,417,1,1,1,1,0 41 | 298,531,1,1,1,1,0 42 | 508,705,1,1,1,1,0 43 | 317,617,1,1,1,1,0 44 | 325,46,1,1,1,1,0 45 | 42,219,1,1,1,1,0 46 | 78,595,1,1,1,1,0 47 | 330,35,1,1,1,1,0 48 | 488,681,1,1,1,1,0 49 | 29,634,1,1,1,1,0 50 | 194,18,1,1,1,1,0 51 | 389,108,1,1,1,1,0 52 | 407,73,1,1,1,1,0 53 | 31,246,1,1,1,1,0 54 | 468,310,1,1,1,1,0 55 | 338,98,1,1,1,1,0 56 | 352,275,1,1,1,1,0 57 | 306,596,1,1,1,1,0 58 | 304,442,1,1,1,1,0 59 | 379,69,1,1,1,1,0 60 | 402,270,1,1,1,1,0 61 | 31,259,1,1,1,1,0 62 | 90,196,1,1,1,1,0 63 | 65,114,1,1,1,1,0 64 | 188,346,1,1,1,1,0 65 | 50,33,1,1,1,1,0 66 | 464,130,1,1,1,1,0 67 | 154,435,1,1,1,1,0 68 | 430,782,1,1,1,1,0 69 | 230,213,1,1,1,1,0 70 | 98,491,1,1,1,1,0 71 | 139,392,1,1,1,1,0 72 | 411,170,1,1,1,1,0 73 | 345,607,1,1,1,1,0 74 | 475,167,1,1,1,1,0 75 | 392,168,1,1,1,1,0 76 | 364,187,1,1,1,1,0 77 | 392,467,1,1,1,1,0 78 | 233,435,1,1,1,1,0 79 | 202,651,1,1,1,1,0 80 | 428,593,1,1,1,1,0 81 | 319,711,1,1,1,1,0 82 | 218,457,1,1,1,1,0 83 | 500,578,1,1,1,1,0 84 | 429,642,1,1,1,1,0 85 | 62,141,1,1,1,1,0 86 | 461,752,1,1,1,1,0 87 | 316,753,1,1,1,1,0 88 | 453,462,1,1,1,1,0 89 | 206,715,1,1,1,1,0 90 | 197,162,1,1,1,1,0 91 | 272,782,1,1,1,1,0 92 | 134,319,1,1,1,1,0 93 | 65,131,1,1,1,1,0 94 | 394,742,1,1,1,1,0 95 | 383,211,1,1,1,1,0 96 | 10,72,1,1,1,1,0 97 | 246,710,1,1,1,1,0 98 | 150,156,1,1,1,1,0 99 | 14,593,1,1,1,1,0 100 | 141,770,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic0_m.csv: -------------------------------------------------------------------------------- 1 | 105,124,91,1,1,1,3 2 | 100,54,126,1,1,1,3 3 | 53,98,30,1,1,1,3 4 | 30,25,23,1,1,1,3 5 | 34,21,126,1,1,1,3 6 | 35,13,119,1,1,1,3 7 | 45,127,125,1,1,1,3 8 | 128,97,54,1,1,1,3 9 | 73,127,99,1,1,1,3 10 | 111,95,99,1,1,1,3 11 | 70,66,102,1,1,1,3 12 | 127,127,84,1,1,1,3 13 | 35,72,69,1,1,1,3 14 | 89,30,123,1,1,1,3 15 | 77,20,109,1,1,1,3 16 | 128,39,42,1,1,1,3 17 | 126,67,31,1,1,1,3 18 | 15,101,73,1,1,1,3 19 | 38,106,38,1,1,1,3 20 | 68,64,23,1,1,1,3 21 | 82,59,79,1,1,1,3 22 | 67,81,10,1,1,1,3 23 | 38,124,119,1,1,1,3 24 | 39,65,50,1,1,1,3 25 | 8,36,20,1,1,1,3 26 | 51,45,107,1,1,1,3 27 | 43,125,78,1,1,1,3 28 | 120,91,118,1,1,1,3 29 | 99,123,93,1,1,1,3 30 | 65,124,126,1,1,1,3 31 | 102,25,31,1,1,1,3 32 | 23,31,114,1,1,1,3 33 | 29,22,107,1,1,1,3 34 | 125,105,123,1,1,1,3 35 | 52,65,58,1,1,1,3 36 | 103,76,124,1,1,1,3 37 | 21,74,43,1,1,1,3 38 | 128,24,30,1,1,1,3 39 | 39,37,90,1,1,1,3 40 | 32,101,124,1,1,1,3 41 | 39,66,110,1,1,1,3 42 | 11,79,17,1,1,1,3 43 | 102,118,76,1,1,1,3 44 | 113,117,27,1,1,1,3 45 | 20,104,48,1,1,1,3 46 | 82,74,70,1,1,1,3 47 | 82,90,37,1,1,1,3 48 | 28,54,126,1,1,1,3 49 | 99,107,63,1,1,1,3 50 | 127,9,113,1,1,1,3 51 | 12,105,8,1,1,1,3 52 | 13,41,53,1,1,1,3 53 | 101,118,127,1,1,1,3 54 | 60,29,32,1,1,1,3 55 | 111,51,101,1,1,1,3 56 | 29,46,109,1,1,1,3 57 | 96,38,33,1,1,1,3 58 | 56,9,21,1,1,1,3 59 | 20,49,42,1,1,1,3 60 | 113,26,56,1,1,1,3 61 | 42,78,125,1,1,1,3 62 | 47,68,118,1,1,1,3 63 | 106,54,11,1,1,1,3 64 | 45,67,107,1,1,1,3 65 | 80,92,37,1,1,1,3 66 | 112,108,72,1,1,1,3 67 | 25,99,93,1,1,1,3 68 | 79,10,128,1,1,1,3 69 | 117,19,84,1,1,1,3 70 | 83,110,38,1,1,1,3 71 | 110,17,21,1,1,1,3 72 | 120,20,108,1,1,1,3 73 | 110,119,96,1,1,1,3 74 | 37,120,46,1,1,1,3 75 | 59,78,103,1,1,1,3 76 | 43,61,111,1,1,1,3 77 | 75,59,69,1,1,1,3 78 | 92,45,51,1,1,1,3 79 | 97,57,106,1,1,1,3 80 | 13,25,68,1,1,1,3 81 | 28,23,44,1,1,1,3 82 | 103,102,116,1,1,1,3 83 | 122,82,43,1,1,1,3 84 | 15,125,44,1,1,1,3 85 | 75,21,30,1,1,1,3 86 | 31,45,32,1,1,1,3 87 | 91,102,93,1,1,1,3 88 | 63,70,24,1,1,1,3 89 | 101,47,43,1,1,1,3 90 | 31,110,80,1,1,1,3 91 | 125,125,46,1,1,1,3 92 | 57,128,108,1,1,1,3 93 | 96,99,52,1,1,1,3 94 | 102,85,124,1,1,1,3 95 | 86,52,8,1,1,1,3 96 | 114,44,91,1,1,1,3 97 | 98,72,106,1,1,1,3 98 | 107,21,93,1,1,1,3 99 | 55,128,95,1,1,1,3 100 | 60,23,97,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic1_m.csv: -------------------------------------------------------------------------------- 1 | 48,94,15,1,1,1,3 2 | 49,57,31,1,1,1,3 3 | 19,42,86,1,1,1,3 4 | 15,19,85,1,1,1,3 5 | 111,109,58,1,1,1,3 6 | 52,30,120,1,1,1,3 7 | 79,58,123,1,1,1,3 8 | 58,75,121,1,1,1,3 9 | 81,68,39,1,1,1,3 10 | 19,50,26,1,1,1,3 11 | 127,52,127,1,1,1,3 12 | 80,37,71,1,1,1,3 13 | 35,45,108,1,1,1,3 14 | 8,122,73,1,1,1,3 15 | 104,12,68,1,1,1,3 16 | 22,49,67,1,1,1,3 17 | 57,88,39,1,1,1,3 18 | 52,68,76,1,1,1,3 19 | 80,117,89,1,1,1,3 20 | 34,127,65,1,1,1,3 21 | 88,77,31,1,1,1,3 22 | 91,60,46,1,1,1,3 23 | 38,86,27,1,1,1,3 24 | 38,123,99,1,1,1,3 25 | 95,86,105,1,1,1,3 26 | 26,122,128,1,1,1,3 27 | 77,48,56,1,1,1,3 28 | 35,107,62,1,1,1,3 29 | 49,95,17,1,1,1,3 30 | 96,39,17,1,1,1,3 31 | 76,41,68,1,1,1,3 32 | 108,116,87,1,1,1,3 33 | 61,35,29,1,1,1,3 34 | 32,80,111,1,1,1,3 35 | 91,34,28,1,1,1,3 36 | 121,89,34,1,1,1,3 37 | 109,40,49,1,1,1,3 38 | 27,43,46,1,1,1,3 39 | 94,44,64,1,1,1,3 40 | 101,69,107,1,1,1,3 41 | 63,110,89,1,1,1,3 42 | 42,102,65,1,1,1,3 43 | 116,82,83,1,1,1,3 44 | 98,120,92,1,1,1,3 45 | 90,83,40,1,1,1,3 46 | 106,38,102,1,1,1,3 47 | 12,103,29,1,1,1,3 48 | 71,35,66,1,1,1,3 49 | 23,127,25,1,1,1,3 50 | 14,97,78,1,1,1,3 51 | 47,44,69,1,1,1,3 52 | 112,65,127,1,1,1,3 53 | 80,15,110,1,1,1,3 54 | 25,127,31,1,1,1,3 55 | 115,80,41,1,1,1,3 56 | 58,24,121,1,1,1,3 57 | 45,47,108,1,1,1,3 58 | 15,59,73,1,1,1,3 59 | 78,33,85,1,1,1,3 60 | 28,119,40,1,1,1,3 61 | 79,84,81,1,1,1,3 62 | 87,11,64,1,1,1,3 63 | 29,30,71,1,1,1,3 64 | 103,20,35,1,1,1,3 65 | 30,102,63,1,1,1,3 66 | 126,58,60,1,1,1,3 67 | 38,33,104,1,1,1,3 68 | 73,30,119,1,1,1,3 69 | 8,90,68,1,1,1,3 70 | 62,105,74,1,1,1,3 71 | 26,40,104,1,1,1,3 72 | 39,62,101,1,1,1,3 73 | 107,94,38,1,1,1,3 74 | 81,41,47,1,1,1,3 75 | 14,96,121,1,1,1,3 76 | 42,36,9,1,1,1,3 77 | 45,87,16,1,1,1,3 78 | 106,116,67,1,1,1,3 79 | 103,122,20,1,1,1,3 80 | 49,14,47,1,1,1,3 81 | 120,15,9,1,1,1,3 82 | 119,35,85,1,1,1,3 83 | 49,71,53,1,1,1,3 84 | 75,81,59,1,1,1,3 85 | 24,78,120,1,1,1,3 86 | 81,51,55,1,1,1,3 87 | 68,29,95,1,1,1,3 88 | 56,23,20,1,1,1,3 89 | 27,111,118,1,1,1,3 90 | 105,23,116,1,1,1,3 91 | 11,20,49,1,1,1,3 92 | 66,20,23,1,1,1,3 93 | 46,54,37,1,1,1,3 94 | 89,81,34,1,1,1,3 95 | 60,114,112,1,1,1,3 96 | 8,76,73,1,1,1,3 97 | 47,101,112,1,1,1,3 98 | 107,94,101,1,1,1,3 99 | 39,91,40,1,1,1,3 100 | 89,28,103,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic2_m.csv: -------------------------------------------------------------------------------- 1 | 71,128,26,1,1,1,3 2 | 118,63,118,1,1,1,3 3 | 126,114,66,1,1,1,3 4 | 80,35,91,1,1,1,3 5 | 101,59,11,1,1,1,3 6 | 30,55,33,1,1,1,3 7 | 65,9,119,1,1,1,3 8 | 48,75,105,1,1,1,3 9 | 67,106,118,1,1,1,3 10 | 38,72,86,1,1,1,3 11 | 84,101,86,1,1,1,3 12 | 115,80,22,1,1,1,3 13 | 117,43,121,1,1,1,3 14 | 65,43,86,1,1,1,3 15 | 75,40,22,1,1,1,3 16 | 106,69,67,1,1,1,3 17 | 89,62,114,1,1,1,3 18 | 41,33,115,1,1,1,3 19 | 110,109,79,1,1,1,3 20 | 71,122,111,1,1,1,3 21 | 34,14,56,1,1,1,3 22 | 50,9,127,1,1,1,3 23 | 19,114,25,1,1,1,3 24 | 78,40,51,1,1,1,3 25 | 124,80,29,1,1,1,3 26 | 120,34,16,1,1,1,3 27 | 125,45,12,1,1,1,3 28 | 25,34,61,1,1,1,3 29 | 109,78,53,1,1,1,3 30 | 78,110,74,1,1,1,3 31 | 41,40,47,1,1,1,3 32 | 104,31,32,1,1,1,3 33 | 119,119,67,1,1,1,3 34 | 57,102,82,1,1,1,3 35 | 49,16,94,1,1,1,3 36 | 59,63,81,1,1,1,3 37 | 90,10,52,1,1,1,3 38 | 102,46,114,1,1,1,3 39 | 127,19,103,1,1,1,3 40 | 68,122,82,1,1,1,3 41 | 38,57,119,1,1,1,3 42 | 47,21,110,1,1,1,3 43 | 113,108,65,1,1,1,3 44 | 104,125,53,1,1,1,3 45 | 124,53,37,1,1,1,3 46 | 93,12,41,1,1,1,3 47 | 9,77,81,1,1,1,3 48 | 92,73,69,1,1,1,3 49 | 60,19,57,1,1,1,3 50 | 46,26,106,1,1,1,3 51 | 22,18,62,1,1,1,3 52 | 89,88,64,1,1,1,3 53 | 74,98,12,1,1,1,3 54 | 12,32,20,1,1,1,3 55 | 43,18,26,1,1,1,3 56 | 72,25,58,1,1,1,3 57 | 41,12,13,1,1,1,3 58 | 79,121,56,1,1,1,3 59 | 24,127,73,1,1,1,3 60 | 17,24,116,1,1,1,3 61 | 51,90,47,1,1,1,3 62 | 33,46,92,1,1,1,3 63 | 86,102,72,1,1,1,3 64 | 13,119,28,1,1,1,3 65 | 114,54,33,1,1,1,3 66 | 101,19,82,1,1,1,3 67 | 13,44,43,1,1,1,3 68 | 12,61,53,1,1,1,3 69 | 21,113,33,1,1,1,3 70 | 94,118,51,1,1,1,3 71 | 17,55,117,1,1,1,3 72 | 25,32,54,1,1,1,3 73 | 14,88,16,1,1,1,3 74 | 72,109,83,1,1,1,3 75 | 36,59,28,1,1,1,3 76 | 89,78,20,1,1,1,3 77 | 27,87,123,1,1,1,3 78 | 68,106,39,1,1,1,3 79 | 97,9,45,1,1,1,3 80 | 34,21,64,1,1,1,3 81 | 8,85,89,1,1,1,3 82 | 36,53,15,1,1,1,3 83 | 123,71,108,1,1,1,3 84 | 105,25,65,1,1,1,3 85 | 79,95,11,1,1,1,3 86 | 102,113,48,1,1,1,3 87 | 90,100,13,1,1,1,3 88 | 126,83,86,1,1,1,3 89 | 19,108,67,1,1,1,3 90 | 83,26,73,1,1,1,3 91 | 8,22,69,1,1,1,3 92 | 25,19,14,1,1,1,3 93 | 81,83,43,1,1,1,3 94 | 76,102,108,1,1,1,3 95 | 35,117,53,1,1,1,3 96 | 27,43,66,1,1,1,3 97 | 22,115,53,1,1,1,3 98 | 46,23,116,1,1,1,3 99 | 25,121,117,1,1,1,3 100 | 105,55,92,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic3_m.csv: -------------------------------------------------------------------------------- 1 | 75,54,67,1,1,1,3 2 | 16,95,40,1,1,1,3 3 | 127,57,80,1,1,1,3 4 | 51,40,111,1,1,1,3 5 | 95,104,107,1,1,1,3 6 | 61,121,93,1,1,1,3 7 | 81,72,117,1,1,1,3 8 | 76,19,64,1,1,1,3 9 | 117,82,93,1,1,1,3 10 | 120,103,61,1,1,1,3 11 | 98,112,57,1,1,1,3 12 | 60,36,100,1,1,1,3 13 | 34,73,31,1,1,1,3 14 | 119,62,48,1,1,1,3 15 | 108,31,123,1,1,1,3 16 | 50,36,60,1,1,1,3 17 | 51,45,127,1,1,1,3 18 | 104,35,98,1,1,1,3 19 | 17,64,62,1,1,1,3 20 | 68,31,85,1,1,1,3 21 | 70,55,29,1,1,1,3 22 | 104,112,114,1,1,1,3 23 | 24,77,65,1,1,1,3 24 | 46,120,13,1,1,1,3 25 | 115,22,68,1,1,1,3 26 | 14,17,57,1,1,1,3 27 | 40,83,17,1,1,1,3 28 | 122,61,11,1,1,1,3 29 | 24,21,44,1,1,1,3 30 | 96,26,124,1,1,1,3 31 | 39,81,92,1,1,1,3 32 | 20,126,99,1,1,1,3 33 | 29,10,63,1,1,1,3 34 | 8,75,107,1,1,1,3 35 | 103,11,22,1,1,1,3 36 | 31,49,101,1,1,1,3 37 | 120,122,118,1,1,1,3 38 | 9,66,58,1,1,1,3 39 | 128,102,49,1,1,1,3 40 | 75,106,114,1,1,1,3 41 | 58,59,20,1,1,1,3 42 | 46,43,35,1,1,1,3 43 | 72,110,26,1,1,1,3 44 | 87,128,63,1,1,1,3 45 | 81,118,63,1,1,1,3 46 | 80,16,13,1,1,1,3 47 | 47,56,74,1,1,1,3 48 | 52,28,60,1,1,1,3 49 | 51,123,65,1,1,1,3 50 | 90,74,109,1,1,1,3 51 | 50,75,15,1,1,1,3 52 | 124,112,108,1,1,1,3 53 | 86,30,56,1,1,1,3 54 | 38,77,94,1,1,1,3 55 | 65,83,104,1,1,1,3 56 | 75,103,57,1,1,1,3 57 | 32,69,36,1,1,1,3 58 | 125,17,123,1,1,1,3 59 | 71,48,113,1,1,1,3 60 | 113,10,23,1,1,1,3 61 | 81,16,18,1,1,1,3 62 | 124,123,34,1,1,1,3 63 | 119,41,110,1,1,1,3 64 | 52,63,16,1,1,1,3 65 | 43,98,59,1,1,1,3 66 | 125,123,41,1,1,1,3 67 | 47,24,103,1,1,1,3 68 | 35,117,8,1,1,1,3 69 | 40,16,125,1,1,1,3 70 | 10,56,71,1,1,1,3 71 | 12,126,78,1,1,1,3 72 | 82,61,102,1,1,1,3 73 | 15,23,54,1,1,1,3 74 | 23,60,77,1,1,1,3 75 | 82,73,103,1,1,1,3 76 | 91,51,121,1,1,1,3 77 | 52,10,106,1,1,1,3 78 | 22,18,117,1,1,1,3 79 | 111,66,50,1,1,1,3 80 | 112,14,99,1,1,1,3 81 | 30,120,19,1,1,1,3 82 | 8,122,122,1,1,1,3 83 | 10,39,104,1,1,1,3 84 | 89,88,70,1,1,1,3 85 | 119,99,28,1,1,1,3 86 | 17,42,23,1,1,1,3 87 | 43,122,12,1,1,1,3 88 | 96,100,80,1,1,1,3 89 | 23,123,9,1,1,1,3 90 | 93,74,77,1,1,1,3 91 | 112,62,78,1,1,1,3 92 | 106,19,15,1,1,1,3 93 | 71,87,35,1,1,1,3 94 | 36,110,26,1,1,1,3 95 | 89,108,68,1,1,1,3 96 | 47,75,44,1,1,1,3 97 | 115,18,126,1,1,1,3 98 | 123,17,103,1,1,1,3 99 | 104,54,106,1,1,1,3 100 | 43,106,40,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic4_m.csv: -------------------------------------------------------------------------------- 1 | 10,83,23,1,1,1,3 2 | 38,11,98,1,1,1,3 3 | 44,53,108,1,1,1,3 4 | 20,44,124,1,1,1,3 5 | 68,16,28,1,1,1,3 6 | 19,76,52,1,1,1,3 7 | 64,25,46,1,1,1,3 8 | 104,58,128,1,1,1,3 9 | 34,61,67,1,1,1,3 10 | 69,90,126,1,1,1,3 11 | 75,128,14,1,1,1,3 12 | 49,33,126,1,1,1,3 13 | 107,117,116,1,1,1,3 14 | 114,47,17,1,1,1,3 15 | 84,40,91,1,1,1,3 16 | 62,99,76,1,1,1,3 17 | 98,14,124,1,1,1,3 18 | 33,87,38,1,1,1,3 19 | 18,86,110,1,1,1,3 20 | 21,103,125,1,1,1,3 21 | 63,93,28,1,1,1,3 22 | 104,62,24,1,1,1,3 23 | 66,54,87,1,1,1,3 24 | 54,42,9,1,1,1,3 25 | 55,69,56,1,1,1,3 26 | 27,66,103,1,1,1,3 27 | 73,41,89,1,1,1,3 28 | 53,84,121,1,1,1,3 29 | 80,92,44,1,1,1,3 30 | 20,14,56,1,1,1,3 31 | 61,53,90,1,1,1,3 32 | 68,13,12,1,1,1,3 33 | 46,24,75,1,1,1,3 34 | 125,71,39,1,1,1,3 35 | 120,54,37,1,1,1,3 36 | 65,58,23,1,1,1,3 37 | 96,110,46,1,1,1,3 38 | 63,23,37,1,1,1,3 39 | 81,128,64,1,1,1,3 40 | 91,124,52,1,1,1,3 41 | 37,78,95,1,1,1,3 42 | 75,92,53,1,1,1,3 43 | 66,9,48,1,1,1,3 44 | 86,67,107,1,1,1,3 45 | 128,92,111,1,1,1,3 46 | 94,79,97,1,1,1,3 47 | 87,37,88,1,1,1,3 48 | 55,90,72,1,1,1,3 49 | 78,77,96,1,1,1,3 50 | 85,13,62,1,1,1,3 51 | 92,69,118,1,1,1,3 52 | 82,35,44,1,1,1,3 53 | 68,38,123,1,1,1,3 54 | 68,110,70,1,1,1,3 55 | 106,109,25,1,1,1,3 56 | 77,74,40,1,1,1,3 57 | 33,78,103,1,1,1,3 58 | 77,85,97,1,1,1,3 59 | 125,12,37,1,1,1,3 60 | 58,102,113,1,1,1,3 61 | 81,111,44,1,1,1,3 62 | 36,66,74,1,1,1,3 63 | 8,14,17,1,1,1,3 64 | 52,103,46,1,1,1,3 65 | 41,10,45,1,1,1,3 66 | 61,96,12,1,1,1,3 67 | 85,79,83,1,1,1,3 68 | 54,11,50,1,1,1,3 69 | 57,114,10,1,1,1,3 70 | 101,67,34,1,1,1,3 71 | 77,112,16,1,1,1,3 72 | 28,57,86,1,1,1,3 73 | 86,124,76,1,1,1,3 74 | 37,75,53,1,1,1,3 75 | 102,98,95,1,1,1,3 76 | 31,80,30,1,1,1,3 77 | 44,83,101,1,1,1,3 78 | 123,65,10,1,1,1,3 79 | 70,109,119,1,1,1,3 80 | 14,106,63,1,1,1,3 81 | 17,37,69,1,1,1,3 82 | 44,18,40,1,1,1,3 83 | 43,27,92,1,1,1,3 84 | 55,67,45,1,1,1,3 85 | 115,70,61,1,1,1,3 86 | 19,95,57,1,1,1,3 87 | 19,25,13,1,1,1,3 88 | 26,90,15,1,1,1,3 89 | 19,18,123,1,1,1,3 90 | 78,39,25,1,1,1,3 91 | 15,33,8,1,1,1,3 92 | 71,112,73,1,1,1,3 93 | 73,53,77,1,1,1,3 94 | 87,101,18,1,1,1,3 95 | 29,90,128,1,1,1,3 96 | 34,127,27,1,1,1,3 97 | 94,66,102,1,1,1,3 98 | 65,79,17,1,1,1,3 99 | 73,48,121,1,1,1,3 100 | 65,26,99,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic5_m.csv: -------------------------------------------------------------------------------- 1 | 49,110,110,1,1,1,3 2 | 66,44,88,1,1,1,3 3 | 104,71,71,1,1,1,3 4 | 28,94,64,1,1,1,3 5 | 43,52,106,1,1,1,3 6 | 116,35,90,1,1,1,3 7 | 109,62,104,1,1,1,3 8 | 91,113,45,1,1,1,3 9 | 90,61,74,1,1,1,3 10 | 11,48,39,1,1,1,3 11 | 117,86,88,1,1,1,3 12 | 52,17,54,1,1,1,3 13 | 69,106,128,1,1,1,3 14 | 56,76,94,1,1,1,3 15 | 109,81,104,1,1,1,3 16 | 101,107,57,1,1,1,3 17 | 91,61,105,1,1,1,3 18 | 65,38,90,1,1,1,3 19 | 99,97,52,1,1,1,3 20 | 109,16,52,1,1,1,3 21 | 10,42,28,1,1,1,3 22 | 12,115,89,1,1,1,3 23 | 18,95,63,1,1,1,3 24 | 100,69,109,1,1,1,3 25 | 97,22,22,1,1,1,3 26 | 93,18,20,1,1,1,3 27 | 57,39,50,1,1,1,3 28 | 121,39,46,1,1,1,3 29 | 86,74,52,1,1,1,3 30 | 61,27,102,1,1,1,3 31 | 58,14,9,1,1,1,3 32 | 62,16,33,1,1,1,3 33 | 45,95,65,1,1,1,3 34 | 8,113,8,1,1,1,3 35 | 81,8,109,1,1,1,3 36 | 19,89,91,1,1,1,3 37 | 78,69,111,1,1,1,3 38 | 36,54,103,1,1,1,3 39 | 95,74,35,1,1,1,3 40 | 91,79,49,1,1,1,3 41 | 88,9,33,1,1,1,3 42 | 80,82,33,1,1,1,3 43 | 60,69,27,1,1,1,3 44 | 39,118,43,1,1,1,3 45 | 36,88,36,1,1,1,3 46 | 124,86,95,1,1,1,3 47 | 112,41,38,1,1,1,3 48 | 100,39,50,1,1,1,3 49 | 55,115,58,1,1,1,3 50 | 15,66,95,1,1,1,3 51 | 19,11,105,1,1,1,3 52 | 101,88,10,1,1,1,3 53 | 117,31,14,1,1,1,3 54 | 77,89,82,1,1,1,3 55 | 17,124,53,1,1,1,3 56 | 67,113,8,1,1,1,3 57 | 33,56,39,1,1,1,3 58 | 92,14,93,1,1,1,3 59 | 36,61,33,1,1,1,3 60 | 33,110,40,1,1,1,3 61 | 104,34,124,1,1,1,3 62 | 115,75,62,1,1,1,3 63 | 37,56,72,1,1,1,3 64 | 97,125,59,1,1,1,3 65 | 54,95,98,1,1,1,3 66 | 33,126,31,1,1,1,3 67 | 59,127,51,1,1,1,3 68 | 128,117,23,1,1,1,3 69 | 126,126,112,1,1,1,3 70 | 26,74,58,1,1,1,3 71 | 68,49,10,1,1,1,3 72 | 73,73,54,1,1,1,3 73 | 122,28,54,1,1,1,3 74 | 9,92,102,1,1,1,3 75 | 124,123,57,1,1,1,3 76 | 109,79,60,1,1,1,3 77 | 119,69,125,1,1,1,3 78 | 69,30,86,1,1,1,3 79 | 41,28,32,1,1,1,3 80 | 36,22,84,1,1,1,3 81 | 113,94,74,1,1,1,3 82 | 12,75,104,1,1,1,3 83 | 113,13,24,1,1,1,3 84 | 85,21,8,1,1,1,3 85 | 78,99,110,1,1,1,3 86 | 85,83,25,1,1,1,3 87 | 115,93,111,1,1,1,3 88 | 13,50,60,1,1,1,3 89 | 54,16,72,1,1,1,3 90 | 66,23,8,1,1,1,3 91 | 44,68,122,1,1,1,3 92 | 73,106,8,1,1,1,3 93 | 68,40,116,1,1,1,3 94 | 43,65,66,1,1,1,3 95 | 124,37,96,1,1,1,3 96 | 47,56,120,1,1,1,3 97 | 21,15,67,1,1,1,3 98 | 119,105,105,1,1,1,3 99 | 95,12,93,1,1,1,3 100 | 117,60,44,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic6_m.csv: -------------------------------------------------------------------------------- 1 | 20,75,74,1,1,1,3 2 | 68,96,28,1,1,1,3 3 | 128,74,103,1,1,1,3 4 | 81,33,92,1,1,1,3 5 | 17,72,62,1,1,1,3 6 | 36,90,65,1,1,1,3 7 | 18,9,85,1,1,1,3 8 | 76,119,59,1,1,1,3 9 | 85,71,67,1,1,1,3 10 | 49,27,113,1,1,1,3 11 | 58,84,45,1,1,1,3 12 | 38,22,86,1,1,1,3 13 | 93,10,50,1,1,1,3 14 | 103,88,27,1,1,1,3 15 | 42,111,125,1,1,1,3 16 | 62,99,56,1,1,1,3 17 | 17,13,11,1,1,1,3 18 | 18,67,18,1,1,1,3 19 | 55,92,60,1,1,1,3 20 | 9,30,42,1,1,1,3 21 | 108,19,114,1,1,1,3 22 | 75,118,99,1,1,1,3 23 | 88,55,48,1,1,1,3 24 | 67,24,116,1,1,1,3 25 | 50,119,69,1,1,1,3 26 | 125,39,86,1,1,1,3 27 | 74,30,67,1,1,1,3 28 | 72,117,41,1,1,1,3 29 | 126,100,27,1,1,1,3 30 | 67,87,73,1,1,1,3 31 | 12,69,86,1,1,1,3 32 | 67,50,28,1,1,1,3 33 | 49,88,77,1,1,1,3 34 | 82,18,107,1,1,1,3 35 | 73,26,13,1,1,1,3 36 | 45,18,29,1,1,1,3 37 | 9,9,66,1,1,1,3 38 | 31,34,75,1,1,1,3 39 | 69,40,124,1,1,1,3 40 | 51,91,108,1,1,1,3 41 | 11,12,80,1,1,1,3 42 | 84,53,62,1,1,1,3 43 | 85,46,50,1,1,1,3 44 | 24,32,93,1,1,1,3 45 | 93,28,43,1,1,1,3 46 | 95,28,94,1,1,1,3 47 | 58,110,91,1,1,1,3 48 | 54,108,126,1,1,1,3 49 | 92,18,30,1,1,1,3 50 | 111,29,29,1,1,1,3 51 | 40,97,73,1,1,1,3 52 | 65,93,47,1,1,1,3 53 | 57,128,37,1,1,1,3 54 | 41,41,45,1,1,1,3 55 | 12,117,37,1,1,1,3 56 | 113,37,123,1,1,1,3 57 | 23,19,122,1,1,1,3 58 | 36,13,66,1,1,1,3 59 | 37,59,89,1,1,1,3 60 | 106,95,115,1,1,1,3 61 | 21,67,48,1,1,1,3 62 | 66,106,54,1,1,1,3 63 | 64,60,51,1,1,1,3 64 | 124,115,82,1,1,1,3 65 | 84,14,83,1,1,1,3 66 | 44,54,114,1,1,1,3 67 | 68,50,74,1,1,1,3 68 | 87,18,77,1,1,1,3 69 | 19,75,40,1,1,1,3 70 | 96,41,23,1,1,1,3 71 | 123,37,25,1,1,1,3 72 | 59,24,124,1,1,1,3 73 | 128,65,123,1,1,1,3 74 | 49,114,38,1,1,1,3 75 | 63,71,73,1,1,1,3 76 | 101,96,106,1,1,1,3 77 | 110,23,67,1,1,1,3 78 | 40,77,23,1,1,1,3 79 | 122,80,116,1,1,1,3 80 | 37,11,86,1,1,1,3 81 | 88,27,10,1,1,1,3 82 | 18,28,110,1,1,1,3 83 | 79,128,69,1,1,1,3 84 | 51,86,18,1,1,1,3 85 | 21,117,59,1,1,1,3 86 | 48,120,30,1,1,1,3 87 | 27,103,91,1,1,1,3 88 | 65,44,70,1,1,1,3 89 | 126,39,111,1,1,1,3 90 | 65,127,97,1,1,1,3 91 | 42,23,78,1,1,1,3 92 | 92,101,88,1,1,1,3 93 | 12,102,89,1,1,1,3 94 | 66,97,88,1,1,1,3 95 | 93,73,77,1,1,1,3 96 | 109,72,81,1,1,1,3 97 | 33,60,12,1,1,1,3 98 | 19,103,95,1,1,1,3 99 | 50,109,81,1,1,1,3 100 | 79,75,90,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic7_m.csv: -------------------------------------------------------------------------------- 1 | 46,28,111,1,1,1,3 2 | 38,92,49,1,1,1,3 3 | 113,115,64,1,1,1,3 4 | 107,41,17,1,1,1,3 5 | 45,77,108,1,1,1,3 6 | 34,93,17,1,1,1,3 7 | 51,49,20,1,1,1,3 8 | 24,100,11,1,1,1,3 9 | 93,31,115,1,1,1,3 10 | 41,30,111,1,1,1,3 11 | 89,108,114,1,1,1,3 12 | 102,68,52,1,1,1,3 13 | 64,45,60,1,1,1,3 14 | 15,73,73,1,1,1,3 15 | 96,107,13,1,1,1,3 16 | 57,44,31,1,1,1,3 17 | 44,35,36,1,1,1,3 18 | 46,51,33,1,1,1,3 19 | 8,15,91,1,1,1,3 20 | 109,56,109,1,1,1,3 21 | 104,35,24,1,1,1,3 22 | 110,37,32,1,1,1,3 23 | 93,59,80,1,1,1,3 24 | 15,64,55,1,1,1,3 25 | 111,67,40,1,1,1,3 26 | 52,46,60,1,1,1,3 27 | 80,120,35,1,1,1,3 28 | 95,33,104,1,1,1,3 29 | 42,73,115,1,1,1,3 30 | 63,80,97,1,1,1,3 31 | 104,42,83,1,1,1,3 32 | 123,40,71,1,1,1,3 33 | 56,114,112,1,1,1,3 34 | 63,117,100,1,1,1,3 35 | 64,69,53,1,1,1,3 36 | 56,102,46,1,1,1,3 37 | 53,28,33,1,1,1,3 38 | 54,70,91,1,1,1,3 39 | 43,120,53,1,1,1,3 40 | 95,109,114,1,1,1,3 41 | 110,106,80,1,1,1,3 42 | 69,78,108,1,1,1,3 43 | 128,69,106,1,1,1,3 44 | 70,39,43,1,1,1,3 45 | 125,128,35,1,1,1,3 46 | 92,102,14,1,1,1,3 47 | 74,22,13,1,1,1,3 48 | 117,84,30,1,1,1,3 49 | 121,112,26,1,1,1,3 50 | 10,101,39,1,1,1,3 51 | 57,13,120,1,1,1,3 52 | 98,85,107,1,1,1,3 53 | 73,76,14,1,1,1,3 54 | 113,46,117,1,1,1,3 55 | 19,76,105,1,1,1,3 56 | 121,127,81,1,1,1,3 57 | 51,113,63,1,1,1,3 58 | 36,18,54,1,1,1,3 59 | 100,46,25,1,1,1,3 60 | 100,37,39,1,1,1,3 61 | 76,67,89,1,1,1,3 62 | 23,67,128,1,1,1,3 63 | 13,61,114,1,1,1,3 64 | 126,12,98,1,1,1,3 65 | 36,85,73,1,1,1,3 66 | 127,88,122,1,1,1,3 67 | 58,8,98,1,1,1,3 68 | 93,112,59,1,1,1,3 69 | 54,31,44,1,1,1,3 70 | 60,71,51,1,1,1,3 71 | 38,84,87,1,1,1,3 72 | 122,111,38,1,1,1,3 73 | 19,18,52,1,1,1,3 74 | 87,92,77,1,1,1,3 75 | 116,115,47,1,1,1,3 76 | 85,24,19,1,1,1,3 77 | 60,49,125,1,1,1,3 78 | 60,32,71,1,1,1,3 79 | 57,110,75,1,1,1,3 80 | 16,49,19,1,1,1,3 81 | 83,113,28,1,1,1,3 82 | 75,109,70,1,1,1,3 83 | 23,44,18,1,1,1,3 84 | 15,15,93,1,1,1,3 85 | 38,100,42,1,1,1,3 86 | 31,23,64,1,1,1,3 87 | 48,34,76,1,1,1,3 88 | 96,57,66,1,1,1,3 89 | 34,26,20,1,1,1,3 90 | 53,29,97,1,1,1,3 91 | 117,16,56,1,1,1,3 92 | 118,20,40,1,1,1,3 93 | 121,24,77,1,1,1,3 94 | 14,29,118,1,1,1,3 95 | 24,63,120,1,1,1,3 96 | 78,36,73,1,1,1,3 97 | 112,39,42,1,1,1,3 98 | 27,43,104,1,1,1,3 99 | 26,121,95,1,1,1,3 100 | 97,122,87,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic8_m.csv: -------------------------------------------------------------------------------- 1 | 56,125,108,1,1,1,3 2 | 51,9,109,1,1,1,3 3 | 113,113,90,1,1,1,3 4 | 48,77,25,1,1,1,3 5 | 59,107,34,1,1,1,3 6 | 61,110,16,1,1,1,3 7 | 31,33,73,1,1,1,3 8 | 12,15,22,1,1,1,3 9 | 85,66,52,1,1,1,3 10 | 8,125,97,1,1,1,3 11 | 78,38,106,1,1,1,3 12 | 115,43,16,1,1,1,3 13 | 121,88,8,1,1,1,3 14 | 88,38,37,1,1,1,3 15 | 125,57,105,1,1,1,3 16 | 105,53,35,1,1,1,3 17 | 36,109,85,1,1,1,3 18 | 67,52,40,1,1,1,3 19 | 107,23,47,1,1,1,3 20 | 103,46,100,1,1,1,3 21 | 67,96,31,1,1,1,3 22 | 94,120,99,1,1,1,3 23 | 91,16,76,1,1,1,3 24 | 91,123,119,1,1,1,3 25 | 115,77,24,1,1,1,3 26 | 101,18,128,1,1,1,3 27 | 20,29,53,1,1,1,3 28 | 80,28,114,1,1,1,3 29 | 125,108,36,1,1,1,3 30 | 88,9,75,1,1,1,3 31 | 115,16,85,1,1,1,3 32 | 91,15,109,1,1,1,3 33 | 37,75,33,1,1,1,3 34 | 68,83,87,1,1,1,3 35 | 32,70,22,1,1,1,3 36 | 37,80,62,1,1,1,3 37 | 41,21,10,1,1,1,3 38 | 81,9,20,1,1,1,3 39 | 124,24,50,1,1,1,3 40 | 72,47,120,1,1,1,3 41 | 81,57,97,1,1,1,3 42 | 39,120,75,1,1,1,3 43 | 79,13,94,1,1,1,3 44 | 58,40,31,1,1,1,3 45 | 48,89,128,1,1,1,3 46 | 103,47,52,1,1,1,3 47 | 96,127,120,1,1,1,3 48 | 61,39,33,1,1,1,3 49 | 96,52,111,1,1,1,3 50 | 57,71,67,1,1,1,3 51 | 95,97,97,1,1,1,3 52 | 117,58,91,1,1,1,3 53 | 15,99,79,1,1,1,3 54 | 38,49,27,1,1,1,3 55 | 102,88,57,1,1,1,3 56 | 40,21,80,1,1,1,3 57 | 70,75,97,1,1,1,3 58 | 76,44,97,1,1,1,3 59 | 125,92,62,1,1,1,3 60 | 20,39,80,1,1,1,3 61 | 65,36,81,1,1,1,3 62 | 117,10,74,1,1,1,3 63 | 113,46,44,1,1,1,3 64 | 29,17,102,1,1,1,3 65 | 80,11,48,1,1,1,3 66 | 83,71,115,1,1,1,3 67 | 56,90,79,1,1,1,3 68 | 128,106,68,1,1,1,3 69 | 25,72,67,1,1,1,3 70 | 34,125,116,1,1,1,3 71 | 63,118,85,1,1,1,3 72 | 87,96,95,1,1,1,3 73 | 55,55,69,1,1,1,3 74 | 124,93,53,1,1,1,3 75 | 61,68,17,1,1,1,3 76 | 86,42,22,1,1,1,3 77 | 58,59,90,1,1,1,3 78 | 93,125,44,1,1,1,3 79 | 13,81,69,1,1,1,3 80 | 89,57,52,1,1,1,3 81 | 90,115,18,1,1,1,3 82 | 83,116,104,1,1,1,3 83 | 62,86,102,1,1,1,3 84 | 37,88,110,1,1,1,3 85 | 109,44,13,1,1,1,3 86 | 120,53,51,1,1,1,3 87 | 8,74,32,1,1,1,3 88 | 27,76,73,1,1,1,3 89 | 28,62,75,1,1,1,3 90 | 25,89,16,1,1,1,3 91 | 43,86,66,1,1,1,3 92 | 18,60,56,1,1,1,3 93 | 60,91,14,1,1,1,3 94 | 20,58,109,1,1,1,3 95 | 76,47,57,1,1,1,3 96 | 15,103,33,1,1,1,3 97 | 81,77,80,1,1,1,3 98 | 64,81,39,1,1,1,3 99 | 95,13,70,1,1,1,3 100 | 42,109,94,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/batch3/GEMM/traffic9_m.csv: -------------------------------------------------------------------------------- 1 | 77,119,94,1,1,1,3 2 | 106,17,56,1,1,1,3 3 | 86,112,85,1,1,1,3 4 | 48,15,123,1,1,1,3 5 | 87,40,72,1,1,1,3 6 | 101,28,119,1,1,1,3 7 | 70,66,94,1,1,1,3 8 | 97,69,97,1,1,1,3 9 | 113,8,102,1,1,1,3 10 | 94,53,106,1,1,1,3 11 | 112,38,99,1,1,1,3 12 | 102,28,114,1,1,1,3 13 | 91,39,49,1,1,1,3 14 | 72,87,88,1,1,1,3 15 | 35,31,55,1,1,1,3 16 | 97,47,100,1,1,1,3 17 | 109,122,13,1,1,1,3 18 | 43,84,27,1,1,1,3 19 | 99,48,26,1,1,1,3 20 | 100,122,91,1,1,1,3 21 | 79,116,114,1,1,1,3 22 | 67,22,47,1,1,1,3 23 | 27,50,44,1,1,1,3 24 | 61,46,19,1,1,1,3 25 | 55,57,112,1,1,1,3 26 | 78,87,8,1,1,1,3 27 | 39,105,103,1,1,1,3 28 | 113,19,51,1,1,1,3 29 | 39,59,70,1,1,1,3 30 | 44,29,96,1,1,1,3 31 | 46,12,15,1,1,1,3 32 | 11,16,21,1,1,1,3 33 | 75,91,115,1,1,1,3 34 | 56,51,123,1,1,1,3 35 | 76,103,41,1,1,1,3 36 | 100,54,80,1,1,1,3 37 | 34,107,117,1,1,1,3 38 | 114,40,24,1,1,1,3 39 | 37,35,55,1,1,1,3 40 | 11,48,28,1,1,1,3 41 | 45,30,64,1,1,1,3 42 | 75,12,24,1,1,1,3 43 | 11,99,115,1,1,1,3 44 | 27,24,26,1,1,1,3 45 | 108,47,23,1,1,1,3 46 | 87,94,89,1,1,1,3 47 | 19,114,43,1,1,1,3 48 | 116,12,54,1,1,1,3 49 | 93,53,116,1,1,1,3 50 | 48,14,70,1,1,1,3 51 | 74,55,22,1,1,1,3 52 | 74,8,20,1,1,1,3 53 | 24,125,89,1,1,1,3 54 | 45,89,36,1,1,1,3 55 | 123,58,82,1,1,1,3 56 | 8,67,123,1,1,1,3 57 | 74,93,18,1,1,1,3 58 | 68,96,104,1,1,1,3 59 | 30,114,102,1,1,1,3 60 | 34,86,10,1,1,1,3 61 | 91,52,69,1,1,1,3 62 | 122,53,80,1,1,1,3 63 | 115,16,55,1,1,1,3 64 | 114,45,39,1,1,1,3 65 | 117,104,23,1,1,1,3 66 | 18,116,70,1,1,1,3 67 | 47,61,79,1,1,1,3 68 | 83,93,31,1,1,1,3 69 | 117,128,41,1,1,1,3 70 | 85,56,27,1,1,1,3 71 | 34,113,34,1,1,1,3 72 | 119,62,126,1,1,1,3 73 | 59,38,26,1,1,1,3 74 | 93,64,121,1,1,1,3 75 | 49,34,13,1,1,1,3 76 | 17,19,26,1,1,1,3 77 | 96,10,122,1,1,1,3 78 | 99,105,103,1,1,1,3 79 | 118,70,97,1,1,1,3 80 | 23,75,91,1,1,1,3 81 | 55,105,122,1,1,1,3 82 | 54,118,75,1,1,1,3 83 | 120,111,12,1,1,1,3 84 | 18,50,63,1,1,1,3 85 | 104,122,31,1,1,1,3 86 | 81,42,124,1,1,1,3 87 | 25,41,116,1,1,1,3 88 | 119,45,17,1,1,1,3 89 | 56,42,30,1,1,1,3 90 | 71,52,127,1,1,1,3 91 | 119,27,72,1,1,1,3 92 | 33,84,39,1,1,1,3 93 | 19,112,87,1,1,1,3 94 | 28,24,45,1,1,1,3 95 | 29,74,115,1,1,1,3 96 | 78,28,59,1,1,1,3 97 | 54,84,85,1,1,1,3 98 | 112,18,61,1,1,1,3 99 | 46,85,67,1,1,1,3 100 | 33,91,126,1,1,1,3 101 | -------------------------------------------------------------------------------- /traffic/gen_traffic_v1.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | 5 | 6 | 7 | 8 | 9 | def gen_CONV_traffic(batchdir = "try/", num_layers = 100): 10 | os.makedirs(batchdir, exist_ok=True) 11 | dim_range = [[1, 256], [1, 256], [3, 225], [3, 225], [1,3], [1, 3], [1]] 12 | for tr in range(10): 13 | outfile_t = batchdir+"traffic{}_m.csv".format(tr) 14 | with open(outfile_t, "w") as fd: 15 | for lay in range(num_layers): 16 | dim = [random.randint(*dim_range[i]) for i in range(2)] 17 | r = random.randint(*dim_range[2]) 18 | dim += [r, r] 19 | r = random.choice(dim_range[4]) 20 | dim += [r, r] 21 | t = random.choice(dim_range[6]) 22 | dim += [t] 23 | fd.write("{},{},{},{},{},{},{}\n".format(*dim)) 24 | 25 | def gen_GEMM_traffic(batchdir = "try/", num_layers = 100): 26 | os.makedirs(batchdir, exist_ok=True) 27 | dim_range = [[8, 128], [8, 128], [8, 128]] 28 | for tr in range(10): 29 | outfile_t = batchdir+"traffic{}_m.csv".format(tr) 30 | with open(outfile_t, "w") as fd: 31 | for lay in range(num_layers): 32 | dim = [random.randint(*dim_range[i]) for i in range(3)] 33 | dim += [1,1,1,3] 34 | fd.write("{},{},{},{},{},{},{}\n".format(*dim)) 35 | 36 | 37 | def gen_FNN_traffic(batchdir = "try/", num_layers = 100): 38 | os.makedirs(batchdir, exist_ok=True) 39 | dim_range = [[8, 512], [8, 812]] 40 | for tr in range(10): 41 | outfile_t = batchdir+"traffic{}_m.csv".format(tr) 42 | with open(outfile_t, "w") as fd: 43 | for lay in range(num_layers): 44 | dim = [random.randint(*dim_range[i]) for i in range(2)] 45 | dim += [1,1,1,1,0] 46 | fd.write("{},{},{},{},{},{},{}\n".format(*dim)) 47 | 48 | gen_FNN_traffic("./batch3/FNN/") 49 | gen_GEMM_traffic("./batch3/GEMM/") 50 | gen_CONV_traffic("./batch3/CONV/") -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts0.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts1.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts10.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts2.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts3.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts4.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts5.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts6.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts7.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts8.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch1/description/insts9.csv: -------------------------------------------------------------------------------- 1 | 16,16,16 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts0.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts1.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts2.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts3.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts4.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts5.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts6.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts7.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts8.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch2/description/insts9.csv: -------------------------------------------------------------------------------- 1 | 1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts0.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts1.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts2.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts3.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts4.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts5.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts6.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts7.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts8.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch3/description/insts9.csv: -------------------------------------------------------------------------------- 1 | 1,1,1 -------------------------------------------------------------------------------- /traffic_insts/batch_conv/insts1.csv: -------------------------------------------------------------------------------- 1 | 1,1,512,256,28,28,1,1,1 2 | 1,1,256,1024,14,14,1,1,1 3 | 1,1,48,192,14,14,3,3,1 4 | 1,1,512,512,14,14,3,3,1 5 | 1,1,192,240,56,56,1,1,1 6 | 1,1,1024,512,14,14,1,1,1 7 | 1,1,1024,256,14,14,1,1,1 8 | 1,1,160,512,14,14,1,1,1 9 | 1,1,232,232,7,7,1,1,1 10 | 1,1,64,192,28,28,1,1,1 11 | 1,1,48,192,14,14,3,3,1 12 | 1,1,1,1152,7,7,5,5,2 13 | 1,1,384,192,7,7,3,3,1 14 | 1,1,192,864,14,14,1,1,1 15 | 1,1,256,512,28,28,1,1,1 16 | 1,1,192,1344,7,7,1,1,1 17 | 1,1,128,128,56,56,3,3,1 18 | 1,1,384,192,7,7,3,3,1 19 | 1,1,1,72,56,56,3,3,2 20 | 1,1,480,80,14,14,1,1,1 21 | 1,1,96,576,14,14,1,1,1 22 | 1,1,120,40,28,28,1,1,1 23 | 1,1,512,512,28,28,1,1,1 24 | 1,1,128,832,7,7,1,1,1 25 | 1,1,256,1024,14,14,1,1,1 26 | 1,1,1024,512,14,14,1,1,1 27 | 1,1,192,1248,14,14,1,1,1 28 | 1,1,116,116,14,14,1,1,1 29 | 1,1,512,128,28,28,1,1,1 30 | 1,1,48,192,28,28,3,3,1 31 | 1,1,128,256,56,56,1,1,1 32 | 1,1,192,1152,7,7,1,1,1 33 | 1,1,32,528,14,14,1,1,1 34 | 1,1,512,512,14,14,3,3,1 35 | 1,1,192,576,28,28,1,1,1 36 | 1,1,192,1536,7,7,1,1,1 37 | 1,1,256,128,28,28,1,1,1 38 | 1,1,160,832,7,7,1,1,1 39 | 1,1,48,192,28,28,3,3,1 40 | 1,1,64,24,14,14,3,3,1 41 | 1,1,192,1104,14,14,1,1,1 42 | 1,1,256,256,14,14,3,3,1 43 | 1,1,1,72,56,56,3,3,2 44 | 1,1,256,256,14,14,3,3,1 45 | 1,1,512,512,14,14,3,3,1 46 | 1,1,116,116,14,14,1,1,1 47 | 1,1,128,32,27,27,3,3,1 48 | 1,1,256,64,27,27,3,3,1 49 | 1,1,232,232,7,7,1,1,1 50 | 1,1,128,256,56,56,1,1,1 51 | 1,1,112,512,14,14,1,1,1 52 | 1,1,192,1440,7,7,1,1,1 53 | 1,1,1,384,14,14,3,3,2 54 | 1,1,512,512,28,28,3,3,1 55 | 1,1,1,58,28,28,3,3,2 56 | 1,1,2048,1024,7,7,1,1,1 57 | 1,1,192,1440,7,7,1,1,1 58 | 1,1,48,192,14,14,3,3,1 59 | 1,1,48,192,28,28,3,3,1 60 | 1,1,1152,192,7,7,1,1,1 61 | 1,1,192,528,28,28,1,1,1 62 | 1,1,256,1024,14,14,1,1,1 63 | 1,1,192,576,7,7,1,1,1 64 | 1,1,128,256,56,56,1,1,1 65 | 1,1,512,1024,14,14,1,1,1 66 | 1,1,1,384,14,14,3,3,2 67 | 1,1,1,384,14,14,3,3,2 68 | 1,1,128,528,14,14,1,1,1 69 | 1,1,512,512,7,7,3,3,1 70 | 1,1,512,512,14,14,3,3,1 71 | 1,1,192,1440,7,7,1,1,1 72 | 1,1,128,48,7,7,3,3,1 73 | 1,1,512,256,28,28,1,1,1 74 | 1,1,192,1296,14,14,1,1,1 75 | 1,1,256,512,28,28,1,1,1 76 | 1,1,72,24,56,56,1,1,1 77 | 1,1,232,232,7,7,1,1,1 78 | 1,1,192,2112,7,7,1,1,1 79 | 1,1,116,116,14,14,1,1,1 80 | 1,1,32,144,28,28,1,1,1 81 | 1,1,80,480,14,14,1,1,1 82 | 1,1,256,256,14,14,3,3,1 83 | 1,1,192,1584,7,7,1,1,1 84 | 1,1,1,192,28,28,3,3,2 85 | 1,1,48,832,7,7,1,1,1 86 | 1,1,256,256,28,28,3,3,1 87 | 1,1,192,576,7,7,1,1,1 88 | 1,1,1,116,14,14,3,3,2 89 | 1,1,32,192,28,28,1,1,1 90 | 1,1,192,1392,14,14,1,1,1 91 | 1,1,48,192,7,7,3,3,1 92 | 1,1,58,58,28,28,1,1,1 93 | 1,1,32,256,27,27,1,1,1 94 | 1,1,192,1776,7,7,1,1,1 95 | 1,1,1,24,56,56,3,3,2 96 | 1,1,2048,1024,7,7,1,1,1 97 | 1,1,116,116,14,14,1,1,1 98 | 1,1,40,120,28,28,1,1,1 99 | 1,1,256,832,7,7,1,1,1 100 | 1,1,192,576,28,28,1,1,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_conv/insts2.csv: -------------------------------------------------------------------------------- 1 | 1,1,192,672,28,28,1,1,1 2 | 1,1,256,1024,14,14,1,1,1 3 | 1,1,1,116,14,14,3,3,2 4 | 1,1,256,64,13,13,1,1,1 5 | 1,1,24,48,56,56,1,1,1 6 | 1,1,512,512,28,28,3,3,1 7 | 1,1,320,1152,7,7,1,1,1 8 | 1,1,16,480,14,14,1,1,1 9 | 1,1,512,1024,14,14,1,1,1 10 | 1,1,116,116,28,28,1,1,1 11 | 1,1,48,192,14,14,3,3,1 12 | 1,1,1024,2048,7,7,1,1,1 13 | 1,1,192,1728,7,7,1,1,1 14 | 1,1,32,256,28,28,1,1,1 15 | 1,1,64,256,28,28,1,1,1 16 | 1,1,144,512,14,14,1,1,1 17 | 1,1,1024,512,14,14,1,1,1 18 | 1,1,192,1488,14,14,1,1,1 19 | 1,1,320,160,7,7,3,3,1 20 | 1,1,512,512,14,14,3,3,1 21 | 1,1,512,1024,14,14,1,1,1 22 | 1,1,384,64,14,14,1,1,1 23 | 1,1,48,192,14,14,3,3,1 24 | 1,1,128,528,4,4,1,1,1 25 | 1,1,1024,512,14,14,1,1,1 26 | 1,1,512,256,28,28,1,1,1 27 | 1,1,96,32,28,28,3,3,1 28 | 1,1,24,48,56,56,1,1,1 29 | 1,1,160,576,7,7,1,1,1 30 | 1,1,1024,1024,7,7,3,3,1 31 | 1,1,256,256,14,14,3,3,1 32 | 1,1,48,192,28,28,3,3,1 33 | 1,1,128,128,28,28,3,3,1 34 | 1,1,48,192,14,14,3,3,1 35 | 1,1,1,72,56,56,3,3,2 36 | 1,1,64,64,56,56,3,3,1 37 | 1,1,1000,512,13,13,1,1,1 38 | 1,1,256,64,56,56,1,1,1 39 | 1,1,1,384,14,14,3,3,2 40 | 1,1,512,1024,14,14,1,1,1 41 | 1,1,384,64,14,14,1,1,1 42 | 1,1,48,192,14,14,3,3,1 43 | 1,1,1024,512,28,28,1,1,1 44 | 1,1,2048,512,7,7,1,1,1 45 | 1,1,1024,512,14,14,1,1,1 46 | 1,1,64,24,14,14,3,3,1 47 | 1,1,64,64,56,56,3,3,1 48 | 1,1,192,1152,7,7,1,1,1 49 | 1,1,160,512,14,14,1,1,1 50 | 1,1,1024,512,14,14,1,1,1 51 | 1,1,1,192,28,28,3,3,2 52 | 1,1,116,116,14,14,1,1,1 53 | 1,1,1,192,28,28,3,3,2 54 | 1,1,256,256,28,28,3,3,1 55 | 1,1,1,116,14,14,3,3,2 56 | 1,1,1152,192,7,7,1,1,1 57 | 1,1,192,624,28,28,1,1,1 58 | 1,1,192,816,14,14,1,1,1 59 | 1,1,256,528,14,14,1,1,1 60 | 1,1,1,384,14,14,3,3,2 61 | 1,1,256,256,14,14,3,3,1 62 | 1,1,48,192,7,7,3,3,1 63 | 1,1,40,72,28,28,1,1,1 64 | 1,1,1,116,14,14,3,3,2 65 | 1,1,512,1024,14,14,1,1,1 66 | 1,1,512,512,14,14,3,3,1 67 | 1,1,48,192,14,14,3,3,1 68 | 1,1,116,116,14,14,1,1,1 69 | 1,1,512,2048,7,7,1,1,1 70 | 1,1,192,768,14,14,1,1,1 71 | 1,1,1024,256,14,14,1,1,1 72 | 1,1,1,192,28,28,3,3,2 73 | 1,1,512,256,28,28,1,1,1 74 | 1,1,24,96,56,56,1,1,1 75 | 1,1,192,1248,14,14,1,1,1 76 | 1,1,116,116,14,14,1,1,1 77 | 1,1,192,48,27,27,1,1,1 78 | 1,1,48,192,14,14,3,3,1 79 | 1,1,48,192,7,7,3,3,1 80 | 1,1,128,64,56,56,1,1,1 81 | 1,1,64,3,224,224,3,3,1 82 | 1,1,64,16,54,54,1,1,1 83 | 1,1,48,192,14,14,3,3,1 84 | 1,1,116,116,14,14,1,1,1 85 | 1,1,192,1536,7,7,1,1,1 86 | 1,1,116,116,28,28,1,1,1 87 | 1,1,48,192,14,14,3,3,1 88 | 1,1,48,192,14,14,3,3,1 89 | 1,1,192,1968,14,14,1,1,1 90 | 1,1,192,1968,7,7,1,1,1 91 | 1,1,1,576,14,14,3,3,2 92 | 1,1,1,32,112,112,3,3,2 93 | 1,1,128,512,14,14,1,1,1 94 | 1,1,512,512,7,7,3,3,1 95 | 1,1,48,192,14,14,3,3,1 96 | 1,1,1024,2048,7,7,1,1,1 97 | 1,1,48,192,28,28,3,3,1 98 | 1,1,1,960,7,7,3,3,2 99 | 1,1,48,192,14,14,3,3,1 100 | 1,1,48,192,14,14,3,3,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_conv/insts3.csv: -------------------------------------------------------------------------------- 1 | 1,1,48,192,7,7,3,3,1 2 | 1,1,64,64,56,56,3,3,1 3 | 1,1,48,192,14,14,3,3,1 4 | 1,1,256,1024,14,14,1,1,1 5 | 1,1,960,160,7,7,1,1,1 6 | 1,1,128,32,54,54,3,3,1 7 | 1,1,512,1024,14,14,1,1,1 8 | 1,1,48,832,7,7,1,1,1 9 | 1,1,64,64,56,56,3,3,1 10 | 1,1,256,512,28,28,1,1,1 11 | 1,1,1152,192,7,7,1,1,1 12 | 1,1,512,512,28,28,3,3,1 13 | 1,1,80,480,14,14,1,1,1 14 | 1,1,96,16,112,112,1,1,1 15 | 1,1,1024,1024,7,7,3,3,1 16 | 1,1,512,512,28,28,1,1,1 17 | 1,1,32,512,14,14,1,1,1 18 | 1,1,192,1728,14,14,1,1,1 19 | 1,1,32,256,28,28,1,1,1 20 | 1,1,512,512,14,14,3,3,1 21 | 1,1,1,960,7,7,3,3,2 22 | 1,1,1152,192,7,7,1,1,1 23 | 1,1,128,832,7,7,1,1,1 24 | 1,1,256,832,7,7,1,1,1 25 | 1,1,1,480,14,14,5,5,2 26 | 1,1,192,288,56,56,1,1,1 27 | 1,1,48,192,7,7,3,3,1 28 | 1,1,72,24,56,56,1,1,1 29 | 1,1,48,192,14,14,3,3,1 30 | 1,1,48,832,7,7,1,1,1 31 | 1,1,512,1024,14,14,1,1,1 32 | 1,1,116,116,14,14,1,1,1 33 | 1,1,192,1584,14,14,1,1,1 34 | 1,1,116,116,14,14,1,1,1 35 | 1,1,192,96,56,56,1,1,1 36 | 1,1,256,256,28,28,3,3,1 37 | 1,1,120,40,28,28,1,1,1 38 | 1,1,96,576,14,14,1,1,1 39 | 1,1,64,64,56,56,3,3,1 40 | 1,1,576,96,14,14,1,1,1 41 | 1,1,48,192,28,28,3,3,1 42 | 1,1,24,72,56,56,1,1,1 43 | 1,1,1,192,28,28,3,3,2 44 | 1,1,384,64,14,14,1,1,1 45 | 1,1,240,40,28,28,1,1,1 46 | 1,1,1024,512,14,14,1,1,1 47 | 1,1,256,832,7,7,1,1,1 48 | 1,1,512,1024,14,14,1,1,1 49 | 1,1,192,1152,14,14,1,1,1 50 | 1,1,1024,512,14,14,1,1,1 51 | 1,1,128,32,14,14,3,3,1 52 | 1,1,48,192,7,7,3,3,1 53 | 1,1,116,116,14,14,1,1,1 54 | 1,1,1024,512,14,14,1,1,1 55 | 1,1,232,232,7,7,1,1,1 56 | 1,1,48,192,7,7,3,3,1 57 | 1,1,512,128,28,28,1,1,1 58 | 1,1,192,1632,14,14,1,1,1 59 | 1,1,512,256,56,56,1,1,1 60 | 1,1,32,256,27,27,1,1,1 61 | 1,1,64,3,224,224,7,7,1 62 | 1,1,48,192,14,14,3,3,1 63 | 1,1,256,832,7,7,1,1,1 64 | 1,1,512,1024,14,14,1,1,1 65 | 1,1,144,24,56,56,1,1,1 66 | 1,1,512,1024,14,14,1,1,1 67 | 1,1,256,512,28,28,1,1,1 68 | 1,1,48,192,14,14,3,3,1 69 | 1,1,192,2160,7,7,1,1,1 70 | 1,1,192,384,14,14,1,1,1 71 | 1,1,1024,1024,7,7,3,3,1 72 | 1,1,1,480,14,14,5,5,2 73 | 1,1,48,192,14,14,3,3,1 74 | 1,1,1,960,7,7,3,3,2 75 | 1,1,1,1152,7,7,3,3,2 76 | 1,1,256,128,56,56,1,1,1 77 | 1,1,192,1680,14,14,1,1,1 78 | 1,1,128,256,56,56,1,1,1 79 | 1,1,1,232,7,7,3,3,2 80 | 1,1,232,232,7,7,1,1,1 81 | 1,1,512,512,14,14,3,3,1 82 | 1,1,48,192,28,28,3,3,1 83 | 1,1,1024,1024,7,7,3,3,1 84 | 1,1,48,192,14,14,3,3,1 85 | 1,1,192,1728,14,14,1,1,1 86 | 1,1,48,192,14,14,3,3,1 87 | 1,1,192,832,7,7,1,1,1 88 | 1,1,192,240,56,56,1,1,1 89 | 1,1,48,192,7,7,3,3,1 90 | 1,1,128,256,56,56,1,1,1 91 | 1,1,48,192,28,28,3,3,1 92 | 1,1,48,192,14,14,3,3,1 93 | 1,1,48,192,14,14,3,3,1 94 | 1,1,192,336,28,28,1,1,1 95 | 1,1,1024,256,14,14,1,1,1 96 | 1,1,48,192,28,28,3,3,1 97 | 1,1,48,192,14,14,3,3,1 98 | 1,1,1,576,14,14,3,3,2 99 | 1,1,192,1104,14,14,1,1,1 100 | 1,1,192,1152,14,14,1,1,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_conv/insts5.csv: -------------------------------------------------------------------------------- 1 | 1,1,48,192,14,14,3,3,1 2 | 1,1,512,1024,14,14,1,1,1 3 | 1,1,128,96,28,28,3,3,1 4 | 1,1,192,480,14,14,1,1,1 5 | 1,1,192,480,14,14,1,1,1 6 | 1,1,128,128,28,28,3,3,1 7 | 1,1,128,64,112,112,3,3,1 8 | 1,1,1024,512,14,14,1,1,1 9 | 1,1,512,1024,14,14,1,1,1 10 | 1,1,48,192,14,14,3,3,1 11 | 1,1,144,24,56,56,1,1,1 12 | 1,1,512,1024,14,14,1,1,1 13 | 1,1,48,192,7,7,3,3,1 14 | 1,1,48,192,14,14,3,3,1 15 | 1,1,512,512,7,7,3,3,1 16 | 1,1,128,256,28,28,1,1,1 17 | 1,1,128,528,14,14,1,1,1 18 | 1,1,128,32,54,54,3,3,1 19 | 1,1,192,1968,7,7,1,1,1 20 | 1,1,48,192,14,14,3,3,1 21 | 1,1,48,192,14,14,3,3,1 22 | 1,1,48,192,14,14,3,3,1 23 | 1,1,1,58,28,28,3,3,2 24 | 1,1,1,480,14,14,5,5,2 25 | 1,1,32,528,14,14,1,1,1 26 | 1,1,1024,1024,14,14,3,3,1 27 | 1,1,48,192,28,28,3,3,1 28 | 1,1,1,116,14,14,3,3,2 29 | 1,1,1,232,7,7,3,3,2 30 | 1,1,128,512,28,28,1,1,1 31 | 1,1,256,128,56,56,3,3,1 32 | 1,1,128,128,56,56,3,3,1 33 | 1,1,1,192,28,28,3,3,2 34 | 1,1,96,576,14,14,1,1,1 35 | 1,1,512,512,7,7,3,3,1 36 | 1,1,1,1152,7,7,5,5,2 37 | 1,1,48,192,14,14,3,3,1 38 | 1,1,64,192,14,14,1,1,1 39 | 1,1,24,512,14,14,1,1,1 40 | 1,1,512,1024,14,14,1,1,1 41 | 1,1,48,192,7,7,3,3,1 42 | 1,1,96,480,14,14,1,1,1 43 | 1,1,1056,2112,14,14,1,1,1 44 | 1,1,116,116,14,14,1,1,1 45 | 1,1,512,512,14,14,3,3,1 46 | 1,1,96,384,14,14,1,1,1 47 | 1,1,192,1920,7,7,1,1,1 48 | 1,1,48,192,14,14,3,3,1 49 | 1,1,128,512,28,28,1,1,1 50 | 1,1,48,192,14,14,3,3,1 51 | 1,1,232,232,14,14,1,1,1 52 | 1,1,1,1152,7,7,3,3,2 53 | 1,1,1,116,28,28,3,3,2 54 | 1,1,64,64,56,56,3,3,1 55 | 1,1,192,1440,14,14,1,1,1 56 | 1,1,48,192,7,7,3,3,1 57 | 1,1,64,64,56,56,3,3,1 58 | 1,1,256,64,56,56,1,1,1 59 | 1,1,64,3,224,224,3,3,1 60 | 1,1,576,96,14,14,1,1,1 61 | 1,1,48,192,14,14,3,3,1 62 | 1,1,64,64,56,56,3,3,1 63 | 1,1,192,576,7,7,1,1,1 64 | 1,1,192,2016,14,14,1,1,1 65 | 1,1,512,1024,14,14,1,1,1 66 | 1,1,128,96,28,28,3,3,1 67 | 1,1,512,512,7,7,3,3,1 68 | 1,1,1,58,28,28,3,3,2 69 | 1,1,116,116,28,28,1,1,1 70 | 1,1,128,256,56,56,1,1,1 71 | 1,1,1152,192,7,7,1,1,1 72 | 1,1,192,1920,14,14,1,1,1 73 | 1,1,192,480,14,14,1,1,1 74 | 1,1,116,116,14,14,1,1,1 75 | 1,1,1024,2048,7,7,1,1,1 76 | 1,1,112,512,14,14,1,1,1 77 | 1,1,1024,1024,7,7,3,3,1 78 | 1,1,512,512,28,28,3,3,1 79 | 1,1,192,336,28,28,1,1,1 80 | 1,1,116,116,14,14,1,1,1 81 | 1,1,256,256,28,28,3,3,1 82 | 1,1,512,256,28,28,1,1,1 83 | 1,1,48,192,56,56,3,3,1 84 | 1,1,128,256,56,56,1,1,1 85 | 1,1,192,528,28,28,1,1,1 86 | 1,1,512,512,7,7,3,3,1 87 | 1,1,192,32,28,28,1,1,1 88 | 1,1,1,384,14,14,3,3,2 89 | 1,1,256,512,28,28,1,1,1 90 | 1,1,32,832,7,7,1,1,1 91 | 1,1,48,192,14,14,3,3,1 92 | 1,1,1024,512,14,14,1,1,1 93 | 1,1,1,960,7,7,3,3,2 94 | 1,1,192,1488,7,7,1,1,1 95 | 1,1,384,192,13,13,3,3,1 96 | 1,1,512,256,28,28,3,3,1 97 | 1,1,192,384,56,56,1,1,1 98 | 1,1,256,128,14,14,3,3,1 99 | 1,1,64,24,14,14,3,3,1 100 | 1,1,128,256,56,56,1,1,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_conv/insts6.csv: -------------------------------------------------------------------------------- 1 | 1,1,48,192,7,7,3,3,1 2 | 1,1,512,512,7,7,3,3,1 3 | 1,1,128,128,56,56,3,3,1 4 | 1,1,48,192,14,14,3,3,1 5 | 1,1,512,256,28,28,1,1,1 6 | 1,1,1024,512,14,14,1,1,1 7 | 1,1,116,116,14,14,1,1,1 8 | 1,1,1152,192,7,7,1,1,1 9 | 1,1,512,1024,14,14,1,1,1 10 | 1,1,48,256,27,27,1,1,1 11 | 1,1,48,192,14,14,3,3,1 12 | 1,1,512,256,28,28,1,1,1 13 | 1,1,192,48,27,27,1,1,1 14 | 1,1,1280,320,7,7,1,1,1 15 | 1,1,512,256,28,28,1,1,1 16 | 1,1,80,480,14,14,1,1,1 17 | 1,1,64,384,14,14,1,1,1 18 | 1,1,64,3,224,224,3,3,1 19 | 1,1,192,144,56,56,1,1,1 20 | 1,1,1,144,56,56,3,3,2 21 | 1,1,48,192,14,14,3,3,1 22 | 1,1,48,192,14,14,3,3,1 23 | 1,1,192,576,7,7,1,1,1 24 | 1,1,48,192,14,14,3,3,1 25 | 1,1,192,1104,7,7,1,1,1 26 | 1,1,116,116,14,14,1,1,1 27 | 1,1,1,576,14,14,3,3,2 28 | 1,1,192,832,7,7,1,1,1 29 | 1,1,64,3,224,224,3,3,1 30 | 1,1,1024,256,14,14,1,1,1 31 | 1,1,24,72,56,56,1,1,1 32 | 1,1,128,256,56,56,1,1,1 33 | 1,1,48,192,28,28,3,3,1 34 | 1,1,192,240,56,56,1,1,1 35 | 1,1,1024,256,14,14,1,1,1 36 | 1,1,384,64,14,14,1,1,1 37 | 1,1,192,1584,14,14,1,1,1 38 | 1,1,128,512,4,4,1,1,1 39 | 1,1,48,256,27,27,1,1,1 40 | 1,1,256,128,28,28,3,3,1 41 | 1,1,256,256,14,14,3,3,1 42 | 1,1,48,192,14,14,3,3,1 43 | 1,1,192,1152,7,7,1,1,1 44 | 1,1,192,576,14,14,1,1,1 45 | 1,1,192,1152,7,7,1,1,1 46 | 1,1,48,192,14,14,3,3,1 47 | 1,1,256,256,28,28,3,3,1 48 | 1,1,116,116,28,28,1,1,1 49 | 1,1,192,1824,14,14,1,1,1 50 | 1,1,192,1584,14,14,1,1,1 51 | 1,1,16,192,28,28,1,1,1 52 | 1,1,1024,512,14,14,1,1,1 53 | 1,1,1,72,56,56,5,5,2 54 | 1,1,80,480,14,14,1,1,1 55 | 1,1,96,576,14,14,1,1,1 56 | 1,1,192,912,14,14,1,1,1 57 | 1,1,192,624,14,14,1,1,1 58 | 1,1,192,2112,7,7,1,1,1 59 | 1,1,116,116,14,14,1,1,1 60 | 1,1,116,116,14,14,1,1,1 61 | 1,1,256,64,27,27,1,1,1 62 | 1,1,192,384,28,28,1,1,1 63 | 1,1,232,232,7,7,1,1,1 64 | 1,1,128,32,27,27,1,1,1 65 | 1,1,1,144,56,56,3,3,2 66 | 1,1,32,3,224,224,3,3,1 67 | 1,1,1,144,56,56,3,3,2 68 | 1,1,192,1248,14,14,1,1,1 69 | 1,1,960,160,7,7,1,1,1 70 | 1,1,32,192,28,28,1,1,1 71 | 1,1,116,116,14,14,1,1,1 72 | 1,1,48,192,28,28,3,3,1 73 | 1,1,1,480,14,14,5,5,2 74 | 1,1,64,64,56,56,3,3,1 75 | 1,1,128,128,28,28,3,3,1 76 | 1,1,64,512,14,14,1,1,1 77 | 1,1,512,512,7,7,3,3,1 78 | 1,1,48,192,7,7,3,3,1 79 | 1,1,256,256,14,14,3,3,1 80 | 1,1,64,64,56,56,3,3,1 81 | 1,1,128,32,14,14,3,3,1 82 | 1,1,1,116,28,28,3,3,2 83 | 1,1,384,192,13,13,3,3,1 84 | 1,1,48,192,56,56,3,3,1 85 | 1,1,16,32,112,112,1,1,1 86 | 1,1,320,1152,7,7,1,1,1 87 | 1,1,192,240,28,28,1,1,1 88 | 1,1,64,3,224,224,7,7,1 89 | 1,1,192,128,28,28,3,3,1 90 | 1,1,48,192,28,28,3,3,1 91 | 1,1,48,192,7,7,3,3,1 92 | 1,1,2048,1024,7,7,1,1,1 93 | 1,1,48,192,7,7,3,3,1 94 | 1,1,384,768,28,28,1,1,1 95 | 1,1,1024,512,14,14,1,1,1 96 | 1,1,256,256,14,14,3,3,1 97 | 1,1,48,192,56,56,3,3,1 98 | 1,1,256,256,56,56,1,1,1 99 | 1,1,1,116,14,14,3,3,2 100 | 1,1,1000,512,13,13,1,1,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_conv/insts8.csv: -------------------------------------------------------------------------------- 1 | 1,1,58,24,56,56,1,1,1 2 | 1,1,256,256,56,56,3,3,1 3 | 1,1,48,832,7,7,1,1,1 4 | 1,1,116,116,14,14,1,1,1 5 | 1,1,1,32,112,112,3,3,2 6 | 1,1,512,512,14,14,3,3,1 7 | 1,1,48,192,14,14,3,3,1 8 | 1,1,256,512,28,28,1,1,1 9 | 1,1,48,192,14,14,3,3,1 10 | 1,1,48,192,28,28,3,3,1 11 | 1,1,48,192,14,14,3,3,1 12 | 1,1,256,256,28,28,3,3,1 13 | 1,1,1,192,28,28,3,3,2 14 | 1,1,48,192,7,7,3,3,1 15 | 1,1,48,192,14,14,3,3,1 16 | 1,1,48,192,14,14,3,3,1 17 | 1,1,160,960,7,7,1,1,1 18 | 1,1,192,528,14,14,1,1,1 19 | 1,1,48,192,56,56,3,3,1 20 | 1,1,24,3,224,224,3,3,1 21 | 1,1,64,3,224,224,7,7,1 22 | 1,1,384,64,14,14,1,1,1 23 | 1,1,64,16,54,54,3,3,1 24 | 1,1,192,816,14,14,1,1,1 25 | 1,1,256,64,56,56,1,1,1 26 | 1,1,128,128,56,56,3,3,1 27 | 1,1,72,24,56,56,1,1,1 28 | 1,1,128,32,27,27,3,3,1 29 | 1,1,192,1824,7,7,1,1,1 30 | 1,1,256,128,56,56,1,1,1 31 | 1,1,120,40,28,28,1,1,1 32 | 1,1,116,116,14,14,1,1,1 33 | 1,1,192,1728,7,7,1,1,1 34 | 1,1,116,116,14,14,1,1,1 35 | 1,1,128,256,56,56,1,1,1 36 | 1,1,128,64,112,112,3,3,1 37 | 1,1,512,512,14,14,3,3,1 38 | 1,1,48,192,7,7,3,3,1 39 | 1,1,192,1104,14,14,1,1,1 40 | 1,1,192,288,56,56,1,1,1 41 | 1,1,128,64,56,56,1,1,1 42 | 1,1,64,512,14,14,1,1,1 43 | 1,1,128,512,28,28,1,1,1 44 | 1,1,208,96,14,14,3,3,1 45 | 1,1,16,32,112,112,1,1,1 46 | 1,1,512,512,14,14,3,3,1 47 | 1,1,48,192,14,14,3,3,1 48 | 1,1,256,256,56,56,1,1,1 49 | 1,1,128,96,28,28,3,3,1 50 | 1,1,48,192,14,14,3,3,1 51 | 1,1,32,192,28,28,1,1,1 52 | 1,1,1,1152,7,7,5,5,2 53 | 1,1,256,1024,14,14,1,1,1 54 | 1,1,384,64,14,14,1,1,1 55 | 1,1,48,16,14,14,3,3,1 56 | 1,1,192,1920,14,14,1,1,1 57 | 1,1,256,128,56,56,1,1,1 58 | 1,1,320,160,7,7,3,3,1 59 | 1,1,384,832,7,7,1,1,1 60 | 1,1,1,116,14,14,3,3,2 61 | 1,1,1,120,28,28,5,5,2 62 | 1,1,64,24,14,14,3,3,1 63 | 1,1,72,24,56,56,1,1,1 64 | 1,1,256,128,56,56,1,1,1 65 | 1,1,192,480,28,28,1,1,1 66 | 1,1,58,24,28,28,1,1,1 67 | 1,1,64,512,14,14,1,1,1 68 | 1,1,512,256,56,56,1,1,1 69 | 1,1,116,116,14,14,1,1,1 70 | 1,1,256,1024,14,14,1,1,1 71 | 1,1,16,128,54,54,1,1,1 72 | 1,1,192,816,14,14,1,1,1 73 | 1,1,48,832,7,7,1,1,1 74 | 1,1,192,1344,7,7,1,1,1 75 | 1,1,1000,512,13,13,1,1,1 76 | 1,1,512,256,56,56,1,1,1 77 | 1,1,32,256,28,28,1,1,1 78 | 1,1,48,192,7,7,3,3,1 79 | 1,1,48,192,28,28,3,3,1 80 | 1,1,512,512,14,14,3,3,1 81 | 1,1,64,480,14,14,1,1,1 82 | 1,1,192,240,28,28,1,1,1 83 | 1,1,384,64,14,14,1,1,1 84 | 1,1,64,192,28,28,1,1,1 85 | 1,1,48,192,14,14,3,3,1 86 | 1,1,48,192,14,14,3,3,1 87 | 1,1,48,192,28,28,3,3,1 88 | 1,1,192,1680,14,14,1,1,1 89 | 1,1,1,576,14,14,3,3,2 90 | 1,1,128,128,28,28,3,3,1 91 | 1,1,1,116,14,14,3,3,2 92 | 1,1,48,192,14,14,3,3,1 93 | 1,1,128,128,56,56,3,3,1 94 | 1,1,192,2112,7,7,1,1,1 95 | 1,1,384,64,14,14,1,1,1 96 | 1,1,128,64,112,112,3,3,1 97 | 1,1,512,512,14,14,3,3,1 98 | 1,1,1,120,28,28,5,5,2 99 | 1,1,512,1024,14,14,1,1,1 100 | 1,1,384,64,14,14,1,1,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_random/insts0.csv: -------------------------------------------------------------------------------- 1 | 1,1,221,196,11,11,1,1,1 2 | 1,1,243,95,225,225,1,1,1 3 | 1,1,1,89,198,198,1,1,1 4 | 1,1,21,52,20,20,3,3,1 5 | 1,1,132,129,158,158,1,1,1 6 | 1,1,79,101,96,96,1,1,1 7 | 1,1,22,208,184,184,1,1,1 8 | 1,1,125,239,75,75,3,3,1 9 | 1,1,119,250,56,56,1,1,1 10 | 1,1,229,143,8,8,3,3,1 11 | 1,1,238,30,99,99,3,3,1 12 | 1,1,241,37,26,26,3,3,1 13 | 1,1,198,50,100,100,3,3,1 14 | 1,1,232,1,223,223,3,3,1 15 | 1,1,8,90,185,185,3,3,1 16 | 1,1,241,179,94,94,3,3,1 17 | 1,1,253,66,78,78,3,3,1 18 | 1,1,181,120,173,173,3,3,1 19 | 1,1,102,32,79,79,1,1,1 20 | 1,1,39,129,158,158,3,3,1 21 | 1,1,228,96,11,11,3,3,1 22 | 1,1,229,39,146,146,1,1,1 23 | 1,1,34,209,122,122,1,1,1 24 | 1,1,247,42,101,101,1,1,1 25 | 1,1,38,8,11,11,1,1,1 26 | 1,1,67,118,3,3,3,3,1 27 | 1,1,222,32,59,59,3,3,1 28 | 1,1,177,145,114,114,3,3,1 29 | 1,1,230,113,179,179,1,1,1 30 | 1,1,6,254,12,12,3,3,1 31 | 1,1,74,782,1,1,1,1,0 32 | 1,1,410,375,1,1,1,1,0 33 | 1,1,108,505,1,1,1,1,0 34 | 1,1,286,32,1,1,1,1,0 35 | 1,1,241,231,1,1,1,1,0 36 | 1,1,14,768,1,1,1,1,0 37 | 1,1,473,807,1,1,1,1,0 38 | 1,1,124,55,1,1,1,1,0 39 | 1,1,218,473,1,1,1,1,0 40 | 1,1,41,415,1,1,1,1,0 41 | 1,1,24,123,1,1,1,1,0 42 | 1,1,264,446,1,1,1,1,0 43 | 1,1,261,619,1,1,1,1,0 44 | 1,1,89,11,1,1,1,1,0 45 | 1,1,363,639,1,1,1,1,0 46 | 1,1,503,275,1,1,1,1,0 47 | 1,1,186,719,1,1,1,1,0 48 | 1,1,150,508,1,1,1,1,0 49 | 1,1,328,739,1,1,1,1,0 50 | 1,1,146,453,1,1,1,1,0 51 | 1,1,170,220,1,1,1,1,0 52 | 1,1,378,611,1,1,1,1,0 53 | 1,1,254,173,1,1,1,1,0 54 | 1,1,293,584,1,1,1,1,0 55 | 1,1,485,421,1,1,1,1,0 56 | 1,1,259,370,1,1,1,1,0 57 | 1,1,336,13,1,1,1,1,0 58 | 1,1,470,366,1,1,1,1,0 59 | 1,1,210,688,1,1,1,1,0 60 | 1,1,349,199,1,1,1,1,0 61 | 1,1,105,124,91,1,1,1,3 62 | 1,1,100,54,126,1,1,1,3 63 | 1,1,53,98,30,1,1,1,3 64 | 1,1,30,25,23,1,1,1,3 65 | 1,1,34,21,126,1,1,1,3 66 | 1,1,35,13,119,1,1,1,3 67 | 1,1,45,127,125,1,1,1,3 68 | 1,1,128,97,54,1,1,1,3 69 | 1,1,73,127,99,1,1,1,3 70 | 1,1,111,95,99,1,1,1,3 71 | 1,1,70,66,102,1,1,1,3 72 | 1,1,127,127,84,1,1,1,3 73 | 1,1,35,72,69,1,1,1,3 74 | 1,1,89,30,123,1,1,1,3 75 | 1,1,77,20,109,1,1,1,3 76 | 1,1,128,39,42,1,1,1,3 77 | 1,1,126,67,31,1,1,1,3 78 | 1,1,15,101,73,1,1,1,3 79 | 1,1,38,106,38,1,1,1,3 80 | 1,1,68,64,23,1,1,1,3 81 | 1,1,82,59,79,1,1,1,3 82 | 1,1,67,81,10,1,1,1,3 83 | 1,1,38,124,119,1,1,1,3 84 | 1,1,39,65,50,1,1,1,3 85 | 1,1,8,36,20,1,1,1,3 86 | 1,1,51,45,107,1,1,1,3 87 | 1,1,43,125,78,1,1,1,3 88 | 1,1,120,91,118,1,1,1,3 89 | 1,1,99,123,93,1,1,1,3 90 | 1,1,65,124,126,1,1,1,3 91 | 1,1,229,1,208,208,3,3,1 92 | 1,1,122,213,100,100,3,3,1 93 | 1,1,193,59,48,48,1,1,1 94 | 1,1,248,114,153,153,3,3,1 95 | 1,1,36,46,12,12,1,1,1 96 | 1,1,162,103,20,20,1,1,1 97 | 1,1,203,72,67,67,1,1,1 98 | 1,1,44,48,155,155,3,3,1 99 | 1,1,70,4,77,77,3,3,1 100 | 1,1,200,158,167,167,1,1,1 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_random/insts1.csv: -------------------------------------------------------------------------------- 1 | 1,1,149,171,202,202,1,1,1 2 | 1,1,152,252,207,207,3,3,1 3 | 1,1,62,193,50,50,3,3,1 4 | 1,1,139,218,50,50,3,3,1 5 | 1,1,13,252,57,57,3,3,1 6 | 1,1,56,176,151,151,1,1,1 7 | 1,1,93,92,67,67,1,1,1 8 | 1,1,56,172,126,126,1,1,1 9 | 1,1,208,5,184,184,3,3,1 10 | 1,1,132,13,208,208,3,3,1 11 | 1,1,229,198,166,166,3,3,1 12 | 1,1,141,203,140,140,3,3,1 13 | 1,1,79,41,75,75,3,3,1 14 | 1,1,55,44,93,93,1,1,1 15 | 1,1,223,206,36,36,1,1,1 16 | 1,1,188,10,34,34,3,3,1 17 | 1,1,81,157,46,46,3,3,1 18 | 1,1,182,47,155,155,1,1,1 19 | 1,1,51,191,111,111,3,3,1 20 | 1,1,106,139,122,122,1,1,1 21 | 1,1,216,194,182,182,3,3,1 22 | 1,1,134,235,55,55,1,1,1 23 | 1,1,78,176,32,32,3,3,1 24 | 1,1,166,99,14,14,3,3,1 25 | 1,1,207,74,130,130,3,3,1 26 | 1,1,214,58,34,34,1,1,1 27 | 1,1,59,34,154,154,3,3,1 28 | 1,1,150,248,135,135,1,1,1 29 | 1,1,30,144,208,208,3,3,1 30 | 1,1,40,121,108,108,3,3,1 31 | 1,1,139,53,85,85,3,3,1 32 | 1,1,252,96,180,180,1,1,1 33 | 1,1,153,179,213,213,3,3,1 34 | 1,1,65,170,36,36,1,1,1 35 | 1,1,85,118,97,97,3,3,1 36 | 1,1,93,3,18,18,3,3,1 37 | 1,1,155,158,84,84,3,3,1 38 | 1,1,118,14,146,146,3,3,1 39 | 1,1,235,158,129,129,3,3,1 40 | 1,1,217,60,186,186,1,1,1 41 | 1,1,248,352,1,1,1,1,0 42 | 1,1,486,282,1,1,1,1,0 43 | 1,1,114,14,1,1,1,1,0 44 | 1,1,475,35,1,1,1,1,0 45 | 1,1,350,251,1,1,1,1,0 46 | 1,1,404,257,1,1,1,1,0 47 | 1,1,142,713,1,1,1,1,0 48 | 1,1,444,795,1,1,1,1,0 49 | 1,1,322,356,1,1,1,1,0 50 | 1,1,233,614,1,1,1,1,0 51 | 1,1,399,764,1,1,1,1,0 52 | 1,1,82,56,1,1,1,1,0 53 | 1,1,225,335,1,1,1,1,0 54 | 1,1,279,342,1,1,1,1,0 55 | 1,1,98,175,1,1,1,1,0 56 | 1,1,201,207,1,1,1,1,0 57 | 1,1,252,451,1,1,1,1,0 58 | 1,1,74,659,1,1,1,1,0 59 | 1,1,248,313,1,1,1,1,0 60 | 1,1,434,391,1,1,1,1,0 61 | 1,1,366,404,1,1,1,1,0 62 | 1,1,76,75,1,1,1,1,0 63 | 1,1,146,694,1,1,1,1,0 64 | 1,1,373,279,1,1,1,1,0 65 | 1,1,481,479,1,1,1,1,0 66 | 1,1,182,195,1,1,1,1,0 67 | 1,1,153,243,1,1,1,1,0 68 | 1,1,116,543,1,1,1,1,0 69 | 1,1,489,741,1,1,1,1,0 70 | 1,1,337,468,1,1,1,1,0 71 | 1,1,48,94,15,1,1,1,3 72 | 1,1,49,57,31,1,1,1,3 73 | 1,1,19,42,86,1,1,1,3 74 | 1,1,15,19,85,1,1,1,3 75 | 1,1,111,109,58,1,1,1,3 76 | 1,1,52,30,120,1,1,1,3 77 | 1,1,79,58,123,1,1,1,3 78 | 1,1,58,75,121,1,1,1,3 79 | 1,1,81,68,39,1,1,1,3 80 | 1,1,19,50,26,1,1,1,3 81 | 1,1,127,52,127,1,1,1,3 82 | 1,1,80,37,71,1,1,1,3 83 | 1,1,35,45,108,1,1,1,3 84 | 1,1,8,122,73,1,1,1,3 85 | 1,1,104,12,68,1,1,1,3 86 | 1,1,22,49,67,1,1,1,3 87 | 1,1,57,88,39,1,1,1,3 88 | 1,1,52,68,76,1,1,1,3 89 | 1,1,80,117,89,1,1,1,3 90 | 1,1,34,127,65,1,1,1,3 91 | 1,1,88,77,31,1,1,1,3 92 | 1,1,91,60,46,1,1,1,3 93 | 1,1,38,86,27,1,1,1,3 94 | 1,1,38,123,99,1,1,1,3 95 | 1,1,95,86,105,1,1,1,3 96 | 1,1,26,122,128,1,1,1,3 97 | 1,1,77,48,56,1,1,1,3 98 | 1,1,35,107,62,1,1,1,3 99 | 1,1,49,95,17,1,1,1,3 100 | 1,1,96,39,17,1,1,1,3 -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts0.csv: -------------------------------------------------------------------------------- 1 | 1,1,4096,4096,1,1,1,1,0 2 | 1,1,4096,4096,1,1,1,1,0 3 | 1,1,128,64,1,1,1,1,0 4 | 1,1,512,512,1,1,1,1,0 5 | 1,1,512,512,1,1,1,1,0 6 | 1,1,1024,1024,1,1,1,1,0 7 | 1,1,200,200,1,1,1,1,0 8 | 1,1,128,256,1,1,1,1,0 9 | 1,1,2,80,1,1,1,1,0 10 | 1,1,512,13,1,1,1,1,0 11 | 1,1,256,256,1,1,1,1,0 12 | 1,1,1024,512,1,1,1,1,0 13 | 1,1,2048,2048,1,1,1,1,0 14 | 1,1,128,64,1,1,1,1,0 15 | 1,1,512,512,1,1,1,1,0 16 | 1,1,4096,4096,1,1,1,1,0 17 | 1,1,80,200,1,1,1,1,0 18 | 1,1,2048,2048,1,1,1,1,0 19 | 1,1,4096,4096,1,1,1,1,0 20 | 1,1,4096,4096,1,1,1,1,0 21 | 1,1,4096,4096,1,1,1,1,0 22 | 1,1,1024,512,1,1,1,1,0 23 | 1,1,4096,4096,1,1,1,1,0 24 | 1,1,4096,4096,1,1,1,1,0 25 | 1,1,1024,1024,1,1,1,1,0 26 | 1,1,256,256,1,1,1,1,0 27 | 1,1,4096,4096,1,1,1,1,0 28 | 1,1,128,64,1,1,1,1,0 29 | 1,1,256,256,1,1,1,1,0 30 | 1,1,128,256,1,1,1,1,0 31 | 1,1,256,512,1,1,1,1,0 32 | 1,1,256,2048,1,1,1,1,0 33 | 1,1,200,200,1,1,1,1,0 34 | 1,1,80,200,1,1,1,1,0 35 | 1,1,32,128,1,1,1,1,0 36 | 1,1,2048,2048,1,1,1,1,0 37 | 1,1,1,64,1,1,1,1,0 38 | 1,1,256,512,1,1,1,1,0 39 | 1,1,256,256,1,1,1,1,0 40 | 1,1,2048,2048,1,1,1,1,0 41 | 1,1,4096,4096,1,1,1,1,0 42 | 1,1,512,512,1,1,1,1,0 43 | 1,1,1,128,1,1,1,1,0 44 | 1,1,200,200,1,1,1,1,0 45 | 1,1,128,256,1,1,1,1,0 46 | 1,1,64,256,1,1,1,1,0 47 | 1,1,64,256,1,1,1,1,0 48 | 1,1,128,256,1,1,1,1,0 49 | 1,1,128,2048,1,1,1,1,0 50 | 1,1,256,512,1,1,1,1,0 51 | 1,1,1024,512,1,1,1,1,0 52 | 1,1,4096,4096,1,1,1,1,0 53 | 1,1,1,4096,1,1,1,1,0 54 | 1,1,4096,4096,1,1,1,1,0 55 | 1,1,1024,1024,1,1,1,1,0 56 | 1,1,1,4096,1,1,1,1,0 57 | 1,1,256,256,1,1,1,1,0 58 | 1,1,4096,4096,1,1,1,1,0 59 | 1,1,4096,4096,1,1,1,1,0 60 | 1,1,2,80,1,1,1,1,0 61 | 1,1,512,512,1,1,1,1,0 62 | 1,1,512,512,1,1,1,1,0 63 | 1,1,4096,2048,1,1,1,1,0 64 | 1,1,1024,1024,1,1,1,1,0 65 | 1,1,1024,1024,1,1,1,1,0 66 | 1,1,4096,4096,1,1,1,1,0 67 | 1,1,2048,2048,1,1,1,1,0 68 | 1,1,512,512,1,1,1,1,0 69 | 1,1,256,128,1,1,1,1,0 70 | 1,1,256,256,1,1,1,1,0 71 | 1,1,4096,4096,1,1,1,1,0 72 | 1,1,2048,2048,1,1,1,1,0 73 | 1,1,128,512,1,1,1,1,0 74 | 1,1,4096,4096,1,1,1,1,0 75 | 1,1,256,128,1,1,1,1,0 76 | 1,1,4096,4096,1,1,1,1,0 77 | 1,1,256,512,1,1,1,1,0 78 | 1,1,512,512,1,1,1,1,0 79 | 1,1,1,64,1,1,1,1,0 80 | 1,1,256,256,1,1,1,1,0 81 | 1,1,32,128,1,1,1,1,0 82 | 1,1,4096,4096,1,1,1,1,0 83 | 1,1,4096,4096,1,1,1,1,0 84 | 1,1,1024,1024,1,1,1,1,0 85 | 1,1,128,512,1,1,1,1,0 86 | 1,1,256,512,1,1,1,1,0 87 | 1,1,4096,4096,1,1,1,1,0 88 | 1,1,1,128,1,1,1,1,0 89 | 1,1,200,200,1,1,1,1,0 90 | 1,1,80,200,1,1,1,1,0 91 | 1,1,128,256,1,1,1,1,0 92 | 1,1,1,128,1,1,1,1,0 93 | 1,1,512,512,1,1,1,1,0 94 | 1,1,32,128,1,1,1,1,0 95 | 1,1,1,1024,1,1,1,1,0 96 | 1,1,256,256,1,1,1,1,0 97 | 1,1,4096,4096,1,1,1,1,0 98 | 1,1,4096,4096,1,1,1,1,0 99 | 1,1,4096,4096,1,1,1,1,0 100 | 1,1,2048,2048,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts1.csv: -------------------------------------------------------------------------------- 1 | 1,1,512,512,1,1,1,1,0 2 | 1,1,200,200,1,1,1,1,0 3 | 1,1,128,512,1,1,1,1,0 4 | 1,1,256,512,1,1,1,1,0 5 | 1,1,256,512,1,1,1,1,0 6 | 1,1,4096,2048,1,1,1,1,0 7 | 1,1,64,128,1,1,1,1,0 8 | 1,1,1024,1024,1,1,1,1,0 9 | 1,1,256,256,1,1,1,1,0 10 | 1,1,256,256,1,1,1,1,0 11 | 1,1,1024,1024,1,1,1,1,0 12 | 1,1,80,200,1,1,1,1,0 13 | 1,1,1,1024,1,1,1,1,0 14 | 1,1,512,512,1,1,1,1,0 15 | 1,1,256,512,1,1,1,1,0 16 | 1,1,200,200,1,1,1,1,0 17 | 1,1,128,2048,1,1,1,1,0 18 | 1,1,2048,2048,1,1,1,1,0 19 | 1,1,2048,2048,1,1,1,1,0 20 | 1,1,256,128,1,1,1,1,0 21 | 1,1,256,256,1,1,1,1,0 22 | 1,1,128,64,1,1,1,1,0 23 | 1,1,4096,4096,1,1,1,1,0 24 | 1,1,4096,4096,1,1,1,1,0 25 | 1,1,512,512,1,1,1,1,0 26 | 1,1,4096,4096,1,1,1,1,0 27 | 1,1,64,512,1,1,1,1,0 28 | 1,1,512,512,1,1,1,1,0 29 | 1,1,64,128,1,1,1,1,0 30 | 1,1,4096,4096,1,1,1,1,0 31 | 1,1,128,2048,1,1,1,1,0 32 | 1,1,4096,4096,1,1,1,1,0 33 | 1,1,1,64,1,1,1,1,0 34 | 1,1,64,512,1,1,1,1,0 35 | 1,1,512,512,1,1,1,1,0 36 | 1,1,256,256,1,1,1,1,0 37 | 1,1,2048,2048,1,1,1,1,0 38 | 1,1,256,2048,1,1,1,1,0 39 | 1,1,200,200,1,1,1,1,0 40 | 1,1,256,128,1,1,1,1,0 41 | 1,1,1024,1024,1,1,1,1,0 42 | 1,1,80,200,1,1,1,1,0 43 | 1,1,512,13,1,1,1,1,0 44 | 1,1,80,200,1,1,1,1,0 45 | 1,1,1,1024,1,1,1,1,0 46 | 1,1,256,256,1,1,1,1,0 47 | 1,1,128,64,1,1,1,1,0 48 | 1,1,2048,2048,1,1,1,1,0 49 | 1,1,256,256,1,1,1,1,0 50 | 1,1,80,200,1,1,1,1,0 51 | 1,1,512,512,1,1,1,1,0 52 | 1,1,4096,4096,1,1,1,1,0 53 | 1,1,4096,4096,1,1,1,1,0 54 | 1,1,1,1024,1,1,1,1,0 55 | 1,1,128,2048,1,1,1,1,0 56 | 1,1,1,256,1,1,1,1,0 57 | 1,1,1024,1024,1,1,1,1,0 58 | 1,1,4096,4096,1,1,1,1,0 59 | 1,1,1,1024,1,1,1,1,0 60 | 1,1,4096,4096,1,1,1,1,0 61 | 1,1,80,200,1,1,1,1,0 62 | 1,1,4096,4096,1,1,1,1,0 63 | 1,1,2048,128,1,1,1,1,0 64 | 1,1,4096,4096,1,1,1,1,0 65 | 1,1,4096,4096,1,1,1,1,0 66 | 1,1,4096,4096,1,1,1,1,0 67 | 1,1,1,64,1,1,1,1,0 68 | 1,1,256,256,1,1,1,1,0 69 | 1,1,200,200,1,1,1,1,0 70 | 1,1,256,128,1,1,1,1,0 71 | 1,1,256,256,1,1,1,1,0 72 | 1,1,256,128,1,1,1,1,0 73 | 1,1,4096,4096,1,1,1,1,0 74 | 1,1,4096,4096,1,1,1,1,0 75 | 1,1,4096,4096,1,1,1,1,0 76 | 1,1,256,128,1,1,1,1,0 77 | 1,1,4096,4096,1,1,1,1,0 78 | 1,1,2048,2048,1,1,1,1,0 79 | 1,1,4096,4096,1,1,1,1,0 80 | 1,1,4096,4096,1,1,1,1,0 81 | 1,1,4096,4096,1,1,1,1,0 82 | 1,1,512,512,1,1,1,1,0 83 | 1,1,256,2048,1,1,1,1,0 84 | 1,1,1024,1024,1,1,1,1,0 85 | 1,1,4096,4096,1,1,1,1,0 86 | 1,1,1,4096,1,1,1,1,0 87 | 1,1,1024,1024,1,1,1,1,0 88 | 1,1,2,80,1,1,1,1,0 89 | 1,1,256,512,1,1,1,1,0 90 | 1,1,256,256,1,1,1,1,0 91 | 1,1,64,256,1,1,1,1,0 92 | 1,1,4096,4096,1,1,1,1,0 93 | 1,1,256,128,1,1,1,1,0 94 | 1,1,256,256,1,1,1,1,0 95 | 1,1,512,512,1,1,1,1,0 96 | 1,1,256,2048,1,1,1,1,0 97 | 1,1,64,512,1,1,1,1,0 98 | 1,1,256,256,1,1,1,1,0 99 | 1,1,2048,2048,1,1,1,1,0 100 | 1,1,128,256,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts2.csv: -------------------------------------------------------------------------------- 1 | 1,1,256,2048,1,1,1,1,0 2 | 1,1,128,256,1,1,1,1,0 3 | 1,1,4096,4096,1,1,1,1,0 4 | 1,1,4096,4096,1,1,1,1,0 5 | 1,1,512,512,1,1,1,1,0 6 | 1,1,512,512,1,1,1,1,0 7 | 1,1,128,64,1,1,1,1,0 8 | 1,1,256,256,1,1,1,1,0 9 | 1,1,256,512,1,1,1,1,0 10 | 1,1,1024,1024,1,1,1,1,0 11 | 1,1,256,256,1,1,1,1,0 12 | 1,1,256,128,1,1,1,1,0 13 | 1,1,2048,2048,1,1,1,1,0 14 | 1,1,4096,4096,1,1,1,1,0 15 | 1,1,64,256,1,1,1,1,0 16 | 1,1,4096,4096,1,1,1,1,0 17 | 1,1,64,512,1,1,1,1,0 18 | 1,1,1024,1024,1,1,1,1,0 19 | 1,1,2048,2048,1,1,1,1,0 20 | 1,1,256,2048,1,1,1,1,0 21 | 1,1,128,512,1,1,1,1,0 22 | 1,1,1024,512,1,1,1,1,0 23 | 1,1,2048,2048,1,1,1,1,0 24 | 1,1,64,128,1,1,1,1,0 25 | 1,1,2048,2048,1,1,1,1,0 26 | 1,1,1,1024,1,1,1,1,0 27 | 1,1,2048,2048,1,1,1,1,0 28 | 1,1,256,256,1,1,1,1,0 29 | 1,1,1024,1024,1,1,1,1,0 30 | 1,1,80,200,1,1,1,1,0 31 | 1,1,1,128,1,1,1,1,0 32 | 1,1,512,512,1,1,1,1,0 33 | 1,1,1,4096,1,1,1,1,0 34 | 1,1,1024,512,1,1,1,1,0 35 | 1,1,2048,2048,1,1,1,1,0 36 | 1,1,128,256,1,1,1,1,0 37 | 1,1,256,256,1,1,1,1,0 38 | 1,1,4096,4096,1,1,1,1,0 39 | 1,1,4096,4096,1,1,1,1,0 40 | 1,1,32,128,1,1,1,1,0 41 | 1,1,512,512,1,1,1,1,0 42 | 1,1,256,512,1,1,1,1,0 43 | 1,1,4096,4096,1,1,1,1,0 44 | 1,1,256,512,1,1,1,1,0 45 | 1,1,256,256,1,1,1,1,0 46 | 1,1,1,1024,1,1,1,1,0 47 | 1,1,4096,4096,1,1,1,1,0 48 | 1,1,64,256,1,1,1,1,0 49 | 1,1,256,256,1,1,1,1,0 50 | 1,1,4096,4096,1,1,1,1,0 51 | 1,1,128,2048,1,1,1,1,0 52 | 1,1,2,80,1,1,1,1,0 53 | 1,1,4096,4096,1,1,1,1,0 54 | 1,1,1024,1024,1,1,1,1,0 55 | 1,1,512,512,1,1,1,1,0 56 | 1,1,2048,2048,1,1,1,1,0 57 | 1,1,1,128,1,1,1,1,0 58 | 1,1,256,512,1,1,1,1,0 59 | 1,1,256,256,1,1,1,1,0 60 | 1,1,2048,2048,1,1,1,1,0 61 | 1,1,256,256,1,1,1,1,0 62 | 1,1,2048,2048,1,1,1,1,0 63 | 1,1,128,256,1,1,1,1,0 64 | 1,1,2048,2048,1,1,1,1,0 65 | 1,1,512,512,1,1,1,1,0 66 | 1,1,256,256,1,1,1,1,0 67 | 1,1,2048,2048,1,1,1,1,0 68 | 1,1,512,1024,1,1,1,1,0 69 | 1,1,1024,1024,1,1,1,1,0 70 | 1,1,4096,4096,1,1,1,1,0 71 | 1,1,80,200,1,1,1,1,0 72 | 1,1,2048,2048,1,1,1,1,0 73 | 1,1,64,256,1,1,1,1,0 74 | 1,1,1,64,1,1,1,1,0 75 | 1,1,2048,128,1,1,1,1,0 76 | 1,1,256,256,1,1,1,1,0 77 | 1,1,2048,2048,1,1,1,1,0 78 | 1,1,256,512,1,1,1,1,0 79 | 1,1,512,512,1,1,1,1,0 80 | 1,1,1,256,1,1,1,1,0 81 | 1,1,1024,512,1,1,1,1,0 82 | 1,1,2,80,1,1,1,1,0 83 | 1,1,128,256,1,1,1,1,0 84 | 1,1,512,512,1,1,1,1,0 85 | 1,1,2048,128,1,1,1,1,0 86 | 1,1,1,1024,1,1,1,1,0 87 | 1,1,256,512,1,1,1,1,0 88 | 1,1,4096,4096,1,1,1,1,0 89 | 1,1,128,256,1,1,1,1,0 90 | 1,1,2048,2048,1,1,1,1,0 91 | 1,1,128,64,1,1,1,1,0 92 | 1,1,512,13,1,1,1,1,0 93 | 1,1,32,128,1,1,1,1,0 94 | 1,1,1,1024,1,1,1,1,0 95 | 1,1,256,256,1,1,1,1,0 96 | 1,1,1024,1024,1,1,1,1,0 97 | 1,1,4096,4096,1,1,1,1,0 98 | 1,1,4096,4096,1,1,1,1,0 99 | 1,1,256,256,1,1,1,1,0 100 | 1,1,512,13,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts3.csv: -------------------------------------------------------------------------------- 1 | 1,1,2048,2048,1,1,1,1,0 2 | 1,1,1024,1024,1,1,1,1,0 3 | 1,1,2048,2048,1,1,1,1,0 4 | 1,1,256,256,1,1,1,1,0 5 | 1,1,128,512,1,1,1,1,0 6 | 1,1,256,512,1,1,1,1,0 7 | 1,1,512,512,1,1,1,1,0 8 | 1,1,4096,4096,1,1,1,1,0 9 | 1,1,256,2048,1,1,1,1,0 10 | 1,1,4096,4096,1,1,1,1,0 11 | 1,1,80,200,1,1,1,1,0 12 | 1,1,128,256,1,1,1,1,0 13 | 1,1,2048,2048,1,1,1,1,0 14 | 1,1,80,200,1,1,1,1,0 15 | 1,1,256,256,1,1,1,1,0 16 | 1,1,128,2048,1,1,1,1,0 17 | 1,1,4096,4096,1,1,1,1,0 18 | 1,1,128,64,1,1,1,1,0 19 | 1,1,2048,2048,1,1,1,1,0 20 | 1,1,1024,512,1,1,1,1,0 21 | 1,1,1024,512,1,1,1,1,0 22 | 1,1,4096,4096,1,1,1,1,0 23 | 1,1,2048,2048,1,1,1,1,0 24 | 1,1,256,256,1,1,1,1,0 25 | 1,1,256,256,1,1,1,1,0 26 | 1,1,256,256,1,1,1,1,0 27 | 1,1,512,512,1,1,1,1,0 28 | 1,1,4096,4096,1,1,1,1,0 29 | 1,1,256,256,1,1,1,1,0 30 | 1,1,256,256,1,1,1,1,0 31 | 1,1,512,512,1,1,1,1,0 32 | 1,1,4096,4096,1,1,1,1,0 33 | 1,1,80,200,1,1,1,1,0 34 | 1,1,4096,4096,1,1,1,1,0 35 | 1,1,4096,4096,1,1,1,1,0 36 | 1,1,2048,2048,1,1,1,1,0 37 | 1,1,256,256,1,1,1,1,0 38 | 1,1,4096,4096,1,1,1,1,0 39 | 1,1,4096,4096,1,1,1,1,0 40 | 1,1,4096,4096,1,1,1,1,0 41 | 1,1,128,512,1,1,1,1,0 42 | 1,1,512,512,1,1,1,1,0 43 | 1,1,1,64,1,1,1,1,0 44 | 1,1,128,256,1,1,1,1,0 45 | 1,1,128,512,1,1,1,1,0 46 | 1,1,4096,4096,1,1,1,1,0 47 | 1,1,4096,4096,1,1,1,1,0 48 | 1,1,256,128,1,1,1,1,0 49 | 1,1,4096,4096,1,1,1,1,0 50 | 1,1,1024,512,1,1,1,1,0 51 | 1,1,1,1024,1,1,1,1,0 52 | 1,1,32,128,1,1,1,1,0 53 | 1,1,4096,4096,1,1,1,1,0 54 | 1,1,512,13,1,1,1,1,0 55 | 1,1,2,80,1,1,1,1,0 56 | 1,1,256,512,1,1,1,1,0 57 | 1,1,4096,4096,1,1,1,1,0 58 | 1,1,4096,4096,1,1,1,1,0 59 | 1,1,4096,4096,1,1,1,1,0 60 | 1,1,4096,4096,1,1,1,1,0 61 | 1,1,4096,4096,1,1,1,1,0 62 | 1,1,2048,2048,1,1,1,1,0 63 | 1,1,2048,128,1,1,1,1,0 64 | 1,1,1024,512,1,1,1,1,0 65 | 1,1,4096,4096,1,1,1,1,0 66 | 1,1,4096,4096,1,1,1,1,0 67 | 1,1,64,256,1,1,1,1,0 68 | 1,1,4096,4096,1,1,1,1,0 69 | 1,1,4096,2048,1,1,1,1,0 70 | 1,1,1,1024,1,1,1,1,0 71 | 1,1,1,256,1,1,1,1,0 72 | 1,1,2048,2048,1,1,1,1,0 73 | 1,1,256,256,1,1,1,1,0 74 | 1,1,2,80,1,1,1,1,0 75 | 1,1,32,128,1,1,1,1,0 76 | 1,1,2048,128,1,1,1,1,0 77 | 1,1,256,128,1,1,1,1,0 78 | 1,1,4096,4096,1,1,1,1,0 79 | 1,1,4096,4096,1,1,1,1,0 80 | 1,1,32,128,1,1,1,1,0 81 | 1,1,512,512,1,1,1,1,0 82 | 1,1,256,256,1,1,1,1,0 83 | 1,1,256,128,1,1,1,1,0 84 | 1,1,2,80,1,1,1,1,0 85 | 1,1,128,256,1,1,1,1,0 86 | 1,1,128,512,1,1,1,1,0 87 | 1,1,1024,512,1,1,1,1,0 88 | 1,1,256,512,1,1,1,1,0 89 | 1,1,512,512,1,1,1,1,0 90 | 1,1,256,512,1,1,1,1,0 91 | 1,1,512,512,1,1,1,1,0 92 | 1,1,256,256,1,1,1,1,0 93 | 1,1,4096,4096,1,1,1,1,0 94 | 1,1,128,256,1,1,1,1,0 95 | 1,1,2048,2048,1,1,1,1,0 96 | 1,1,4096,4096,1,1,1,1,0 97 | 1,1,128,256,1,1,1,1,0 98 | 1,1,200,200,1,1,1,1,0 99 | 1,1,4096,4096,1,1,1,1,0 100 | 1,1,256,512,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts4.csv: -------------------------------------------------------------------------------- 1 | 1,1,32,128,1,1,1,1,0 2 | 1,1,2,80,1,1,1,1,0 3 | 1,1,256,256,1,1,1,1,0 4 | 1,1,4096,4096,1,1,1,1,0 5 | 1,1,2048,2048,1,1,1,1,0 6 | 1,1,32,128,1,1,1,1,0 7 | 1,1,512,512,1,1,1,1,0 8 | 1,1,4096,4096,1,1,1,1,0 9 | 1,1,256,2048,1,1,1,1,0 10 | 1,1,2048,2048,1,1,1,1,0 11 | 1,1,4096,4096,1,1,1,1,0 12 | 1,1,1,128,1,1,1,1,0 13 | 1,1,32,128,1,1,1,1,0 14 | 1,1,2048,2048,1,1,1,1,0 15 | 1,1,4096,4096,1,1,1,1,0 16 | 1,1,1024,512,1,1,1,1,0 17 | 1,1,512,512,1,1,1,1,0 18 | 1,1,64,128,1,1,1,1,0 19 | 1,1,128,256,1,1,1,1,0 20 | 1,1,4096,2048,1,1,1,1,0 21 | 1,1,256,2048,1,1,1,1,0 22 | 1,1,512,13,1,1,1,1,0 23 | 1,1,4096,4096,1,1,1,1,0 24 | 1,1,2048,2048,1,1,1,1,0 25 | 1,1,32,128,1,1,1,1,0 26 | 1,1,512,512,1,1,1,1,0 27 | 1,1,256,128,1,1,1,1,0 28 | 1,1,4096,4096,1,1,1,1,0 29 | 1,1,1024,1024,1,1,1,1,0 30 | 1,1,2048,128,1,1,1,1,0 31 | 1,1,2,80,1,1,1,1,0 32 | 1,1,256,512,1,1,1,1,0 33 | 1,1,4096,4096,1,1,1,1,0 34 | 1,1,1,1024,1,1,1,1,0 35 | 1,1,4096,4096,1,1,1,1,0 36 | 1,1,4096,4096,1,1,1,1,0 37 | 1,1,4096,4096,1,1,1,1,0 38 | 1,1,1024,1024,1,1,1,1,0 39 | 1,1,4096,4096,1,1,1,1,0 40 | 1,1,512,13,1,1,1,1,0 41 | 1,1,4096,4096,1,1,1,1,0 42 | 1,1,2048,2048,1,1,1,1,0 43 | 1,1,2048,2048,1,1,1,1,0 44 | 1,1,1,4096,1,1,1,1,0 45 | 1,1,4096,4096,1,1,1,1,0 46 | 1,1,512,13,1,1,1,1,0 47 | 1,1,128,512,1,1,1,1,0 48 | 1,1,1,1024,1,1,1,1,0 49 | 1,1,1,64,1,1,1,1,0 50 | 1,1,64,512,1,1,1,1,0 51 | 1,1,1,256,1,1,1,1,0 52 | 1,1,512,13,1,1,1,1,0 53 | 1,1,4096,4096,1,1,1,1,0 54 | 1,1,512,512,1,1,1,1,0 55 | 1,1,4096,4096,1,1,1,1,0 56 | 1,1,128,256,1,1,1,1,0 57 | 1,1,256,256,1,1,1,1,0 58 | 1,1,512,512,1,1,1,1,0 59 | 1,1,4096,4096,1,1,1,1,0 60 | 1,1,2048,2048,1,1,1,1,0 61 | 1,1,256,128,1,1,1,1,0 62 | 1,1,1,256,1,1,1,1,0 63 | 1,1,4096,4096,1,1,1,1,0 64 | 1,1,4096,4096,1,1,1,1,0 65 | 1,1,4096,4096,1,1,1,1,0 66 | 1,1,2048,2048,1,1,1,1,0 67 | 1,1,256,512,1,1,1,1,0 68 | 1,1,256,256,1,1,1,1,0 69 | 1,1,256,256,1,1,1,1,0 70 | 1,1,256,512,1,1,1,1,0 71 | 1,1,1024,1024,1,1,1,1,0 72 | 1,1,4096,4096,1,1,1,1,0 73 | 1,1,4096,4096,1,1,1,1,0 74 | 1,1,1024,1024,1,1,1,1,0 75 | 1,1,512,1024,1,1,1,1,0 76 | 1,1,512,1024,1,1,1,1,0 77 | 1,1,256,512,1,1,1,1,0 78 | 1,1,200,200,1,1,1,1,0 79 | 1,1,4096,4096,1,1,1,1,0 80 | 1,1,1024,512,1,1,1,1,0 81 | 1,1,512,512,1,1,1,1,0 82 | 1,1,4096,4096,1,1,1,1,0 83 | 1,1,4096,4096,1,1,1,1,0 84 | 1,1,128,64,1,1,1,1,0 85 | 1,1,4096,2048,1,1,1,1,0 86 | 1,1,256,256,1,1,1,1,0 87 | 1,1,64,512,1,1,1,1,0 88 | 1,1,64,512,1,1,1,1,0 89 | 1,1,256,256,1,1,1,1,0 90 | 1,1,4096,4096,1,1,1,1,0 91 | 1,1,256,128,1,1,1,1,0 92 | 1,1,1024,1024,1,1,1,1,0 93 | 1,1,256,256,1,1,1,1,0 94 | 1,1,128,64,1,1,1,1,0 95 | 1,1,2048,2048,1,1,1,1,0 96 | 1,1,200,200,1,1,1,1,0 97 | 1,1,256,512,1,1,1,1,0 98 | 1,1,2048,2048,1,1,1,1,0 99 | 1,1,4096,4096,1,1,1,1,0 100 | 1,1,1024,1024,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts5.csv: -------------------------------------------------------------------------------- 1 | 1,1,64,512,1,1,1,1,0 2 | 1,1,128,256,1,1,1,1,0 3 | 1,1,64,512,1,1,1,1,0 4 | 1,1,1024,512,1,1,1,1,0 5 | 1,1,256,128,1,1,1,1,0 6 | 1,1,2048,2048,1,1,1,1,0 7 | 1,1,64,128,1,1,1,1,0 8 | 1,1,512,512,1,1,1,1,0 9 | 1,1,256,2048,1,1,1,1,0 10 | 1,1,2,80,1,1,1,1,0 11 | 1,1,4096,4096,1,1,1,1,0 12 | 1,1,4096,2048,1,1,1,1,0 13 | 1,1,2,80,1,1,1,1,0 14 | 1,1,1024,1024,1,1,1,1,0 15 | 1,1,2048,2048,1,1,1,1,0 16 | 1,1,200,200,1,1,1,1,0 17 | 1,1,4096,4096,1,1,1,1,0 18 | 1,1,128,2048,1,1,1,1,0 19 | 1,1,512,512,1,1,1,1,0 20 | 1,1,256,512,1,1,1,1,0 21 | 1,1,64,512,1,1,1,1,0 22 | 1,1,256,256,1,1,1,1,0 23 | 1,1,1,64,1,1,1,1,0 24 | 1,1,4096,4096,1,1,1,1,0 25 | 1,1,256,512,1,1,1,1,0 26 | 1,1,512,512,1,1,1,1,0 27 | 1,1,256,128,1,1,1,1,0 28 | 1,1,2,80,1,1,1,1,0 29 | 1,1,4096,4096,1,1,1,1,0 30 | 1,1,4096,2048,1,1,1,1,0 31 | 1,1,2048,2048,1,1,1,1,0 32 | 1,1,128,256,1,1,1,1,0 33 | 1,1,256,256,1,1,1,1,0 34 | 1,1,128,256,1,1,1,1,0 35 | 1,1,4096,4096,1,1,1,1,0 36 | 1,1,4096,4096,1,1,1,1,0 37 | 1,1,2,80,1,1,1,1,0 38 | 1,1,256,512,1,1,1,1,0 39 | 1,1,1024,512,1,1,1,1,0 40 | 1,1,2048,2048,1,1,1,1,0 41 | 1,1,128,2048,1,1,1,1,0 42 | 1,1,512,1024,1,1,1,1,0 43 | 1,1,64,512,1,1,1,1,0 44 | 1,1,2048,2048,1,1,1,1,0 45 | 1,1,80,200,1,1,1,1,0 46 | 1,1,128,64,1,1,1,1,0 47 | 1,1,4096,4096,1,1,1,1,0 48 | 1,1,2048,128,1,1,1,1,0 49 | 1,1,128,64,1,1,1,1,0 50 | 1,1,4096,4096,1,1,1,1,0 51 | 1,1,64,256,1,1,1,1,0 52 | 1,1,128,256,1,1,1,1,0 53 | 1,1,80,200,1,1,1,1,0 54 | 1,1,4096,4096,1,1,1,1,0 55 | 1,1,1,4096,1,1,1,1,0 56 | 1,1,256,256,1,1,1,1,0 57 | 1,1,256,512,1,1,1,1,0 58 | 1,1,256,256,1,1,1,1,0 59 | 1,1,2048,2048,1,1,1,1,0 60 | 1,1,1,1024,1,1,1,1,0 61 | 1,1,1,128,1,1,1,1,0 62 | 1,1,2,80,1,1,1,1,0 63 | 1,1,64,512,1,1,1,1,0 64 | 1,1,256,256,1,1,1,1,0 65 | 1,1,4096,4096,1,1,1,1,0 66 | 1,1,4096,4096,1,1,1,1,0 67 | 1,1,4096,4096,1,1,1,1,0 68 | 1,1,256,512,1,1,1,1,0 69 | 1,1,1,64,1,1,1,1,0 70 | 1,1,2,80,1,1,1,1,0 71 | 1,1,1024,1024,1,1,1,1,0 72 | 1,1,4096,4096,1,1,1,1,0 73 | 1,1,512,512,1,1,1,1,0 74 | 1,1,2048,2048,1,1,1,1,0 75 | 1,1,256,128,1,1,1,1,0 76 | 1,1,128,64,1,1,1,1,0 77 | 1,1,4096,4096,1,1,1,1,0 78 | 1,1,512,512,1,1,1,1,0 79 | 1,1,4096,4096,1,1,1,1,0 80 | 1,1,1,1024,1,1,1,1,0 81 | 1,1,200,200,1,1,1,1,0 82 | 1,1,4096,4096,1,1,1,1,0 83 | 1,1,1024,1024,1,1,1,1,0 84 | 1,1,4096,4096,1,1,1,1,0 85 | 1,1,2048,128,1,1,1,1,0 86 | 1,1,256,512,1,1,1,1,0 87 | 1,1,512,512,1,1,1,1,0 88 | 1,1,2,80,1,1,1,1,0 89 | 1,1,4096,4096,1,1,1,1,0 90 | 1,1,4096,4096,1,1,1,1,0 91 | 1,1,256,128,1,1,1,1,0 92 | 1,1,256,256,1,1,1,1,0 93 | 1,1,256,256,1,1,1,1,0 94 | 1,1,256,256,1,1,1,1,0 95 | 1,1,4096,4096,1,1,1,1,0 96 | 1,1,80,200,1,1,1,1,0 97 | 1,1,256,2048,1,1,1,1,0 98 | 1,1,1024,1024,1,1,1,1,0 99 | 1,1,2048,2048,1,1,1,1,0 100 | 1,1,2048,2048,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts6.csv: -------------------------------------------------------------------------------- 1 | 1,1,512,512,1,1,1,1,0 2 | 1,1,256,256,1,1,1,1,0 3 | 1,1,1024,512,1,1,1,1,0 4 | 1,1,512,1024,1,1,1,1,0 5 | 1,1,4096,4096,1,1,1,1,0 6 | 1,1,128,256,1,1,1,1,0 7 | 1,1,4096,4096,1,1,1,1,0 8 | 1,1,512,512,1,1,1,1,0 9 | 1,1,64,512,1,1,1,1,0 10 | 1,1,256,256,1,1,1,1,0 11 | 1,1,256,512,1,1,1,1,0 12 | 1,1,4096,4096,1,1,1,1,0 13 | 1,1,128,256,1,1,1,1,0 14 | 1,1,512,512,1,1,1,1,0 15 | 1,1,4096,4096,1,1,1,1,0 16 | 1,1,64,128,1,1,1,1,0 17 | 1,1,4096,4096,1,1,1,1,0 18 | 1,1,128,256,1,1,1,1,0 19 | 1,1,2048,2048,1,1,1,1,0 20 | 1,1,32,128,1,1,1,1,0 21 | 1,1,512,1024,1,1,1,1,0 22 | 1,1,4096,4096,1,1,1,1,0 23 | 1,1,4096,4096,1,1,1,1,0 24 | 1,1,4096,4096,1,1,1,1,0 25 | 1,1,512,512,1,1,1,1,0 26 | 1,1,2,80,1,1,1,1,0 27 | 1,1,2048,2048,1,1,1,1,0 28 | 1,1,512,512,1,1,1,1,0 29 | 1,1,256,128,1,1,1,1,0 30 | 1,1,256,128,1,1,1,1,0 31 | 1,1,256,256,1,1,1,1,0 32 | 1,1,4096,4096,1,1,1,1,0 33 | 1,1,128,256,1,1,1,1,0 34 | 1,1,1,128,1,1,1,1,0 35 | 1,1,128,64,1,1,1,1,0 36 | 1,1,512,1024,1,1,1,1,0 37 | 1,1,2048,128,1,1,1,1,0 38 | 1,1,128,256,1,1,1,1,0 39 | 1,1,128,512,1,1,1,1,0 40 | 1,1,2048,2048,1,1,1,1,0 41 | 1,1,1,256,1,1,1,1,0 42 | 1,1,64,256,1,1,1,1,0 43 | 1,1,512,512,1,1,1,1,0 44 | 1,1,1,128,1,1,1,1,0 45 | 1,1,256,512,1,1,1,1,0 46 | 1,1,256,256,1,1,1,1,0 47 | 1,1,512,1024,1,1,1,1,0 48 | 1,1,80,200,1,1,1,1,0 49 | 1,1,1,4096,1,1,1,1,0 50 | 1,1,2048,2048,1,1,1,1,0 51 | 1,1,4096,4096,1,1,1,1,0 52 | 1,1,4096,2048,1,1,1,1,0 53 | 1,1,32,128,1,1,1,1,0 54 | 1,1,80,200,1,1,1,1,0 55 | 1,1,4096,4096,1,1,1,1,0 56 | 1,1,4096,4096,1,1,1,1,0 57 | 1,1,1,128,1,1,1,1,0 58 | 1,1,64,256,1,1,1,1,0 59 | 1,1,512,1024,1,1,1,1,0 60 | 1,1,2048,2048,1,1,1,1,0 61 | 1,1,4096,4096,1,1,1,1,0 62 | 1,1,4096,4096,1,1,1,1,0 63 | 1,1,256,256,1,1,1,1,0 64 | 1,1,2048,2048,1,1,1,1,0 65 | 1,1,256,256,1,1,1,1,0 66 | 1,1,4096,4096,1,1,1,1,0 67 | 1,1,4096,4096,1,1,1,1,0 68 | 1,1,64,256,1,1,1,1,0 69 | 1,1,256,256,1,1,1,1,0 70 | 1,1,256,512,1,1,1,1,0 71 | 1,1,1,256,1,1,1,1,0 72 | 1,1,64,512,1,1,1,1,0 73 | 1,1,128,64,1,1,1,1,0 74 | 1,1,80,200,1,1,1,1,0 75 | 1,1,1024,1024,1,1,1,1,0 76 | 1,1,128,2048,1,1,1,1,0 77 | 1,1,2048,2048,1,1,1,1,0 78 | 1,1,4096,4096,1,1,1,1,0 79 | 1,1,4096,4096,1,1,1,1,0 80 | 1,1,128,256,1,1,1,1,0 81 | 1,1,64,128,1,1,1,1,0 82 | 1,1,512,512,1,1,1,1,0 83 | 1,1,4096,2048,1,1,1,1,0 84 | 1,1,32,128,1,1,1,1,0 85 | 1,1,256,256,1,1,1,1,0 86 | 1,1,4096,4096,1,1,1,1,0 87 | 1,1,1,1024,1,1,1,1,0 88 | 1,1,4096,4096,1,1,1,1,0 89 | 1,1,4096,4096,1,1,1,1,0 90 | 1,1,4096,4096,1,1,1,1,0 91 | 1,1,64,256,1,1,1,1,0 92 | 1,1,4096,4096,1,1,1,1,0 93 | 1,1,512,512,1,1,1,1,0 94 | 1,1,128,256,1,1,1,1,0 95 | 1,1,128,256,1,1,1,1,0 96 | 1,1,4096,2048,1,1,1,1,0 97 | 1,1,4096,4096,1,1,1,1,0 98 | 1,1,32,128,1,1,1,1,0 99 | 1,1,4096,2048,1,1,1,1,0 100 | 1,1,1024,512,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts7.csv: -------------------------------------------------------------------------------- 1 | 1,1,256,256,1,1,1,1,0 2 | 1,1,2048,2048,1,1,1,1,0 3 | 1,1,256,256,1,1,1,1,0 4 | 1,1,2048,2048,1,1,1,1,0 5 | 1,1,4096,4096,1,1,1,1,0 6 | 1,1,1024,512,1,1,1,1,0 7 | 1,1,2048,2048,1,1,1,1,0 8 | 1,1,2,80,1,1,1,1,0 9 | 1,1,4096,4096,1,1,1,1,0 10 | 1,1,2,80,1,1,1,1,0 11 | 1,1,4096,4096,1,1,1,1,0 12 | 1,1,2048,2048,1,1,1,1,0 13 | 1,1,1024,1024,1,1,1,1,0 14 | 1,1,2048,2048,1,1,1,1,0 15 | 1,1,4096,4096,1,1,1,1,0 16 | 1,1,128,256,1,1,1,1,0 17 | 1,1,256,2048,1,1,1,1,0 18 | 1,1,1024,1024,1,1,1,1,0 19 | 1,1,2048,2048,1,1,1,1,0 20 | 1,1,512,512,1,1,1,1,0 21 | 1,1,2048,2048,1,1,1,1,0 22 | 1,1,1024,1024,1,1,1,1,0 23 | 1,1,200,200,1,1,1,1,0 24 | 1,1,32,128,1,1,1,1,0 25 | 1,1,1024,1024,1,1,1,1,0 26 | 1,1,4096,4096,1,1,1,1,0 27 | 1,1,256,256,1,1,1,1,0 28 | 1,1,256,2048,1,1,1,1,0 29 | 1,1,1,256,1,1,1,1,0 30 | 1,1,200,200,1,1,1,1,0 31 | 1,1,512,13,1,1,1,1,0 32 | 1,1,4096,4096,1,1,1,1,0 33 | 1,1,1024,1024,1,1,1,1,0 34 | 1,1,128,2048,1,1,1,1,0 35 | 1,1,2048,2048,1,1,1,1,0 36 | 1,1,1024,1024,1,1,1,1,0 37 | 1,1,128,64,1,1,1,1,0 38 | 1,1,4096,4096,1,1,1,1,0 39 | 1,1,1024,1024,1,1,1,1,0 40 | 1,1,1,1024,1,1,1,1,0 41 | 1,1,256,128,1,1,1,1,0 42 | 1,1,512,1024,1,1,1,1,0 43 | 1,1,1024,512,1,1,1,1,0 44 | 1,1,4096,4096,1,1,1,1,0 45 | 1,1,4096,4096,1,1,1,1,0 46 | 1,1,256,128,1,1,1,1,0 47 | 1,1,1024,1024,1,1,1,1,0 48 | 1,1,2,80,1,1,1,1,0 49 | 1,1,128,512,1,1,1,1,0 50 | 1,1,4096,4096,1,1,1,1,0 51 | 1,1,2048,2048,1,1,1,1,0 52 | 1,1,512,512,1,1,1,1,0 53 | 1,1,80,200,1,1,1,1,0 54 | 1,1,2048,2048,1,1,1,1,0 55 | 1,1,1,128,1,1,1,1,0 56 | 1,1,2048,2048,1,1,1,1,0 57 | 1,1,512,512,1,1,1,1,0 58 | 1,1,4096,2048,1,1,1,1,0 59 | 1,1,1,128,1,1,1,1,0 60 | 1,1,200,200,1,1,1,1,0 61 | 1,1,2,80,1,1,1,1,0 62 | 1,1,4096,4096,1,1,1,1,0 63 | 1,1,4096,4096,1,1,1,1,0 64 | 1,1,64,512,1,1,1,1,0 65 | 1,1,1,128,1,1,1,1,0 66 | 1,1,200,200,1,1,1,1,0 67 | 1,1,4096,4096,1,1,1,1,0 68 | 1,1,512,512,1,1,1,1,0 69 | 1,1,1024,1024,1,1,1,1,0 70 | 1,1,64,512,1,1,1,1,0 71 | 1,1,1,128,1,1,1,1,0 72 | 1,1,256,256,1,1,1,1,0 73 | 1,1,1,256,1,1,1,1,0 74 | 1,1,80,200,1,1,1,1,0 75 | 1,1,128,256,1,1,1,1,0 76 | 1,1,256,128,1,1,1,1,0 77 | 1,1,512,1024,1,1,1,1,0 78 | 1,1,1,256,1,1,1,1,0 79 | 1,1,1,128,1,1,1,1,0 80 | 1,1,1,4096,1,1,1,1,0 81 | 1,1,256,512,1,1,1,1,0 82 | 1,1,2048,2048,1,1,1,1,0 83 | 1,1,80,200,1,1,1,1,0 84 | 1,1,128,256,1,1,1,1,0 85 | 1,1,4096,4096,1,1,1,1,0 86 | 1,1,2048,2048,1,1,1,1,0 87 | 1,1,2048,2048,1,1,1,1,0 88 | 1,1,256,256,1,1,1,1,0 89 | 1,1,2048,128,1,1,1,1,0 90 | 1,1,128,64,1,1,1,1,0 91 | 1,1,2048,2048,1,1,1,1,0 92 | 1,1,2048,2048,1,1,1,1,0 93 | 1,1,256,256,1,1,1,1,0 94 | 1,1,128,64,1,1,1,1,0 95 | 1,1,128,256,1,1,1,1,0 96 | 1,1,128,64,1,1,1,1,0 97 | 1,1,4096,4096,1,1,1,1,0 98 | 1,1,2048,2048,1,1,1,1,0 99 | 1,1,512,512,1,1,1,1,0 100 | 1,1,80,200,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts8.csv: -------------------------------------------------------------------------------- 1 | 1,1,128,64,1,1,1,1,0 2 | 1,1,80,200,1,1,1,1,0 3 | 1,1,2048,2048,1,1,1,1,0 4 | 1,1,4096,4096,1,1,1,1,0 5 | 1,1,2,80,1,1,1,1,0 6 | 1,1,1,256,1,1,1,1,0 7 | 1,1,4096,4096,1,1,1,1,0 8 | 1,1,4096,4096,1,1,1,1,0 9 | 1,1,2048,2048,1,1,1,1,0 10 | 1,1,256,256,1,1,1,1,0 11 | 1,1,4096,4096,1,1,1,1,0 12 | 1,1,200,200,1,1,1,1,0 13 | 1,1,256,128,1,1,1,1,0 14 | 1,1,256,128,1,1,1,1,0 15 | 1,1,4096,4096,1,1,1,1,0 16 | 1,1,4096,4096,1,1,1,1,0 17 | 1,1,4096,4096,1,1,1,1,0 18 | 1,1,256,256,1,1,1,1,0 19 | 1,1,128,64,1,1,1,1,0 20 | 1,1,1024,512,1,1,1,1,0 21 | 1,1,256,256,1,1,1,1,0 22 | 1,1,1024,1024,1,1,1,1,0 23 | 1,1,256,256,1,1,1,1,0 24 | 1,1,128,64,1,1,1,1,0 25 | 1,1,1024,1024,1,1,1,1,0 26 | 1,1,4096,4096,1,1,1,1,0 27 | 1,1,2048,2048,1,1,1,1,0 28 | 1,1,2048,2048,1,1,1,1,0 29 | 1,1,2048,2048,1,1,1,1,0 30 | 1,1,4096,4096,1,1,1,1,0 31 | 1,1,64,512,1,1,1,1,0 32 | 1,1,80,200,1,1,1,1,0 33 | 1,1,128,256,1,1,1,1,0 34 | 1,1,1,128,1,1,1,1,0 35 | 1,1,80,200,1,1,1,1,0 36 | 1,1,4096,4096,1,1,1,1,0 37 | 1,1,4096,4096,1,1,1,1,0 38 | 1,1,1024,1024,1,1,1,1,0 39 | 1,1,256,512,1,1,1,1,0 40 | 1,1,128,256,1,1,1,1,0 41 | 1,1,256,256,1,1,1,1,0 42 | 1,1,1,256,1,1,1,1,0 43 | 1,1,4096,4096,1,1,1,1,0 44 | 1,1,2,80,1,1,1,1,0 45 | 1,1,4096,4096,1,1,1,1,0 46 | 1,1,128,64,1,1,1,1,0 47 | 1,1,4096,4096,1,1,1,1,0 48 | 1,1,128,256,1,1,1,1,0 49 | 1,1,128,256,1,1,1,1,0 50 | 1,1,1,64,1,1,1,1,0 51 | 1,1,2048,128,1,1,1,1,0 52 | 1,1,1,4096,1,1,1,1,0 53 | 1,1,512,512,1,1,1,1,0 54 | 1,1,256,256,1,1,1,1,0 55 | 1,1,256,128,1,1,1,1,0 56 | 1,1,2048,2048,1,1,1,1,0 57 | 1,1,1024,512,1,1,1,1,0 58 | 1,1,4096,4096,1,1,1,1,0 59 | 1,1,2,80,1,1,1,1,0 60 | 1,1,128,256,1,1,1,1,0 61 | 1,1,1,256,1,1,1,1,0 62 | 1,1,128,256,1,1,1,1,0 63 | 1,1,256,256,1,1,1,1,0 64 | 1,1,128,256,1,1,1,1,0 65 | 1,1,512,512,1,1,1,1,0 66 | 1,1,1024,1024,1,1,1,1,0 67 | 1,1,64,256,1,1,1,1,0 68 | 1,1,4096,2048,1,1,1,1,0 69 | 1,1,128,2048,1,1,1,1,0 70 | 1,1,2048,2048,1,1,1,1,0 71 | 1,1,2048,128,1,1,1,1,0 72 | 1,1,4096,4096,1,1,1,1,0 73 | 1,1,4096,4096,1,1,1,1,0 74 | 1,1,128,256,1,1,1,1,0 75 | 1,1,2048,128,1,1,1,1,0 76 | 1,1,4096,4096,1,1,1,1,0 77 | 1,1,256,128,1,1,1,1,0 78 | 1,1,256,512,1,1,1,1,0 79 | 1,1,2048,2048,1,1,1,1,0 80 | 1,1,4096,4096,1,1,1,1,0 81 | 1,1,4096,4096,1,1,1,1,0 82 | 1,1,256,256,1,1,1,1,0 83 | 1,1,256,256,1,1,1,1,0 84 | 1,1,1024,1024,1,1,1,1,0 85 | 1,1,4096,4096,1,1,1,1,0 86 | 1,1,4096,4096,1,1,1,1,0 87 | 1,1,128,256,1,1,1,1,0 88 | 1,1,512,512,1,1,1,1,0 89 | 1,1,32,128,1,1,1,1,0 90 | 1,1,512,13,1,1,1,1,0 91 | 1,1,128,2048,1,1,1,1,0 92 | 1,1,1024,1024,1,1,1,1,0 93 | 1,1,256,512,1,1,1,1,0 94 | 1,1,64,256,1,1,1,1,0 95 | 1,1,128,256,1,1,1,1,0 96 | 1,1,4096,4096,1,1,1,1,0 97 | 1,1,256,256,1,1,1,1,0 98 | 1,1,1,4096,1,1,1,1,0 99 | 1,1,1,256,1,1,1,1,0 100 | 1,1,2048,2048,1,1,1,1,0 101 | -------------------------------------------------------------------------------- /traffic_insts/batch_recom/insts9.csv: -------------------------------------------------------------------------------- 1 | 1,1,1,4096,1,1,1,1,0 2 | 1,1,4096,4096,1,1,1,1,0 3 | 1,1,4096,4096,1,1,1,1,0 4 | 1,1,2,80,1,1,1,1,0 5 | 1,1,256,128,1,1,1,1,0 6 | 1,1,512,512,1,1,1,1,0 7 | 1,1,2048,2048,1,1,1,1,0 8 | 1,1,2,80,1,1,1,1,0 9 | 1,1,4096,4096,1,1,1,1,0 10 | 1,1,256,2048,1,1,1,1,0 11 | 1,1,2048,2048,1,1,1,1,0 12 | 1,1,4096,2048,1,1,1,1,0 13 | 1,1,128,2048,1,1,1,1,0 14 | 1,1,4096,4096,1,1,1,1,0 15 | 1,1,32,128,1,1,1,1,0 16 | 1,1,1,64,1,1,1,1,0 17 | 1,1,1,4096,1,1,1,1,0 18 | 1,1,4096,2048,1,1,1,1,0 19 | 1,1,32,128,1,1,1,1,0 20 | 1,1,1,1024,1,1,1,1,0 21 | 1,1,1,64,1,1,1,1,0 22 | 1,1,4096,4096,1,1,1,1,0 23 | 1,1,4096,4096,1,1,1,1,0 24 | 1,1,2048,2048,1,1,1,1,0 25 | 1,1,512,512,1,1,1,1,0 26 | 1,1,4096,4096,1,1,1,1,0 27 | 1,1,4096,4096,1,1,1,1,0 28 | 1,1,2,80,1,1,1,1,0 29 | 1,1,128,256,1,1,1,1,0 30 | 1,1,64,512,1,1,1,1,0 31 | 1,1,4096,4096,1,1,1,1,0 32 | 1,1,256,512,1,1,1,1,0 33 | 1,1,256,256,1,1,1,1,0 34 | 1,1,4096,4096,1,1,1,1,0 35 | 1,1,256,256,1,1,1,1,0 36 | 1,1,256,512,1,1,1,1,0 37 | 1,1,512,512,1,1,1,1,0 38 | 1,1,4096,4096,1,1,1,1,0 39 | 1,1,1,1024,1,1,1,1,0 40 | 1,1,64,128,1,1,1,1,0 41 | 1,1,2048,2048,1,1,1,1,0 42 | 1,1,256,512,1,1,1,1,0 43 | 1,1,512,13,1,1,1,1,0 44 | 1,1,256,128,1,1,1,1,0 45 | 1,1,32,128,1,1,1,1,0 46 | 1,1,2048,2048,1,1,1,1,0 47 | 1,1,1024,1024,1,1,1,1,0 48 | 1,1,512,512,1,1,1,1,0 49 | 1,1,2048,2048,1,1,1,1,0 50 | 1,1,4096,4096,1,1,1,1,0 51 | 1,1,2,80,1,1,1,1,0 52 | 1,1,200,200,1,1,1,1,0 53 | 1,1,2048,2048,1,1,1,1,0 54 | 1,1,4096,4096,1,1,1,1,0 55 | 1,1,4096,4096,1,1,1,1,0 56 | 1,1,1,1024,1,1,1,1,0 57 | 1,1,1,128,1,1,1,1,0 58 | 1,1,4096,4096,1,1,1,1,0 59 | 1,1,256,256,1,1,1,1,0 60 | 1,1,2048,2048,1,1,1,1,0 61 | 1,1,256,2048,1,1,1,1,0 62 | 1,1,128,256,1,1,1,1,0 63 | 1,1,512,512,1,1,1,1,0 64 | 1,1,64,128,1,1,1,1,0 65 | 1,1,256,2048,1,1,1,1,0 66 | 1,1,4096,4096,1,1,1,1,0 67 | 1,1,2048,2048,1,1,1,1,0 68 | 1,1,2048,2048,1,1,1,1,0 69 | 1,1,256,512,1,1,1,1,0 70 | 1,1,256,128,1,1,1,1,0 71 | 1,1,128,256,1,1,1,1,0 72 | 1,1,4096,4096,1,1,1,1,0 73 | 1,1,1,4096,1,1,1,1,0 74 | 1,1,4096,4096,1,1,1,1,0 75 | 1,1,1,64,1,1,1,1,0 76 | 1,1,256,128,1,1,1,1,0 77 | 1,1,2048,2048,1,1,1,1,0 78 | 1,1,4096,4096,1,1,1,1,0 79 | 1,1,256,256,1,1,1,1,0 80 | 1,1,128,256,1,1,1,1,0 81 | 1,1,1,256,1,1,1,1,0 82 | 1,1,2,80,1,1,1,1,0 83 | 1,1,4096,4096,1,1,1,1,0 84 | 1,1,4096,4096,1,1,1,1,0 85 | 1,1,256,256,1,1,1,1,0 86 | 1,1,256,512,1,1,1,1,0 87 | 1,1,128,2048,1,1,1,1,0 88 | 1,1,4096,4096,1,1,1,1,0 89 | 1,1,2,80,1,1,1,1,0 90 | 1,1,4096,4096,1,1,1,1,0 91 | 1,1,32,128,1,1,1,1,0 92 | 1,1,1,1024,1,1,1,1,0 93 | 1,1,2048,2048,1,1,1,1,0 94 | 1,1,256,256,1,1,1,1,0 95 | 1,1,80,200,1,1,1,1,0 96 | 1,1,256,256,1,1,1,1,0 97 | 1,1,80,200,1,1,1,1,0 98 | 1,1,512,13,1,1,1,1,0 99 | 1,1,4096,4096,1,1,1,1,0 100 | 1,1,4096,4096,1,1,1,1,0 101 | --------------------------------------------------------------------------------