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run_component.py
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run_component.py
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#!/usr/bin/env python3
import torch
import numpy as np
from smart_home_dataset import SmartHomeDataset
from classifier import Classifier
from torch import optim
import utils
import callbacks as cb
import time
from generative_replay_learner import GenerativeReplayLearner;
import arg_params
import json
import os
import copy
import torch.multiprocessing as mp
from run_main import *
def select_hidden_unit(args):
if args.data_dir == "pamap":
args.hidden_units = 1000
elif args.data_dir == "dsads":
args.hidden_units = 1000
elif args.data_dir == "housea":
args.hidden_units = 200
else:
args.hidden_units = 500
return args.hidden_units
if __name__ == "__main__":
parser = arg_params.get_parser()
args = parser.parse_args()
print("Arguments")
print(args)
result_folder = args.results_dir
print("\n")
print("STEP1: load datasets")
base_dataset = select_dataset(args)
methods = [
("offline", 0), ("sg-cgan", 0), #("mp-gan", 0), ("mp-wgan", 0), ("sg-cwgan", 0),
("offline", 1), ("sg-cgan", 1), #("mp-gan", 1), ("mp-wgan", 1), ("sg-cwgan", 1),
("offline", 2), ("sg-cgan", 2), #("mp-gan", 2), ("mp-wgan", 2), ("sg-cwgan", 2),
("offline", 3), ("sg-cgan", 3), #("mp-gan", 3), ("mp-wgan", 3), ("sg-cwgan", 3),
("offline", 4), ("sg-cgan", 4), #("mp-gan", 4), ("mp-wgan", 4), ("sg-cwgan", 4),
("offline", 5), ("sg-cgan", 5), #("mp-gan", 5), ("mp-wgan", 5), ("sg-cwgan", 5),
("offline", 6), ("sg-cgan", 6), #("mp-gan", 6), ("mp-wgan", 6), ("sg-cwgan", 6),
]
jobs = []
# pool = mp.Pool()
start = time.time()
ntask = 10
tasks = []
if args.task_order is not None:
ft = open(args.task_order)
tasks = [line.strip().split(";") for line in ft]
base_args = args
for task_order in range(ntask):
if args.task_order is not None:
base_dataset.permu_task_order(tasks[task_order])
else:
base_dataset.permu_task_order()
identity = {
"task_order": None,
"method": None,
"train_session": None,
"task_index": None,
"no_of_test": None,
"no_of_correct_prediction": None,
"accuracy": None,
"solver_training_time": None,
"generator_training_time": None,
}
identity["task_order"] = task_order
if args.task_order is None:
save_order(result_folder, task_order, base_dataset.classes)
traindata, testdata = base_dataset.train_test_split()
dataset = traindata
train_datasets, config, classes_per_task = dataset.split(tasks=args.tasks)
test_datasets, _, _ = testdata.split(tasks=args.tasks)
over_dataset = traindata.resampling()
over_train_datasets, _, __ = over_dataset.split(tasks=args.tasks)
print("******* Run ",task_order,"*******")
print("\n")
for method in methods:
m, cmd = method
identity["method"] = m
args = copy.deepcopy(base_args)
args.self_verify = False
args.oversampling = False
args.solver_ewc = False
args.solver_distill = False
args.generator_noise = False
_train_datasets = train_datasets
# cmd==0; no extra helps
if cmd==0:
args.self_verify = False
args.oversampling = False
args.solver_ewc = False
args.solver_distill = False
args.generator_noise = False
elif cmd==1:
args.self_verify = True
elif cmd==2:
args.oversampling = True
_train_datasets = over_train_datasets
elif cmd==3:
args.solver_ewc = True
elif cmd==4:
args.solver_distill = True
elif cmd==5:
args.generator_noise = True
else:
args.self_verify = True
args.oversampling = True
args.solver_ewc = False
args.solver_distill = True
args.generator_noise = True
_train_datasets = over_train_datasets
train_datasets = _train_datasets
base_hidden_units = select_hidden_unit(args)
args.critic_fc_units = base_hidden_units
args.generator_fc_units = base_hidden_units
# pool.apply_async(run_model, args=(identity, method, args, config, _train_datasets, test_datasets, True))
run_model(identity, method, args, config, train_datasets, test_datasets, True)
# pool.close()
# pool.join()
training_time = time.time() - start
print(training_time)
# clearup_tmp_file(result_folder, ntask, methods)