def test_testCase16():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(3989, 3259),
3,
[{"id": 0, "position_x": 302, "position_y": 6109},
{"id": 1, "position_x": 3671, "position_y": 981},
{"id": 2, "position_x": 6863, "position_y": 809}]
,
11,
[{"id": 0, "position_x": 208, "position_y": 156},
{"id": 1, "position_x": 10129, "position_y": 711},
{"id": 2, "position_x": 13229, "position_y": 413},
{"id": 3, "position_x": 203, "position_y": 3627},
{"id": 4, "position_x": 7310, "position_y": 3912},
{"id": 5, "position_x": 9814, "position_y": 3223},
{"id": 6, "position_x": 13556, "position_y": 3668},
{"id": 7, "position_x": 3923, "position_y": 6251},
{"id": 8, "position_x": 6720, "position_y": 6574},
{"id": 9, "position_x": 10387, "position_y": 6136},
{"id": 10, "position_x": 13093, "position_y": 6253}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase1():
simulator = Simulator()
initial_game = simulator.initialize_game(Position(0, 0), 1, [{"id": 0, "position_x": 8250, "position_y": 4500}], 1, [{"id": 0, "position_x": 8250, "position_y": 8999}])
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score == 10
def test_initialize_game():
simulator = Simulator()
inital_game = simulator.initialize_game(
Position(0, 0),
3,
[{"id": 0, "position_x": 500, "position_y": 600},
{"id": 1, "position_x": 700, "position_y": 300},
{"id": 2, "position_x": 100, "position_y": 900}],
1,
[{"id": 0, "position_x": 2000, "position_y": 1000}]
)
assert len(inital_game.humans) == 3
assert len(inital_game.zombies) == 1
inital_game = simulator.initialize_game(
Position(0, 0),
4,
[],
2,
[]
)
assert len(inital_game.humans) == 4
assert len(inital_game.zombies) == 2
def test_run():
np.random.seed(123)
torch.manual_seed(2)
cfg_file = open('cfg.log', "w", 1)
acc_file = open('acc.log', "w", 1)
cr_file = open('cr.log', "w", 1)
# setup target network
# so that we only do this once
sim = Simulator()
cgen = C_Generator(explore=True)
num_cfg = 100 # number of cfgs to be explored
selected_ranges = [10*i for i in range(1,10)]+[100*i for i in range(1,8)]+[782]
print('Num batches:',num_cfg,sim.num_batches)
TF = Transformer('Tiled')
# the pareto front can be restarted, need to try
for bi in range(num_cfg):
# DDPG-based generator
C_param = cgen.get()
# apply the compression param chosen by the generator
map50s,crs = sim.get_multi_point(selected_ranges, TF=TF, C_param=np.copy(C_param))
# optimize generator
cgen.optimize((map50s[-1],crs[-1]),False)
# write logs
cfg_file.write(' '.join([str(n) for n in C_param])+'\n')
acc_file.write(' '.join([str(n) for n in map50s])+'\n')
cr_file.write(' '.join([str(n) for n in crs])+'\n')
def test_testCase21():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(7992, 8304),
15,
[{"id": 0, "position_x": 757, "position_y": 3545},
{"id": 1, "position_x": 510, "position_y": 8170},
{"id": 2, "position_x": 1119, "position_y": 733},
{"id": 3, "position_x": 1416, "position_y": 7409},
{"id": 4, "position_x": 1110, "position_y": 8488},
{"id": 5, "position_x": 2118, "position_y": 1983},
{"id": 6, "position_x": 3167, "position_y": 480},
{"id": 7, "position_x": 6576, "position_y": 664},
{"id": 8, "position_x": 8704, "position_y": 1276},
{"id": 9, "position_x": 13340, "position_y": 5663},
{"id": 10, "position_x": 13808, "position_y": 4731},
{"id": 11, "position_x": 15355, "position_y": 3528},
{"id": 12, "position_x": 15495, "position_y": 5035},
{"id": 13, "position_x": 15182, "position_y": 6184},
{"id": 14, "position_x": 15564, "position_y": 7640}]
,
7,
[{"id": 0, "position_x": 3996, "position_y": 4152},
{"id": 1, "position_x": 3996, "position_y": 4844},
{"id": 2, "position_x": 3996, "position_y": 7612},
{"id": 3, "position_x": 5328, "position_y": 1384},
{"id": 4, "position_x": 7992, "position_y": 3460},
{"id": 5, "position_x": 11322, "position_y": 5536},
{"id": 6, "position_x": 11322, "position_y": 8304}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def speed_test(EXP_NAME):
np.random.seed(123)
torch.manual_seed(2)
sim = Simulator(train=False)
TF = Transformer(name=EXP_NAME)
datarange = [66,70]
eval_file = open(EXP_NAME+'_spdtest.log', "w", 1)
if EXP_NAME in ['Tiled','TiledLegacy']:
if EXP_NAME == 'Tiled':
selected_ranges = [32,42,51,58,72,197]
else:
selected_ranges = [50, 58, 69, 85, 108,170]
with open(EXP_NAME+'_MOBO_pf.log','r') as f:
for lidx,line in enumerate(f.readlines()):
if lidx not in selected_ranges:continue
print(EXP_NAME,lidx)
tmp = line.strip().split(' ')
acc,cr = float(tmp[0]),float(tmp[1])
C_param = np.array([float(n) for n in tmp[2:]])
acc1,cr1 = sim.get_one_point(datarange, TF=TF, C_param=C_param)
else:
if EXP_NAME == 'JPEG':
rate_ranges = [7,11,15,21,47,100]
elif EXP_NAME == 'JPEG2000':
rate_ranges = range(6)
elif EXP_NAME == 'WebP':
rate_ranges = [0,5,37,100]
for r in rate_ranges:
print(EXP_NAME,r)
acc,cr = sim.get_one_point(datarange, TF=TF, C_param=r)
m,s = TF.get_compression_time()
eval_file.write(f"{m:.3f} {s:.3f}\n")
def pareto_front_approx(comp_name,EXP_NAME):
cfg_file = open(comp_name+'_'+EXP_NAME+'_cfg.log', "w", 1)
acc_file = open(comp_name+'_'+EXP_NAME+'_acc.log', "w", 1)
cr_file = open(comp_name+'_'+EXP_NAME+'_cr.log', "w", 1)
# test wigh 500 iter
start = time.perf_counter()
# setup target network
# so that we only do this once
sim = Simulator(train=True)
cgen = C_Generator(name=EXP_NAME,explore=True)
num_cfg = 500 # number of cfgs to be explored
datarange = [0,100]
print(EXP_NAME,'num configs:',num_cfg, 'total batches:', sim.num_batches)
TF = Transformer(comp_name)
# the pareto front can be restarted, need to try
for bi in range(num_cfg):
print(bi)
# DDPG-based generator
C_param = cgen.get()
# apply the compression param chosen by the generator
map50,cr = sim.get_one_point(datarange=datarange, TF=TF, C_param=np.copy(C_param))
# optimize generator
cgen.optimize((map50,cr),False)
# write logs
cfg_file.write(' '.join([str(n) for n in C_param])+'\n')
acc_file.write(str(float(map50))+'\n')
cr_file.write(str(cr)+'\n')
# test wigh 500 iter
end = time.perf_counter()
with open(EXP_NAME+'_time.log','w',1) as f:
f.write(str(end-start)+'s')
def test_testCase20():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(0, 4500),
2,
[{"id": 0, "position_x": 7000, "position_y": 3500},
{"id": 1, "position_x": 0, "position_y": 500},
{"id": 2, "position_x": 7000, "position_y": 5500},
{"id": 3, "position_x": 3500, "position_y": 1000},
{"id": 4, "position_x": 9250, "position_y": 8000},
{"id": 5, "position_x": 13000, "position_y": 4500}]
,
2,
[{"id": 0, "position_x": 3600, "position_y": 3500},
{"id": 1, "position_x": 3700, "position_y": 4500},
{"id": 2, "position_x": 3400, "position_y": 6500},
{"id": 3, "position_x": 9000, "position_y": 3500},
{"id": 4, "position_x": 8990, "position_y": 4500},
{"id": 5, "position_x": 9000, "position_y": 5500},
{"id": 6, "position_x": 11000, "position_y": 4000},
{"id": 7, "position_x": 9100, "position_y": 10}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase11():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(9000, 684),
3,
[{"id": 0, "position_x": 15999, "position_y": 4500},
{"id": 1, "position_x": 8000, "position_y": 7999},
{"id": 2, "position_x": 0, "position_y": 4500}]
,
9,
[{"id": 0, "position_x": 0, "position_y": 3033},
{"id": 1, "position_x": 1500, "position_y": 6251},
{"id": 2, "position_x": 3000, "position_y": 2502},
{"id": 3, "position_x": 4500, "position_y": 6556},
{"id": 4, "position_x": 6000, "position_y": 3905},
{"id": 5, "position_x": 7500, "position_y": 5472},
{"id": 6, "position_x": 10500, "position_y": 2192},
{"id": 7, "position_x": 12000, "position_y": 6568},
{"id": 8, "position_x": 13500, "position_y": 7448}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase9():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(8000, 4500),
4,
[
{"id": 0, "position_x": 4000, "position_y": 2250},
{"id": 1, "position_x": 4000, "position_y": 6750},
{"id": 2, "position_x": 12000, "position_y": 2250},
{"id": 3, "position_x": 12000, "position_y": 6750}
],
12,
[
{"id": 0, "position_x": 4000, "position_y": 3375},
{"id": 1, "position_x": 12000, "position_y": 3375},
{"id": 2, "position_x": 4000, "position_y": 4500},
{"id": 3, "position_x": 12000, "position_y": 4500},
{"id": 4, "position_x": 4000, "position_y": 5625},
{"id": 5, "position_x": 12000, "position_y": 5625},
{"id": 6, "position_x": 6000, "position_y": 2250},
{"id": 7, "position_x": 8000, "position_y": 2250},
{"id": 8, "position_x": 10000, "position_y": 2250},
{"id": 9, "position_x": 6000, "position_y": 6750},
{"id": 10, "position_x": 8000, "position_y": 6750},
{"id": 11, "position_x": 10000, "position_y": 6750}
]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase7():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(0, 4000),
2,
[
{"id": 0, "position_x": 0, "position_y": 1000},
{"id": 1, "position_x": 0, "position_y": 8000}
],
16,
[
{"id": 0, "position_x": 5000, "position_y": 1000},
{"id": 1, "position_x": 5000, "position_y": 8000},
{"id": 2, "position_x": 7000, "position_y": 1000},
{"id": 3, "position_x": 7000, "position_y": 8000},
{"id": 4, "position_x": 9000, "position_y": 1000},
{"id": 5, "position_x": 9000, "position_y": 8000},
{"id": 6, "position_x": 11000, "position_y": 1000},
{"id": 7, "position_x": 11000, "position_y": 8000},
{"id": 8, "position_x": 13000, "position_y": 1000},
{"id": 9, "position_x": 13000, "position_y": 8000},
{"id": 10, "position_x": 14000, "position_y": 1000},
{"id": 11, "position_x": 14000, "position_y": 8000},
{"id": 12, "position_x": 14500, "position_y": 1000},
{"id": 13, "position_x": 14500, "position_y": 8000},
{"id": 14, "position_x": 15000, "position_y": 1000},
{"id": 15, "position_x": 15000, "position_y": 8000}
]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def __init__(self): super().__init__(n_var=6, n_obj=2, n_constr=0, xl=np.array([-.5]*6), xu=np.array([.5]*6)) self.sim = Simulator(train=True) self.TF = Transformer(comp_name) self.datarange = [0,100] self.cfg_file = open(comp_name+'_NSGA2_cfg.log', "w", 1) self.acc_file = open(comp_name+'_NSGA2_acc.log', "w", 1) self.cr_file = open(comp_name+'_NSGA2_cr.log', "w", 1) self.iter = 0
def objective(x):
sim = Simulator(train=True)
TF = Transformer(comp_name)
datarange = [0,100]
print('Iter:',d['iter'],x)
acc,cr = sim.get_one_point(datarange=datarange, TF=TF, C_param=x)
d['cfg_file'].write(' '.join([str(n) for n in x])+'\n')
d['acc_file'].write(str(float(acc))+'\n')
d['cr_file'].write(str(cr)+'\n')
d['iter'] += 1
return np.array([float(acc),cr])
def test_testCase10():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(8000, 0),
3,
[
{"id": 0, "position_x": 0, "position_y": 4500},
{"id": 1, "position_x": 15999, "position_y": 4500},
{"id": 2, "position_x": 8000, "position_y": 7999}
]
,
23,
[
{"id": 0, "position_x": 2000, "position_y": 1200},
{"id": 1, "position_x": 3000, "position_y": 1800},
{"id": 2, "position_x": 4000, "position_y": 2400},
{"id": 3, "position_x": 5000, "position_y": 3000},
{"id": 4, "position_x": 6000, "position_y": 3600},
{"id": 5, "position_x": 9000, "position_y": 5400},
{"id": 6, "position_x": 10000, "position_y": 6000},
{"id": 7, "position_x": 11000, "position_y": 6600},
{"id": 8, "position_x": 12000, "position_y": 7200},
{"id": 9, "position_x": 13000, "position_y": 7800},
{"id": 10, "position_x": 14000, "position_y": 8400},
{"id": 11, "position_x": 14000, "position_y": 600},
{"id": 12, "position_x": 13000, "position_y": 1200},
{"id": 13, "position_x": 12000, "position_y": 1800},
{"id": 14, "position_x": 11000, "position_y": 2400},
{"id": 15, "position_x": 10000, "position_y": 3000},
{"id": 16, "position_x": 9000, "position_y": 3600},
{"id": 17, "position_x": 6000, "position_y": 5400},
{"id": 18, "position_x": 5000, "position_y": 6000},
{"id": 19, "position_x": 4000, "position_y": 6600},
{"id": 20, "position_x": 3000, "position_y": 7200},
{"id": 21, "position_x": 2000, "position_y": 7800},
{"id": 22, "position_x": 1000, "position_y": 8400}
]
)
# pr = cProfile.Profile()
# pr.enable()
final_score, move = initial_game.get_best_move()
# pr.disable()
# s = io.StringIO()
# sortby = 'tottime'
# ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
# ps.print_stats()
# print(s.getvalue())
pytest.total_score += final_score
assert final_score > 0
def test_testCase4():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(8000, 2000),
1,
[{"id": 0, "position_x": 8000, "position_y": 4500}],
2,
[{"id": 0, "position_x": 2000, "position_y": 6500},
{"id": 1, "position_x": 14000, "position_y": 6500}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase3():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(10999, 0),
1,
[{"id": 0, "position_x": 8000, "position_y": 5500},
{"id": 1, "position_x": 4000, "position_y": 5500}],
1,
[{"id": 0, "position_x": 1250, "position_y": 5500},
{"id": 1, "position_x": 15999, "position_y": 5500}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase2():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(5000, 0),
1,
[{"id": 0, "position_x": 950, "position_y": 6000},
{"id": 1, "position_x": 8000, "position_y": 6100}],
1,
[{"id": 0, "position_x": 3100, "position_y": 7000},
{"id": 1, "position_x": 11500, "position_y": 7100}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_play_round_move():
simulator = Simulator()
inital_game = simulator.initialize_game(
Position(0, 0),
1,
[{"id": 0, "position_x": 8250, "position_y": 4500}],
1,
[{"id": 0, "position_x": 8250, "position_y": 8999}]
)
ash_target_position = Position(8250, 8999)
updated_game, round_score = simulator.play_round(inital_game.ash, inital_game.humans, inital_game.zombies, ash_target_position)
assert updated_game.zombies[0].position.is_equal(Position(8250, 8599))
assert updated_game.ash.position.is_equal(Position(675, 737))
def test_testCase5():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(7500, 2000),
2,
[{"id": 0, "position_x": 9000, "position_y": 1200},
{"id": 1, "position_x": 4000, "position_y": 6000}],
3,
[{"id": 0, "position_x": 2000, "position_y": 1500},
{"id": 1, "position_x": 13900, "position_y": 6500},
{"id": 2, "position_x": 7000, "position_y": 7500}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase17():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(3989, 3259),
4,
[{"id": 0, "position_x": 3647, "position_y": 384},
{"id": 1, "position_x": 60, "position_y": 3262},
{"id": 2, "position_x": 2391, "position_y": 1601},
{"id": 3, "position_x": 2363, "position_y": 3422}]
,
30,
[{"id": 0, "position_x": 6485, "position_y": 499},
{"id": 1, "position_x": 7822, "position_y": 446},
{"id": 2, "position_x": 9202, "position_y": 826},
{"id": 3, "position_x": 11060, "position_y": 253},
{"id": 4, "position_x": 12568, "position_y": 808},
{"id": 5, "position_x": 14148, "position_y": 650},
{"id": 6, "position_x": 6571, "position_y": 1893},
{"id": 7, "position_x": 8484, "position_y": 2013},
{"id": 8, "position_x": 9669, "position_y": 1968},
{"id": 9, "position_x": 7570, "position_y": 3338},
{"id": 10, "position_x": 9780, "position_y": 3611},
{"id": 11, "position_x": 8360, "position_y": 4767},
{"id": 12, "position_x": 9804, "position_y": 4154},
{"id": 13, "position_x": 10935, "position_y": 4977},
{"id": 14, "position_x": 12310, "position_y": 4614},
{"id": 15, "position_x": 13891, "position_y": 4302},
{"id": 16, "position_x": 913, "position_y": 5636},
{"id": 17, "position_x": 2410, "position_y": 5912},
{"id": 18, "position_x": 3952, "position_y": 6143},
{"id": 19, "position_x": 4615, "position_y": 5995},
{"id": 20, "position_x": 6568, "position_y": 6085},
{"id": 21, "position_x": 8204, "position_y": 5579},
{"id": 22, "position_x": 9049, "position_y": 5470},
{"id": 23, "position_x": 30, "position_y": 6798},
{"id": 24, "position_x": 1798, "position_y": 6682},
{"id": 25, "position_x": 3247, "position_y": 7664},
{"id": 26, "position_x": 5005, "position_y": 7319},
{"id": 27, "position_x": 6415, "position_y": 7094},
{"id": 28, "position_x": 8159, "position_y": 7447},
{"id": 29, "position_x": 9550, "position_y": 6847}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def generate_image_samples(EXP_NAME):
sim = Simulator(train=True)
TF = Transformer(name=EXP_NAME,snapshot=True)
datarange = [0,1]#sim.num_batches]
selected_lines = [92,152]
# replace pf file later
with open(EXP_NAME+'_MOBO_pf.log','r') as f:
for lcnt,line in enumerate(f.readlines()):
if lcnt not in selected_lines:
continue
tmp = line.strip().split(' ')
acc,cr = float(tmp[0]),float(tmp[1])
C_param = np.array([float(n) for n in tmp[2:]])
acc1,cr1 = sim.get_one_point(datarange, TF=TF, C_param=C_param)
print(acc1,cr1,C_param)
break
m,s = TF.get_compression_time()
print(m,s)
def test_testCase14():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(8020, 3500),
3,
[{"id": 0, "position_x": 11000, "position_y": 1000},
{"id": 1, "position_x": 11000, "position_y": 6000},
{"id": 2, "position_x": 4000, "position_y": 3500}]
,
5,
[{"id": 0, "position_x": 15000, "position_y": 1000},
{"id": 1, "position_x": 15000, "position_y": 6000},
{"id": 2, "position_x": 120, "position_y": 3500},
{"id": 3, "position_x": 0, "position_y": 4000},
{"id": 4, "position_x": 120, "position_y": 3000}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_play_round_kill_zombies():
simulator = Simulator()
inital_game = simulator.initialize_game(
Position(0, 0),
3,
[{"id": 0, "position_x": 8250, "position_y": 4500},
{"id": 1, "position_x": 8000, "position_y": 7000},
{"id": 2, "position_x": 2500, "position_y": 1000}],
2,
[{"id": 0, "position_x": 100, "position_y": 100},
{"id": 0, "position_x": 200, "position_y": 200}]
)
ash_target_position = Position(8250, 8999)
updated_game, round_score = simulator.play_round(inital_game.ash, inital_game.humans, inital_game.zombies, ash_target_position)
assert inital_game.zombies_count == 2
assert updated_game.zombies_count == 0
assert len(updated_game.zombies) == 0
assert round_score == 270
def evaluation(EXP_NAME):
np.random.seed(123)
torch.manual_seed(2)
sim = Simulator(train=False)
TF = Transformer(name=EXP_NAME)
datarange = [0,sim.num_batches]
eval_file = open(EXP_NAME+'_eval.log', "w", 1)
if EXP_NAME in ['Tiled', 'TiledLegacy','ROI']:
with open(EXP_NAME+'_MOBO_pf.log','r') as f:
for line in f.readlines()[::-1]:
tmp = line.strip().split(' ')
acc,cr = float(tmp[0]),float(tmp[1])
C_param = np.array([float(n) for n in tmp[2:]])
acc1,cr1 = sim.get_one_point(datarange, TF=TF, C_param=C_param)
eval_file.write(f"{acc1:.3f} {cr1:.3f} {acc:.3f} {cr:.3f}\n")
elif EXP_NAME == 'RAW':
acc,cr = sim.get_one_point(datarange, TF=None, C_param=None)
eval_file.write(f"{acc:.3f} {cr:.3f}\n")
elif EXP_NAME == 'Scale':
for i in range(1,33):
acc,cr = sim.get_one_point(datarange, TF=TF, C_param=i)
eval_file.write(f"{acc:.3f} {cr:.3f}\n")
else:
for i in range(101):
print(EXP_NAME,i)
acc,cr = sim.get_one_point(datarange, TF=TF, C_param=i)
eval_file.write(f"{acc:.3f} {cr:.3f}\n")
def test_testCase18():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(3989, 3259),
8,
[{"id": 0, "position_x": 647, "position_y": 384},
{"id": 1, "position_x": 60, "position_y": 1262},
{"id": 2, "position_x": 1391, "position_y": 1601},
{"id": 3, "position_x": 1363, "position_y": 422},
{"id": 4, "position_x": 15470, "position_y": 384},
{"id": 5, "position_x": 15060, "position_y": 1262},
{"id": 6, "position_x": 11391, "position_y": 1601},
{"id": 7, "position_x": 11363, "position_y": 422}]
,
18,
[{"id": 0, "position_x": 7900, "position_y": 1579},
{"id": 1, "position_x": 8500, "position_y": 2470},
{"id": 2, "position_x": 7500, "position_y": 3798},
{"id": 3, "position_x": 6500, "position_y": 4682},
{"id": 4, "position_x": 9000, "position_y": 5664},
{"id": 5, "position_x": 7500, "position_y": 6319},
{"id": 6, "position_x": 8500, "position_y": 7094},
{"id": 7, "position_x": 7800, "position_y": 8447},
{"id": 8, "position_x": 8100, "position_y": 8847},
{"id": 9, "position_x": 0, "position_y": 7000},
{"id": 10, "position_x": 1000, "position_y": 7900},
{"id": 11, "position_x": 3000, "position_y": 8500},
{"id": 12, "position_x": 5000, "position_y": 7500},
{"id": 13, "position_x": 7000, "position_y": 6500},
{"id": 14, "position_x": 9000, "position_y": 7000},
{"id": 15, "position_x": 11000, "position_y": 7500},
{"id": 16, "position_x": 13000, "position_y": 8500},
{"id": 17, "position_x": 15000, "position_y": 7800}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase15():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(3900, 5000),
4,
[{"id": 0, "position_x": 3000, "position_y": 3000},
{"id": 1, "position_x": 3000, "position_y": 5000},
{"id": 2, "position_x": 3000, "position_y": 7000},
{"id": 3, "position_x": 12000, "position_y": 3500}]
,
6,
[{"id": 0, "position_x": 10000, "position_y": 1000},
{"id": 1, "position_x": 10000, "position_y": 6000},
{"id": 2, "position_x": 15500, "position_y": 2000},
{"id": 3, "position_x": 15500, "position_y": 3600},
{"id": 4, "position_x": 15500, "position_y": 5000},
{"id": 5, "position_x": 0, "position_y": 1200}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase13():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(4920, 6810),
10,
[{"id": 0, "position_x": 50, "position_y": 4810},
{"id": 1, "position_x": 14820, "position_y": 3870},
{"id": 2, "position_x": 10869, "position_y": 8250},
{"id": 3, "position_x": 9695, "position_y": 7220},
{"id": 4, "position_x": 10160, "position_y": 5600},
{"id": 5, "position_x": 12988, "position_y": 5820},
{"id": 6, "position_x": 14892, "position_y": 5180},
{"id": 7, "position_x": 881, "position_y": 1210},
{"id": 8, "position_x": 7258, "position_y": 2130},
{"id": 9, "position_x": 13029, "position_y": 6990}]
,
14,
[{"id": 0, "position_x": 11048, "position_y": 720},
{"id": 1, "position_x": 2155, "position_y": 1650},
{"id": 2, "position_x": 9618, "position_y": 2820},
{"id": 3, "position_x": 12157, "position_y": 3770},
{"id": 4, "position_x": 2250, "position_y": 5180},
{"id": 5, "position_x": 8617, "position_y": 4890},
{"id": 6, "position_x": 7028, "position_y": 960},
{"id": 7, "position_x": 1518, "position_y": 280},
{"id": 8, "position_x": 7996, "position_y": 4080},
{"id": 9, "position_x": 13029, "position_y": 150},
{"id": 10, "position_x": 3119, "position_y": 4600},
{"id": 11, "position_x": 3339, "position_y": 4150},
{"id": 12, "position_x": 894, "position_y": 7340},
{"id": 13, "position_x": 7550, "position_y": 7550}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
def test_testCase12():
simulator = Simulator()
initial_game = simulator.initialize_game(
Position(8000, 4000),
2,
[{"id": 0, "position_x": 0, "position_y": 4000},
{"id": 1, "position_x": 15000, "position_y": 4000}]
,
9,
[{"id": 0, "position_x": 4333, "position_y": 1800},
{"id": 1, "position_x": 4333, "position_y": 3600},
{"id": 2, "position_x": 4333, "position_y": 5400},
{"id": 3, "position_x": 4333, "position_y": 7200},
{"id": 4, "position_x": 10666, "position_y": 1800},
{"id": 5, "position_x": 10666, "position_y": 3600},
{"id": 6, "position_x": 10666, "position_y": 5400},
{"id": 7, "position_x": 10666, "position_y": 7200},
{"id": 8, "position_x": 0, "position_y": 7200}]
)
final_score, move = initial_game.get_best_move()
pytest.total_score += final_score
assert final_score > 0
class MyProblem(Problem):
def __init__(self):
super().__init__(n_var=6, n_obj=2, n_constr=0, xl=np.array([-.5]*6), xu=np.array([.5]*6))
self.sim = Simulator(train=True)
self.TF = Transformer(comp_name)
self.datarange = [0,100]
self.cfg_file = open(comp_name+'_NSGA2_cfg.log', "w", 1)
self.acc_file = open(comp_name+'_NSGA2_acc.log', "w", 1)
self.cr_file = open(comp_name+'_NSGA2_cr.log', "w", 1)
self.iter = 0
def _evaluate(self, x, out, *args, **kwargs):
points = []
for row in range(x.shape[0]):
acc,cr = self.sim.get_one_point(datarange=self.datarange, TF=self.TF, C_param=x[row,:])
points += [[float(acc),cr]]
self.cfg_file.write(' '.join([str(n) for n in x[row,:]])+'\n')
self.acc_file.write(str(float(acc))+'\n')
self.cr_file.write(str(cr)+'\n')
print('Iter:',self.iter)
self.iter += 1
out["F"] = np.array(points)
def dual_train(net):
np.random.seed(123)
criterion = nn.MSELoss(reduction='sum')
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
log_file = open('training.log', "w", 1)
log_file.write('Training...\n')
# setup target network
# so that we only do this once
sim = Simulator(10)
cgen = C_Generator()
num_cfg = 1#sim.point_per_sim//batch_size
print('Num batches:',num_cfg,sim.point_per_sim)
for epoch in range(10):
running_loss = 0.0
TF = Transformer('Tiled')
# the pareto front can be restarted, need to try
for bi in range(num_cfg):
inputs,labels = [],[]
# DDPG-based generator
C_param = cgen.get()
# batch result of mAP and compression ratio
batch_acc, batch_cr = [],[]
for k in range(batch_size):
di = bi*batch_size + k # data index
# start counting the compressed size
TF.reset()
# apply the compression param chosen by the generator
fetch_start = time.perf_counter()
# the function to get results from cloud model
sim_result = sim.get_one_point(index=di, TF=TF, C_param=np.copy(C_param))
fetch_end = time.perf_counter()
# get the compression ratio
cr = TF.get_compression_ratio()
batch_acc += [sim_result]
batch_cr += [cr]
print_str = str(di)+str(C_param)+'\t'+str(sim_result)+'\t'+str(cr)+'\t'+str(fetch_end-fetch_start)
print(print_str)
log_file.write(print_str+'\n')
inputs.append(C_param)
labels.append(sim_result) # accuracy of IoU=0.5
# optimize generator
cgen.optimize((np.mean(batch_acc),np.mean(batch_cr)),False)
log_file.write(print_str+'\n')
# transform to tensor
inputs = torch.FloatTensor(inputs)#.cuda()
labels = torch.FloatTensor(labels)#.cuda()
# zero gradient
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
val_loss = abs(torch.mean(labels.cpu()-outputs.cpu()))
print_str = '{:d}, {:d}, loss {:.6f}, val loss {:.6f}'.format(epoch + 1, bi + 1, loss.item(), val_loss)
print(print_str)
log_file.write(print_str + '\n')
print_str = str(cgen.paretoFront.data.keys())
print(print_str)
cgen.optimize(None,True)
torch.save(net.state_dict(), PATH)