def simulatorOrdersPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting simulator benchmark test " + str(testNum) +
" on orders", BLUE)
orderList.orders = []
plant.machines = machines[:]
times = [0]
for i in range(1, len(orders) + 1):
pprint("PERF Number of orders = " + str(i), BLUE)
orderList.orders = orders[:i]
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(plant)
simulator.simulate(schedule)
t = time() - t
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "simulator-orders-" + str(testNum)) + FORMAT,
times, 800, 800, (255, 255, 255), 5, True, True, True, None, None,
None, None)
def setup_network(self):
config = BertConfig.from_json_file(cfg.CONFIG_FILE)
if cfg.TRAIN.FROM_PRETRAINED:
model = BaseBertPreTraining.from_pretrained(
cfg.TRAIN.FROM_PRETRAINED, config)
else:
model = BaseBertPreTraining(config)
model.to(self.device)
if args.local_rank != -1:
self.model = torch.nn.parallel.DistributedDataParallel(
model,
find_unused_parameters=True,
device_ids=[self.args.local_rank],
output_device=self.args.local_rank,
broadcast_buffers=False)
elif self.n_gpu > 1:
self.model = torch.nn.DataParallel(model)
else:
self.model = model
epoch_steps = len(self.train_dataset_loader)
n_steps = epoch_steps * cfg.SOLVER.NUM_TRAIN_EPOCHS
self.optim = Optimizer(self.model,
epoch_steps=epoch_steps,
n_steps=n_steps)
def bench(self):
recipes = []
for o in self.orderList.orders:
recipes.append(o.recipe.recipe[:])
o.recipe.recipe = []
machines = self.plant.machines[:]
self.plant.machines = []
i = self.startValue
while i <= len(machines):
pprint("PERF Number of machines = " + str(i), BLUE)
self.plant.machines = machines[:i]
for j, o in enumerate(self.orderList.orders):
o.recipe.recipe = recipes[j][:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 10
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def setup_network(self):
model = models.create(cfg.MODEL.TYPE)
self.trainer = model.cuda()
self.checkpointer = CaptionCheckpointer(
self.trainer, os.path.join(cfg.ROOT_DIR, "snapshot"))
if self.args.resume > 0:
self.checkpointer.load(
self.snapshot_path("caption_model", self.args.resume))
self.predictor = models.create(cfg.MODEL.TYPE).cuda()
self.predictor.load_state_dict(self.trainer.state_dict())
self.optim = Optimizer(self.trainer)
def mix(self):
if len(self.loaded_songs) == 0:
self.message.set("No loaded songs...")
return
first_goal = MixGoal(0.0, 0.0, 0.0, 0.0, 1)
goals = [first_goal]
self.analyze_songs()
print("Chosen Style: {}".format(self.style))
print("Maximum Length: {}".format(self.max_length))
print("First Song: {}".format(self.first_song))
dj = Optimizer(self.loaded_songs, goals, Style_Lib[self.style].value,
self.max_length, self.first_song)
mix = dj.generate_mixtape()
self.log_message(mix)
comp = composer.composer_parser(mix)
comp.compose()
def main_loop(self):
#optimizer = T4l_optimizer(self.optimizer_config)
optimizer = Optimizer(self.optimizer_config)
broker = Broker(self.broker_config, optimizer)
self.print_info()
broker.schedule_tasks(self.tasks)
def main_loop(self):
optimizer = Optimizer(self.optimizer_config)
broker = LocalBroker(self.broker_config, optimizer)
self.print_info()
broker.schedule_tasks(self.tasks)
if broker.sys_info is not None:
broker.sys_info.stop_server.set()
def bench(self):
orders = self.orderList.orders[:]
self.orderList.orders = []
i = self.startValue
while i <= len(orders):
pprint("PERF Number of orders = " + str(i), BLUE)
self.orderList.orders = orders[:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(self.plant)
simulator.simulate(schedule)
t = time() - t
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def test_warm_start(self):
params = Parameter()
params.set("wheel_base", 2.7)
params.set("dt", 0.2)
params.set("function_tolerance", 1e-6)
params.set("max_num_iterations", 1000)
initial_state = np.array([[0., 0., 0., 10.], [2., 0., 0., 10.],
[4., 0., 0., 10.], [6., 0., 0., 10.]])
opt_vec = np.array(
[[0., 0.], [-0.00581466, -0.02969236], [-0.0100573, -0.05063586],
[-0.01290205, -0.06336555], [-0.0145111, -0.06958468],
[-0.01504364, -0.07147759], [-0.01466189, -0.0712203],
[-0.01353391, -0.07066254], [-0.01183398, -0.0711591],
[-0.00974178, -0.0735261], [-0.00744058, -0.07806701],
[-0.00509113, -0.08413195], [-0.0028139, -0.09089476],
[-0.00069383, -0.0976187], [0.00121458, -0.10373539],
[0.00288213, -0.10887319], [0.00430011, -0.11284776],
[0.00547573, -0.11562908], [0.00642782, -0.11729807],
[0.00718276, -0.11800159], [0.0077706, -0.11791354],
[0.00821974, -0.11720583], [0.00855625, -0.11603267],
[0.0088038, -0.11452536], [0.0089836, -0.11278997],
[0.00911419, -0.11090794], [0.00921137, -0.10893774],
[0.00928801, -0.10691821], [0.00935393, -0.10487191],
[0., 0.00059502]])
ref_line = np.array([[0., 4.], [1000., 4.]])
obstacle_outline0 = np.array([[14., 1.7], [22., 1.7], [22., 4.7],
[14., 4.7], [14., 1.7]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceLineCost(params, 100.)
ref_cost.SetReferenceLine(ref_line)
jerk_cost = JerkCost(params, 10000.)
outline = ObjectOutline()
obstacle_outline1 = obstacle_outline0 + np.array([[30.0, -2.]])
outline.Add(obstacle_outline0, 0.)
outline.Add(obstacle_outline1, 6.)
object_cost = StaticObjectCost(params, 2.5, 10000.)
object_cost.AddObjectOutline(outline)
input_cost = InputCost(params, 10.)
input_cost.SetLowerBound(np.array([[-0.2, -1.0]]))
input_cost.SetUpperBound(np.array([[0.2, 1.0]]))
speed_cost = SpeedCost(params, 10.)
speed_cost.SetDesiredSpeed(10.)
# optimization problem
functor = opt.AddFastSingleTrackFunctor(
initial_state, params,
[jerk_cost, ref_cost, input_cost, object_cost, speed_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
def optimize(self):
simulator = Simulator(self.plant)
evaluator = Evaluator.fromXmlFile(self.configFilename, self.plant)
optimizer = Optimizer.fromXmlFile(self.configFilename, self.plant,
self.orderList, simulator, evaluator)
result = optimizer.run()
best = bestSolution(result)
best.unNormalize(self.normValue)
print best
def bench(self):
val = 4
i = self.startValue
while i <= 6:
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in self.orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(self.plant)
simulator.simulate(schedule)
t = time() - t
self.addCairoPlotTime(t)
self.addGnuPlotTime((i + 1) * val, t)
i += 1
def optimizerMachinesPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting optimizer benchmark test " + str(testNum) +
" on machines", BLUE)
orderList.orders = orders[:]
recipes = []
for o in orderList.orders:
recipes.append(o.recipe.recipe[:])
o.recipe.recipe = []
plant.machines = []
times = [0]
for i in range(1, len(machines) + 1):
pprint("PERF Number of machines = " + str(i), BLUE)
plant.machines = machines[:i]
for j, o in enumerate(orderList.orders):
o.recipe.recipe = recipes[j][:i]
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 10
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "optimizer-machines-" + str(testNum)) + FORMAT,
times, 800, 800, (255, 255, 255), 5, True, True, True, None, None,
None, None)
def bench(self):
i = 10
while i <= 200:
pprint("PERF Mutation Range = " + str(i), BLUE)
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = i
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 20
def setup_network(self):
model = models.create(cfg.MODEL.TYPE)
if self.distributed:
# this should be removed if we update BatchNorm stats
self.model = torch.nn.parallel.DistributedDataParallel(
model.to(self.device),
device_ids=[self.args.local_rank],
output_device=self.args.local_rank,
broadcast_buffers=False)
else:
self.model = torch.nn.DataParallel(model).cuda()
if self.args.resume > 0:
self.model.load_state_dict(
torch.load(self.snapshot_path("caption_model",
self.args.resume),
map_location=lambda storage, loc: storage))
self.optim = Optimizer(self.model)
self.xe_criterion = losses.create(cfg.LOSSES.XE_TYPE).cuda()
self.rl_criterion = losses.create(cfg.LOSSES.RL_TYPE).cuda()
def simulatorLargeValuesPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting simulator benchmark test " + str(testNum) +
" with large values", BLUE)
orderList.orders = orders[:]
plant.machines = machines[:]
times = [0]
val = 4
for i in range(1, 7):
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(plant)
simulator.simulate(schedule)
t = time() - t
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "simulator-largevalues-" + str(testNum)) +
FORMAT, times, 800, 800, (255, 255, 255), 5, True, True, True, None,
None, None, None)
def optimize(self):
simulator = Simulator(self.plant)
evaluator = Evaluator.fromXmlFile(self.configFilename, self.plant,
self.normValue)
optimizer = Optimizer.fromXmlFile(self.configFilename, self.plant,
self.orderList, simulator, evaluator)
result = optimizer.run()
best = bestSolution(result)
best.unNormalize(self.normValue)
# for item in best:
# if "kettle" in item[1]:
# print item[0], item[2]
print best
def optimizerLargeValuesPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting optimizer benchmark test " + str(testNum) +
" with large values", BLUE)
orderList.orders = orders[:6]
plant.machines = machines[:]
times = [0]
val = 2
for i in range(1, 6):
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
optimizer.populationSize = 5
optimizer.iterations = 5
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 500
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "optimizer-largevalues-" + str(testNum)) +
FORMAT, times, 800, 800, (255, 255, 255), 5, True, True, True, None,
None, None, None)
def bench(self):
recipes = []
for o in self.orderList.orders:
recipes.append(o.recipe.recipe[:])
o.recipe.recipe = []
machines = self.plant.machines[:]
self.plant.machines = []
i = self.startValue
while i <= len(machines):
pprint("PERF Number of machines = " + str(i), BLUE)
self.plant.machines = machines[:i]
for j, o in enumerate(self.orderList.orders):
o.recipe.recipe = recipes[j][:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(self.plant)
simulator.simulate(schedule)
t = time() - t
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def bench(self):
orders = self.orderList.orders[:]
self.orderList.orders = []
i = self.startValue
while i <= len(orders):
pprint("PERF Number of orders = " + str(i), BLUE)
self.orderList.orders = orders[:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 10
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def run(self):
simulator = Simulator(self.plant)
evaluator = Evaluator.fromXmlFile(self.configFilename, self.plant)
optimizer = Optimizer.fromXmlFile(self.configFilename, self.plant,
self.orderList, simulator, evaluator)
scheduler = Scheduler(self.plant, self.orderList)
result = scheduler.start()
if result != None:
solutions = parseSolutions(result, self.plant, self.orderList)
for s in solutions:
s.loadStartTimes(self.plant)
result = optimizer.run(solutions)
best = bestSolution(result)
best.unNormalize(self.normValue)
print best
def bench(self):
val = 2
i = self.startValue
while i < 10:
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in self.orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 500
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime((i + 1) * val, t)
i += 1
def optimizerMutationRangePerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting optimizer benchmark test " + str(testNum) +
" on mutation range", BLUE)
orderList.orders = orders[:]
plant.machines = machines[:]
times = [0]
for i in range(10, 200, 20):
pprint("PERF Selection rate = " + str(i), BLUE)
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
optimizer.populationSize = 5
optimizer.iterations = 5
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = i
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "optimizer-mutationrange-" + str(testNum)) +
FORMAT, times, 800, 800, (255, 255, 255), 5, True, True, True, None,
None, None, None)
from multiprocessing import Queue, Process, Lock, Manager
from Exchange import MyExchange
from optimizer.optimizer import Optimizer
optimizer = Optimizer(symbol='ETH/USD', recentPerformanceTimeDuration=5)
if __name__ == "__main__":
optimizer.screen_for_best_parameter()
def test_single_track_optimizer(self):
params = Parameter()
params.set("wheel_base", 2.7)
params.set("dt", 0.2)
params.set("function_tolerance", 1e-8)
params.set("max_num_iterations", 1000)
initial_state = np.array([[0., 0., 0., 10.],
[2., 0., 0., 10.],
[4., 0., 0., 10.],
[6., 0., 0., 10.]])
opt_vec = np.array([[ 0. , 0. ],
[-0.00581466, -0.02969236],
[-0.0100573 , -0.05063586],
[-0.01290205, -0.06336555],
[-0.0145111 , -0.06958468],
[-0.01504364, -0.07147759],
[-0.01466189, -0.0712203 ],
[-0.01353391, -0.07066254],
[-0.01183398, -0.0711591 ],
[-0.00974178, -0.0735261 ],
[-0.00744058, -0.07806701],
[-0.00509113, -0.08413195],
[-0.0028139 , -0.09089476],
[-0.00069383, -0.0976187 ],
[ 0.00121458, -0.10373539],
[ 0.00288213, -0.10887319],
[ 0.00430011, -0.11284776],
[ 0.00547573, -0.11562908],
[ 0.00642782, -0.11729807],
[ 0.00718276, -0.11800159],
[ 0.0077706 , -0.11791354],
[ 0.00821974, -0.11720583],
[ 0.00855625, -0.11603267],
[ 0.0088038 , -0.11452536],
[ 0.0089836 , -0.11278997],
[ 0.00911419, -0.11090794],
[ 0.00921137, -0.10893774],
[ 0.00928801, -0.10691821],
[ 0.00935393, -0.10487191],
[ 0. , 0.00059502]])
ref_line = np.array([[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 2.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 4.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 6.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 8.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 9.99939997e+00, -4.30463875e-03, -4.30592393e-03,
9.99406153e+00],
[ 1.19971306e+01, -2.03405218e-02, -1.17478308e-02,
9.98393436e+00],
[ 1.39923704e+01, -5.32967584e-02, -2.12840277e-02,
9.97126125e+00],
[ 1.59845144e+01, -1.06379431e-01, -3.19953707e-02,
9.95734431e+00],
[ 1.79731414e+01, -1.81067097e-01, -4.30841450e-02,
9.94304879e+00],
[ 1.99579824e+01, -2.77367780e-01, -5.38759931e-02,
9.92880473e+00],
[ 2.19388867e+01, -3.94078028e-01, -6.38232626e-02,
9.91467222e+00],
[ 2.39157899e+01, -5.29044532e-01, -7.25085470e-02,
9.90044040e+00],
[ 2.58886800e+01, -6.79428615e-01, -7.96477572e-02,
9.88573518e+00],
[ 2.78575651e+01, -8.41973335e-01, -8.50921200e-02,
9.87012178e+00],
[ 2.98224543e+01, -1.01325561e+00, -8.88112060e-02,
9.85329539e+00],
[ 3.17833555e+01, -1.18989285e+00, -9.08631109e-02,
9.83511644e+00],
[ 3.37402750e+01, -1.36869381e+00, -9.13680809e-02,
9.81559270e+00],
[ 3.56932177e+01, -1.54676003e+00, -9.04859136e-02,
9.79484562e+00],
[ 3.76421873e+01, -1.72154515e+00, -8.83971179e-02,
9.77307098e+00],
[ 3.95871864e+01, -1.89087947e+00, -8.52877092e-02,
9.75050143e+00],
[ 4.15282170e+01, -2.05296693e+00, -8.13374631e-02,
9.72737561e+00],
[ 4.34652803e+01, -2.20636175e+00, -7.67114427e-02,
9.70391600e+00],
[ 4.53983772e+01, -2.34993117e+00, -7.15546057e-02,
9.68031568e+00],
[ 4.73275086e+01, -2.48281085e+00, -6.59892879e-02,
9.65673297e+00],
[ 4.92526750e+01, -2.60435938e+00, -6.01165980e-02,
9.63329181e+00],
[ 5.11738771e+01, -2.71411662e+00, -5.40182610e-02,
9.61008527e+00],
[ 5.30911154e+01, -2.81176608e+00, -4.77585100e-02,
9.58718020e+00],
[ 5.50043903e+01, -2.89710162e+00, -4.13860399e-02,
9.56462221e+00],
[ 5.69137022e+01, -2.96999832e+00, -3.49360318e-02,
9.54244062e+00],
[ 5.88190514e+01, -3.03038789e+00, -2.84322483e-02,
9.52065307e+00],
[ 6.07204383e+01, -3.07823825e+00, -2.18892006e-02,
9.49926943e+00],
[ 6.26178630e+01, -3.11353783e+00, -1.53143860e-02,
9.47829505e+00],
[ 6.45133116e+01, -3.14256773e+00, -1.53143860e-02,
9.47841405e+00]])
obstacle_outline0 = np.array([[14., 1.7],
[22., 1.7],
[22., 4.7],
[14., 4.7],
[14., 1.7]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceCost(params, 100.)
ref_cost.SetReference(ref_line)
jerk_cost = JerkCost(params, 10000.)
outline = ObjectOutline()
obstacle_outline1 = obstacle_outline0 + np.array([[30.0, -3.]])
outline.Add(obstacle_outline0, 0.)
outline.Add(obstacle_outline1, 6.)
object_cost = StaticObjectCost(params, 2.5, 10000.)
object_cost.AddObjectOutline(outline)
input_cost = InputCost(params, 10.)
input_cost.SetLowerBound(np.array([[-0.2, -1.0]]))
input_cost.SetUpperBound(np.array([[0.2, 1.0]]))
# optimization problem
functor = opt.AddFastSingleTrackFunctor(initial_state,
params,
[jerk_cost,
object_cost,
input_cost,
ref_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
inputs = opt.Result()
trajectory = GenerateTrajectorySingleTrack(initial_state, inputs, params)
cmap, norm = GetColorMap(0., 6.)
fig, ax = plt.subplots(nrows=1, ncols=2)
for t in np.arange(0, 6, 0.5):
poly = outline.Query(t)
ax[0].plot(poly[:, 0], poly[:, 1], color=cmap(norm(t)))
colorbar_axis = ax[0].plot(trajectory[:, 0], trajectory[:, 1])
for i, pt in enumerate(trajectory[:]):
ax[0].plot(pt[0], pt[1], color=cmap(norm(params.set("dt", 0.2)*i)), marker='o')
ax[0].axis("equal")
ax[1].plot(inputs[:-1, 0], label="Steering angle", marker='o')
ax[1].plot(inputs[:-1, 1], label="Acceleration", marker='o', color="green")
ax[1].legend()
plt.show()
print(repr(inputs))
print(repr(trajectory))
class Trainer(object):
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
if cfg.SEED > 0:
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
self.num_gpus = torch.cuda.device_count()
self.distributed = self.num_gpus > 1
if self.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
self.device = torch.device("cuda")
self.rl_stage = False
self.setup_logging()
self.setup_dataset()
self.setup_network()
self.val_evaler = Evaler(
eval_ids = cfg.DATA_LOADER.VAL_ID,
gv_feat = cfg.DATA_LOADER.VAL_GV_FEAT,
att_feats = cfg.DATA_LOADER.VAL_ATT_FEATS,
eval_annfile = cfg.INFERENCE.VAL_ANNFILE
)
self.test_evaler = Evaler(
eval_ids = cfg.DATA_LOADER.TEST_ID,
gv_feat = cfg.DATA_LOADER.TEST_GV_FEAT,
att_feats = cfg.DATA_LOADER.TEST_ATT_FEATS,
eval_annfile = cfg.INFERENCE.TEST_ANNFILE
)
self.scorer = Scorer()
def setup_logging(self):
self.logger = logging.getLogger(cfg.LOGGER_NAME)
self.logger.setLevel(logging.INFO)
if self.distributed and dist.get_rank() > 0:
return
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.INFO)
formatter = logging.Formatter("[%(levelname)s: %(asctime)s] %(message)s")
ch.setFormatter(formatter)
self.logger.addHandler(ch)
if not os.path.exists(cfg.ROOT_DIR):
os.makedirs(cfg.ROOT_DIR)
fh = logging.FileHandler(os.path.join(cfg.ROOT_DIR, cfg.LOGGER_NAME + '.txt'))
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
self.logger.addHandler(fh)
self.logger.info('Training with config:')
self.logger.info(pprint.pformat(cfg))
def setup_network(self):
model = models.create(cfg.MODEL.TYPE)
if self.distributed:
# this should be removed if we update BatchNorm stats
self.model = torch.nn.parallel.DistributedDataParallel(
model.to(self.device),
device_ids = [self.args.local_rank],
output_device = self.args.local_rank,
broadcast_buffers = False
)
else:
self.model = torch.nn.DataParallel(model).cuda()
if self.args.resume > 0:
self.model.load_state_dict(
torch.load(self.snapshot_path("caption_model", self.args.resume),
map_location=lambda storage, loc: storage)
)
self.optim = Optimizer(self.model)
self.xe_criterion = losses.create(cfg.LOSSES.XE_TYPE).cuda()
self.rl_criterion = losses.create(cfg.LOSSES.RL_TYPE).cuda()
def setup_dataset(self):
self.coco_set = datasets.coco_dataset.CocoDataset(
image_ids_path = cfg.DATA_LOADER.TRAIN_ID,
input_seq = cfg.DATA_LOADER.INPUT_SEQ_PATH,
target_seq = cfg.DATA_LOADER.TARGET_SEQ_PATH,
gv_feat_path = cfg.DATA_LOADER.TRAIN_GV_FEAT,
att_feats_folder = cfg.DATA_LOADER.TRAIN_ATT_FEATS,
seq_per_img = cfg.DATA_LOADER.SEQ_PER_IMG,
max_feat_num = cfg.DATA_LOADER.MAX_FEAT
)
def setup_loader(self, epoch):
self.training_loader = datasets.data_loader.load_train(
self.distributed, epoch, self.coco_set)
def eval(self, epoch):
if (epoch + 1) % cfg.SOLVER.TEST_INTERVAL != 0:
return None
if self.distributed and dist.get_rank() > 0:
return None
val_res = self.val_evaler(self.model, 'val_' + str(epoch + 1))
self.logger.info('######## Epoch (VAL)' + str(epoch + 1) + ' ########')
self.logger.info(str(val_res))
test_res = self.test_evaler(self.model,'test_' + str(epoch + 1))
self.logger.info('######## Epoch (TEST)' + str(epoch + 1) + ' ########')
self.logger.info(str(test_res))
val = 0
for score_type, weight in zip(cfg.SCORER.TYPES, cfg.SCORER.WEIGHTS):
val -= val_res[score_type] * weight
return val
def snapshot_path(self, name, epoch):
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
return os.path.join(snapshot_folder, name + "_" + str(epoch) + ".pth")
def save_model(self, epoch):
if (epoch + 1) % cfg.SOLVER.SNAPSHOT_ITERS != 0:
return
if self.distributed and dist.get_rank() > 0:
return
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
if not os.path.exists(snapshot_folder):
os.mkdir(snapshot_folder)
torch.save(self.model.state_dict(), self.snapshot_path("caption_model", epoch+1))
def make_kwargs(self, indices, input_seq, target_seq, gv_feat, att_feats, att_mask):
seq_mask = (input_seq > 0).type(torch.cuda.LongTensor)
seq_mask[:,0] += 1
seq_mask_sum = seq_mask.sum(-1)
max_len = int(seq_mask_sum.max())
input_seq = input_seq[:, 0:max_len].contiguous()
target_seq = target_seq[:, 0:max_len].contiguous()
kwargs = {
cfg.PARAM.INDICES: indices,
cfg.PARAM.INPUT_SENT: input_seq,
cfg.PARAM.TARGET_SENT: target_seq,
cfg.PARAM.GLOBAL_FEAT: gv_feat,
cfg.PARAM.ATT_FEATS: att_feats,
cfg.PARAM.ATT_FEATS_MASK: att_mask
}
return kwargs
def scheduled_sampling(self, epoch):
if epoch > cfg.TRAIN.SCHEDULED_SAMPLING.START:
frac = (epoch - cfg.TRAIN.SCHEDULED_SAMPLING.START) // cfg.TRAIN.SCHEDULED_SAMPLING.INC_EVERY
ss_prob = min(cfg.TRAIN.SCHEDULED_SAMPLING.INC_PROB * frac, cfg.TRAIN.SCHEDULED_SAMPLING.MAX_PROB)
self.model.module.ss_prob = ss_prob
def display(self, iteration, data_time, batch_time, losses, loss_info):
if iteration % cfg.SOLVER.DISPLAY != 0:
return
if self.distributed and dist.get_rank() > 0:
return
info_str = ' (DataTime/BatchTime: {:.3}/{:.3}) losses = {:.5}'.format(data_time.avg, batch_time.avg, losses.avg)
self.logger.info('Iteration ' + str(iteration) + info_str +', lr = ' + str(self.optim.get_lr()))
for name in sorted(loss_info):
self.logger.info(' ' + name + ' = ' + str(loss_info[name]))
data_time.reset()
batch_time.reset()
losses.reset()
def forward(self, kwargs):
if self.rl_stage == False:
logit = self.model(**kwargs)
loss, loss_info = self.xe_criterion(logit, kwargs[cfg.PARAM.TARGET_SENT])
else:
ids = kwargs[cfg.PARAM.INDICES]
gv_feat = kwargs[cfg.PARAM.GLOBAL_FEAT]
att_feats = kwargs[cfg.PARAM.ATT_FEATS]
att_mask = kwargs[cfg.PARAM.ATT_FEATS_MASK]
# max
kwargs['BEAM_SIZE'] = 1
kwargs['GREEDY_DECODE'] = True
kwargs[cfg.PARAM.GLOBAL_FEAT] = gv_feat
kwargs[cfg.PARAM.ATT_FEATS] = att_feats
kwargs[cfg.PARAM.ATT_FEATS_MASK] = att_mask
self.model.eval()
with torch.no_grad():
seq_max, logP_max = self.model.module.decode(**kwargs)
self.model.train()
rewards_max, rewards_info_max = self.scorer(ids, seq_max.data.cpu().numpy().tolist())
rewards_max = utils.expand_numpy(rewards_max)
ids = utils.expand_numpy(ids)
gv_feat = utils.expand_tensor(gv_feat, cfg.DATA_LOADER.SEQ_PER_IMG)
att_feats = utils.expand_tensor(att_feats, cfg.DATA_LOADER.SEQ_PER_IMG)
att_mask = utils.expand_tensor(att_mask, cfg.DATA_LOADER.SEQ_PER_IMG)
# sample
kwargs['BEAM_SIZE'] = 1
kwargs['GREEDY_DECODE'] = False
kwargs[cfg.PARAM.GLOBAL_FEAT] = gv_feat
kwargs[cfg.PARAM.ATT_FEATS] = att_feats
kwargs[cfg.PARAM.ATT_FEATS_MASK] = att_mask
seq_sample, logP_sample = self.model.module.decode(**kwargs)
rewards_sample, rewards_info_sample = self.scorer(ids, seq_sample.data.cpu().numpy().tolist())
rewards = rewards_sample - rewards_max
rewards = torch.from_numpy(rewards).float().cuda()
loss = self.rl_criterion(seq_sample, logP_sample, rewards)
loss_info = {}
for key in rewards_info_sample:
loss_info[key + '_sample'] = rewards_info_sample[key]
for key in rewards_info_max:
loss_info[key + '_max'] = rewards_info_max[key]
return loss, loss_info
def train(self):
self.model.train()
self.optim.zero_grad()
iteration = 0
for epoch in range(cfg.SOLVER.MAX_EPOCH):
if epoch == cfg.TRAIN.REINFORCEMENT.START:
self.rl_stage = True
self.setup_loader(epoch)
start = time.time()
data_time = AverageMeter()
batch_time = AverageMeter()
losses = AverageMeter()
for _, (indices, input_seq, target_seq, gv_feat, att_feats, att_mask) in enumerate(self.training_loader):
data_time.update(time.time() - start)
input_seq = input_seq.cuda()
target_seq = target_seq.cuda()
gv_feat = gv_feat.cuda()
att_feats = att_feats.cuda()
att_mask = att_mask.cuda()
kwargs = self.make_kwargs(indices, input_seq, target_seq, gv_feat, att_feats, att_mask)
loss, loss_info = self.forward(kwargs)
loss.backward()
utils.clip_gradient(self.optim.optimizer, self.model,
cfg.SOLVER.GRAD_CLIP_TYPE, cfg.SOLVER.GRAD_CLIP)
self.optim.step()
self.optim.zero_grad()
self.optim.scheduler_step('Iter')
batch_time.update(time.time() - start)
start = time.time()
losses.update(loss.item())
self.display(iteration, data_time, batch_time, losses, loss_info)
iteration += 1
if self.distributed:
dist.barrier()
self.save_model(epoch)
val = self.eval(epoch)
self.optim.scheduler_step('Epoch', val)
self.scheduled_sampling(epoch)
if self.distributed:
dist.barrier()
class Trainer(object):
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
self.setup_gpu()
self.setup_logging()
self.setup_loader()
self.setup_network()
def setup_gpu(self):
if args.local_rank == -1:
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.n_gpu = torch.cuda.device_count()
self.distributed = False
else:
torch.cuda.set_device(args.local_rank)
self.device = torch.device("cuda", args.local_rank)
self.n_gpu = 1
torch.distributed.init_process_group(
backend="nccl",
init_method="env://",
timeout=timedelta(minutes=180))
self.distributed = True
print("device: {} n_gpu: {}, distributed training: {}".format(
self.device, self.n_gpu, bool(args.local_rank != -1)))
if cfg.SEED > 0:
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
def setup_logging(self):
self.logger = logging.getLogger(cfg.LOGGER_NAME)
self.logger.setLevel(logging.INFO)
if self.distributed and dist.get_rank() > 0:
return
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
"[%(levelname)s: %(asctime)s] %(message)s")
ch.setFormatter(formatter)
self.logger.addHandler(ch)
fh = logging.FileHandler(
os.path.join(cfg.ROOT_DIR, cfg.LOGGER_NAME + '.txt'))
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
self.logger.addHandler(fh)
self.logger.info('Training with config:')
self.logger.info(pprint.pformat(cfg))
def setup_loader(self):
self.tokenizer = BertTokenizer.from_pretrained(
cfg.TRAIN.BERT_MODEL, do_lower_case=cfg.TRAIN.DO_LOWER_CASE)
self.train_dataset_loader, self.train_dataset, self.train_sampler = load_concap_train(
args.local_rank, self.tokenizer)
def setup_network(self):
config = BertConfig.from_json_file(cfg.CONFIG_FILE)
if cfg.TRAIN.FROM_PRETRAINED:
model = BaseBertPreTraining.from_pretrained(
cfg.TRAIN.FROM_PRETRAINED, config)
else:
model = BaseBertPreTraining(config)
model.to(self.device)
if args.local_rank != -1:
self.model = torch.nn.parallel.DistributedDataParallel(
model,
find_unused_parameters=True,
device_ids=[self.args.local_rank],
output_device=self.args.local_rank,
broadcast_buffers=False)
elif self.n_gpu > 1:
self.model = torch.nn.DataParallel(model)
else:
self.model = model
epoch_steps = len(self.train_dataset_loader)
n_steps = epoch_steps * cfg.SOLVER.NUM_TRAIN_EPOCHS
self.optim = Optimizer(self.model,
epoch_steps=epoch_steps,
n_steps=n_steps)
def display(self, iteration, batch_time, losses, loss_info):
if iteration % cfg.SOLVER.DISPLAY != 0:
return
if self.distributed and dist.get_rank() > 0:
return
info_str = ' (BatchTime: {:.3}) losses = {:.5}'.format(
batch_time.avg, losses.avg)
self.logger.info('Iteration ' + str(iteration) + info_str + ', lr = ' +
str(self.optim.get_lr()))
for name in sorted(loss_info):
self.logger.info(' ' + name + ' = ' + str(loss_info[name]))
batch_time.reset()
losses.reset()
def snapshot_path(self, name, epoch):
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
return os.path.join(snapshot_folder, name + "_" + str(epoch) + ".bin")
def save_model(self, epoch):
if (epoch + 1) % cfg.SOLVER.SNAPSHOT_ITERS != 0:
return
if self.distributed and dist.get_rank() > 0:
return
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
if not os.path.exists(snapshot_folder):
os.mkdir(snapshot_folder)
model_to_save = (self.model.module
if hasattr(self.model, "module") else self.model)
torch.save(model_to_save.state_dict(),
self.snapshot_path("pytorch_model", epoch + 1))
def train(self):
max_num_iter = len(self.train_dataset_loader)
for epochId in range(int(cfg.SOLVER.NUM_TRAIN_EPOCHS)):
if self.train_sampler is not None:
self.train_sampler.set_epoch(epochId)
self.model.train()
start = time.time()
batch_time = AverageMeter()
for step, batch in enumerate(self.train_dataset_loader):
iterId = step + (epochId * max_num_iter)
self.optim.zero_grad()
batch = tuple(
t.cuda(device=self.device, non_blocking=True)
for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, image_feat, \
image_loc, imgfeat_cls_prob, imgfeat_label, imgfeat_mask = (batch)
# TODO Train model
self.save_model(epochId)
if self.distributed:
dist.barrier()
logging.info('Using default depth of 1')
labels_true = change_label_depth(b.labels, depth=1)
else:
labels_true = change_label_depth(b.labels, depth=args.ldepth)
# Choose appropriate clusterer and initialize.
if args.algorithm == 'km':
c = KMClust(v, labels_true)
elif args.algorithm == 'lda':
c = LDAClust(v, labels_true)
else:
logging.error('Clusterer name not recognized. Exiting...')
sys.exit()
logging.info('Clustering...')
o = Optimizer()
o.optimize(c, args.n_clusters, one_run=(not args.optimize))
o.latest_clusterer.export_csv_doc_topic()
file_name = o.latest_clusterer.export_csv_topic_word()
# Generate the heatmap for the last clusterer
generate_heatmap(file_name)
# Plot the Purities and Topic categories so far
if args.optimize:
o.plot_current()
if args.search:
# Compute wordclouds for the latest clusterer
og = OfficialGoogler()
def test_triple_int_optimizer(self):
params = Parameter()
params.set("dt", 0.2)
params.set("function_tolerance", 1e-6)
params.set("max_num_iterations", 1000)
initial_state = np.array([[0., 10., 0., 0., 10., 0., 0., 1., 0.],
[2., 10., 0., 2., 10., 0., 0.2, 1., 0.],
[4., 10., 0., 4., 10., 0., 0.4, 1., 0.],
[6., 10., 0., 6., 10., 0., 0.6, 1., 0.]])
opt_vec = np.zeros(shape=(30, 3))
ref_line = np.array([[10., -100.], [50.1, 100.1]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceLineCost(params, 10.)
ref_cost.SetReferenceLine(ref_line)
jerk_cost = JerkCost(params, 100.)
speed_cost = SpeedCost(params, 10.)
speed_cost.SetDesiredSpeed(20.)
# optimization problem
functor = opt.AddTripleIntFunctor(initial_state, params,
[ref_cost, jerk_cost, speed_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
inputs = opt.Result()
trajectory = GenerateTrajectoryTripleInt(initial_state, inputs, params)
fig = plt.figure()
plt.subplot(144)
ax = fig.add_subplot(144, projection='3d')
plt.plot(trajectory[:, 0],
trajectory[:, 3],
trajectory[:, 6],
marker='o')
plt.plot([10, 50.1], [-100, 100], [0., 0.], marker='o')
plt.axis("equal")
plt.subplot(141)
plt.plot(trajectory[:-3, 1], label="vx", marker='o')
plt.plot(trajectory[:-3, 4], label="vy", marker='o', color="green")
plt.plot(trajectory[:-3, 7], label="vz", marker='o', color="red")
plt.xlabel("v [m/s]")
plt.legend()
plt.subplot(142)
plt.plot(trajectory[:, 0], trajectory[:, 3], marker='o')
plt.plot([10, 50.1], [-100, 100], marker='o')
plt.axis("equal")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.subplot(143)
plt.plot(trajectory[:, 3], trajectory[:, 6], marker='o')
plt.axis("equal")
plt.xlabel("y [m]")
plt.ylabel("z [m]")
# fig, ax = plt.subplots(nrows=1, ncols=2)
# colorbar_axis = ax[0].plot(trajectory[:, 0], trajectory[:, 3])
# ax[0].axis("equal")
# ax[1].plot(inputs[:-1, 0], label="ax", marker='o')
# ax[1].plot(inputs[:-1, 1], label="ay", marker='o', color="green")
# ax[1].plot(inputs[:-1, 2], label="az", marker='o', color="red")
# ax[1].legend()
plt.show()
print(repr(trajectory))
print(repr(inputs))
class Trainer(object):
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
if cfg.SEED > 0:
np.random.seed(int(cfg.SEED))
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
self.setup_logging()
self.setup_dataset()
self.setup_network()
self.val_evaler = Evaler(eval_ids=cfg.DATA_LOADER.VAL_ID,
gv_feat=cfg.DATA_LOADER.VAL_GV_FEAT,
att_feats=cfg.DATA_LOADER.VAL_ATT_FEATS,
eval_annfile=cfg.INFERENCE.VAL_ANNFILE)
self.test_evaler = Evaler(eval_ids=cfg.DATA_LOADER.TEST_ID,
gv_feat=cfg.DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.TEST_ANNFILE)
self.scorer = Scorer()
self._init_ppo()
def _init_ppo(self):
self.nenvs = 4 * cfg.TRAIN.BATCH_SIZE
self.noptepochs = 1
self.envsperbatch = cfg.TRAIN.BATCH_SIZE
self.clip_range = 0.1
assert self.nenvs % cfg.TRAIN.BATCH_SIZE == 0
self.batch_next = None
self.mv_approxkl = 0
self.mv_violate = 0
self.mv_entropy = 0
self.mv_total = 0
def setup_logging(self):
self.logger = logging.getLogger(cfg.LOGGER_NAME)
self.logger.setLevel(logging.INFO)
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
"[%(levelname)s: %(asctime)s] %(message)s")
ch.setFormatter(formatter)
self.logger.addHandler(ch)
if not os.path.exists(cfg.ROOT_DIR):
os.makedirs(cfg.ROOT_DIR)
fh = logging.FileHandler(
os.path.join(cfg.ROOT_DIR, cfg.LOGGER_NAME + '.txt'))
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
self.logger.addHandler(fh)
self.logger.info('Training with config:')
self.logger.info(pprint.pformat(cfg))
def setup_network(self):
model = models.create(cfg.MODEL.TYPE)
self.trainer = model.cuda()
self.checkpointer = CaptionCheckpointer(
self.trainer, os.path.join(cfg.ROOT_DIR, "snapshot"))
if self.args.resume > 0:
self.checkpointer.load(
self.snapshot_path("caption_model", self.args.resume))
self.predictor = models.create(cfg.MODEL.TYPE).cuda()
self.predictor.load_state_dict(self.trainer.state_dict())
self.optim = Optimizer(self.trainer)
def setup_dataset(self):
self.coco_set = datasets.coco_dataset.CocoDataset(
image_ids_path=cfg.DATA_LOADER.TRAIN_ID,
input_seq=cfg.DATA_LOADER.INPUT_SEQ_PATH,
target_seq=cfg.DATA_LOADER.TARGET_SEQ_PATH,
gv_feat_path=cfg.DATA_LOADER.TRAIN_GV_FEAT,
att_feats_folder=cfg.DATA_LOADER.TRAIN_ATT_FEATS,
seq_per_img=1,
max_feat_num=cfg.DATA_LOADER.MAX_FEAT)
def setup_loader(self, epoch):
self.training_loader = datasets.data_loader.load_train(
False, epoch, self.coco_set)
def snapshot_path(self, name, epoch):
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
return os.path.join(snapshot_folder, name + "_" + str(epoch) + ".pth")
def save_model(self, iteration):
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
if not os.path.exists(snapshot_folder):
os.mkdir(snapshot_folder)
torch.save(self.trainer.state_dict(),
self.snapshot_path("caption_model", iteration))
def get_batch(self):
epoch = 0
while True:
self.setup_loader(epoch)
for x in self.training_loader:
yield epoch, x
epoch += 1
def _set_kwargs(self, kwargs, repeat_factor):
# sample
kwargs['GREEDY_DECODE'] = False
kwargs["NEED_PD"] = True
kwargs["REPEAT_FACTOR"] = repeat_factor
return kwargs
def _prefix_rewards(self, kwargs, seq_prefix):
kwargs[cfg.PARAM.MAX_GEN_LEN] = seq_prefix.shape[-1]
kwargs[cfg.PARAM.GEN_RESULT] = utils.expand_tensor(seq_prefix, 5)
with torch.no_grad():
seq_sample = self.predictor.extend_trajectory(
**kwargs).detach().cpu().numpy().tolist()
return seq_sample
def _sample_trajectory(self, kwargs):
self._set_kwargs(kwargs, 1)
with torch.no_grad():
seq_sample, log_prob_sample = self.predictor.decode(**kwargs)
seq_sample_list = seq_sample.detach().cpu().numpy().tolist()
log_prob_sample, seq_sample = [
_.detach().cpu().numpy()
for _ in [log_prob_sample, seq_sample]
]
indices = kwargs[cfg.PARAM.INDICES]
rewards_sample, _ = self.scorer(indices, seq_sample_list)
repeat_factor = 5
kwargs = self._set_kwargs(kwargs, repeat_factor)
kwargs['gx'], kwargs['encoder_out'], kwargs['p_att_feats'] , kwargs['att_mask']= \
self.predictor.init_gx_encoder_out_p_att_feats_att_mask(**kwargs)
indices = utils.expand_numpy(kwargs[cfg.PARAM.INDICES], repeat_factor)
advs = np.zeros_like(seq_sample, dtype=np.float32)
for k in range(cfg.MODEL.SEQ_LEN):
baseline, _ = self.scorer(
indices,
self._prefix_rewards(
kwargs,
torch.from_numpy(seq_sample[:, :k]).cuda()))
baseline = baseline.reshape(-1, repeat_factor)
advs[:, k] = rewards_sample - baseline.mean(-1)
if seq_sample[:, k].sum() == 0:
break
seq_sample = seq_sample[:, None, :]
advs = np.clip(advs[:, None, :], -1, 1)
#advs = advs[:, None, :]
log_prob_sample = log_prob_sample[:, None, ...]
return seq_sample, log_prob_sample, advs
def runner_run(self, iteration):
mb_indices = []
mb_gv_feat = []
mb_att_feats = []
mb_att_mask = []
mb_sample_logprobs = []
mb_gen_result = []
mb_advs = []
for _ in range(self.nenvs // cfg.TRAIN.BATCH_SIZE):
# data - indices, input_seq, target_seq, gv_feat, att_feats, att_mask
epoch, data = next(self.batch_next)
iteration += 1
indices = data[0]
mb_indices.append(indices.reshape(-1, 1))
for x, y in zip(data[-3:],
[mb_gv_feat, mb_att_feats, mb_att_mask]):
y.append(x.numpy())
gv_feat, att_feats, att_mask = [_.cuda() for _ in data[-3:]]
kwargs = {
cfg.PARAM.INDICES: indices,
cfg.PARAM.GLOBAL_FEAT: gv_feat,
cfg.PARAM.ATT_FEATS: att_feats,
cfg.PARAM.ATT_FEATS_MASK: att_mask
}
seq_sample, log_prob_sample, rewards = self._sample_trajectory(
kwargs)
trajectory = [log_prob_sample, seq_sample, rewards]
for x, y in zip(trajectory,
[mb_sample_logprobs, mb_gen_result, mb_advs]):
y.append(x)
max_att_num = np.max([_.shape[1] for _ in mb_att_feats])
for k, x in enumerate(mb_att_feats):
after = max_att_num - x.shape[1]
mb_att_feats[k] = np.pad(x, ((0, 0), (0, after), (0, 0)),
mode="constant")
mb_att_mask[k] = np.pad(mb_att_mask[k], ((0, 0), (0, after)),
mode="constant")
mb_indices, mb_gv_feat, mb_att_feats, mb_att_mask, \
mb_sample_logprobs, mb_gen_result, mb_advs = [np.vstack(_) for _ in [
mb_indices, mb_gv_feat, mb_att_feats, mb_att_mask,
mb_sample_logprobs, mb_gen_result, mb_advs
]]
return iteration, epoch, [
mb_indices, mb_gv_feat, mb_att_feats, mb_att_mask,
mb_sample_logprobs, mb_gen_result, mb_advs
]
def mb_train(self, kwargs):
kwargs = self._set_kwargs(kwargs, 1)
_, neglogpac = self.trainer.decode(**kwargs)
sample_logprobs, gen_result, advs = [
kwargs[_] for _ in
[cfg.PARAM.SAMPLE_LOGPROBS, cfg.PARAM.GEN_RESULT, cfg.PARAM.ADVS]
]
trajectory = [sample_logprobs, gen_result, advs]
for k, _ in enumerate(trajectory):
trajectory[k] = _.view(-1, *_.shape[2:])
sample_logprobs, gen_result, advs = trajectory
mask = gen_result > 0
mask = torch.cat(
[mask.new_full((mask.shape[0], 1), True), mask[:, :-1]], 1)
kl_div = torch.exp(sample_logprobs) * (sample_logprobs - neglogpac)
kl_div = kl_div.sum(-1)
kl_div = torch.masked_select(kl_div, mask)
entropy = torch.sum(torch.exp(neglogpac) * (-neglogpac), dim=-1)
entropy = entropy[mask].mean()
neglogpac = torch.gather(neglogpac, 2,
gen_result.unsqueeze(-1)).squeeze(-1)
sample_logprobs = torch.gather(sample_logprobs, 2,
gen_result.unsqueeze(-1)).squeeze(-1)
advs_close_zero = (-1e-5 < advs) & (advs < 1e-5)
mask &= ~advs_close_zero
neglogpac = -torch.masked_select(neglogpac, mask)
oldneglogpac = -torch.masked_select(sample_logprobs, mask)
advs = torch.masked_select(advs, mask)
ratio = torch.exp(oldneglogpac - neglogpac)
pg_losses = -advs * ratio
pg_losses2 = -advs * torch.clamp(ratio, 1.0 - self.clip_range,
1.0 + self.clip_range)
self.mv_total = 0.9 * self.mv_total + 0.1 * pg_losses.shape[0]
pg_loss = torch.max(pg_losses,
pg_losses2).sum() / (cfg.TRAIN.BATCH_SIZE * 16)
mask_positive = (advs > 0) & (ratio > 1 + self.clip_range)
mask_negative = (advs < 0) & (ratio < 1 - self.clip_range)
mask_total = mask_positive | mask_negative
kl_div = kl_div.mean()
loss = pg_loss
self.mv_approxkl = 0.9 * self.mv_approxkl + 0.1 * kl_div.item()
self.mv_entropy = 0.9 * self.mv_entropy + 0.1 * entropy.item()
self.mv_violate = 0.9 * self.mv_violate + 0.1 * mask_total.sum().item()
return loss
def train(self):
self.batch_next = self.get_batch()
# eval - crucial to disable dropout
self.trainer.eval()
self.predictor.eval()
epoch, iteration = 0, 0
val_current = None
val_best = self._compute_val(iteration)
self.logger.info("val @iteration 0: {}".format(val_best))
while True:
iteration, epoch, data = self.runner_run(iteration)
envinds = np.arange(self.nenvs)
for _ in range(self.noptepochs):
np.random.shuffle(envinds)
for start in range(0, self.nenvs, self.envsperbatch):
end = start + self.envsperbatch
mbenvinds = envinds[start:end]
indices = data[0][mbenvinds].reshape(-1)
gv_feat, att_feats, att_mask, sample_logprobs, gen_result, advs = \
[torch.from_numpy(x).cuda()
for x in [_[mbenvinds] for _ in data[1:]]]
kwargs = {
cfg.PARAM.INDICES: indices,
cfg.PARAM.GLOBAL_FEAT: gv_feat,
cfg.PARAM.ATT_FEATS: att_feats,
cfg.PARAM.ATT_FEATS_MASK: att_mask,
cfg.PARAM.SAMPLE_LOGPROBS: sample_logprobs,
cfg.PARAM.GEN_RESULT: gen_result,
cfg.PARAM.ADVS: advs
}
loss = self.mb_train(kwargs)
self.optim.zero_grad()
loss.backward()
#torch.nn.utils.clip_grad_norm_(self.trainer.parameters(), 0.5, 2)
self.optim.step()
time.sleep(1)
if iteration % 64 == 0:
self.predictor.load_state_dict(self.trainer.state_dict())
val_current = self._compute_val(iteration)
if val_best is None:
val_best = val_current
self.logger.info(
"val_current @iteration {}: {}, val_predictor: {}".format(
iteration, val_current, val_best))
self.logger.info("mv_approxkl: {}".format(self.mv_approxkl))
self.logger.info("mv_entropy: {}".format(self.mv_entropy))
self.logger.info("mv_violate: {}".format(self.mv_violate))
self.logger.info("mv_total: {}".format(self.mv_total))
if val_best <= val_current:
self.save_model(23)
val_best = val_current
if iteration == 3584:
break
def _compute_val(self, iteration):
val_res = self.val_evaler(self.trainer, 'val_' + str(iteration), 1)
val = 0
for score_type, weight in zip(cfg.SCORER.TYPES, cfg.SCORER.WEIGHTS):
# plus!!!
val += val_res[score_type] * weight
test_res = self.test_evaler(self.trainer, 'test_' + str(iteration), 2)
self.logger.info('######## Iter (TEST) ' + str(iteration) +
' ########')
self.logger.info(str(test_res))
# crucial!
self.trainer.eval()
return val
def test_single_track_optimizer(self):
params = Parameter()
params.set("wheel_base", 2.7)
params.set("dt", 0.2)
params.set("function_tolerance", 1e-6)
params.set("max_num_iterations", 1000)
params.set("num_threads", 1)
initial_state = np.array([[0., 0., 0., 10.], [2., 0., 0., 10.],
[4., 0., 0., 10.], [6., 0., 0., 10.]])
opt_vec = np.zeros(shape=(30, 2))
ref_line = np.array([[0., 4.], [1000., 4.]])
obstacle_outline0 = np.array([[14., 1.7], [22., 1.7], [22., 4.7],
[14., 4.7], [14., 1.7]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceLineCost(params, 100.)
ref_cost.SetReferenceLine(ref_line)
jerk_cost = JerkCost(params, 10000.)
outline = ObjectOutline()
obstacle_outline1 = obstacle_outline0 + np.array([[30.0, -2.]])
outline.Add(obstacle_outline0, 0.)
outline.Add(obstacle_outline1, 6.)
object_cost = StaticObjectCost(params, 2.5, 10000.)
object_cost.AddObjectOutline(outline)
input_cost = InputCost(params, 10.)
input_cost.SetLowerBound(np.array([[-0.2, -1.0]]))
input_cost.SetUpperBound(np.array([[0.2, 1.0]]))
speed_cost = SpeedCost(params, 10.)
speed_cost.SetDesiredSpeed(10.)
# optimization problem
functor = opt.AddFastSingleTrackFunctor(
initial_state, params,
[jerk_cost, ref_cost, input_cost, object_cost, speed_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
inputs = opt.Result()
trajectory = GenerateTrajectorySingleTrack(initial_state, inputs,
params)
cmap, norm = GetColorMap(0., 6.)
fig, ax = plt.subplots(nrows=1, ncols=2)
for t in np.arange(0, 6, 0.5):
poly = outline.Query(t)
ax[0].plot(poly[:, 0], poly[:, 1], color=cmap(norm(t)))
colorbar_axis = ax[0].plot(trajectory[:, 0], trajectory[:, 1])
for i, pt in enumerate(trajectory[:]):
ax[0].plot(pt[0],
pt[1],
color=cmap(norm(params.set("dt", 0.2) * i)),
marker='o')
ax[0].axis("equal")
ax[1].plot(inputs[:-1, 0], label="Steering angle", marker='o')
ax[1].plot(inputs[:-1, 1],
label="Acceleration",
marker='o',
color="green")
ax[1].legend()
plt.show()
print(repr(trajectory))
print(repr(inputs))