def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
logging.info("args = %s", args)
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled=True
model = SearchSpace()
model.cuda()
optimizer = torch.optim.SGD(model.parameters(), args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
train_samples = Rain800(args.data+'training/', args.steps*args.batch_size, args.patch_size)
train_queue = torch.utils.data.DataLoader(train_samples, batch_size=args.batch_size, pin_memory=True)
val_samples = Rain800(args.data+'test_syn/', 30*args.batch_size, args.patch_size)
valid_queue = torch.utils.data.DataLoader(val_samples, batch_size=args.batch_size, pin_memory=True)
best_psnr = 0
best_psnr_epoch = 0
best_ssim = 0
best_ssim_epoch = 0
best_loss = float("inf")
best_loss_epoch = 0
for epoch in range(args.epochs):
lr = scheduler.get_lr()[0]
logging.info('epoch %d/%d lr %e', epoch+1, args.epochs, lr)
# training
train(epoch, train_queue, valid_queue, model, architect, optimizer, lr)
# validation
psnr, ssim, loss = infer(valid_queue, model)
if psnr > best_psnr and not math.isinf(psnr):
utils.save(model, os.path.join(args.save, 'best_psnr_weights.pt'))
best_psnr_epoch = epoch+1
best_psnr = psnr
if ssim > best_ssim:
utils.save(model, os.path.join(args.save, 'best_ssim_weights.pt'))
best_ssim_epoch = epoch+1
best_ssim = ssim
if loss < best_loss:
utils.save(model, os.path.join(args.save, 'best_loss_weights.pt'))
best_loss_epoch = epoch+1
best_loss = loss
scheduler.step()
logging.info('psnr:%6f ssim:%6f loss:%6f -- best_psnr:%6f best_ssim:%6f best_loss:%6f', psnr, ssim, loss, best_psnr, best_ssim, best_loss)
logging.info('arch:%s', torch.argmax(model.arch_parameters()[0], dim=1))
logging.info('BEST_LOSS(epoch):%6f(%d), BEST_PSNR(epoch):%6f(%d), BEST_SSIM(epoch):%6f(%d)', best_loss, best_loss_epoch, best_psnr, best_psnr_epoch, best_ssim, best_ssim_epoch)
utils.save(model, os.path.join(args.save, 'last_weights.pt'))
def test_trainer():
search_space = SearchSpace(model_output_shape=2)
tokens = search_space.generate_token()
# controller = Controller(tokens=tokens)
trainer = Trainer()
# samples = controller.generate_sequence()
samples = [[65, 146, 143, 201, 281, 382]]
architectures = search_space.create_models(samples=samples,
model_input_shape=(128, 128, 3))
epoch_performance = trainer.train_models(samples=samples,
architectures=architectures)
assert len(epoch_performance) != 0
def test_search_space():
search_space = SearchSpace(target_classes=2)
vocab = search_space.mapping
vocab_decoded = search_space.decode_sequence(vocab)
vocab_encoded = search_space.encode_sequence(vocab_decoded)
sample_sequence = [8, 8, 30]
input_shape = np.shape(np.zeros(13))
model = search_space.create_architecture(sample_sequence, input_shape)
keras.utils.plot_model(model, to_file="model.png", show_shapes=True)
assert len(vocab) == 30
assert len(vocab_decoded) == 30
assert len(vocab_encoded) == 30
def test_controller_rnn_trainer():
search_space = SearchSpace(model_output_shape=2)
tokens = search_space.generate_token()
controller = Controller(tokens=tokens)
# samples = controller.generate_sequence()
manual_epoch_performance = {
(320, 96, 338, 84, 176, 382): (0.968, 0), # (acc, lat)
(22, 47, 225, 315, 223, 382): (0.87, 0),
(74, 204, 73, 236, 309, 382): (0.74, 0),
(110, 60, 191, 270, 199, 382): (0.51, 0)
}
loss_avg = controller.train_controller_rnn(
epoch_performance=manual_epoch_performance)
print(loss_avg)
def test_search_space():
search_space = SearchSpace(model_output_shape=2)
token = search_space.generate_token()
dense_tokens = [x for x, y in token.items()
if "Dense" in y] # dense layers start from 865
sample_sequence = [52, 146, 31, 119, 138, 244]
translated_sequence = search_space.translate_sequence(sample_sequence)
assert len(translated_sequence) == 4
model = search_space.create_model(sequence=sample_sequence,
model_input_shape=(128, 128, 3))
keras.utils.plot_model(model, to_file="model.png", show_shapes=True)
print(model.summary())
assert len(token) == 890
def test_controller_generate_sequence_naive():
search_space = SearchSpace(model_output_shape=2)
tokens = search_space.generate_token()
controller = Controller(tokens=tokens)
# samples = controller.generate_sequence_naive(mode="b")
# for sequence in samples:
# sequence_ = sequence
# print(sequence_)
# sequences_random = controller.generate_sequence_naive(mode="r")
for i in range(20):
sequences_random = controller.generate_sequence_naive(mode="r_var_len")
print(sequences_random)
print("Done.")
def rrt_star_test(index=0):
t0 = tic()
mapfile = [
'./maps/single_cube.txt', './maps/maze.txt', './maps/window.txt',
'./maps/tower.txt', './maps/flappy_bird.txt', './maps/room.txt',
'./maps/monza.txt'
]
name = [
'single_cube', 'maze', 'window', 'tower', 'flappy_bird', 'room',
'monza'
]
start_list = [(2.3, 2.3, 1.3), (0.0, 0.0, 1.0), (0.2, -4.9, 0.2),
(2.5, 4.0, 0.5), (0.5, 2.5, 5.5), (1.0, 5.0, 1.5),
(0.5, 1.0, 4.9)]
goal_list = [(7.0, 7.0, 5.5), (12.0, 12.0, 5.0), (6.0, 18.0, 3.0),
(4.0, 2.5, 19.5), (19.0, 2.5, 5.5), (9.0, 7.0, 1.5),
(3.8, 1.0, 0.1)]
print('Running RRT* ' + name[index] + ' test...\n')
start = start_list[index]
goal = goal_list[index]
boundary, blocks = load_map(mapfile[index])
dimension = np.array([(boundary[0, 0], boundary[0, 3]),
(boundary[0, 1], boundary[0, 4]),
(boundary[0, 2], boundary[0, 5])])
new_blocks = np.zeros((1, 6))
for block in blocks:
new_blocks = np.concatenate((new_blocks, [block[:6]]))
new_blocks = np.delete(new_blocks, 0, 0)
Q = np.array([(0.5, 4)])
r = 0.05
max_samples = 102400
prc = 0.1
rewire = 10
X = SearchSpace(dimension, new_blocks)
rrt = RRTStar(X, Q, start, goal, max_samples, r, prc, rewire)
path = rrt.rrt_star()
plot_rrt(name[index], X, rrt, path, new_blocks, start, goal)
toc(t0, name[index] + ' RRT*')
pathlength = np.sum(
np.sqrt(np.sum(np.diff(np.array(path), axis=0)**2, axis=1)))
print('Path length is:', pathlength)
def optimize(self, run_f, params):
search_tree = SearchSpace(params)
lb = search_tree.get_lb()
ub = search_tree.get_ub()
f = param_decorator(run_f, search_tree)
gs = RandomSearch(self.num_runs, lb, ub, self.sobol)
start = timeit.default_timer()
best_params, score = gs.optimize(f)
end = timeit.default_timer() - start
best_params = search_tree.transform(best_params)
Result = namedtuple('Result', ['params', 'score', 'time'])
return Result(best_params, score, end)
def optimize(self, run_f, params, parallel=False):
search_tree = SearchSpace(params)
lb = search_tree.get_lb()
ub = search_tree.get_ub()
f = Evaluator(run_f, search_tree)
algorithm = self.algorithm(lb, ub, parallel, *self.args, **self.kwargs)
start = timeit.default_timer()
best_params, score = algorithm.run(f)
end = timeit.default_timer() - start
best_params = search_tree.transform(best_params)
# Result = namedtuple('Result', ['params', 'score', 'time'])
return Result(best_params, score, end)
def optimize(self, run_f, params):
search_tree = SearchSpace(params)
lb = search_tree.get_lb()
ub = search_tree.get_ub()
f = param_decorator(run_f, search_tree)
pso = PSO(self.num_generations, self.num_particles, lb, ub, self.phi1,
self.phi2)
start = timeit.default_timer()
best_params, score = pso.optimize(f)
end = timeit.default_timer() - start
best_params = search_tree.transform(best_params)
Result = namedtuple('Result', ['params', 'score', 'time'])
return Result(best_params, score, end)
def generate_models():
if config.search_space["mode"] == "MobileNets":
search_space = SearchSpaceMn(model_output_shape=2)
else:
search_space = SearchSpace(model_output_shape=2)
if os.listdir(latency_dataset):
raise ValueError("Dataset folder is not empty.")
tokens = search_space.generate_token()
controller = Controller(tokens=tokens)
sequences = []
df = pd.DataFrame(columns=["model", "params [K]", "sipeed_latency [ms]", "kmodel_memory [KB]", "cpu_latency [ms]",
"accuracy", "token_sequence", "length", "model_info"])
i = 0
while i < no_of_examples:
sequence = controller.generate_sequence_naive(mode="r_var_len")
if (sequence in sequences) or (not search_space.check_sequence(sequence)):
continue
try:
architecture = search_space.create_model(sequence=sequence, model_input_shape=model_input_shape)
except Exception as e:
# print(sequence)
# print(e)
continue
sequences.append(sequence)
i += 1
i_str = format(i, f"0{len(str(no_of_examples))}d") # add 0s
file_name = f"model_{i_str}"
architecture.save(f"{latency_dataset}/{file_name}.h5")
model_params = round(architecture.count_params()/1000, 4)
model_info = search_space.translate_sequence(sequence)
model_info_json = json.dumps(dict(zip(range(len(model_info)), model_info)))
df = df.append({"model": file_name, "params [K]": model_params,
"token_sequence": sequence, "length": len(sequence),
"model_info": model_info_json}, ignore_index=True)
print(file_name, ", length:", len(sequence))
df.to_csv(f"{latency_dataset}/table.csv", index=False)
def __init__(self, tokens):
self.max_no_of_layers = config.controller["max_no_of_layers"]
self.agent_lr = config.controller["agent_lr"]
self.min_reward = config.controller["min_reward"]
self.min_plays = config.controller["min_plays"]
self.max_plays = config.controller["max_plays"]
self.alpha = config.controller["alpha"]
self.gamma = config.controller["gamma"]
self.model_input_shape = config.emnas["model_input_shape"]
self.valid_sequence_timeout = config.controller[
"valid_sequence_timeout"]
self.tokens = tokens
self.len_search_space = len(tokens) + 1
self.end_token = list(tokens.keys())[-1]
self.model = self.rl_agent()
self.states = []
self.gradients = []
self.rewards = []
self.probs = []
if config.search_space["mode"] == "MobileNets":
self.search_space = SearchSpaceMn(
config.emnas["model_output_shape"])
else:
self.search_space = SearchSpace(config.emnas["model_output_shape"])
def initialize(self, width, height, robot_pos, goal_pos, map_data):
""" Initialize the Moving Target D* Lite algorithm """
# Search variables
self.km = 0
self.open_list = OpenList()
self.deleted_list = []
self.path = []
# Create a search space
self.search_space = SearchSpace(width, height)
self.search_space.load_search_space_from_map(map_data)
# Get the node the robot is on (row, col is the pos argument)
self.start_node = self.search_space.get_node(robot_pos)
# Get the node the goal is on (row, col is the pos argument)
self.goal_node = self.search_space.get_node(goal_pos)
# Set the robot's node's rhs = 0
self.start_node.set_rhs(0)
# Add the robot's node to the open list
self.start_node.set_key(self.calculate_key(self.start_node))
self.open_list.insert(self.start_node)
from optimization_problem import OptimizationProblem
from bayes_optimizer import BayesOptimizer
from search_space import SearchSpace, Parameter
import random
from sklearn.gaussian_process import GaussianProcessRegressor
from benchmark import branin
from numpy import average
branin_problem = OptimizationProblem(
SearchSpace(
[
Parameter("", random.uniform, a=-5, b=10),
Parameter("", random.uniform, a=0, b=15),
]
),
function=branin,
optimal_value=-0.397887,
)
branin_optimizer = BayesOptimizer(
branin_problem, GaussianProcessRegressor(), max_iterations=1000, epsilon=0.01
)
def objective(array, *args, **kwargs):
return average([branin_optimizer.optimize(start_sample_size=array[0], sample_size=array[1]) for i in range(20)]) * -1
bayes_optimization_problem = OptimizationProblem(
SearchSpace(
[
# Parameter("target", random.choice, [branin_optimizer]),
def test_controller_sample_generator():
search_space = SearchSpace(model_output_shape=2)
tokens = search_space.generate_token()
controller = Controller(tokens=tokens)
samples = controller.generate_sequence()
print(samples)
# Obstacles = np.array([
# (20, 20, 20, 20, 20, 20, 20, 40, 40, 40, 40, 40, 40, 40),
# (10, 10, 10, 10, 10, 10, 10, 18, 18, 18, 18, 18, 18, 18)
# ])
x_init = (0, 0, 0, 0, 0, 0, 0) # starting location
x_goal = (0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5) # goal location
Q = np.array([(.05, .05, .05, .05, .05, .05, .05)]) # length of tree edges
r = .01 # length of smallest edge to check for intersection with obstacles
max_samples = 100 # max number of samples to take before timing out
prc = 0.1 # probability of checking for a connection to goal
# create search space
#X = SearchSpace(X_dimensions, Obstacles)
X = SearchSpace(X_dimensions) #can set obstacles to none.
# create rrt_search
rrt = RRT(X, Q, x_init, x_goal, max_samples, r, prc)
print('rrt built. Searching for path...')
path = rrt.rrt_search()
#TODO: figure out plotting with plotly
# plot
#plot = Plot("rrt_2d")
#plot.plot_tree(X, rrt.trees)
#if path is not None:
# plot.plot_path(X, path)
#plot.plot_obstacles(X, Obstacles)
#plot.plot_start(X, x_init)
#plot.plot_goal(X, x_goal)
# obstacles (q1 lower, q2 lower, ..., qn lower, q1 upper, q2 upper,..., qn upper), each obstacle should have 2n coordinates
Obstacles = np.array([(20, 20, 20, 20, 20, 20, 20, 40, 40, 40, 40, 40, 40, 40),
(10, 10, 10, 10, 10, 10, 10, 18, 18, 18, 18, 18, 18, 18)
])
x_init = (0, 0, 0, 0, 0, 0, 0) # starting location
x_goal = (50, 50, 50, 50, 50, 50, 50) # goal location
Q = np.array([(8, 4)]) # length of tree edges
r = 1 # length of smallest edge to check for intersection with obstacles
max_samples = 1024 # max number of samples to take before timing out
prc = 0.1 # probability of checking for a connection to goal
# create search space
X = SearchSpace(X_dimensions, Obstacles)
# create rrt_search
rrt = RRT(X, Q, x_init, x_goal, max_samples, r, prc)
path = rrt.rrt_search()
#TODO: figure out plotting with plotly
# plot
#plot = Plot("rrt_2d")
#plot.plot_tree(X, rrt.trees)
#if path is not None:
# plot.plot_path(X, path)
#plot.plot_obstacles(X, Obstacles)
#plot.plot_start(X, x_init)
#plot.plot_goal(X, x_goal)
#plot.draw(auto_open=True)
model_input_shape = config.emnas["model_input_shape"]
search_mode = config.emnas["search_mode"]
naive_threshold = config.emnas["naive_threshold"]
naive_timeout = config.emnas["naive_timeout"]
no_of_episodes = config.emnas["no_of_episodes"]
log_path = config.emnas["log_path"]
max_no_of_layers = config.controller["max_no_of_layers"]
dynamic_min_reward = config.controller["dynamic_min_reward"]
variance_threshold = config.controller["variance_threshold"]
valid_actions = config.controller["valid_actions"]
valid_sequence_timeout = config.controller["valid_sequence_timeout"]
if config.search_space["mode"] == "MobileNets":
search_space = SearchSpaceMn(model_output_shape=model_output_shape)
else:
search_space = SearchSpace(model_output_shape=model_output_shape)
tokens = search_space.generate_token()
controller = Controller(tokens=tokens)
trainer = Trainer(tokens)
def _plot(history, path):
img_size = (24, 5)
fig = plt.figure(figsize=img_size)
plt.plot(np.arange(0, len(history["loss"])), history["loss"])
plt.title("Agent loss")
plt.xlabel("Episode")
plt.ylabel("Loss")
plt.grid()
plt.savefig(path + "/fig_1.png", bbox_inches="tight", pad_inches=0.2)
def main():
parser = argparse.ArgumentParser(description='TrainingContainer')
parser.add_argument('--algorithm-settings',
type=str,
default="",
help="algorithm settings")
parser.add_argument('--search-space',
type=str,
default="",
help="search space for the neural architecture search")
parser.add_argument('--num-layers',
type=str,
default="",
help="number of layers of the neural network")
args = parser.parse_args()
# Get Algorithm Settings
algorithm_settings = args.algorithm_settings.replace("\'", "\"")
algorithm_settings = json.loads(algorithm_settings)
print(">>> Algorithm settings")
for key, value in algorithm_settings.items():
if len(key) > 13:
print("{}\t{}".format(key, value))
elif len(key) < 5:
print("{}\t\t\t{}".format(key, value))
else:
print("{}\t\t{}".format(key, value))
print()
num_epochs = int(algorithm_settings["num_epochs"])
w_lr = float(algorithm_settings["w_lr"])
w_lr_min = float(algorithm_settings["w_lr_min"])
w_momentum = float(algorithm_settings["w_momentum"])
w_weight_decay = float(algorithm_settings["w_weight_decay"])
w_grad_clip = float(algorithm_settings["w_grad_clip"])
alpha_lr = float(algorithm_settings["alpha_lr"])
alpha_weight_decay = float(algorithm_settings["alpha_weight_decay"])
batch_size = int(algorithm_settings["batch_size"])
num_workers = int(algorithm_settings["num_workers"])
init_channels = int(algorithm_settings["init_channels"])
print_step = int(algorithm_settings["print_step"])
num_nodes = int(algorithm_settings["num_nodes"])
stem_multiplier = int(algorithm_settings["stem_multiplier"])
# Get Search Space
search_space = args.search_space.replace("\'", "\"")
search_space = json.loads(search_space)
search_space = SearchSpace(search_space)
# Get Num Layers
num_layers = int(args.num_layers)
print("Number of layers {}\n".format(num_layers))
# Set GPU Device
# Currently use only first available GPU
# TODO: Add multi GPU support
# TODO: Add functionality to select GPU
all_gpus = list(range(torch.cuda.device_count()))
if len(all_gpus) > 0:
device = torch.device("cuda")
torch.cuda.set_device(all_gpus[0])
np.random.seed(2)
torch.manual_seed(2)
torch.cuda.manual_seed_all(2)
torch.backends.cudnn.benchmark = True
print(">>> Use GPU for Training <<<")
print("Device ID: {}".format(torch.cuda.current_device()))
print("Device name: {}".format(torch.cuda.get_device_name(0)))
print("Device availability: {}\n".format(torch.cuda.is_available()))
else:
device = torch.device("cpu")
print(">>> Use CPU for Training <<<")
# Get dataset with meta information
# TODO: Add support for more dataset
input_channels, num_classes, train_data = utils.get_dataset()
criterion = nn.CrossEntropyLoss().to(device)
model = NetworkCNN(init_channels, input_channels, num_classes, num_layers,
criterion, search_space, num_nodes, stem_multiplier)
model = model.to(device)
# Weights optimizer
w_optim = torch.optim.SGD(model.getWeights(),
w_lr,
momentum=w_momentum,
weight_decay=w_weight_decay)
# Alphas optimizer
alpha_optim = torch.optim.Adam(model.getAlphas(),
alpha_lr,
betas=(0.5, 0.999),
weight_decay=alpha_weight_decay)
# Split data to train/validation
num_train = len(train_data)
split = num_train // 2
indices = list(range(num_train))
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(
indices[:split])
valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(
indices[split:])
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(train_data,
batch_size=batch_size,
sampler=valid_sampler,
num_workers=num_workers,
pin_memory=True)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(w_optim,
num_epochs,
eta_min=w_lr_min)
architect = Architect(model, w_momentum, w_weight_decay)
# Start training
best_top1 = 0.
for epoch in range(num_epochs):
lr_scheduler.step()
lr = lr_scheduler.get_lr()[0]
model.print_alphas()
# Training
print(">>> Training")
train(train_loader, valid_loader, model, architect, w_optim,
alpha_optim, lr, epoch, num_epochs, device, w_grad_clip,
print_step)
# Validation
print("\n>>> Validation")
cur_step = (epoch + 1) * len(train_loader)
top1 = validate(valid_loader, model, epoch, cur_step, num_epochs,
device, print_step)
# Print genotype
genotype = model.genotype(search_space)
print("\nModel genotype = {}".format(genotype))
# Modify best top1
if top1 > best_top1:
best_top1 = top1
best_genotype = genotype
print("Final best Prec@1 = {:.4%}".format(best_top1))
print("\nBest-Genotype={}".format(str(best_genotype).replace(" ", "")))
from optimization_problem import OptimizationProblem
from benchmark import branin
from bayes_optimizer import BayesOptimizer
from search_space import SearchSpace, Parameter
import random
from sklearn.gaussian_process import GaussianProcessRegressor
branin_problem = OptimizationProblem(
SearchSpace([
Parameter("", random.uniform, a=-5, b=10),
Parameter("", random.uniform, a=0, b=15),
]),
function=branin,
optimal_value=-0.397887,
)
sum = 0
optimizer = BayesOptimizer(branin_problem,
GaussianProcessRegressor(),
epsilon=0.0001,
max_iterations=3000)
for i in range(1):
sum += optimizer.optimize(
debug=True,
start_sample_size=200,
sample_size=1000,
)
print("\n\n\n\nAverage iterations is ", sum)
