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_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(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_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_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 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 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 __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)
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 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)
class mt_dstar_lite:
def __init__(self):
""" Initialize the Moving Target D* Lite algorithm """
# Moving Target
self.km = 0
self.search_space = None
# Lists
self.open_list = None
self.deleted_list = None
self.path = None
# Nodes
self.start_node = None
self.goal_node = None
self.old_start = None
self.old_goal = None
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)
def calculate_key(self, node):
""" Calculates a node key based on its G and RHS values, as well as the distance to the goal """
key_1 = min(
min(node.G, node.rhs) + self.heuristic(node, self.goal_node) +
self.km, Node.INFINITY)
key_2 = min(min(node.G, node.rhs), Node.INFINITY)
return (key_1, key_2)
def heuristic(self, node1, node2):
""" Compute the distance from one node to another """
return sqrt((node2.row - node1.row)**2 + (node2.col - node1.col)**2)
def cost(self, node1, node2):
""" Computer actual cost incurred by moving from one node to another """
if node1.cost < Node.INFINITY and node2.cost < Node.INFINITY:
return self.heuristic(node1, node2) + node1.cost + node2.cost
else:
return Node.INFINITY
def update_state(self, node):
""" Updates a node's status based on the progression of the search """
# If the node is on the open list and is inconsistent, update it
if node.G != node.rhs and self.open_list.contains(node):
node.set_key(self.calculate_key(node))
self.open_list.update(node)
# If the node is not on the open list, but is inconsistent, then add it to the open list
elif node.G != node.rhs and not self.open_list.contains(node):
node.set_key(self.calculate_key(node))
self.open_list.insert(node)
# If the node is on the open list and is consistent, close it by removing it from the open list
elif node.G == node.rhs and self.open_list.contains(node):
self.open_list.delete(node)
def compute_cost_minimal_path(self):
""" Finds the best path from the start state to the goal state """
while (self.open_list.top_key() < self.calculate_key(
self.goal_node)) or (self.goal_node.rhs > self.goal_node.G):
# Get the highest priority node from the open list
u_node = self.open_list.top()
# Compute keys for this node
old_key = u_node.key
new_key = self.calculate_key(u_node)
# If the new key is greater than the old key, update the open list
if old_key < new_key:
u_node.set_key(new_key)
self.open_list.update(u_node)
# Otherwise, if the node's G value is higher than its RHS value, close the node and update its successors
elif u_node.G > u_node.rhs:
# Make the node consistent and remove it from the open list
u_node.set_g(u_node.rhs)
self.open_list.delete(u_node)
# Update successors
for s_node in self.search_space.get_successors(u_node):
if s_node != self.start_node and s_node.rhs > u_node.G + self.cost(
u_node, s_node):
s_node.set_rhs(u_node.G + self.cost(u_node, s_node))
s_node.set_par(u_node)
self.update_state(s_node)
# Otherwise make the node inconsistent and calculate the parent nodes of its successors
else:
# Set G value to infinity
u_node.set_g(Node.INFINITY)
# Update successors
succ = self.search_space.get_successors(u_node)
succ.append(u_node)
for s_node in succ:
if s_node != self.start_node and s_node.par == u_node:
# Set the RHS to be the minimum one-step lookahead
s_rhs = min([
node.G + self.cost(node, s_node) for node in
self.search_space.get_successors(s_node)
])
s_node.set_rhs(s_rhs)
if s_node.rhs >= Node.INFINITY:
s_node.set_rhs(Node.INFINITY)
s_node.set_par(None)
else:
# Set the parent node pointer based on the parent node that has the minimum path cost to goal from this node
par_nodes = [
node for node in
self.search_space.get_successors(s_node)
]
s_par = [
node.G + self.cost(node, s_node)
for node in par_nodes
]
s_par_node_idx = s_par.index(min(s_par))
s_par_node = par_nodes[s_par_node_idx]
s_node.set_par(s_par_node)
self.update_state(s_node)
def optimized_deletion(self):
""" """
# Initialize deletion
self.deleted_list = []
self.start_node.set_par(None)
# Go through the nodes in the search tree that aren't on the path from the current start node to the goal node
for s_node in self.search_space.get_deleteable_nodes(self.start_node):
# Reset the node's data
s_node.set_par(None)
s_node.set_rhs(Node.INFINITY)
s_node.set_g(Node.INFINITY)
# Get this node off the open list and add it to the deleted list
if self.open_list.contains(s_node):
self.open_list.delete(s_node)
# Add this node to the deleted list
self.deleted_list.append(s_node)
# Go through the deleted list and update its costs
for node in self.deleted_list:
# Get the best cost from the successors
for succ_node in self.search_space.get_successors(node):
if node.rhs > succ_node.G + self.cost(succ_node, node):
node.set_rhs(succ_node.G + self.cost(succ_node, node))
node.set_par(succ_node)
# Add the node to the open list if it has a finite cost
if node.rhs < Node.INFINITY:
node.set_key(self.calculate_key(node))
self.open_list.insert(node)
def get_best_path(self):
""" Get the best path using the parent pointers """
# Start at the start and keep track of the footsteps
node = self.goal_node
path = [(node.row, node.col)]
# Go until the goal is found
while node != self.start_node:
# Add the next node to the path
next_node = node.par
path.append((next_node.row, next_node.col))
node = next_node
# The best path should be found by traversing the pointers
return path
# NOTE: we assume that the hunter never catches the target. We also assume that the edge costs change
# during every configuration space update for now - the SLAM node will (in the future) provide new maps only when the
# robot has moved. Since the target cannot move off the path unless the map updates, and the map only updates to show new
# edge costs, we don't need to check for the conditions in the while loop that waits for the hunter to follow the target,
# as described in the original algorithm
def plan(self):
""" Plan the robot's initial path """
# Set the last robot positions
self.old_start = self.start_node
self.old_goal = self.goal_node
# Find a low cost path from the start to the goal
self.compute_cost_minimal_path()
# If the goal has a RHS of infinity, then there is no path
if self.goal_node.rhs >= Node.INFINITY:
#self.search_space.print_search_space_rhs()
print("Get rekt " + str(self.goal_node.rhs))
return None
# Figure out path by following the parent pointers (par) to the goal node
self.path = self.get_best_path()
return self.path
# NOTE: Per the note above, we must unwrap the main while loop since there is a discontinuity caused by the "hunter follows target"
# while loop. Therefore, plan() should only be called by an external class the first time we want to plan,
# and all subsequent external planning requests should be to the following replan() function
def replan(self, robot_pos, goal_pos, new_map):
# 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)
# Update the km search parameter
self.km = self.km + self.heuristic(self.goal_node, self.old_goal)
# If the robot has moved, delete the nodes that need updating and revalue them
if self.old_start != self.start_node:
# Perform optimized deletion to update the out-of-date parts of the search tree
self.optimized_deletion()
# Shift the map anyways because our environment grows
# TODO
# Update all changed edge costs from the map update
for c_node in self.search_space.update_map(new_map):
for succ in self.search_space.get_successors(c_node):
# If the old cost was bigger than the new cost (i.e., the current cost is low now)
if c_node.cost < Node.INFINITY:
if succ != self.start_node and succ.rhs > c_node.G + self.cost(
c_node, succ):
succ.set_par(c_node)
succ.set_rhs(c_node.G + self.cost(c_node, succ))
# Otherwise the cost has increased and we need to reroute the successors that map to this node
else:
# Only update successors that map to the current node (and that aren't the start node)
if succ != self.start_node and succ.par is c_node:
# Set the RHS to be the minimum one-step lookahead
succ_rhs = min([
node.G + self.cost(node, succ)
for node in self.search_space.get_successors(succ)
])
succ.set_rhs(succ_rhs)
if succ.rhs >= Node.INFINITY:
succ.set_rhs(Node.INFINITY)
succ.set_par(None)
else:
# Set the parent node pointer based on the parent node that has the minimum path cost to goal from this node
par_nodes = [
node for node in
self.search_space.get_successors(succ)
]
s_par = [
node.G + self.cost(node, succ)
for node in par_nodes
]
s_par_node_idx = s_par.index(min(s_par))
s_par_node = par_nodes[s_par_node_idx]
succ.set_par(s_par_node)
# update the state of the successor
self.update_state(succ)
# Plan the path after adjustments
return self.plan()
def get_search_space_map(self):
return self.search_space.get_search_space_rhs_map()
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 to_resource_graph(self, space: SearchSpace, *args, **kwargs):
return space.to_resource_graph(self.arch, self.input_shape,
self.num_classes, *args, **kwargs)
def to_keras_model(self, space: SearchSpace, *args, **kwargs):
return space.to_keras_model(self.arch, self.input_shape,
self.num_classes, *args, **kwargs)
def __init__(self, wrapped_arch: Architecture, space: SearchSpace,
input_shape, num_classes):
self.arch = wrapped_arch
self.input_shape = input_shape
self.num_classes = num_classes
self.val_error = None # Filled out by the experiment caller
self.test_error = None
rg = space.to_resource_graph(self.arch, input_shape, num_classes)
self.resource_features = self.compute_resource_features(rg)
# Here we convert the architecture into a representation compatible with Dragonfly's kernel,
# which is:
# - `layer_labels`: a string describing the kind of a layer ("ip" and "op" for input and
# output layers, or e.g. "DWConv2D-3" for a 3x3 DW convolution);
# - `edge_list`: a list of tuples representing connections between layers (indices are
# determined by the position of the latter in the `layer_labels` list);
# - `units`: number of units in a layer, which Dragonfly uses to compute "layer mass".
# The conversion can be done for any `Architecture` from its resource graph representation
layer_labels = []
edge_list = []
units = []
mass = [
] # -1 marks non-processing layers, mass will be filled out later
layers = [t for t in rg.tensors.values()
if not t.is_constant] # input and layer output tensors
tensor_name_to_id = {t.name: i for i, t in enumerate(layers)}
# Add input tensors as "input layers"
for tensor in rg.inputs:
layer_labels.append("ip")
units.append(tensor.shape[-1])
mass.append(-1)
# Add each operator as its own layer and connect its input layers to itself
for op in rg.operators.values():
layer_labels.append(op_to_label(op))
units.append(op.output.shape[-1])
inputs = [i for i in op.inputs if i.name in tensor_name_to_id]
edge_list.extend(
(tensor_name_to_id[i.name], tensor_name_to_id[op.output.name])
for i in inputs)
mass.append(macs(op))
# Invent a dummy layer for each output tensor
for tensor in rg.outputs:
i = len(layer_labels)
layer_labels.append("op")
units.append(tensor.shape[-1])
edge_list.append((tensor_name_to_id[tensor.name], i))
mass.append(-1)
num_layers = len(layer_labels)
conn_mat = dok_matrix((num_layers, num_layers))
for (a, b) in edge_list:
conn_mat[a, b] = 1
non_proc_layer_mass = max(0.1 * sum(f for f in mass if f != -1),
100) # according to Dragonfly
self.layer_masses = np.array([(non_proc_layer_mass if f == -1 else f)
for f in mass],
dtype=np.float) / 1000
super().__init__("unas-net",
layer_labels,
conn_mat,
num_units_in_each_layer=units,
all_layer_label_classes=all_possible_labels(),
layer_label_similarities=None)
class Controller(object):
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 rl_agent(self):
model_output_shape = (self.max_no_of_layers - 1, self.len_search_space)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(512,
input_shape=(self.max_no_of_layers - 1, ),
activation="relu"))
model.add(keras.layers.Dense(256, activation="relu"))
model.add(keras.layers.Dense(128, activation="relu"))
model.add(keras.layers.Dense(64, activation="relu"))
model.add(keras.layers.Dense(32, activation="relu"))
model.add(keras.layers.Dense(16, activation="relu"))
model.add(keras.layers.Dense(16, activation="relu"))
model.add(keras.layers.Dense(32, activation="relu"))
model.add(keras.layers.Dense(64, activation="relu"))
model.add(keras.layers.Dense(128, activation="relu"))
model.add(keras.layers.Dense(256, activation="relu"))
model.add(keras.layers.Dense(512, activation="relu"))
model.add(
keras.layers.Dense(model_output_shape[0] * model_output_shape[1],
activation="softmax"))
model.add(keras.layers.Reshape(model_output_shape))
model.compile(loss="categorical_crossentropy",
optimizer=keras.optimizers.Adam(lr=self.agent_lr))
return model
def get_all_action(self, state: np.ndarray) -> (List, np.ndarray, bool):
true_sequence = False
actions = []
distributions = self.model.predict(state)
for distribution in distributions[0]:
distribution /= np.sum(distribution)
action = np.random.choice(self.len_search_space, 1,
p=distribution)[0]
action = 1 if action == 0 else action
actions.append(int(action))
if action == self.end_token:
break
sequence = actions + [self.end_token
] if self.end_token not in actions else actions
valid_sequence = self.search_space.check_sequence(sequence)
if valid_sequence:
valid_model = self.search_space.create_models(
samples=[sequence], model_input_shape=self.model_input_shape)
true_sequence = True if (valid_model[0] is not None
and valid_sequence is True) else False
if len(actions) < self.max_no_of_layers - 1:
for _ in range((self.max_no_of_layers - 1) - len(actions)):
actions.append(0)
return actions, distributions, true_sequence
def get_valid_action(self, state: np.ndarray) -> (List, np.ndarray, int):
true_sequence = False
counter = 0
while not true_sequence:
counter += 1
actions = []
distributions = self.model.predict(state)
for distribution in distributions[0]:
distribution /= np.sum(distribution)
action = np.random.choice(self.len_search_space,
1,
p=distribution)[0]
action = 1 if action == 0 else action
actions.append(int(action))
if action == self.end_token:
break
sequence = actions + [
self.end_token
] if self.end_token not in actions else actions
valid_sequence = self.search_space.check_sequence(sequence)
if valid_sequence:
valid_model = self.search_space.create_models(
samples=[sequence],
model_input_shape=self.model_input_shape)
true_sequence = True if (valid_model[0] is not None
and valid_sequence is True) else False
if counter > self.valid_sequence_timeout:
return None, None, None # timeout
if len(actions) < self.max_no_of_layers - 1:
for _ in range((self.max_no_of_layers - 1) - len(actions)):
actions.append(0)
return actions, distributions, counter - 1
def remember(self, state, actions, prob, reward):
model_output_shape = (self.max_no_of_layers - 1, self.len_search_space)
encoded_action = np.zeros(model_output_shape, np.float32)
for i, action in enumerate(actions):
encoded_action[i][action] = 1
self.gradients.append(encoded_action - prob)
self.states.append(state)
self.rewards.append(reward)
self.probs.append(prob)
def clear_memory(self):
self.states.clear()
self.gradients.clear()
self.rewards.clear()
self.probs.clear()
def get_discounted_rewards(self, rewards_in):
discounted_rewards = []
cumulative_total_return = 0
for reward in rewards_in[::-1]:
cumulative_total_return = (cumulative_total_return *
self.gamma) + reward
discounted_rewards.insert(0, cumulative_total_return)
mean_rewards = np.mean(discounted_rewards)
std_rewards = np.std(discounted_rewards)
norm_discounted_rewards = (discounted_rewards -
mean_rewards) / (std_rewards + 1e-7)
return norm_discounted_rewards
def update_policy(self):
states_ = np.vstack(self.states)
gradients_ = np.vstack(self.gradients)
rewards_ = np.vstack(self.rewards)
discounted_rewards = self.get_discounted_rewards(rewards_)
discounted_rewards = discounted_rewards.reshape(
discounted_rewards.shape[0], discounted_rewards.shape[1],
discounted_rewards.shape[1])
gradients_ *= discounted_rewards
gradients_ = self.alpha * gradients_ + np.vstack(self.probs)
history = self.model.train_on_batch(states_, gradients_)
self.clear_memory()
return history
def generate_sequence_naive(self, mode: str):
token_keys = list(self.tokens.keys())
if mode == "b": # Brute-force
space = itertools.permutations(token_keys,
self.max_no_of_layers - 1)
return space
if mode == "r": # Random
sequence = []
sequence_length = np.random.randint(3, self.max_no_of_layers)
for i in range(sequence_length):
token = np.random.choice(token_keys)
sequence.append(token)
return sequence
if mode == "r_var_len":
sequence = []
length = np.random.randint(12 - 1, self.max_no_of_layers, 1)[0]
for i in range(length):
token = np.random.choice(token_keys)
sequence.append(token)
sequence.append(token_keys[-1])
return sequence
# 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)
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)
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)
# 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)
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)
