class DblockNas(SearchAlgorithm):
"""DnetNas.
:param search_space: input search_space
:type: SeachSpace
"""
config = DblockNasConfig()
def __init__(self, search_space=None, **kwargs):
"""Init DnetNas."""
super(DblockNas, self).__init__(search_space, **kwargs)
# ea or random
self.max_sample = self.config.range.max_sample
self.min_sample = self.config.range.min_sample
self.sample_count = 0
logging.info("inited DblockNas")
self.pareto_front = ParetoFront(self.config.pareto.object_count,
self.config.pareto.max_object_ids)
self._best_desc_file = 'nas_model_desc.json'
@property
def is_completed(self):
"""Check if NAS is finished."""
return self.sample_count > self.max_sample
def search(self):
"""Search in search_space and return a sample."""
sample = {}
while sample is None or 'code' not in sample:
# pareto_dict = self.pareto_front.get_pareto_front()
# pareto_list = list(pareto_dict.values())
sample_desc = self.search_space.sample()
sample = self.codec.encode(sample_desc)
if not self.pareto_front._add_to_board(id=self.sample_count + 1,
config=sample):
sample = None
self.sample_count += 1
sample_desc = self.codec.decode(sample)
logging.info(f"sample: {sample_desc['network.backbone.encoding']}")
return dict(worker_id=self.sample_count, encoded_desc=sample_desc)
def update(self, record):
"""Use train and evaluate result to update algorithm.
:param performance: performance value from trainer or evaluator
"""
perf = record.get("rewards")
worker_id = record.get("worker_id")
logging.info("update performance={}".format(perf))
self.pareto_front.add_pareto_score(worker_id, perf)
@property
def max_samples(self):
"""Get max samples number."""
return self.max_sample
def __init__(self, search_space=None, **kwargs):
"""Init DnetNas."""
super(DblockNas, self).__init__(search_space, **kwargs)
# ea or random
self.max_sample = self.config.range.max_sample
self.min_sample = self.config.range.min_sample
self.sample_count = 0
logging.info("inited DblockNas")
self.pareto_front = ParetoFront(self.config.pareto.object_count,
self.config.pareto.max_object_ids)
self._best_desc_file = 'nas_model_desc.json'
def __init__(self, search_space=None, **kwargs):
"""Init BackboneNas."""
super(BackboneNas, self).__init__(search_space, **kwargs)
# ea or random
self.num_mutate = self.config.policy.num_mutate
self.random_ratio = self.config.policy.random_ratio
self.max_sample = self.config.range.max_sample
self.min_sample = self.config.range.min_sample
self.sample_count = 0
logging.info("inited BackboneNas")
self.pareto_front = ParetoFront(self.config.pareto.object_count,
self.config.pareto.max_object_ids)
self._best_desc_file = 'nas_model_desc.json'
def __init__(self, search_space=None, **kwargs):
"""Init DnetNas."""
super(DnetNas, self).__init__(search_space, **kwargs)
# ea or random
self.num_mutate = self.config.policy.num_mutate
self.random_ratio = self.config.policy.random_ratio
self.max_sample = self.config.range.max_sample
self.min_sample = self.config.range.min_sample
self.sample_count = 0
logging.info("inited DnetNas")
self.pareto_front = ParetoFront(self.config.pareto.object_count,
self.config.pareto.max_object_ids)
self._best_desc_file = 'nas_model_desc.json'
block_nas_folder = ModelConfig.models_folder.format(
local_base_path=self.local_base_path)
logging.info(f'folder: {block_nas_folder}')
base_reports = ReportServer().load_records_from_model_folder(
block_nas_folder)
logging.info(f'base_reports: {base_reports}')
self.base_block = base_reports[0].desc['backbone']['encoding'].split(
'_')[0]
class BackboneNas(SearchAlgorithm):
"""BackboneNas.
:param search_space: input search_space
:type: SeachSpace
"""
config = BackboneNasConfig()
def __init__(self, search_space=None, **kwargs):
"""Init BackboneNas."""
super(BackboneNas, self).__init__(search_space, **kwargs)
# ea or random
self.num_mutate = self.config.policy.num_mutate
self.random_ratio = self.config.policy.random_ratio
self.max_sample = self.config.range.max_sample
self.min_sample = self.config.range.min_sample
self.sample_count = 0
logging.info("inited BackboneNas")
self.pareto_front = ParetoFront(self.config.pareto.object_count,
self.config.pareto.max_object_ids)
self._best_desc_file = 'nas_model_desc.json'
@property
def is_completed(self):
"""Check if NAS is finished."""
return self.sample_count > self.max_sample
def search(self):
"""Search in search_space and return a sample."""
sample = {}
while sample is None or 'code' not in sample:
pareto_dict = self.pareto_front.get_pareto_front()
pareto_list = list(pareto_dict.values())
if self.pareto_front.size < self.min_sample or random.random(
) < self.random_ratio or len(pareto_list) == 0:
sample_desc = self.search_space.sample()
sample = self.codec.encode(sample_desc)
else:
sample = pareto_list[0]
if sample is not None and 'code' in sample:
code = sample['code']
code = self.ea_sample(code)
sample['code'] = code
if not self.pareto_front._add_to_board(id=self.sample_count + 1,
config=sample):
sample = None
self.sample_count += 1
logging.info(sample)
sample_desc = self.codec.decode(sample)
print(sample_desc)
return dict(worker_id=self.sample_count, encoded_desc=sample_desc)
def random_sample(self):
"""Random sample from search_space."""
sample_desc = self.search_space.sample()
sample = self.codec.encode(sample_desc, is_random=True)
return sample
def ea_sample(self, code):
"""Use EA op to change a arch code.
:param code: list of code for arch
:type code: list
:return: changed code
:rtype: list
"""
new_arch = code.copy()
self._insert(new_arch)
self._remove(new_arch)
self._swap(new_arch[0], self.num_mutate // 2)
self._swap(new_arch[1], self.num_mutate // 2)
return new_arch
def update(self, record):
"""Use train and evaluate result to update algorithm.
:param performance: performance value from trainer or evaluator
"""
perf = record.get("original_rewards")
worker_id = record.get("worker_id")
logging.info("update performance={}".format(perf))
self.pareto_front.add_pareto_score(worker_id, perf)
def _insert(self, arch):
"""Random insert to arch code.
:param arch: input arch code
:type arch: list
:return: changed arch code
:rtype: list
"""
idx = np.random.randint(low=0, high=len(arch[0]))
arch[0].insert(idx, 1)
idx = np.random.randint(low=0, high=len(arch[1]))
arch[1].insert(idx, 1)
return arch
def _remove(self, arch):
"""Random remove one from arch code.
:param arch: input arch code
:type arch: list
:return: changed arch code
:rtype: list
"""
# random pop arch[0]
ones_index = [i for i, char in enumerate(arch[0]) if char == 1]
idx = random.choice(ones_index)
arch[0].pop(idx)
# random pop arch[1]
ones_index = [i for i, char in enumerate(arch[1]) if char == 1]
idx = random.choice(ones_index)
arch[1].pop(idx)
return arch
def _swap(self, arch, R):
"""Random swap one in arch code.
:param arch: input arch code
:type arch: list
:return: changed arch code
:rtype: list
"""
while True:
not_ones_index = [i for i, char in enumerate(arch) if char != 1]
idx = random.choice(not_ones_index)
r = random.randint(1, R)
direction = -r if random.random() > 0.5 else r
try:
arch[idx], arch[idx + direction] = arch[idx +
direction], arch[idx]
break
except Exception:
continue
return arch
@property
def max_samples(self):
"""Get max samples number."""
return self.max_sample
class DnetNas(SearchAlgorithm):
"""DnetNas.
:param search_space: input search_space
:type: SeachSpace
"""
config = DnetNasConfig()
def __init__(self, search_space=None, **kwargs):
"""Init DnetNas."""
super(DnetNas, self).__init__(search_space, **kwargs)
# ea or random
self.num_mutate = self.config.policy.num_mutate
self.random_ratio = self.config.policy.random_ratio
self.max_sample = self.config.range.max_sample
self.min_sample = self.config.range.min_sample
self.sample_count = 0
logging.info("inited DnetNas")
self.pareto_front = ParetoFront(self.config.pareto.object_count,
self.config.pareto.max_object_ids)
self._best_desc_file = 'nas_model_desc.json'
block_nas_folder = ModelConfig.models_folder.format(
local_base_path=self.local_base_path)
logging.info(f'folder: {block_nas_folder}')
base_reports = ReportServer().load_records_from_model_folder(
block_nas_folder)
logging.info(f'base_reports: {base_reports}')
self.base_block = base_reports[0].desc['backbone']['encoding'].split(
'_')[0]
@property
def is_completed(self):
"""Check if NAS is finished."""
return self.sample_count > self.max_sample
def search(self):
"""Search in search_space and return a sample."""
sample = {}
while sample is None or 'code' not in sample:
pareto_dict = self.pareto_front.get_pareto_front()
pareto_list = list(pareto_dict.values())
if self.pareto_front.size < self.min_sample or random.random(
) < self.random_ratio or len(pareto_list) == 0:
sample_desc = self.search_space.sample()
sample_desc.update({'block_coding': self.base_block})
sample = self.codec.encode(sample_desc)
else:
sample = pareto_list[0]
if sample is not None and 'code' in sample:
code = sample['code']
code = self.ea_sample(code)
sample['code'] = code
if not self.pareto_front._add_to_board(id=self.sample_count + 1,
config=sample):
sample = None
self.sample_count += 1
sample_desc = self.codec.decode(sample)
logging.info(f"sample: {sample_desc['network.backbone.encoding']}")
return dict(worker_id=self.sample_count, encoded_desc=sample_desc)
def ea_sample(self, code):
"""Use EA op to change a arch code.
:param code: list of code for arch
:type code: list
:return: changed code
:rtype: list
"""
new_arch = code.copy()
random_value = random.randint(0, 2)
if random_value == 0:
self._insert(new_arch)
elif random_value == 1:
self._remove(new_arch)
else:
self._swap(new_arch)
return new_arch
def update(self, record):
"""Use train and evaluate result to update algorithm.
:param performance: performance value from trainer or evaluator
"""
perf = record.get("rewards")
worker_id = record.get("worker_id")
logging.info("update performance={}".format(perf))
self.pareto_front.add_pareto_score(worker_id, perf)
def _insert(self, arch):
"""Random insert to arch code.
:param arch: input arch code
:type arch: list
:return: changed arch code
:rtype: list
"""
macro_coding = arch['network.backbone.macro_coding']
macro_coding_list = list(macro_coding)
idx = np.random.randint(low=0, high=len(macro_coding))
macro_coding_list.insert(idx, '1')
macro_coding_new = ''.join(macro_coding_list)
arch['network.backbone.macro_coding'] = macro_coding_new
print(f'insert: {macro_coding} --> {macro_coding_new}')
return arch
def _remove(self, arch):
"""Random remove one from arch code.
:param arch: input arch code
:type arch: list
:return: changed arch code
:rtype: list
"""
macro_coding = arch['network.backbone.macro_coding']
macro_coding_list = list(macro_coding)
while True:
idx = np.random.randint(low=0, high=len(macro_coding))
if macro_coding_list[idx] == '1':
macro_coding_list.pop(idx)
break
macro_coding_new = ''.join(macro_coding_list)
arch['network.backbone.macro_coding'] = macro_coding_new
print(f'remove: {macro_coding} --> {macro_coding_new}')
return arch
def _swap(self, arch):
"""Random swap one in arch code.
:param arch: input arch code
:type arch: list
:return: changed arch code
:rtype: list
"""
macro_coding = arch['network.backbone.macro_coding']
macro_coding_list = list(macro_coding)
variant_indexes = []
for index in range(len(macro_coding) - 1):
if macro_coding[index] != macro_coding[index + 1]:
variant_indexes.append(index)
while True:
idx = np.random.randint(low=0, high=len(variant_indexes))
index = variant_indexes[idx]
origin_code = macro_coding_list[index]
if index == len(macro_coding) - 2 and origin_code == '-':
continue
else:
break
macro_coding_list[index] = macro_coding_list[index + 1]
macro_coding_list[index + 1] = origin_code
macro_coding_new = ''.join(macro_coding_list)
arch['network.backbone.macro_coding'] = macro_coding_new
print(f'swap: {macro_coding} --> {macro_coding_new}')
return arch
@property
def max_samples(self):
"""Get max samples number."""
return self.max_sample