Reformulate via black
This commit is contained in:
D-X-Y
@@ -10,212 +10,245 @@ from pathlib import Path
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import torch
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import torch.nn as nn
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from torch.distributions import Categorical
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from nas_201_api import NASBench201API as API
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from models import CellStructure, get_search_spaces
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from nas_201_api import NASBench201API as API
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from models import CellStructure, get_search_spaces
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from R_EA import train_and_eval
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class Policy(nn.Module):
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def __init__(self, max_nodes, search_space):
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super(Policy, self).__init__()
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self.max_nodes = max_nodes
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self.search_space = deepcopy(search_space)
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self.edge2index = {}
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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self.edge2index[node_str] = len(self.edge2index)
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self.arch_parameters = nn.Parameter(1e-3 * torch.randn(len(self.edge2index), len(search_space)))
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def __init__(self, max_nodes, search_space):
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super(Policy, self).__init__()
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self.max_nodes = max_nodes
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self.search_space = deepcopy(search_space)
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self.edge2index = {}
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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self.edge2index[ node_str ] = len(self.edge2index)
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self.arch_parameters = nn.Parameter( 1e-3*torch.randn(len(self.edge2index), len(search_space)) )
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def generate_arch(self, actions):
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genotypes = []
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for i in range(1, self.max_nodes):
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xlist = []
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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op_name = self.search_space[actions[self.edge2index[node_str]]]
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xlist.append((op_name, j))
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genotypes.append(tuple(xlist))
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return CellStructure(genotypes)
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def generate_arch(self, actions):
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genotypes = []
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for i in range(1, self.max_nodes):
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xlist = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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op_name = self.search_space[ actions[ self.edge2index[ node_str ] ] ]
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xlist.append((op_name, j))
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genotypes.append( tuple(xlist) )
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return CellStructure( genotypes )
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def genotype(self):
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genotypes = []
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for i in range(1, self.max_nodes):
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xlist = []
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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with torch.no_grad():
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weights = self.arch_parameters[self.edge2index[node_str]]
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op_name = self.search_space[weights.argmax().item()]
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xlist.append((op_name, j))
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genotypes.append(tuple(xlist))
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return CellStructure(genotypes)
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def genotype(self):
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genotypes = []
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for i in range(1, self.max_nodes):
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xlist = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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with torch.no_grad():
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weights = self.arch_parameters[ self.edge2index[node_str] ]
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op_name = self.search_space[ weights.argmax().item() ]
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xlist.append((op_name, j))
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genotypes.append( tuple(xlist) )
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return CellStructure( genotypes )
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def forward(self):
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alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
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return alphas
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def forward(self):
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alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
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return alphas
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class ExponentialMovingAverage(object):
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"""Class that maintains an exponential moving average."""
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"""Class that maintains an exponential moving average."""
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def __init__(self, momentum):
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self._numerator = 0
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self._denominator = 0
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self._momentum = momentum
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def __init__(self, momentum):
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self._numerator = 0
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self._denominator = 0
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self._momentum = momentum
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def update(self, value):
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self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
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self._denominator = self._momentum * self._denominator + (1 - self._momentum)
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def update(self, value):
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self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
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self._denominator = self._momentum * self._denominator + (1 - self._momentum)
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def value(self):
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"""Return the current value of the moving average"""
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return self._numerator / self._denominator
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def value(self):
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"""Return the current value of the moving average"""
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return self._numerator / self._denominator
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def select_action(policy):
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probs = policy()
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m = Categorical(probs)
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action = m.sample()
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#policy.saved_log_probs.append(m.log_prob(action))
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return m.log_prob(action), action.cpu().tolist()
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probs = policy()
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m = Categorical(probs)
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action = m.sample()
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# policy.saved_log_probs.append(m.log_prob(action))
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return m.log_prob(action), action.cpu().tolist()
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def main(xargs, nas_bench):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads( xargs.workers )
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(xargs.workers)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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if xargs.dataset == 'cifar10':
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dataname = 'cifar10-valid'
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else:
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dataname = xargs.dataset
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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config_path = 'configs/nas-benchmark/algos/R-EA.config'
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config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split) , num_workers=xargs.workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
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logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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extra_info = {'config': config, 'train_loader': train_loader, 'valid_loader': valid_loader}
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else:
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config_path = 'configs/nas-benchmark/algos/R-EA.config'
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config = load_config(config_path, None, logger)
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extra_info = {'config': config, 'train_loader': None, 'valid_loader': None}
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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search_space = get_search_spaces('cell', xargs.search_space_name)
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policy = Policy(xargs.max_nodes, search_space)
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optimizer = torch.optim.Adam(policy.parameters(), lr=xargs.learning_rate)
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#optimizer = torch.optim.SGD(policy.parameters(), lr=xargs.learning_rate)
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eps = np.finfo(np.float32).eps.item()
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baseline = ExponentialMovingAverage(xargs.EMA_momentum)
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logger.log('policy : {:}'.format(policy))
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logger.log('optimizer : {:}'.format(optimizer))
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logger.log('eps : {:}'.format(eps))
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if xargs.dataset == "cifar10":
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dataname = "cifar10-valid"
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else:
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dataname = xargs.dataset
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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split_Fpath = "configs/nas-benchmark/cifar-split.txt"
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log("Load split file from {:}".format(split_Fpath))
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(config_path, {"class_num": class_num, "xshape": xshape}, logger)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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logger.log(
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"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
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)
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)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {"config": config, "train_loader": train_loader, "valid_loader": valid_loader}
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else:
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(config_path, None, logger)
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extra_info = {"config": config, "train_loader": None, "valid_loader": None}
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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# nas dataset load
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logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
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search_space = get_search_spaces("cell", xargs.search_space_name)
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policy = Policy(xargs.max_nodes, search_space)
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optimizer = torch.optim.Adam(policy.parameters(), lr=xargs.learning_rate)
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# optimizer = torch.optim.SGD(policy.parameters(), lr=xargs.learning_rate)
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eps = np.finfo(np.float32).eps.item()
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baseline = ExponentialMovingAverage(xargs.EMA_momentum)
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logger.log("policy : {:}".format(policy))
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logger.log("optimizer : {:}".format(optimizer))
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logger.log("eps : {:}".format(eps))
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# REINFORCE
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# attempts = 0
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x_start_time = time.time()
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logger.log('Will start searching with time budget of {:} s.'.format(xargs.time_budget))
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total_steps, total_costs, trace = 0, 0, []
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#for istep in range(xargs.RL_steps):
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while total_costs < xargs.time_budget:
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start_time = time.time()
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log_prob, action = select_action( policy )
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arch = policy.generate_arch( action )
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reward, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
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trace.append( (reward, arch) )
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# accumulate time
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if total_costs + cost_time < xargs.time_budget:
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total_costs += cost_time
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else: break
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# nas dataset load
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logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
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baseline.update(reward)
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# calculate loss
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policy_loss = ( -log_prob * (reward - baseline.value()) ).sum()
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optimizer.zero_grad()
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policy_loss.backward()
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optimizer.step()
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# accumulate time
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total_costs += time.time() - start_time
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total_steps += 1
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logger.log('step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}'.format(total_steps, baseline.value(), policy_loss.item(), policy.genotype()))
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#logger.log('----> {:}'.format(policy.arch_parameters))
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#logger.log('')
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# REINFORCE
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# attempts = 0
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x_start_time = time.time()
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logger.log("Will start searching with time budget of {:} s.".format(xargs.time_budget))
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total_steps, total_costs, trace = 0, 0, []
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# for istep in range(xargs.RL_steps):
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while total_costs < xargs.time_budget:
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start_time = time.time()
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log_prob, action = select_action(policy)
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arch = policy.generate_arch(action)
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reward, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
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trace.append((reward, arch))
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# accumulate time
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if total_costs + cost_time < xargs.time_budget:
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total_costs += cost_time
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else:
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break
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# best_arch = policy.genotype() # first version
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best_arch = max(trace, key=lambda x: x[0])[1]
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logger.log('REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).'.format(total_steps, total_costs, time.time()-x_start_time))
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info = nas_bench.query_by_arch(best_arch, '200')
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if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
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else : logger.log('{:}'.format(info))
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logger.log('-'*100)
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch( best_arch )
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baseline.update(reward)
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# calculate loss
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policy_loss = (-log_prob * (reward - baseline.value())).sum()
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optimizer.zero_grad()
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policy_loss.backward()
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optimizer.step()
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# accumulate time
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total_costs += time.time() - start_time
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total_steps += 1
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logger.log(
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"step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}".format(
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total_steps, baseline.value(), policy_loss.item(), policy.genotype()
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)
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)
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# logger.log('----> {:}'.format(policy.arch_parameters))
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# logger.log('')
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# best_arch = policy.genotype() # first version
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best_arch = max(trace, key=lambda x: x[0])[1]
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logger.log(
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"REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).".format(
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total_steps, total_costs, time.time() - x_start_time
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)
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)
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info = nas_bench.query_by_arch(best_arch, "200")
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if info is None:
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logger.log("Did not find this architecture : {:}.".format(best_arch))
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else:
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logger.log("{:}".format(info))
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logger.log("-" * 100)
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("The REINFORCE Algorithm")
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parser.add_argument('--data_path', type=str, help='Path to dataset')
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parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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# channels and number-of-cells
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parser.add_argument('--search_space_name', type=str, help='The search space name.')
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parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
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parser.add_argument('--channel', type=int, help='The number of channels.')
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parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
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parser.add_argument('--learning_rate', type=float, help='The learning rate for REINFORCE.')
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#parser.add_argument('--RL_steps', type=int, help='The steps for REINFORCE.')
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parser.add_argument('--EMA_momentum', type=float, help='The momentum value for EMA.')
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parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')
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parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
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parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
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args = parser.parse_args()
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#if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
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nas_bench = None
|
||||
else:
|
||||
print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
|
||||
nas_bench = API(args.arch_nas_dataset)
|
||||
if args.rand_seed < 0:
|
||||
save_dir, all_indexes, num = None, [], 500
|
||||
for i in range(num):
|
||||
print ('{:} : {:03d}/{:03d}'.format(time_string(), i, num))
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
save_dir, index = main(args, nas_bench)
|
||||
all_indexes.append( index )
|
||||
torch.save(all_indexes, save_dir / 'results.pth')
|
||||
else:
|
||||
main(args, nas_bench)
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("The REINFORCE Algorithm")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
# channels and number-of-cells
|
||||
parser.add_argument("--search_space_name", type=str, help="The search space name.")
|
||||
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
|
||||
parser.add_argument("--channel", type=int, help="The number of channels.")
|
||||
parser.add_argument("--num_cells", type=int, help="The number of cells in one stage.")
|
||||
parser.add_argument("--learning_rate", type=float, help="The learning rate for REINFORCE.")
|
||||
# parser.add_argument('--RL_steps', type=int, help='The steps for REINFORCE.')
|
||||
parser.add_argument("--EMA_momentum", type=float, help="The momentum value for EMA.")
|
||||
parser.add_argument("--time_budget", type=int, help="The total time cost budge for searching (in seconds).")
|
||||
# log
|
||||
parser.add_argument("--workers", type=int, default=2, help="number of data loading workers (default: 2)")
|
||||
parser.add_argument("--save_dir", type=str, help="Folder to save checkpoints and log.")
|
||||
parser.add_argument(
|
||||
"--arch_nas_dataset", type=str, help="The path to load the architecture dataset (tiny-nas-benchmark)."
|
||||
)
|
||||
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
|
||||
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
|
||||
if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
|
||||
nas_bench = None
|
||||
else:
|
||||
print("{:} build NAS-Benchmark-API from {:}".format(time_string(), args.arch_nas_dataset))
|
||||
nas_bench = API(args.arch_nas_dataset)
|
||||
if args.rand_seed < 0:
|
||||
save_dir, all_indexes, num = None, [], 500
|
||||
for i in range(num):
|
||||
print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
save_dir, index = main(args, nas_bench)
|
||||
all_indexes.append(index)
|
||||
torch.save(all_indexes, save_dir / "results.pth")
|
||||
else:
|
||||
main(args, nas_bench)
|
||||
|
||||
Reference in New Issue
Block a user