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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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import os, sys, copy, torch, numpy as np
from collections import OrderedDict
def print_information(information, extra_info=None, show=False):
dataset_names = information.get_dataset_names()
strings = [information.arch_str, 'datasets : {:}, extra-info : {:}'.format(dataset_names, extra_info)]
def metric2str(loss, acc):
return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc)
for ida, dataset in enumerate(dataset_names):
flop, param, latency = information.get_comput_costs(dataset)
str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency > 0 else None)
train_loss, train_acc = information.get_metrics(dataset, 'train')
if dataset == 'cifar10-valid':
valid_loss, valid_acc = information.get_metrics(dataset, 'x-valid')
str2 = '{:14s} train : [{:}], valid : [{:}]'.format(dataset, metric2str(train_loss, train_acc), metric2str(valid_loss, valid_acc))
elif dataset == 'cifar10':
test__loss, test__acc = information.get_metrics(dataset, 'ori-test')
str2 = '{:14s} train : [{:}], test : [{:}]'.format(dataset, metric2str(train_loss, train_acc), metric2str(test__loss, test__acc))
else:
valid_loss, valid_acc = information.get_metrics(dataset, 'x-valid')
test__loss, test__acc = information.get_metrics(dataset, 'x-test')
str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_loss, train_acc), metric2str(valid_loss, valid_acc), metric2str(test__loss, test__acc))
strings += [str1, str2]
if show: print('\n'.join(strings))
return strings
class AANASBenchAPI(object):
def __init__(self, file_path_or_dict, verbose=True):
if isinstance(file_path_or_dict, str):
if verbose: print('try to create AA-NAS-Bench api from {:}'.format(file_path_or_dict))
assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict)
file_path_or_dict = torch.load(file_path_or_dict)
else:
file_path_or_dict = copy.deepcopy( file_path_or_dict )
assert isinstance(file_path_or_dict, dict), 'It should be a dict instead of {:}'.format(type(file_path_or_dict))
keys = ('meta_archs', 'arch2infos', 'evaluated_indexes')
for key in keys: assert key in file_path_or_dict, 'Can not find key[{:}] in the dict'.format(key)
self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
self.arch2infos = OrderedDict()
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
self.arch2infos[xkey] = ArchResults.create_from_state_dict( file_path_or_dict['arch2infos'][xkey] )
self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
self.archstr2index = {}
for idx, arch in enumerate(self.meta_archs):
#assert arch.tostr() not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch.tostr()])
assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch])
self.archstr2index[ arch ] = idx
def __getitem__(self, index):
return copy.deepcopy( self.meta_archs[index] )
def __len__(self):
return len(self.meta_archs)
def __repr__(self):
return ('{name}({num}/{total} architectures)'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs)))
def query_index_by_arch(self, arch):
if isinstance(arch, str):
if arch in self.archstr2index: arch_index = self.archstr2index[ arch ]
else : arch_index = -1
elif hasattr(arch, 'tostr'):
if arch.tostr() in self.archstr2index: arch_index = self.archstr2index[ arch.tostr() ]
else : arch_index = -1
else: arch_index = -1
return arch_index
def query_by_arch(self, arch):
arch_index = self.query_index_by_arch(arch)
if arch_index == -1: return None
if arch_index in self.arch2infos:
strings = print_information(self.arch2infos[ arch_index ], 'arch-index={:}'.format(arch_index))
return '\n'.join(strings)
else:
print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index))
return None
def query_by_index(self, arch_index, dataname):
assert arch_index in self.arch2infos, 'arch_index [{:}] does not in arch2info'.format(arch_index)
archInfo = copy.deepcopy( self.arch2infos[ arch_index ] )
assert dataname in archInfo.get_dataset_names(), 'invalid dataset-name : {:}'.format(dataname)
info = archInfo.query(dataname)
return info
def query_meta_info_by_index(self, arch_index):
assert arch_index in self.arch2infos, 'arch_index [{:}] does not in arch2info'.format(arch_index)
archInfo = copy.deepcopy( self.arch2infos[ arch_index ] )
return archInfo
def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None):
best_index, highest_accuracy = -1, None
for i, idx in enumerate(self.evaluated_indexes):
flop, param, latency = self.arch2infos[idx].get_comput_costs(dataset)
if FLOP_max is not None and flop > FLOP_max : continue
if Param_max is not None and param > Param_max: continue
loss, accuracy = self.arch2infos[idx].get_metrics(dataset, metric_on_set)
if best_index == -1:
best_index, highest_accuracy = idx, accuracy
elif highest_accuracy < accuracy:
best_index, highest_accuracy = idx, accuracy
return best_index
def arch(self, index):
assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
return copy.deepcopy(self.meta_archs[index])
def show(self, index=-1):
if index == -1: # show all architectures
print(self)
for i, idx in enumerate(self.evaluated_indexes):
print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
print('arch : {:}'.format(self.meta_archs[idx]))
strings = print_information(self.arch2infos[idx])
print('>' * 20)
print('\n'.join(strings))
print('<' * 20)
else:
if 0 <= index < len(self.meta_archs):
if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
else:
strings = print_information(self.arch2infos[index])
print('\n'.join(strings))
else:
print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
class ArchResults(object):
def __init__(self, arch_index, arch_str):
self.arch_index = int(arch_index)
self.arch_str = copy.deepcopy(arch_str)
self.all_results = dict()
self.dataset_seed = dict()
self.clear_net_done = False
def get_comput_costs(self, dataset):
x_seeds = self.dataset_seed[dataset]
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
flops = [result.flop for result in results]
params = [result.params for result in results]
lantencies = [result.get_latency() for result in results]
return np.mean(flops), np.mean(params), np.mean(lantencies)
def get_metrics(self, dataset, setname, iepoch=None):
x_seeds = self.dataset_seed[dataset]
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
loss, accuracy = [], []
for result in results:
if setname == 'train':
info = result.get_train(iepoch)
else:
info = result.get_eval(setname, iepoch)
loss.append( info['loss'] )
accuracy.append( info['accuracy'] )
return float(np.mean(loss)), float(np.mean(accuracy))
def show(self, is_print=False):
return print_information(self, None, is_print)
def get_dataset_names(self):
return list(self.dataset_seed.keys())
def query(self, dataset, seed=None):
if seed is None:
x_seeds = self.dataset_seed[dataset]
return [self.all_results[ (dataset, seed) ] for seed in x_seeds]
else:
return self.all_results[ (dataset, seed) ]
def arch_idx_str(self):
return '{:06d}'.format(self.arch_index)
def update(self, dataset_name, seed, result):
if dataset_name not in self.dataset_seed:
self.dataset_seed[dataset_name] = []
assert seed not in self.dataset_seed[dataset_name], '{:}-th arch alreadly has this seed ({:}) on {:}'.format(self.arch_index, seed, dataset_name)
self.dataset_seed[ dataset_name ].append( seed )
self.dataset_seed[ dataset_name ] = sorted( self.dataset_seed[ dataset_name ] )
assert (dataset_name, seed) not in self.all_results
self.all_results[ (dataset_name, seed) ] = result
self.clear_net_done = False
def state_dict(self):
state_dict = dict()
for key, value in self.__dict__.items():
if key == 'all_results': # contain the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
assert isinstance(_v, ResultsCount), 'invalid type of value for {:}/{:} : {:}'.format(key, _k, type(_v))
xvalue[_k] = _v.state_dict()
else:
xvalue = value
state_dict[key] = xvalue
return state_dict
def load_state_dict(self, state_dict):
new_state_dict = dict()
for key, value in state_dict.items():
if key == 'all_results': # to convert to the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
xvalue[_k] = ResultsCount.create_from_state_dict(_v)
else: xvalue = value
new_state_dict[key] = xvalue
self.__dict__.update(new_state_dict)
@staticmethod
def create_from_state_dict(state_dict_or_file):
x = ArchResults(-1, -1)
if isinstance(state_dict_or_file, str): # a file path
state_dict = torch.load(state_dict_or_file)
elif isinstance(state_dict_or_file, dict):
state_dict = state_dict_or_file
else:
raise ValueError('invalid type of state_dict_or_file : {:}'.format(type(state_dict_or_file)))
x.load_state_dict(state_dict)
return x
def clear_params(self):
for key, result in self.all_results.items():
result.net_state_dict = None
self.clear_net_done = True
def __repr__(self):
return ('{name}(arch-index={index}, arch={arch}, {num} runs, clear={clear})'.format(name=self.__class__.__name__, index=self.arch_index, arch=self.arch_str, num=len(self.all_results), clear=self.clear_net_done))
class ResultsCount(object):
def __init__(self, name, state_dict, train_accs, train_losses, params, flop, arch_config, seed, epochs, latency):
self.name = name
self.net_state_dict = state_dict
self.train_accs = copy.deepcopy(train_accs)
self.train_losses = copy.deepcopy(train_losses)
self.arch_config = copy.deepcopy(arch_config)
self.params = params
self.flop = flop
self.seed = seed
self.epochs = epochs
self.latency = latency
# evaluation results
self.reset_eval()
def reset_eval(self):
self.eval_names = []
self.eval_accs = {}
self.eval_losses = {}
def update_latency(self, latency):
self.latency = copy.deepcopy( latency )
def get_latency(self):
if self.latency is None: return -1
else: return sum(self.latency) / len(self.latency)
def update_eval(self, name, accs, losses):
assert name not in self.eval_names, '{:} has already added'.format(name)
self.eval_names.append( name )
self.eval_accs[name] = copy.deepcopy( accs )
self.eval_losses[name] = copy.deepcopy( losses )
def __repr__(self):
num_eval = len(self.eval_names)
return ('{name}({xname}, arch={arch}, FLOP={flop:.2f}M, Param={param:.3f}MB, seed={seed}, {num_eval} eval-sets)'.format(name=self.__class__.__name__, xname=self.name, arch=self.arch_config['arch_str'], flop=self.flop, param=self.params, seed=self.seed, num_eval=num_eval))
def valid_evaluation_set(self):
return self.eval_names
def get_train(self, iepoch=None):
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
return {'loss': self.train_losses[iepoch], 'accuracy': self.train_accs[iepoch]}
def get_eval(self, name, iepoch=None):
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
return {'loss': self.eval_losses[name][iepoch], 'accuracy': self.eval_accs[name][iepoch]}
def get_net_param(self):
return self.net_state_dict
def state_dict(self):
_state_dict = {key: value for key, value in self.__dict__.items()}
return _state_dict
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
@staticmethod
def create_from_state_dict(state_dict):
x = ResultsCount(None, None, None, None, None, None, None, None, None, None)
x.load_state_dict(state_dict)
return x