blob: 8fd5e8f4451682ad43ad25cd41e95131682d66fc [file] [log] [blame]
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import random
from src.controller.core.sample import Sampler
from src.search_space.core.model_params import ModelMicroCfg
from src.search_space.core.space import SpaceWrapper
class SequenceSampler(Sampler):
def __init__(self, space: SpaceWrapper):
super().__init__(space)
self.arch_gene = self.space.sample_all_models()
def sample_next_arch(self, sorted_model: list = None) -> (str, ModelMicroCfg):
"""
Sample one random architecture, can sample max 10k architectures.
:return: arch_id, architecture
"""
try:
arch_id, arch_micro = self.arch_gene.__next__()
return arch_id, arch_micro
except Exception as e:
if "StopIteration" in str(e):
print("the end")
return None, None
else:
print("Error", str(e))
return None, None
def fit_sampler(self, score: float):
pass