def get_uniform_sample_cand(*, timeout=500):
idx = np.random.randint(len(bins))
l, r = bins[idx]
for i in range(timeout):
cand = get_random_cand()
if l * 1e6 <= get_cand_flops(cand) <= r * 1e6:
return cand
return get_random_cand()
def get_uniform_sample_cand(*,timeout=500):
idx = np.random.randint(len(bins))
l, r = bins[idx]
for i in range(timeout):
cand = get_random_cand()
if l*1e6 <= get_cand_flops(cand) <= r*1e6:
# print("the {} iters is {}.\n".format(iters, cand))
return cand
return get_random_cand()
def main():
info = torch.load('../Search/log/checkpoint.pth.tar')['vis_dict']
cands = sorted([cand for cand in info if 'err' in info[cand]],
key=lambda cand: info[cand]['err'])[:1]
dst_dir = 'data'
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
for cand in cands:
flops = get_cand_flops(cand)
dst_path = os.path.join(dst_dir, str(cand))
if os.path.exists(dst_path):
continue
print(cand, flops)
print(cand, info[cand]['err'], flops)
os.system('cp -r {} \'{}\''.format('template', dst_path))
with open(os.path.join(dst_path, 'arch.pkl'), 'wb') as f:
pickle.dump(cand, f)
def is_legal(self, cand):
assert isinstance(cand, tuple) and len(cand) == self.nr_layer
if cand not in self.vis_dict:
self.vis_dict[cand] = {}
info = self.vis_dict[cand]
if 'visited' in info:
return False
if 'flops' not in info:
info['flops'] = get_cand_flops(cand)
print(cand, info['flops'])
if info['flops'] > self.flops_limit:
print('flops limit exceed')
return False
info['err'] = get_cand_err(self.model, cand, self.args)
info['visited'] = True
return True
