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AutoML_architecture.py
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# -*- coding: utf-8 -*-
import os
os.environ['OMP_NUM_THREADS'] = '1'
import sys
import pickle
import logging
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torchvision.models as models
import numpy as np
from pthflops import count_ops
from hyperas import optim as hyperas_optim
from hyperopt import Trials, STATUS_OK, tpe
from hyperas.distributions import choice, uniform
from hyperas.utils import eval_hyperopt_space
from data_utils import *
from train_tools import *
from models import *
from counting import *
def _logging():
fpath = './results/AutoML/architecture_search.log'
logger = logging.getLogger('Architecture Search')
logger.setLevel(logging.DEBUG)
if not logger.handlers:
handler = logging.FileHandler(fpath)
handler.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
def _get_conf():
with open('./tmp.pickle', 'rb') as f:
conf_name = pickle.load(f)
opt = ConfLoader(conf_name).opt
return opt
def _get_num_params(model):
num = 0
for params in model.parameters():
num += params.view(-1).shape[0]
return num
def data():
opt = _get_conf()
DATASETTER = {'cifar10': cifar_10_setter,
'cifar100': cifar_100_setter}
CRITERION = {'mse': nn.MSELoss,
'cross_entropy': nn.CrossEntropyLoss,
'label_smoothing': LabelSmoothingLoss}
OPTIMIZER = {'sgd': optim.SGD,
'adam': optim.Adam,
'adagrad': optim.Adagrad,
'rmsprop': optim.RMSprop,
'radam': RAdam}
SCHEDULER = {'step': lr_scheduler.StepLR,
'multistep': lr_scheduler.MultiStepLR,
'cosine': lr_scheduler.CosineAnnealingLR}
dataloaders, dataset_sizes = DATASETTER[opt.data.dataset](batch_size=opt.data.batch_size,
valid_size=opt.data.valid_size,
root=opt.data.root,
fixed_valid=opt.data.fixed_valid,
autoaugment=opt.data.autoaugment,
aug_policy=opt.data.aug_policy)
return dataloaders, dataset_sizes
def create_model(dataloaders, dataset_sizes):
opt = _get_conf()
logger = _logging()
#don't know = {{choice(['i', 'dont', 'know'])}}
stride_where = {{choice(['third', 'fourth', 'none'])}}
if stride_where == 'five':
blocks_args = [
'r%s_k%s_s1_e1_i24_o16_se%.2f' % ({{choice([1, 2])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i16_o24_se%.2f' % ({{choice([1, 2])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s2_e6_i24_o40_se%.2f'% ({{choice([1, 2, 3])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i40_o56_se%.2f' % ({{choice([2, 3])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i56_o72_se%.2f' % ({{choice([2, 3])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s2_e6_i72_o88_se%.2f' % ({{choice([2, 3, 4])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i188_o104_se%.2f' % ({{choice([1, 2])}}, {{choice([2, 3])}}, {{uniform(0.2, 0.4)}})
]
elif stride_where == 'six':
blocks_args = [
'r%s_k%s_s1_e1_i24_o16_se%.2f' % ({{choice([1, 2])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i16_o24_se%.2f' % ({{choice([1, 2])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i24_o40_se%.2f'% ({{choice([1, 2, 3])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s2_e6_i40_o56_se%.2f' % ({{choice([2, 3])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i56_o72_se%.2f' % ({{choice([2, 3])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s2_e6_i72_o88_se%.2f' % ({{choice([2, 3, 4])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i88_o104_se%.2f' % ({{choice([1, 2])}}, {{choice([2, 3])}}, {{uniform(0.2, 0.4)}})
]
else:
blocks_args = [
'r%s_k%s_s1_e1_i24_o16_se%.2f' % ({{choice([1, 2])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i16_o24_se%.2f' % ({{choice([1, 2])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i24_o40_se%.2f'% ({{choice([1, 2, 3])}}, {{choice([3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i40_o56_se%.2f' % ({{choice([1, 2])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i56_o72_se%.2f' % ({{choice([1, 2])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s2_e6_i72_o88_se%.2f' % ({{choice([2, 3, 4])}}, {{choice([2, 3, 5])}}, {{uniform(0.2, 0.4)}}),
'r%s_k%s_s1_e6_i88_o104_se%.2f' % ({{choice([1, 2])}}, {{choice([2, 3])}}, {{uniform(0.2, 0.4)}})
]
blocks_args, global_params = efficientnet(blocks_args=blocks_args,
activation='swish',
activation_param={},
resolution_coefficient=1,
width_coefficient=1,
depth_coefficient=1,
image_size=opt.model.param.image_size,
num_classes=opt.model.param.num_classes)
model = EfficientNet(blocks_args,
global_params)
criterion = CRITERION[opt.criterion.algo](**opt.criterion.param) if opt.criterion.get('param') else CRITERION[opt.criterion.algo]()
optimizer = OPTIMIZER[opt.optimizer.algo](model.parameters(), **opt.optimizer.param) if opt.optimizer.get('param') else OPTIMIZER[opt.optimizer.algo](model.parameters())
# if not use scheduler, you can skip in config json file
if opt.scheduler.get('enabled', False):
scheduler_type = lr_scheduler.MultiStepLR if opt.scheduler.type == 'multistep' else lr_scheduler.CosineAnnealingLR if opt.scheduler.type == 'cosine' else lr_scheduler.StepLR
scheduler = scheduler_type(optimizer, **opt.scheduler.param)
else:
scheduler = None
train_handler = TrainHandler(model,
dataloaders,
dataset_sizes,
criterion,
optimizer,
scheduler,
device=opt.trainhandler.device,
path=opt.trainhandler.path,
mixup=opt.trainhandler.mixup.enabled,
alpha=opt.trainhandler.mixup.alpha,
precision=opt.trainhandler.precision)
train_handler.set_name(opt.trainhandler.name)
train_losses, valid_losses, train_accs, valid_accs = train_handler.train_model(num_epochs=opt.trainhandler.train.num_epochs)
_, valid_loss = sorted(valid_losses, key = lambda x: x[1])[0]
_, valid_acc = sorted(valid_accs, key = lambda x: x[1], reverse=True)[0]
logger.info("model valid loss and valid acc : {:.4f} and {:.2f}%".format(valid_loss, valid_acc*100))
conv_stem = {'kernel': 3, 'stride': 2, 'out_channel': 24}
last_ops = {'out_channel': 150, 'num_classes': 100}
activation = 'swish'
input_size = 32
use_bias = False
counter = MicroNetCounter(conv_stem, blocks_args, global_params, last_ops, activation, input_size, use_bias, add_bits_base=32, mul_bits_base=32)
# Constants
INPUT_BITS = 16
ACCUMULATOR_BITS = 16
PARAMETER_BITS = INPUT_BITS
SUMMARIZE_BLOCKS = True
SPARSITY = 0
params, flops, _, _ = counter.print_summary(SPARSITY, PARAMETER_BITS, ACCUMULATOR_BITS, INPUT_BITS, summarize_blocks=SUMMARIZE_BLOCKS)
logger.info("flops: {:.4f}M, params: {:.4f}MBytes".format(flops, params))
logger.info('score: {:.4f} + {:.4f} = {:.4f}'.format(flops/(10490), params/(36.5*4), flops/(10490) + params/(36.5*4)))
logger.info('#'*50)
return {'loss': valid_loss, 'status': STATUS_OK, 'model': train_handler.model}
if __name__ == "__main__":
conf_name = sys.argv[1]
with open('./tmp.pickle', 'wb') as f:
pickle.dump(conf_name, f)
fpath = './results/AutoML'
if not os.path.isdir(fpath):
os.makedirs(fpath)
if os.path.isfile('./results/AutoML/architecture_search.log'):
os.remove('./results/AutoML/architecture_search.log')
opt = ConfLoader(conf_name).opt
logger = _logging()
DATASETTER = {'cifar10': cifar_10_setter,
'cifar100': cifar_100_setter}
CRITERION = {'mse': nn.MSELoss,
'cross_entropy': nn.CrossEntropyLoss,
'label_smoothing': LabelSmoothingLoss}
OPTIMIZER = {'sgd': optim.SGD,
'adam': optim.Adam,
'adagrad': optim.Adagrad,
'rmsprop': optim.RMSprop,
'radam': RAdam}
SCHEDULER = {'step': lr_scheduler.StepLR,
'multistep': lr_scheduler.MultiStepLR,
'cosine': lr_scheduler.CosineAnnealingLR}
trials = Trials()
best_run, best_model, space = hyperas_optim.minimize(model=create_model,
data=data,
algo=tpe.suggest,
functions=[_get_conf, _logging, _get_num_params],
max_evals=1,
trials=trials,
eval_space=True,
return_space=True)
logger.info("Best performing model chosen hyper-parameters: %s" % best_run)
dataloaders, dataset_sizes = DATASETTER[opt.data.dataset](batch_size=opt.data.batch_size,
valid_size=opt.data.valid_size,
root=opt.data.root,
fixed_valid=opt.data.fixed_valid,
autoaugment=opt.data.autoaugment,
aug_policy=opt.data.aug_policy)
for t, trial in enumerate(trials):
vals = trial.get('misc').get('vals')
print("Trial %s vals: %s" % (t, vals))
tmp = {}
for k,v in list(vals.items()):
tmp[k] = v[0]
logger.info('Trial %d : %s' % (t, eval_hyperopt_space(space, tmp)))
criterion = CRITERION[opt.criterion.algo](**opt.criterion.param) if opt.criterion.get('param') else CRITERION[opt.criterion.algo]()
optimizer = OPTIMIZER[opt.optimizer.algo](best_model.parameters(), **opt.optimizer.param) if opt.optimizer.get('param') else OPTIMIZER[opt.optimizer.algo](model.parameters())
# if not use scheduler, you can skip in config json file
if opt.scheduler.get('enabled', False):
scheduler_type = lr_scheduler.MultiStepLR if opt.scheduler.type == 'multistep' else lr_scheduler.CosineAnnealingLR if opt.scheduler.type == 'cosine' else lr_scheduler.StepLR
scheduler = scheduler_type(optimizer, **opt.scheduler.param)
else:
scheduler = None
train_handler = TrainHandler(best_model,
dataloaders,
dataset_sizes,
criterion,
optimizer,
scheduler,
device=opt.trainhandler.device,
path=opt.trainhandler.path,
mixup=opt.trainhandler.mixup.enabled,
alpha=opt.trainhandler.mixup.alpha,
precision=opt.trainhandler.precision)
train_handler.test_model()
os.remove('./tmp.pickle')