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tune_optuna_NAS.py
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tune_optuna_NAS.py
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"""
NAS search for lightweight model(but with empty weights).
- Author: Junghoon Kim, Jongsun Shin
"""
import optuna
import torch
import torch.nn as nn
import torch.optim as optim
from src.dataloader import create_dataloader
from src.model import Model
from src.utils.torch_utils import model_info, check_runtime
from src.trainer import TorchTrainer, count_model_params
from typing import Any, Dict, List, Tuple
from optuna.pruners import HyperbandPruner
from subprocess import _args_from_interpreter_flags
import argparse
# from optuna.integration.wandb import WeightsAndBiasesCallback
import pandas as pd
from optuna.visualization import plot_contour
from optuna.visualization import plot_edf
from optuna.visualization import plot_intermediate_values
from optuna.visualization import plot_optimization_history
from optuna.visualization import plot_parallel_coordinate
from optuna.visualization import plot_param_importances
from optuna.visualization import plot_slice
import yaml
import os
EPOCH = 100
DATA_PATH = "../data" # type your data path here that contains test, train and val directories
RESULT_MODEL_PATH = "./result_model.pt" # result model will be saved in this path
def search_hyperparam(trial: optuna.trial.Trial) -> Dict[str, Any]:
"""Search hyperparam from user-specified search space."""
epochs = trial.suggest_int("epochs", low=20, high=20, step=2)
img_size = trial.suggest_categorical("img_size", [96, 112, 168, 224])
n_select = trial.suggest_int("n_select", low=0, high=6, step=2)
batch_size = trial.suggest_int("batch_size", low=32, high=128, step=32)
return {
"EPOCHS": epochs,
"IMG_SIZE": img_size,
"n_select": n_select,
"BATCH_SIZE": batch_size,
}
def search_model(trial: optuna.trial.Trial) -> List[Any]:
"""Search model structure from user-specified search space."""
model = []
n_stride = 0
MAX_NUM_STRIDE = 5
UPPER_STRIDE = 2 # 5(224 example): 224, 112, 56, 28, 14, 7
n_layers = trial.suggest_int("n_layers", 8, 12)
stride = 1
input_max = 64
input_min = 32
module_info = {}
### 몇개의 레이어를 쌓을지도 search하게 했습니다.
for i in range(n_layers):
out_channel = trial.suggest_int(f"{i+1}units", input_min, input_max)
block = trial.suggest_categorical(
f"m{i+1}",
["Conv", "DWConv", "InvertedResidualv2", "InvertedResidualv3", "MBConv"],
)
repeat = trial.suggest_int(f"m{i+1}/repeat", 1, 5)
m_stride = trial.suggest_int(f"m{i+1}/stride", low=1, high=UPPER_STRIDE)
if m_stride == 2:
stride += 1
if n_stride == 0:
m_stride = 2
if block == "Conv":
activation = trial.suggest_categorical(f"m{i+1}/activation", ["ReLU", "Hardswish"])
# Conv args: [out_channel, kernel_size, stride, padding, groups, activation]
args = [out_channel, 3, m_stride, None, 1, activation]
elif block == "DWConv":
activation = trial.suggest_categorical(f"m{i+1}/activation", ["ReLU", "Hardswish"])
# DWConv args: [out_channel, kernel_size, stride, padding_size, activation]
args = [out_channel, 3, 1, None, activation]
elif block == "InvertedResidualv2":
c = trial.suggest_int(f"m{i+1}/v2_c", low=input_min, high=input_max, step=16)
t = trial.suggest_int(f"m{i+1}/v2_t", low=1, high=4)
args = [c, t, m_stride]
elif block == "InvertedResidualv3":
kernel = trial.suggest_int(f"m{i+1}/kernel_size", low=3, high=5, step=2)
t = round(trial.suggest_float(f"m{i+1}/v3_t", low=1.0, high=6.0, step=0.1), 1)
c = trial.suggest_int(f"m{i+1}/v3_c", low=input_min, high=input_max, step=8)
se = trial.suggest_categorical(f"m{i+1}/v3_se", [0, 1])
hs = trial.suggest_categorical(f"m{i+1}/v3_hs", [0, 1])
# k t c SE HS s
args = [kernel, t, c, se, hs, m_stride]
elif block == "MBConv":
kernel = trial.suggest_int(f"m{i+1}/kernel_size", low=3, high=5, step=2)
c = trial.suggest_int(f"m{i+1}/efb0_c", low=input_min, high=input_max, step=8)
# args=[_,c]
in_features = out_channel
model.append([repeat, block, args])
if i % 2:
input_max *= 2
input_max = min(input_max, 160)
module_info[f"block{i+1}"] = {"type": block, "repeat": repeat, "stride": stride}
# last layer
last_dim = trial.suggest_int("last_dim", low=128, high=1024, step=128)
# We can setup fixed structure as well
model.append([1, "Conv", [last_dim, 1, 1]])
model.append([1, "GlobalAvgPool", []])
model.append([1, "FixedConv", [6, 1, 1, None, 1, None]])
return model, module_info
def objective(trial: optuna.trial.Trial, device, fp16) -> Tuple[float, int, float]:
"""Optuna objective.
Args:
trial
Returns:
float: score1(e.g. accuracy)
int: score2(e.g. params)
"""
model_config: Dict[str, Any] = {}
model_config["input_channel"] = 3
# img_size = trial.suggest_categorical("img_size", [32, 64, 128])
img_size = 32
model_config["INPUT_SIZE"] = [img_size, img_size]
model_config["depth_multiple"] = trial.suggest_categorical(
"depth_multiple", [0.25, 0.5, 0.75, 1.0]
)
model_config["width_multiple"] = trial.suggest_categorical(
"width_multiple", [0.25, 0.5, 0.75, 1.0]
)
model_config["backbone"], module_info = search_model(trial)
hyperparams = search_hyperparam(trial)
model = Model(model_config, verbose=True)
model.to(device)
model.model.to(device)
# check ./data_configs/data.yaml for config information
data_config: Dict[str, Any] = {}
data_config["DATA_PATH"] = DATA_PATH
data_config["DATASET"] = "TACO"
data_config["AUG_TRAIN"] = "randaugment_train"
data_config["AUG_TEST"] = "simple_augment_test"
data_config["AUG_TRAIN_PARAMS"] = {
"n_select": hyperparams["n_select"],
}
data_config["AUG_TEST_PARAMS"] = None
data_config["BATCH_SIZE"] = hyperparams["BATCH_SIZE"]
data_config["VAL_RATIO"] = 0.8
data_config["IMG_SIZE"] = hyperparams["IMG_SIZE"]
data_config["INIT_LR"] = 0.0001
data_config["EPOCHS"] = 100
"""이부분이 config를 저장하는 부분입니다. 위의 lr,fp16,epochs는 원래 함수에는 없지만
config를 바로 사용할 수 있게 추가했습니다."""
k = 1
file_name = f"search_model/model_{k}.yaml"
while os.path.exists(file_name):
print(k)
k += 1
file_name = f"search_model/model_{k}.yaml"
print(model_config)
"model config와 data config를 저장"
with open(f"search_model/model_{k}.yaml", "w") as outfile:
yaml.dump(model_config, outfile)
with open(f"search_model/data_{k}.yaml", "w") as outfile:
yaml.dump(data_config, outfile)
mean_time = check_runtime(
model.model,
[model_config["input_channel"]] + model_config["INPUT_SIZE"],
device,
)
model_info(model, verbose=True)
train_loader, val_loader, test_loader = create_dataloader(data_config)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=0.1,
steps_per_epoch=len(train_loader),
epochs=hyperparams["EPOCHS"],
pct_start=0.05,
)
scaler = torch.cuda.amp.GradScaler() if fp16 and device != torch.device("cpu") else None
trainer = TorchTrainer(
model,
criterion,
optimizer,
scheduler,
device=device,
verbose=1,
model_path=RESULT_MODEL_PATH,
scaler=scaler,
)
trainer.train(train_loader, hyperparams["EPOCHS"], val_dataloader=val_loader)
loss, f1_score, acc_percent = trainer.test(model, test_dataloader=val_loader)
params_nums = count_model_params(model)
model_info(model, verbose=True)
return f1_score, params_nums, mean_time
def get_best_trial_with_condition(optuna_study: optuna.study.Study) -> Dict[str, Any]:
"""Get best trial that satisfies the minimum condition(e.g. accuracy > 0.8).
Args:
study : Optuna study object to get trial.
Returns:
best_trial : Best trial that satisfies condition.
"""
df = optuna_study.trials_dataframe().rename(
columns={
"values_0": "acc_percent",
"values_1": "params_nums",
"values_2": "mean_time",
}
)
## minimum condition : accuracy >= threshold
threshold = 0.7
minimum_cond = df.acc_percent >= threshold
if minimum_cond.any():
df_min_cond = df.loc[minimum_cond]
## get the best trial idx with lowest parameter numbers
best_idx = df_min_cond.loc[
df_min_cond.params_nums == df_min_cond.params_nums.min()
].acc_percent.idxmax()
best_trial_ = optuna_study.trials[best_idx]
print("Best trial which satisfies the condition")
print(df.loc[best_idx])
else:
print("No trials satisfies minimum condition")
best_trial_ = None
return best_trial_
def tune(gpu_id, storage: str = None, fp16: bool = False):
if not torch.cuda.is_available():
device = torch.device("cpu")
elif 0 <= gpu_id < torch.cuda.device_count():
device = torch.device(f"cuda:{gpu_id}")
sampler = optuna.samplers.MOTPESampler()
if storage is not None:
rdb_storage = optuna.storages.RDBStorage(url=storage)
else:
rdb_storage = None
study = optuna.create_study(
directions=["maximize", "minimize", "minimize"],
study_name="automl",
sampler=sampler,
storage=rdb_storage,
load_if_exists=True,
)
study.optimize(lambda trial: objective(trial, device, fp16), n_trials=10)
pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED]
complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]
print("Study statistics: ")
print(" Number of finished trials: ", len(study.trials))
print(" Number of pruned trials: ", len(pruned_trials))
print(" Number of complete trials: ", len(complete_trials))
print("Best trials:")
best_trials = study.best_trials
## trials that satisfies Pareto Fronts
for tr in best_trials:
print(f" value1:{tr.values[0]}, value2:{tr.values[1]}")
for key, value in tr.params.items():
print(f" {key}:{value}")
best_trial = get_best_trial_with_condition(study)
print(best_trial)
df = study.trials_dataframe(attrs=("number", "value", "params", "state"))
df.to_csv("search_results.csv", index=False)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Optuna tuner.")
parser.add_argument("--gpu", default=0, type=int, help="GPU id to use")
parser.add_argument("--storage", default="", type=str, help="Optuna database storage path.")
parser.add_argument("--fp16", default=False, type=bool, help="train to fp16")
args = parser.parse_args()
tune(args.gpu, storage=args.storage if args.storage != "" else None, fp16=args.fp16)