def pred_directory(args):
"""指定ディレクトリについてモデル予測"""
# ### test data load ### #
d_cls = get_train_valid_test.LabeledDataset(
[args["img_rows"], args["img_cols"], args["channels"]],
args["batch_size"],
valid_batch_size=args["batch_size"],
)
if args["is_flow"]:
# 指定ディレクトリの前処理済み画像、ラベル、ファイルパスロード
d_cls.X_test, d_cls.y_test, test_paths = base_dataset.load_my_data(
args["test_data_dir"],
classes=args["classes"],
img_height=args["img_rows"],
img_width=args["img_cols"],
channel=args["channels"],
is_pytorch=False,
)
d_cls.create_test_generator()
elif args["is_flow_from_directory"]:
d_cls.create_my_generator_flow_from_directory(
args["train_data_dir"],
args["classes"],
test_data_dir=args["test_data_dir"],
color_mode=args["color_mode"],
class_mode=args["class_mode"],
my_IDG_options={"rescale": 1 / 255.0},
)
# binaryラベルのgeneratorをマルチタスクgeneratorに変換するラッパー
if args["n_multitask"] > 1 and args["multitask_pred_n_node"] == 1:
d_cls.test_gen = get_train_valid_test.binary_generator_multi_output_wrapper(
d_cls.test_gen
)
# generator predict TTA
# load_model = keras.models.load_model(os.path.join(args['output_dir'], 'best_val_loss.h5'))
load_model = keras.models.load_model(
os.path.join(args["output_dir"], "best_val_accuracy.h5")
)
pred_tta = base_predict.predict_tta_generator(
load_model,
d_cls.test_gen,
TTA=args["TTA"],
TTA_rotate_deg=args["TTA_rotate_deg"],
TTA_crop_num=args["TTA_crop_num"],
TTA_crop_size=args["TTA_crop_size"],
resize_size=[args["img_rows"], args["img_cols"]],
)
pred_tta_df = base_predict.get_predict_generator_results(
pred_tta, d_cls.test_gen, classes_list=args["classes"]
)
# 混同行列作成
base_predict.conf_matrix_from_pred_classes_generator(
pred_tta_df, args["classes"], args["output_dir"]
)
def trial_train_directory(self, trial, args):
keras.backend.clear_session()
# ### train validation data load ### #
d_cls = get_train_valid_test.LabeledDataset(
[args["img_rows"], args["img_cols"], args["channels"]],
args["batch_size"],
valid_batch_size=args["batch_size"],
train_samples=len(util.find_img_files(args["train_data_dir"])),
valid_samples=len(util.find_img_files(
args["validation_data_dir"])),
)
if args["is_flow"]:
# 指定ディレクトリの前処理済み画像、ラベル、ファイルパスロード
d_cls.X_train, d_cls.y_train, train_paths = base_dataset.load_my_data(
args["train_data_dir"],
classes=args["classes"],
img_height=args["img_rows"],
img_width=args["img_cols"],
channel=args["channels"],
is_pytorch=False,
)
d_cls.X_valid, d_cls.y_valid, valid_paths = base_dataset.load_my_data(
args["validation_data_dir"],
classes=args["classes"],
img_height=args["img_rows"],
img_width=args["img_cols"],
channel=args["channels"],
is_pytorch=False,
)
d_cls.X_train, d_cls.X_valid = d_cls.X_train * 255.0, d_cls.X_valid * 255.0
d_cls.create_my_generator_flow(
my_IDG_options=args["my_IDG_options"])
elif args["is_flow_from_directory"]:
d_cls.create_my_generator_flow_from_directory(
args["train_data_dir"],
args["classes"],
valid_data_dir=args["validation_data_dir"],
color_mode=args["color_mode"],
class_mode=args["class_mode"],
my_IDG_options=args["my_IDG_options"],
)
# d_cls.train_gen_augmentor = d_cls.create_augmentor_util_from_directory(args['train_data_dir']
# , args['batch_size']
# , augmentor_options=args['train_augmentor_options'])
# binaryラベルのgeneratorをマルチタスクgeneratorに変換するラッパー
if args["n_multitask"] > 1 and args["multitask_pred_n_node"] == 1:
d_cls.train_gen = get_train_valid_test.binary_generator_multi_output_wrapper(
d_cls.train_gen)
d_cls.valid_gen = get_train_valid_test.binary_generator_multi_output_wrapper(
d_cls.valid_gen)
# ### model ### #
os.makedirs(args["output_dir"], exist_ok=True)
if args["choice_model"] == "model_paper":
model = model_paper.create_paper_cnn(
input_shape=(args["img_cols"], args["img_rows"],
args["channels"]),
num_classes=args["num_classes"],
activation=args["activation"],
)
else:
model, orig_model = define_model.get_fine_tuning_model(
args["output_dir"],
args["img_rows"],
args["img_cols"],
args["channels"],
args["num_classes"],
args["choice_model"],
trainable=args["trainable"],
fcpool=args["fcpool"],
fcs=args["fcs"],
drop=args["drop"],
activation=args["activation"],
weights=args["weights"],
)
optim = define_model.get_optimizers(choice_optim=args["choice_optim"],
lr=args["lr"],
decay=args["decay"])
model.compile(loss=args["loss"],
optimizer=optim,
metrics=args["metrics"])
cb = my_callback.get_base_cb(
args["output_dir"],
args["num_epoch"],
early_stopping=20,
monitor="val_" + args["metrics"][0],
metric=args["metrics"][0],
) # args['num_epoch']//3
cb.append(OptunaCallback(trial, True))
# ### train ### #
hist = model.fit(
d_cls.train_gen,
steps_per_epoch=d_cls.init_train_steps_per_epoch,
epochs=args["num_epoch"],
validation_data=d_cls.valid_gen,
validation_steps=d_cls.init_valid_steps_per_epoch,
verbose=2, # 1:ログをプログレスバーで標準出力 2:最低限の情報のみ出す
callbacks=cb,
)
return hist
def train_directory(args):
"""指定ディレクトリについてgenerator作ってモデル学習"""
print("train_directory")
# ### train validation data load ### #
d_cls = get_train_valid_test.LabeledDataset(
[args["img_rows"], args["img_cols"], args["channels"]],
args["batch_size"],
valid_batch_size=args["batch_size"],
train_samples=len(util.find_img_files(args["train_data_dir"])),
valid_samples=len(util.find_img_files(args["validation_data_dir"])),
)
if args["is_flow"]:
# 指定ディレクトリの前処理済み画像、ラベル、ファイルパスロード
d_cls.X_train, d_cls.y_train, train_paths = base_dataset.load_my_data(
args["train_data_dir"],
classes=args["classes"],
img_height=args["img_rows"],
img_width=args["img_cols"],
channel=args["channels"],
is_pytorch=False,
)
d_cls.X_valid, d_cls.y_valid, valid_paths = base_dataset.load_my_data(
args["validation_data_dir"],
classes=args["classes"],
img_height=args["img_rows"],
img_width=args["img_cols"],
channel=args["channels"],
is_pytorch=False,
)
d_cls.X_train, d_cls.X_valid = d_cls.X_train * 255.0, d_cls.X_valid * 255.0
d_cls.create_my_generator_flow(my_IDG_options=args["my_IDG_options"])
elif args["is_flow_from_directory"]:
d_cls.create_my_generator_flow_from_directory(
args["train_data_dir"],
args["classes"],
valid_data_dir=args["validation_data_dir"],
color_mode=args["color_mode"],
class_mode=args["class_mode"],
my_IDG_options=args["my_IDG_options"],
)
# d_cls.train_gen_augmentor = d_cls.create_augmentor_util_from_directory(args['train_data_dir']
# , args['batch_size']
# , augmentor_options=args['train_augmentor_options'])
# binaryラベルのgeneratorをマルチタスクgeneratorに変換するラッパー
if args["n_multitask"] > 1 and args["multitask_pred_n_node"] == 1:
d_cls.train_gen = get_train_valid_test.binary_generator_multi_output_wrapper(
d_cls.train_gen)
d_cls.valid_gen = get_train_valid_test.binary_generator_multi_output_wrapper(
d_cls.valid_gen)
# ### model ### #
os.makedirs(args["output_dir"], exist_ok=True)
if args["choice_model"] == "model_paper":
model = model_paper.create_paper_cnn(
input_shape=(args["img_cols"], args["img_rows"], args["channels"]),
num_classes=args["num_classes"],
activation=args["activation"],
)
else:
model, orig_model = define_model.get_fine_tuning_model(
args["output_dir"],
args["img_rows"],
args["img_cols"],
args["channels"],
args["num_classes"],
args["choice_model"],
trainable=args["trainable"],
fcpool=args["fcpool"],
fcs=args["fcs"],
drop=args["drop"],
activation=args["activation"],
weights=args["weights"],
)
optim = define_model.get_optimizers(choice_optim=args["choice_optim"],
lr=args["lr"],
decay=args["decay"])
model.compile(loss=args["loss"], optimizer=optim, metrics=args["metrics"])
cb = my_callback.get_base_cb(
args["output_dir"],
args["num_epoch"],
early_stopping=args["num_epoch"] // 4,
monitor="val_" + args["metrics"][0],
metric=args["metrics"][0],
)
# lr_finder
if args["is_lr_finder"] == True:
# 最適な学習率確認して関数抜ける
lr_finder.run(
model,
d_cls.train_gen,
args["batch_size"],
d_cls.init_train_steps_per_epoch,
output_dir=args["output_dir"],
)
return
# ### train ### #
start_time = time.time()
hist = model.fit(
d_cls.train_gen,
steps_per_epoch=d_cls.init_train_steps_per_epoch,
epochs=args["num_epoch"],
validation_data=d_cls.valid_gen,
validation_steps=d_cls.init_valid_steps_per_epoch,
verbose=2, # 1:ログをプログレスバーで標準出力 2:最低限の情報のみ出す
callbacks=cb,
)
end_time = time.time()
print("Elapsed Time : {:.2f}sec".format(end_time - start_time))
model.save(os.path.join(args["output_dir"], "model_last_epoch.h5"))
plot_log.plot_results(
args["output_dir"],
os.path.join(args["output_dir"], "tsv_logger.tsv"),
acc_metric=args["metrics"][0],
)
return hist
