def main(fold,gender_train,gender_test):
# parser config
config_file = 'config_file.ini'
cp = ConfigParser()
cp.read(config_file)
root_output_dir= cp["DEFAULT"].get("output_dir")
# default config
print(root_output_dir,gender_train)
output_dir= root_output_dir + gender_train+'/Fold_'+str(fold)+'/output/'
base_model_name = cp["DEFAULT"].get("base_model_name")
class_names = cp["DEFAULT"].get("class_names").split(",")
image_source_dir = cp["DEFAULT"].get("image_source_dir")
# train config
image_dimension = cp["TRAIN"].getint("image_dimension")
# test config
batch_size = cp["TEST"].getint("batch_size")
test_steps = cp["TEST"].get("test_steps")
use_best_weights = cp["TEST"].getboolean("use_best_weights")
# parse weights file path
output_weights_name = cp["TRAIN"].get("output_weights_name")
weights_path = os.path.join(output_dir, output_weights_name)
best_weights_path = os.path.join(output_dir, f"best_{output_weights_name}")
# get test sample count
test_counts, _ = get_sample_counts(root_output_dir+gender_train+'/Fold_'+str(fold),str(gender_test), class_names)
# compute steps
if test_steps == "auto":
test_steps = int(test_counts / batch_size)
else:
try:
test_steps = int(test_steps)
except ValueError:
raise ValueError(f"""
test_steps: {test_steps} is invalid,
please use 'auto' or integer.
""")
print(f"** test_steps: {test_steps} **")
print("** load model **")
if use_best_weights:
print("** use best weights **")
model_weights_path = best_weights_path
else:
print("** use last weights **")
model_weights_path = weights_path
model_factory = ModelFactory()
model = model_factory.get_model(
class_names,
model_name=base_model_name,
use_base_weights=False,
weights_path=model_weights_path)
print("** load test generator **")
test_sequence = AugmentedImageSequence(
dataset_csv_file=os.path.join(root_output_dir+gender_train+'/Fold_'+str(fold), str(gender_test)+".csv"),
class_names=class_names,
source_image_dir=image_source_dir,
batch_size=batch_size,
target_size=(image_dimension, image_dimension),
augmenter=None,
steps=test_steps,
shuffle_on_epoch_end=False,
)
print("** make prediction **")
y_hat = model.predict_generator(test_sequence, verbose=1)
y = test_sequence.get_y_true()
y_pred_dir = output_dir + "y_pred_run_" + str(fold)+"_train"+gender_train+"_"+gender_test+ ".csv"
y_true_dir = output_dir + "y_true_run_" + str(fold)+"_train"+gender_train+"_"+gender_test+ ".csv"
np.savetxt(y_pred_dir, y_hat, delimiter=",")
np.savetxt(y_true_dir, y, delimiter=",")
def main():
# parser config
config_file = "./config.ini"
cp = ConfigParser()
cp.read(config_file)
# default config
output_dir = cp["DEFAULT"].get("output_dir")
base_model_name = cp["DEFAULT"].get("base_model_name")
class_names = cp["DEFAULT"].get("class_names").split(",")
image_source_dir = cp["DEFAULT"].get("image_source_dir")
# train config
image_dimension = cp["TRAIN"].getint("image_dimension")
# test config
batch_size = cp["TEST"].getint("batch_size")
test_steps = cp["TEST"].get("test_steps")
use_best_weights = cp["TEST"].getboolean("use_best_weights")
# parse weights file path
output_weights_name = cp["TRAIN"].get("output_weights_name")
weights_path = os.path.join(output_dir, output_weights_name)
best_weights_path = os.path.join(output_dir, f"best_{output_weights_name}")
# get test sample count
test_counts, _ = get_sample_counts(output_dir, "test", class_names)
# compute steps
if test_steps == "auto":
test_steps = int(test_counts / batch_size)
else:
try:
test_steps = int(test_steps)
except ValueError:
raise ValueError(f"""
test_steps: {test_steps} is invalid,
please use 'auto' or integer.
""")
print(f"** test_steps: {test_steps} **")
print("** load model **")
if use_best_weights:
print("** use best weights **")
model_weights_path = best_weights_path
else:
print("** use last weights **")
model_weights_path = weights_path
model_factory = ModelFactory()
model = model_factory.get_model(class_names,
model_name=base_model_name,
use_base_weights=False,
weights_path=model_weights_path)
print("** load test generator **")
test_sequence = AugmentedImageSequence(
dataset_csv_file=os.path.join(output_dir, "test.csv"),
class_names=class_names,
source_image_dir=image_source_dir,
batch_size=batch_size,
target_size=(image_dimension, image_dimension),
augmenter=None,
steps=test_steps,
shuffle_on_epoch_end=False,
)
print("** make prediction **")
logs = []
starttime = time()
y_hat = model.predict_generator(test_sequence, verbose=1)
logs.append(time() - starttime)
print("time: " + str(logs))
y = test_sequence.get_y_true()
test_log_path = os.path.join(output_dir, "test.log")
print(f"** write log to {test_log_path} **")
aurocs = []
with open(test_log_path, "w") as f:
for i in range(len(class_names)):
try:
score = roc_auc_score(y[:, i], y_hat[:, i])
aurocs.append(score)
except ValueError:
score = 0
f.write(f"{class_names[i]}: {score}\n")
mean_auroc = np.mean(aurocs)
f.write("-------------------------\n")
f.write(f"mean auroc: {mean_auroc}\n")
print(f"mean auroc: {mean_auroc}")
def main():
# parser config
config_file = "./config.ini"
cp = ConfigParser()
cp.read(config_file)
# default config
output_dir = cp["DEFAULT"].get("output_dir")
if not os.path.exists(output_dir):
os.mkdir(output_dir)
base_model_name = cp["DEFAULT"].get("base_model_name")
class_names = cp["DEFAULT"].get("class_names").split(",")
image_source_dir = cp["DEFAULT"].get("image_source_dir")
data_set_dir = cp["TRAIN"].get("dataset_csv_dir")
# train config
image_dimension = cp["TRAIN"].getint("image_dimension")
# test config
batch_size = cp["TEST"].getint("batch_size")
test_steps = cp["TEST"].get("test_steps")
use_best_weights = cp["TEST"].getboolean("use_best_weights")
# parse weights file path
input_weights_name = cp["TRAIN"].get("input_weights_name")
weights_path = os.path.join(data_set_dir, input_weights_name)
best_weights_path = os.path.join(data_set_dir,
"best_{}".format(input_weights_name))
# get test sample count
test_counts, _ = get_sample_counts(data_set_dir, "test", class_names)
# compute steps
if test_steps == "auto":
test_steps = int(test_counts / batch_size)
else:
try:
test_steps = int(test_steps)
except ValueError:
raise ValueError("""
test_steps: {} is invalid,
please use 'auto' or integer.
""".format(test_steps))
print("** test_steps: {} **".format(test_steps))
print("** load model **")
if use_best_weights:
print("** use best weights **")
model_weights_path = best_weights_path
else:
print("** use last weights **")
model_weights_path = weights_path
model_factory = ModelFactory()
model = model_factory.get_model(class_names,
model_name=base_model_name,
use_base_weights=False,
weights_path=model_weights_path)
print("** load test generator **")
test_sequence = AugmentedImageSequence(
# dataset_csv_file=os.path.join(output_dir, "dev.csv"),
dataset_csv_file=os.path.join(data_set_dir, "test.csv"),
# dataset_csv_file=os.path.join(data_set_dir, "MIMIC_dataset.csv"),
class_names=class_names,
source_image_dir=image_source_dir,
batch_size=batch_size,
target_size=(image_dimension, image_dimension),
augmenter=None,
steps=test_steps,
shuffle_on_epoch_end=False,
)
# test_sequence.dataset_df.to_csv(os.path.join(output_dir, 'test_data_frame.csv'))
print("** make prediction **")
y_hat = model.predict_generator(test_sequence, verbose=1)
y = test_sequence.get_y_true()
# np.savetxt(os.path.join(output_dir, 'y_hat_1205_default_weight.txt'), y_hat)
np.savetxt(os.path.join(output_dir, 'y_0430.txt'), y)
test_log_path = os.path.join(output_dir, "test.log")
print("** write log to {} **".format(test_log_path))
aurocs = []
with open(test_log_path, "w") as f:
for i in range(len(class_names)):
try:
score = roc_auc_score(y[:, i], y_hat[:, i])
aurocs.append(score)
except ValueError:
score = 0
f.write("{}: {}\n".format(class_names[i], score))
mean_auroc = np.mean(aurocs)
f.write("-------------------------\n")
f.write("mean auroc: {}\n".format(mean_auroc))
print("mean auroc: {}".format(mean_auroc))
