for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# get the original_dataset
train_dataset, valid_dataset, test_dataset, train_count, valid_count, test_count = generate_datasets(
)
# load the model
# model = get_model()
# model.load_weights(filepath=save_model_dir)
model = tf.saved_model.load(save_model_dir)
# Get the accuracy on the test set
loss_object = tf.keras.metrics.SparseCategoricalCrossentropy()
test_loss = tf.keras.metrics.Mean()
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
@tf.function
def test_step(images, labels):
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
for features in test_dataset:
test_images, test_labels = process_features(features)
test_step(test_images, test_labels)
print("loss: {:.5f}, test accuracy: {:.5f}".format(
test_loss.result(), test_accuracy.result()))
print("The accuracy on test set is: {:.3f}%".format(
test_accuracy.result() * 100))
args = parser.parse_args()
# get the original_dataset
train_dataset, valid_dataset, test_dataset, train_count, valid_count, test_count = generate_datasets(
)
# load the model
model = get_model(args.idx)
model.load_weights(filepath=save_model_dir)
# model = tf.saved_model.load(save_model_dir)
# Get the accuracy on the test set
loss_object = tf.keras.metrics.SparseCategoricalCrossentropy()
test_loss = tf.keras.metrics.Mean()
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
# @tf.function
def test_step(images, labels):
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
for features in test_dataset:
test_images, test_labels = process_features(features,
data_augmentation=False)
test_step(test_images, test_labels)
print("loss: {:.5f}, test accuracy: {:.5f}".format(
test_loss.result(), test_accuracy.result()))
print("The accuracy on test set is: {:.3f}%".format(
test_accuracy.result() * 100))
def main(argv):
# Need the user to provide system argv for job_id and product_id, it is prepared for frontend calling
if len(argv) < 2 or len(argv) > 3:
print(
"ERROR: Format error, refer to the usage: python test.py job_id product_id"
)
elif not argv[1].isdigit():
print("ERROR: Format error, job_id must be in int format")
elif not argv[1].isalnum():
print(
"ERROR: Format error, product_id must be consistent by character or number, without special character"
)
else:
print("INFO: Start evaluating model " +
datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
# GPU settings
gpus = tf.config.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# Folder generate for log file and model saving
log_dir, save_model_dir = folder_preparation(argv[1], argv[2])
# get the original_dataset
train_dataset, valid_dataset, test_dataset, train_count, valid_count, test_count = generate_datasets(
)
# load the model
model = get_model()
model.load_weights(filepath=save_model_dir + "model")
# model = tf.saved_model.load(save_model_dir)
# Get the accuracy on the test set
loss_object = tf.keras.metrics.SparseCategoricalCrossentropy()
test_loss = tf.keras.metrics.Mean()
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
# @tf.function
def test_step(images, labels):
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
return tf.math.argmax(predictions, axis=1).numpy()
batch = 0
for features in test_dataset:
batch += 1
test_images, test_labels = process_features(
features, data_augmentation=False)
predict_labels = test_step(test_images, test_labels)
print(
"loss: {:.5f}, test accuracy: {:.5f}, predict_labels:{}, test_labels:{}"
.format(test_loss.result(), test_accuracy.result(),
predict_labels, test_labels))
file = open(log_dir + "test_result_step" + ".log", "a")
file.write("test\t")
file.write(datetime.datetime.now().strftime("%Y%m%d%H%M%S") + "\t")
file.write(str(batch) + "\t")
file.write(str(test_accuracy.result().numpy()) + "\t")
file.write(str(predict_labels) + "\t")
file.write(str(test_labels) + "\n")
file.close()
print("The accuracy on test set is: {:.3f}%".format(
test_accuracy.result() * 100))
file = open(log_dir + "test_result" + ".log", "a")
file.write(datetime.datetime.now().strftime("%Y%m%d%H%M%S") + "\t")
file.write(str(test_accuracy.result()) + "\n")
file.close()