def test_validation_order(test_config):
"""Test that calls to model.read_data() produce identical data when shuffle is false"""
test_config["train"]["shuffle"] = False
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Create model
mod.create()
mod.read_data(validation_split=True)
first_sample = []
for image, label in mod.val_split:
first_sample.append(label)
first_sample = np.vstack(first_sample)
#Second call
mod.read_data(validation_split=True)
second_sample = []
for image, label in mod.val_split:
second_sample.append(label)
second_sample = np.vstack(second_sample)
assert np.array_equal(first_sample, second_sample)
def test_evaluate(test_config):
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Create
mod.create()
mod.read_data(validation_split=True)
#Method 1, class eval method
print("Before evaluation")
y_pred, y_true = mod.evaluate(mod.val_split)
print("evaluated")
test_acc = keras_metrics.CategoricalAccuracy()
test_acc.update_state(y_true=y_true, y_pred=y_pred)
method1_eval_accuracy = test_acc.result().numpy()
assert y_pred.shape == y_true.shape
#Method 2, keras eval method
metric_list = mod.model.evaluate(mod.val_split)
metric_dict = {}
for index, value in enumerate(metric_list):
metric_dict[mod.model.metrics_names[index]] = value
assert method1_eval_accuracy == metric_dict["acc"]
#F1 requires integer, not softmax
f1s = metrics.f1_scores(y_true, y_pred)
def test_AttentionModel(test_config, validation_split):
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Create model
mod.create()
mod.read_data(validation_split=validation_split)
#initial weights
initial_weight = mod.model.layers[1].get_weights()
#How many batches and ensure no overlap in data
train_image_data = []
test_image_data = []
if validation_split:
train_counter = 0
for data, label in mod.train_split:
train_image_data.append(data)
train_counter += data.shape[0]
test_counter = 0
for data, label in mod.val_split:
test_image_data.append(data)
test_counter += data.shape[0]
assert train_counter > test_counter
#No test in train batches
assert all([
not np.array_equal(y, x) for x in train_image_data
for y in test_image_data
])
#Spatial block of train and test
test_center_pixel = test_image_data[0][0, 2, 2, 0].numpy()
test_pixel_image = test_image_data[0][0, :, :, 0].numpy()
for x in train_image_data:
assert not test_center_pixel in x[:, 2, 2, 0]
#train
mod.train()
final_weight = mod.model.layers[1].get_weights()
#assert training took place
assert not np.array_equal(final_weight, initial_weight)
#assert val acc exists if split
if validation_split:
assert "val_acc" in list(mod.model.history.history.keys())
def test_split_data(test_config):
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
mod.read_data(validation_split=True)
#Assert tfrecords are split
assert all([x not in mod.train_split_records for x in mod.test_split_records])
def test_submodel_AttentionModel(test_config, submodel):
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Create model
mod.create()
mod.read_data(mode="submodel")
mod.train(submodel=submodel)
for batch in mod.train_split:
len(batch)
def test_predict_raster(test_config, predict_tfrecords):
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Create
mod.create()
mod.read_data()
results = mod.predict_raster(predict_tfrecords, batch_size=2)
predicted_raster = visualize.create_raster(results)
#Equals size of the input raster
assert predicted_raster.shape == (12,15)
def test_evaluate(test_config):
#Create class
mod = main.AttentionModel()
#Replace config for testing env
for key, value in test_config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Create
mod.create()
mod.read_data(validation_split=True)
metric_list = [
keras_metrics.TopKCategoricalAccuracy(k=2, name="top_k"),
keras_metrics.CategoricalAccuracy(name="acc")
]
mod.model.compile(loss="categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(
lr=float(mod.config['train']['learning_rate'])),
metrics=metric_list)
#Method 1, class eval method
print("Before evaluation")
y_pred, y_true = mod.evaluate(mod.val_split)
print("evaluated")
test_acc = keras_metrics.CategoricalAccuracy()
test_acc.update_state(y_true=y_true, y_pred = y_pred)
method1_eval_accuracy = test_acc.result().numpy()
assert y_pred.shape == y_true.shape
#Method 2, keras eval method
metric_list = mod.model.evaluate(mod.val_split)
metric_dict = {}
for index, value in enumerate(metric_list):
metric_dict[mod.model.metrics_names[index]] = value
assert method1_eval_accuracy == metric_dict["acc"]
#F1 requires integer, not softmax
f1s = metrics.f1_scores( y_true, y_pred)