def test_neuron_indices_to_target_var_u_net(self):
"""Ensures correct output from neuron_indices_to_target_var.
In this case, neural-net type is U-net.
"""
num_tests = len(U_NET_NEURON_INDICES_LIST)
for i in range(num_tests):
this_target_name, this_height_m_agl = (
neural_net.neuron_indices_to_target_var(
neuron_indices=U_NET_NEURON_INDICES_LIST[i],
example_dict=copy.deepcopy(EXAMPLE_DICT),
net_type_string=neural_net.U_NET_TYPE_STRING))
self.assertTrue(this_target_name == U_NET_TARGET_NAMES[i])
self.assertTrue(this_height_m_agl == U_NET_HEIGHTS_M_AGL[i])
def _run(model_file_name, example_file_name, num_examples, example_dir_name,
example_id_file_name, layer_name, is_layer_output, neuron_indices,
ideal_activation, output_file_name):
"""Makes saliency map for each example, according to one model.
This is effectively the main method.
:param model_file_name: See documentation at top of file.
:param example_file_name: Same.
:param num_examples: Same.
:param example_dir_name: Same.
:param example_id_file_name: Same.
:param layer_name: Same.
:param is_layer_output: Same.
:param neuron_indices: Same.
:param ideal_activation: Same.
:param output_file_name: Same.
"""
print('Reading model from: "{0:s}"...'.format(model_file_name))
model_object = neural_net.read_model(model_file_name)
metafile_name = neural_net.find_metafile(
model_dir_name=os.path.split(model_file_name)[0],
raise_error_if_missing=True
)
print('Reading metadata from: "{0:s}"...'.format(metafile_name))
metadata_dict = neural_net.read_metafile(metafile_name)
predictor_matrix, _, example_id_strings = (
misc_utils.get_examples_for_inference(
model_metadata_dict=metadata_dict,
example_file_name=example_file_name,
num_examples=num_examples, example_dir_name=example_dir_name,
example_id_file_name=example_id_file_name
)
)
print(SEPARATOR_STRING)
generator_option_dict = metadata_dict[neural_net.TRAINING_OPTIONS_KEY]
if is_layer_output:
dummy_example_dict = {
example_utils.SCALAR_TARGET_NAMES_KEY:
generator_option_dict[neural_net.SCALAR_TARGET_NAMES_KEY],
example_utils.VECTOR_TARGET_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY],
example_utils.HEIGHTS_KEY:
generator_option_dict[neural_net.HEIGHTS_KEY]
}
target_field_name, target_height_m_agl = (
neural_net.neuron_indices_to_target_var(
neuron_indices=neuron_indices,
example_dict=copy.deepcopy(dummy_example_dict),
net_type_string=metadata_dict[neural_net.NET_TYPE_KEY]
)
)
else:
target_field_name = None
target_height_m_agl = None
print('Target field and height = {0:s}, {1:s}'.format(
str(target_field_name), str(target_height_m_agl)
))
print('Computing saliency for neuron {0:s} in layer "{1:s}"...'.format(
str(neuron_indices), layer_name
))
saliency_matrix = saliency.get_saliency_one_neuron(
model_object=model_object, predictor_matrix=predictor_matrix,
layer_name=layer_name, neuron_indices=neuron_indices,
ideal_activation=ideal_activation
)
net_type_string = metadata_dict[neural_net.NET_TYPE_KEY]
if net_type_string == neural_net.DENSE_NET_TYPE_STRING:
dummy_example_dict = {
example_utils.SCALAR_PREDICTOR_NAMES_KEY:
generator_option_dict[neural_net.SCALAR_PREDICTOR_NAMES_KEY],
example_utils.VECTOR_PREDICTOR_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_PREDICTOR_NAMES_KEY],
example_utils.HEIGHTS_KEY:
generator_option_dict[neural_net.HEIGHTS_KEY]
}
dummy_example_dict = neural_net.predictors_numpy_to_dict(
predictor_matrix=saliency_matrix, example_dict=dummy_example_dict,
net_type_string=metadata_dict[neural_net.NET_TYPE_KEY]
)
scalar_saliency_matrix = (
dummy_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY]
)
vector_saliency_matrix = (
dummy_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY]
)
else:
num_scalar_predictors = len(
generator_option_dict[neural_net.SCALAR_PREDICTOR_NAMES_KEY]
)
scalar_saliency_matrix = saliency_matrix[..., -num_scalar_predictors:]
vector_saliency_matrix = saliency_matrix[..., :-num_scalar_predictors]
print('Writing saliency maps to: "{0:s}"...'.format(output_file_name))
saliency.write_file(
netcdf_file_name=output_file_name,
scalar_saliency_matrix=scalar_saliency_matrix,
vector_saliency_matrix=vector_saliency_matrix,
example_id_strings=example_id_strings, model_file_name=model_file_name,
layer_name=layer_name, neuron_indices=neuron_indices,
ideal_activation=ideal_activation, target_field_name=target_field_name,
target_height_m_agl=target_height_m_agl
)
def _run(gradcam_file_name, use_log_scale, prediction_file_name,
output_dir_name):
"""Plots Grad-CAM output (class-activation maps).
This is effectively the main method.
:param gradcam_file_name: See documentation at top of file.
:param use_log_scale: Same.
:param prediction_file_name: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name
)
print('Reading class-activation maps from: "{0:s}"...'.format(
gradcam_file_name
))
gradcam_dict = gradcam.read_file(gradcam_file_name)
example_id_strings = gradcam_dict[gradcam.EXAMPLE_IDS_KEY]
output_neuron_indices = gradcam_dict[gradcam.OUTPUT_NEURONS_KEY]
model_file_name = gradcam_dict[gradcam.MODEL_FILE_KEY]
model_metafile_name = neural_net.find_metafile(
model_dir_name=os.path.split(model_file_name)[0]
)
print('Reading model metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = neural_net.read_metafile(model_metafile_name)
generator_option_dict = model_metadata_dict[neural_net.TRAINING_OPTIONS_KEY]
dummy_example_dict = {
example_utils.VECTOR_TARGET_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY],
example_utils.HEIGHTS_KEY: generator_option_dict[neural_net.HEIGHTS_KEY]
}
target_field_name, target_height_m_agl = (
neural_net.neuron_indices_to_target_var(
neuron_indices=output_neuron_indices,
example_dict=dummy_example_dict,
net_type_string=model_metadata_dict[neural_net.NET_TYPE_KEY]
)
)
predicted_target_values, actual_target_values = (
plot_saliency_maps._get_target_values(
prediction_file_name=prediction_file_name,
model_metadata_dict=model_metadata_dict,
example_id_strings=example_id_strings,
target_field_name=target_field_name,
target_height_m_agl=target_height_m_agl
)
)
print(SEPARATOR_STRING)
num_examples = len(example_id_strings)
for i in range(num_examples):
this_title_string = 'Actual and predicted {0:s}'.format(
target_field_name
)
if target_height_m_agl is not None:
this_title_string += ' at {0:.2f} km AGL'.format(
METRES_TO_KM * target_height_m_agl
)
this_title_string += ' = {0:.2f}, {1:.2f}'.format(
actual_target_values[i], predicted_target_values[i]
)
_plot_one_example(
gradcam_dict=gradcam_dict, example_index=i,
model_metadata_dict=model_metadata_dict, use_log_scale=use_log_scale,
title_string=this_title_string, output_dir_name=output_dir_name
)