def export_run(self, run_num, model):
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
def write_predictions(self, model, weight_file_name):
#write predictions for various scenarios to file, return path to files for each scenario
run_num = 'run_{}'.format(weight_file_name)
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
return [val_with_targ, pred_with_targ, val_no_targ, pred_no_targ, val_following, pred_following]
model.fit_generator(
train_iter,
steps_per_epoch=steps_per_epoch, # the number of batches per epoch,
epochs=num_epochs, # the number of epochs to train for,
validation_data=val_iter, # validation iterator
validation_steps=
validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers=workers)
weight_file_name = 'model_weights_' + run
model_tools.save_network(model, weight_file_name)
run_num = 'run_' + run
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(
model, run_num, 'patrol_with_targ', 'evaluation')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(
model, run_num, 'patrol_non_targ', 'evaluation')
val_following, pred_following = model_tools.write_predictions_grade_set(
model, run_num, 'following_images', 'evaluation')
print(
"--------------------------------------------------------------------------------"
)
print("Scores for while the quad is following behind the target")
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(
val_following, pred_following)
print("true_pos1: " + str(true_pos1))
print("false_pos1: " + str(false_pos1))
model_tools.save_network(model, weight_file_name)
# ## Prediction<a id='prediction'></a>
# In[123]:
# If you need to load a model which you previously trained you can uncomment the codeline that calls the function below.
# weight_file_name = 'model_weights'
# restored_model = model_tools.load_network(weight_file_name)
# In[124]:
# generate predictions, save in the runs, directory.
run_number = 'run1'
validation_path, output_path = model_tools.write_predictions_grade_set(
model, run_number, 'validation')
# In[127]:
# take a look at predictions
# validation_path = 'validation'
im_files = plotting_tools.get_im_file_sample(run_number, validation_path)
for i in range(10):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# ## Evaluation<a id='evaluation'></a>
# Let's evaluate your model!
# In[128]:
def train_net(x):
"""Trains the network with the suggested hyper-parameters passed in x
argument. Returns -final_score as a result to let ski-opt library find the
desired minimum, corresponding to the maximum final_score"""
learning_rate = x[0] # 0.001
batch_size = x[1] # 32
num_epochs = x[2] # 12
layers_num = x[3] #2
conv_layers_num = x[4] #1
external_features = x[5] #128
internal_features = x[6] #16
conv_features = x[7] #16
print()
print("learning_rate", learning_rate)
print("batch_size", batch_size)
print("num_epochs", num_epochs)
print("layers_num", layers_num)
print("conv_layers_num", conv_layers_num)
print("external_features", external_features)
print("internal_features", internal_features)
print("conv_features", conv_features)
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes, layers_num,
external_features, internal_features,
conv_layers_num, conv_features)
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate),
loss='categorical_crossentropy')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data',
'train_combined'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data',
'validation'),
image_shape=image_shape)
model.fit_generator(
train_iter,
steps_per_epoch=steps_per_epoch, # the number of batches per epoch,
epochs=num_epochs, # the number of epochs to train for,
validation_data=val_iter, # validation iterator
validation_steps=
validation_steps, # the number of batches to validate on
workers=workers)
run_num = 'run_1'
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(
model, run_num, 'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(
model, run_num, 'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(
model, run_num, 'following_images', 'sample_evaluation_data')
# Scores for while the quad is following behind the target.
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(
val_following, pred_following)
# Scores for images while the quad is on patrol and the target is not
# visible
true_pos2, false_pos2, false_neg2, iou2 = scoring_utils.score_run_iou(
val_no_targ, pred_no_targ)
# This score measures how well the neural network can detect the target
# from far away
true_pos3, false_pos3, false_neg3, iou3 = scoring_utils.score_run_iou(
val_with_targ, pred_with_targ)
# Sum all the true positives, etc from the three datasets to get a weight
# for the score
true_pos = true_pos1 + true_pos2 + true_pos3
false_pos = false_pos1 + false_pos2 + false_pos3
false_neg = false_neg1 + false_neg2 + false_neg3
weight = true_pos / (true_pos + false_neg + false_pos)
# The IoU for the dataset that never includes the hero is excluded from
# grading
final_IoU = (iou1 + iou3) / 2
# And the final grade score is
final_score = final_IoU * weight
weight_file_name = 'model_weights_' + str(final_score)
model_tools.save_network(model, weight_file_name)
print("Saved", weight_file_name)
print("final_score", final_score)
print()
return -final_score