def optimize_cnn(hype_space):
"""Build a convolutional neural network and train it."""
try:
model, model_name, result, _ = build_and_train(hype_space)
# Save training results to disks with unique filenames
save_json_result(model_name, result)
K.clear_session()
del model
return result
except Exception as err:
try:
K.clear_session()
except:
pass
err_str = str(err)
print(err_str)
traceback_str = str(traceback.format_exc())
print(traceback_str)
return {
'status': STATUS_FAIL,
'err': err_str,
'traceback': traceback_str
}
print("\n\n")
def optimize_cnn(hype_space):
"""Build a convolutional neural network and train it."""
if not is_gpu_available():
tf.logging.warning('GPUs are not available')
tf.logging.debug("Hyperspace: ", hype_space)
tf.logging.debug("\n")
try:
model, model_name, result, _ = build_and_train(
hype_space, log_for_tensorboard=True)
tf.logging.info("Training ended with success:")
tf.logging.info("Model name: %s", model_name)
# Save training results to disks with unique filenames
save_json_result(model_name, result)
export_model(model_name)
K.clear_session()
del model
tf.logging.info('before return result')
return result
except Exception as err:
err_str = str(err)
tf.logging.error(err_str)
traceback_str = str(traceback.format_exc())
tf.logging.error(traceback_str)
return {
'status': STATUS_FAIL,
'err': err_str,
'traceback': traceback_str
}
def save(self, result):
"""Save results in json format file."""
utils.save_json_result(result, self.config.json_file,
self.config.std_json_path,
self.config.service_chain,
self.config.service_chain_count,
self.config.flow_count, self.config.frame_sizes)
def optimize_cnn(hype_space):
"""Build a convolutional neural network and train it."""
if not is_gpu_available():
tf.logging.warning('GPUs are not available')
tf.logging.debug("Hyperspace: ", hype_space)
tf.logging.debug("\n")
try:
model, model_name, result, _ = build_and_train(hype_space)
tf.logging.info("Training ended with success:")
tf.logging.info("Model name: ", model_name)
# Save training results to disks with unique filenames
# TODO do we need this? this save to json on disc not to mongo. Not sure if we want always save to disc
save_json_result(model_name, result)
K.clear_session()
del model
tf.logging.info('before return result')
return result
except Exception as err:
try:
K.clear_session()
except:
pass
err_str = str(err)
tf.logging.error(err_str)
traceback_str = str(traceback.format_exc())
tf.logging.error(traceback_str)
return {
'status': STATUS_FAIL,
'err': err_str,
'traceback': traceback_str
}
def lle(space):
n_neighbors = int(space['n_neighbors'])
method = space['method']
vertices, colors = get_all_vertices_dk_atlas_w_colors()
print(space)
lle = LLE(n_neighbors=n_neighbors, n_components=2, method=method, neighbors_algorithm='auto')
lle_xy = lle.fit_transform(vertices)
centers = get_centers_of_rois_xy(lle_xy)
avg_distance = avg_distance_between_center_of_masses(centers)
model_name = 'lle_{}_{}'.format(method, avg_distance)
result = {
'loss': -avg_distance,
'space': space,
'status': STATUS_OK
}
save_json_result(model_name, result)
save_2d_roi_map(lle_xy, colors, centers, model_name)
return result
def optimize_cnn(hype_space):
"""Build a convolutional neural network and train it."""
try:
model, model_name, result, model_uuid = build_and_train(
hype_space, save_best_weights=True)
# Save training results to disks with unique filenames
save_json_result(model_name, result)
# Save .png plot of the model according to its hyper-parameters
plot(result['space'], model_uuid)
K.clear_session()
del model
return result
except Exception as err:
try:
K.clear_session()
except:
pass
err_str = str(err)
print(err_str)
traceback_str = str(traceback.format_exc())
print(traceback_str)
print("\n\n")
return {
'status': STATUS_FAIL,
'err': err_str,
'traceback': traceback_str
}
def optimize_rnn(hype_space):
model, model_name, result = build_and_train_rnn(hype_space)
# Save training results to disks with unique filenames
save_json_result(model_name, result)
tf.keras.backend.clear_session()
del model
print("\n\n")
return result
def build_and_train(hype_space, save_best_weights=False):
train_path = '/home/comp/e4252392/retraindata4frcnn.txt'
config_output_filename = '/home/comp/e4252392/hyperopt/hyperopt_config.pickle'
num_epochs = 20
#for retrain best model only
diagnose_path = '/home/comp/e4252392/hyperopt/models/hyperopt_loss_ap_plt.npy'
real_model_path = '/home/comp/e4252392/hyperopt/models/hyperopt_model_plt_'
print("Hyperspace:")
print(hype_space)
C = config.Config()
C.num_rois = int(hype_space['num_rois']) #why int?
# C.anchor_box_scales = hype_space['anchor_box_scales']
# C.base_net_weights = '/home/comp/e4252392/second_res_more_epoch.h5'
C.base_net_weights = 'model_frcnn.hdf5'
#data
all_imgs, classes_count, class_mapping = get_data(train_path)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
with open(config_output_filename, 'wb') as config_f:
pickle.dump(C, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(config_output_filename))
random.shuffle(all_imgs)
num_imgs = len(all_imgs)
train_imgs = [s for s in all_imgs]
print('Num train samples {}'.format(len(train_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs,
classes_count,
C,
nn.get_img_output_length,
K.image_dim_ordering(),
mode='train')
#data
# build_model
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)
else:
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
shared_layers = nn.nn_base(int(hype_space['kernel_size']),
img_input,
trainable=True)
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = nn.rpn(int(hype_space['kernel_size']), shared_layers, num_anchors)
classifier = nn.classifier(int(hype_space['kernel_size']),
shared_layers,
roi_input,
C.num_rois,
nb_classes=len(classes_count),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
try:
print('loading weights from {}'.format(C.base_net_weights))
model_rpn.load_weights(C.base_net_weights, by_name=True)
model_classifier.load_weights(C.base_net_weights, by_name=True)
except:
print(
'Could not load pretrained model weights. Weights can be found in the keras application folder \
https://github.com/fchollet/keras/tree/master/keras/applications')
# optimizer = Adam(lr=1e-5)
# optimizer_classifier = Adam(lr=1e-5)
optimizer = Adam(lr=hype_space['optimizer_lr'],
decay=hype_space['optimizer_decay'])
optimizer_classifier = Adam(lr=hype_space['optimizer_lr'],
decay=hype_space['optimizer_decay'])
model_rpn.compile(optimizer=optimizer,
loss=[
thelosses.rpn_loss_cls(num_anchors),
thelosses.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
thelosses.class_loss_cls,
thelosses.class_loss_regr(len(classes_count) - 1)
],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
sgd = SGD(lr=hype_space['sgd_lr'], decay=hype_space['sgd_decay'])
model_all.compile(optimizer=sgd, loss='mae')
# build_model
#build_and_train
epoch_length = 10
iter_num = 0
losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()
best_loss = np.Inf
print('Starting training')
loss_array = []
ap_array = []
epoch_array = []
epoch_array.append(0)
result = {}
model_name = ''
for epoch_num in range(num_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))
while True:
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
# train
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
K.image_dim_ordering(),
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
# train
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
iter_num += 1
progbar.update(
iter_num,
[('rpn_cls', np.mean(losses[:iter_num, 0])),
('rpn_regr', np.mean(losses[:iter_num, 1])),
('detector_cls', np.mean(losses[:iter_num, 2])),
('detector_regr', np.mean(losses[:iter_num, 3]))])
if iter_num == epoch_length:
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(sum(
rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print(
'Classifier accuracy for bounding boxes from RPN: {}'
.format(class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(
loss_class_cls))
print('Loss Detector regression: {}'.format(
loss_class_regr))
print('Elapsed time: {}'.format(time.time() -
start_time))
# result
curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
iter_num = 0
start_time = time.time()
if curr_loss < best_loss:
if C.verbose:
print(
'Total loss decreased from {} to {}, saving weights'
.format(best_loss, curr_loss))
best_loss = curr_loss
if save_best_weights:
real_model_path = real_model_path + str(
epoch_num + 1) + '.hdf5'
model_all.save_weights(real_model_path,
overwrite=True)
print("Best weights so far saved to " +
real_model_path + ". best_loss = " +
str(best_loss))
epoch_array.append(epoch_num + 1)
loss_array.append([
loss_rpn_cls, loss_rpn_regr, loss_class_cls,
loss_class_regr, best_loss
])
album_ap, logo_ap, mAP = measure_map.measure_map(
config_output_filename, real_model_path)
ap_array.append([album_ap, logo_ap, mAP])
np.save(diagnose_path,
[epoch_array, loss_array, ap_array])
else:
album_ap = 'not applicable'
logo_ap = 'not applicable'
mAP = 'not applicable'
model_name = "model_{}_{}".format(
str(best_loss),
str(uuid.uuid4())[:5])
result = {
'loss': best_loss,
'loss_rpn_cls': loss_rpn_cls,
'loss_rpn_regr': loss_rpn_regr,
'loss_class_cls': loss_class_cls,
'loss_class_regr': loss_class_regr,
'album_ap': album_ap,
'logo_ap': logo_ap,
'mAP': mAP,
'model_name': model_name,
'space': hype_space,
'status': STATUS_OK
}
print("RESULT UPDATED.")
print("Model name: {}".format(model_name))
# result
break
except Exception as e:
print('Exception: {}'.format(e))
continue
print('Training complete, exiting.')
print("BEST MODEL: {}".format(model_name))
print("FINAL RESULT:")
print_json(result)
save_json_result(model_name, result)
try:
K.clear_session()
del model_all, model_rpn, model_classifier
except Exception as err:
try:
K.clear_session()
except:
pass
err_str = str(err)
print(err_str)
traceback_str = str(traceback.format_exc())
print(traceback_str)
return {
'status': STATUS_FAIL,
'err': err_str,
'traceback': traceback_str
}
print("\n\n")
return model_name, result