def finetune_model(prog_path,nb_epoch,train_iters,val_iters,training_image_path,training_map_path,weight_path,output_folder,test_images,test_output,shuffle=True):
#config settings
imgs_train_path = training_image_path
maps_train_path = training_map_path
#nb_imgs_train = train_iters
#imgs_val_path = training_image_path
#maps_val_path = training_map_path
#nb_imgs_val = val_iters
#b_s, shape_r, shape_c, shape_r_gt, shape_c_gt, _= config()
#start finetunings
model = ml_net_model(img_cols=shape_c, img_rows=shape_r, downsampling_factor_product=10,weight_path=weight_path)
datagen = ImageDataGenerator()
sgd = SGD(lr=1e-4, decay=0.0005, momentum=0.9, nesterov=True)
print("Compile ML-Net Model for finetuning")
model.compile(sgd, loss)
print("Load weights ML-Net")
model.load_weights(weight_path + '/mlnet_salicon_weights.pkl')
model = prepare_finetune(model,4,sgd)
#prepare model
timestamp = time.localtime()
timestamp = prog_path + 'checkpoints/' + str(timestamp.tm_mon) + '_' + str(timestamp.tm_mday) + '_' + str(timestamp.tm_hour) + '_' + str(timestamp.tm_min)
if not os.path.exists(timestamp):
os.makedirs(timestamp)
print('Finetuning model for ' + str(nb_epoch) + ' epochs')
for ep in range(nb_epoch):
ep_loss = 0
num_batches = 0
batch_estimate = int(np.ceil(len(imgs_train_path) / b_s))
if shuffle:
rand_order = np.arange(len(imgs_train_path))
np.random.shuffle(rand_order)
ar_images = np.asarray(imgs_train_path)
ar_maps = np.asarray(maps_train_path)
ar_images = ar_images[rand_order]
ar_maps = ar_maps[rand_order]
it_imgs_train_path = ar_images.tolist()
it_maps_train_path = ar_maps.tolist()
else:
it_imgs_train_path = np.copy(it_imgs_train_path).tolist()
it_maps_train_path = np.copy(it_maps_train_path).tolist()
for X_train, Y_train in generator(b_s, it_imgs_train_path, it_maps_train_path, shape_r, shape_c, shape_r_gt, shape_c_gt):
if X_train.shape[0] == 0:
break
else:
ep_loss += model.train_on_batch(X_train, Y_train)
num_batches += 1
sys.stdout.write('\r' + str(num_batches) + '/' + str(batch_estimate))
sys.stdout.flush()
print('mean loss across batches', ep_loss / num_batches)
model_pointer = timestamp + '/' + str(ep) + '.h5'
model.save(model_pointer)
#Now make predictions
prediction_paths = make_predictions(model,test_images,test_output)
return model_pointer, prediction_paths
if f.endswith('.jpg')
]
images.sort()
counter = 0
while True:
yield preprocess_images(images[counter:counter + b_s], shape_r,
shape_c)
counter = (counter + b_s) % len(images)
if __name__ == '__main__':
phase = sys.argv[1]
model = ml_net_model(img_cols=shape_c,
img_rows=shape_r,
downsampling_factor_product=10)
sgd = SGD(lr=1e-3, decay=0.0005, momentum=0.9, nesterov=True)
print("Compile ML-Net Model")
model.compile(sgd, loss)
if phase == 'train':
print("Training ML-Net")
model.fit_generator(
generator(b_s=b_s),
nb_imgs_train,
nb_epoch=nb_epoch,
validation_data=generator(b_s=b_s, phase_gen='val'),
nb_val_samples=nb_imgs_val,
callbacks=[
EarlyStopping(patience=5),
def generator_test(b_s, imgs_test_path):
images = [imgs_test_path + f for f in os.listdir(imgs_test_path) if f.endswith('.jpg')]
images.sort()
counter = 0
while True:
yield preprocess_images(images[counter:counter + b_s], shape_r, shape_c)
counter = (counter + b_s) % len(images)
if __name__ == '__main__':
phase = sys.argv[1]
model = ml_net_model(img_cols=shape_c, img_rows=shape_r, downsampling_factor_product=10)
sgd = SGD(lr=1e-3, decay=0.0005, momentum=0.9, nesterov=True)
print("Compile ML-Net Model")
model.compile(sgd, loss)
if phase == 'train':
print("Training ML-Net")
model.fit_generator(generator(b_s=b_s), nb_imgs_train, nb_epoch=nb_epoch,
validation_data=generator(b_s=b_s, phase_gen='val'), nb_val_samples=nb_imgs_val,
callbacks=[EarlyStopping(patience=5),
ModelCheckpoint('weights.mlnet.{epoch:02d}-{val_loss:.4f}.pkl', save_best_only=True)])
elif phase == "test":
# path of output folder
output_folder = ''
verbose = True
# parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("--seq")
parser.add_argument("--pred_dir")
args = parser.parse_args()
assert args.seq is not None, 'Please provide a correct dreyeve sequence'
assert args.pred_dir is not None, 'Please provide a correct pred_dir'
dreyeve_dir = 'Z:/DATA' # local
# dreyeve_dir = '/gpfs/work/IscrC_DeepVD/dabati/DREYEVE/data/' # cineca
# get the model
model = ml_net_model(img_rows=shape_r, img_cols=shape_c)
model.compile(optimizer='adam', loss='kld') # do we need this?
model.load_weights('weights.mlnet.07-0.0193.pkl') # load weights
# set up some directories
pred_dir = join(args.pred_dir, '{:02d}'.format(int(args.seq)), 'output')
makedirs([pred_dir])
sequence_dir = join(dreyeve_dir, '{:02d}'.format(int(args.seq)))
for sample in tqdm(range(15, 7500 - 1)):
X = load_dreyeve_sample(sequence_dir=sequence_dir,
sample=sample,
shape_c=shape_c,
shape_r=shape_r)
# predict sample
def load_dreyeve_sample(sequence_dir, sample, shape_r, shape_c):
filename = join(sequence_dir, 'frames', '{}.jpg'.format(sample))
X = preprocess_images([filename], shape_r, shape_c)
return X
if __name__ == '__main__':
shape_r = 480
shape_c = 640
# get the model
model_bdda = ml_net_model(img_rows=shape_r, img_cols=shape_c)
model_bdda.compile(optimizer='adam', loss='kld')
model_sage = ml_net_model(img_rows=shape_r, img_cols=shape_c)
model_sage.compile(optimizer='adam', loss='kld')
model_path_bdda = "../pretrained_models/mlnet/weights.mlnet.bdda.pkl"
model_path_sage = "../pretrained_models/mlnet/weights.mlnet.sage.pkl"
model_bdda.load_weights(model_path_bdda)
model_sage.load_weights(model_path_sage)
demo_dir = 'demo_images/'
X = load_dreyeve_sample(sequence_dir=demo_dir,
sample=16,
shape_c=shape_c,
