def train(n_classes=11,
batch_size=16,
epochs=100,
width=960,
height=720,
crop_factor_x=2,
crop_factor_y=1.25,
init_lr=1e-4,
median_frequency=.15,
zoom_augmentation=.2,
dataset_path='datasets/endoscopy',
weights_path='weights/endoscopy/model',
preprocess='imagenet'):
CONFIG = {}
CONFIG['n_classes'] = n_classes
CONFIG['batch_size'] = batch_size
CONFIG['epochs'] = epochs
CONFIG['width'] = width
CONFIG['height'] = height
CONFIG['crop_factor_x'] = crop_factor_x
CONFIG['crop_factor_y'] = crop_factor_y
CONFIG['width_train'] = int(
CONFIG['width'] /
CONFIG['crop_factor_x']) # will be cropped from width_test size
CONFIG['height_train'] = int(
CONFIG['height'] /
CONFIG['crop_factor_y']) # will be cropped from height_test size
CONFIG['init_lr'] = init_lr
CONFIG['median_frequency'] = median_frequency
CONFIG['zoom_augmentation'] = zoom_augmentation
CONFIG['dataset_path'] = dataset_path
CONFIG['weights_path'] = weights_path
CONFIG['preprocess'] = preprocess
assert CONFIG['width'] * (
1 - CONFIG['zoom_augmentation']) >= CONFIG['width_train']
assert CONFIG['height'] * (
1 - CONFIG['zoom_augmentation']) >= CONFIG['height_train']
# GPU to use
n_gpu = 0
os.environ["CUDA_VISIBLE_DEVICES"] = str(n_gpu)
# Loader
loader = Loader.Loader(dataFolderPath=CONFIG['dataset_path'],
n_classes=CONFIG['n_classes'],
width=CONFIG['width'],
height=CONFIG['height'],
median_frequency=CONFIG['median_frequency'])
print('Dataset loaded...')
# build model
#model = MiniNetv2.MiniNetv2p(num_classes=CONFIG['n_classes'])
model = ResNet50.ResNet50Seg(CONFIG['n_classes'],
input_shape=(None, None, 3),
weights='imagenet')
# optimizer
learning_rate = tf.Variable(CONFIG['init_lr'])
optimizer = tf.keras.optimizers.Adam(learning_rate)
loss_function = tf.keras.losses.CategoricalCrossentropy()
# restore if model saved and show number of params
restore_state(model, CONFIG['weights_path'])
init_model(model, (1, CONFIG['width'], CONFIG['height'], 3))
get_params(model)
# Train
print('Training...')
_train(loader=loader,
optimizer=optimizer,
loss_function=loss_function,
model=model,
config=CONFIG,
lr=learning_rate,
name_best_model=CONFIG['weights_path'],
evaluation=True,
preprocess_mode=CONFIG['preprocess'])
print('Testing model')
test_acc, test_miou = get_metrics(loader,
model,
loader.n_classes,
train=False,
flip_inference=True,
scales=[1, 2, 1.5, 0.5, 0.75],
write_images=True,
preprocess_mode=CONFIG['preprocess'],
time_exect=True)
print('Test accuracy: ' + str(test_acc.numpy()))
print('Test miou: ' + str(test_miou.numpy()))
model = Segception.Efficient(num_classes=n_classes, weights='imagenet', input_shape=(None, None, channels))
# optimizer
learning_rate = tfe.Variable(lr)
optimizer = tf.train.AdamOptimizer(learning_rate)
# Init models (optional, just for get_params function)
init_model(model, input_shape=(batch_size, width, height, channels))
variables_to_restore = model.variables
variables_to_save = model.variables
variables_to_optimize = model.variables
# Init saver. can use also ckpt = tfe.Checkpoint((model=model, optimizer=optimizer,learning_rate=learning_rate, global_step=global_step)
saver_model = tfe.Saver(var_list=variables_to_save)
restore_model = tfe.Saver(var_list=variables_to_restore)
# restore if model saved and show number of params
restore_state(restore_model, name_best_model)
get_params(model)
train(loader=loader, optimizer=optimizer, model=model, epochs=epochs, batch_size=batch_size, augmenter='segmentation', lr=learning_rate,
init_lr=lr, saver=saver_model, variables_to_optimize=variables_to_optimize, name_best_model=name_best_model,
evaluation=True, aux_loss=False, preprocess_mode=preprocess_mode)
# Test best model
print('Testing model')
test_acc, test_miou = get_metrics(loader, model, loader.n_classes, train=False, flip_inference=True, scales=[0.75, 1, 1.5],
write_images=True, preprocess_mode=preprocess_mode, time_exect=False)
print('Test accuracy: ' + str(test_acc.numpy()))
print('Test miou: ' + str(test_miou))
height=CONFIG['height'],
median_frequency=CONFIG['median_frequency'])
print('Dataset loaded...')
# build model
#model = MiniNetv2.MiniNetv2p(num_classes=CONFIG['n_classes'])
model = ResNet50.ResNet50Seg(CONFIG['n_classes'],
input_shape=(None, None, 3),
weights='imagenet')
# optimizer
learning_rate = tf.Variable(CONFIG['init_lr'])
optimizer = tf.keras.optimizers.Adam(learning_rate)
loss_function = tf.keras.losses.CategoricalCrossentropy()
# restore if model saved and show number of params
restore_state(model, args.weights_path)
init_model(model, (1, CONFIG['width'], CONFIG['height'], 3))
get_params(model)
# Train
print('Training...')
_train(loader=loader,
optimizer=optimizer,
loss_function=loss_function,
model=model,
config=CONFIG,
lr=learning_rate,
name_best_model=args.weights_path,
evaluation=True,
preprocess_mode=args.preprocess)