def main():
# capture the config path from the run arguments
# then process the json configration file
try:
args = get_args()
config = process_config(args.config)
except Exception as e:
print(e)
print('Missing or invalid arguments')
exit(0)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
sess = tf.Session()
# create instance of the model you want
model = Model(config)
# load model if exist
model.load(sess)
# create your data generator
data = IVUSDataGenerator(config)
# create tensorboard logger
logger = Logger(sess, config)
# create trainer and path all previous components to it
trainer = SigmoidTrainer(sess, model, data, config, logger)
# train the model
trainer.train()
def main():
# get json configuration filepath from the run argument
# process the json configuration file
args = get_args()
config = process_config(args.config)
# create the experiment directories
log_dir, checkpoint_dir = create_dirs(config)
print('Create the data generator')
data_loader = DataLoader(config)
print('Create the model')
model = CycleGANAttrModel(config, config['weights_path'])
model.build_model()
print('model ready loading data now')
print('Create the trainer')
trainer = CycleGANModelTrainer(model, data_loader.get_trainA_data(),
data_loader.get_trainB_data(),
data_loader.get_testA_data(),
data_loader.get_testB_data(), config,
log_dir, checkpoint_dir)
# print('Start training the model.')
trainer.train()
def main():
# get json configuration filepath from the run argument
# process the json configuration file
try:
args = get_args()
config, log_dir, checkpoint_dir = process_config(args.config)
except:
print('missing or invalid arguments')
print('Unexpected error:', sys.exc_info()[0])
# create the experiment directories
create_dirs([log_dir, checkpoint_dir])
print('Create the data generator')
data_loader = WikiArtDataLoader(config)
print('Create the model')
model = ResNet50AttrModel(config)
print('model ready loading data now')
print('Create the trainer')
trainer = ResNet50ModelTrainer(model.model, data_loader.get_train_data(), data_loader.get_val_data(), config, log_dir, checkpoint_dir)
print('Start training the model.')
trainer.train()
def main():
try:
args = get_args()
config = process_config(args.config)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
ktf.set_session(sess)
create_dirs([config.callbacks.tensorboard_log_dir,
config.callbacks.checkpoint_dir,
config.path.chache_path])
print("Create the data generator.")
data_loader = SuperResolutionDataLoader(config)
print("Create the model.")
model = SuperResolutionModel(config)
print("Create the trainer.")
trainer = SuperResolutionTrainer(model.model,
data_loader.generate_train_data(),
config)
print("Start training...!")
trainer.train()
except Exception as err:
print("missing or invalid arguments: {0}".format(err))
exit(0)
def main():
start_time = time.time()
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
configurations = process_config(args.config)
except:
print("unable to create configurations ")
exit(0)
scores = []
for config in configurations:
experiment_result = experiment(config)
scores.append(experiment_result)
# results is a list of future objects
results = compss_wait_on(scores)
end_time = time.time()
elapsed_time = end_time - start_time
with open('./results/cifar_times.txt', 'a') as the_file:
the_file.write("Computing Units = " + str(os.environ['VAR']) + "\n")
the_file.write("Number of Nodes = " + str(args.nodes) + "\n")
the_file.write("Elapsed time = " + str(elapsed_time) + "\n")
the_file.write("\n")
the_file.write("\n")
with open('./results/cifar_scores.txt', 'a') as the_file:
the_file.write(str(results) + "\n")
def init() -> None:
"""
The main function of the project used to initialise all the required classes
used when training the model
"""
# get input arguments
args = get_args()
# get static config information
config = process_config()
# combine both into dictionary
config = {**config, **args}
# initialise model
model = RawModel(config)
# create your data generators for each mode
train_data = TFRecordDataLoader(config, mode="train")
val_data = TFRecordDataLoader(config, mode="val")
test_data = TFRecordDataLoader(config, mode="test")
# initialise the estimator
trainer = RawTrainer(config, model, train_data, val_data, test_data)
# start training
trainer.run()
def main():
try:
args = get_args()
config = process_config(args.config)
except:
print("Missing Arguments")
exit(0)
sess = tf.Session()
data = DataLoader(sess, "./data/celebA/images/", "./data/celebA/list_attr_celeba.txt", 178, 128, 16, "train")
stargan = StarGAN(sess, config, data)
stargan.train()
def main():
try:
FLAG = process_config()
except:
print("missing or invalid arguments")
exit(0)
if FLAG.GPU_options:
session_config = tf.ConfigProto()
session_config.gpu_options.per_process_gpu_memory_fraction = 0.9
session_config.gpu_options.allow_growth = True
sess = tf.Session(config=session_config)
else:
sess = tf.Session()
model = yolov3(FLAG)
model.build()
model.init_saver()
model.load(sess)
image_test = Image.open('images/timg.jpg')
resized_image = image_test.resize(size=(416, 416))
image_data = np.array(resized_image, dtype='float32') / 255.0
img_hw = tf.placeholder(dtype=tf.float32, shape=[2])
boxes, scores, classes = model.pedict(img_hw,
iou_threshold=0.5,
score_threshold=0.5)
begin_time = time.time()
boxes_, scores_, classes_, conv0 = sess.run(
[boxes, scores, classes, model.feature_extractor.conv0],
feed_dict={
img_hw: [image_test.size[1], image_test.size[0]],
model.x: [image_data]
})
end_time = time.time()
print(end_time - begin_time)
# print conv0
image_draw = draw_boxes(np.array(image_test, dtype=np.float32) / 255,
boxes_,
classes_,
FLAG.names,
scores=scores_)
fig = plt.figure(frameon=False)
ax = plt.Axes(fig, [0, 0, 1, 1])
ax.set_axis_off()
fig.add_axes(ax)
plt.imshow(image_draw)
fig.savefig('prediction.jpg')
plt.show()
sess.close()
def main():
# get json configuration filepath from the run argument
# process the json configuration file
args = get_args()
config = process_config(args.config)
print('Create the data generator')
data_loader = DataLoader(config)
if config['all_weights_in_folder']:
weights = np.array(glob(os.path.dirname(os.path.abspath(config['weights_path'])) + '/*.hdf5'))
else:
weights = np.array([config['weights_path']])
for weight in weights:
weightnum = int(os.path.basename(weight).split('-')[-1:][0][:-5])
print('Create the model for weight #%s' % (weightnum))
model = CycleGANAttrModel(config, weight, is_train=False)
predict_set = config['predict_set'] # either a, b, both
model.build_predict_model(predict_set)
print('model ready loading data now')
os.makedirs('images/%s' % config['dataset_name'], exist_ok=True)
if predict_set=='both' or predict_set=='a':
testA_datagen = DataGenerator(img_filenames=data_loader.get_testA_data(), batch_size=1, target_size=(config['predict_img_height'], config['predict_img_width']))
testA_generator = iter(testA_datagen)
num_images = len(testA_datagen)
for i in range(num_images):
imgs_A = next(testA_generator)
fake_B = model.predict_g_AB.predict(imgs_A)
imageio.imwrite("images/%s/%i_a_transl_%i.png" % (config['dataset_name'], weightnum, i), ((fake_B[0]+1)*127.5).astype(np.uint8))
if predict_set=='both':
reconstr_A = model.predict_g_BA.predict(fake_B)
imageio.imwrite("images/%s/%i_a_recon_%i.png" % (config['dataset_name'], weightnum, i), ((reconstr_A[0]+1)*127.5).astype(np.uint8))
if predict_set=='both' or predict_set=='b':
testB_datagen = DataGenerator(img_filenames=data_loader.get_testB_data(), batch_size=1, target_size=(config['predict_img_height'], config['predict_img_width']))
testB_generator = iter(testB_datagen)
num_images = len(testB_datagen)
for i in range(num_images):
imgs_B = next(testB_generator)
fake_A = model.predict_g_BA.predict(imgs_B)
imageio.imwrite("images/%s/%i_b_transl_%i.png" % (config['dataset_name'], weightnum, i), ((fake_A[0]+1)*127.5).astype(np.uint8))
if predict_set=='both':
reconstr_B = model.predict_g_AB.predict(fake_A)
imageio.imwrite("images/%s/%i_b_recon_%i.png" % (config['dataset_name'], weightnum, i), ((reconstr_B[0]+1)*127.5).astype(np.uint8))
def infer():
# get json configuration filepath from the run argument
# process the json configuration file
try:
config = 'input_params_for_inference.json'
config, _, _ = process_config(config)
except:
print('missing or invalid arguments')
print('Unexpected error:', sys.exc_info()[0])
print('Create the data generator')
data_loader = WikiArtDataLoader(config)
print('Create the model')
model = ResNet50AttrModel(config)
print('model ready loading data now')
print('Create the trainer')
trainer = ResNet50ModelTrainer(model.model, data_loader.get_train_data(), data_loader.get_val_data(), config, '', '')
print('Infer.')
trainer.predict()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(
(shape[3], shape[2], shape[0], shape[1]))
# remember to transpose to column-major
var_weights = np.transpose(var_weights, (2, 3, 1, 0))
ptr += num_params
assign_ops.append(tf.assign(var1, var_weights,
validate_shape=True))
i += 1
return assign_ops
if __name__ == '__main__':
try:
FLAG = process_config()
except:
print("missing or invalid arguments")
exit(0)
if FLAG.npz:
if os.path.exists(FLAG.npz_path):
print "darknet53.conv.74.npz already exists"
else:
print FLAG.config_path
load_weights_for_finetune(FLAG)
elif FLAG.ckpt:
detections = yolov3(FLAG)
# with tf.variable_scope('detector'):
detections.build()
import os
import matplotlib.pyplot as plt
from config import Config
# from model.ACM_SinGAN import SinGAN_ACM as SinGAN
from model.SinGAN import SinGAN
from utils.image import read_img, torch2np
from utils.utils import process_config, adjust_scales, calcul_sr_scale
if __name__ == '__main__':
process_config(Config)
Config.infer_dir = f'{Config.exp_dir}/infer_pyramid' if Config.save_all_pyramid else f'{Config.exp_dir}/infer'
singan = SinGAN(config=Config)
singan.load_trained_weights()
inference_img = read_img(Config)
os.makedirs(Config.infer_dir, exist_ok=True)
plt.imsave(f'{Config.exp_dir}/real.png',
torch2np(inference_img),
vmin=0,
vmax=1)
if Config.mode == 'train':
adjust_scales(inference_img, Config)
start_img_input = singan.create_inference_input()
elif Config.mode == 'train_SR':
in_scale, iter_num = calcul_sr_scale(Config)
Config.scale_factor = 1 / in_scale
Config.scale_factor_init = 1 / in_scale
Config.scale_w = 1
