if dataset_name == 'cubes':
prepare_cubes()
# Semantic Segmentations
if dataset_name == 'human_seg':
prepare_seg_from_meshcnn('human_body')
if dataset_name == 'coseg':
prepare_seg_from_meshcnn('coseg', 'coseg_aliens')
prepare_seg_from_meshcnn('coseg', 'coseg_chairs')
prepare_seg_from_meshcnn('coseg', 'coseg_vases')
if __name__ == '__main__':
utils.config_gpu(False)
np.random.seed(1)
if len(sys.argv) != 2:
print('Use: python dataset_prepare.py <dataset name>')
print('For example: python dataset_prepare.py cubes')
print('Another example: python dataset_prepare.py all')
else:
dataset_name = sys.argv[1]
if dataset_name == 'all':
for dataset_name in ['cubes', 'human_seg', 'coseg', 'modelnet40']:
prepare_one_dataset(dataset_name)
else:
prepare_one_dataset(dataset_name)
parser.add_argument('--checkpoint_path', type=str, default='checkpoint.h5')
parser.add_argument('--gpu', type=str, default='')
parser.add_argument('--allow_growth', default=False, action='store_true')
parser.add_argument("--model", type=str, default="johnson")
args = parser.parse_args()
# Arguments parsed
# Check loss weights
args.style_weight = std_input_list(args.style_weight, args.nb_classes,
'Style weight')
args.content_weight = std_input_list(args.content_weight, args.nb_classes,
'Content weight')
args.tv_weight = std_input_list(args.tv_weight, args.nb_classes,
'TV weight')
config_gpu(args.gpu, args.allow_growth)
print('Creating model...', args.model)
class_targets = K.placeholder(shape=(None, ), dtype=tf.int32)
# The model will be trained with 256 x 256 images of the coco dataset.
if (args.model == "unet"):
model = unet_model(256,
width_factor=args.width_factor,
nb_classes=args.nb_classes,
targets=class_targets)
else:
# model = pastiche_model(256, width_factor=args.width_factor)
model = pastiche_model(256,
width_factor=args.width_factor,
nb_classes=args.nb_classes,
targets=class_targets)
BatchNormalization,
AveragePooling2D,
Flatten,
)
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import (ModelCheckpoint, LearningRateScheduler,
ReduceLROnPlateau)
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import backend as K
from tensorflow.python.framework.ops import disable_eager_execution
from utils import config_gpu
_ = config_gpu()
disable_eager_execution()
def lr_schedule(epoch):
lr = 1e-3
if epoch > 180:
lr *= 0.5e-3
elif epoch > 160:
lr *= 1e-3
elif epoch > 120:
lr *= 1e-2
elif epoch > 80:
lr *= 1e-1
def init_parse():
import argparse
parser = argparse.ArgumentParser(
description='C3AE retry')
parser.add_argument(
'-s', '--save_path', default="./model/c3ae_model_v2_$message$_{epoch}_{val_age_mae:02f}-{val_gender_acc:.3f}", type=str,
help='the best model to save')
parser.add_argument(
'-l', '--log_dir', default="./logs", type=str,
help='the tensorboard log to save')
parser.add_argument(
'-r', '--r_factor', default=2, type=int,
help='the r factor of SE')
parser.add_argument(
'--source', default="wiki", type=str,
choices=['asia', 'wiki', 'imdb', 'wiki|imdb', "utk", "utk|asia", "afad", "afad|utk|asia"],
help='"wiki|imdb" or regrex pattern of feather')
parser.add_argument(
'--dataset', default="./dataset/data/", type=str,
help='the path of dataset to load')
parser.add_argument(
'-m', "--message", default="", type=str,
help='message')
parser.add_argument(
'-p', '--pretrain_path', dest="pretrain_path", default="", type=str,
help='the pretrain path')
parser.add_argument(
'-b', '--batch_size', default=128, type=int,
help='batch size degfault=64')
parser.add_argument(
'-a', '--activation', default="relu", type=str,
help='relu|leakrelu|mish')
parser.add_argument(
'-c', '--category', default=10, type=int,
help='category nums degfault=10, n+2')
parser.add_argument(
'-gpu', dest="gpu", action='store_true',
help='config of GPU')
parser.add_argument(
'-fz', dest="freeze", action='store_true',
help='freeze model')
parser.add_argument(
'-test', dest="test", action='store_true',
help='test')
parser.add_argument(
'-se', "--se-net", dest="se_net", action='store_true',
help='use SE-NET')
parser.add_argument(
'-white', '--white-norm', dest="white_norm", action='store_true',
help='use white norm')
parser.add_argument(
'-d', '--dropout', default="0.2", type=float,
help='dropout rate of erasing')
parser.add_argument(
'-lr', '--learning-rate', default="0.002", type=float,
help='learning rate')
parser.add_argument(
'-fp16', dest="fp16", action='store_true',
help='mix precision training')
params = parser.parse_args()
params.save_path = params.save_path.replace("$message$", params.message)
print("!!-----", params.save_path)
config_gpu() if params.gpu else config_cpu()
return params
'16-04_B',
'16-04_C',
'aliens',
'vases',
'chairs',
None,
None,
None,
][6:]
return jobs, job_parts
if __name__ == '__main__':
np.random.seed(0)
utils.config_gpu()
if len(sys.argv) <= 1:
print('Use: python train_val.py <job> <part>')
print(
'<job> can be one of the following: shrec11 / coseg / human_seg / cubes / modelnet40'
)
print('<job> can be also "all" to run all of the above.')
print('<part> should be used in case of shrec11 or coseg datasets.')
print(
'For shrec11 it should be one of the follows: 10-10_A / 10-10_B / 10-10_C / 16-04_A / 16-04_B / 16-04_C'
)
print(
'For coseg it should be one of the follows: aliens / vases / chairs'
)
print('For example: python train_val.py shrec11 10-10_A')
def init_parse():
import argparse
parser = argparse.ArgumentParser(description='C3AE retry')
parser.add_argument('-s',
'--save_path',
default="./model/c3ae_model_v2.h5",
type=str,
help='the best model to save')
parser.add_argument('-l',
'--log_dir',
default="./logs",
type=str,
help='the tensorboard log to save')
parser.add_argument('-r',
'--r_factor',
default=2,
type=int,
help='the r factor of SE')
parser.add_argument('--source',
default="wiki",
type=str,
choices=['wiki', 'imdb', 'wiki|imdb'],
help='"wiki|imdb" or regrex pattern of feather')
parser.add_argument('--dataset',
default="./dataset/data/",
type=str,
help='the path of dataset to load')
parser.add_argument('-p',
'--pretrain_path',
dest="pretrain_path",
default="",
type=str,
help='the pretrain path')
parser.add_argument('-b',
'--batch_size',
default=50,
type=int,
help='batch size degfault=50')
parser.add_argument('-w',
'--weight_factor',
default=10,
type=int,
help='age feature weight=10')
parser.add_argument('-c',
'--category',
default=10,
type=int,
help='category nums degfault=10, n+2')
parser.add_argument('-gpu',
dest="gpu",
action='store_true',
help='config of GPU')
parser.add_argument('-se',
"--se-net",
dest="se_net",
action='store_true',
help='use SE-NET')
parser.add_argument('-white',
'--white-norm',
dest="white_norm",
action='store_true',
help='use white norm')
parser.add_argument('-d',
'--dropout',
default="0.2",
type=float,
help='dropout rate of erasing')
parser.add_argument('-lr',
'--learning-rate',
default="0.002",
type=float,
help='learning rate')
params = parser.parse_args()
if params.gpu:
config_gpu()
return params
# epsilon = K.epsilon()
# y_pred = K.clip(y_pred, epsilon, 1. - epsilon)
#
# # Calculate Cross Entropy
# cross_entropy = -y_true * K.log(y_pred)
#
# # Calculate Focal Loss
# loss = alpha * K.pow(1 - y_pred, gamma) * cross_entropy
#
# # Sum the losses in mini_batch
# return K.sum(loss, axis=1)
#
# return categorical_focal_loss_func
if __name__ == '__main__':
config_gpu()
LOAD_MODEL_WEIGHTS = True
train_config = [CityScapesFineConfig]
train_images_dir = [ds.train_images for ds in train_config]
train_labels_dir = [ds.train_labels for ds in train_config]
val_images_dir = [ds.val_images for ds in train_config]
val_labels_dir = [ds.val_labels for ds in train_config]
batch_size = 5
lr_max = .001
image_size = (320, 640)
# image_size = (1664, 832)
loss = tf.losses.categorical_crossentropy
(-1, predictions.shape[-1]))
for w_step in range(all_seq.size):
models[name]['pred'][
all_seq[w_step]] += predictions4vertex[w_step]
models[name]['pred_count'][all_seq[w_step]] += 1
postprocess_vertex_predictions(models)
e_acc_after_postproc, v_acc_after_postproc, f_acc_after_postproc = calc_final_accuracy(
models)
return [e_acc_after_postproc, e_acc_after_postproc], dnn_model
if __name__ == '__main__':
from train_val import get_params
utils.config_gpu(1)
np.random.seed(0)
tf.random.set_seed(0)
if len(sys.argv) != 4:
print('<>'.join(sys.argv))
print(
'Use: python evaluate_segmentation.py <job> <part> <trained model directory>'
)
print(
'For example: python evaluate_segmentation.py coseg chairs pretrained/0009-14.11.2020..07.08__coseg_chairs'
)
else:
logdir = sys.argv[3]
job = sys.argv[1]
job_part = sys.argv[2]
for i, name in enumerate(labels):
this_type = confusion[i]
n_this_type = this_type.sum()
accuracy_this_type = this_type[i] / n_this_type
if n_this_type:
acc_per_class.append(accuracy_this_type)
this_type_ = this_type.copy()
this_type_[i] = -1
mean_acc_per_class = np.mean(acc_per_class)
return [mean_accuracy_all_faces, mean_acc_per_class], dnn_model
if __name__ == '__main__':
from train_val import get_params
utils.config_gpu(True)
np.random.seed(0)
tf.random.set_seed(0)
if len(sys.argv) != 4:
print(
'Use: python evaluate_classification.py <job> <part> <trained model directory>'
)
print(
'For example: python evaluate_classification.py shrec11 10-10_A pretrained/0001-09.11.2020..19.57__shrec11_10-10_A'
)
else:
logdir = sys.argv[3]
job = sys.argv[1]
job_part = sys.argv[2]
params = get_params(job, job_part)
