text stringlengths 1 93.6k |
|---|
print ('test,train: ',len(dataset.dataset_files['test']), \
|
len(dataset.dataset_files['train']))
|
dataset.test_batch_size = 8
|
global network
|
network = LSCCNN(args, nofreeze=True, name='scale_4', output_downscale=4)
|
load_model_VGG16(network)
|
model_save_path = os.path.join(model_save_dir, 'train2')
|
if not os.path.exists(model_save_path):
|
os.makedirs(model_save_path)
|
os.makedirs(os.path.join(model_save_path, 'snapshots'))
|
train_networks(network=network,
|
dataset=dataset,
|
network_functions=networkFunctions(),
|
log_path=model_save_path)
|
print('\n-------\nDONE.')
|
if __name__ == '__main__':
|
args = parser.parse_args()
|
# -- Assign GPU
|
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
|
# -- Assertions
|
assert(args.dataset)
|
assert len(args.trained_model) in [0, 1]
|
# -- Setting seeds for reproducability
|
np.random.seed(11)
|
random.seed(11)
|
torch.manual_seed(11)
|
torch.backends.cudnn.enabled = False
|
torch.backends.cudnn.deterministic = True
|
torch.backends.cudnn.benchmark = False
|
torch.cuda.manual_seed(11)
|
torch.cuda.manual_seed_all(11)
|
# -- Dataset paths
|
if args.dataset == "parta":
|
dataset_paths = {'test': ['../dataset/ST_partA/test_data/images',
|
'../dataset/ST_partA/test_data/ground_truth'],
|
'train': ['../dataset/ST_partA/train_data/images',
|
'../dataset/ST_partA/train_data/ground_truth']}
|
validation_set = 30
|
path = '../dataset/stparta_dotmaps_predscale0.5_rgb_ddcnn++_test_val_30'
|
output_downscale = 2
|
elif args.dataset == "partb":
|
dataset_paths = {'test': ['../dataset/ST_partB/test_data/images',
|
'../dataset/ST_partB/test_data/ground_truth'],
|
'train': ['../dataset/ST_partB/train_data/images',
|
'../dataset/ST_partB/train_data/ground_truth']}
|
validation_set = 80
|
output_downscale = 2
|
path = "../dataset/stpartb_dotmaps_predscale0.5_rgb_ddcnn++_test/"
|
elif args.dataset == "ucfqnrf":
|
dataset_paths = {'test': ['../dataset/UCF-QNRF_ECCV18/Test/images',
|
'../dataset/UCF-QNRF_ECCV18/Test/ground_truth'],
|
'train': ['../dataset/UCF-QNRF_ECCV18/Train/images',
|
'../dataset/UCF-QNRF_ECCV18/Train/ground_truth']}
|
validation_set = 240
|
output_downscale = 2
|
path = '../dataset/qnrf_dotmaps_predictionScale_'+str(output_downscale)
|
model_save_dir = './models'
|
batch_size = args.batch_size
|
crop_size = 224
|
dataset = DataReader(path)
|
# -- Train the model
|
train()
|
# <FILESEP>
|
from os import environ
|
from os.path import abspath
|
from typing import Any
|
import fabric
|
def regen_apidoc(ctx: Any, src: str, dest: str, is_nspkg: bool = False) -> None:
|
ctx.run(f"rm -rf {dest}", replace_env=False, pty=True)
|
nsopt = " --implicit-namespaces" if is_nspkg else ""
|
if "READTHEDOCS" in environ:
|
cmd = "sphinx-apidoc"
|
else:
|
cmd = "./env/bin/sphinx-apidoc"
|
ctx.run(
|
f"{cmd} -o {dest} -f{nsopt} -e -M {src}",
|
replace_env=False,
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.