text stringlengths 1 93.6k |
|---|
print(colorize("[+] blind match: %s" % ", ".join("'%s' (%d%%)" % (format_name(matches[i][1]), matches[i][0]) for i in xrange(min(len(matches), MAX_MATCHES) if matches[0][0] != 100 else 1))))
|
print()
|
def main():
|
if "--version" not in sys.argv:
|
print(BANNER)
|
parse_args()
|
init()
|
run()
|
load_data()
|
if __name__ == "__main__":
|
try:
|
main()
|
except KeyboardInterrupt:
|
exit(colorize("\r[x] Ctrl-C pressed"))
|
# <FILESEP>
|
import yaml
|
import argparse
|
import tensorflow as tf
|
from models.generator import GeneratorBuilder
|
from models.discriminator import DiscriminatorBuilder
|
from models.spatial_prediction import SpatialPredictorBuilder
|
from models.content_predictor import ContentPredictorBuilder
|
from coord_handler import CoordHandler
|
from patch_handler import PatchHandler
|
from data_loader import DataLoader
|
from trainer import Trainer
|
from evaluator import Evaluator
|
from logger import Logger
|
from fid_utils import fid
|
def precompute_parameters(config):
|
full_image_size = config["data_params"]["full_image_size"]
|
micro_patch_size = config["data_params"]["micro_patch_size"]
|
macro_patch_size = config["data_params"]["macro_patch_size"]
|
# Let NxM micro matches to compose a macro patch,
|
# `ratio_macro_to_micro` is N or M
|
ratio_macro_to_micro = [
|
macro_patch_size[0] // micro_patch_size[0],
|
macro_patch_size[1] // micro_patch_size[1],
|
]
|
num_micro_compose_macro = ratio_macro_to_micro[0] * ratio_macro_to_micro[1]
|
# Let NxM micro matches to compose a full image,
|
# `ratio_full_to_micro` is N or M
|
ratio_full_to_micro = [
|
full_image_size[0] // micro_patch_size[0],
|
full_image_size[1] // micro_patch_size[1],
|
]
|
num_micro_compose_full = ratio_full_to_micro[0] * ratio_full_to_micro[1]
|
config["data_params"]["ratio_macro_to_micro"] = ratio_macro_to_micro
|
config["data_params"]["ratio_full_to_micro"] = ratio_full_to_micro
|
config["data_params"]["num_micro_compose_macro"] = num_micro_compose_macro
|
config["data_params"]["num_micro_compose_full"] = num_micro_compose_full
|
if __name__ == "__main__":
|
parser = argparse.ArgumentParser()
|
parser.add_argument('--config', type=str, required=True)
|
# testing arguments
|
parser.add_argument("--test", action="store_true", help="Run testing generation only.")
|
parser.add_argument("--n_samples", default=1024, help="Generate N sample in testing mode.")
|
parser.add_argument("--test_output_dir", default="./test_outputs/", help="Directory that will contain the generated images.")
|
args = parser.parse_args()
|
with open(args.config) as f:
|
config = yaml.load(f)
|
# Basic protect. Otherwise, I don't know what will happen. OuO
|
micro_size = config["data_params"]['micro_patch_size']
|
macro_size = config["data_params"]['macro_patch_size']
|
full_size = config["data_params"]['full_image_size']
|
assert macro_size[0] % micro_size[0] == 0
|
assert macro_size[1] % micro_size[1] == 0
|
assert full_size[0] % micro_size[0] == 0
|
assert full_size[1] % micro_size[1] == 0
|
# Pre-compute some frequently used parameters
|
precompute_parameters(config)
|
# Create model builders
|
coord_handler = CoordHandler(config)
|
patch_handler = PatchHandler(config)
|
g_builder = GeneratorBuilder(config)
|
d_builder = DiscriminatorBuilder(config)
|
cp_builder = SpatialPredictorBuilder(config)
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.