Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
print('\nSaving backup checkpoint at [{}]\n'.format(base_dir))
save_ckpt(X0_eval, X1_eval, net, net_ema, opt_DSM, opt_CTM, avgmeter, best_PSNR, base_dir, avgmeter.idx)
# Evaluating Quick FID
if avgmeter.idx % FID_iter == 0:
curr_PSNR_G, curr_SSIM_G, curr_LPIPS_G = eval_inverse(sampler, t1, t0, net_ema, n_FID, tweedie=False, verbose=True)
curr_PSNR_g, curr_SSIM_g, curr_LPIPS_g = eval_inverse(sampler, t1, t1, net_ema, n_FID, tweedie=True, verbose=True)
if curr_PSNR_G > best_PSNR:
best_PSNR = curr_PSNR_G
save_ckpt(X0_eval, X1_eval, net, net_ema, opt_DSM, opt_CTM, avgmeter, best_PSNR, ckpt_dir, avgmeter.idx, best=True)
def main():
parser = argparse.ArgumentParser()
# Basic experiment settings
parser.add_argument('--datasets', type=str, nargs='+', default=['cifar10','gaussian'])
parser.add_argument('--data_roots', type=str, nargs='+', default=['../data','../data'])
parser.add_argument('--base_dir', type=str, default='results/cifar10')
parser.add_argument('--ckpt_name', type=str, default=None)
# p(X0,X1) settings
# inverse tasks = {'sr4x-pool', 'sr4x-bicubic', 'inpaint-center', 'inpaint-random', 'blur-uni', 'blur-gauss'}
parser.add_argument('--size', type=int, default=32)
parser.add_argument('--X1_eps_std', type=float, default=0.0)
parser.add_argument('--vars', type=float, nargs='+', default=[0.25,1.0,0.0])
parser.add_argument('--coupling', type=str, default='independent')
parser.add_argument('--coupling_bs', type=int, default=64)
# ODE settings
parser.add_argument('--disc_steps', type=int, default=1024)
parser.add_argument('--init_steps', type=int, default=8)
parser.add_argument('--double_iter', type=int, default=None)
parser.add_argument('--solver', type=str, default='heun')
parser.add_argument('--discretization', type=str, default='edm_n2i')
parser.add_argument('--smin', type=float, default=0.002)
parser.add_argument('--smax', type=float, default=80.0)
parser.add_argument('--edm_rho', type=int, default=7)
parser.add_argument('--t_sm_dists', type=str, nargs='+', default=[])
parser.add_argument('--t_ctm_dists', type=float, nargs='+', default=[1.2,2])
parser.add_argument('--param', type=str, default='LIN')
parser.add_argument('--ODE_N', type=int, default=1)
# Optimization settings
parser.add_argument('--bs', type=int, default=64)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--rho', type=float, default=0.01)
parser.add_argument('--lmda_CTM', type=float, default=0.1)
parser.add_argument('--ctm_distance', type=str, default='l1')
parser.add_argument('--ema_decay', type=float, default=0.9)
parser.add_argument('--n_grad_accum', type=int, default=1)
parser.add_argument('--compare_zero', action='store_true')
parser.add_argument('--use_pcgrad', action='store_true')
parser.add_argument('--offline', action='store_true')
# Model settings
parser.add_argument('--nc', type=int, default=3)
parser.add_argument('--model_channels', type=int, default=128)
parser.add_argument('--num_blocks', type=int, default=4)
parser.add_argument('--dropout', type=float, default=0.1)
# Evaluation settings
parser.add_argument('--v_iter', type=int, default=25)
parser.add_argument('--s_iter', type=int, default=250)
parser.add_argument('--b_iter', type=int, default=25000)
parser.add_argument('--FID_iter', type=int, default=250)
parser.add_argument('--n_FID', type=int, default=5000)
parser.add_argument('--FID_bs', type=int, default=500)
parser.add_argument('--n_viz', type=int, default=100)
parser.add_argument('--n_save', type=int, default=2)
args = parser.parse_args()
def print_args(**kwargs):
print('\nTraining with settings :\n')
pprint.pprint(kwargs)
print_args(**vars(args))
train(**vars(args))
if __name__ == '__main__':
main()
# <FILESEP>
from common import (
Example,
Fact,
Rule,
Theory,
TheoryAssertionInstance,
TheoryAssertionRepresentation,
supported_theorem_provers,
)
import json
import pytest
from theory_label_generator import call_theorem_prover
def create_example_object():
fact1 = Fact(polarity="+", predicate="green", arguments=["'Erin'"])
fact2 = Fact(polarity="+", predicate="blue", arguments=["'Fiona'"])
fact3 = Fact(polarity="+", predicate="big", arguments=["'Charlie'"])
rules = [
Rule(