def lista():
template_conf_path = "template.yaml"
with open(template_conf_path, 'r') as f:
conf = yaml.load(f)
conf['subnet_name'] = 'lista'
Ts = [1]
Cs = [128]
Ks = [0]
budget = 128 * 4 * 32
grid = [(T, C, K) for T in Ts for C in Cs for K in Ks if K < C]
flag = False
for T, C, K in grid:
conf['T'] = T
conf['n_c'] = C
conf['e_rank'] = K
conf['s_rank'] = C // 4
conf['mb_size'] = budget / (T * C)
conf['path_tmp'] = 'tmp/lista'
run_model.train(conf)
psnr, bl_psnr = run_model.eval_te(conf)
with open('notes/log_lista.txt', 'a') as f:
f.write('T: %03d C: %03d K: %03d PSNR: %.2f (%.2f)\n' % \
(T, C, K, psnr, bl_psnr))
def lista():
template_conf_path = "template.yaml"
with open(template_conf_path, 'r') as f:
conf = yaml.load(f)
conf['subnet_name'] = 'lista'
Ts = [1]
Cs = [128]
Ks = [0]
budget = 128 * 4 * 32
grid = [(T, C, K) for T in Ts for C in Cs for K in Ks if K < C]
flag = False
for T, C, K in grid:
conf['T'] = T
conf['n_c'] = C
conf['e_rank'] = K
conf['s_rank'] = C // 4
conf['mb_size'] = budget / (T * C)
conf['path_tmp'] = 'tmp/lista'
run_model.train(conf)
psnr, bl_psnr = run_model.eval_te(conf)
with open('notes/log_lista.txt', 'a') as f:
f.write('T: %03d C: %03d K: %03d PSNR: %.2f (%.2f)\n' % \
(T, C, K, psnr, bl_psnr))
def lcod():
template_conf_path = "template.yaml"
with open(template_conf_path, 'r') as f:
conf = yaml.load(f)
Ts = [8]
Cs = [32]
Ks = [28]
budget = 128 * 4 * 16
grid = [(T, C, K) for T in Ts for C in Cs for K in Ks if K < C]
flag = False
for T, C, K in grid:
print("T: %d C: %03d K: %02d" % (T, C, K))
time.sleep(2)
conf['T'] = T
conf['n_c'] = C
conf['e_rank'] = K
conf['mb_size'] = budget / (T * C)
conf['path_tmp'] = 'tmp/%03d_%03d_%03d' % (T, C, K)
run_model.train(conf, False)
psnr, bl_psnr = run_model.eval_te(conf)
with open('notes/log.txt', 'a') as f:
f.write('T: %03d C: %03d K: %03d PSNR: %.2f (%.2f)\n' % \
(T, C, K, psnr, bl_psnr))
def lcod():
template_conf_path = "template.yaml"
with open(template_conf_path, 'r') as f:
conf = yaml.load(f)
Ts = [8]
Cs = [32]
Ks = [28]
budget = 128 * 4 * 16
grid = [(T, C, K) for T in Ts for C in Cs for K in Ks if K < C]
flag = False
for T, C, K in grid:
print("T: %d C: %03d K: %02d" % (T, C, K))
time.sleep(2)
conf['T'] = T
conf['n_c'] = C
conf['e_rank'] = K
conf['mb_size'] = budget / (T * C)
conf['path_tmp'] = 'tmp/%03d_%03d_%03d' % (T, C, K)
run_model.train(conf, False)
psnr, bl_psnr = run_model.eval_te(conf)
with open('notes/log.txt', 'a') as f:
f.write('T: %03d C: %03d K: %03d PSNR: %.2f (%.2f)\n' % \
(T, C, K, psnr, bl_psnr))