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jblitzar
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github-python

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self.c_table = name
def make_table(self):
n_items = len(self.items)
size = 1 << (ceil(log2(n_items) + 0.5))
table = [None] * size
if self.lower:
fun = (lambda s: hash32trans(s.lower())) \
if self.config.lexicon_hash_bits == 32 \
else (lambda s: hash64trans(s.lower()))
else:
fun = (lambda s: hash32(1, s.encode('utf-8'))) \
if self.config.lexicon_hash_bits == 32 \
else (lambda s: hash64(1, s.encode('utf-8')))
for key, value in self.items:
key_hash = fun(key)
i = key_hash % size
while not table[i] is None:
if table[i][0] == key_hash: break
i = (i + 1) % size
if table[i] is None:
table[i] = (key_hash, value+1)
return table
def lookup(self, form):
if 'normalize' in form.get_translations():
self.lower = True
return Lookup(form, self)
@staticmethod
def from_file(name, filename, config):
with open(filename, 'r', encoding='utf-8') as f:
items = [tuple(line.rstrip('\n').split('\t')) for line in f]
assert all(len(t) == 2 for t in items)
return WCLexicon(name, [(key, int(s)) for key, s in items], config)
def c_emit(self, f):
table = self.make_table()
f.write('#define %s 0x%x\n\n' % (
self.c_size, len(table)))
def c_kv(entry):
if entry is None: return '{ 0, 0 }'
key_hash, value = entry
return '{ 0x%x, %d }' % (key_hash, value+1)
body = '\n'.join(
' %s%s' % (c_kv(t), '' if i == len(table)-1 else ',')
for i,t in enumerate(table))
f.write('static const hash%d_kv_label %s[%s] = {\n%s\n};\n\n' % (
self.config.lexicon_hash_bits, self.c_table, self.c_size, body))
f.write('''
static inline label %s_get_wc(uint%d_t key) {
size_t i = key & 0x%x;
for (;;) {
if (%s[i].hash == key) return %s[i].value;
if (%s[i].value == 0) return 0;
i = (i + 1) & 0x%x;
}
}
''' % (self.name, self.config.lexicon_hash_bits, len(table)-1,
self.c_table, self.c_table, self.c_table, len(table)-1))
def c_lookup(self, c_key):
return '%s_get_wc(%s)' % (self.name, c_key)
# <FILESEP>
from gatgnn.data import *
from gatgnn.model import *
from gatgnn.pytorch_early_stopping import *
from gatgnn.file_setter import use_property
from gatgnn.utils import *
# MOST CRUCIAL DATA PARAMETERS
parser = argparse.ArgumentParser(description='GATGNN')
parser.add_argument('--property', default='bulk-modulus',
choices=['absolute-energy','band-gap','bulk-modulus',
'fermi-energy','formation-energy',
'poisson-ratio','shear-modulus','new-property'],
help='material property to train (default: bulk-modulus)')
parser.add_argument('--data_src', default='CGCNN',choices=['CGCNN','MEGNET','NEW'],
help='selection of the materials dataset to use (default: CGCNN)')
# MOST CRUCIAL MODEL PARAMETERS
parser.add_argument('--num_layers',default=3, type=int,
help='number of AGAT layers to use in model (default:3)')
parser.add_argument('--num_neurons',default=64, type=int,
help='number of neurons to use per AGAT Layer(default:64)')
parser.add_argument('--num_heads',default=4, type=int,
help='number of Attention-Heads to use per AGAT Layer (default:4)')
parser.add_argument('--use_hidden_layers',default=True, type=bool,
help='option to use hidden layers following global feature summation (default:True)')
parser.add_argument('--global_attention',default='composition', choices=['composition','cluster']
,help='selection of the unpooling method as referenced in paper GI M-1 to GI M-4 (default:composition)')
parser.add_argument('--cluster_option',default='fixed', choices=['fixed','random','learnable'],
help='selection of the cluster unpooling strategy referenced in paper GI M-1 to GI M-4 (default: fixed)')