code stringlengths 3 6.57k |
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keyid (string) |
profile (dict) |
key (string) |
format(name) |
format(name) |
format(name, is_deleted['error']) |
format(name) |
setdefault('old', 'Stream {0} exists'.format(name) |
setdefault('new', 'Stream {0} deleted'.format(name) |
_add_changes(ret, changes_old, changes_new, comments) |
join(comments) |
rdBase.DisableLog('rdApp.*') |
sys.path.append('../src/') |
argparse.ArgumentParser(description='SMILES generation') |
parser.add_argument('-fn','--filename', type=str, help='Path to the fine-tuning txt file', required=True) |
parser.add_argument('-m','--model_path', type=str, help='Path to a pretrained model', required=True) |
parser.add_argument('-v','--verbose', type=bool, help='Verbose', required=True) |
int_to_smile(array, indices_token, pad_char) |
str(int(x) |
all_mols.append(''.join(new_mol) |
replace(pad_char, '') |
one_hot_encode(token_lists, n_chars) |
np.zeros((len(token_lists) |
len(token_lists[0]) |
enumerate(token_lists) |
enumerate(token_list) |
int(token) |
sample(model, temp, start_char, end_char, max_len, indices_token, token_indices) |
len(indices_token) |
str(seed_token[0]) |
len(generated) |
one_hot_encode([seed_token], n_chars) |
model.predict(x_seed, verbose=0) |
get_token_proba(logits, temp) |
str(next_char_ind) |
get_token_proba(preds, temp) |
np.asarray(preds) |
astype('float64') |
np.log(preds) |
np.exp(preds) |
np.sum(exp_preds) |
np.argmax(np.random.multinomial(1, probas, 1) |
softmax(preds) |
np.exp(preds) |
np.sum(np.exp(preds) |
time.time() |
vars(parser.parse_args() |
filename.split('/') |
replace('.txt','') |
configparser.ConfigParser() |
config.read('parameters.ini') |
print('\nSTART SAMPLING') |
os.makedirs(save_path, exist_ok=True) |
float(config['EXPERIMENTS']['temp']) |
int(config['EXPERIMENTS']['n_sample']) |
warnings.warn('You will sample more than 5000 SMILES; this will take a while') |
int(config['PROCESSING']['max_len']) |
model_path.split('/') |
replace('.h5', '') |
print(f'Sampling from model saved at epoch {epoch}') |
load_model(model_path) |
range(n_sample) |
hp.save_obj(generated_smi, f'{save_path}{epoch}_{temp}') |
time.time() |
print(f'SAMPLING DONE for model from epoch {epoch} in {end-start:.2f} seconds') |
VpcSecurityGroupCreateTask(object) |
__init__(self, task=None) |
torch.cuda.current_device() |
torch.cuda.device_count() |
print('Multiple GPUs detected! Turning off JIT.') |
Concat(nn.Module) |
__init__(self, nets, extra_params) |
super() |
__init__() |
nn.ModuleList(nets) |
forward(self, x) |
torch.cat([net(x) |
defaultdict(lambda: None) |
PredictionModule(nn.Module) |
The (c) |
size (must be a multiple of 4) |
ratios (one list per scale) |
__init__(self, in_channels, out_channels=1024, aspect_ratios=[[1]], scales=[1], parent=None, index=0) |
super() |
__init__() |
sum(len(x) |
len(scales) |
make_net(in_channels, cfg.extra_head_net) |
Bottleneck(out_channels, out_channels // 4) |
nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=True) |
nn.BatchNorm2d(out_channels) |
nn.Conv2d(out_channels, self.num_priors * 4, **cfg.head_layer_params) |
nn.Conv2d(out_channels, self.num_priors * self.num_classes, **cfg.head_layer_params) |
nn.Conv2d(out_channels, self.num_priors * self.mask_dim, **cfg.head_layer_params) |
nn.Conv2d(out_channels, self.num_priors, **cfg.head_layer_params) |
nn.Conv2d(out_channels, self.num_priors * cfg.num_instance_coeffs, **cfg.head_layer_params) |
make_extra(num_layers) |
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) |
nn.ReLU(inplace=True) |
range(num_layers) |
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