def read_keys(_dir, _filter, column_names):
data = {}
for cn in column_names:
data.update({cn: []})
dirs = ls_dir(_dir)
print(len(dirs), dirs)
assert len(dirs) > 0, f'filter {_filter} not found in {_dir}'
for d in dirs:
model_name = d.split('/')[-1]
run_data = {}
for cn in column_names:
run_data.update({cn: []})
if _filter is not None and not '':
if _filter not in model_name:
logger.info(f'skipping {model_name} | no match with {_filter}')
continue
if not os.path.isfile('{}/progress.csv'.format(d)):
logger.info('skipping {} | file not found'.format(model_name))
continue
dic = csv.DictReader(open(f'{d}/progress.csv'))
for cn in column_names:
assert cn in dic.fieldnames, f'{cn} not in {dic.fieldnames}'
for row in dic:
for cn in column_names:
run_data[cn].append(row[cn])
for cn in column_names:
data[cn].append(run_data[cn])
for cn in column_names:
data[cn] = np.asarray(data[cn], dtype=np.float32)
return data
from forkan.models import VAE
from forkan.datasets.dsprites import load_dsprites
from macode.vae.plot_helper import bars, plot_losses
logger = logging.getLogger(__name__)
network = 'dsprites'
filter = ''
plt_shape = [1, 10]
# whether to plot sigma-bars, kl plots and losses
modes = [True, False]
models_dir = '{}vae-{}/'.format(model_path, network)
dirs = ls_dir(models_dir)
for d in dirs:
ds_name = d.split('/')[-1].split('-')[0]
model_name = d.split('/')[-1]
if filter is not None and not '':
if filter not in model_name:
logger.info('skipping {}'.format(model_name))
continue
# sigma bars
if modes[0]:
(data, _) = load_dsprites('translation', repetitions=10)
v = VAE(load_from=model_name)