def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
params = FLAGS.flag_values_dict()
tf.set_random_seed(params['seed'])
params['results_dir'] = utils.make_subdir(params['training_results_dir'],
params['expname'])
params['figdir'] = utils.make_subdir(params['results_dir'], 'figs')
params['ckptdir'] = utils.make_subdir(params['results_dir'], 'ckpts')
params['logdir'] = utils.make_subdir(params['results_dir'], 'logs')
params['tensordir'] = utils.make_subdir(params['results_dir'], 'tensors')
conv_dims = [int(x) for x in params['conv_dims'].split(',')]
conv_sizes = [int(x) for x in params['conv_sizes'].split(',')]
dense_sizes = [int(x) for x in params['dense_sizes'].split(',')]
params['n_layers'] = len(conv_dims)
clf = classifier.CNN(conv_dims,
conv_sizes,
dense_sizes,
params['n_classes'],
onehot=True)
utils.checkpoint_model(clf, params['ckptdir'], 'initmodel')
itr_train, itr_valid, itr_test = dataset_utils.load_dset_supervised_onehot(
)
train_cnn.train_classifier(clf, itr_train, itr_valid, params)
train_cnn.test_classifier(clf, itr_test, params, 'test')
train_data = utils.aggregate_batches(itr_train, 1000,
['train_x_infl', 'train_y_infl'])
validation_data = utils.aggregate_batches(itr_valid, 1000,
['valid_x_infl', 'valid_y_infl'])
test_data = utils.aggregate_batches(itr_test, 1000,
['test_x_infl', 'test_y_infl'])
utils.save_tensors(
train_data.items() + validation_data.items() + test_data.items(),
params['tensordir'])
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
params = FLAGS.flag_values_dict()
tf.set_random_seed(params['seed'])
params['results_dir'] = utils.make_subdir(params['results_dir'],
params['expname'])
params['figdir'] = utils.make_subdir(params['results_dir'], 'figs')
params['ckptdir'] = utils.make_subdir(params['results_dir'], 'ckpts')
params['logdir'] = utils.make_subdir(params['results_dir'], 'logs')
params['tensordir'] = utils.make_subdir(params['results_dir'], 'tensors')
ood_classes = [int(x) for x in params['ood_classes'].split(',')]
# We assume we train on all non-OOD classes.
all_classes = range(params['n_classes'])
ind_classes = [x for x in all_classes if x not in ood_classes]
(itr_train, itr_valid, itr_test,
itr_test_ood) = dataset_utils.load_dset_ood_supervised(
ind_classes, ood_classes)
conv_dims = [int(x) for x in params['conv_dims'].split(',')]
conv_sizes = [int(x) for x in params['conv_sizes'].split(',')]
clf = CNN(conv_dims, conv_sizes, params['n_classes'])
params['n_layers'] = len(conv_dims)
train_cnn.train_classifier(clf, itr_train, itr_valid, params)
train_cnn.test_classifier(clf, itr_test, params, 'test')
params['tensordir'] = utils.make_subdir(params['results_dir'],
'train_tensors')
train_cnn.test_classifier(clf, itr_train, params, 'train')
params['tensordir'] = utils.make_subdir(params['results_dir'],
'ood_tensors')
train_cnn.test_classifier(clf, itr_test_ood, params, 'ood')
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
params = FLAGS.flag_values_dict()
tf.set_random_seed(params['seed'])
params['results_dir'] = utils.make_subdir(params['training_results_dir'],
params['expname'])
params['figdir'] = utils.make_subdir(params['results_dir'], 'figs')
params['ckptdir'] = utils.make_subdir(params['results_dir'], 'ckpts')
params['logdir'] = utils.make_subdir(params['results_dir'], 'logs')
params['tensordir'] = utils.make_subdir(params['results_dir'], 'tensors')
# Load the classification model.
conv_dims = [
int(x) for x in (
params['conv_dims'].split(',') if params['conv_dims'] else [])
]
conv_sizes = [
int(x) for x in (
params['conv_sizes'].split(',') if params['conv_sizes'] else [])
]
dense_sizes = [
int(x) for x in (
params['dense_sizes'].split(',') if params['dense_sizes'] else [])
]
params['n_layers'] = len(conv_dims)
clf = classifier.CNN(conv_dims,
conv_sizes,
dense_sizes,
params['n_classes'],
onehot=True)
# Checkpoint the initialized model, in case we want to re-run it from there.
utils.checkpoint_model(clf, params['ckptdir'], 'initmodel')
# Load the "in-distribution" and "out-of-distribution" classes as
# separate splits.
ood_classes = [int(x) for x in params['ood_classes'].split(',')]
# We assume we train on all non-OOD classes.
all_classes = range(params['n_classes'])
ind_classes = [x for x in all_classes if x not in ood_classes]
(itr_train, itr_valid, itr_test,
itr_test_ood) = dataset_utils.load_dset_ood_supervised_onehot(
ind_classes,
ood_classes,
label_noise=(params['label_noise']),
dset_name=params['dataset_name'])
# Train and test the model in-distribution, and save test outputs.
train_cnn.train_classifier(clf, itr_train, itr_valid, params)
train_cnn.test_classifier(clf, itr_test, params, 'test')
# Save model outputs on the training set.
params['tensordir'] = utils.make_subdir(params['results_dir'],
'train_tensors')
train_cnn.test_classifier(clf, itr_train, params, 'train')
# Save model outputs on the OOD set.
params['tensordir'] = utils.make_subdir(params['results_dir'],
'ood_tensors')
train_cnn.test_classifier(clf, itr_test_ood, params, 'ood')
params['tensordir'] = utils.make_subdir(params['results_dir'], 'tensors')
# Save to disk samples of size 1000 from the train, valid, test and OOD sets.
train_data = utils.aggregate_batches(itr_train, 1000,
['train_x_infl', 'train_y_infl'])
validation_data = utils.aggregate_batches(itr_valid, 1000,
['valid_x_infl', 'valid_y_infl'])
test_data = utils.aggregate_batches(itr_test, 1000,
['test_x_infl', 'test_y_infl'])
ood_data = utils.aggregate_batches(itr_test_ood, 1000,
['ood_x_infl', 'ood_y_infl'])
utils.save_tensors(
train_data.items() + validation_data.items() + test_data.items() +
ood_data.items(), params['tensordir'])