def fruits_vs_chairs_L3(template=None):
"""
:param template: Template of features to sample from
:return: scope evaluation of fruits vs chair classification for dataset 1
"""
dataset = dataset1()
fruits_and_chairs_1 = lambda x: (x['category'] in frozenset(['fruits', 'chairs'])) and \
(x['obj'] in frozenset(dataset.obj_set1)) and \
(x['obj'] not in frozenset(Broken_objects))
# fruits_and_chairs_1 = {'obj': dataset.obj_set1, 'category': ['Fruits', 'Chairs']}
if template is None:
template = devthor_new_new_params.l3_params
eval_config = {
'npc_train': 40,
'npc_test': 40,
'npc_validate': 0,
'num_splits': 4,
'split_by': 'category',
'labelfunc': 'category',
'train_q': fruits_and_chairs_1,
'test_q': fruits_and_chairs_1,
'metric_screen': 'classifier',
'metric_kwargs': {'model_type': 'MCC2'}}
return scope.dp_sym_loss(template, dataset, eval_config)
def eight_way_imagenet_screen(template=None):
dataset = Imagenet
splits, labels = dataset.get_eight_way_splits()
if template is None:
template = devthor_new_new_params.l4_params
eval_config = {
'precomp_splits': splits,
'validations': [],
'labels': labels,
'metric_screen': 'classifier',
'metric_kwargs': {'model_type': 'MCC2'}
}
return scope.dp_sym_loss(template, dataset, eval_config)
