def alternative_evaluation(input_path, output_path, n, log_step=1):
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
log = section_logger(0)
dataset = AD20kFasterRCNN(
'/home/dani/Documentos/Proyectos/Doctorado/Datasets/ADE20K/ADE20K_2016_07_26/ade20ktrain.csv',
'/home/dani/Documentos/Proyectos/Doctorado/Datasets/ADE20K/ADE20K_2016_07_26/images/training',
transforms=get_transform(train=True))
dataset_test = AD20kFasterRCNN(
'/home/dani/Documentos/Proyectos/Doctorado/Datasets/ADE20K/ADE20K_2016_07_26/ade20ktest.csv',
'/home/dani/Documentos/Proyectos/Doctorado/Datasets/ADE20K/ADE20K_2016_07_26/images/validation',
transforms=get_transform(train=False),
labels=dataset.labels,
is_test=True)
nlp = spacy.load("en_core_web_md")
num_classes = len(dataset.labels)
model = load_frcnn(input_path, num_classes, device)
num_examples = 0
results = []
metaparameters = generate_metaparameters(10,
METAPARAMETER_DEF,
static=False)
for id, objects in enumerate(dataset_test):
image = objects[0].to(device)
predictions = model([image])
if id % log_step == 0:
log("Processing image [{}] with {} predictions and {} boxes".
format(id, len(predictions[0]['boxes']),
len(objects[1]['boxes'])))
for meta_id in range(len(metaparameters['iou_threshold'])):
iou_threshold = metaparameters['iou_threshold'][meta_id]
nlp_threshold = metaparameters['iou_threshold'][meta_id]
wnt_threshold = metaparameters['iou_threshold'][meta_id]
partial_results = nlp_evaluation(id,
objects,
predictions[0],
dataset.labels,
nlp,
iou_threshold=iou_threshold,
nlp_threshold=nlp_threshold,
wnt_threshold=wnt_threshold)
results.append(partial_results)
if num_examples < n:
num_examples += 1
else:
break
results_df = pd.concat(results)
results_df.to_csv(os.path.join(output_path, 'FRCNN_ad20k_evaluations.csv'))
def test_to_list(self):
results = mt.generate_metaparameters(15, INTEGER_DEF_1, static=True)
result_list = mt.to_list(results)
self.assertEqual(len(result_list), 15)
self.assertEqual(list(result_list[0].keys())[0], 'Integer1')
def test_static_generation(self):
results = mt.generate_metaparameters(15, INTEGER_DEF_1, static=True)
self.assertEqual(len(results['Integer1']), 15)
self.assertEqual(sum([e == 5 for e in results['Integer1']]), 15)
def test_generate_multiple_numbers(self):
results = mt.generate_metaparameters(10, SMALL_FLOAT_DEF_1)
self.assertEqual(len(results['SmallFloat1']), 10)
def test_generate_smallfloats(self):
results = mt.generate_metaparameters(1, SMALL_FLOAT_DEF_1)
self.assertEqual(len(results['SmallFloat1']), 1)
self.assertGreater(results['SmallFloat1'][0], np.power(10, -5.))
self.assertLess(results['SmallFloat1'][0], np.power(10, -1.))
def test_generate_integers(self):
results = mt.generate_metaparameters(1, INTEGER_DEF_1)
self.assertEqual(len(results['Integer1']), 1)
self.assertGreater(results['Integer1'][0], 10)
self.assertLess(results['Integer1'][0], 100)