def test_yolo_tiny_portuguese(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset=EXPERIMENT_DATASET,
language="português",
yolo_weight_type="tiny",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
for ext in ['png', 'jpg']:
data = datasets.image_testdata(kind='objects', ext=ext)
with server.Server() as s:
response = s.test(data=data, timeout=10)
if 'tensor' in response.keys():
tensor_shape = response["tensor"]['shape']
self.assertEqual(tensor_shape[1], 6) # outputs 6 features
else: # is a ndarray
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 6) # 6 features
names = response["names"]
self.assertEqual(len(names), 6) # 6 feature names
def test_yolo_empty_output(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset=EXPERIMENT_DATASET,
score_threshold=0.9999,
iou_threshold=0.9999,
yolo_weight_type="tiny",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.image_testdata(kind='text', ext='png')
with server.Server() as s:
response = s.test(data=data, timeout=10)
if 'tensor' in response.keys():
tensor_shape = response["tensor"]['shape']
self.assertEqual(tensor_shape[1], 6) # outputs 6 features
else: # is a ndarray
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 6) # 6 features
names = response["names"]
self.assertEqual(len(names), 6) # 6 feature names
def test_experiment_ocr_output_data(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/ocr_dataset.zip",
target="target",
filter_type="incluir",
model_features="input_image",
bbox_conf=60,
segmentation_mode="Considere um único bloco de texto uniforme",
ocr_engine="Mecanismo de redes neurais com apenas LSTM",
language="por",
bbox_return="np_array",
image_return_format="N/A"),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
for ext in ['png', 'jpg']:
data = datasets.image_testdata(kind='text', ext=ext)
with server.Server() as s:
response = s.test(data=data, timeout=10)
print(response)
for bbox in response['ndarray']:
xmin, ymin, xmax, ymax, text = bbox
self.assertGreater(xmax, xmin, "BoundingBox incorreta.")
self.assertGreater(ymax, ymin, "BoundingBox incorreta.")
def test_experiment_face_detection_cuda(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/football_teams.zip",
device="cuda",
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
data = datasets.image_testdata(kind='people', ext='jpg')
with server.Server() as s:
response = s.test(data=data, timeout=10)
if 'tensor' in response.keys():
tensor_shape = response["tensor"]['shape']
self.assertEqual(tensor_shape[1], 5) # output 5 features
else: # is a ndarray
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 5) # 5 features
names = response["names"]
self.assertEqual(len(names), 5) # 5 feature names
def test_experiment_face_detection_without_people(self):
papermill.execute_notebook(
"Experiment.ipynb",
"/dev/null",
parameters=dict(
dataset="/tmp/data/football_teams.zip",
image_size=64,
margin=5,
min_face_size=10,
factor=0.709,
keep_all=True,
device="cpu",
seed=7,
inference_batch_size=2,
input_square_transformation_size=128,
),
)
papermill.execute_notebook(
"Deployment.ipynb",
"/dev/null",
)
for ext in ['png', 'jpg']:
data = datasets.image_testdata(kind='objects', ext=ext)
with server.Server() as s:
response = s.test(data=data, timeout=10)
if 'tensor' in response.keys():
tensor_shape = response["tensor"]['shape']
self.assertEqual(tensor_shape[1], 5) # outputs 5 features
else: # is a ndarray
ndarray = response["ndarray"]
self.assertEqual(len(ndarray[0]), 5) # 5 features
names = response["names"]
self.assertEqual(len(names), 5) # 5 feature names