def test_set_yolo_parameters():
model = dpp.ObjectDetectionModel()
with pytest.raises(RuntimeError):
model.set_yolo_parameters()
model.set_image_dimensions(448, 448, 3)
model.set_yolo_parameters()
with pytest.raises(TypeError):
model.set_yolo_parameters(True, ['plant', 'knat'],
[(100, 30), (200, 10), (50, 145)])
with pytest.raises(TypeError):
model.set_yolo_parameters(13, ['plant', 'knat'], [(100, 30), (200, 10),
(50, 145)])
with pytest.raises(TypeError):
model.set_yolo_parameters([13], ['plant', 'knat'],
[(100, 30), (200, 10), (50, 145)])
with pytest.raises(TypeError):
model.set_yolo_parameters([13, 13], 'plant', [(100, 30), (200, 10),
(50, 145)])
with pytest.raises(TypeError):
model.set_yolo_parameters([13, 13], ['plant', 2],
[(100, 30), (200, 10), (50, 145)])
with pytest.raises(TypeError):
model.set_yolo_parameters([13, 13], ['plant', 'knat'], 100)
with pytest.raises(TypeError):
model.set_yolo_parameters([13, 13], ['plant', 'knat'], [(100, 30),
(200, 10), 50])
with pytest.raises(TypeError):
model.set_yolo_parameters([13, 13], ['plant', 'knat'], [(100, 30),
(200, 10),
(145, )])
with pytest.raises(TypeError):
model.set_yolo_parameters([13, 13], ['plant', 'knat'], [(100, 30),
(200, 10),
(145, 'a')])
model.set_yolo_parameters([13, 13], ['plant', 'knat'],
[(100, 30), (200, 10), (50, 145)])
with pytest.raises(TypeError):
model.set_patch_size(1.0, 1)
with pytest.raises(ValueError):
model.set_patch_size(-1, 1)
with pytest.raises(TypeError):
model.set_patch_size(1, 1.0)
with pytest.raises(ValueError):
model.set_patch_size(1, -1)
@pytest.mark.parametrize(
"model,bad_loss,good_loss",
[(dpp.ClassificationModel(), 'l2', 'softmax cross entropy'),
(dpp.RegressionModel(), 'softmax cross entropy', 'l2'),
(dpp.SemanticSegmentationModel(), 'l2', 'sigmoid cross entropy'),
(dpp.ObjectDetectionModel(), 'l2', 'yolo'),
(dpp.CountCeptionModel(), 'l2', 'l1'),
(dpp.HeatmapObjectCountingModel(), 'l1', 'sigmoid cross entropy')])
def test_set_loss_function(model, bad_loss, good_loss):
with pytest.raises(TypeError):
model.set_loss_function(0)
with pytest.raises(ValueError):
model.set_loss_function(bad_loss)
model.set_loss_function(good_loss)
def test_set_yolo_parameters():
model = dpp.ObjectDetectionModel()
with pytest.raises(RuntimeError):
model.set_yolo_parameters()
model.set_image_dimensions(448, 448, 3)
#
# Demonstrates the process of training a YOLO-based object detector in DPP.
#
import deepplantphenomics as dpp
model = dpp.ObjectDetectionModel(debug=True,
save_checkpoints=False,
tensorboard_dir='tensor_logs',
report_rate=20)
# 3 channels for colour, 1 channel for greyscale
channels = 3
# Setup and hyper-parameters
model.set_batch_size(1)
model.set_number_of_threads(4)
model.set_image_dimensions(448, 448, channels)
model.set_resize_images(False)
model.set_patch_size(448, 448)
# model.set_yolo_parameters() is not called here because we are using all of the default values
model.set_test_split(0.1)
model.set_validation_split(0)
model.set_learning_rate(0.000001)
model.set_weight_initializer('xavier')
model.set_maximum_training_epochs(100)
model.load_yolo_dataset_from_directory('./yolo_data',
label_file='labels.json',
image_dir='images')