def test_add_output_layer():
model1 = dpp.ClassificationModel()
model2 = dpp.SemanticSegmentationModel()
model3 = dpp.CountCeptionModel()
model1.set_image_dimensions(5, 5, 3)
model2.set_image_dimensions(5, 5, 3)
with pytest.raises(RuntimeError):
model1.add_output_layer(2.5, 3)
model1.add_input_layer()
model2.add_input_layer()
model3.add_input_layer()
with pytest.raises(TypeError):
model1.add_output_layer("2")
with pytest.raises(ValueError):
model1.add_output_layer(-0.4)
with pytest.raises(TypeError):
model1.add_output_layer(2.0, 3.4)
with pytest.raises(ValueError):
model1.add_output_layer(2.0, -4)
with pytest.raises(RuntimeError):
model2.add_output_layer(
output_size=3
) # Semantic segmentation needed for this runtime error to occur
model1.add_output_layer(2.5, 3)
assert isinstance(model1._last_layer(), dpp.layers.fullyConnectedLayer)
with pytest.warns(Warning):
model2.add_output_layer(regularization_coefficient=2.0)
assert isinstance(model2._last_layer(), dpp.layers.convLayer)
model3.add_output_layer()
assert isinstance(model3._last_layer(), dpp.layers.inputLayer)
#
# Demonstrates training a general-purpose image classifier on the popular CIFAR10 image classification dataset.
# Assumes that you downloaded CIFAR10 image files via nvidia DIGITS.
#
import deepplantphenomics as dpp
model = dpp.ClassificationModel(debug=True, load_from_saved=False)
# 3 channels for colour, 1 channel for greyscale
channels = 3
# Setup and hyper-parameters
model.set_image_dimensions(32, 32, channels)
model.set_regularization_coefficient(0.004)
model.set_batch_size(32)
model.set_learning_rate(0.0001)
model.set_maximum_training_epochs(25)
# Augmentation options
model.set_augmentation_flip_horizontal(True)
model.set_augmentation_crop(True)
model.set_augmentation_brightness_and_contrast(True)
# Load dataset
model.load_cifar10_dataset_from_directory('./data/cifar10')
# Use a VGG-16 network
model.use_predefined_model('vgg-16')
def test_set_patch_size(model):
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()