def __init__(self, batch_size=32):
"""A network which provides segmentation masks from plant images"""
m_path, _ = os.path.split(__file__)
checkpoint_path = os.path.join(m_path, 'network_states',
self.__dir_name)
import deepplantpheno as dpp
self.model = dpp.DPPModel(debug=False, load_from_saved=checkpoint_path)
# Define model hyperparameters
self.model.set_problem_type('semantic_segmentation')
self.model.set_batch_size(batch_size)
self.model.set_number_of_threads(1)
self.model.set_image_dimensions(self.img_height, self.img_width, 3)
self.model.set_resize_images(True)
# Define a model architecture
self.model.add_input_layer()
self.model.add_convolutional_layer(filter_dimension=[3, 3, 3, 16],
stride_length=1,
activation_function='relu')
self.model.add_convolutional_layer(filter_dimension=[3, 3, 16, 32],
stride_length=1,
activation_function='relu')
self.model.add_convolutional_layer(filter_dimension=[5, 5, 32, 32],
stride_length=1,
activation_function='relu')
self.model.add_output_layer()
def __init__(self, height, width, batch_size=4):
"""A network which predicts bounding box coordinates via a convolutional neural net"""
# Set original image dimensions
self.original_img_height = height
self.original_img_width = width
m_path, _ = os.path.split(__file__)
checkpoint_path = os.path.join(m_path, 'network_states',
self.__dir_name)
import deepplantpheno as dpp
self.model = dpp.DPPModel(debug=False, load_from_saved=checkpoint_path)
self.model.clear_preprocessors()
# Define model hyperparameters
self.model.set_batch_size(batch_size)
self.model.set_number_of_threads(1)
self.model.set_original_image_dimensions(self.original_img_height,
self.original_img_width)
self.model.set_image_dimensions(self.img_height, self.img_width, 3)
self.model.set_resize_images(True)
self.model.set_problem_type('regression')
self.model.set_num_regression_outputs(4)
# Define a model architecture
self.model.add_input_layer()
self.model.add_convolutional_layer(filter_dimension=[5, 5, 3, 16],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 16, 64],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 64, 64],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 64, 64],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_fully_connected_layer(output_size=384,
activation_function='relu')
self.model.add_output_layer()
def __init__(self, batch_size=8):
"""A network which predicts rosette leaf count via a convolutional neural net"""
m_path, _ = os.path.split(__file__)
checkpoint_path = os.path.join(m_path, 'network_states',
self.__dir_name)
import deepplantpheno as dpp
self.model = dpp.DPPModel(debug=False, load_from_saved=checkpoint_path)
self.model.clear_preprocessors()
# Define model hyperparameters
self.model.set_batch_size(batch_size)
self.model.set_number_of_threads(1)
self.model.set_image_dimensions(self.img_height, self.img_width, 3)
self.model.set_resize_images(True)
self.model.set_problem_type('regression')
self.model.set_augmentation_crop(True, crop_ratio=0.9)
# Define a model architecture
self.model.add_input_layer()
self.model.add_convolutional_layer(filter_dimension=[5, 5, 3, 32],
stride_length=1,
activation_function='tanh')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 32, 32],
stride_length=1,
activation_function='tanh')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[3, 3, 32, 64],
stride_length=1,
activation_function='tanh')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[3, 3, 64, 64],
stride_length=1,
activation_function='tanh')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_fully_connected_layer(output_size=1024,
activation_function='tanh')
self.model.add_output_layer()
def __init__(self, batch_size=32):
"""A network which predicts arabidopsis strain/mutant via a convolutional neural net"""
m_path, _ = os.path.split(__file__)
checkpoint_path = os.path.join(m_path, 'network_states',
self.__dir_name)
import deepplantpheno as dpp
self.model = dpp.DPPModel(debug=False, load_from_saved=checkpoint_path)
# Define model hyperparameters
self.model.set_batch_size(batch_size)
self.model.set_number_of_threads(1)
self.model.set_image_dimensions(self.img_height, self.img_width, 3)
self.model.set_resize_images(True)
self.model.set_augmentation_crop(True)
# Define a model architecture
self.model.add_input_layer()
self.model.add_convolutional_layer(filter_dimension=[5, 5, 3, 32],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 32, 64],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 64, 64],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_convolutional_layer(filter_dimension=[5, 5, 64, 64],
stride_length=1,
activation_function='relu')
self.model.add_pooling_layer(kernel_size=3, stride_length=2)
self.model.add_fully_connected_layer(output_size=4096,
activation_function='relu')
self.model.add_dropout_layer(0.5)
self.model.add_fully_connected_layer(output_size=4096,
activation_function='relu')
self.model.add_dropout_layer(0.5)
self.model.add_output_layer(output_size=5)