def test_train_main(identity_config, tmp_path):
config_reset()
train_config = tmp_path / 'config.yaml'
with open(train_config, 'w') as f:
f.write('''
train:
steps: 5
epochs: 3
network:
layers:
- Input:
shape: [1, 1, num_bands]
- Conv2D:
filters: 2
kernel_size: [1, 1]
activation: relu
padding: same
batch_size: 1
validation:
steps: 2
callbacks:
- ExponentialLRScheduler:
start_epoch: 2
''')
args = 'delta train --config %s --config %s' % (identity_config,
train_config)
main(args.split())
def test_train_validate(identity_config, binary_identity_tiff_filenames, tmp_path):
config_reset()
train_config = tmp_path / 'config.yaml'
with open(train_config, 'w') as f:
f.write('''
train:
steps: 5
epochs: 3
network:
layers:
- Input:
shape: [~, ~, num_bands]
- Conv2D:
filters: 2
kernel_size: [1, 1]
activation: relu
padding: same
batch_size: 1
validation:
from_training: false
images:
nodata_value: ~
files: [%s]
labels:
nodata_value: ~
files: [%s]
steps: 2
callbacks:
- ExponentialLRScheduler:
start_epoch: 2
''' % (binary_identity_tiff_filenames[0][0], binary_identity_tiff_filenames[1][0]))
args = 'delta train --config %s --config %s' % (identity_config, train_config)
main(args.split())
def test_predict_main(identity_config, tmp_path):
config_reset()
model_path = tmp_path / 'model.h5'
inputs = tf.keras.layers.Input((32, 32, 2))
tf.keras.Model(inputs, inputs).save(model_path)
args = 'delta classify --config %s %s' % (identity_config, model_path)
old = os.getcwd()
os.chdir(tmp_path) # put temporary outputs here
main(args.split())
os.chdir(old)
def test_validate_main(identity_config):
config_reset()
args = 'delta validate --config %s' % (identity_config, )
main(args.split())