def test_mypytorchtask():
logger.set_level(logger.DEBUG)
saver = Saver()
task = MyPytorchTask(saver=saver,
model=MyPytorchModel,
optimizer='SGD',
optimizer_args=dict(lr=0.1),
loss='CrossEntropyLoss',
metrics=['acc', 'lrs'])
task.set_hps({'num_epochs': 5})
task.execute()
task.finalize()
assert saver.load_ml(key='mypytorch')['_num_epochs'] == 5
def test_saver():
saver = Saver(mode='shelve')
assert saver._serial_id == 0
saver.open()
assert saver._state == 'open'
saver['key0'] = 'value0'
assert saver.keys('shelve') == ['key0']
assert saver['key0'] == 'value0'
assert saver._shelve['key0'] == 'value0'
# change shelve to dict
saver.set_mode('dict')
saver['key1'] = 'value1'
assert saver.keys('dict') == ['key1']
assert saver['key1'] == 'value1'
assert saver._dict['key1'] == 'value1'
assert 'key0' not in saver._dict
assert 'key1' not in saver._shelve
# save dict objects to shelve
saver.save()
assert set(saver.keys()) == set(['key0', 'key1'])
assert 'key0' not in saver._dict
assert 'key1' not in saver._dict
assert 'key0' in saver._shelve
assert 'key1' in saver._shelve
# move data from shelve to dict
saver.to_memory('key0')
assert 'key0' not in saver._shelve
assert 'key0' in saver._dict
# move data from dict to shelve
saver.to_storage('key0')
assert 'key0' in saver._shelve
assert 'key0' not in saver._dict
saver.set_mode('shelve')
saver.dump_ml(key='ml0', param0='param0', param1='param1')
assert saver.load_ml(key='ml0')['param0'] == 'param0'
saver.close()
assert saver._state == 'close'
def test_keras_ensemble():
logger.set_level(logger.DEBUG)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
saver = Saver()
storegate = build_storegate()
args_mlptask = {
# BaseTask
'saver': saver,
'storegate': storegate,
# KerasBaseTask
'optimizer': 'adam',
'num_epochs': 2,
'max_patience': 1,
'loss': 'mse',
'run_eagerly': True,
# MLPTask
'activation': 'relu',
'activation_last': 'sigmoid',
}
subtask0 = MLPTask(name='subtask0',
input_var_names=['var0', 'var1'],
output_var_names=['output0'],
true_var_names=['label'],
layers=[4, 1],
**args_mlptask)
subtask1 = MLPTask(name='subtask1',
input_var_names=['var0', 'var1'],
output_var_names=['output0'],
true_var_names=['label'],
layers=[4, 1],
**args_mlptask)
subtask0.execute()
subtask1.execute()
task = EnsembleTask(
subtasks=[subtask0, subtask1],
dropout_rate=None,
individual_loss=True,
individual_loss_weights=1.0,
saver=saver,
storegate=storegate,
save_weights=True,
# do_training=False,
phases=["train", "valid", "test"],
)
assert task.get_inputs()[0].shape[1] == len(['var0', 'var1'])
assert task.input_var_names == ['var0', 'var1']
assert task.output_var_names == ['output0']
task.execute()
task.finalize()
task2 = EnsembleTask(
name='EnsembleTask2',
subtasks=[subtask0, subtask1],
dropout_rate=None,
individual_loss=True,
individual_loss_weights=1.0,
saver=saver,
storegate=storegate,
phases=["test"],
load_weights=saver.load_ml(task.get_unique_id())['model_path'],
)
task2.execute()
task2.finalize()
y_pred = task.predict(phase='test')
y_pred_load = task2.predict(phase='test')
assert (np.array_equal(y_pred, y_pred_load))
def test_keras_mlp():
logger.set_level(logger.DEBUG)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
storegate = build_storegate()
saver = Saver()
args_task = {
# BaseTask
'saver': saver,
'storegate': storegate,
# MLBaseTask
'phases': None,
'save_weights': True,
# KerasBaseTask
'input_var_names': ['var0', 'var1'],
'output_var_names': 'output0',
'true_var_names': 'label',
'optimizer': 'adam',
'num_epochs': 2,
'max_patience': 1,
'loss': 'binary_crossentropy',
'run_eagerly': True,
# MLPTask
'layers': [8, 1],
'activation': 'relu',
'activation_last': 'sigmoid',
'batch_norm': True,
}
task = MLPTask(**args_task)
assert task._layers == [8, 1]
task.set_hps({
'layers': [4, 1],
'input_shapes': None,
'output_var_names': 'output0'
})
assert task._layers == [4, 1]
assert task._input_shapes == [len(['var0', 'var1'])]
assert task.get_inputs()[0].shape[1] == len(['var0', 'var1'])
task.execute()
task.finalize()
# Validate model save/load
args_task['phases'] = ['test']
args_task['save_weights'] = False
args_task['load_weights'] = saver.load_ml(task._name)['model_path']
args_task['layers'] = [4, 1]
task2 = MLPTask(**args_task)
# Fail due to call models before defining the model
with pytest.raises(ValueError):
y_pred_load = task2.predict(phase='test')
task2.execute()
task2.finalize()
y_pred = task.predict(phase='test')
y_pred_load = task2.predict(phase='test')
# assert (np.sum(np.square(y_pred - y_pred_load)) < 1e-10)
assert (np.array_equal(y_pred, y_pred_load))
def test_keras_conv2d():
logger.set_level(logger.DEBUG)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
storegate = build_storegate()
saver = Saver()
args_task = {
# BaseTask
'saver': saver,
'storegate': storegate,
# MLBaseTask
'phases': None,
'save_weights': True,
# KerasBaseTask
'input_var_names': ['var0'],
'output_var_names': ['output0'],
'optimizer': 'adam',
'num_epochs': 2,
'max_patience': 1,
'loss': 'binary_crossentropy',
'run_eagerly': True,
# MLPTask
'true_var_names': ['label'],
'layers': [4, 1],
'input_shapes': (1, 3, 3, 1),
'activation': 'relu',
'activation_last': 'sigmoid',
# Conv2DTask
'conv2d_layers': [
('conv2d', {
'filters': 4,
'kernel_size': (2, 2)
}),
('maxpooling2d', {
'pool_size': (2, 2)
}),
('upsampling2d', {
'size': (2, 2)
}),
],
}
from multiml.task.keras import Conv2DTask
task = Conv2DTask(**args_task)
task.execute()
task.finalize()
# Validate model save/load
args_task['phases'] = ['test']
args_task['save_weights'] = False
args_task['load_weights'] = saver.load_ml(task._name)['model_path']
task2 = Conv2DTask(**args_task)
task2.execute()
task2.finalize()
y_pred = task.predict(phase='test')
y_pred_load = task2.predict(phase='test')
assert (np.array_equal(y_pred, y_pred_load))