def test_model_forward_no_satellite(configuration_conv3d):
config_file = "tests/configs/model/conv3d_sat_nwp.yaml"
config = load_config(config_file)
config['include_future_satellite'] = False
# start model
model = Model(**config)
dataset_configuration = configuration_conv3d
dataset_configuration.input_data.nwp.nwp_image_size_pixels = 16
# create fake data loader
train_dataset = FakeDataset(configuration=dataset_configuration)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=None)
x = next(iter(train_dataloader))
# run data through model
y = model(x)
# check out put is the correct shape
assert len(y.shape) == 2
assert y.shape[0] == 2
assert y.shape[1] == model.forecast_len_30
def test_train(configuration_conv3d):
config_file = "tests/configs/model/conv3d_gsp.yaml"
config = load_config(config_file)
dataset_configuration = configuration_conv3d
# start model
model = Model(**config)
# create fake data loader
train_dataset = FakeDataset(configuration=dataset_configuration)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=None)
# fit model
trainer = pl.Trainer(gpus=0, max_epochs=1)
trainer.fit(model, train_dataloader)
# predict over training set
_ = trainer.predict(model, train_dataloader)
def configuration_conv3d():
config_file = "tests/configs/model/conv3d.yaml"
config = load_config(config_file)
dataset_configuration = Configuration()
dataset_configuration.process.batch_size = 2
dataset_configuration.input_data.default_history_minutes = config[
'history_minutes']
dataset_configuration.input_data.default_forecast_minutes = config[
'forecast_minutes']
dataset_configuration.input_data = dataset_configuration.input_data.set_all_to_defaults(
)
dataset_configuration.input_data.nwp.nwp_image_size_pixels = 2
dataset_configuration.input_data.satellite.satellite_image_size_pixels = config[
'image_size_pixels']
dataset_configuration.input_data.satellite.forecast_minutes = config[
'forecast_minutes']
dataset_configuration.input_data.satellite.history_minutes = config[
'history_minutes']
return dataset_configuration
def test_model_forward(configuration_conv3d):
config_file = "tests/configs/model/conv3d.yaml"
config = load_config(config_file)
dataset_configuration = configuration_conv3d
# start model
model = Model(**config)
# create fake data loader
train_dataset = FakeDataset(configuration=dataset_configuration)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=None)
x = next(iter(train_dataloader))
# run data through model
y = model(x)
# check out put is the correct shape
assert len(y.shape) == 2
assert y.shape[0] == 2
assert y.shape[1] == model.forecast_len_5
def test_init():
config_file = "tests/configs/model/conv3d_sat_nwp.yaml"
config = load_config(config_file)
_ = Model(**config)
def test_init():
config_file = "configs/model/conv3d.yaml"
config = load_config(config_file)
_ = Model(**config)