def test__get_model_encdec_diag_birnn_skip(setup_args):
setup_args.model = 'encdec_diag_birnn_skip'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, EncDecDiagBiRNNSkip)
import torch.optim as optim
from motornn.utils.parser import get_parser_with_args
from motornn.utils.helpers import (get_file_names, get_dataloaders, get_model,
get_loss_function, Log)
from motornn.utils.runner import Runner
parser = get_parser_with_args()
args = parser.parse_args()
weight_path, log_path = get_file_names(args)
print(weight_path, log_path)
logger = Log(log_path, 'w')
train_loader, val_loader = get_dataloaders(args)
model = get_model(args)
criterion = get_loss_function(args)
optimizer = optim.SGD(model.parameters(), lr=args.lr)
runner = Runner(args.gpu, model, optimizer, criterion, train_loader,
val_loader)
best_smape = 1000
logger.write_model(model)
for epoch in range(args.epochs):
runner.set_epoch_metrics()
train_metrics = runner.train_model()
val_metrics = runner.eval_model()
def test__get_model_deep_encdec(setup_args):
setup_args.model = 'deep_encdec'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, DeepEncDec)
def test__get_model_encdec_skip(setup_args):
setup_args.model = 'encdec_skip'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, EncDecSkip)
def test__get_model_shallow_encdec(setup_args):
setup_args.model = 'shallow_encdec'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, ShallowEncDec)
def test__get_model_deep_lstm(setup_args):
setup_args.model = 'deep_lstm'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, DeepLSTM)
def test__get_model_shallow_lstm(setup_args):
setup_args.model = 'shallow_lstm'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, ShallowLSTM)
def test__get_model_deep_rnn(setup_args):
setup_args.model = 'deep_rnn'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, DeepRNN)
def test__get_model_shallow_rnn(setup_args):
setup_args.model = 'shallow_rnn'
model = get_model(setup_args)
assert isinstance(model, nn.Module)
assert isinstance(model, ShallowRNN)
