def test_no_fc():
no_fc_net_spec = deepcopy(net_spec)
no_fc_net_spec['fc_hid_layers'] = []
net = RecurrentNet(no_fc_net_spec, in_dim, out_dim)
assert isinstance(net, nn.Module)
assert not hasattr(net, 'fc_model')
assert hasattr(net, 'rnn_model')
assert hasattr(net, 'model_tail')
assert not hasattr(net, 'model_tails')
y = net.forward(x)
assert y.shape == (batch_size, out_dim)
def test_multitails():
net = RecurrentNet(net_spec, in_dim, [3, 4])
assert isinstance(net, nn.Module)
assert hasattr(net, 'fc_model')
assert hasattr(net, 'rnn_model')
assert not hasattr(net, 'model_tail')
assert hasattr(net, 'model_tails')
assert len(net.model_tails) == 2
y = net.forward(x)
assert len(y) == 2
assert y[0].shape == (batch_size, 3)
assert y[1].shape == (batch_size, 4)
def test_variant(bidirectional, cell_type):
var_net_spec = deepcopy(net_spec)
var_net_spec['bidirectional'] = bidirectional
var_net_spec['cell_type'] = cell_type
net = RecurrentNet(var_net_spec, in_dim, out_dim)
assert isinstance(net, nn.Module)
assert hasattr(net, 'fc_model')
assert hasattr(net, 'rnn_model')
assert hasattr(net, 'model_tail')
assert not hasattr(net, 'model_tails')
assert net.rnn_model.bidirectional == bidirectional
y = net.forward(x)
assert y.shape == (batch_size, out_dim)
def test_init():
net = RecurrentNet(net_spec, in_dim, out_dim)
assert isinstance(net, nn.Module)
assert hasattr(net, 'fc_model')
assert hasattr(net, 'rnn_model')
assert hasattr(net, 'model_tail')
assert not hasattr(net, 'model_tails')
assert net.rnn_model.bidirectional == False
"optim_spec": {
"name": "Adam",
"lr": 0.02
},
"lr_scheduler_spec": {
"name": "StepLR",
"step_size": 30,
"gamma": 0.1
},
"gpu": True
}
in_dim = 10
out_dim = 3
batch_size = 16
seq_len = net_spec['seq_len']
net = RecurrentNet(net_spec, in_dim, out_dim)
x = torch.rand((batch_size, seq_len, in_dim))
def test_init():
net = RecurrentNet(net_spec, in_dim, out_dim)
assert isinstance(net, nn.Module)
assert hasattr(net, 'fc_model')
assert hasattr(net, 'rnn_model')
assert hasattr(net, 'model_tail')
assert not hasattr(net, 'model_tails')
assert net.rnn_model.bidirectional == False
def test_forward():
y = net.forward(x)