def test_model_construction(config_simple):
"""
Tests whether a model and its loss can be constructed.
"""
model, criterion = factories.construct(config_simple['model']['name'])
net = model(config_simple['model'])
loss = criterion(config_simple['model'])
net.eval()
net.train()
def test_model_full(config_full):
"""
Tests whether a model can be trained.
Including parsers and trainval in the execution.
Parameters
----------
config: dict
Generated by a fixture above, dummy config to allow networks to run.
It is mostly empty, we rely on networks default config.
"""
config = config_full
model, criterion = factories.construct(config['model']['name'])
net = model(config['model'])
loss = criterion(config['model'])
if not hasattr(net, "INPUT_SCHEMA"):
pytest.skip('No test defined for network of %s' %
config['model']['name'])
if not hasattr(loss, "INPUT_SCHEMA"):
pytest.skip('No test defined for criterion of %s' %
config['model']['name'])
# Setup configuration to have all necessary I/O keys
config['iotool']['dataset']['schema'] = {}
config['model']['network_input'] = []
config['model']['loss_input'] = []
for i, x in enumerate(net.INPUT_SCHEMA + loss.INPUT_SCHEMA):
parser_name = x[0]
parser_return_types = x[1]
config['iotool']['dataset']['schema'][i] = [x[0]]
for t in parser_return_types:
config['iotool']['dataset']['schema'][i].extend(
branch[parser_name][t])
if i < len(net.INPUT_SCHEMA):
config['model']['network_input'].append(i)
else:
config['model']['loss_input'].append(i)
process_config(config)
# Try opening LArCV data file
try:
handlers = prepare(config)
except FileNotFoundError:
pytest.skip('File not found to test the loader.')
train_loop(config, handlers)
def test_model_forward(config_simple, N, num_voxels_low, num_voxels_high):
"""
Test whether a model can be trained.
Using only numpy input arrays, should also test with parsers running.
Parameters
----------
config: dict
Generated by a fixture above, dummy config to allow networks to run.
It is mostly empty, we rely on networks default config.
N: int
Spatial size
num_voxels_low: int, optional
Lower boundary for generating (random) number of voxels.
num_voxels_high: int, optional
Upper boundary for generating (random) number of voxels.
"""
config = config_simple
model, criterion = factories.construct(config['model']['name'])
net = model(config['model'])
loss = criterion(config['model'])
if not hasattr(net, "INPUT_SCHEMA"):
pytest.skip('No test defined for network of %s' % config['model']['name'])
net_input, voxels = generate_data(N, net.INPUT_SCHEMA,
num_voxels_low=num_voxels_low,
num_voxels_high=num_voxels_high)
output = net.forward(net_input)
if not hasattr(loss, "INPUT_SCHEMA"):
pytest.skip('No test defined for criterion of %s' % config['model']['name'])
loss_input = generate_data(N, loss.INPUT_SCHEMA,
num_voxels_low=num_voxels_low,
num_voxels_high=num_voxels_high,
voxels=voxels,
loss=True)[0]
res = loss.forward(output, *loss_input)
res['loss'].backward()
def config(request):
"""
Fixture to generate a basic configuration dictionary given a model name.
"""
model_name = request.param
model, criterion = factories.construct(model_name)
if 'chain' in model_name:
model_config = {'name': model_name, 'modules': {}}
for module in model.MODULES:
model_config['modules'][module] = {}
else:
model_config = {'name': model_name, 'modules': {model_name: {}}}
model_config['network_input'] = ['input_data', 'segment_label']
model_config['loss_input'] = ['segment_label']
iotool_config = {
'batch_size': 1,
'minibatch_size': 1,
}
config = {'iotool': iotool_config, 'training': {}, 'model': model_config}
return config