def init(config_filename, log_verbosity):
"""
:param str config_filename: filename to config-file
:param int log_verbosity:
"""
rnn.init_better_exchook()
rnn.init_thread_join_hack()
if config_filename:
print("Using config file %r." % config_filename)
assert os.path.exists(config_filename)
rnn.init_config(config_filename=config_filename, command_line_options=[])
global config
config = rnn.config
config.set("log", None)
config.set("log_verbosity", log_verbosity)
config.set("use_tensorflow", True)
rnn.init_log()
print("Returnn compile-native-op starting up.", file=log.v1)
rnn.returnn_greeting()
rnn.init_backend_engine()
assert util.BackendEngine.is_tensorflow_selected(), "this is only for TensorFlow"
rnn.init_faulthandler()
rnn.init_config_json_network()
if 'network' in config.typed_dict:
print("Loading network")
from returnn.tf.network import TFNetwork
network = TFNetwork(
name="root",
config=config,
rnd_seed=1,
train_flag=False,
eval_flag=True,
search_flag=False)
network.construct_from_dict(config.typed_dict["network"])
def test_Updater_add_check_numerics_ops():
class _Layer(DummyLayer):
def _get_loss_value(self):
return tf_compat.v1.log(self.x)
from returnn.tf.network import TFNetwork, ExternData
from returnn.config import Config
with make_scope() as session:
config = Config()
config.set("debug_add_check_numerics_ops", True)
network = TFNetwork(extern_data=ExternData(), train_flag=True)
network.add_layer(name="output", layer_class=_Layer, initial_value=1.0)
network.initialize_params(session=session)
updater = Updater(config=config, network=network)
updater.set_learning_rate(1.0, session=session)
updater.set_trainable_vars(network.get_trainable_params())
updater.init_optimizer_vars(session=session)
# Should succeed.
session.run(updater.get_optim_op())
# One gradient descent step from ln(x), x = 1.0: gradient is 1.0 / x, thus x - 1.0 = 0.0.
assert_almost_equal(
session.run(network.get_default_output_layer().output.placeholder),
0.0)
try:
# Now, should fail.
session.run(updater.get_optim_op())
except tf.errors.InvalidArgumentError as exc:
print("Expected exception: %r" % exc)
else:
assert False, "should have raised an exception"
def test_Updater_CustomUpdate():
with make_scope() as session:
from returnn.tf.network import TFNetwork, ExternData
from returnn.config import Config
from returnn.tf.util.basic import CustomUpdate
config = Config()
network = TFNetwork(extern_data=ExternData(), train_flag=True)
layer = network.add_layer(name="output",
layer_class=DummyLayer,
initial_value=4.0)
assert isinstance(layer, DummyLayer)
network.initialize_params(session=session)
class CustomUpdateAdd13(CustomUpdate):
def update_var(self, var):
return tf_compat.v1.assign_add(var, 13.0)
CustomUpdateAdd13().set_on_var(layer.x)
updater = Updater(config=config, network=network)
updater.set_learning_rate(1000.0, session=session) # should be ignored
updater.set_trainable_vars(network.get_trainable_params())
updater.init_optimizer_vars(session=session)
session.run(updater.get_optim_op())
# Should have applied CustomUpdateAdd13.
assert_almost_equal(
session.run(network.get_default_output_layer().output.placeholder),
17.0)
def _run_returnn_standalone_net_dict(self):
print(">>> Constructing RETURNN model, load TF checkpoint, run...")
with tf.compat.v1.Session() as session:
from returnn.config import Config
from returnn.tf.network import TFNetwork
config = Config({
"extern_data": {
"data": self._returnn_in_data_dict
},
"debug_print_layer_output_template": True,
})
network = TFNetwork(config=config, name="root")
network.construct_from_dict(self._returnn_net_dict)
network.load_params_from_file(
filename=self._tf_checkpoint_save_path, session=session)
x = network.extern_data.get_default_input_data()
y = network.get_default_output_layer().output
feed_dict = self._make_tf_feed_dict(x)
y_, y_size = session.run((y.placeholder, y.size_placeholder),
feed_dict=feed_dict)
assert isinstance(y_, numpy.ndarray)
print("Output shape:", y_.shape)
numpy.testing.assert_allclose(self._out_returnn_np, y_)
print(">>>> Looks good!")
print()
def init(config_filename, command_line_options, args):
"""
:param str config_filename:
:param list[str] command_line_options:
:param args: argparse.Namespace
"""
global config, engine, dataset
rnn.init(config_filename=config_filename,
command_line_options=command_line_options,
config_updates={
"log": None,
"need_data": False
},
extra_greeting="RETURNN dump-forward starting up.")
config = rnn.config
engine = rnn.engine
dataset_str = args.dataset
if dataset_str in {"train", "dev", "eval", "search_data"}:
dataset_str = "config:%s" % dataset_str
extra_dataset_kwargs = {}
if args.reset_partition_epoch:
print("NOTE: We are resetting partition epoch to %i." %
(args.reset_partition_epoch, ))
extra_dataset_kwargs["partition_epoch"] = args.reset_partition_epoch
if args.reset_seq_ordering:
print("NOTE: We will use %r seq ordering." %
(args.reset_seq_ordering, ))
extra_dataset_kwargs["seq_ordering"] = args.reset_seq_ordering
if args.reset_epoch_wise_filter:
extra_dataset_kwargs["epoch_wise_filter"] = eval(
args.reset_epoch_wise_filter)
dataset = init_dataset(dataset_str, extra_kwargs=extra_dataset_kwargs)
if hasattr(dataset,
"epoch_wise_filter") and args.reset_epoch_wise_filter is None:
if dataset.epoch_wise_filter:
print("NOTE: Resetting epoch_wise_filter to None.")
dataset.epoch_wise_filter = None
if args.reset_partition_epoch:
assert dataset.partition_epoch == args.reset_partition_epoch
if args.reset_seq_ordering:
assert dataset.seq_ordering == args.reset_seq_ordering
config.set("task", "eval")
if args.load:
config.set("load", args.load)
epoch, model_epoch_filename = Engine.get_epoch_model(config)
engine.pretrain = pretrain_from_config(config)
engine.custom_get_net_dict = config.typed_value("get_network")
net_dict = engine.get_net_dict_for_epoch(epoch)
engine.make_tf_session()
engine.network = TFNetwork(name="root")
engine.network.construct_layer(net_dict, args.layer)
print("Load model:", model_epoch_filename)
engine.network.load_params_from_file(model_epoch_filename,
session=engine.tf_session)
def test_Updater_simple_batch():
with make_scope() as session:
from returnn.tf.network import TFNetwork, ExternData
from returnn.config import Config
from returnn.datasets.generating import Task12AXDataset
dataset = Task12AXDataset()
dataset.init_seq_order(epoch=1)
extern_data = ExternData()
extern_data.init_from_dataset(dataset)
config = Config()
network = TFNetwork(extern_data=extern_data, train_flag=True)
network.construct_from_dict({
"layer1": {
"class": "linear",
"activation": "tanh",
"n_out": 13,
"from": "data:data"
},
"layer2": {
"class": "linear",
"activation": "tanh",
"n_out": 13,
"from": ["layer1"]
},
"output": {
"class": "softmax",
"loss": "ce",
"target": "classes",
"from": ["layer2"]
}
})
network.initialize_params(session=session)
updater = Updater(config=config, network=network)
updater.set_learning_rate(1.0, session=session)
updater.set_trainable_vars(network.get_trainable_params())
updater.init_optimizer_vars(session=session)
from returnn.tf.data_pipeline import FeedDictDataProvider
batches = dataset.generate_batches(
recurrent_net=network.recurrent,
batch_size=100,
max_seqs=10,
max_seq_length=sys.maxsize,
used_data_keys=network.used_data_keys)
data_provider = FeedDictDataProvider(tf_session=session,
extern_data=extern_data,
data_keys=network.used_data_keys,
dataset=dataset,
batches=batches)
feed_dict, _ = data_provider.get_feed_dict(single_threaded=True)
session.run(updater.get_optim_op(), feed_dict=feed_dict)
def test_Updater_GradientDescent():
with make_scope() as session:
from returnn.tf.network import TFNetwork, ExternData
from returnn.config import Config
config = Config()
network = TFNetwork(extern_data=ExternData(), train_flag=True)
network.add_layer(name="output", layer_class=DummyLayer, initial_value=5.0, loss_value_factor=3.0)
network.initialize_params(session=session)
updater = Updater(config=config, network=network)
updater.set_learning_rate(1.0, session=session)
updater.set_trainable_vars(network.get_trainable_params())
updater.init_optimizer_vars(session=session)
session.run(updater.get_optim_op())
# One gradient descent step from 3.0 * x: gradient is 3, thus 5 - 3 = 2.
assert_almost_equal(session.run(network.get_default_output_layer().output.placeholder), 2.0)
def test_base_get_output_shape_from_returnn_conv2d_dynamic():
with Naming.make_instance() as naming:
assert isinstance(naming, Naming)
x = torch.Tensor(64, 1, 11, 13)
x_ = naming.register_tensor(x)
x_.returnn_data = Data(name="x", shape=(1, None, None), feature_dim_axis=1)
x_.returnn_axis_from_torch_axis = {0: 0, 1: 1, 2: 2, 3: 3}
net = TFNetwork(extern_data=ExternData())
# E.g. conv layer, with padding "same".
layer = InternalLayer(name="layer", network=net, out_type={"shape": (None, None, 32)})
torch_shape, returnn_axis_from_torch_axis = torch.nn.Module._base_get_output_shape_from_returnn(
inputs_flat=[x], layer=layer)
assert returnn_axis_from_torch_axis == {0: 0, 1: 3, 2: 1, 3: 2}
assert torch_shape == (64, 32, 11, 13)
def _run_returnn_standalone_python(self):
print(
">>> Constructing RETURNN model via Python code, load TF checkpoint, run..."
)
with tf.compat.v1.Session() as session:
with Naming.make_instance(
) as naming: # we expect this to work with the default settings
model_func = self._model_func
# Wrap the model_func in a module.
# We assume this would be flattened away in the namespace.
# All named modules should thus have the same names.
class DummyModule(torch_returnn.nn.Module):
def get_returnn_name(self) -> str:
return "" # also avoid that this name becomes a prefix anywhere
def forward(self, *inputs):
return model_func(wrapped_import_torch_returnn,
*inputs)
dummy_mod = DummyModule()
net_dict = dummy_mod.as_returnn_net_dict(
self._returnn_in_data_dict)
from returnn.config import Config
from returnn.tf.network import TFNetwork
config = Config({
"extern_data": {
"data": self._returnn_in_data_dict
},
"debug_print_layer_output_template": True,
})
network = TFNetwork(config=config, name="root")
network.construct_from_dict(net_dict)
network.load_params_from_file(
filename=self._tf_checkpoint_save_path, session=session)
x = network.extern_data.get_default_input_data()
y = network.get_default_output_layer().output
feed_dict = self._make_tf_feed_dict(x)
y_, y_size = session.run((y.placeholder, y.size_placeholder),
feed_dict=feed_dict)
assert isinstance(y_, numpy.ndarray)
print("Output shape:", y_.shape)
numpy.testing.assert_allclose(self._out_returnn_np, y_)
print(">>>> Looks good!")
print()
def test_base_get_output_shape_from_returnn_2d_reorder_dynamic():
with Naming.make_instance() as naming:
assert isinstance(naming, Naming)
x = torch.Tensor(64, 1, 11, 13)
x_ = naming.register_tensor(x)
x_.returnn_data = Data(name="x", shape=(1, None, None), feature_dim_axis=1, auto_create_placeholders=True)
x_.returnn_axis_from_torch_axis = {0: 0, 1: 1, 2: 2, 3: 3}
y_data = x_.returnn_data.copy_move_axis(2, 3)
assert y_data.get_dim_tag(3) == x_.returnn_data.get_dim_tag(2)
net = TFNetwork(extern_data=ExternData())
# E.g. softmax_over_spatial with axis="stag:time1"
layer = InternalLayer(name="layer", network=net, output=y_data)
# We expect from all Torch modules, that they don't reorder the spatial axes.
# (If they do, they explicitly would overwrite the output shape logic.)
torch_shape, returnn_axis_from_torch_axis = torch.nn.Module._base_get_output_shape_from_returnn(
inputs_flat=[x], layer=layer)
assert returnn_axis_from_torch_axis == {0: 0, 1: 1, 2: 3, 3: 2}
assert torch_shape == (64, 1, 11, 13)
def __init__(self,
*,
parent: Optional[ReturnnContext] = None,
name: Optional[str] = None):
self.parent = parent
if parent:
assert name
self.config = parent.config
self.tf_name_scope = parent.network.get_absolute_name_scope_prefix(
) + LayerBase.cls_get_tf_scope_name(name)
assert parent.network.extern_data.data
self.sub_net_layer = (
parent.network.add_layer(
name=name,
layer_class=SubnetworkLayer,
# This is just a placeholder, will be replaced in define_output.
sources=[parent.network.get_layer("data")],
subnetwork={"output": {
"class": "copy"
}})) # type: SubnetworkLayer
self._dummy_sub_output = self.sub_net_layer.subnetwork.layers[
"output"]
else:
self.config = Config({
# "debug_print_layer_output_template": True,
})
self.tf_name_scope = ""
self.sub_net_layer = None
self._dummy_sub_output = None
if self.sub_net_layer:
self.network = self.sub_net_layer.subnetwork
else:
assert not parent
self.network = TFNetwork(
extern_data=ExternData(),
config=self.config,
name="root",
absolute_name_prefix=(self.tf_name_scope +
"/") if self.tf_name_scope else "")
