def test_count_parameters_empty(self):
module = snt.Module()
snt.allow_empty_variables(module)
# No variables.
self.assertEqual(0, parameter_overview.count_parameters(module))
# Single variable.
module.var = tf.Variable([0, 1])
self.assertEqual(2, parameter_overview.count_parameters(module))
def test_get_parameter_overview_empty(self):
module = snt.Module()
snt.allow_empty_variables(module)
# No variables.
self.assertEqual(EMPTY_PARAMETER_OVERVIEW,
parameter_overview.get_parameter_overview(module))
module.conv = snt.Conv2D(output_channels=2, kernel_shape=3)
# Variables not yet created (happens in the first forward pass).
self.assertEqual(EMPTY_PARAMETER_OVERVIEW,
parameter_overview.get_parameter_overview(module))
def to_sonnet_module(transformation: types.TensorTransformation) -> snt.Module:
"""Convert a tensor transformation to a Sonnet Module."""
if isinstance(transformation, snt.Module):
return transformation
# Use snt.Sequential to convert any tensor transformation to a snt.Module.
module = snt.Sequential([transformation])
# Wrap the module to allow it to return an empty variable tuple.
return snt.allow_empty_variables(module)
def build_agent_model(n_actions, agent_size, batch_size, kpt_encoder_type,
kpt_cnn_channels):
# agent
model_dict = {
"agent_net": RecurrentQNet(agent_size, n_actions, batch_size)
}
# keypoint encoder
if kpt_encoder_type == "cnn":
model_dict["kpt_encoder"] = KptConvEncoder(kpt_cnn_channels,
agent_size)
elif kpt_encoder_type == "gnn":
model_dict["kpt_encoder"] = snt.allow_empty_variables(KptGnnEncoder())
model_dict["node_enc"] = NodeEncoder(output_dim=agent_size)
model_dict["pos_net"] = PositionalEncoder(kpt_cnn_channels)
return model_dict
def to_sonnet_module(transformation: types.TensorValuedCallable) -> snt.Module:
"""Convert a tensor transformation to a Sonnet Module.
Args:
transformation: A Callable that takes one or more (nested) Tensors, and
returns one or more (nested) Tensors.
Returns:
A Sonnet Module that wraps the transformation.
"""
if isinstance(transformation, snt.Module):
return transformation
module = TransformationWrapper(transformation)
# Wrap the module to allow it to return an empty variable tuple.
return snt.allow_empty_variables(module)
_IMAGE_SIZE = 224
_BATCH_SIZE = 1
_NUM_CLASSES = 100
# Learning rate for the sonnet optimizer
_LEARNING_RATE = 0.01
_NUM_EPOCHS = 200
_MOMENTUM = 0.9
train_generator = MSASLDataLoader(ANNOTATION_FILE_PATH_TRAIN, FRAMES_DIR_PATH, 1, height=224, width=224, color_mode='rgb', shuffle=True, frames_threshold=28)
data_shape = train_generator.get_data_dim()
print('DATA SHAPE (frames per sample, height, width, color_channels)' + str(data_shape))
print('DATA LEN (number of batches)' + str(train_generator.batch_size))
with tf.name_scope('RGB'):
i3d_model = i3d.InceptionI3d(num_classes=_NUM_CLASSES, spatial_squeeze=True, final_endpoint='Logits')
snt.allow_empty_variables(i3d_model)
X_train, y_train = train_generator[0]
print(train_generator[1])
print("Y_Train: ")
print(y_train.shape)
print(y_train)
X_train = tf.cast(X_train, tf.float32)
print('X SHAPE ' + str(X_train.shape))
print("=====================VARIABLES===================")
for variable in tf.compat.v1.global_variables():
print(variable.name)
if variable.name.split('/')[0] == 'RGB':
print(variable.name)
print(tf.compat.v1.get_default_graph().get_name_scope())
print("-------------------------------------------------")