def clear_screenshots(self):
"""
## Clear screenshots
"""
path = pathlib.Path(self.info.screenshots_path)
if path.exists():
util.rm_tree(path)
path.mkdir(parents=True)
def clear_checkpoints(self):
"""
## Clear old checkpoints
We run this when running a new fresh trial
"""
path = pathlib.Path(self.info.checkpoint_path)
if path.exists():
util.rm_tree(path)
def clear_summaries(self):
"""
## Clear TensorBoard summaries
We run this when running a new fresh trial
"""
path = pathlib.Path(self.info.summary_path)
if path.exists():
util.rm_tree(path)
def save_checkpoint_numpy(self,
session: tf.Session,
global_step: int):
"""
## Save model as a set of numpy arrays
"""
checkpoints_path = pathlib.Path(self.info.checkpoint_path)
if not checkpoints_path.exists():
checkpoints_path.mkdir()
checkpoint_path = checkpoints_path / str(global_step)
assert not checkpoint_path.exists()
checkpoint_path.mkdir()
values = session.run(self.__variables)
# Save each variable
files = {}
for variable, value in zip(self.__variables, values):
file_name = tf_util.variable_name_to_file_name(
tf_util.strip_variable_name(variable.name))
file_name = f"{file_name}.npy"
files[variable.name] = file_name
np.save(str(checkpoint_path / file_name), value)
# Save header
with open(str(checkpoint_path / "info.json"), "w") as f:
f.write(json.dumps(files))
# Delete old checkpoints
for c in checkpoints_path.iterdir():
if c.name != checkpoint_path.name:
util.rm_tree(c)
def save_embeddings(*,
path: Path,
embeddings: np.ndarray,
images: Optional[np.ndarray],
labels: Optional[List[str]]):
if path.exists():
util.rm_tree(path)
path.mkdir(parents=True)
sess = tf.compat.v1.InteractiveSession()
name = random_string()
embeddings_variable = tf.Variable(embeddings, trainable=False, name=name)
tf.global_variables_initializer().run()
saver = tf.compat.v1.train.Saver()
writer = tf.compat.v1.summary.FileWriter(str(path), sess.graph)
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embeddings_variable.name
if labels is not None:
embed.metadata_path = 'metadata.tsv'
save_labels(path / 'metadata.tsv', labels)
if images is not None:
embed.sprite.image_path = "sprites.png"
save_sprite_image(path / 'sprites.png', images)
embed.sprite.single_image_dim.extend([images.shape[1], images.shape[2]])
projector.visualize_embeddings(writer, config)
saver.save(sess, str(path / 'a_model.ckpt'), global_step=0)