def main(unused_argv):
gan_lib.MODELS.update({
"MultiGAN": multi_gan.MultiGAN,
"MultiGANBackground": multi_gan_background.MultiGANBackground
})
params.PARAMETERS.update({
"MultiGAN": multi_gan.MultiGANHyperParams,
"MultiGANBackground": multi_gan_background.MultiGANBackgroundHyperParams
})
gan_lib.DATASETS.update(dataset.get_datasets())
params.DATASET_PARAMS.update(dataset.get_dataset_params())
task_workdir = FLAGS.eval_task_workdir
task = simple_task_pb2.Task()
with open(os.path.join(task_workdir, "task"), "r") as f:
text_format.Parse(f.read(), task)
options = task_utils.ParseOptions(task)
out_dir = os.path.join(FLAGS.out_dir, GetModelDir(options))
if not tf.gfile.IsDirectory(out_dir):
tf.gfile.MakeDirs(out_dir)
task_string = text_format.MessageToString(task)
print("\nWill evaluate task\n%s\n\n", task_string)
EvalTask(options, task_workdir, out_dir)
def MakeDimensions(dim_dict, extra_dims=None, base_task=None):
"""Creates a task proto from given dictionary."""
task = simple_task_pb2.Task()
if base_task is not None:
task.MergeFrom(base_task)
if extra_dims is not None:
dim_dict = copy.copy(dim_dict)
dim_dict.update(extra_dims)
dim_dict = collections.OrderedDict(sorted(dim_dict.items()))
for key, value in six.iteritems(dim_dict):
if key in ("_proto", "_prefix"):
# We skip the special keys.
continue
dimension = task.dimensions.add()
dimension.parameter = key
if isinstance(value, str):
dimension.string_value = value
elif isinstance(value, bool):
dimension.bool_value = value
elif isinstance(value, int):
dimension.int_value = value
elif isinstance(value, float):
dimension.float_value = value
elif isinstance(value, tuple):
# Convert the tuple in a string
dimension.string_value = str(value)
else:
# We skip the values that are of other type,
# e.g. proto (this is the case of the "grid" argument that is
# used in many functions).
del task.dimensions[-1]
continue
return task
def GetTaskProtoFromCheckpoint(checkpoint_path):
task_file = os.path.join(os.path.dirname(checkpoint_path), "../task")
if not tf.gfile.Exists(task_file):
logging.warning("Cant find task_file at %s", task_file)
assert False, "Task file %s not found." % task_file
content = tf.gfile.Open(task_file, mode="r").read()
return text_format.Parse(content, simple_task_pb2.Task())