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import torch
from swapae.evaluation.base_evaluator import BaseEvaluator
import swapae.util as util
def find_evaluator_using_name(filename):
target_class_name = filename
module_name = 'swapae.evaluation.' + filename
eval_class = util.find_class_in_module(target_class_name, module_name)
assert issubclass(eval_class, BaseEvaluator), \
"Class %s should be a subclass of BaseEvaluator" % eval_class
return eval_class
def find_evaluator_classes(opt):
if len(opt.evaluation_metrics) == 0:
return []
eval_metrics = opt.evaluation_metrics.split(",")
all_classes = []
target_phases = []
for metric in eval_metrics:
if metric.startswith("train"):
target_phases.append("train")
metric = metric[len("train"):]
elif metric.startswith("test"):
target_phases.append("test")
metric = metric[len("test"):]
else:
target_phases.append("test")
metric_class = find_evaluator_using_name("%s_evaluator" % metric)
all_classes.append(metric_class)
return all_classes, target_phases
class GroupEvaluator(BaseEvaluator):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.add_argument("--evaluation_metrics", default="structure_style_grid_generation")
opt, _ = parser.parse_known_args()
evaluator_classes, _ = find_evaluator_classes(opt)
for eval_class in evaluator_classes:
parser = eval_class.modify_commandline_options(parser, is_train)
return parser
def __init__(self, opt, target_phase=None):
super().__init__(opt, target_phase=None)
self.opt = opt
evaluator_classes, target_phases = find_evaluator_classes(opt)
self.evaluators = [cls(opt, target_phase=phs) for cls, phs in zip(evaluator_classes, target_phases)]
def evaluate(self, model, dataset, nsteps=None):
original_phase = dataset.phase
metrics = {}
for i, evaluator in enumerate(self.evaluators):
print("Entering evaluation using %s on %s images" % (type(evaluator).__name__, evaluator.target_phase))
dataset.set_phase(evaluator.target_phase)
with torch.no_grad():
new_metrics = evaluator.evaluate(model, dataset, nsteps)
metrics.update(new_metrics)
print("Finished evaluation of %s" % type(evaluator).__name__)
dataset.set_phase(original_phase)
return metrics
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