def offline_evaluate(pred_func, output_file):
df = get_eval_dataflow()
all_results = eval_on_dataflow(
df, lambda img: detect_one_image(img, pred_func))
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_evaluation_scores(output_file)
def _eval(self):
all_results = eval_on_dataflow(
self.df, lambda img: detect_one_image(img, self.pred))
output_file = os.path.join(logger.get_logger_dir(),
'outputs{}.json'.format(self.global_step))
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_evaluation_scores(output_file)
def _eval(self):
all_results = eval_on_dataflow(self.df, lambda img: detect_one_image(img, self.pred))
output_file = os.path.join(
logger.get_logger_dir(), 'outputs{}.json'.format(self.global_step))
with open(output_file, 'w') as f:
json.dump(all_results, f)
scores = print_evaluation_scores(output_file)
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
def offline_evaluate(pred_func, output_file):
df = get_test_dataflow(add_mask=False)
all_results = eval_on_dataflow(
df, lambda img: detect_one_image(img, pred_func))
#print(all_results)
# input()
with open(output_file, 'w') as f:
json.dump(all_results, f, cls=MyEncoder)
ret = print_evaluation_scores(output_file)
print(ret)
def _eval(self):
all_results, local_score = eval_on_dataflow(
self.df, lambda img: detect_one_image(img, self.pred))
"""
output_file = os.path.join(
logger.get_logger_dir(), 'outputs{}.json'.format(self.global_step))
with open(output_file, 'w') as f:
json.dump(all_results, f, cls=MyEncoder)
scores = print_evaluation_scores(output_file)
"""
scores = {}
scores['local'] = local_score
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
def offline_evaluate(model_path, output_file):
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(model_path),
input_names=['image'],
output_names=[
'fastrcnn_fg_probs',
'fastrcnn_fg_boxes',
]))
df = get_eval_dataflow()
df = PrefetchDataZMQ(df, 1)
all_results = eval_on_dataflow(df, lambda img: detect_one_image(img, pred))
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_evaluation_scores(output_file)
def offline_evaluate(pred_func, output_file):
df = get_test_dataflow()
all_results = eval_on_dataflow(
df, lambda img: detect_one_image(img, pred_func))
print(all_results)
if args.visualize or args.evaluate or args.predict:
# autotune is too slow for inference
os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
assert args.load
print_config()
if args.evaluate:
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['image'],
output_names=get_model_output_names()))
df = get_test_dataflow(add_mask=True)
df.reset_state()
all_results, local_score = eval_on_dataflow(
df, lambda img: detect_one_image(img, pred))
print("F2 Score: ", local_score)
elif args.predict:
imgs = Detection.load_many(
config.BASEDIR, config.TEST_DATASET,
add_gt=False) # to load the class names into caches
# filter with zero-ship
imgs = [(img['image_data'], img['id']) for img in imgs]
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['image'],
output_names=get_model_output_names()))
predict_many(pred, imgs)
else:
