diff --git a/configs/quick_start/ResNet50_vd_multilabel.yam b/configs/quick_start/ResNet50_vd_multilabel.yaml similarity index 82% rename from configs/quick_start/ResNet50_vd_multilabel.yam rename to configs/quick_start/ResNet50_vd_multilabel.yaml index d615bab65705ac2c76eb2164d1084f995f585d7f..fe785b4b54c12cef8040d48868febb11b99ddb96 100644 --- a/configs/quick_start/ResNet50_vd_multilabel.yam +++ b/configs/quick_start/ResNet50_vd_multilabel.yaml @@ -34,8 +34,8 @@ OPTIMIZER: TRAIN: batch_size: 256 num_workers: 4 - file_list: "./dataset/NUS-SCENE-dataset/multilabel_train_list.txt" - data_dir: "./dataset/NUS-SCENE-dataset/images" + file_list: "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/multilabel_train_list.txt" + data_dir: "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images" shuffle_seed: 0 transforms: - DecodeImage: @@ -59,8 +59,8 @@ TRAIN: VALID: batch_size: 64 num_workers: 4 - file_list: "./dataset/NUS-SCENE-dataset/multilabel_test_list.txt" - data_dir: "./dataset/NUS-SCENE-dataset/images" + file_list: "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/multilabel_test_list.txt" + data_dir: "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images" shuffle_seed: 0 transforms: - DecodeImage: diff --git a/ppcls/modeling/loss.py b/ppcls/modeling/loss.py index 52d2a876d35b7c38795c41f2487f9b9dac4cfff1..5e7abd643fc2c1a3bada3209210d03a9cacfb3f4 100644 --- a/ppcls/modeling/loss.py +++ b/ppcls/modeling/loss.py @@ -89,8 +89,8 @@ class MultiLabelLoss(Loss): def __init__(self, class_dim=1000, epsilon=None): super(MultiLabelLoss, self).__init__(class_dim, epsilon) - def __call__(self, input, target, use_pure_fp16=False): - cost = self._binary_crossentropy(input, target, use_pure_fp16) + def __call__(self, input, target): + cost = self._binary_crossentropy(input, target) return cost diff --git a/tools/infer/infer.py b/tools/infer/infer.py index a0ba7d224052710f726f16fc41f202524980641c..87fe9f32035017c7c142e1e8c97e2ba56fec9348 100644 --- a/tools/infer/infer.py +++ b/tools/infer/infer.py @@ -72,10 +72,15 @@ def main(): for number, result_dict in enumerate(batch_result_list): filename = img_path_list[number].split("/")[-1] clas_ids = result_dict["clas_ids"] - scores_str = "[{}]".format(", ".join("{:.2f}".format( - r) for r in result_dict["scores"])) - print("File:{}, Top-{} result: class id(s): {}, score(s): {}". - format(filename, args.top_k, clas_ids, scores_str)) + if multilabel: + print("File:{}, multilabel result: ".format(filename)) + for id, score in zip(clas_ids, result_dict["scores"]): + print("\tclass id: {}, probability: {:.2f}".format(id, score)) + else: + scores_str = "[{}]".format(", ".join("{:.2f}".format( + r) for r in result_dict["scores"])) + print("File:{}, Top-{} result: class id(s): {}, score(s): {}". + format(filename, args.top_k, clas_ids, scores_str)) if args.pre_label_image: save_prelabel_results(clas_ids[0], img_path_list[number],