Merge remote-tracking branch 'refs/remotes/upstream/develop' into hub_L_a

docs/en/tutorials/getting_started_en.md

@@ -267,6 +267,8 @@ Among them:
 + `enable_mkldnn`: Wheter to use `MKL-DNN`, default by `False`. When both `use_gpu` and `enable_mkldnn` are set to `True`, GPU is used to run and `enable_mkldnn` will be ignored.
 + `resize_short`: The length of the shortest side of the image that be scaled proportionally, default by `256`;
 + `resize`: The side length of the image that be center cropped from resize_shorted image, default by `224`;
++ `enable_calc_topk`: Whether to calculate top-k accuracy of the predction, default by `False`. Top-k accuracy will be printed out when set as `True`.
++ `gt_label_path`: Image name and label file, used when `enable_calc_topk` is `True` to get image list and labels.
 
 **Note**: If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `resize_short=384`, `resize=384`.
 

docs/zh_CN/tutorials/getting_started.md

@@ -278,6 +278,9 @@ python tools/infer/predict.py \
 + `enable_mkldnn`：是否启用`MKL-DNN`加速，默认为`False`。注意`enable_mkldnn`与`use_gpu`同时为`True`时，将忽略`enable_mkldnn`，而使用GPU运行。
 + `resize_short`: 对输入图像进行等比例缩放，表示最短边的尺寸，默认值：`256`
 + `resize`: 对`resize_short`操作后的进行居中裁剪，表示裁剪的尺寸，默认值：`224`
++ `enable_calc_topk`: 是否计算预测结果的Topk精度指标，默认为`False`，
++ `gt_label_path`: 图像文件名以及真值标签文件，当`enable_calc_topk`为True时生效，用于读取待预测的图像列表及其标签。
+
 
 **注意**: 如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸，需要设置参数`resize_short=384`, `resize=384`。
 

tools/infer/predict.py

@@ -20,7 +20,8 @@ import time
 import sys
 sys.path.insert(0, ".")
 from ppcls.utils import logger
-from tools.infer.utils import parse_args, get_image_list, create_paddle_predictor, preprocess, postprocess
+from tools.infer.utils import parse_args, create_paddle_predictor, preprocess, postprocess
+from tools.infer.utils import get_image_list, get_image_list_from_label_file, calc_topk_acc
 
 
 class Predictor(object):
@@ -46,7 +47,19 @@ class Predictor(object):
         return batch_output
 
     def normal_predict(self):
-        image_list = get_image_list(self.args.image_file)
+        if self.args.enable_calc_topk:
+            assert self.args.gt_label_path is not None and os.path.exists(self.args.gt_label_path), \
+                "gt_label_path shoule not be None and must exist, please check its path."
+            image_list, gt_labels = get_image_list_from_label_file(
+                self.args.image_file, self.args.gt_label_path)
+            predicts_map = {
+                "prediction": [],
+                "gt_label": [],
+            }
+        else:
+            image_list = get_image_list(self.args.image_file)
+            gt_labels = None
+
         batch_input_list = []
         img_name_list = []
         cnt = 0
@@ -64,6 +77,8 @@ class Predictor(object):
                 img_name = img_path.split("/")[-1]
                 img_name_list.append(img_name)
                 cnt += 1
+                if self.args.enable_calc_topk:
+                    predicts_map["gt_label"].append(gt_labels[idx])
 
             if cnt % args.batch_size == 0 or (idx + 1) == len(image_list):
                 batch_outputs = self.predict(np.array(batch_input_list))
@@ -74,12 +89,20 @@ class Predictor(object):
                     clas_ids = result_dict["clas_ids"]
                     scores_str = "[{}]".format(", ".join("{:.2f}".format(
                         r) for r in result_dict["scores"]))
-                    print(
+                    logger.info(
                         "File:{}, Top-{} result: class id(s): {}, score(s): {}".
                         format(filename, self.args.top_k, clas_ids,
                                scores_str))
+
+                    if self.args.enable_calc_topk:
+                        predicts_map["prediction"].append(clas_ids)
+
                 batch_input_list = []
                 img_name_list = []
+        if self.args.enable_calc_topk:
+            topk_acc = calc_topk_acc(predicts_map)
+            for idx, acc in enumerate(topk_acc):
+                logger.info("Top-{} acc: {:.5f}".format(idx + 1, acc))
 
     def benchmark_predict(self):
         test_num = 500

tools/infer/utils.py

@@ -74,6 +74,12 @@ def parse_args():
     # parameters for test hubserving
     parser.add_argument("--server_url", type=str)
 
+    # enable_calc_metric, when set as true, topk acc will be calculated
+    parser.add_argument("--enable_calc_topk", type=str2bool, default=False)
+    # groudtruth label path
+    # data format for each line: $image_name $class_id
+    parser.add_argument("--gt_label_path", type=str, default=None)
+
     return parser.parse_args()
 
 
@@ -128,7 +134,6 @@ def preprocess(img, args):
 def postprocess(batch_outputs, topk=5, multilabel=False):
     batch_results = []
     for probs in batch_outputs:
-        results = []
         if multilabel:
             index = np.where(probs >= 0.5)[0].astype('int32')
         else:
@@ -159,6 +164,42 @@ def get_image_list(img_file):
     return imgs_lists
 
 
+def get_image_list_from_label_file(image_path, label_file_path):
+    imgs_lists = []
+    gt_labels = []
+    with open(label_file_path, "r") as fin:
+        lines = fin.readlines()
+        for line in lines:
+            image_name, label = line.strip("\n").split()
+            label = int(label)
+            imgs_lists.append(os.path.join(image_path, image_name))
+            gt_labels.append(int(label))
+    return imgs_lists, gt_labels
+
+
+def calc_topk_acc(info_map):
+    '''
+    calc_topk_acc
+    input:
+        info_map(dict): keys are prediction and gt_label
+    output:
+        topk_acc(list): top-k accuracy list
+    '''
+    gt_label = np.array(info_map["gt_label"])
+    prediction = np.array(info_map["prediction"])
+
+    gt_label = np.reshape(gt_label, (-1, 1)).repeat(
+        prediction.shape[1], axis=1)
+    correct = np.equal(prediction, gt_label)
+    topk_acc = []
+    for idx in range(prediction.shape[1]):
+        if idx > 0:
+            correct[:, idx] = np.logical_or(correct[:, idx],
+                                            correct[:, idx - 1])
+        topk_acc.append(1.0 * np.sum(correct[:, idx]) / correct.shape[0])
+    return topk_acc
+
+
 def save_prelabel_results(class_id, input_file_path, output_dir):
     output_dir = os.path.join(output_dir, str(class_id))
     if not os.path.isdir(output_dir):