Merge pull request #1960 from zhiboniu/dev_attr

support attribute infer

deploy/configs/inference_attr.yaml

+Global:
+  infer_imgs: "./images/Pedestrain_Attr.jpg"
+  inference_model_dir: "../inference/"
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: False
+  cpu_num_threads: 10
+  enable_benchmark: True
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        size: [192, 256]
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+        channel_num: 3
+    - ToCHWImage:
+
+PostProcess:
+  main_indicator: Attribute
+  Attribute:
+    threshold: 0.5  #default threshold
+    glasses_threshold: 0.3  #threshold only for glasses
+    hold_threshold: 0.6 #threshold only for hold
+  
\ No newline at end of file

deploy/python/postprocess.py

@@ -64,9 +64,17 @@ class ThreshOutput(object):
         for idx, probs in enumerate(x):
             score = probs[1]
             if score < self.threshold:
-                result = {"class_ids": [0], "scores":  [1 - score], "label_names": [self.label_0]}
+                result = {
+                    "class_ids": [0],
+                    "scores": [1 - score],
+                    "label_names": [self.label_0]
+                }
             else:
-                result = {"class_ids": [1], "scores": [score], "label_names": [self.label_1]}
+                result = {
+                    "class_ids": [1],
+                    "scores": [score],
+                    "label_names": [self.label_1]
+                }
             if file_names is not None:
                 result["file_name"] = file_names[idx]
             y.append(result)
@@ -179,3 +187,96 @@ class Binarize(object):
             byte[:, i:i + 1] = np.dot(x[:, i * 8:(i + 1) * 8], self.unit)
 
         return byte
+
+
+class Attribute(object):
+    def __init__(self,
+                 threshold=0.5,
+                 glasses_threshold=0.3,
+                 hold_threshold=0.6):
+        self.threshold = threshold
+        self.glasses_threshold = glasses_threshold
+        self.hold_threshold = hold_threshold
+
+    def __call__(self, batch_preds, file_names=None):
+        # postprocess output of predictor
+        age_list = ['AgeLess18', 'Age18-60', 'AgeOver60']
+        direct_list = ['Front', 'Side', 'Back']
+        bag_list = ['HandBag', 'ShoulderBag', 'Backpack']
+        upper_list = ['UpperStride', 'UpperLogo', 'UpperPlaid', 'UpperSplice']
+        lower_list = [
+            'LowerStripe', 'LowerPattern', 'LongCoat', 'Trousers', 'Shorts',
+            'Skirt&Dress'
+        ]
+        batch_res = []
+        for res in batch_preds:
+            res = res.tolist()
+            label_res = []
+            # gender 
+            gender = 'Female' if res[22] > self.threshold else 'Male'
+            label_res.append(gender)
+            # age
+            age = age_list[np.argmax(res[19:22])]
+            label_res.append(age)
+            # direction 
+            direction = direct_list[np.argmax(res[23:])]
+            label_res.append(direction)
+            # glasses
+            glasses = 'Glasses: '
+            if res[1] > self.glasses_threshold:
+                glasses += 'True'
+            else:
+                glasses += 'False'
+            label_res.append(glasses)
+            # hat
+            hat = 'Hat: '
+            if res[0] > self.threshold:
+                hat += 'True'
+            else:
+                hat += 'False'
+            label_res.append(hat)
+            # hold obj
+            hold_obj = 'HoldObjectsInFront: '
+            if res[18] > self.hold_threshold:
+                hold_obj += 'True'
+            else:
+                hold_obj += 'False'
+            label_res.append(hold_obj)
+            # bag
+            bag = bag_list[np.argmax(res[15:18])]
+            bag_score = res[15 + np.argmax(res[15:18])]
+            bag_label = bag if bag_score > self.threshold else 'No bag'
+            label_res.append(bag_label)
+            # upper
+            upper_res = res[4:8]
+            upper_label = 'Upper:'
+            sleeve = 'LongSleeve' if res[3] > res[2] else 'ShortSleeve'
+            upper_label += ' {}'.format(sleeve)
+            for i, r in enumerate(upper_res):
+                if r > self.threshold:
+                    upper_label += ' {}'.format(upper_list[i])
+            label_res.append(upper_label)
+            # lower
+            lower_res = res[8:14]
+            lower_label = 'Lower: '
+            has_lower = False
+            for i, l in enumerate(lower_res):
+                if l > self.threshold:
+                    lower_label += ' {}'.format(lower_list[i])
+                    has_lower = True
+            if not has_lower:
+                lower_label += ' {}'.format(lower_list[np.argmax(lower_res)])
+
+            label_res.append(lower_label)
+            # shoe
+            shoe = 'Boots' if res[14] > self.threshold else 'No boots'
+            label_res.append(shoe)
+
+            threshold_list = [0.5] * len(res)
+            threshold_list[1] = self.glasses_threshold
+            threshold_list[18] = self.hold_threshold
+            pred_res = (np.array(res) > np.array(threshold_list)
+                        ).astype(np.int8).tolist()
+
+            batch_res.append([label_res, pred_res])
+        return batch_res

deploy/python/predict_cls.py

@@ -138,13 +138,21 @@ def main(config):
                 continue
             batch_results = cls_predictor.predict(batch_imgs)
             for number, result_dict in enumerate(batch_results):
-                filename = batch_names[number]
-                clas_ids = result_dict["class_ids"]
-                scores_str = "[{}]".format(", ".join("{:.2f}".format(
-                    r) for r in result_dict["scores"]))
-                label_names = result_dict["label_names"]
-                print("{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
-                      format(filename, clas_ids, scores_str, label_names))
+                if "Attribute" in config["PostProcess"]:
+                    filename = batch_names[number]
+                    attr_message = result_dict[0]
+                    pred_res = result_dict[1]
+                    print("{}:\t attributes: {}, \npredict output: {}".format(
+                        filename, attr_message, pred_res))
+                else:
+                    filename = batch_names[number]
+                    clas_ids = result_dict["class_ids"]
+                    scores_str = "[{}]".format(", ".join("{:.2f}".format(
+                        r) for r in result_dict["scores"]))
+                    label_names = result_dict["label_names"]
+                    print(
+                        "{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
+                        format(filename, clas_ids, scores_str, label_names))
             batch_imgs = []
             batch_names = []
     if cls_predictor.benchmark:

ppcls/arch/backbone/legendary_models/resnet.py

@@ -137,8 +137,11 @@ class ConvBNLayer(TheseusLayer):
         weight_attr = ParamAttr(learning_rate=lr_mult, trainable=True)
         bias_attr = ParamAttr(learning_rate=lr_mult, trainable=True)
 
-        self.bn = BatchNorm2D(
-            num_filters, weight_attr=weight_attr, bias_attr=bias_attr)
+        self.bn = BatchNorm(
+            num_filters,
+            param_attr=ParamAttr(learning_rate=lr_mult),
+            bias_attr=ParamAttr(learning_rate=lr_mult),
+            data_layout=data_format)
         self.relu = nn.ReLU()
 
     def forward(self, x):
@@ -287,7 +290,8 @@ class ResNet(TheseusLayer):
                  data_format="NCHW",
                  input_image_channel=3,
                  return_patterns=None,
-                 return_stages=None):
+                 return_stages=None,
+                 **kargs):
         super().__init__()
 
         self.cfg = config

ppcls/configs/Attr/StrongBaselineAttr.yaml

@@ -20,6 +20,7 @@ Arch:
   name: "ResNet50"
   pretrained: True
   class_num: 26
+  infer_add_softmax: False
 
 # loss function config for traing/eval process
 Loss:
@@ -110,5 +111,3 @@ DataLoader:
 Metric:
   Eval:
     - ATTRMetric:
-
-

ppcls/engine/engine.py

@@ -458,7 +458,9 @@ class Engine(object):
 
     def export(self):
         assert self.mode == "export"
-        use_multilabel = self.config["Global"].get("use_multilabel", False)
+        use_multilabel = self.config["Global"].get(
+            "use_multilabel",
+            False) and not "ATTRMetric" in self.config["Metric"]["Eval"][0]
         model = ExportModel(self.config["Arch"], self.model, use_multilabel)
         if self.config["Global"]["pretrained_model"] is not None:
             load_dygraph_pretrain(model.base_model,

ppcls/metric/metrics.py

@@ -390,6 +390,7 @@ class AccuracyScore(MultiLabelMetric):
 def get_attr_metrics(gt_label, preds_probs, threshold):
     """
     index: evaluated label index
+    adapted from "https://github.com/valencebond/Rethinking_of_PAR/blob/master/metrics/pedestrian_metrics.py"
     """
     pred_label = (preds_probs > threshold).astype(int)