Merge pull request #2264 from cuicheng01/add_table_attribute

add table_attribute_code

deploy/configs/PULC/table_attribute/inference_table_attribute.yaml

+Global:
+  infer_imgs: "images/PULC/table_attribute/val_3610.jpg"
+  inference_model_dir: "./models/table_attribute_infer"
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: True
+  cpu_num_threads: 10
+  benchmark: False
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        size: [224, 224]
+    - 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: TableAttribute  
+  TableAttribute:
+    source_threshold: 0.5
+    number_threshold: 0.5
+    color_threshold: 0.5
+    clarity_threshold : 0.5
+    obstruction_threshold: 0.5
+    angle_threshold: 0.5

deploy/python/postprocess.py

@@ -320,3 +320,49 @@ class VehicleAttribute(object):
                         ).astype(np.int8).tolist()
             batch_res.append({"attributes": label_res, "output": pred_res})
         return batch_res
+
+
+class TableAttribute(object):
+    def __init__(
+            self,
+            source_threshold=0.5,
+            number_threshold=0.5,
+            color_threshold=0.5,
+            clarity_threshold=0.5,
+            obstruction_threshold=0.5,
+            angle_threshold=0.5, ):
+        self.source_threshold = source_threshold
+        self.number_threshold = number_threshold
+        self.color_threshold = color_threshold
+        self.clarity_threshold = clarity_threshold
+        self.obstruction_threshold = obstruction_threshold
+        self.angle_threshold = angle_threshold
+
+    def __call__(self, batch_preds, file_names=None):
+        # postprocess output of predictor
+        batch_res = []
+
+        for res in batch_preds:
+            res = res.tolist()
+            label_res = []
+            source = 'Scanned' if res[0] > self.source_threshold else 'Photo'
+            number = 'Little' if res[1] > self.number_threshold else 'Numerous'
+            color = 'Black-and-White' if res[
+                2] > self.color_threshold else 'Multicolor'
+            clarity = 'Clear' if res[3] > self.clarity_threshold else 'Blurry'
+            obstruction = 'Without-Obstacles' if res[
+                4] > self.number_threshold else 'With-Obstacles'
+            angle = 'Horizontal' if res[
+                5] > self.number_threshold else 'Tilted'
+
+            label_res = [source, number, color, clarity, obstruction, angle]
+
+            threshold_list = [
+                self.source_threshold, self.number_threshold,
+                self.color_threshold, self.clarity_threshold,
+                self.obstruction_threshold, self.angle_threshold
+            ]
+            pred_res = (np.array(res) > np.array(threshold_list)
+                        ).astype(np.int8).tolist()
+            batch_res.append({"attributes": label_res, "output": pred_res})
+        return batch_res

deploy/python/predict_cls.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 import os
 
 import cv2
@@ -136,7 +135,8 @@ def main(config):
             for number, result_dict in enumerate(batch_results):
                 if "PersonAttribute" in config[
                         "PostProcess"] or "VehicleAttribute" in config[
-                            "PostProcess"]:
+                            "PostProcess"] or "TableAttribute" in config[
+                                "PostProcess"]:
                     filename = batch_names[number]
                     print("{}:\t {}".format(filename, result_dict))
                 else:

paddleclas.py

@@ -192,7 +192,8 @@ PULC_MODEL_BASE_DOWNLOAD_URL = "https://paddleclas.bj.bcebos.com/models/PULC/inf
 PULC_MODELS = [
     "car_exists", "language_classification", "person_attribute",
     "person_exists", "safety_helmet", "text_image_orientation",
-    "textline_orientation", "traffic_sign", "vehicle_attribute"
+    "textline_orientation", "traffic_sign", "vehicle_attribute",
+    "table_attribute"
 ]
 
 SHITU_MODEL_BASE_DOWNLOAD_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/{}_infer.tar"
@@ -278,6 +279,7 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
     if "thresh" in kwargs and kwargs[
             "thresh"] and "ThreshOutput" in cfg.PostProcess:
         cfg.PostProcess.ThreshOutput.thresh = kwargs["thresh"]
+
     if cfg.get("PostProcess"):
         if "Topk" in cfg.PostProcess:
             if "topk" in kwargs and kwargs["topk"]:
@@ -297,7 +299,25 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
             if "type_threshold" in kwargs and kwargs["type_threshold"]:
                 cfg.PostProcess.VehicleAttribute.type_threshold = kwargs[
                     "type_threshold"]
-
+        if "TableAttribute" in cfg.PostProcess:
+            if "source_threshold" in kwargs and kwargs["source_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "source_threshold"]
+            if "number_threshold" in kwargs and kwargs["number_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "number_threshold"]
+            if "color_threshold" in kwargs and kwargs["color_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "color_threshold"]
+            if "clarity_threshold" in kwargs and kwargs["clarity_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "clarity_threshold"]
+            if "obstruction_threshold" in kwargs and kwargs["obstruction_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "obstruction_threshold"]
+            if "angle_threshold" in kwargs and kwargs["angle_threshold"]:
+                cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[
+                    "angle_threshold"]
     if "save_dir" in kwargs and kwargs["save_dir"]:
         cfg.PostProcess.SavePreLabel.save_dir = kwargs["save_dir"]
 

ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output/"
+  device: "gpu"
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 20
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: "./inference"
+  use_multilabel: True
+
+# model architecture
+Arch:
+  name: "PPLCNet_x1_0"
+  pretrained: True
+  use_ssld: True
+  class_num: 6
+  
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - MultiLabelLoss:
+        weight: 1.0
+        weight_ratio: True
+        size_sum: True
+  Eval:
+    - MultiLabelLoss:
+        weight: 1.0
+        weight_ratio: True
+        size_sum: True
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.01
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.0005
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: MultiLabelDataset
+      image_root: "dataset/table_attribute/"
+      cls_label_path: "dataset/table_attribute/train_list.txt"
+      label_ratio: True
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: [224, 224]
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: True
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+  Eval:
+    dataset:
+      name: MultiLabelDataset
+      image_root: "dataset/table_attribute/"
+      cls_label_path: "dataset/table_attribute/val_list.txt"
+      label_ratio: True
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: [224, 224]
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: deploy/images/PULC/table_attribute/val_3610.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        size: [224, 224]
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: TableAttribute
+    source_threshold: 0.5
+    number_threshold: 0.5
+    color_threshold: 0.5
+    clarity_threshold : 0.5
+    obstruction_threshold: 0.5
+    angle_threshold: 0.5
+
+Metric:
+  Eval:
+    - ATTRMetric:
+
+

ppcls/data/postprocess/__init__.py

@@ -18,7 +18,7 @@ from . import topk, threshoutput
 
 from .topk import Topk, MultiLabelTopk
 from .threshoutput import ThreshOutput
-from .attr_rec import VehicleAttribute, PersonAttribute
+from .attr_rec import VehicleAttribute, PersonAttribute, TableAttribute
 
 
 def build_postprocess(config):

ppcls/data/postprocess/attr_rec.py

@@ -71,7 +71,6 @@ class VehicleAttribute(object):
         return batch_res
 
 
-
 class PersonAttribute(object):
     def __init__(self,
                  threshold=0.5,
@@ -171,3 +170,58 @@ class PersonAttribute(object):
             batch_res.append({"attributes": label_res, "output": pred_res})
         return batch_res
 
+
+class TableAttribute(object):
+    def __init__(
+            self,
+            source_threshold=0.5,
+            number_threshold=0.5,
+            color_threshold=0.5,
+            clarity_threshold=0.5,
+            obstruction_threshold=0.5,
+            angle_threshold=0.5, ):
+        self.source_threshold = source_threshold
+        self.number_threshold = number_threshold
+        self.color_threshold = color_threshold
+        self.clarity_threshold = clarity_threshold
+        self.obstruction_threshold = obstruction_threshold
+        self.angle_threshold = angle_threshold
+
+    def __call__(self, x, file_names=None):
+        if isinstance(x, dict):
+            x = x['logits']
+        assert isinstance(x, paddle.Tensor)
+        if file_names is not None:
+            assert x.shape[0] == len(file_names)
+        x = F.sigmoid(x).numpy()
+
+        # postprocess output of predictor
+        batch_res = []
+        for idx, res in enumerate(x):
+            res = res.tolist()
+            label_res = []
+            source = 'Scanned' if res[0] > self.source_threshold else 'Photo'
+            number = 'Little' if res[1] > self.number_threshold else 'Numerous'
+            color = 'Black-and-White' if res[
+                2] > self.color_threshold else 'Multicolor'
+            clarity = 'Clear' if res[3] > self.clarity_threshold else 'Blurry'
+            obstruction = 'Without-Obstacles' if res[
+                4] > self.number_threshold else 'With-Obstacles'
+            angle = 'Horizontal' if res[
+                5] > self.number_threshold else 'Tilted'
+
+            label_res = [source, number, color, clarity, obstruction, angle]
+
+            threshold_list = [
+                self.source_threshold, self.number_threshold,
+                self.color_threshold, self.clarity_threshold,
+                self.obstruction_threshold, self.angle_threshold
+            ]
+            pred_res = (np.array(res) > np.array(threshold_list)
+                        ).astype(np.int8).tolist()
+            batch_res.append({
+                "attributes": label_res,
+                "output": pred_res,
+                "file_name": file_names[idx]
+            })
+        return batch_res