add Binary general reocg configure

ppcls/arch/backbone/__init__.py

@@ -61,6 +61,7 @@ from ppcls.arch.backbone.model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet3
 from ppcls.arch.backbone.model_zoo.cspnet import CSPDarkNet53
 from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
 from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
+from ppcls.arch.backbone.variant_models.lcnet_variant import PPLCNet_x2_5Tanh
 
 
 def get_apis():

ppcls/arch/backbone/variant_models/__init__.py

@@ -1,2 +1,3 @@
 from .resnet_variant import ResNet50_last_stage_stride1
 from .vgg_variant import VGG19Sigmoid
+from .lcnet_variant import PPLCNet_x2_5Tanh

ppcls/arch/backbone/variant_models/lcnet_variant.py

@@ -0,0 +1,29 @@
+import paddle
+from paddle.nn import Sigmoid
+from paddle.nn import Tanh
+from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x2_5
+
+__all__ = ["PPLCNet_x2_5Tanh"]
+
+
+class TanhSuffix(paddle.nn.Layer):
+    def __init__(self, origin_layer):
+        super(SigmoidSuffix, self).__init__()
+        self.origin_layer = origin_layer
+        self.tanh = Tanh()
+
+    def forward(self, input, res_dict=None, **kwargs):
+        x = self.origin_layer(input)
+        x = self.tanh(x)
+        return x
+
+
+def PPLCNet_x2_5Tanh(pretrained=False, use_ssld=False, **kwargs):
+    def replace_function(origin_layer):
+        new_layer = TanhSuffix(origin_layer)
+        return new_layer
+
+    match_re = "linear_0"
+    model = PPLCNet_x2_5(pretrained=pretrained, use_ssld=use_ssld, **kwargs)
+    model.replace_sub(match_re, replace_function, True)
+    return model

ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_binary.yaml

@@ -0,0 +1,151 @@
+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 100
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+  eval_mode: retrieval
+  use_dali: False
+  to_static: False
+
+  #feature postprocess
+  feature_normalize: False
+  feature_binarize: "sign"
+
+# model architecture
+Arch:
+  name: RecModel
+  infer_output_key: features
+  infer_add_softmax: False
+
+  Backbone:
+    name: PPLCNet_x2_5Tanh
+    pretrained: True
+    use_ssld: True
+    class_num: 512
+  Head:
+    name: FC
+    embedding_size: &embedding_size 512
+    class_num: &n_class 185341
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - DSHSDLoss:
+        weight: 1.0
+        n_class: *n_class
+        bit: *embedding_size
+        alpha: 0.1
+  Eval:
+    - DSHSDLoss:
+        weight: 1.0
+        n_class: *n_class
+        bit: *embedding_size
+        alpha: 0.1
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.04
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.00001
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/all_data
+      cls_label_path: ./dataset/all_data/train_reg_all_data.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 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: 256
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    Query:
+      dataset: 
+        name: VeriWild
+        image_root: ./dataset/Aliproduct/
+        cls_label_path: ./dataset/Aliproduct/val_list.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 0.00392157
+              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
+
+    Gallery:
+      dataset: 
+        name: VeriWild
+        image_root: ./dataset/Aliproduct/
+        cls_label_path: ./dataset/Aliproduct/val_list.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 0.00392157
+              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
+
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]

ppcls/loss/__init__.py

@@ -22,6 +22,8 @@ from .distillationloss import DistillationGTCELoss
 from .distillationloss import DistillationDMLLoss
 from .multilabelloss import MultiLabelLoss
 
+from .deephashloss import DSHSDLoss, LCDSHLoss
+
 
 class CombinedLoss(nn.Layer):
     def __init__(self, config_list):