fix pact bug for circlemargin arcmargin cosmargin

.gitignore

@@ -3,10 +3,11 @@ __pycache__/
 *.sw*
 */workerlog*
 checkpoints/
-output/
+output*/
 pretrained/
 .ipynb_checkpoints/
 *.ipynb*
 _build/
 build/
+log/
 nohup.out

ppcls/arch/gears/arcmargin.py

@@ -24,30 +24,25 @@ class ArcMargin(nn.Layer):
                  margin=0.5,
                  scale=80.0,
                  easy_margin=False):
-        super(ArcMargin, self).__init__()
+        super().__init__()
         self.embedding_size = embedding_size
         self.class_num = class_num
         self.margin = margin
         self.scale = scale
         self.easy_margin = easy_margin
-
-        weight_attr = paddle.ParamAttr(
-            initializer=paddle.nn.initializer.XavierNormal())
-        self.fc = nn.Linear(
-            self.embedding_size,
-            self.class_num,
-            weight_attr=weight_attr,
-            bias_attr=False)
+        self.weight = self.create_parameter(
+            shape=[self.embedding_size, self.class_num],
+            is_bias=False,
+            default_initializer=paddle.nn.initializer.XavierNormal())
 
     def forward(self, input, label=None):
         input_norm = paddle.sqrt(
             paddle.sum(paddle.square(input), axis=1, keepdim=True))
         input = paddle.divide(input, input_norm)
 
-        weight = self.fc.weight
         weight_norm = paddle.sqrt(
-            paddle.sum(paddle.square(weight), axis=0, keepdim=True))
-        weight = paddle.divide(weight, weight_norm)
+            paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
+        weight = paddle.divide(self.weight, weight_norm)
 
         cos = paddle.matmul(input, weight)
         if not self.training or label is None:

ppcls/arch/gears/circlemargin.py

@@ -26,20 +26,19 @@ class CircleMargin(nn.Layer):
         self.embedding_size = embedding_size
         self.class_num = class_num
 
-        weight_attr = paddle.ParamAttr(
-            initializer=paddle.nn.initializer.XavierNormal())
-        self.fc = paddle.nn.Linear(
-            self.embedding_size, self.class_num, weight_attr=weight_attr)
+        self.weight = self.create_parameter(
+            shape=[self.embedding_size, self.class_num],
+            is_bias=False,
+            default_initializer=paddle.nn.initializer.XavierNormal())
 
     def forward(self, input, label):
         feat_norm = paddle.sqrt(
             paddle.sum(paddle.square(input), axis=1, keepdim=True))
         input = paddle.divide(input, feat_norm)
 
-        weight = self.fc.weight
         weight_norm = paddle.sqrt(
-            paddle.sum(paddle.square(weight), axis=0, keepdim=True))
-        weight = paddle.divide(weight, weight_norm)
+            paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
+        weight = paddle.divide(self.weight, weight_norm)
 
         logits = paddle.matmul(input, weight)
         if not self.training or label is None:
@@ -49,9 +48,9 @@ class CircleMargin(nn.Layer):
         alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
         delta_p = 1 - self.margin
         delta_n = self.margin
-        
+
         m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
-        
+
         logits_p = alpha_p * (logits - delta_p)
         logits_n = alpha_n * (logits - delta_n)
         pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)

ppcls/arch/gears/cosmargin.py

@@ -25,13 +25,10 @@ class CosMargin(paddle.nn.Layer):
         self.embedding_size = embedding_size
         self.class_num = class_num
 
-        weight_attr = paddle.ParamAttr(
-            initializer=paddle.nn.initializer.XavierNormal())
-        self.fc = nn.Linear(
-            self.embedding_size,
-            self.class_num,
-            weight_attr=weight_attr,
-            bias_attr=False)
+        self.weight = self.create_parameter(
+            shape=[self.embedding_size, self.class_num],
+            is_bias=False,
+            default_initializer=paddle.nn.initializer.XavierNormal())
 
     def forward(self, input, label):
         label.stop_gradient = True
@@ -40,15 +37,14 @@ class CosMargin(paddle.nn.Layer):
             paddle.sum(paddle.square(input), axis=1, keepdim=True))
         input = paddle.divide(input, input_norm)
 
-        weight = self.fc.weight
         weight_norm = paddle.sqrt(
-            paddle.sum(paddle.square(weight), axis=0, keepdim=True))
-        weight = paddle.divide(weight, weight_norm)
+            paddle.sum(paddle.square(self.weight), axis=0, keepdim=True))
+        weight = paddle.divide(self.weight, weight_norm)
 
         cos = paddle.matmul(input, weight)
         if not self.training or label is None:
             return cos
-        
+
         cos_m = cos - self.margin
 
         one_hot = paddle.nn.functional.one_hot(label, self.class_num)

ppcls/configs/Vehicle/ResNet50.yaml

@@ -2,7 +2,7 @@
 Global:
   checkpoints: null
   pretrained_model: null
-  output_dir: "./output/"
+  output_dir: "./output_vehicle_cls/"
   device: "gpu"
   save_interval: 1
   eval_during_train: True
@@ -51,11 +51,8 @@ Optimizer:
   name: Momentum
   momentum: 0.9
   lr:
-    name: MultiStepDecay
+    name: Cosine
     learning_rate: 0.01
-    milestones: [30, 60, 70, 80, 90, 100, 120, 140]
-    gamma: 0.5
-    verbose: False
     last_epoch: -1
   regularizer:
     name: 'L2'

ppcls/configs/Vehicle/ResNet50_ReID.yaml

@@ -2,7 +2,7 @@
 Global:
   checkpoints: null
   pretrained_model: null
-  output_dir: "./output/"
+  output_dir: "./output_vehicle_reid/"
   device: "gpu"
   save_interval: 1
   eval_during_train: True

ppcls/configs/slim/ResNet50_vehicle_cls_prune.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output_vehicle_cls_prune/"
+  device: "gpu"
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 160
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: "./inference"
+
+Slim:
+  prune:
+    name: fpgm
+    pruned_ratio: 0.3
+
+# model architecture
+Arch:
+  name: "RecModel"
+  infer_output_key: "features"
+  infer_add_softmax: False
+  Backbone: 
+    name: "ResNet50_last_stage_stride1"
+    pretrained: True
+  BackboneStopLayer:
+    name: "adaptive_avg_pool2d_0"
+  Neck:
+    name: "VehicleNeck"
+    in_channels: 2048
+    out_channels: 512
+  Head:
+    name: "ArcMargin"  
+    embedding_size: 512
+    class_num: 431
+    margin: 0.15
+    scale: 32
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+    - SupConLoss:
+        weight: 1.0
+        views: 2
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.01
+    last_epoch: -1
+  regularizer:
+    name: 'L2'
+    coeff: 0.0005
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+        name: "CompCars"
+        image_root: "./dataset/CompCars/image/"
+        label_root: "./dataset/CompCars/label/"
+        bbox_crop: True
+        cls_label_path: "./dataset/CompCars/train_test_split/classification/train_label.txt"
+        transform_ops:
+          - ResizeImage:
+              size: 224
+          - RandFlipImage:
+              flip_code: 1
+          - AugMix:
+              prob: 0.5
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+          - RandomErasing:
+              EPSILON: 0.5
+              sl: 0.02
+              sh: 0.4
+              r1: 0.3
+              mean: [0., 0., 0.]
+
+    sampler:
+        name: DistributedRandomIdentitySampler
+        batch_size: 128
+        num_instances: 2
+        drop_last: False
+        shuffle: True
+    loader:
+        num_workers: 8
+        use_shared_memory: True
+
+  Eval:
+    dataset: 
+        name: "CompCars"
+        image_root: "./dataset/CompCars/image/"
+        label_root: "./dataset/CompCars/label/"
+        cls_label_path: "./dataset/CompCars/train_test_split/classification/test_label.txt"
+        bbox_crop: True
+        transform_ops:
+          - 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: 128
+        drop_last: False
+        shuffle: False
+    loader:
+        num_workers: 8
+        use_shared_memory: True
+
+Metric:
+    Train:
+    - TopkAcc:
+        topk: [1, 5]
+    Eval:
+    - TopkAcc:
+        topk: [1, 5]
+

ppcls/configs/slim/ResNet50_vehicle_cls_quantization.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output_vehicle_cls_pact/"
+  device: "gpu"
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 80
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: "./inference"
+
+Slim:
+  quant:
+    name: pact
+
+# model architecture
+Arch:
+  name: "RecModel"
+  infer_output_key: "features"
+  infer_add_softmax: False
+  Backbone: 
+    name: "ResNet50_last_stage_stride1"
+    pretrained: True
+  BackboneStopLayer:
+    name: "adaptive_avg_pool2d_0"
+  Neck:
+    name: "VehicleNeck"
+    in_channels: 2048
+    out_channels: 512
+  Head:
+    name: "ArcMargin"  
+    embedding_size: 512
+    class_num: 431
+    margin: 0.15
+    scale: 32
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+    - SupConLoss:
+        weight: 1.0
+        views: 2
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    last_epoch: -1
+  regularizer:
+    name: 'L2'
+    coeff: 0.0005
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+        name: "CompCars"
+        image_root: "./dataset/CompCars/image/"
+        label_root: "./dataset/CompCars/label/"
+        bbox_crop: True
+        cls_label_path: "./dataset/CompCars/train_test_split/classification/train_label.txt"
+        transform_ops:
+          - ResizeImage:
+              size: 224
+          - RandFlipImage:
+              flip_code: 1
+          - AugMix:
+              prob: 0.5
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+          - RandomErasing:
+              EPSILON: 0.5
+              sl: 0.02
+              sh: 0.4
+              r1: 0.3
+              mean: [0., 0., 0.]
+
+    sampler:
+        name: DistributedRandomIdentitySampler
+        batch_size: 128
+        num_instances: 2
+        drop_last: False
+        shuffle: True
+    loader:
+        num_workers: 8
+        use_shared_memory: True
+
+  Eval:
+    dataset: 
+        name: "CompCars"
+        image_root: "./dataset/CompCars/image/"
+        label_root: "./dataset/CompCars/label/"
+        cls_label_path: "./dataset/CompCars/train_test_split/classification/test_label.txt"
+        bbox_crop: True
+        transform_ops:
+          - 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: 128
+        drop_last: False
+        shuffle: False
+    loader:
+        num_workers: 8
+        use_shared_memory: True
+
+Metric:
+    Train:
+    - TopkAcc:
+        topk: [1, 5]
+    Eval:
+    - TopkAcc:
+        topk: [1, 5]
+

ppcls/configs/slim/ResNet50_vehicle_reid_prune.yaml

@@ -2,7 +2,7 @@
 Global:
   checkpoints: null
   pretrained_model: null
-  output_dir: "./output/"
+  output_dir: "./output_fpgm/"
   device: "gpu"
   save_interval: 1
   eval_during_train: True

ppcls/configs/slim/ResNet50_vehicle_reid_quantization.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output_vehicle_reid_pact/"
+  device: "gpu"
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 40
+  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"
+
+# for quantizaiton or prune model
+Slim:
+  ## for prune
+  quant:
+    name: pact
+
+# model architecture
+Arch:
+  name: "RecModel"
+  infer_output_key: "features"
+  infer_add_softmax: False
+  Backbone: 
+    name: "ResNet50_last_stage_stride1"
+    pretrained: True
+  BackboneStopLayer:
+    name: "adaptive_avg_pool2d_0"
+  Neck:
+    name: "VehicleNeck"
+    in_channels: 2048
+    out_channels: 512
+  Head:
+    name: "ArcMargin"  
+    embedding_size: 512
+    class_num: 30671
+    margin: 0.15
+    scale: 32
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+    - SupConLoss:
+        weight: 1.0
+        views: 2
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    last_epoch: -1
+  regularizer:
+    name: 'L2'
+    coeff: 0.0005
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+        name: "VeriWild"
+        image_root: "./dataset/VeRI-Wild/images/"
+        cls_label_path: "./dataset/VeRI-Wild/train_test_split/train_list_start0.txt"
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - RandFlipImage:
+              flip_code: 1
+          - AugMix:
+              prob: 0.5
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+          - RandomErasing:
+              EPSILON: 0.5
+              sl: 0.02
+              sh: 0.4
+              r1: 0.3
+              mean: [0., 0., 0.]
+
+    sampler:
+        name: DistributedRandomIdentitySampler
+        batch_size: 64
+        num_instances: 2
+        drop_last: False
+        shuffle: True
+    loader:
+        num_workers: 6
+        use_shared_memory: True
+  Eval:
+    Query:
+      dataset: 
+        name: "VeriWild"
+        image_root: "./dataset/VeRI-Wild/images"
+        cls_label_path: "./dataset/VeRI-Wild/train_test_split/test_3000_id_query.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: 6
+        use_shared_memory: True
+
+    Gallery:
+      dataset: 
+        name: "VeriWild"
+        image_root: "./dataset/VeRI-Wild/images"
+        cls_label_path: "./dataset/VeRI-Wild/train_test_split/test_3000_id.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: 6
+        use_shared_memory: True
+
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]
+    - mAP: {}
+