Merge pull request #762 from Intsigstephon/develop_reg

add RecModel for retrieval

ppcls/arch/__init__.py

@@ -18,11 +18,14 @@ import importlib
 import paddle.nn as nn
 
 from . import backbone
+from . import head
 
 from .backbone import *
-from ppcls.arch.loss_metrics.loss import *
+from .head  import *
 from .utils import *
 
+__all__ = ["build_model", "RecModel"]
+
 
 def build_model(config):
     config = copy.deepcopy(config)
@@ -35,31 +38,31 @@ def build_model(config):
 class RecModel(nn.Layer):
     def __init__(self, **config):
         super().__init__()
+
         backbone_config = config["Backbone"]
         backbone_name = backbone_config.pop("name")
-        self.backbone = getattr(backbone_name)(**backbone_config)
-        if "backbone_stop_layer" in config:
-            backbone_stop_layer = config["backbone_stop_layer"]
-            self.backbone.stop_layer(backbone_stop_layer)
+        self.backbone = eval(backbone_name)(**backbone_config)
 
-        if "Neck" in config:
-            neck_config = config["Neck"]
-            neck_name = neck_config.pop("name")
-            self.neck = getattr(neck_name)(**neck_config)
+        assert "Stoplayer" in config,  "Stoplayer should be specified in retrieval task \
+                please specified a Stoplayer config"
+        stop_layer_config = config["Stoplayer"]
+        self.backbone.stop_after(stop_layer_config["name"])
+        
+        if stop_layer_config.get("embedding_size", 0) > 0:
+            self.neck = nn.Linear(stop_layer_config["output_dim"], stop_layer_config["embedding_size"])
+            embedding_size = stop_layer_config["embedding_size"]
         else:
             self.neck = None
+            embedding_size = stop_layer_config["output_dim"]
+        
+        assert "Head" in config,  "Head should be specified in retrieval task \
+                please specify a Head config"
+        config["Head"]["embedding_size"] = embedding_size
+        self.head = build_head(config["Head"])
 
-        if "Head" in config:
-            head_config = config["Head"]
-            head_name = head_config.pop("name")
-            self.head = getattr(head_name)(**head_config)
-        else:
-            self.head = None
-
-    def forward(self, x):
-        y = self.backbone(x)
+    def forward(self, x, label):
+        x = self.backbone(x)
         if self.neck is not None:
-            y = self.neck(y)
-        if self.head is not None:
-            y = self.head(y)
-        return y
+            x = self.neck(x)
+        y = self.head(x, label)
+        return {"features":x, "logits":y}

ppcls/arch/neck/__init__.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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.
-
-__all__ = ['build_neck"]
-
-def build_neck(config):
-    support_dict = ['FPN', 'FC']
-    module_name = config.pop('name')
-    assert module_name in support_dict, Exception('head only support {}'.format(
-        support_dict))
-    module_class = eval(module_name)(**config)
-    return module_class

ppcls/arch/neck/fc.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import paddle
-import paddle.nn as nn
-
-class FC(nn.Layer):
-    def __init__(self, input_dim, 
-                       embedding_size):
-        super(FC, self).__init__()
-        self.input_dim  = input_dim
-        self.embedding_size = embedding_size
-        weight_attr =  paddle.ParamAttr(initializer = paddle.nn.initializer.XavierNormal())
-        self.fc  =  paddle.nn.Linear(self.input_dim, self.embedding_size, weight_attr=weight_attr)    
-
-    def forward(self, x):
-        x = self.fc(x)
-        return x

ppcls/configs/ImageNet/ResNet/ResNet50.yaml

@@ -18,7 +18,7 @@ Global:
 # model architecture
 Arch:
   name: "ResNet50"
-
+ 
 # loss function config for traing/eval process
 Loss:
   Train:

ppcls/configs/ImageNet/ResNet/ResNet50_retrieval.yml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output/"
+  device: "gpu"
+  class_num: 1000
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 120
+  print_batch_step: 10
+  use_visualdl: False
+  image_shape: [3, 224, 224]
+  infer_imgs:
+
+# model architecture
+Arch:  
+  name: "RecModel"
+  Backbone:
+    name: "ResNet50"
+  Stoplayer: 
+    name: "flatten_0"
+    output_dim: 2048
+    embedding_size: 512
+  Head:
+    name: "ArcMargin"
+    margin: 0.5
+    scale:  80
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.1
+    decay_epochs: [30, 60, 90]
+    values: [0.1, 0.01, 0.001, 0.0001]
+  regularizer:
+    name: 'L2'
+    coeff: 0.0001
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    # Dataset:
+    # Sampler:
+    # Loader:
+    batch_size: 256
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/train_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+  Eval:
+    # TOTO: modify to the latest trainer
+    # Dataset:
+    # Sampler:
+    # Loader:
+    batch_size: 128
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/val_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+Metric:
+    Train:
+    - Topk:
+        k: [1, 5]
+    Eval:
+    - Topk:
+        k: [1, 5]