[Dygraph] add SSD/SSDLite mbv1v3 (#2070)

* fix ssd ssdlite scheduler, add cosdecay

* fix mbv1v3 BatchNorm mean variance

* update ssd mbv1 voc modelzoo

* fix ssd mbv1 warmup, update modelzoo

* fix cosdecay

dygraph/configs/ssd/README.md

@@ -6,9 +6,10 @@
 
 | 骨架网络        | 网络类型       | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP |                           下载                          | 配置文件 |
 | :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
-| VGG             | SSD            |    8    |   240e    |     ----     |  78.2  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/dygraph/configs/ssd_vgg16_300_240e_voc.yml) |
+| VGG             | SSD            |    8    |   240e    |     ----     |  78.2  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ssd_vgg16_300_240e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ssd_vgg16_300_240e_voc.yml) |
+| MobileNet v1    | SSD            |    32    |   120e    |     ----     |  73.1  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ssd_mobilenet_v1_300_120e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ssd_mobilenet_v1_300_120e_voc.yml) |
 
-**注意：** SSD使用4GPU训练，训练240个epoch
+**注意：** SSD-VGG使用4GPU在总batch size为32下训练240个epoch。SSD-MobileNetv1使用2GPU在总batch size为64下训练120周期。
 
 ## Citations
 ```

dygraph/configs/ssd/_base_/optimizer_120e.yml

@@ -4,13 +4,9 @@ LearningRate:
   base_lr: 0.001
   schedulers:
   - !PiecewiseDecay
-    gamma: 0.1
-    milestones:
-    - 80
-    - 100
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
+    milestones: [40, 60, 80, 100]
+    values: [0.001, 0.0005, 0.00025, 0.0001, 0.00001]
+    use_warmup: false
 
 OptimizerBuilder:
   optimizer:

dygraph/configs/ssd/_base_/optimizer_1000e.yml → dygraph/configs/ssd/_base_/optimizer_1700e.yml

-epoch: 1746
+epoch: 1700
 
 LearningRate:
   base_lr: 0.4
   schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones:
-    - 160
-    - 200
+  - !CosineDecay
+    max_epochs: 1700
   - !LinearWarmup
     start_factor: 0.3333333333333333
     steps: 2000

dygraph/configs/ssd/ssdlite_mobilenet_v1_300_coco.yml

 _BASE_: [
   '../datasets/coco_detection.yml',
   '../runtime.yml',
-  '_base_/optimizer_1000e.yml',
+  '_base_/optimizer_1700e.yml',
   '_base_/ssdlite_mobilenet_v1_300.yml',
   '_base_/ssdlite300_reader.yml',
 ]

dygraph/configs/ssd/ssdlite_mobilenet_v3_large_320_coco.yml

 _BASE_: [
   '../datasets/coco_detection.yml',
   '../runtime.yml',
-  '_base_/optimizer_1000e.yml',
+  '_base_/optimizer_1700e.yml',
   '_base_/ssdlite_mobilenet_v3_large_320.yml',
   '_base_/ssdlite320_reader.yml',
 ]

dygraph/configs/ssd/ssdlite_mobilenet_v3_small_320_coco.yml

 _BASE_: [
   '../datasets/coco_detection.yml',
   '../runtime.yml',
-  '_base_/optimizer_1000e.yml',
+  '_base_/optimizer_1700e.yml',
   '_base_/ssdlite_mobilenet_v3_small_320.yml',
   '_base_/ssdlite320_reader.yml',
 ]

dygraph/ppdet/modeling/backbones/mobilenet_v1.py

@@ -58,16 +58,22 @@ class ConvBNLayer(nn.Layer):
                 name=name + "_weights"),
             bias_attr=False)
 
+        param_attr = ParamAttr(
+            name=name + "_bn_scale", regularizer=L2Decay(norm_decay))
+        bias_attr = ParamAttr(
+            name=name + "_bn_offset", regularizer=L2Decay(norm_decay))
         if norm_type == 'sync_bn':
-            batch_norm = nn.SyncBatchNorm
+            self._batch_norm = nn.SyncBatchNorm(
+                out_channels, weight_attr=param_attr, bias_attr=bias_attr)
         else:
-            batch_norm = nn.BatchNorm2D
-        self._batch_norm = batch_norm(
+            self._batch_norm = nn.BatchNorm(
                 out_channels,
-            weight_attr=ParamAttr(
-                name=name + "_bn_scale", regularizer=L2Decay(norm_decay)),
-            bias_attr=ParamAttr(
-                name=name + "_bn_offset", regularizer=L2Decay(norm_decay)))
+                act=None,
+                param_attr=param_attr,
+                bias_attr=bias_attr,
+                use_global_stats=False,
+                moving_mean_name=name + '_bn_mean',
+                moving_variance_name=name + '_bn_variance')
 
     def forward(self, x):
         x = self._conv(x)

dygraph/ppdet/modeling/backbones/mobilenet_v3.py

@@ -67,20 +67,33 @@ class ConvBNLayer(nn.Layer):
             bias_attr=False)
 
         norm_lr = 0. if freeze_norm else lr_mult
-        if norm_type == 'sync_bn':
-            batch_norm = nn.SyncBatchNorm
-        else:
-            batch_norm = nn.BatchNorm2D
-        self.bn = batch_norm(
-            out_c,
-            weight_attr=ParamAttr(
+        param_attr = ParamAttr(
             learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay),
             name=name + "_bn_scale",
-                regularizer=L2Decay(norm_decay)),
-            bias_attr=ParamAttr(
+            trainable=False if freeze_norm else True)
+        bias_attr = ParamAttr(
             learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay),
             name=name + "_bn_offset",
-                regularizer=L2Decay(norm_decay)))
+            trainable=False if freeze_norm else True)
+        global_stats = True if freeze_norm else False
+        if norm_type == 'sync_bn':
+            self.bn = nn.SyncBatchNorm(
+                out_c, weight_attr=param_attr, bias_attr=bias_attr)
+        else:
+            self.bn = nn.BatchNorm(
+                out_c,
+                act=None,
+                param_attr=param_attr,
+                bias_attr=bias_attr,
+                use_global_stats=global_stats,
+                moving_mean_name=name + '_bn_mean',
+                moving_variance_name=name + '_bn_variance')
+        norm_params = self.bn.parameters()
+        if freeze_norm:
+            for param in norm_params:
+                param.stop_gradient = True
 
     def forward(self, x):
         x = self.conv(x)

dygraph/ppdet/optimizer.py

@@ -21,6 +21,7 @@ import paddle
 import paddle.nn as nn
 
 import paddle.optimizer as optimizer
+from paddle.optimizer.lr import CosineAnnealingDecay
 import paddle.regularizer as regularizer
 from paddle import cos
 
@@ -32,6 +33,42 @@ from ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
 
 
+@serializable
+class CosineDecay(object):
+    """
+    Cosine learning rate decay
+
+    Args:
+        max_epochs (int): max epochs for the training process.
+            if you commbine cosine decay with warmup, it is recommended that
+            the max_iters is much larger than the warmup iter
+    """
+
+    def __init__(self, max_epochs=1000, use_warmup=True):
+        self.max_epochs = max_epochs
+        self.use_warmup = use_warmup
+
+    def __call__(self,
+                 base_lr=None,
+                 boundary=None,
+                 value=None,
+                 step_per_epoch=None):
+        assert base_lr is not None, "either base LR or values should be provided"
+
+        max_iters = self.max_epochs * int(step_per_epoch)
+
+        if boundary is not None and value is not None and self.use_warmup:
+            for i in range(int(boundary[-1]), max_iters):
+                boundary.append(i)
+
+                decayed_lr = base_lr * 0.5 * (
+                    math.cos(i * math.pi / max_iters) + 1)
+                value.append(decayed_lr)
+            return optimizer.lr.PiecewiseDecay(boundary, value)
+
+        return optimizer.lr.CosineAnnealingDecay(base_lr, T_max=max_iters)
+
+
 @serializable
 class PiecewiseDecay(object):
     """
@@ -42,7 +79,11 @@ class PiecewiseDecay(object):
         milestones (list): steps at which to decay learning rate
     """
 
-    def __init__(self, gamma=[0.1, 0.01], milestones=[8, 11]):
+    def __init__(self,
+                 gamma=[0.1, 0.01],
+                 milestones=[8, 11],
+                 values=None,
+                 use_warmup=True):
         super(PiecewiseDecay, self).__init__()
         if type(gamma) is not list:
             self.gamma = []
@@ -51,15 +92,26 @@ class PiecewiseDecay(object):
         else:
             self.gamma = gamma
         self.milestones = milestones
+        self.values = values
+        self.use_warmup = use_warmup
 
     def __call__(self,
                  base_lr=None,
                  boundary=None,
                  value=None,
                  step_per_epoch=None):
-        if boundary is not None:
+        if boundary is not None and self.use_warmup:
             boundary.extend([int(step_per_epoch) * i for i in self.milestones])
+        else:
+            # do not use LinearWarmup
+            boundary = [int(step_per_epoch) * i for i in self.milestones]
 
+        # self.values is setted directly in config 
+        if self.values is not None:
+            assert len(self.milestones) + 1 == len(self.values)
+            return optimizer.lr.PiecewiseDecay(boundary, self.values)
+
+        # value is computed by self.gamma
         if value is not None:
             for i in self.gamma:
                 value.append(base_lr * i)
@@ -114,6 +166,11 @@ class LearningRate(object):
         self.schedulers = schedulers
 
     def __call__(self, step_per_epoch):
+        assert len(self.schedulers) >= 1
+        if not self.schedulers[0].use_warmup:
+            return self.schedulers[0](base_lr=self.base_lr,
+                                      step_per_epoch=step_per_epoch)
+
         # TODO: split warmup & decay 
         # warmup
         boundary, value = self.schedulers[1](self.base_lr)
@@ -127,7 +184,6 @@ class LearningRate(object):
 class OptimizerBuilder():
     """
     Build optimizer handles
-
     Args:
         regularizer (object): an `Regularizer` instance
         optimizer (object): an `Optimizer` instance