Add prefix_name in PaddleDetection (#3556)

ppdet/modeling/anchor_heads/yolo_head.py

@@ -41,7 +41,7 @@ class YOLOv3Head(object):
         nms (object): an instance of `MultiClassNMS`
     """
     __inject__ = ['nms']
-    __shared__ = ['num_classes']
+    __shared__ = ['num_classes', 'weight_prefix_name']
 
     def __init__(self,
                  norm_decay=0.,
@@ -56,7 +56,8 @@ class YOLOv3Head(object):
                      nms_top_k=1000,
                      keep_top_k=100,
                      nms_threshold=0.45,
-                     background_label=-1).__dict__):
+                     background_label=-1).__dict__,
+                 weight_prefix_name=''):
         self.norm_decay = norm_decay
         self.num_classes = num_classes
         self.ignore_thresh = ignore_thresh
@@ -64,6 +65,7 @@ class YOLOv3Head(object):
         self.anchor_masks = anchor_masks
         self._parse_anchors(anchors)
         self.nms = nms
+        self.prefix_name = weight_prefix_name
         if isinstance(nms, dict):
             self.nms = MultiClassNMS(**nms)
 
@@ -208,7 +210,7 @@ class YOLOv3Head(object):
                 block,
                 channel=512 // (2**i),
                 is_test=(not is_train),
-                name="yolo_block.{}".format(i))
+                name=self.prefix_name + "yolo_block.{}".format(i))
 
             # out channel number = mask_num * (5 + class_num)
             num_filters = len(self.anchor_masks[i]) * (self.num_classes + 5)
@@ -219,11 +221,12 @@ class YOLOv3Head(object):
                 stride=1,
                 padding=0,
                 act=None,
-                param_attr=ParamAttr(
-                    name="yolo_output.{}.conv.weights".format(i)),
+                param_attr=ParamAttr(name=self.prefix_name +
+                                     "yolo_output.{}.conv.weights".format(i)),
                 bias_attr=ParamAttr(
                     regularizer=L2Decay(0.),
-                    name="yolo_output.{}.conv.bias".format(i)))
+                    name=self.prefix_name +
+                    "yolo_output.{}.conv.bias".format(i)))
             outputs.append(block_out)
 
             if i < len(blocks) - 1:
@@ -235,7 +238,7 @@ class YOLOv3Head(object):
                     stride=1,
                     padding=0,
                     is_test=(not is_train),
-                    name="yolo_transition.{}".format(i))
+                    name=self.prefix_name + "yolo_transition.{}".format(i))
                 # upsample
                 route = self._upsample(route)
 
@@ -272,7 +275,7 @@ class YOLOv3Head(object):
                 ignore_thresh=self.ignore_thresh,
                 downsample_ratio=downsample,
                 use_label_smooth=self.label_smooth,
-                name="yolo_loss" + str(i))
+                name=self.prefix_name + "yolo_loss" + str(i))
             losses.append(fluid.layers.reduce_mean(loss))
             downsample //= 2
 
@@ -304,7 +307,7 @@ class YOLOv3Head(object):
                 class_num=self.num_classes,
                 conf_thresh=self.nms.score_threshold,
                 downsample_ratio=downsample,
-                name="yolo_box" + str(i))
+                name=self.prefix_name + "yolo_box" + str(i))
             boxes.append(box)
             scores.append(fluid.layers.transpose(score, perm=[0, 2, 1]))
 

ppdet/modeling/backbones/darknet.py

@@ -36,14 +36,19 @@ class DarkNet(object):
         norm_type (str): normalization type, 'bn' and 'sync_bn' are supported
         norm_decay (float): weight decay for normalization layer weights
     """
-    __shared__ = ['norm_type']
+    __shared__ = ['norm_type', 'weight_prefix_name']
 
-    def __init__(self, depth=53, norm_type='bn', norm_decay=0.):
+    def __init__(self,
+                 depth=53,
+                 norm_type='bn',
+                 norm_decay=0.,
+                 weight_prefix_name=''):
         assert depth in [53], "unsupported depth value"
         self.depth = depth
         self.norm_type = norm_type
         self.norm_decay = norm_decay
         self.depth_cfg = {53: ([1, 2, 8, 8, 4], self.basicblock)}
+        self.prefix_name = weight_prefix_name
 
     def _conv_norm(self,
                    input,
@@ -143,9 +148,11 @@ class DarkNet(object):
             filter_size=3,
             stride=1,
             padding=1,
-            name="yolo_input")
+            name=self.prefix_name + "yolo_input")
         downsample_ = self._downsample(
-            input=conv, ch_out=conv.shape[1] * 2, name="yolo_input.downsample")
+            input=conv,
+            ch_out=conv.shape[1] * 2,
+            name=self.prefix_name + "yolo_input.downsample")
         blocks = []
         for i, stage in enumerate(stages):
             block = self.layer_warp(
@@ -153,11 +160,11 @@ class DarkNet(object):
                 input=downsample_,
                 ch_out=32 * 2**i,
                 count=stage,
-                name="stage.{}".format(i))
+                name=self.prefix_name + "stage.{}".format(i))
             blocks.append(block)
             if i < len(stages) - 1:  # do not downsaple in the last stage
                 downsample_ = self._downsample(
                     input=block,
                     ch_out=block.shape[1] * 2,
-                    name="stage.{}.downsample".format(i))
+                    name=self.prefix_name + "stage.{}.downsample".format(i))
         return blocks

ppdet/modeling/backbones/mobilenet.py

@@ -37,7 +37,7 @@ class MobileNet(object):
         with_extra_blocks (bool): if extra blocks should be added
         extra_block_filters (list): number of filter for each extra block
     """
-    __shared__ = ['norm_type']
+    __shared__ = ['norm_type', 'weight_prefix_name']
 
     def __init__(self,
                  norm_type='bn',
@@ -46,13 +46,15 @@ class MobileNet(object):
                  conv_learning_rate=1.0,
                  with_extra_blocks=False,
                  extra_block_filters=[[256, 512], [128, 256], [128, 256],
-                                      [64, 128]]):
+                                      [64, 128]],
+                 weight_prefix_name=''):
         self.norm_type = norm_type
         self.norm_decay = norm_decay
         self.conv_group_scale = conv_group_scale
         self.conv_learning_rate = conv_learning_rate
         self.with_extra_blocks = with_extra_blocks
         self.extra_block_filters = extra_block_filters
+        self.prefix_name = weight_prefix_name
 
     def _conv_norm(self,
                    input,
@@ -151,35 +153,42 @@ class MobileNet(object):
 
         blocks = []
         # input 1/1
-        out = self._conv_norm(input, 3, int(32 * scale), 2, 1, name="conv1")
+        out = self._conv_norm(
+            input, 3, int(32 * scale), 2, 1, name=self.prefix_name + "conv1")
         # 1/2
         out = self.depthwise_separable(
-            out, 32, 64, 32, 1, scale, name="conv2_1")
+            out, 32, 64, 32, 1, scale, name=self.prefix_name + "conv2_1")
         out = self.depthwise_separable(
-            out, 64, 128, 64, 2, scale, name="conv2_2")
+            out, 64, 128, 64, 2, scale, name=self.prefix_name + "conv2_2")
         # 1/4
         out = self.depthwise_separable(
-            out, 128, 128, 128, 1, scale, name="conv3_1")
+            out, 128, 128, 128, 1, scale, name=self.prefix_name + "conv3_1")
         out = self.depthwise_separable(
-            out, 128, 256, 128, 2, scale, name="conv3_2")
+            out, 128, 256, 128, 2, scale, name=self.prefix_name + "conv3_2")
         # 1/8
         blocks.append(out)
         out = self.depthwise_separable(
-            out, 256, 256, 256, 1, scale, name="conv4_1")
+            out, 256, 256, 256, 1, scale, name=self.prefix_name + "conv4_1")
         out = self.depthwise_separable(
-            out, 256, 512, 256, 2, scale, name="conv4_2")
+            out, 256, 512, 256, 2, scale, name=self.prefix_name + "conv4_2")
         # 1/16
         blocks.append(out)
         for i in range(5):
             out = self.depthwise_separable(
-                out, 512, 512, 512, 1, scale, name="conv5_" + str(i + 1))
+                out,
+                512,
+                512,
+                512,
+                1,
+                scale,
+                name=self.prefix_name + "conv5_" + str(i + 1))
         module11 = out
 
         out = self.depthwise_separable(
-            out, 512, 1024, 512, 2, scale, name="conv5_6")
+            out, 512, 1024, 512, 2, scale, name=self.prefix_name + "conv5_6")
         # 1/32
         out = self.depthwise_separable(
-            out, 1024, 1024, 1024, 1, scale, name="conv6")
+            out, 1024, 1024, 1024, 1, scale, name=self.prefix_name + "conv6")
         module13 = out
         blocks.append(out)
         if not self.with_extra_blocks:
@@ -187,11 +196,15 @@ class MobileNet(object):
 
         num_filters = self.extra_block_filters
         module14 = self._extra_block(module13, num_filters[0][0],
-                                     num_filters[0][1], 1, 2, "conv7_1")
+                                     num_filters[0][1], 1, 2,
+                                     self.prefix_name + "conv7_1")
         module15 = self._extra_block(module14, num_filters[1][0],
-                                     num_filters[1][1], 1, 2, "conv7_2")
+                                     num_filters[1][1], 1, 2,
+                                     self.prefix_name + "conv7_2")
         module16 = self._extra_block(module15, num_filters[2][0],
-                                     num_filters[2][1], 1, 2, "conv7_3")
+                                     num_filters[2][1], 1, 2,
+                                     self.prefix_name + "conv7_3")
         module17 = self._extra_block(module16, num_filters[3][0],
-                                     num_filters[3][1], 1, 2, "conv7_4")
+                                     num_filters[3][1], 1, 2,
+                                     self.prefix_name + "conv7_4")
         return module11, module13, module14, module15, module16, module17

ppdet/modeling/backbones/resnet.py

@@ -47,7 +47,7 @@ class ResNet(object):
         feature_maps (list): index of stages whose feature maps are returned
         dcn_v2_stages (list): index of stages who select deformable conv v2
     """
-    __shared__ = ['norm_type', 'freeze_norm']
+    __shared__ = ['norm_type', 'freeze_norm', 'weight_prefix_name']
 
     def __init__(self,
                  depth=50,
@@ -57,7 +57,8 @@ class ResNet(object):
                  norm_decay=0.,
                  variant='b',
                  feature_maps=[2, 3, 4, 5],
-                 dcn_v2_stages=[]):
+                 dcn_v2_stages=[],
+                 weight_prefix_name=''):
         super(ResNet, self).__init__()
 
         if isinstance(feature_maps, Integral):
@@ -89,6 +90,7 @@ class ResNet(object):
         self.stage_filters = [64, 128, 256, 512]
         self._c1_out_chan_num = 64
         self.na = NameAdapter(self)
+        self.prefix_name = weight_prefix_name
 
     def _conv_offset(self,
                      input,
@@ -121,6 +123,7 @@ class ResNet(object):
                    act=None,
                    name=None,
                    dcn_v2=False):
+        _name = self.prefix_name + name if self.prefix_name != '' else name
         if not dcn_v2:
             conv = fluid.layers.conv2d(
                 input=input,
@@ -130,9 +133,9 @@ class ResNet(object):
                 padding=(filter_size - 1) // 2,
                 groups=groups,
                 act=None,
-                param_attr=ParamAttr(name=name + "_weights"),
+                param_attr=ParamAttr(name=_name + "_weights"),
                 bias_attr=False,
-                name=name + '.conv2d.output.1')
+                name=_name + '.conv2d.output.1')
         else:
             # select deformable conv"
             offset_mask = self._conv_offset(
@@ -141,7 +144,7 @@ class ResNet(object):
                 stride=stride,
                 padding=(filter_size - 1) // 2,
                 act=None,
-                name=name + "_conv_offset")
+                name=_name + "_conv_offset")
             offset_channel = filter_size**2 * 2
             mask_channel = filter_size**2
             offset, mask = fluid.layers.split(
@@ -160,11 +163,12 @@ class ResNet(object):
                 groups=groups,
                 deformable_groups=1,
                 im2col_step=1,
-                param_attr=ParamAttr(name=name + "_weights"),
+                param_attr=ParamAttr(name=_name + "_weights"),
                 bias_attr=False,
-                name=name + ".conv2d.output.1")
+                name=_name + ".conv2d.output.1")
 
         bn_name = self.na.fix_conv_norm_name(name)
+        bn_name = self.prefix_name + bn_name if self.prefix_name != '' else bn_name
 
         norm_lr = 0. if self.freeze_norm else 1.
         norm_decay = self.norm_decay
@@ -420,7 +424,8 @@ class ResNetC5(ResNet):
                  freeze_norm=True,
                  norm_decay=0.,
                  variant='b',
-                 feature_maps=[5]):
+                 feature_maps=[5],
+                 weight_prefix_name=''):
         super(ResNetC5, self).__init__(depth, freeze_at, norm_type, freeze_norm,
                                        norm_decay, variant, feature_maps)
         self.severed_head = True

ppdet/modeling/backbones/resnext.py

@@ -50,7 +50,8 @@ class ResNeXt(ResNet):
                  norm_decay=True,
                  variant='a',
                  feature_maps=[2, 3, 4, 5],
-                 dcn_v2_stages=[]):
+                 dcn_v2_stages=[],
+                 weight_prefix_name=''):
         assert depth in [50, 101, 152], "depth {} should be 50, 101 or 152"
         super(ResNeXt, self).__init__(depth, freeze_at, norm_type, freeze_norm,
                                       norm_decay, variant, feature_maps)
@@ -80,7 +81,8 @@ class ResNeXtC5(ResNeXt):
                  freeze_norm=True,
                  norm_decay=True,
                  variant='a',
-                 feature_maps=[5]):
+                 feature_maps=[5],
+                 weight_prefix_name=''):
         super(ResNeXtC5, self).__init__(depth, groups, group_width, freeze_at,
                                         norm_type, freeze_norm, norm_decay,
                                         variant, feature_maps)

ppdet/modeling/backbones/senet.py

@@ -56,7 +56,8 @@ class SENet(ResNeXt):
                  variant='d',
                  feature_maps=[2, 3, 4, 5],
                  dcn_v2_stages=[],
-                 std_senet=False):
+                 std_senet=False,
+                 weight_prefix_name=''):
         super(SENet, self).__init__(depth, groups, group_width, freeze_at,
                                     norm_type, freeze_norm, norm_decay, variant,
                                     feature_maps)
@@ -113,7 +114,8 @@ class SENetC5(SENet):
                  freeze_norm=True,
                  norm_decay=0.,
                  variant='d',
-                 feature_maps=[5]):
+                 feature_maps=[5],
+                 weight_prefix_name=''):
         super(SENetC5, self).__init__(depth, groups, group_width, freeze_at,
                                       norm_type, freeze_norm, norm_decay,
                                       variant, feature_maps)