add server side rcnn model (#433)

* add server side rcnn model, 62 fps with coco mAP 41.6% and 20fps with coco mAP 47.8%

configs/rcnn_server_side_det/README.md

+# Practical Server-side detection method base on RCNN
+
+## Introduction
+
+* This is developed by PaddleDetection. Many useful tricks are utilized for the model training process. More details can be seen in the configuration file.
+
+
+## Model Zoo
+
+| Backbone                | Type     | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           |
+| :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: |
+| ResNet50-vd-FPN-Dcnv2         | Faster     |     2     |   3x    |     61.425     |  41.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |
+| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     20.001     |  47.8  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |

configs/rcnn_server_side_det/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml

+architecture: CascadeRCNN
+max_iters: 270000
+snapshot_iter: 30000
+use_gpu: true
+log_smooth_window: 20
+log_iter: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_82.39_pretrained.tar
+weights: output/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side/model_final
+metric: COCO
+num_classes: 81
+
+CascadeRCNN:
+  backbone: ResNet
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: CascadeBBoxHead
+  bbox_assigner: CascadeBBoxAssigner
+
+ResNet:
+  norm_type: bn
+  depth: 50
+  feature_maps: [2, 3, 4, 5]
+  freeze_at: 2
+  variant: d
+  dcn_v2_stages: [3, 4, 5]
+  lr_mult_list: [0.05, 0.05, 0.1, 0.15]
+
+FPN:
+  max_level: 6
+  min_level: 2
+  num_chan: 64
+  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
+
+FPNRPNHead:
+  anchor_generator:
+    anchor_sizes: [32, 64, 128, 256, 512]
+    aspect_ratios: [0.5, 1.0, 2.0]
+    stride: [16.0, 16.0]
+    variance: [1.0, 1.0, 1.0, 1.0]
+  anchor_start_size: 32
+  min_level: 2
+  max_level: 6
+  num_chan: 64
+  rpn_target_assign:
+    rpn_batch_size_per_im: 256
+    rpn_fg_fraction: 0.5
+    rpn_positive_overlap: 0.7
+    rpn_negative_overlap: 0.3
+    rpn_straddle_thresh: 0.0
+  train_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    pre_nms_top_n: 2000
+    post_nms_top_n: 2000
+  test_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    pre_nms_top_n: 500
+    post_nms_top_n: 300
+
+FPNRoIAlign:
+  canconical_level: 4
+  canonical_size: 224
+  min_level: 2
+  max_level: 5
+  box_resolution: 7
+  sampling_ratio: 2
+
+CascadeBBoxAssigner:
+  batch_size_per_im: 512
+  bbox_reg_weights: [10, 20, 30]
+  bg_thresh_lo: [0.0, 0.0, 0.0]
+  bg_thresh_hi: [0.5, 0.6, 0.7]
+  fg_thresh: [0.5, 0.6, 0.7]
+  fg_fraction: 0.25
+
+CascadeBBoxHead:
+  head: CascadeTwoFCHead
+  bbox_loss: BalancedL1Loss
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+
+BalancedL1Loss:
+  alpha: 0.5
+  gamma: 1.5
+  beta: 1.0
+  loss_weight: 1.0
+
+CascadeTwoFCHead:
+  mlp_dim: 1024
+
+LearningRate:
+  base_lr: 0.02
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [180000, 240000]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+
+TrainReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
+  dataset:
+    !COCODataSet
+    image_dir: train2017
+    anno_path: annotations/instances_train2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+  - !RandomFlipImage
+    prob: 0.5
+  - !AutoAugmentImage
+    autoaug_type: v1
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    target_size: [640, 672, 704, 736, 768, 800, 832, 864, 896, 928, 960, 992, 1024]
+    max_size: 1500
+    interp: 1
+    use_cv2: true
+  - !Permute
+    to_bgr: false
+    channel_first: true
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: false
+  batch_size: 2
+  shuffle: true
+  worker_num: 2
+  use_process: false
+
+
+TestReader:
+  inputs_def:
+    # set image_shape if needed
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+  dataset:
+    !ImageFolder
+    anno_path: annotations/instances_val2017.json
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 1500
+    target_size: 1000
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+
+
+
+EvalReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+    # for voc
+    #fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
+  dataset:
+    !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 1500
+    target_size: 1000
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+  drop_empty: false
+  worker_num: 2

configs/rcnn_server_side_det/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml

+architecture: FasterRCNN
+max_iters: 270000
+snapshot_iter: 30000
+use_gpu: true
+log_smooth_window: 20
+log_iter: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_82.39_pretrained.tar
+weights: output/faster_rcnn_dcn_r50_vd_fpn_3x_server_side/model_final
+metric: COCO
+num_classes: 81
+
+FasterRCNN:
+  backbone: ResNet
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: BBoxHead
+  bbox_assigner: LibraBBoxAssigner
+
+ResNet:
+  depth: 50
+  feature_maps: [2, 3, 4, 5]
+  freeze_at: 2
+  norm_type: bn
+  variant: d
+  dcn_v2_stages: [3, 4, 5]
+  lr_mult_list: [0.05, 0.05, 0.1, 0.15]
+
+FPN:
+  max_level: 6
+  min_level: 2
+  num_chan: 64
+  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
+
+FPNRPNHead:
+  anchor_generator:
+    anchor_sizes: [32, 64, 128, 256, 512]
+    aspect_ratios: [0.5, 1.0, 2.0]
+    stride: [16.0, 16.0]
+    variance: [1.0, 1.0, 1.0, 1.0]
+  anchor_start_size: 32
+  max_level: 6
+  min_level: 2
+  num_chan: 64
+  rpn_target_assign:
+    rpn_batch_size_per_im: 256
+    rpn_fg_fraction: 0.5
+    rpn_negative_overlap: 0.3
+    rpn_positive_overlap: 0.7
+    rpn_straddle_thresh: 0.0
+  train_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    post_nms_top_n: 2000
+    pre_nms_top_n: 2000
+  test_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    post_nms_top_n: 300
+    pre_nms_top_n: 500
+
+FPNRoIAlign:
+  canconical_level: 4
+  canonical_size: 224
+  max_level: 5
+  min_level: 2
+  box_resolution: 7
+  sampling_ratio: 2
+
+LibraBBoxAssigner:
+  batch_size_per_im: 512
+  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
+  bg_thresh_hi: 0.5
+  bg_thresh_lo: 0.0
+  fg_fraction: 0.25
+  fg_thresh: 0.5
+
+BBoxHead:
+  head: TwoFCHead
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+  bbox_loss: DiouLoss
+
+DiouLoss:
+  loss_weight: 10.0
+  is_cls_agnostic: false
+  use_complete_iou_loss: true
+
+TwoFCHead:
+  mlp_dim: 1024
+
+LearningRate:
+  base_lr: 0.02
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [180000, 240000]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+
+TrainReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
+  dataset:
+    !COCODataSet
+    image_dir: train2017
+    anno_path: annotations/instances_train2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+  - !RandomFlipImage
+    prob: 0.5
+  - !AutoAugmentImage
+    autoaug_type: v1
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    target_size: [384, 416, 448, 480, 512, 544, 576, 608, 640, 672]
+    max_size: 1000
+    interp: 1
+    use_cv2: true
+  - !Permute
+    to_bgr: false
+    channel_first: true
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: false
+  batch_size: 2
+  shuffle: true
+  worker_num: 2
+  use_process: false
+
+
+TestReader:
+  inputs_def:
+    # set image_shape if needed
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+  dataset:
+    !ImageFolder
+    anno_path: annotations/instances_val2017.json
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 640
+    target_size: 640
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+
+
+
+EvalReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+    # for voc
+    #fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
+  dataset:
+    !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 640
+    target_size: 640
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+  drop_empty: false
+  worker_num: 2

ppdet/modeling/backbones/resnet.py

@@ -53,6 +53,9 @@ class ResNet(object):
         gcb_params (dict): gc blocks config, includes ratio(default as 1.0/16),
                            pooling_type(default as "att") and
                            fusion_types(default as ['channel_add'])
+        lr_mult_list (list): learning rate ratio of different resnet stages(2,3,4,5),
+                             lower learning rate ratio is need for pretrained model 
+                             got using distillation(default as [1.0, 1.0, 1.0, 1.0]).
     """
     __shared__ = ['norm_type', 'freeze_norm', 'weight_prefix_name']
 
@@ -68,7 +71,8 @@ class ResNet(object):
                  weight_prefix_name='',
                  nonlocal_stages=[],
                  gcb_stages=[],
-                 gcb_params=dict()):
+                 gcb_params=dict(),
+                 lr_mult_list=[1., 1., 1., 1.]):
         super(ResNet, self).__init__()
 
         if isinstance(feature_maps, Integral):
@@ -82,6 +86,9 @@ class ResNet(object):
         assert norm_type in ['bn', 'sync_bn', 'affine_channel']
         assert not (len(nonlocal_stages)>0 and depth<50), \
                     "non-local is not supported for resnet18 or resnet34"
+        assert len(lr_mult_list
+                   ) == 4, "lr_mult_list length must be 4 but got {}".format(
+                       len(lr_mult_list))
 
         self.depth = depth
         self.freeze_at = freeze_at
@@ -116,6 +123,10 @@ class ResNet(object):
         self.gcb_stages = gcb_stages
         self.gcb_params = gcb_params
 
+        self.lr_mult_list = lr_mult_list
+        # var denoting curr stage
+        self.stage_num = -1
+
     def _conv_offset(self,
                      input,
                      filter_size,
@@ -148,6 +159,13 @@ class ResNet(object):
                    name=None,
                    dcn_v2=False):
         _name = self.prefix_name + name if self.prefix_name != '' else name
+
+        # need fine lr for distilled model, default as 1.0
+        lr_mult = 1.0
+        mult_idx = max(self.stage_num - 2, 0)
+        mult_idx = min(self.stage_num - 2, 3)
+        lr_mult = self.lr_mult_list[mult_idx]
+
         if not dcn_v2:
             conv = fluid.layers.conv2d(
                 input=input,
@@ -157,7 +175,8 @@ class ResNet(object):
                 padding=(filter_size - 1) // 2,
                 groups=groups,
                 act=None,
-                param_attr=ParamAttr(name=_name + "_weights"),
+                param_attr=ParamAttr(
+                    name=_name + "_weights", learning_rate=lr_mult),
                 bias_attr=False,
                 name=_name + '.conv2d.output.1')
         else:
@@ -187,14 +206,15 @@ class ResNet(object):
                 groups=groups,
                 deformable_groups=1,
                 im2col_step=1,
-                param_attr=ParamAttr(name=_name + "_weights"),
+                param_attr=ParamAttr(
+                    name=_name + "_weights", learning_rate=lr_mult),
                 bias_attr=False,
                 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_lr = 0. if self.freeze_norm else lr_mult
         norm_decay = self.norm_decay
         pattr = ParamAttr(
             name=bn_name + '_scale',
@@ -365,6 +385,8 @@ class ResNet(object):
         """
         assert stage_num in [2, 3, 4, 5]
 
+        self.stage_num = stage_num
+
         stages, block_func = self.depth_cfg[self.depth]
         count = stages[stage_num - 2]
 

ppdet/modeling/ops.py

@@ -552,6 +552,8 @@ class BBoxAssigner(object):
 
 @register
 class LibraBBoxAssigner(object):
+    __shared__ = ['num_classes']
+
     def __init__(self,
                  batch_size_per_im=512,
                  fg_fraction=.25,
@@ -797,6 +799,7 @@ class LibraBBoxAssigner(object):
                 hs = boxes[:, 3] - boxes[:, 1] + 1
                 keep = np.where((ws > 0) & (hs > 0))[0]
                 boxes = boxes[keep]
+                max_overlaps = max_overlaps[keep]
                 fg_inds = np.where(max_overlaps >= fg_thresh)[0]
                 bg_inds = np.where((max_overlaps < bg_thresh_hi) & (
                     max_overlaps >= bg_thresh_lo))[0]

ppdet/modeling/roi_heads/cascade_head.py

@@ -23,6 +23,7 @@ from paddle.fluid.initializer import MSRA
 
 from ppdet.modeling.ops import MultiClassNMS
 from ppdet.modeling.ops import ConvNorm
+from ppdet.modeling.losses import SmoothL1Loss
 from ppdet.core.workspace import register
 
 __all__ = ['CascadeBBoxHead']
@@ -38,16 +39,24 @@ class CascadeBBoxHead(object):
         nms (object): `MultiClassNMS` instance
         num_classes: number of output classes
     """
-    __inject__ = ['head', 'nms']
+    __inject__ = ['head', 'nms', 'bbox_loss']
     __shared__ = ['num_classes']
 
-    def __init__(self, head, nms=MultiClassNMS().__dict__, num_classes=81):
+    def __init__(
+            self,
+            head,
+            nms=MultiClassNMS().__dict__,
+            bbox_loss=SmoothL1Loss().__dict__,
+            num_classes=81, ):
         super(CascadeBBoxHead, self).__init__()
         self.head = head
         self.nms = nms
+        self.bbox_loss = bbox_loss
         self.num_classes = num_classes
         if isinstance(nms, dict):
             self.nms = MultiClassNMS(**nms)
+        if isinstance(bbox_loss, dict):
+            self.bbox_loss = SmoothL1Loss(**bbox_loss)
 
     def get_output(self,
                    roi_feat,
@@ -123,13 +132,11 @@ class CascadeBBoxHead(object):
             loss_cls = fluid.layers.reduce_mean(
                 loss_cls, name='loss_cls_' + str(i)) * rcnn_loss_weight_list[i]
 
-            loss_bbox = fluid.layers.smooth_l1(
+            loss_bbox = self.bbox_loss(
                 x=rcnn_pred[1],
                 y=rcnn_target[2],
                 inside_weight=rcnn_target[3],
-                outside_weight=rcnn_target[4],
-                sigma=1.0,  # detectron use delta = 1./sigma**2
-            )
+                outside_weight=rcnn_target[4])
             loss_bbox = fluid.layers.reduce_mean(
                 loss_bbox,
                 name='loss_bbox_' + str(i)) * rcnn_loss_weight_list[i]

ppdet/modeling/target_assigners.py

@@ -21,7 +21,11 @@ from paddle import fluid
 from ppdet.core.workspace import register
 from ppdet.modeling.ops import BBoxAssigner, MaskAssigner
 
-__all__ = ['BBoxAssigner', 'MaskAssigner', 'CascadeBBoxAssigner']
+__all__ = [
+    'BBoxAssigner',
+    'MaskAssigner',
+    'CascadeBBoxAssigner',
+]
 
 
 @register