Add faster rcnn fpn hrnet 1x & 2x (#2055)

* add hrnet, test=develop

* add comment, test=dygraph

* move config file to hrnet, test=dygraph

* add 2x model

* add hrnet README

* update latest config structure, test=dygraph

dygraph/configs/hrnet/README.md

+# High-resolution networks (HRNets) for object detection
+
+## Introduction
+
+- Deep High-Resolution Representation Learning for Human Pose Estimation: [https://arxiv.org/abs/1902.09212](https://arxiv.org/abs/1902.09212)
+
+```
+@inproceedings{SunXLW19,
+  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
+  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
+  booktitle={CVPR},
+  year={2019}
+}
+```
+
+- High-Resolution Representations for Labeling Pixels and Regions: [https://arxiv.org/abs/1904.04514](https://arxiv.org/abs/1904.04514)
+
+```
+@article{SunZJCXLMWLW19,
+  title={High-Resolution Representations for Labeling Pixels and Regions},
+  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao
+  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
+  journal   = {CoRR},
+  volume    = {abs/1904.04514},
+  year={2019}
+}
+```
+
+## Model Zoo
+
+| Backbone                | Type           | deformable Conv  | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
+| :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
+| HRNetV2p_W18            | Faster         | False |     2     |   1x    |    -      |  35.7  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/faster_rcnn_hrnetv2p_w18_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.yml) |
+| HRNetV2p_W18            | Faster         | False |     2     |   2x    |    -      |  37.7  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/faster_rcnn_hrnetv2p_w18_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.yml) |

dygraph/configs/hrnet/_base_/faster_rcnn_hrnetv2p_w18.yml

+architecture: FasterRCNN
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar
+weights: output/faster_rcnn_hrnetv2p_w18_1x_coco/model_final
+load_static_weights: True
+
+# Model Achitecture
+FasterRCNN:
+  # model anchor info flow
+  anchor: Anchor
+  proposal: Proposal
+  # model feat info flow
+  backbone: HRNet
+  neck: HRFPN
+  rpn_head: RPNHead
+  bbox_head: BBoxHead
+  # post process
+  bbox_post_process: BBoxPostProcess
+
+HRNet:
+  width: 18
+  freeze_at: 0
+  return_idx: [0, 1, 2, 3]
+
+HRFPN:
+  out_channel: 256
+  share_conv: false
+
+RPNHead:
+  rpn_feat:
+    name: RPNFeat
+    feat_in: 256
+    feat_out: 256
+  anchor_per_position: 3
+  rpn_channel: 256
+
+Anchor:
+  anchor_generator:
+    name: AnchorGeneratorRPN
+    aspect_ratios: [0.5, 1.0, 2.0]
+    anchor_start_size: 32
+    stride: [4., 4.]
+  anchor_target_generator:
+    name: AnchorTargetGeneratorRPN
+    batch_size_per_im: 256
+    fg_fraction: 0.5
+    negative_overlap: 0.3
+    positive_overlap: 0.7
+    straddle_thresh: 0.0
+
+Proposal:
+  proposal_generator:
+    name: ProposalGenerator
+    min_size: 0.0
+    nms_thresh: 0.7
+    train_pre_nms_top_n: 2000
+    train_post_nms_top_n: 2000
+    infer_pre_nms_top_n: 1000
+    infer_post_nms_top_n: 1000
+  proposal_target_generator:
+    name: ProposalTargetGenerator
+    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_thresh: [0.5,]
+    fg_fraction: 0.25
+
+BBoxHead:
+  bbox_feat:
+    name: BBoxFeat
+    roi_extractor:
+      name: RoIAlign
+      resolution: 7
+      sampling_ratio: 2
+    head_feat:
+      name: TwoFCHead
+      in_dim: 256
+      mlp_dim: 1024
+  in_feat: 1024
+
+BBoxPostProcess:
+  decode:
+    name: RCNNBox
+    num_classes: 81
+    batch_size: 1
+  nms:
+    name: MultiClassNMS
+    keep_top_k: 100
+    score_threshold: 0.05
+    nms_threshold: 0.5

dygraph/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  './_base_/faster_rcnn_hrnetv2p_w18.yml',
+  '../faster_rcnn/_base_/optimizer_1x.yml',
+  '../faster_rcnn/_base_/faster_fpn_reader.yml',
+  '../runtime.yml',
+]
+
+LearningRate:
+  base_lr: 0.02
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [8, 11]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000
+
+TrainReader:
+  batch_size: 2

dygraph/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  './_base_/faster_rcnn_hrnetv2p_w18.yml',
+  '../faster_rcnn/_base_/optimizer_1x.yml',
+  '../faster_rcnn/_base_/faster_fpn_reader.yml',
+  '../runtime.yml',
+]
+
+weights: output/faster_rcnn_hrnetv2p_w18_2x_coco/model_final
+epoch: 24
+
+LearningRate:
+  base_lr: 0.02
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000
+
+TrainReader:
+  batch_size: 2

dygraph/ppdet/engine/trainer.py

@@ -156,6 +156,7 @@ class Trainer(object):
 
     def train(self):
         assert self.mode == 'train', "Model not in 'train' mode"
+        self.model.train()
 
         # if no given weights loaded, load backbone pretrain weights as default
         if not self._weights_loaded:
@@ -184,7 +185,6 @@ class Trainer(object):
                 self._compose_callback.on_step_begin(self.status)
 
                 # model forward
-                self.model.train()
                 outputs = self.model(data)
                 loss = outputs['loss']
 

dygraph/ppdet/modeling/backbones/__init__.py

@@ -3,9 +3,11 @@ from . import resnet
 from . import darknet
 from . import mobilenet_v1
 from . import mobilenet_v3
+from . import hrnet
 
 from .vgg import *
 from .resnet import *
 from .darknet import *
 from .mobilenet_v1 import *
 from .mobilenet_v3 import *
+from .hrnet import *

dygraph/ppdet/modeling/backbones/darknet.py

+# Copyright (c) 2020 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.
+
 import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F

dygraph/ppdet/modeling/necks/__init__.py

@@ -14,6 +14,8 @@
 
 from . import fpn
 from . import yolo_fpn
+from . import hrfpn
 
 from .fpn import *
 from .yolo_fpn import *
+from .hrfpn import *

dygraph/ppdet/modeling/necks/hrfpn.py

+# Copyright (c) 2020 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.
+
+import paddle
+import paddle.nn.functional as F
+from paddle import ParamAttr
+import paddle.nn as nn
+from paddle.regularizer import L2Decay
+from ppdet.core.workspace import register, serializable
+
+__all__ = ['HRFPN']
+
+
+@register
+class HRFPN(nn.Layer):
+    """
+    Args:
+        in_channel (int): number of input feature channels from backbone
+        out_channel (int): number of output feature channels
+        share_conv (bool): whether to share conv for different layers' reduction
+        spatial_scale (list): feature map scaling factor
+    """
+
+    def __init__(
+            self,
+            in_channel=270,
+            out_channel=256,
+            share_conv=False,
+            spatial_scale=[1. / 4, 1. / 8, 1. / 16, 1. / 32, 1. / 64], ):
+        super(HRFPN, self).__init__()
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+        self.share_conv = share_conv
+        self.spatial_scale = spatial_scale
+
+        self.reduction = nn.Conv2D(
+            in_channels=in_channel,
+            out_channels=out_channel,
+            kernel_size=1,
+            weight_attr=ParamAttr(name='hrfpn_reduction_weights'),
+            bias_attr=False)
+        self.num_out = len(self.spatial_scale)
+        if share_conv:
+            self.fpn_conv = nn.Conv2D(
+                in_channels=out_channel,
+                out_channels=out_channel,
+                kernel_size=3,
+                padding=1,
+                weight_attr=ParamAttr(name='fpn_conv_weights'),
+                bias_attr=False)
+        else:
+            self.fpn_conv = []
+            for i in range(self.num_out):
+                conv_name = "fpn_conv_" + str(i)
+                conv = self.add_sublayer(
+                    conv_name,
+                    nn.Conv2D(
+                        in_channels=out_channel,
+                        out_channels=out_channel,
+                        kernel_size=3,
+                        padding=1,
+                        weight_attr=ParamAttr(name=conv_name + "_weights"),
+                        bias_attr=False))
+                self.fpn_conv.append(conv)
+
+    def forward(self, body_feats):
+        num_backbone_stages = len(body_feats)
+
+        outs = []
+        outs.append(body_feats[0])
+
+        # resize
+        for i in range(1, num_backbone_stages):
+            resized = F.interpolate(
+                body_feats[i], scale_factor=2**i, mode='bilinear')
+            outs.append(resized)
+
+        # concat
+        out = paddle.concat(outs, axis=1)
+        assert out.shape[
+            1] == self.in_channel, 'in_channel should be {}, be received {}'.format(
+                out.shape[1], self.in_channel)
+
+        # reduction
+        out = self.reduction(out)
+
+        # conv
+        outs = [out]
+        for i in range(1, self.num_out):
+            outs.append(F.avg_pool2d(out, kernel_size=2**i, stride=2**i))
+        outputs = []
+
+        for i in range(self.num_out):
+            conv_func = self.fpn_conv if self.share_conv else self.fpn_conv[i]
+            conv = conv_func(outs[i])
+            outputs.append(conv)
+
+        fpn_feat = [outputs[k] for k in range(self.num_out)]
+        return fpn_feat, self.spatial_scale

dygraph/ppdet/utils/checkpoint.py

@@ -95,7 +95,7 @@ def load_weight(model, weight, optimizer=None):
     last_epoch = 0
     if optimizer is not None and os.path.exists(path + '.pdopt'):
         optim_state_dict = paddle.load(path + '.pdopt')
-        # to slove resume bug, will it be fixed in paddle 2.0
+        # to solve resume bug, will it be fixed in paddle 2.0
         for key in optimizer.state_dict().keys():
             if not key in optim_state_dict.keys():
                 optim_state_dict[key] = optimizer.state_dict()[key]
@@ -132,6 +132,9 @@ def load_pretrain_weight(model,
                     weight_name, pre_state_dict[weight_name].shape))
                 param_state_dict[key] = pre_state_dict[weight_name]
             else:
+                if 'backbone' in key:
+                    logger.info('Lack weight: {}, structure name: {}'.format(
+                        weight_name, key))
                 param_state_dict[key] = model_dict[key]
         model.set_dict(param_state_dict)
         return