add WiderNaive-18 base model (#4312)

* fix naive-lite-hrnet backbone

* add WiderNaive-18 model

configs/keypoint/README.md

@@ -23,6 +23,7 @@ COCO数据集
 | HRNet-w32             | 384x288  |     77.8     | [hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams) | [config](./hrnet/hrnet_w32_384x288.yml)                     |
 | HRNet-w32+DarkPose             | 256x192  |     78.0     | [dark_hrnet_w32_256x192.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_256x192.pdparams) | [config](./hrnet/dark_hrnet_w32_256x192.yml)                     |
 | HRNet-w32+DarkPose             | 384x288  |     78.3     | [dark_hrnet_w32_384x288.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_384x288.pdparams) | [config](./hrnet/dark_hrnet_w32_384x288.yml)                     |
+| WiderNaiveHRNet-18         | 256x192  |     67.6(+DARK 68.4)     | [wider_naive_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/wider_naive_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/wider_naive_hrnet_18_256x192_coco.yml)     |
 | LiteHRNet-18                   | 256x192  |     66.5     | [lite_hrnet_18_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_256x192_coco.yml)     |
 | LiteHRNet-18                   | 384x288  |     69.7     | [lite_hrnet_18_384x288_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_18_384x288_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_18_384x288_coco.yml)     |
 | LiteHRNet-30                   | 256x192  |     69.4     | [lite_hrnet_30_256x192_coco.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/lite_hrnet_30_256x192_coco.pdparams) | [config](./lite_hrnet/lite_hrnet_30_256x192_coco.yml)     |

configs/keypoint/lite_hrnet/wider_naive_hrnet_18_256x192_coco.yml

+use_gpu: true
+log_iter: 5
+save_dir: output
+snapshot_epoch: 10
+weights: output/wider_naive_hrnet_18_256x192_coco/model_final
+epoch: 210
+num_joints: &num_joints 17
+pixel_std: &pixel_std 200
+metric: KeyPointTopDownCOCOEval
+num_classes: 1
+train_height: &train_height 256
+train_width: &train_width 192
+trainsize: &trainsize [*train_width, *train_height]
+hmsize: &hmsize [48, 64]
+flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
+
+
+#####model
+architecture: TopDownHRNet
+
+TopDownHRNet:
+  backbone: LiteHRNet
+  post_process: HRNetPostProcess
+  flip_perm: *flip_perm
+  num_joints: *num_joints
+  width: &width 40
+  loss: KeyPointMSELoss
+  use_dark: false
+
+LiteHRNet:
+  network_type: wider_naive
+  freeze_at: -1
+  freeze_norm: false
+  return_idx: [0]
+
+KeyPointMSELoss:
+  use_target_weight: true
+  loss_scale: 1.0
+
+#####optimizer
+LearningRate:
+  base_lr: 0.002
+  schedulers:
+  - !PiecewiseDecay
+    milestones: [170, 200]
+    gamma: 0.1
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 500
+
+OptimizerBuilder:
+  optimizer:
+    type: Adam
+  regularizer:
+    factor: 0.0
+    type: L2
+
+
+#####data
+TrainDataset:
+  !KeypointTopDownCocoDataset
+    image_dir: train2017
+    anno_path: annotations/person_keypoints_train2017.json
+    dataset_dir: dataset/coco
+    num_joints: *num_joints
+    trainsize: *trainsize
+    pixel_std: *pixel_std
+    use_gt_bbox: True
+
+
+EvalDataset:
+  !KeypointTopDownCocoDataset
+    image_dir: val2017
+    anno_path: annotations/person_keypoints_val2017.json
+    dataset_dir: dataset/coco
+    num_joints: *num_joints
+    trainsize: *trainsize
+    pixel_std: *pixel_std
+    use_gt_bbox: True
+    image_thre: 0.0
+
+
+TestDataset:
+  !ImageFolder
+    anno_path: dataset/coco/keypoint_imagelist.txt
+
+worker_num: 2
+global_mean: &global_mean [0.485, 0.456, 0.406]
+global_std: &global_std [0.229, 0.224, 0.225]
+TrainReader:
+  sample_transforms:
+    - RandomFlipHalfBodyTransform:
+        scale: 0.25
+        rot: 30
+        num_joints_half_body: 8
+        prob_half_body: 0.3
+        pixel_std: *pixel_std
+        trainsize: *trainsize
+        upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        flip_pairs: *flip_perm
+    - TopDownAffine:
+        trainsize: *trainsize
+    - ToHeatmapsTopDown:
+        hmsize: *hmsize
+        sigma: 2
+  batch_transforms:
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 64
+  shuffle: true
+  drop_last: false
+
+EvalReader:
+  sample_transforms:
+    - TopDownAffine:
+        trainsize: *trainsize
+  batch_transforms:
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 16
+
+TestReader:
+  inputs_def:
+    image_shape: [3, *train_height, *train_width]
+  sample_transforms:
+    - Decode: {}
+    - TopDownEvalAffine:
+        trainsize: *trainsize
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 1

ppdet/modeling/backbones/lite_hrnet.py

@@ -271,7 +271,7 @@ class ShuffleUnit(nn.Layer):
                  norm_decay=0.):
         super(ShuffleUnit, self).__init__()
         branch_channel = out_channel // 2
-        stride = self.stride
+        self.stride = stride
         if self.stride == 1:
             assert (
                 in_channel == branch_channel * 2,
@@ -544,7 +544,7 @@ class LiteHRNetModule(nn.Layer):
                     norm_decay=norm_decay))
         return nn.Sequential(*layers)
 
-    def _make_naive_branchs(self,
+    def _make_naive_branches(self,
                              num_branches,
                              num_blocks,
                              freeze_norm=False,
@@ -644,7 +644,7 @@ class LiteHRNetModule(nn.Layer):
             out = self.layers(x)
         elif self.module_type == 'NAIVE':
             for i in range(self.num_branches):
-                x[i] = self.layers(x[i])
+                x[i] = self.layers[i](x[i])
             out = x
         if self.with_fuse:
             out_fuse = []