dudate config and modelzoo (#2723)

* Add faster_rcnn_x101_vd_64x4d_fpn_1x.yml
* Release faster_rcnn_r101_vd_fpn_1x.tar, faster_rcnn_x101_vd_64x4d_fpn_1x.tar, mask_rcnn_r50_2x.tar

README.md

@@ -36,16 +36,16 @@ multi-GPU training.
 
 Supported Architectures:
 
-|                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | SENet | MobileNet | DarkNet |
-|--------------------|:------:|------------------------------:|:-----:|:---------:|:-------:|
-| Faster R-CNN       | ✓      |                             ✓ | ✓     | ✗         | ✗       |
-| Faster R-CNN + FPN | ✓      |                             ✓ | ✓     | ✗         | ✗       |
-| Mask R-CNN         | ✓      |                             ✓ | ✓     | ✗         | ✗       |
-| Mask R-CNN + FPN   | ✓      |                             ✓ | ✓     | ✗         | ✗       |
-| Cascade R-CNN      | ✓      |                             ✗ | ✗     | ✗         | ✗       |
-| RetinaNet          | ✓      |                             ✗ | ✗     | ✗         | ✗       |
-| Yolov3             | ✓      |                             ✗ | ✗     | ✓         | ✓       |
-| SSD                | ✗      |                             ✗ | ✗     | ✓         | ✗       |
+|                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet |
+|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|
+| Faster R-CNN       | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       |
+| Faster R-CNN + FPN | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       |
+| Mask R-CNN         | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       |
+| Mask R-CNN + FPN   | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       |
+| Cascade R-CNN      | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       |
+| RetinaNet          | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       |
+| Yolov3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       |
+| SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       |
 
 <a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
 

configs/faster_rcnn_x101_vd_64x4d_fpn_1x.yml

+architecture: FasterRCNN
+train_feed: FasterRCNNTrainFeed
+eval_feed: FasterRCNNEvalFeed
+test_feed: FasterRCNNTestFeed
+max_iters: 180000
+snapshot_iter: 10000
+use_gpu: True
+log_smooth_window: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+weights: output/faster_rcnn_x101_vd_64x4d_fpn_1x/model_final
+metric: COCO
+
+FasterRCNN:
+  backbone: ResNeXt
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: BBoxHead
+  bbox_assigner: BBoxAssigner
+
+ResNeXt:
+  depth: 101
+  feature_maps: [2, 3, 4, 5]
+  freeze_at: 2
+  group_width: 4
+  groups: 64
+  norm_type: affine_channel
+  variant: d
+
+FPN:
+  max_level: 6
+  min_level: 2
+  num_chan: 256
+  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: 256
+  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: 1000
+    pre_nms_top_n: 1000
+
+FPNRoIAlign:
+  canconical_level: 4
+  canonical_size: 224
+  max_level: 5
+  min_level: 2
+  box_resolution: 7
+  sampling_ratio: 2
+
+BBoxAssigner:
+  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
+  num_classes: 81
+
+BBoxHead:
+  head: TwoFCHead
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+  num_classes: 81
+
+TwoFCHead:
+  num_chan: 1024
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [120000, 160000]
+    values: null
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+
+FasterRCNNTrainFeed:
+  # batch size per device
+  batch_size: 1
+  dataset:
+    dataset_dir: dataset/coco
+    image_dir: train2017
+    annotation: annotations/instances_train2017.json
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+  num_workers: 2
+  shuffle: True
+
+FasterRCNNEvalFeed:
+  batch_size: 1
+  dataset:
+    dataset_dir: dataset/coco
+    annotation: annotations/instances_val2017.json
+    image_dir: val2017
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+  num_workers: 2
+
+FasterRCNNTestFeed:
+  batch_size: 1
+  dataset:
+    annotation: annotations/instances_val2017.json
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+  num_workers: 2
+  shuffle: False

docs/MODEL_ZOO.md

@@ -35,6 +35,7 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
 | ResNet50             | Faster         |    1    |   1x    |  35.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar) |
 | ResNet50             | Faster         |    1    |   2x    |  37.1  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_2x.tar) |
 | ResNet50             | Mask           |    1    |   1x    |  36.5  |  32.2   | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_1x.tar) |
+| ResNet50             | Mask           |    1    |   2x    |  38.2  |  33.4   | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_2x.tar) |
 | ResNet50-vd          | Faster         |    1    |   1x    |  36.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar) |
 | ResNet50-FPN         | Faster         |    2    |   1x    |  37.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar) |
 | ResNet50-FPN         | Faster         |    2    |   2x    |  37.7  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar) |
@@ -46,8 +47,9 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
 | ResNet101-FPN        | Faster         |    1    |   1x    |  38.7  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) |
 | ResNet101-FPN        | Faster         |    1    |   2x    |  39.1  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) |
 | ResNet101-FPN        | Mask           |    1    |   1x    |  39.5  |  35.2   | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_fpn_1x.tar) |
-| ResNet101-vd-FPN     | Faster         |    1    |   1x    |  40.0  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) |
+| ResNet101-vd-FPN     | Faster         |    1    |   1x    |  40.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_1x.tar) |
 | ResNet101-vd-FPN     | Faster         |    1    |   2x    |  40.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) |
+| ResNeXt101-vd-FPN    | Faster         |    1    |   1x    |  42.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_1x.tar) |
 | SENet154-vd-FPN      | Faster         |    1    |  1.44x  |  42.9  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_se154_vd_fpn_s1x.tar) |
 | SENet154-vd-FPN      | Mask           |    1    |  1.44x  |  44.0  |  38.7   | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.tar) |