[PaddleDetection] Add VGG-SSD on VOC and COCO dataset. (#3037)

# Add VGG-SSD on VOC and COCO dataset.
# Add config and model zoo. 
# Refine bbox2out and draw_bbox:
    - Add bbox de-normalize in bbox2out function.
    - Remove bbox de-normalize in draw_bbox.

README.md

@@ -38,16 +38,16 @@ multi-GPU training.
 
 Supported Architectures:
 
-|                    | 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   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       |
-| Cascade R-CNN      | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       |
-| RetinaNet          | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       |
-| Yolov3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       |
-| SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       |
+|                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet | VGG |
+|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|:---:|
+| Faster R-CNN       | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
+| Faster R-CNN + FPN | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
+| Mask R-CNN         | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
+| Mask R-CNN + FPN   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
+| 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.
 

README_cn.md

@@ -27,16 +27,16 @@ PaddleDetection的目的是为工业界和学术界提供大量易使用的目
 
 支持的模型结构：
 
-|                    | 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   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       |
-| Cascade R-CNN      | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       |
-| RetinaNet          | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       |
-| Yolov3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       |
-| SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       |
+|                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet | VGG |
+|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|:---:|
+| Faster R-CNN       | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
+| Faster R-CNN + FPN | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
+| Mask R-CNN         | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
+| Mask R-CNN + FPN   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
+| Cascade R-CNN      | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       | ✗   |
+| RetinaNet          | ✓      |                             ✗ | ✗          | ✗     | ✗         | ✗       | ✗   |
+| Yolov3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       | ✗   |
+| SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       | ✓   |
 
 <a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型提供了较大的精度提高和较少的性能损失。
 

configs/ssd/ssd_vgg16_300.yml

+architecture: SSD
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+use_gpu: true
+max_iters: 400000
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: COCO
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_caffe_pretrained.tar
+save_dir: output
+weights: output/ssd_vgg16_300/model_final
+num_classes: 81
+
+SSD:
+  backbone: VGG
+  multi_box_head: MultiBoxHead
+  metric:
+    ap_version: 11point
+    evaluate_difficult: false
+    overlap_threshold: 0.5
+  output_decoder:
+    background_label: 0
+    keep_top_k: 200
+    nms_eta: 1.0
+    nms_threshold: 0.45
+    nms_top_k: 400
+    score_threshold: 0.01
+
+VGG:
+  depth: 16
+  with_extra_blocks: true
+  normalizations: [20., -1, -1, -1, -1, -1]
+
+MultiBoxHead:
+  base_size: 300
+  aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
+  min_ratio: 15
+  max_ratio: 90
+  min_sizes: [30.0, 60.0, 111.0, 162.0, 213.0, 264.0]
+  max_sizes: [60.0, 111.0, 162.0, 213.0, 264.0, 315.0]
+  steps: [8, 16, 32, 64, 100, 300]
+  offset: 0.5
+  flip: true
+  kernel_size: 3
+  pad: 1
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [280000, 360000]
+  - !LinearWarmup
+    start_factor: 0.3333333333333333
+    steps: 500
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  dataset:
+    dataset_dir: dataset/coco
+    annotation: annotations/instances_train2017.json
+    image_dir: train2017
+  image_shape: [3, 300, 300]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !RandomDistort
+    brightness_lower: 0.875
+    brightness_upper: 1.125
+    is_order: true
+  - !ExpandImage
+    max_ratio: 4
+    mean: [104, 117, 123]
+    prob: 0.5
+  - !CropImage
+    avoid_no_bbox: true
+    batch_sampler:
+    - [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
+    satisfy_all: false
+  - !ResizeImage
+    interp: 1
+    target_size: 300
+    use_cv2: false
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]
+
+SSDEvalFeed:
+  batch_size: 16
+  dataset:
+    dataset_dir: dataset/coco
+    annotation: annotations/instances_val2017.json
+    image_dir: val2017
+  drop_last: false
+  image_shape: [3, 300, 300]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    target_size: 300
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]
+
+SSDTestFeed:
+  batch_size: 1
+  dataset:
+    annotation: dataset/coco/annotations/instances_val2017.json
+  image_shape: [3, 300, 300]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    max_size: 0
+    target_size: 300
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]

configs/ssd/ssd_vgg16_300_voc.yml

+architecture: SSD
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+use_gpu: true
+max_iters: 120001
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: VOC
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_caffe_pretrained.tar
+save_dir: output
+weights: output/ssd_vgg16_300_voc/model_final/
+num_classes: 21
+
+SSD:
+  backbone: VGG
+  multi_box_head: MultiBoxHead
+  metric:
+    ap_version: 11point
+    evaluate_difficult: false
+    overlap_threshold: 0.5
+  output_decoder:
+    background_label: 0
+    keep_top_k: 200
+    nms_eta: 1.0
+    nms_threshold: 0.45
+    nms_top_k: 400
+    score_threshold: 0.01
+
+VGG:
+  depth: 16
+  with_extra_blocks: true
+  normalizations: [20., -1, -1, -1, -1, -1]
+
+MultiBoxHead:
+  base_size: 300
+  aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
+  min_ratio: 20
+  max_ratio: 90
+  min_sizes: [30.0, 60.0, 111.0, 162.0, 213.0, 264.0]
+  max_sizes: [60.0, 111.0, 162.0, 213.0, 264.0, 315.0]
+  steps: [8, 16, 32, 64, 100, 300]
+  offset: 0.5
+  flip: true
+  min_max_aspect_ratios_order: true
+  kernel_size: 3
+  pad: 1
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [80000, 100000]
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  dataset:
+    dataset_dir: dataset/voc
+    annotation: VOCdevkit/VOC_all/ImageSets/Main/train.txt
+    image_dir: VOCdevkit/VOC_all/JPEGImages
+    use_default_label: true
+  image_shape: [3, 300, 300]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !RandomDistort
+    brightness_lower: 0.875
+    brightness_upper: 1.125
+    is_order: true
+  - !ExpandImage
+    max_ratio: 4
+    mean: [104, 117, 123]
+    prob: 0.5
+  - !CropImage
+    avoid_no_bbox: true
+    batch_sampler:
+    - [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
+    satisfy_all: false
+  - !ResizeImage
+    interp: 1
+    target_size: 300
+    use_cv2: False
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]
+
+SSDEvalFeed:
+  batch_size: 32
+  dataset:
+    dataset_dir: dataset/voc
+    annotation: VOCdevkit/VOC_all/ImageSets/Main/val.txt
+    image_dir: VOCdevkit/VOC_all/JPEGImages
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 300, 300]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !ResizeImage
+    interp: 1
+    target_size: 300
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]  
+    std: [1, 1, 1]
+
+SSDTestFeed:
+  batch_size: 1
+  dataset:
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 300, 300]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    max_size: 0
+    target_size: 300
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]

configs/ssd/ssd_vgg16_512.yml

+architecture: SSD
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+use_gpu: true
+max_iters: 400000
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: COCO
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_caffe_pretrained.tar
+save_dir: output
+weights: output/ssd_vgg16_512/model_final
+num_classes: 81
+
+SSD:
+  backbone: VGG
+  multi_box_head: MultiBoxHead
+  metric:
+    ap_version: 11point
+    evaluate_difficult: false
+    overlap_threshold: 0.5
+  output_decoder:
+    background_label: 0
+    keep_top_k: 200
+    nms_eta: 1.0
+    nms_threshold: 0.45
+    nms_top_k: 400
+    score_threshold: 0.01
+
+VGG:
+  depth: 16
+  with_extra_blocks: true
+  normalizations: [20., -1, -1, -1, -1, -1, -1]
+  extra_block_filters: [[256, 512, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 1, 4]]
+
+
+MultiBoxHead:
+  base_size: 512
+  aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
+  min_ratio: 15
+  max_ratio: 90
+  min_sizes: [20.0, 51.0, 133.0, 215.0, 296.0, 378.0, 460.0]
+  max_sizes: [51.0, 133.0, 215.0, 296.0, 378.0, 460.0, 542.0]
+  steps: [8, 16, 32, 64, 128, 256, 512]
+  offset: 0.5
+  flip: true
+  kernel_size: 3
+  pad: 1
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [280000, 360000]
+  - !LinearWarmup
+    start_factor: 0.3333333333333333
+    steps: 500
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  dataset:
+    dataset_dir: dataset/coco
+    annotation: annotations/instances_train2017.json
+    image_dir: train2017
+  image_shape: [3, 512, 512]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !RandomDistort
+    brightness_lower: 0.875
+    brightness_upper: 1.125
+    is_order: true
+  - !ExpandImage
+    max_ratio: 4
+    mean: [104, 117, 123]
+    prob: 0.5
+  - !CropImage
+    avoid_no_bbox: true
+    batch_sampler:
+    - [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
+    satisfy_all: false
+  - !ResizeImage
+    interp: 1
+    target_size: 512
+    use_cv2: false
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]
+
+SSDEvalFeed:
+  batch_size: 8
+  dataset:
+    dataset_dir: dataset/coco
+    annotation: annotations/instances_val2017.json
+    image_dir: val2017
+  drop_last: false
+  image_shape: [3, 512, 512]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    target_size: 512
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]
+
+SSDTestFeed:
+  batch_size: 1
+  dataset:
+    annotation: dataset/coco/annotations/instances_val2017.json
+  image_shape: [3, 512, 512]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    max_size: 0
+    target_size: 512
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [1, 1, 1]

configs/ssd/ssd_vgg16_512_voc.yml

+architecture: SSD
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+use_gpu: true
+max_iters: 120000
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: VOC
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_caffe_pretrained.tar
+save_dir: output
+weights: output/ssd_vgg16_512_voc/model_final/
+num_classes: 21
+
+SSD:
+  backbone: VGG
+  multi_box_head: MultiBoxHead
+  metric:
+    ap_version: 11point
+    evaluate_difficult: false
+    overlap_threshold: 0.5
+  output_decoder:
+    background_label: 0
+    keep_top_k: 200
+    nms_eta: 1.0
+    nms_threshold: 0.45
+    nms_top_k: 400
+    score_threshold: 0.01
+
+VGG:
+  depth: 16
+  with_extra_blocks: true
+  normalizations: [20., -1, -1, -1, -1, -1, -1]
+  extra_block_filters: [[256, 512, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 1, 1, 4]]
+
+
+MultiBoxHead:
+  base_size: 512
+  aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
+  min_ratio: 20
+  max_ratio: 90
+  min_sizes: [20.0, 51.0, 133.0, 215.0, 296.0, 378.0, 460.0]
+  max_sizes: [51.0, 133.0, 215.0, 296.0, 378.0, 460.0, 542.0]
+  steps: [8, 16, 32, 64, 128, 256, 512]
+  offset: 0.5
+  flip: true
+  kernel_size: 3
+  pad: 1
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [80000, 100000]
+  - !LinearWarmup
+    start_factor: 0.3333333333333333
+    steps: 500
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  dataset:
+    dataset_dir: dataset/voc
+    annotation: VOCdevkit/VOC_all/ImageSets/Main/train.txt
+    image_dir: VOCdevkit/VOC_all/JPEGImages
+    use_default_label: true
+  image_shape: [3, 512, 512]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !RandomDistort
+    brightness_lower: 0.875
+    brightness_upper: 1.125
+    is_order: true
+  - !ExpandImage
+    max_ratio: 4
+    mean: [123, 117, 104]
+    prob: 0.5
+  - !CropImage
+    avoid_no_bbox: true
+    batch_sampler:
+    - [1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0]
+    - [1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]
+    satisfy_all: false
+  - !ResizeImage
+    interp: 1
+    target_size: 512
+    use_cv2: false
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [123, 117, 104]
+    std: [1, 1, 1]
+
+SSDEvalFeed:
+  batch_size: 32
+  dataset:
+    dataset_dir: dataset/voc
+    annotation: VOCdevkit/VOC_all/ImageSets/Main/val.txt
+    image_dir: VOCdevkit/VOC_all/JPEGImages
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 512, 512]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !ResizeImage
+    interp: 1
+    target_size: 512
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [123, 117, 104]
+    std: [1, 1, 1]
+
+SSDTestFeed:
+  batch_size: 1
+  dataset:
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 512, 512]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    max_size: 0
+    target_size: 512
+    use_cv2: false
+  - !Permute
+    to_bgr: false
+  - !NormalizeImage
+    is_scale: false
+    mean: [123, 117, 104]
+    std: [1, 1, 1]

docs/MODEL_ZOO.md

@@ -118,11 +118,21 @@ results of image size 608/416/320 above.
 
 **Notes:** In RetinaNet, the base LR is changed to 0.01 for minibatch size 16.
 
+### SSD
+
+| Backbone     | Size | Image/gpu | Lr schd | Box AP | Download  |
+| VGG16        | 300  |     8   |   40w  |  25.1  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300.tar) |
+| VGG16        | 512  |     8   |   40w  |  29.1  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_512.tar) |
+
+**Notes:** VGG-SSD is trained in 4 GPU with total batch size as 32 and trained 400000 iters.
+
 ### SSD on Pascal VOC
 
 | Backbone     | Size | Image/gpu | Lr schd | Box AP | Download  |
 | :----------- | :--: | :-----: | :-----: | :----: | :-------: |
-| MobileNet v1 | 300  |    32   |   120e  |  73.13  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) |
+| MobileNet v1 | 300  |    32   |   120e  |  73.2  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) |
+| VGG16        | 300  |     8   |   240e  |  77.5  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300_voc.tar) |
+| VGG16        | 512  |     8   |   240e  |  80.2  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_512_voc.tar) |
 
-**Notes:** SSD is trained in 2 GPU with totoal batch size as 64 and trained 120 epoches. SSD training data augmentations: randomly color distortion,
+**NOTE**: MobileNet-SSD is trained in 2 GPU with totoal batch size as 64 and trained 120 epoches. VGG-SSD is trained in 4 GPU with total batch size as 32 and trained 240 epoches. SSD training data augmentations: randomly color distortion,
 randomly cropping, randomly expansion, randomly flipping.

docs/MODEL_ZOO_cn.md

@@ -115,10 +115,20 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
 
 **注意事项:** RetinaNet系列模型中，在总batch size为16下情况下，初始学习率改为0.01。
 
+### SSD
+
+|  骨架网络     | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 |
+| VGG16        | 300  |     8   |   40万  |  25.1  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300.tar) |
+| VGG16        | 512  |     8   |   40万  |  29.1  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_512.tar) |
+
+**注意事项:** VGG-SSD在总batch size为32下训练40万轮。
+
 ### SSD 基于Pascal VOC数据集
 
 |  骨架网络     | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载  |
 | :----------- | :--: | :-----: | :-----: | :----: | :-------: |
-| MobileNet v1 | 300  |    32   |   120e  |  73.13  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) |
+| MobileNet v1 | 300  |    32   |   120e  |  73.2  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) |
+| VGG16        | 300  |     8   |   240e  |  77.5  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_300_voc.tar) |
+| VGG16        | 512  |     8   |   240e  |  80.2  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssd_vgg16_512_voc.tar) |
 
-**注意事项:** SSD在2卡，总batch size为64下训练120轮。数据增强包括：随机颜色失真，随机剪裁，随机扩张，随机翻转。
+**注意事项:** MobileNet-SSD在2卡，总batch size为64下训练120周期。VGG-SSD在总batch size为32下训练240周期。数据增强包括：随机颜色失真，随机剪裁，随机扩张，随机翻转。

ppdet/data/data_feed.py

@@ -28,9 +28,10 @@ from ppdet.data.transform.operators import (
     DecodeImage, MixupImage, NormalizeBox, NormalizeImage, RandomDistort,
     RandomFlipImage, RandomInterpImage, ResizeImage, ExpandImage, CropImage,
     Permute)
+
 from ppdet.data.transform.arrange_sample import (
-    ArrangeRCNN, ArrangeTestRCNN, ArrangeSSD, ArrangeTestSSD, ArrangeYOLO,
-    ArrangeEvalYOLO, ArrangeTestYOLO)
+    ArrangeRCNN, ArrangeTestRCNN, ArrangeSSD, ArrangeEvalSSD, ArrangeTestSSD, 
+    ArrangeYOLO, ArrangeEvalYOLO, ArrangeTestYOLO)
 
 __all__ = [
     'PadBatch', 'MultiScale', 'RandomShape', 'DataSet', 'CocoDataSet',
@@ -690,7 +691,7 @@ class SSDTrainFeed(DataFeed):
 
     def __init__(self,
                  dataset=VocDataSet().__dict__,
-                 fields=['image', 'gt_box', 'gt_label', 'is_difficult'],
+                 fields=['image', 'gt_box', 'gt_label'],
                  image_shape=[3, 300, 300],
                  sample_transforms=[
                      DecodeImage(to_rgb=True, with_mixup=False),
@@ -723,8 +724,6 @@ class SSDTrainFeed(DataFeed):
                  bufsize=10,
                  use_process=True):
         sample_transforms.append(ArrangeSSD())
-        if isinstance(dataset, dict):
-            dataset = VocDataSet(**dataset)
         super(SSDTrainFeed, self).__init__(
             dataset,
             fields,
@@ -736,6 +735,7 @@ class SSDTrainFeed(DataFeed):
             samples=samples,
             drop_last=drop_last,
             num_workers=num_workers,
+            bufsize=bufsize,
             use_process=use_process)
         self.mode = 'TRAIN'
 
@@ -747,7 +747,8 @@ class SSDEvalFeed(DataFeed):
     def __init__(
             self,
             dataset=VocDataSet(VOC_VAL_ANNOTATION).__dict__,
-            fields=['image', 'gt_box', 'gt_label', 'is_difficult'],
+            fields=['image', 'im_shape', 'im_id', 'gt_box',
+                         'gt_label', 'is_difficult'],
             image_shape=[3, 300, 300],
             sample_transforms=[
                 DecodeImage(to_rgb=True, with_mixup=False),
@@ -767,9 +768,7 @@ class SSDEvalFeed(DataFeed):
             num_workers=8,
             bufsize=10,
             use_process=False):
-        sample_transforms.append(ArrangeSSD())
-        if isinstance(dataset, dict):
-            dataset = VocDataSet(**dataset)
+        sample_transforms.append(ArrangeEvalSSD())
         super(SSDEvalFeed, self).__init__(
             dataset,
             fields,
@@ -781,6 +780,7 @@ class SSDEvalFeed(DataFeed):
             samples=samples,
             drop_last=drop_last,
             num_workers=num_workers,
+            bufsize=bufsize,
             use_process=use_process)
         self.mode = 'VAL'
 
@@ -791,7 +791,7 @@ class SSDTestFeed(DataFeed):
 
     def __init__(self,
                  dataset=SimpleDataSet(VOC_TEST_ANNOTATION).__dict__,
-                 fields=['image', 'im_id'],
+                 fields=['image', 'im_id', 'im_shape'],
                  image_shape=[3, 300, 300],
                  sample_transforms=[
                      DecodeImage(to_rgb=True),
@@ -823,7 +823,9 @@ class SSDTestFeed(DataFeed):
             shuffle=shuffle,
             samples=samples,
             drop_last=drop_last,
-            num_workers=num_workers)
+            num_workers=num_workers,
+            bufsize=bufsize,
+            use_process=use_process)
         self.mode = 'TEST'
 
 

ppdet/data/transform/arrange_sample.py

@@ -131,15 +131,10 @@ class ArrangeTestRCNN(BaseOperator):
 class ArrangeSSD(BaseOperator):
     """
     Transform dict to tuple format needed for training.
-
-    Args:
-        is_mask (bool): whether to use include mask data
     """
 
-    def __init__(self, is_mask=False):
+    def __init__(self):
         super(ArrangeSSD, self).__init__()
-        self.is_mask = is_mask
-        assert isinstance(self.is_mask, bool), "wrong type for is_mask"
 
     def __call__(self, sample, context=None):
         """
@@ -154,10 +149,40 @@ class ArrangeSSD(BaseOperator):
         im = sample['image']
         gt_bbox = sample['gt_bbox']
         gt_class = sample['gt_class']
-        difficult = sample['difficult']
-        outs = (im, gt_bbox, gt_class, difficult)
+        outs = (im, gt_bbox, gt_class)
         return outs
 
+@register_op
+class ArrangeEvalSSD(BaseOperator):
+    """
+    Transform dict to tuple format needed for training.
+    """
+
+    def __init__(self):
+        super(ArrangeEvalSSD, self).__init__()
+
+    def __call__(self, sample, context=None):
+        """
+        Args:
+            sample: a dict which contains image
+                    info and annotation info.
+            context: a dict which contains additional info.
+        Returns:
+            sample: a tuple containing the following items: (image)
+        """
+        im = sample['image']
+        if len(sample['gt_bbox']) != len(sample['gt_class']):
+            raise ValueError("gt num mismatch: bbox and class.")
+        im_id = sample['im_id']
+        h = sample['h']
+        w = sample['w']
+        im_shape = np.array((h, w))
+        gt_bbox = sample['gt_bbox']
+        gt_class = sample['gt_class']
+        difficult = sample['difficult']
+        outs = (im, im_shape, im_id, gt_bbox, gt_class, difficult)
+
+        return outs
 
 @register_op
 class ArrangeTestSSD(BaseOperator):
@@ -168,10 +193,8 @@ class ArrangeTestSSD(BaseOperator):
         is_mask (bool): whether to use include mask data
     """
 
-    def __init__(self, is_mask=False):
+    def __init__(self):
         super(ArrangeTestSSD, self).__init__()
-        self.is_mask = is_mask
-        assert isinstance(self.is_mask, bool), "wrong type for is_mask"
 
     def __call__(self, sample, context=None):
         """
@@ -184,7 +207,10 @@ class ArrangeTestSSD(BaseOperator):
         """
         im = sample['image']
         im_id = sample['im_id']
-        outs = (im, im_id)
+        h = sample['h']
+        w = sample['w']
+        im_shape = np.array((h, w))
+        outs = (im, im_id, im_shape)
         return outs
 
 

ppdet/modeling/architectures/ssd.py

@@ -63,7 +63,6 @@ class SSD(object):
         if mode == 'train' or mode == 'eval':
             gt_box = feed_vars['gt_box']
             gt_label = feed_vars['gt_label']
-            difficult = feed_vars['is_difficult']
 
         body_feats = self.backbone(im)
         locs, confs, box, box_var = self.multi_box_head(
@@ -76,17 +75,7 @@ class SSD(object):
             return {'loss': loss}
         else:
             pred = self.output_decoder(locs, confs, box, box_var)
-            if mode == 'eval':
-                map_eval = self.metric(
-                    pred,
-                    gt_label,
-                    gt_box,
-                    difficult,
-                    class_num=self.num_classes)
-                _, accum_map = map_eval.get_map_var()
-                return {'map': map_eval, 'accum_map': accum_map}
-            else:
-                return {'bbox': pred}
+            return {'bbox': pred}
 
     def train(self, feed_vars):
         return self.build(feed_vars, 'train')
@@ -99,5 +88,5 @@ class SSD(object):
 
     def is_bbox_normalized(self):
         # SSD use output_decoder in output layers, bbox is normalized
-        # to range [0, 1], is_bbox_normalized is used in infer.py
+        # to range [0, 1], is_bbox_normalized is used in eval.py and infer.py
         return True

ppdet/modeling/backbones/__init__.py

@@ -20,6 +20,7 @@ from . import darknet
 from . import mobilenet
 from . import senet
 from . import fpn
+from . import vgg
 
 from .resnet import *
 from .resnext import *
@@ -27,3 +28,4 @@ from .darknet import *
 from .mobilenet import *
 from .senet import *
 from .fpn import *
+from .vgg import *

ppdet/modeling/backbones/vgg.py

+# Copyright (c) 2019 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import fluid
+from paddle.fluid.param_attr import ParamAttr
+
+from ppdet.core.workspace import register
+
+__all__ = ['VGG']
+
+
+@register
+class VGG(object):
+    """
+    VGG, see https://arxiv.org/abs/1409.1556
+
+    Args:
+        depth (int): the VGG net depth (16 or 19)
+        normalizations (list): params list of init scale in l2 norm, skip init
+            scale if param is -1.
+        with_extra_blocks (bool): whether or not extra blocks should be added
+        extra_block_filters (list): in each extra block, params:
+            [in_channel, out_channel, padding_size, stride_size, filter_size]
+    """
+
+    def __init__(self,
+                 depth=16,
+                 with_extra_blocks=False,
+                 normalizations=[20., -1, -1, -1, -1, -1],
+                 extra_block_filters=[[256, 512, 1, 2, 3], [128, 256, 1, 2, 3],
+                                      [128, 256, 0, 1, 3], [128, 256, 0, 1, 3]]):
+        assert depth in [16, 19], \
+            "depth {} not in [16, 19]"
+
+        self.depth = depth
+        self.depth_cfg = {
+            16: [2, 2, 3, 3, 3],
+            19: [2, 2, 4, 4, 4]
+        }
+        self.with_extra_blocks = with_extra_blocks
+        self.normalizations = normalizations
+        self.extra_block_filters = extra_block_filters
+
+    def __call__(self, input):
+        layers = []
+        layers += self._vgg_block(input)
+
+        if not self.with_extra_blocks:
+            return layers[-1]
+
+        layers += self._add_extras_block(layers[-1])
+        norm_cfg = self.normalizations
+        for k, v in enumerate(layers):
+            if not norm_cfg[k] == -1:
+                layers[k] = self._l2_norm_scale(v, init_scale=norm_cfg[k])
+
+        return layers
+
+    def _vgg_block(self, input):
+        nums = self.depth_cfg[self.depth]
+        vgg_base = [64, 128, 256, 512, 512]
+        conv = input
+        layers = []
+        for k, v in enumerate(vgg_base):
+            conv = self._conv_block(conv, v, nums[k], name="conv{}_".format(k + 1))
+            layers.append(conv)
+            if k == 4:
+                conv = self._pooling_block(conv, 3, 1, pool_padding=1)
+            else:
+                conv = self._pooling_block(conv, 2, 2)
+
+        fc6 = self._conv_layer(conv, 1024, 3, 1, 6, dilation=6, name="fc6")
+        fc7 = self._conv_layer(fc6, 1024, 1, 1, 0, name="fc7")
+
+        return [layers[3], fc7]
+
+    def _add_extras_block(self, input):
+        cfg = self.extra_block_filters
+        conv = input
+        layers = []
+        for k, v in enumerate(cfg):
+            assert len(v) == 5, "extra_block_filters size not fix"
+            conv = self._extra_block(conv, v[0], v[1],
+                                     v[2], v[3], v[4], name="conv{}_".format(6 + k))
+            layers.append(conv)
+
+        return layers
+
+    def _conv_block(self, input, num_filter, groups, name=None):
+        conv = input
+        for i in range(groups):
+            conv = self._conv_layer(
+                input=conv,
+                num_filters=num_filter,
+                filter_size=3,
+                stride=1,
+                padding=1,
+                act='relu',
+                name=name + str(i + 1))
+        return conv
+
+    def _extra_block(self,
+                     input,
+                     num_filters1,
+                     num_filters2,
+                     padding_size,
+                     stride_size,
+                     filter_size,
+                     name=None):
+        # 1x1 conv
+        conv_1 = self._conv_layer(
+            input=input,
+            num_filters=int(num_filters1),
+            filter_size=1,
+            stride=1,
+            act='relu',
+            padding=0,
+            name=name + "1")
+
+        # 3x3 conv
+        conv_2 = self._conv_layer(
+            input=conv_1,
+            num_filters=int(num_filters2),
+            filter_size=filter_size,
+            stride=stride_size,
+            act='relu',
+            padding=padding_size,
+            name=name + "2")
+        return conv_2
+
+    def _conv_layer(self,
+                   input,
+                   num_filters,
+                   filter_size,
+                   stride,
+                   padding,
+                   dilation=1,
+                   act='relu',
+                   use_cudnn=True,
+                   name=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            act=act,
+            use_cudnn=use_cudnn,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=ParamAttr(name=name + "_biases"),
+            name=name + '.conv2d.output.1')
+        return conv
+
+    def _pooling_block(self,
+                       conv,
+                       pool_size,
+                       pool_stride,
+                       pool_padding=0,
+                       ceil_mode=True):
+        pool = fluid.layers.pool2d(
+            input=conv,
+            pool_size=pool_size,
+            pool_type='max',
+            pool_stride=pool_stride,
+            pool_padding=pool_padding,
+            ceil_mode=ceil_mode)
+        return pool
+
+    def _l2_norm_scale(self, input, init_scale=1.0, channel_shared=False):
+        from paddle.fluid.layer_helper import LayerHelper
+        from paddle.fluid.initializer import Constant
+        helper = LayerHelper("Scale")
+        l2_norm = fluid.layers.l2_normalize(
+            input, axis=1)  # l2 norm along channel
+        shape = [1] if channel_shared else [input.shape[1]]
+        scale = helper.create_parameter(
+            attr=helper.param_attr,
+            shape=shape,
+            dtype=input.dtype,
+            default_initializer=Constant(init_scale))
+        out = fluid.layers.elementwise_mul(
+            x=l2_norm, y=scale, axis=-1 if channel_shared else 1,
+            name="conv4_3_norm_scale")
+        return out

ppdet/modeling/ops.py

@@ -255,22 +255,30 @@ class MultiBoxHead(object):
     def __init__(self,
                  min_ratio=20,
                  max_ratio=90,
+                 base_size=300,
                  min_sizes=[60.0, 105.0, 150.0, 195.0, 240.0, 285.0],
                  max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0],
                  aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.],
                                 [2., 3.]],
-                 base_size=300,
+                 steps=None,
                  offset=0.5,
-                 flip=True):
+                 flip=True,
+                 min_max_aspect_ratios_order=False,
+                 kernel_size=1,
+                 pad=0):
         super(MultiBoxHead, self).__init__()
         self.min_ratio = min_ratio
         self.max_ratio = max_ratio
+        self.base_size = base_size
         self.min_sizes = min_sizes
         self.max_sizes = max_sizes
         self.aspect_ratios = aspect_ratios
-        self.base_size = base_size
+        self.steps = steps
         self.offset = offset
         self.flip = flip
+        self.min_max_aspect_ratios_order = min_max_aspect_ratios_order
+        self.kernel_size = kernel_size
+        self.pad = pad
 
 
 @register

ppdet/utils/coco_eval.py

@@ -66,7 +66,12 @@ def proposal_eval(results, anno_file, outfile, max_dets=(100, 300, 1000)):
     # flush coco evaluation result
     sys.stdout.flush()
 
-def bbox_eval(results, anno_file, outfile, with_background=True):
+
+def bbox_eval(results,
+              anno_file,
+              outfile,
+              with_background=True,
+              is_bbox_normalized=False):
     assert 'bbox' in results[0]
     assert outfile.endswith('.json')
 
@@ -79,7 +84,9 @@ def bbox_eval(results, anno_file, outfile, with_background=True):
         {i + int(with_background): catid
          for i, catid in enumerate(cat_ids)})
 
-    xywh_results = bbox2out(results, clsid2catid)
+    xywh_results = bbox2out(
+        results, clsid2catid, is_bbox_normalized=is_bbox_normalized)
+
     if len(xywh_results) == 0:
         logger.warning("The number of valid bbox detected is zero.\n \
             Please use reasonable model and check input data.\n \
@@ -111,6 +118,7 @@ def mask_eval(results, anno_file, outfile, resolution, thresh_binarize=0.5):
 
     cocoapi_eval(outfile, 'segm', coco_gt=coco_gt)
 
+
 def cocoapi_eval(jsonfile,
                  style,
                  coco_gt=None,
@@ -141,6 +149,7 @@ def cocoapi_eval(jsonfile,
     coco_eval.summarize()
     return coco_eval.stats
 
+
 def proposal2out(results, is_bbox_normalized=False):
     xywh_res = []
     for t in results:
@@ -180,6 +189,13 @@ def proposal2out(results, is_bbox_normalized=False):
 
 
 def bbox2out(results, clsid2catid, is_bbox_normalized=False):
+    """
+    Args:
+        results: request a dict, should include: `bbox`, `im_id`,
+                 if is_bbox_normalized=True, also need `im_shape`.
+        clsid2catid: class id to category id map of COCO2017 dataset.
+        is_bbox_normalized: whether or not bbox is normalized.
+    """
     xywh_res = []
     for t in results:
         bboxes = t['bbox'][0]
@@ -202,6 +218,11 @@ def bbox2out(results, clsid2catid, is_bbox_normalized=False):
                             clip_bbox([xmin, ymin, xmax, ymax])
                     w = xmax - xmin
                     h = ymax - ymin
+                    im_height, im_width = t['im_shape'][0][i].tolist()
+                    xmin *= im_width
+                    ymin *= im_height
+                    w *= im_width
+                    h *= im_height
                 else:
                     w = xmax - xmin + 1
                     h = ymax - ymin + 1

ppdet/utils/eval_utils.py

@@ -113,8 +113,11 @@ def eval_results(results,
             output = 'bbox.json'
             if output_directory:
                 output = os.path.join(output_directory, 'bbox.json')
+
             box_ap_stats = bbox_eval(results, anno_file, output,
-                                     with_background)
+                                     with_background,
+                                     is_bbox_normalized=is_bbox_normalized)
+
         if 'mask' in results[0]:
             output = 'mask.json'
             if output_directory:

ppdet/utils/visualizer.py

@@ -31,8 +31,7 @@ def visualize_results(image,
                       catid2name,
                       threshold=0.5,
                       bbox_results=None,
-                      mask_results=None,
-                      is_bbox_normalized=False):
+                      mask_results=None):
     """
     Visualize bbox and mask results
     """
@@ -40,7 +39,7 @@ def visualize_results(image,
         image = draw_mask(image, im_id, mask_results, threshold)
     if bbox_results:
         image = draw_bbox(image, im_id, catid2name, bbox_results,
-                          threshold, is_bbox_normalized)
+                          threshold)
     return image
 
 
@@ -69,8 +68,7 @@ def draw_mask(image, im_id, segms, threshold, alpha=0.7):
     return Image.fromarray(img_array.astype('uint8'))
 
 
-def draw_bbox(image, im_id, catid2name, bboxes, threshold,
-              is_bbox_normalized=False):
+def draw_bbox(image, im_id, catid2name, bboxes, threshold):
     """
     Draw bbox on image
     """
@@ -86,12 +84,6 @@ def draw_bbox(image, im_id, catid2name, bboxes, threshold,
             continue
 
         xmin, ymin, w, h = bbox
-        if is_bbox_normalized:
-            im_width, im_height = image.size
-            xmin *= im_width
-            ymin *= im_height
-            w *= im_width
-            h *= im_height
         xmax = xmin + w
         ymax = ymin + h
 

tools/infer.py

@@ -186,7 +186,7 @@ def main():
     if cfg['metric'] == 'COCO':
         extra_keys = ['im_info', 'im_id', 'im_shape']
     if cfg['metric'] == 'VOC':
-        extra_keys = ['im_id']
+        extra_keys = ['im_id', 'im_shape']
     keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
 
     # parse dataset category
@@ -235,7 +235,7 @@ def main():
             image = visualize_results(image,
                                       int(im_id), catid2name,
                                       FLAGS.draw_threshold, bbox_results,
-                                      mask_results, is_bbox_normalized)
+                                      mask_results)
             save_name = get_save_image_name(FLAGS.output_dir, image_path)
             logger.info("Detection bbox results save in {}".format(save_name))
             image.save(save_name, quality=95)