Add AutoAugment (#309)

* add autoaugment, which is validated on faster-rcnn

configs/autoaugment/README.md

+# Learning Data Augmentation Strategies for Object Detection
+
+## Introduction
+
+- Learning Data Augmentation Strategies for Object Detection: [https://arxiv.org/abs/1906.11172](https://arxiv.org/abs/1906.11172)
+
+```
+@article{Zoph2019LearningDA,
+  title={Learning Data Augmentation Strategies for Object Detection},
+  author={Barret Zoph and Ekin Dogus Cubuk and Golnaz Ghiasi and Tsung-Yi Lin and Jonathon Shlens and Quoc V. Le},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1906.11172}
+}
+```
+
+
+## Model Zoo
+
+| Backbone                | Type     | AutoAug policy | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           |
+| :---------------------- | :-------------:| :-------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: |
+| ResNet50-vd-FPN         | Faster     |   v1 |  2     |   3x    |     22.800     |  39.9  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_aa_3x.tar) |
+| ResNet101-vd-FPN         | Faster     |   v1 |  2     |   3x    |     17.652     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_aa_3x.tar) |

configs/autoaugment/faster_rcnn_r101_vd_fpn_aa_3x.yml

+architecture: FasterRCNN
+max_iters: 270000
+snapshot_iter: 30000
+use_gpu: true
+log_smooth_window: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
+weights: output/faster_rcnn_r101_vd_fpn_aa_3x/model_final
+metric: COCO
+num_classes: 81
+
+FasterRCNN:
+  backbone: ResNet
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: BBoxHead
+  bbox_assigner: BBoxAssigner
+
+ResNet:
+  depth: 101
+  feature_maps: [2, 3, 4, 5]
+  freeze_at: 2
+  norm_type: bn
+  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
+
+BBoxHead:
+  head: TwoFCHead
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+
+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
+
+_READER_: '../faster_fpn_reader.yml'
+TrainReader:
+  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: 800
+    max_size: 1333
+    interp: 1
+    use_cv2: true
+  - !Permute
+    to_bgr: false
+    channel_first: true
+  batch_size: 2
+  use_process: true

configs/autoaugment/faster_rcnn_r50_vd_fpn_aa_3x.yml

+architecture: FasterRCNN
+max_iters: 270000
+snapshot_iter: 30000
+use_gpu: true
+log_smooth_window: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+weights: output/faster_rcnn_r50_vd_fpn_aa_3x/model_final
+metric: COCO
+num_classes: 81
+
+FasterRCNN:
+  backbone: ResNet
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: BBoxHead
+  bbox_assigner: BBoxAssigner
+
+ResNet:
+  depth: 50
+  feature_maps: [2, 3, 4, 5]
+  freeze_at: 2
+  norm_type: bn
+  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
+
+BBoxHead:
+  head: TwoFCHead
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+
+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
+
+_READER_: '../faster_fpn_reader.yml'
+TrainReader:
+  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: 800
+    max_size: 1333
+    interp: 1
+    use_cv2: true
+  - !Permute
+    to_bgr: false
+    channel_first: true
+  batch_size: 2
+  use_process: true

ppdet/data/transform/operators.py

@@ -435,6 +435,75 @@ class RandomFlipImage(BaseOperator):
         return sample
 
 
+@register_op
+class AutoAugmentImage(BaseOperator):
+    def __init__(self, is_normalized=False, autoaug_type="v1"):
+        """
+        Args:
+            is_normalized (bool): whether the bbox scale to [0,1]
+            autoaug_type (str): autoaug type, support v0, v1, v2, v3, test
+        """
+        super(AutoAugmentImage, self).__init__()
+        self.is_normalized = is_normalized
+        self.autoaug_type = autoaug_type
+        if not isinstance(self.is_normalized, bool):
+            raise TypeError("{}: input type is invalid.".format(self))
+
+    def __call__(self, sample, context=None):
+        """
+        Learning Data Augmentation Strategies for Object Detection, see https://arxiv.org/abs/1906.11172
+        """
+        samples = sample
+        batch_input = True
+        if not isinstance(samples, Sequence):
+            batch_input = False
+            samples = [samples]
+        for sample in samples:
+            gt_bbox = sample['gt_bbox']
+            im = sample['image']
+            if not isinstance(im, np.ndarray):
+                raise TypeError("{}: image is not a numpy array.".format(self))
+            if len(im.shape) != 3:
+                raise ImageError("{}: image is not 3-dimensional.".format(self))
+            if len(gt_bbox) == 0:
+                continue
+
+            # gt_boxes : [x1, y1, x2, y2]
+            # norm_gt_boxes: [y1, x1, y2, x2]
+            height, width, _ = im.shape
+            norm_gt_bbox = np.ones_like(gt_bbox, dtype=np.float32)
+            if not self.is_normalized:
+                norm_gt_bbox[:, 0] = gt_bbox[:, 1] / float(height)
+                norm_gt_bbox[:, 1] = gt_bbox[:, 0] / float(width)
+                norm_gt_bbox[:, 2] = gt_bbox[:, 3] / float(height)
+                norm_gt_bbox[:, 3] = gt_bbox[:, 2] / float(width)
+            else:
+                norm_gt_bbox[:, 0] = gt_bbox[:, 1]
+                norm_gt_bbox[:, 1] = gt_bbox[:, 0]
+                norm_gt_bbox[:, 2] = gt_bbox[:, 3]
+                norm_gt_bbox[:, 3] = gt_bbox[:, 2]
+
+            from .autoaugment_utils import distort_image_with_autoaugment
+            im, norm_gt_bbox = distort_image_with_autoaugment(im, norm_gt_bbox,
+                                                              self.autoaug_type)
+            if not self.is_normalized:
+                gt_bbox[:, 0] = norm_gt_bbox[:, 1] * float(width)
+                gt_bbox[:, 1] = norm_gt_bbox[:, 0] * float(height)
+                gt_bbox[:, 2] = norm_gt_bbox[:, 3] * float(width)
+                gt_bbox[:, 3] = norm_gt_bbox[:, 2] * float(height)
+            else:
+                gt_bbox[:, 0] = norm_gt_bbox[:, 1]
+                gt_bbox[:, 1] = norm_gt_bbox[:, 0]
+                gt_bbox[:, 2] = norm_gt_bbox[:, 3]
+                gt_bbox[:, 3] = norm_gt_bbox[:, 2]
+
+            sample['gt_bbox'] = gt_bbox
+            sample['image'] = im
+
+        sample = samples if batch_input else samples[0]
+        return sample
+
+
 @register_op
 class NormalizeImage(BaseOperator):
     def __init__(self,