[Face Detection] add facedetection config and eval code (#3466)

* Add face detection configs and main code.
* Integration multi-scale evaluation.

configs/face_detection/blazeface.yml

+architecture: BlazeFace
+max_iters: 320000
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+pretrain_weights: 
+use_gpu: true
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: WIDERFACE
+save_dir: output
+weights: output/blazeface/model_final/
+# 1(label_class) + 1(background)
+num_classes: 2
+
+BlazeFace:
+  backbone: BlazeNet
+  output_decoder:
+    keep_top_k: 750
+    nms_threshold: 0.3
+    nms_top_k: 5000
+    score_threshold: 0.01
+  min_sizes: [[16.,24.], [32., 48., 64., 80., 96., 128.]]
+  use_density_prior_box: false
+
+BlazeNet:
+  with_extra_blocks: true
+  lite_edition: false
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [240000, 300000]
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.0
+    type: RMSPropOptimizer
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  use_process: True
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_train_bbx_gt.txt
+    image_dir: WIDER_train/images
+  image_shape: [3, 640, 640]
+  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
+    prob: 0.5
+  - !CropImageWithDataAchorSampling
+    anchor_sampler:
+    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
+    batch_sampler:
+    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    target_size: 640
+  - !RandomInterpImage
+    target_size: 640
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDEvalFeed:
+  batch_size: 1
+  use_process: false
+  fields: ['image', 'im_id', 'gt_box']
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: annotFile.txt #wider_face_split/wider_face_val_bbx_gt.txt   
+    image_dir: WIDER_val/images
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDTestFeed:
+  batch_size: 1
+  use_process: false
+  dataset:
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]

configs/face_detection/blazeface_nas.yml

+architecture: BlazeFace
+max_iters: 320000
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+pretrain_weights: 
+use_gpu: true
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: WIDERFACE
+save_dir: output
+weights: output/blazeface_nas/model_final/
+# 1(label_class) + 1(background)
+num_classes: 2
+
+BlazeFace:
+  backbone: BlazeNet
+  output_decoder:
+    keep_top_k: 750
+    nms_threshold: 0.3
+    nms_top_k: 5000
+    score_threshold: 0.01
+  min_sizes: [[16.,24.], [32., 48., 64., 80., 96., 128.]]
+  use_density_prior_box: false
+
+BlazeNet:
+  blaze_filters: [[12, 12], [12, 12, 2], [12, 12]]
+  double_blaze_filters: [[12, 16, 24, 2], [24, 12, 24], [24, 16, 72, 2], [72, 12, 72]]
+  with_extra_blocks: true
+  lite_edition: false
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [240000, 300000]
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.0
+    type: RMSPropOptimizer
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  use_process: True
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_train_bbx_gt.txt
+    image_dir: WIDER_train/images
+  image_shape: [3, 640, 640]
+  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
+    prob: 0.5
+  - !CropImageWithDataAchorSampling
+    anchor_sampler:
+    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
+    batch_sampler:
+    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    target_size: 640
+  - !RandomInterpImage
+    target_size: 640
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDEvalFeed:
+  batch_size: 1
+  use_process: false
+  fields: ['image', 'im_id', 'gt_box']
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_val_bbx_gt.txt
+    image_dir: WIDER_val/images
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDTestFeed:
+  batch_size: 1
+  use_process: false
+  dataset:
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]

configs/face_detection/faceboxes.yml

+architecture: FaceBoxes
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+pretrain_weights:
+use_gpu: true
+max_iters: 320000
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: WIDERFACE
+save_dir: output
+weights: output/faceboxes/model_final/
+# 1(label_class) + 1(background)
+num_classes: 2
+
+FaceBoxes:
+  backbone: FaceBoxNet
+  densities: [[4, 2, 1], [1], [1]]
+  fixed_sizes: [[32., 64., 128.], [256.], [512.]]
+  output_decoder:
+    keep_top_k: 750
+    nms_threshold: 0.3
+    nms_top_k: 5000
+    score_threshold: 0.01
+
+FaceBoxNet:
+  with_extra_blocks: true
+  lite_edition: false
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [240000, 300000]
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.0
+    type: RMSPropOptimizer
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  use_process: True
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_train_bbx_gt.txt
+    image_dir: WIDER_train/images
+  image_shape: [3, 640, 640]
+  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
+    prob: 0.5
+  - !CropImageWithDataAchorSampling
+    anchor_sampler:
+    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
+    batch_sampler:
+    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    target_size: 640
+  - !RandomInterpImage
+    target_size: 640
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDEvalFeed:
+  batch_size: 1
+  use_process: false
+  fields: ['image', 'im_id', 'gt_box']
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_val_bbx_gt.txt
+    image_dir: WIDER_val/images
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDTestFeed:
+  batch_size: 1
+  use_process: false
+  dataset:
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]

configs/face_detection/faceboxes_lite.yml

+architecture: FaceBoxes
+train_feed: SSDTrainFeed
+eval_feed: SSDEvalFeed
+test_feed: SSDTestFeed
+pretrain_weights:
+use_gpu: true
+max_iters: 320000
+snapshot_iter: 10000
+log_smooth_window: 20
+log_iter: 20
+metric: WIDERFACE
+save_dir: output
+weights: output/faceboxes_lite/model_final/
+# 1(label_class) + 1(background)
+num_classes: 2
+
+FaceBoxes:
+  backbone: FaceBoxNet
+  densities: [[2, 1, 1], [1, 1]]
+  fixed_sizes: [[16., 32., 64.], [96., 128.]]
+  output_decoder:
+    keep_top_k: 750
+    nms_threshold: 0.3
+    nms_top_k: 5000
+    score_threshold: 0.01
+
+FaceBoxNet:
+  with_extra_blocks: true
+  lite_edition: true
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [240000, 300000]
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.0
+    type: RMSPropOptimizer
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+SSDTrainFeed:
+  batch_size: 8
+  use_process: True
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_train_bbx_gt.txt
+    image_dir: WIDER_train/images
+  image_shape: [3, 640, 640]
+  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
+    prob: 0.5
+  - !CropImageWithDataAchorSampling
+    anchor_sampler:
+    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
+    batch_sampler:
+    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
+    target_size: 640
+  - !RandomInterpImage
+    target_size: 640
+  - !RandomFlipImage
+    is_normalized: true
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDEvalFeed:
+  batch_size: 1
+  use_process: false
+  fields: ['image', 'im_id', 'gt_box']
+  dataset:
+    dataset_dir: dataset/wider_face
+    annotation: wider_face_split/wider_face_val_bbx_gt.txt
+    image_dir: WIDER_val/images
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeBox {}
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]
+
+SSDTestFeed:
+  batch_size: 1
+  use_process: false
+  dataset:
+    use_default_label: true
+  drop_last: false
+  image_shape: [3, 640, 640]
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !ResizeImage
+    interp: 1
+    target_size: 640
+    use_cv2: false
+  - !Permute {}
+  - !NormalizeImage
+    is_scale: false
+    mean: [104, 117, 123]
+    std: [127.502231, 127.502231, 127.502231]

ppdet/data/data_feed.py

@@ -781,7 +781,7 @@ class SSDEvalFeed(DataFeed):
             bufsize=10,
             use_process=False,
             memsize=None):
-        sample_transforms.append(ArrangeEvalSSD())
+        sample_transforms.append(ArrangeEvalSSD(fields))
         super(SSDEvalFeed, self).__init__(
             dataset,
             fields,

ppdet/data/transform/arrange_sample.py

@@ -200,8 +200,9 @@ class ArrangeEvalSSD(BaseOperator):
     Transform dict to tuple format needed for training.
     """
 
-    def __init__(self):
+    def __init__(self, fields):
         super(ArrangeEvalSSD, self).__init__()
+        self.fields = fields
 
     def __call__(self, sample, context=None):
         """
@@ -212,17 +213,25 @@ class ArrangeEvalSSD(BaseOperator):
         Returns:
             sample: a tuple containing the following items: (image)
         """
-        im = sample['image']
+        outs = []
         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)
+        for field in self.fields:
+            if field == 'im_shape':
+                h = sample['h']
+                w = sample['w']
+                im_shape = np.array((h, w))
+                outs.append(im_shape)
+            elif field == 'is_difficult':
+                outs.append(sample['difficult'])
+            elif field == 'gt_box':
+                outs.append(sample['gt_bbox'])
+            elif field == 'gt_label':
+                outs.append(sample['gt_class'])
+            else:
+                outs.append(sample[field])
+
+        outs = tuple(outs)
 
         return outs
 

ppdet/data/transform/op_helper.py

@@ -102,7 +102,8 @@ def bbox_area_sampling(bboxes, labels, scores, target_size, min_size):
         else:
             new_bboxes.append(bbox)
             new_labels.append(labels[i])
-            new_scores.append(scores[i])
+            if scores is not None and scores.size != 0:
+                new_scores.append(scores[i])
     bboxes = np.array(new_bboxes)
     labels = np.array(new_labels)
     scores = np.array(new_scores)

ppdet/data/transform/operators.py

@@ -640,7 +640,7 @@ class CropImageWithDataAchorSampling(BaseOperator):
         self.sampling_prob = sampling_prob
         self.min_size = min_size
         self.avoid_no_bbox = avoid_no_bbox
-        self.scale_array = np.array(das_anchor_scales)
+        self.das_anchor_scales = np.array(das_anchor_scales)
 
     def __call__(self, sample, context):
         """
@@ -674,8 +674,8 @@ class CropImageWithDataAchorSampling(BaseOperator):
                     if found >= sampler[0]:
                         break
                     sample_bbox = data_anchor_sampling(
-                        gt_bbox, image_width, image_height, self.scale_array,
-                        self.target_size)
+                        gt_bbox, image_width, image_height,
+                        self.das_anchor_scales, self.target_size)
                     if sample_bbox == 0:
                         break
                     if satisfy_sample_constraint_coverage(sampler, sample_bbox,

ppdet/modeling/architectures/blazeface.py

@@ -108,9 +108,7 @@ class BlazeFace(object):
                         use_density_prior_box=False):
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
-            compile_shape = [
-                trans.shape[0], np.prod(trans.shape[1:]) // last_dim, last_dim
-            ]
+            compile_shape = [0, -1, last_dim]
             return fluid.layers.reshape(trans, shape=compile_shape)
 
         def _is_list_or_tuple_(data):

ppdet/modeling/architectures/faceboxes.py

@@ -93,9 +93,7 @@ class FaceBoxes(object):
     def _multi_box_head(self, inputs, image, num_classes=2):
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
-            compile_shape = [
-                trans.shape[0], np.prod(trans.shape[1:]) // last_dim, last_dim
-            ]
+            compile_shape = [0, -1, last_dim]
             return fluid.layers.reshape(trans, shape=compile_shape)
 
         def _is_list_or_tuple_(data):

ppdet/modeling/backbones/faceboxnet.py

@@ -238,7 +238,6 @@ class FaceBoxNet(object):
             use_cudnn=use_cudnn,
             param_attr=parameter_attr,
             bias_attr=False)
-        print("{}:{}".format(name, conv.shape))
         return fluid.layers.batch_norm(input=conv, act=act)
 
     def _conv_norm_crelu(

ppdet/utils/widerface_eval_utils.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
+
+import os
+import numpy as np
+
+from ppdet.data.source.widerface_loader import widerface_label
+from ppdet.utils.coco_eval import bbox2out
+
+import logging
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    'get_shrink', 'bbox_vote', 'save_widerface_bboxes', 'save_fddb_bboxes',
+    'to_chw_bgr', 'bbox2out', 'get_category_info'
+]
+
+
+def to_chw_bgr(image):
+    """
+    Transpose image from HWC to CHW and from RBG to BGR.
+    Args:
+        image (np.array): an image with HWC and RBG layout.
+    """
+    # HWC to CHW
+    if len(image.shape) == 3:
+        image = np.swapaxes(image, 1, 2)
+        image = np.swapaxes(image, 1, 0)
+    # RBG to BGR
+    image = image[[2, 1, 0], :, :]
+    return image
+
+
+def bbox_vote(det):
+    order = det[:, 4].ravel().argsort()[::-1]
+    det = det[order, :]
+    if det.shape[0] == 0:
+        dets = np.array([[10, 10, 20, 20, 0.002]])
+        det = np.empty(shape=[0, 5])
+    while det.shape[0] > 0:
+        # IOU
+        area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
+        xx1 = np.maximum(det[0, 0], det[:, 0])
+        yy1 = np.maximum(det[0, 1], det[:, 1])
+        xx2 = np.minimum(det[0, 2], det[:, 2])
+        yy2 = np.minimum(det[0, 3], det[:, 3])
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        o = inter / (area[0] + area[:] - inter)
+
+        # nms
+        merge_index = np.where(o >= 0.3)[0]
+        det_accu = det[merge_index, :]
+        det = np.delete(det, merge_index, 0)
+        if merge_index.shape[0] <= 1:
+            if det.shape[0] == 0:
+                try:
+                    dets = np.row_stack((dets, det_accu))
+                except:
+                    dets = det_accu
+            continue
+        det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
+        max_score = np.max(det_accu[:, 4])
+        det_accu_sum = np.zeros((1, 5))
+        det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4],
+                                      axis=0) / np.sum(det_accu[:, -1:])
+        det_accu_sum[:, 4] = max_score
+        try:
+            dets = np.row_stack((dets, det_accu_sum))
+        except:
+            dets = det_accu_sum
+    dets = dets[0:750, :]
+    # Only keep 0.3 or more
+    keep_index = np.where(dets[:, 4] >= 0.01)[0]
+    dets = dets[keep_index, :]
+    return dets
+
+
+def get_shrink(height, width):
+    """
+    Args:
+        height (int): image height.
+        width (int): image width.
+    """
+    # avoid out of memory
+    max_shrink_v1 = (0x7fffffff / 577.0 / (height * width))**0.5
+    max_shrink_v2 = ((678 * 1024 * 2.0 * 2.0) / (height * width))**0.5
+
+    def get_round(x, loc):
+        str_x = str(x)
+        if '.' in str_x:
+            str_before, str_after = str_x.split('.')
+            len_after = len(str_after)
+            if len_after >= 3:
+                str_final = str_before + '.' + str_after[0:loc]
+                return float(str_final)
+            else:
+                return x
+
+    max_shrink = get_round(min(max_shrink_v1, max_shrink_v2), 2) - 0.3
+    if max_shrink >= 1.5 and max_shrink < 2:
+        max_shrink = max_shrink - 0.1
+    elif max_shrink >= 2 and max_shrink < 3:
+        max_shrink = max_shrink - 0.2
+    elif max_shrink >= 3 and max_shrink < 4:
+        max_shrink = max_shrink - 0.3
+    elif max_shrink >= 4 and max_shrink < 5:
+        max_shrink = max_shrink - 0.4
+    elif max_shrink >= 5:
+        max_shrink = max_shrink - 0.5
+
+    shrink = max_shrink if max_shrink < 1 else 1
+    return shrink, max_shrink
+
+
+def save_widerface_bboxes(image_path, bboxes_scores, output_dir):
+    image_name = image_path.split('/')[-1]
+    image_class = image_path.split('/')[-2]
+    odir = os.path.join(output_dir, image_class)
+    if not os.path.exists(odir):
+        os.makedirs(odir)
+
+    ofname = os.path.join(odir, '%s.txt' % (image_name[:-4]))
+    f = open(ofname, 'w')
+    f.write('{:s}\n'.format(image_class + '/' + image_name))
+    f.write('{:d}\n'.format(bboxes_scores.shape[0]))
+    for box_score in bboxes_scores:
+        xmin, ymin, xmax, ymax, score = box_score
+        f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(xmin, ymin, (
+            xmax - xmin + 1), (ymax - ymin + 1), score))
+    f.close()
+    logger.info("The predicted result is saved as {}".format(ofname))
+
+
+def save_fddb_bboxes(bboxes_scores,
+                     output_dir,
+                     output_fname='pred_fddb_res.txt'):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    predict_file = os.path.join(output_dir, output_fname)
+    f = open(predict_file, 'w')
+    for image_path, dets in bboxes_scores.iteritems():
+        f.write('{:s}\n'.format(image_path))
+        f.write('{:d}\n'.format(dets.shape[0]))
+        for box_score in dets:
+            xmin, ymin, xmax, ymax, score = box_score
+            width, height = xmax - xmin, ymax - ymin
+            f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'
+                    .format(xmin, ymin, width, height, score))
+    logger.info("The predicted result is saved as {}".format(predict_file))
+    return predict_file
+
+
+def get_category_info(anno_file=None,
+                      with_background=True,
+                      use_default_label=False):
+    if use_default_label or anno_file is None \
+            or not os.path.exists(anno_file):
+        logger.info("Not found annotation file {}, load "
+                    "wider-face categories.".format(anno_file))
+        return widerfaceall_category_info(with_background)
+    else:
+        logger.info("Load categories from {}".format(anno_file))
+        return get_category_info_from_anno(anno_file, with_background)
+
+
+def get_category_info_from_anno(anno_file, with_background=True):
+    """
+    Get class id to category id map and category id
+    to category name map from annotation file.
+    Args:
+        anno_file (str): annotation file path
+        with_background (bool, default True):
+            whether load background as class 0.
+    """
+    cats = []
+    with open(anno_file) as f:
+        for line in f.readlines():
+            cats.append(line.strip())
+
+    if cats[0] != 'background' and with_background:
+        cats.insert(0, 'background')
+    if cats[0] == 'background' and not with_background:
+        cats = cats[1:]
+
+    clsid2catid = {i: i for i in range(len(cats))}
+    catid2name = {i: name for i, name in enumerate(cats)}
+
+    return clsid2catid, catid2name
+
+
+def widerfaceall_category_info(with_background=True):
+    """
+    Get class id to category id map and category id
+    to category name map of mixup wider_face dataset
+
+    Args:
+        with_background (bool, default True):
+            whether load background as class 0.
+    """
+    label_map = widerface_label(with_background)
+    label_map = sorted(label_map.items(), key=lambda x: x[1])
+    cats = [l[0] for l in label_map]
+
+    if with_background:
+        cats.insert(0, 'background')
+
+    clsid2catid = {i: i for i in range(len(cats))}
+    catid2name = {i: name for i, name in enumerate(cats)}
+
+    return clsid2catid, catid2name

tools/face_eval.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
+
+import os
+
+import paddle.fluid as fluid
+import numpy as np
+from PIL import Image
+from collections import OrderedDict
+
+import ppdet.utils.checkpoint as checkpoint
+from ppdet.utils.cli import ArgsParser
+from ppdet.utils.check import check_gpu
+from ppdet.utils.widerface_eval_utils import get_shrink, bbox_vote, \
+    save_widerface_bboxes, save_fddb_bboxes, to_chw_bgr
+from ppdet.core.workspace import load_config, merge_config, create
+from ppdet.modeling.model_input import create_feed
+
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
+
+def face_img_process(image,
+                     mean=[104., 117., 123.],
+                     std=[127.502231, 127.502231, 127.502231]):
+    img = np.array(image)
+    img = to_chw_bgr(img)
+    img = img.astype('float32')
+    img -= np.array(mean)[:, np.newaxis, np.newaxis].astype('float32')
+    img /= np.array(std)[:, np.newaxis, np.newaxis].astype('float32')
+    img = [img]
+    img = np.array(img)
+    return img
+
+
+def face_eval_run(exe,
+                  compile_program,
+                  fetches,
+                  img_root_dir,
+                  gt_file,
+                  pred_dir='output/pred',
+                  eval_mode='widerface'):
+    # load ground truth files
+    with open(gt_file, 'r') as f:
+        gt_lines = f.readlines()
+    imid2path = []
+    pos_gt = 0
+    while pos_gt < len(gt_lines):
+        name_gt = gt_lines[pos_gt].strip('\n\t').split()[0]
+        imid2path.append(name_gt)
+        pos_gt += 1
+        n_gt = int(gt_lines[pos_gt].strip('\n\t').split()[0])
+        pos_gt += 1 + n_gt
+    logger.info('The ground truth file load {} images'.format(len(imid2path)))
+
+    dets_dist = OrderedDict()
+    for iter_id, im_path in enumerate(imid2path):
+        image_path = os.path.join(img_root_dir, im_path)
+        if eval_mode == 'fddb':
+            image_path += '.jpg'
+        image = Image.open(image_path).convert('RGB')
+        shrink, max_shrink = get_shrink(image.size[1], image.size[0])
+
+        det0 = detect_face(exe, compile_program, fetches, image, shrink)
+        det1 = flip_test(exe, compile_program, fetches, image, shrink)
+        [det2, det3] = multi_scale_test(exe, compile_program, fetches, image,
+                                        max_shrink)
+        det4 = multi_scale_test_pyramid(exe, compile_program, fetches, image,
+                                        max_shrink)
+        det = np.row_stack((det0, det1, det2, det3, det4))
+        dets = bbox_vote(det)
+        if eval_mode == 'widerface':
+            save_widerface_bboxes(image_path, dets, pred_dir)
+        else:
+            dets_dist[im_path] = dets
+        if iter_id % 100 == 0:
+            logger.info('Test iter {}'.format(iter_id))
+    if eval_mode == 'fddb':
+        save_fddb_bboxes(dets_dist, pred_dir)
+    logger.info("Finish evaluation.")
+
+
+def detect_face(exe, compile_program, fetches, image, shrink):
+    image_shape = [3, image.size[1], image.size[0]]
+    if shrink != 1:
+        h, w = int(image_shape[1] * shrink), int(image_shape[2] * shrink)
+        image = image.resize((w, h), Image.ANTIALIAS)
+        image_shape = [3, h, w]
+
+    img = face_img_process(image)
+    detection, = exe.run(compile_program,
+                         feed={'image': img},
+                         fetch_list=[fetches['bbox']],
+                         return_numpy=False)
+    detection = np.array(detection)
+    # layout: xmin, ymin, xmax. ymax, score
+    if np.prod(detection.shape) == 1:
+        logger.info("No face detected")
+        return np.array([[0, 0, 0, 0, 0]])
+    det_conf = detection[:, 1]
+    det_xmin = image_shape[2] * detection[:, 2] / shrink
+    det_ymin = image_shape[1] * detection[:, 3] / shrink
+    det_xmax = image_shape[2] * detection[:, 4] / shrink
+    det_ymax = image_shape[1] * detection[:, 5] / shrink
+
+    det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
+    return det
+
+
+def flip_test(exe, compile_program, fetches, image, shrink):
+    img = image.transpose(Image.FLIP_LEFT_RIGHT)
+    det_f = detect_face(exe, compile_program, fetches, img, shrink)
+    det_t = np.zeros(det_f.shape)
+    # image.size: [width, height]
+    det_t[:, 0] = image.size[0] - det_f[:, 2]
+    det_t[:, 1] = det_f[:, 1]
+    det_t[:, 2] = image.size[0] - det_f[:, 0]
+    det_t[:, 3] = det_f[:, 3]
+    det_t[:, 4] = det_f[:, 4]
+    return det_t
+
+
+def multi_scale_test(exe, compile_program, fetches, image, max_shrink):
+    # Shrink detecting is only used to detect big faces
+    st = 0.5 if max_shrink >= 0.75 else 0.5 * max_shrink
+    det_s = detect_face(exe, compile_program, fetches, image, st)
+    index = np.where(
+        np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1)
+        > 30)[0]
+    det_s = det_s[index, :]
+    # Enlarge one times
+    bt = min(2, max_shrink) if max_shrink > 1 else (st + max_shrink) / 2
+    det_b = detect_face(exe, compile_program, fetches, image, bt)
+
+    # Enlarge small image x times for small faces
+    if max_shrink > 2:
+        bt *= 2
+        while bt < max_shrink:
+            det_b = np.row_stack((det_b, detect_face(exe, compile_program,
+                                                     fetches, image, bt)))
+            bt *= 2
+        det_b = np.row_stack((det_b, detect_face(exe, compile_program, fetches,
+                                                 image, max_shrink)))
+
+    # Enlarged images are only used to detect small faces.
+    if bt > 1:
+        index = np.where(
+            np.minimum(det_b[:, 2] - det_b[:, 0] + 1,
+                       det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
+        det_b = det_b[index, :]
+    # Shrinked images are only used to detect big faces.
+    else:
+        index = np.where(
+            np.maximum(det_b[:, 2] - det_b[:, 0] + 1,
+                       det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
+        det_b = det_b[index, :]
+    return det_s, det_b
+
+
+def multi_scale_test_pyramid(exe, compile_program, fetches, image, max_shrink):
+    # Use image pyramids to detect faces
+    det_b = detect_face(exe, compile_program, fetches, image, 0.25)
+    index = np.where(
+        np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
+        > 30)[0]
+    det_b = det_b[index, :]
+
+    st = [0.75, 1.25, 1.5, 1.75]
+    for i in range(len(st)):
+        if st[i] <= max_shrink:
+            det_temp = detect_face(exe, compile_program, fetches, image, st[i])
+            # Enlarged images are only used to detect small faces.
+            if st[i] > 1:
+                index = np.where(
+                    np.minimum(det_temp[:, 2] - det_temp[:, 0] + 1,
+                               det_temp[:, 3] - det_temp[:, 1] + 1) < 100)[0]
+                det_temp = det_temp[index, :]
+            # Shrinked images are only used to detect big faces.
+            else:
+                index = np.where(
+                    np.maximum(det_temp[:, 2] - det_temp[:, 0] + 1,
+                               det_temp[:, 3] - det_temp[:, 1] + 1) > 30)[0]
+                det_temp = det_temp[index, :]
+            det_b = np.row_stack((det_b, det_temp))
+    return det_b
+
+
+def main():
+    """
+    Main evaluate function
+    """
+    cfg = load_config(FLAGS.config)
+    if 'architecture' in cfg:
+        main_arch = cfg.architecture
+    else:
+        raise ValueError("'architecture' not specified in config file.")
+
+    merge_config(FLAGS.opt)
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
+    if 'eval_feed' not in cfg:
+        eval_feed = create(main_arch + 'EvalFeed')
+    else:
+        eval_feed = create(cfg.eval_feed)
+
+    # define executor
+    place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # build program
+    model = create(main_arch)
+    startup_prog = fluid.Program()
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            _, feed_vars = create_feed(eval_feed, use_pyreader=False)
+            fetches = model.eval(feed_vars)
+
+    eval_prog = eval_prog.clone(True)
+
+    # load model
+    exe.run(startup_prog)
+    if 'weights' in cfg:
+        checkpoint.load_params(exe, eval_prog, cfg.weights)
+
+    assert cfg.metric in ['WIDERFACE'], \
+            "unknown metric type {}".format(cfg.metric)
+
+    annotation_file = getattr(eval_feed.dataset, 'annotation', None)
+    dataset_dir = FLAGS.dataset_dir if FLAGS.dataset_dir else \
+        getattr(eval_feed.dataset, 'dataset_dir', None)
+    img_root_dir = dataset_dir
+    if FLAGS.eval_mode == "widerface":
+        image_dir = getattr(eval_feed.dataset, 'image_dir', None)
+        img_root_dir = os.path.join(dataset_dir, image_dir)
+    gt_file = os.path.join(dataset_dir, annotation_file)
+    pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
+    face_eval_run(
+        exe,
+        eval_prog,
+        fetches,
+        img_root_dir,
+        gt_file,
+        pred_dir=pred_dir,
+        eval_mode=FLAGS.eval_mode)
+
+
+if __name__ == '__main__':
+    parser = ArgsParser()
+    parser.add_argument(
+        "-d",
+        "--dataset_dir",
+        default=None,
+        type=str,
+        help="Dataset path, same as DataFeed.dataset.dataset_dir")
+    parser.add_argument(
+        "-f",
+        "--output_eval",
+        default=None,
+        type=str,
+        help="Evaluation file directory, default is current directory.")
+    parser.add_argument(
+        "-e",
+        "--eval_mode",
+        default="widerface",
+        type=str,
+        help="Evaluation mode, include `widerface` and `fddb`, default is `widerface`."
+    )
+    FLAGS = parser.parse_args()
+    main()

tools/infer.py

@@ -186,12 +186,12 @@ def main():
         save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)
 
     # parse infer fetches
-    assert cfg.metric in ['COCO', 'VOC'], \
+    assert cfg.metric in ['COCO', 'VOC', 'WIDERFACE'], \
             "unknown metric type {}".format(cfg.metric)
     extra_keys = []
     if cfg['metric'] == 'COCO':
         extra_keys = ['im_info', 'im_id', 'im_shape']
-    if cfg['metric'] == 'VOC':
+    if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
         extra_keys = ['im_id', 'im_shape']
     keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
 
@@ -200,6 +200,8 @@ def main():
         from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
     if cfg.metric == "VOC":
         from ppdet.utils.voc_eval import bbox2out, get_category_info
+    if cfg.metric == "WIDERFACE":
+        from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
 
     anno_file = getattr(test_feed.dataset, 'annotation', None)
     with_background = getattr(test_feed, 'with_background', True)

tools/train.py

@@ -154,6 +154,8 @@ def main():
             extra_keys = ['im_info', 'im_id', 'im_shape']
         if cfg.metric == 'VOC':
             extra_keys = ['gt_box', 'gt_label', 'is_difficult']
+        if cfg.metric == 'WIDERFACE':
+            extra_keys = ['im_id', 'im_shape', 'gt_box']
         eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
                                                          extra_keys)