[dev] add ppyoloe_plus configs and alter NormalizeImage (#6675)

* [dev] add ppyoloe_plus configs and alter NormalizeImage

* alter other NormalizeImage

* alter cpp NormalizeImage

configs/ppyoloe/_base_/optimizer_80e.yml

+epoch: 80
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/ppyoloe/_base_/ppyoloe_plus_crn.yml

+architecture: YOLOv3
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+
+YOLOv3:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+CSPResNet:
+  layers: [3, 6, 6, 3]
+  channels: [64, 128, 256, 512, 1024]
+  return_idx: [1, 2, 3]
+  use_large_stem: True
+  use_alpha: True
+
+CustomCSPPAN:
+  out_channels: [768, 384, 192]
+  stage_num: 1
+  block_num: 3
+  act: 'swish'
+  spp: true
+
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: 30
+  use_varifocal_loss: True
+  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7

configs/ppyoloe/_base_/ppyoloe_plus_reader.yml

+worker_num: 4
+eval_height: &eval_height 640
+eval_width: &eval_width 640
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCrop: {}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+    - PadGT: {}
+  batch_size: 8
+  shuffle: true
+  drop_last: true
+  use_shared_memory: true
+  collate_batch: true
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 2
+
+TestReader:
+  inputs_def:
+    image_shape: [3, *eval_height, *eval_width]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *eval_size, keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1

configs/ppyoloe/legacy_model/README.md

+# PP-YOLOE Legacy Model Zoo (2022.03)
+
+## Legacy Model Zoo
+|          Model           | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
+|:------------------------:|:-------:|:-------:|:--------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:|:---------------:| :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  | 400 |     8      |    32    | cspresnet-s |     640     |       43.4        |        43.6         |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](./ppyoloe_crn_s_400e_coco.yml)                   |
+| PP-YOLOE-s                  | 300 |     8      |    32    | cspresnet-s |     640     |       43.0        |        43.2         |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  | 300 |     8      |    28    | cspresnet-m |     640     |       49.0        |        49.1         |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  | 300 |     8      |    20    | cspresnet-l |     640     |       51.4        |        51.6         |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  | 300 |     8      |    16    | cspresnet-x |     640     |       52.3        |        52.4         |   98.42   |  206.59  |   45.0    |   95.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)                   |
+
+### Comprehensive Metrics
+|          Model           | Epoch | AP<sup>0.5:0.95 | AP<sup>0.5 |  AP<sup>0.75  | AP<sup>small  | AP<sup>medium | AP<sup>large | AR<sup>small | AR<sup>medium | AR<sup>large | download | config  |
+|:----------------------:|:-----:|:---------------:|:----------:|:-------------:| :------------:| :-----------: | :----------: |:------------:|:-------------:|:------------:| :-----: | :-----: |
+| PP-YOLOE-s             | 400 |      43.4      |     60.0    |     47.5      |     25.7      |      47.8     |     59.2     |     43.9     |      70.8     |   81.9         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](./ppyoloe_crn_s_400e_coco.yml)|
+| PP-YOLOE-s             | 300 |      43.0      |     59.6    |     47.2      |     26.0      |      47.4     |     58.7     |     45.1     |      70.6     |   81.4         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)|
+| PP-YOLOE-m             | 300 |      49.0      |     65.9    |     53.8      |     30.9      |      53.5     |     65.3     |     50.9     |      74.4     |   84.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)|
+| PP-YOLOE-l             | 300 |      51.4      |     68.6    |     56.2      |     34.8      |      56.1     |     68.0     |     53.1     |      76.8     |   85.6         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)|
+| PP-YOLOE-x             | 300 |      52.3      |     69.5    |     56.8      |     35.1      |      57.0     |     68.6     |     55.5     |      76.9     |   85.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)|
+
+
+**Notes:**
+
+- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset.
+- The model weights in the table of Comprehensive Metrics are **the same as** that in the original Model Zoo, and evaluated on **val2017**.
+- PP-YOLOE used 8 GPUs for training, if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
+- PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, **CUDA 10.2**, **CUDNN 7.6.5**, **TensorRT 6.0.1.8** in TensorRT mode.
+
+## Appendix
+
+Ablation experiments of PP-YOLOE.
+
+| NO.  |        Model                 | Box AP<sup>val</sup> | Params(M) | FLOPs(G) | V100 FP32 FPS |
+| :--: | :---------------------------: | :------------------: | :-------: | :------: | :-----------: |
+|  A   | PP-YOLOv2          |         49.1         |   54.58   |  115.77   |     68.9     |
+|  B   | A + Anchor-free    |         48.8         |   54.27   |  114.78   |     69.8     |
+|  C   | B + CSPRepResNet   |         49.5         |   47.42   |  101.87   |     85.5     |
+|  D   | C + TAL            |         50.4         |   48.32   |  104.75   |     84.0     |
+|  E   | D + ET-Head        |         50.9         |   52.20   |  110.07   |     78.1     |

configs/ppyoloe/_base_/optimizer_300e.yml → configs/ppyoloe/legacy_model/_base_/optimizer_300e.yml

 epoch: 300
 
 LearningRate:
-  base_lr: 0.025
+  base_lr: 0.01
   schedulers:
     - !CosineDecay
       max_epochs: 360

configs/ppyoloe/legacy_model/_base_/optimizer_400e.yml

+epoch: 400
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+    - !CosineDecay
+      max_epochs: 480
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/ppyoloe/_base_/ppyoloe_reader.yml → configs/ppyoloe/legacy_model/_base_/ppyoloe_reader.yml

@@ -15,7 +15,7 @@ TrainReader:
     - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
     - Permute: {}
     - PadGT: {}
-  batch_size: 20
+  batch_size: 8
   shuffle: true
   drop_last: true
   use_shared_memory: true

configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml → configs/ppyoloe/legacy_model/ppyoloe_crn_l_300e_coco.yml

 _BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
   './_base_/optimizer_300e.yml',
   './_base_/ppyoloe_crn.yml',
   './_base_/ppyoloe_reader.yml',

configs/ppyoloe/ppyoloe_crn_l_36e_coco_xpu.yml → configs/ppyoloe/legacy_model/ppyoloe_crn_l_36e_coco_xpu.yml

 _BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
   './_base_/optimizer_36e_xpu.yml',
   './_base_/ppyoloe_reader.yml',
 ]

configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml → configs/ppyoloe/legacy_model/ppyoloe_crn_m_300e_coco.yml

 _BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
   './_base_/optimizer_300e.yml',
   './_base_/ppyoloe_crn.yml',
   './_base_/ppyoloe_reader.yml',
@@ -13,9 +13,3 @@ weights: output/ppyoloe_crn_m_300e_coco/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_m_pretrained.pdparams
 depth_mult: 0.67
 width_mult: 0.75
-
-TrainReader:
-  batch_size: 28
-
-LearningRate:
-  base_lr: 0.035

configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml → configs/ppyoloe/legacy_model/ppyoloe_crn_s_300e_coco.yml

 _BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
   './_base_/optimizer_300e.yml',
   './_base_/ppyoloe_crn.yml',
   './_base_/ppyoloe_reader.yml',
@@ -13,9 +13,3 @@ weights: output/ppyoloe_crn_s_300e_coco/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_s_pretrained.pdparams
 depth_mult: 0.33
 width_mult: 0.50
-
-TrainReader:
-  batch_size: 32
-
-LearningRate:
-  base_lr: 0.04

configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml → configs/ppyoloe/legacy_model/ppyoloe_crn_s_400e_coco.yml

 _BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
-  './_base_/optimizer_300e.yml',
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  './_base_/optimizer_400e.yml',
   './_base_/ppyoloe_crn.yml',
   './_base_/ppyoloe_reader.yml',
 ]
@@ -14,33 +14,5 @@ pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_s
 depth_mult: 0.33
 width_mult: 0.50
 
-TrainReader:
-  batch_size: 32
-
-epoch: 400
-LearningRate:
-  base_lr: 0.04
-  schedulers:
-    - !CosineDecay
-      max_epochs: 480
-    - !LinearWarmup
-      start_factor: 0.
-      epochs: 5
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0005
-    type: L2
-
-
 PPYOLOEHead:
   static_assigner_epoch: 133
-  nms:
-    name: MultiClassNMS
-    nms_top_k: 1000
-    keep_top_k: 300
-    score_threshold: 0.01
-    nms_threshold: 0.7

configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml → configs/ppyoloe/legacy_model/ppyoloe_crn_x_300e_coco.yml

 _BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
   './_base_/optimizer_300e.yml',
   './_base_/ppyoloe_crn.yml',
   './_base_/ppyoloe_reader.yml',
@@ -13,9 +13,3 @@ weights: output/ppyoloe_crn_x_300e_coco/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_x_pretrained.pdparams
 depth_mult: 1.33
 width_mult: 1.25
-
-TrainReader:
-  batch_size: 16
-
-LearningRate:
-  base_lr: 0.02

configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_80e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_l_80e_coco/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
+depth_mult: 1.0
+width_mult: 1.0

configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_80e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_80e_coco/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_80e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_s_80e_coco/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
+depth_mult: 0.33
+width_mult: 0.50

configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_80e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_x_80e_coco/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams
+depth_mult: 1.33
+width_mult: 1.25

deploy/cpp/include/preprocess_op.h

@@ -65,7 +65,8 @@ class NormalizeImage : public PreprocessOp {
   virtual void Init(const YAML::Node& item) {
     mean_ = item["mean"].as<std::vector<float>>();
     scale_ = item["std"].as<std::vector<float>>();
-    is_scale_ = item["is_scale"].as<bool>();
+    if (item["is_scale"]) is_scale_ = item["is_scale"].as<bool>();
+    if (item["norm_type"]) norm_type_ = item["norm_type"].as<std::string>();
   }
 
   virtual void Run(cv::Mat* im, ImageBlob* data);
@@ -75,6 +76,7 @@ class NormalizeImage : public PreprocessOp {
   std::vector<float> mean_;
   std::vector<float> scale_;
   bool is_scale_ = true;
+  std::string norm_type_ = "mean_std";
 };
 
 class Permute : public PreprocessOp {

deploy/cpp/src/preprocess_op.cc

@@ -34,14 +34,16 @@ void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) {
     e /= 255.0;
   }
   (*im).convertTo(*im, CV_32FC3, e);
-  for (int h = 0; h < im->rows; h++) {
-    for (int w = 0; w < im->cols; w++) {
-      im->at<cv::Vec3f>(h, w)[0] =
-          (im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
-      im->at<cv::Vec3f>(h, w)[1] =
-          (im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
-      im->at<cv::Vec3f>(h, w)[2] =
-          (im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
+  if (norm_type_ == "mean_std"){
+    for (int h = 0; h < im->rows; h++) {
+      for (int w = 0; w < im->cols; w++) {
+        im->at<cv::Vec3f>(h, w)[0] =
+            (im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
+        im->at<cv::Vec3f>(h, w)[1] =
+            (im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
+        im->at<cv::Vec3f>(h, w)[2] =
+            (im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
+      }
     }
   }
 }

deploy/python/preprocess.py

@@ -275,13 +275,14 @@ class NormalizeImage(object):
         mean (list): im - mean
         std (list): im / std
         is_scale (bool): whether need im / 255
-        is_channel_first (bool): if True: image shape is CHW, else: HWC
+        norm_type (str): type in ['mean_std', 'none']
     """
 
-    def __init__(self, mean, std, is_scale=True):
+    def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
         self.mean = mean
         self.std = std
         self.is_scale = is_scale
+        self.norm_type = norm_type
 
     def __call__(self, im, im_info):
         """
@@ -293,13 +294,15 @@ class NormalizeImage(object):
             im_info (dict): info of processed image
         """
         im = im.astype(np.float32, copy=False)
-        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-        std = np.array(self.std)[np.newaxis, np.newaxis, :]
-
         if self.is_scale:
-            im = im / 255.0
-        im -= mean
-        im /= std
+            scale = 1.0 / 255.0
+            im *= scale
+
+        if self.norm_type == 'mean_std':
+            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+            std = np.array(self.std)[np.newaxis, np.newaxis, :]
+            im -= mean
+            im /= std
         return im, im_info
 
 

deploy/serving/python/preprocess_ops.py

@@ -87,13 +87,14 @@ class NormalizeImage(object):
         mean (list): im - mean
         std (list): im / std
         is_scale (bool): whether need im / 255
-        is_channel_first (bool): if True: image shape is CHW, else: HWC
+        norm_type (str): type in ['mean_std', 'none']
     """
 
-    def __init__(self, mean, std, is_scale=True):
+    def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
         self.mean = mean
         self.std = std
         self.is_scale = is_scale
+        self.norm_type = norm_type
 
     def __call__(self, im, im_info):
         """
@@ -105,13 +106,15 @@ class NormalizeImage(object):
             im_info (dict): info of processed image
         """
         im = im.astype(np.float32, copy=False)
-        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-        std = np.array(self.std)[np.newaxis, np.newaxis, :]
-
         if self.is_scale:
-            im = im / 255.0
-        im -= mean
-        im /= std
+            scale = 1.0 / 255.0
+            im *= scale
+
+        if self.norm_type == 'mean_std':
+            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+            std = np.array(self.std)[np.newaxis, np.newaxis, :]
+            im -= mean
+            im /= std
         return im, im_info
 
 

deploy/third_engine/onnx/preprocess.py

@@ -91,13 +91,14 @@ class NormalizeImage(object):
         mean (list): im - mean
         std (list): im / std
         is_scale (bool): whether need im / 255
-        is_channel_first (bool): if True: image shape is CHW, else: HWC
+        norm_type (str): type in ['mean_std', 'none']
     """
 
-    def __init__(self, mean, std, is_scale=True):
+    def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
         self.mean = mean
         self.std = std
         self.is_scale = is_scale
+        self.norm_type = norm_type
 
     def __call__(self, im, im_info):
         """
@@ -109,13 +110,15 @@ class NormalizeImage(object):
             im_info (dict): info of processed image
         """
         im = im.astype(np.float32, copy=False)
-        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-        std = np.array(self.std)[np.newaxis, np.newaxis, :]
-
         if self.is_scale:
-            im = im / 255.0
-        im -= mean
-        im /= std
+            scale = 1.0 / 255.0
+            im *= scale
+
+        if self.norm_type == 'mean_std':
+            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+            std = np.array(self.std)[np.newaxis, np.newaxis, :]
+            im -= mean
+            im /= std
         return im, im_info
 
 

ppdet/data/transform/operators.py

@@ -359,19 +359,26 @@ class RandomErasingImage(BaseOperator):
 
 @register_op
 class NormalizeImage(BaseOperator):
-    def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1],
-                 is_scale=True):
+    def __init__(self,
+                 mean=[0.485, 0.456, 0.406],
+                 std=[0.229, 0.224, 0.225],
+                 is_scale=True,
+                 norm_type='mean_std'):
         """
         Args:
             mean (list): the pixel mean
             std (list): the pixel variance
+            is_scale (bool): scale the pixel to [0,1]
+            norm_type (str): type in ['mean_std', 'none']
         """
         super(NormalizeImage, self).__init__()
         self.mean = mean
         self.std = std
         self.is_scale = is_scale
+        self.norm_type = norm_type
         if not (isinstance(self.mean, list) and isinstance(self.std, list) and
-                isinstance(self.is_scale, bool)):
+                isinstance(self.is_scale, bool) and
+                self.norm_type in ['mean_std', 'none']):
             raise TypeError("{}: input type is invalid.".format(self))
         from functools import reduce
         if reduce(lambda x, y: x * y, self.std) == 0:
@@ -380,20 +387,20 @@ class NormalizeImage(BaseOperator):
     def apply(self, sample, context=None):
         """Normalize the image.
         Operators:
-            1.(optional) Scale the image to [0,1]
-            2. Each pixel minus mean and is divided by std
+            1.(optional) Scale the pixel to [0,1]
+            2.(optional) Each pixel minus mean and is divided by std
         """
         im = sample['image']
         im = im.astype(np.float32, copy=False)
-        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-        std = np.array(self.std)[np.newaxis, np.newaxis, :]
-
         if self.is_scale:
-            im = im / 255.0
-
-        im -= mean
-        im /= std
-
+            scale = 1.0 / 255.0
+            im *= scale
+
+        if self.norm_type == 'mean_std':
+            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+            std = np.array(self.std)[np.newaxis, np.newaxis, :]
+            im -= mean
+            im /= std
         sample['image'] = im
         return sample