[cherry-pick] fix yolox cpp infer

configs/yolox/_base_/yolox_reader.yml

@@ -27,8 +27,8 @@ TrainReader:
 EvalReader:
  sample_transforms:
  - Decode: {}
- - Resize: {target_size: 640, keep_ratio: True}
- - Pad: {size: 640, fill_value: [114., 114., 114.]}
+ - Resize: {target_size: [640, 640], keep_ratio: True, interp: 1}
+ - Pad: {size: [640, 640], fill_value: [114., 114., 114.]}
  - Permute: {}
  batch_size: 4
 
@@ -38,7 +38,7 @@ TestReader:
  image_shape: [3, 640, 640]
  sample_transforms:
  - Decode: {}
- - Resize: {target_size: 640, keep_ratio: True}
- - Pad: {size: 640, fill_value: [114., 114., 114.]}
+ - Resize: {target_size: [640, 640], keep_ratio: True, interp: 1}
+ - Pad: {size: [640, 640], fill_value: [114., 114., 114.]}
  - Permute: {}
  batch_size: 1

configs/yolox/yolox_nano_300e_coco.yml

@@ -64,8 +64,8 @@ TrainReader:
 EvalReader:
  sample_transforms:
  - Decode: {}
- - Resize: {target_size: 416, keep_ratio: True}
- - Pad: {size: 416, fill_value: [114., 114., 114.]}
+ - Resize: {target_size: [416, 416], keep_ratio: True, interp: 1}
+ - Pad: {size: [416, 416], fill_value: [114., 114., 114.]}
  - Permute: {}
  batch_size: 8
 
@@ -75,7 +75,7 @@ TestReader:
  image_shape: [3, 416, 416]
  sample_transforms:
  - Decode: {}
- - Resize: {target_size: 416, keep_ratio: True}
- - Pad: {size: 416, fill_value: [114., 114., 114.]}
+ - Resize: {target_size: [416, 416], keep_ratio: True, interp: 1}
+ - Pad: {size: [416, 416], fill_value: [114., 114., 114.]}
  - Permute: {}
  batch_size: 1

configs/yolox/yolox_tiny_300e_coco.yml

@@ -52,8 +52,8 @@ TrainReader:
 EvalReader:
  sample_transforms:
  - Decode: {}
- - Resize: {target_size: 416, keep_ratio: True}
- - Pad: {size: 416, fill_value: [114., 114., 114.]}
+ - Resize: {target_size: [416, 416], keep_ratio: True, interp: 1}
+ - Pad: {size: [416, 416], fill_value: [114., 114., 114.]}
  - Permute: {}
  batch_size: 8
 
@@ -63,7 +63,7 @@ TestReader:
  image_shape: [3, 416, 416]
  sample_transforms:
  - Decode: {}
- - Resize: {target_size: 416, keep_ratio: True}
- - Pad: {size: 416, fill_value: [114., 114., 114.]}
+ - Resize: {target_size: [416, 416], keep_ratio: True, interp: 1}
+ - Pad: {size: [416, 416], fill_value: [114., 114., 114.]}
  - Permute: {}
  batch_size: 1

deploy/cpp/include/preprocess_op.h

@@ -161,6 +161,20 @@ class WarpAffine : public PreprocessOp {
  int pad_ = 31;
 };
 
+class Pad : public PreprocessOp {
+ public:
+ virtual void Init(const YAML::Node& item) {
+ size_ = item["size"].as<std::vector<int>>();
+ fill_value_ = item["fill_value"].as<std::vector<float>>();
+ }
+
+ virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+ std::vector<int> size_;
+ std::vector<float> fill_value_;
+};
+
 void CropImg(cv::Mat& img,
  cv::Mat& crop_img,
  std::vector<int>& area,
@@ -203,6 +217,8 @@ class Preprocessor {
  return std::make_shared<TopDownEvalAffine>();
  } else if (name == "WarpAffine") {
  return std::make_shared<WarpAffine>();
+ }else if (name == "Pad") {
+ return std::make_shared<Pad>();
  }
  std::cerr << "can not find function of OP: " << name
  << " and return: nullptr" << std::endl;

deploy/cpp/src/preprocess_op.cc

@@ -229,6 +229,23 @@ void WarpAffine::Run(cv::Mat* im, ImageBlob* data) {
  };
 }
 
+void Pad::Run(cv::Mat* im, ImageBlob* data) {
+ int h = size_[0];
+ int w = size_[1];
+ int rh = im->rows;
+ int rw = im->cols;
+ if (h == rh && w == rw){
+ data->in_net_im_ = im->clone();
+ return;
+ }
+ cv::copyMakeBorder(
+ *im, *im, 0, h - rh, 0, w - rw, cv::BORDER_CONSTANT, cv::Scalar(114));
+ data->in_net_im_ = im->clone();
+ data->in_net_shape_ = {
+ static_cast<float>(im->rows), static_cast<float>(im->cols),
+ };
+}
+
 // Preprocessor op running order
 const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
  "TopDownEvalAffine",
@@ -237,7 +254,8 @@ const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
  "WarpAffine",
  "NormalizeImage",
  "PadStride",
- "Permute"};
+ "Permute",
+ "Pad",};
 
 void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
  for (const auto& name : RUN_ORDER) {

deploy/python/preprocess.py

@@ -247,77 +247,30 @@ def __call__(self, im, im_info):
 
 
 class Pad(object):
- def __init__(self,
- size=None,
- size_divisor=32,
- pad_mode=0,
- offsets=None,
- fill_value=(127.5, 127.5, 127.5)):
+ def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
  """
- Pad image to a specified size or multiple of size_divisor.
+ Pad image to a specified size.
  Args:
- size (int, Sequence): image target size, if None, pad to multiple of size_divisor, default None
- size_divisor (int): size divisor, default 32
- pad_mode (int): pad mode, currently only supports four modes [-1, 0, 1, 2]. if -1, use specified offsets
- if 0, only pad to right and bottom. if 1, pad according to center. if 2, only pad left and top
- offsets (list): [offset_x, offset_y], specify offset while padding, only supported pad_mode=-1
- fill_value (bool): rgb value of pad area, default (127.5, 127.5, 127.5)
+ size (list[int]): image target size
+ fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
  """
  super(Pad, self).__init__()
  if isinstance(size, int):
  size = [size, size]
-
- assert pad_mode in [
- -1, 0, 1, 2
- ], 'currently only supports four modes [-1, 0, 1, 2]'
- if pad_mode == -1:
- assert offsets, 'if pad_mode is -1, offsets should not be None'
-
  self.size = size
- self.size_divisor = size_divisor
- self.pad_mode = pad_mode
  self.fill_value = fill_value
- self.offsets = offsets
-
- def apply_image(self, image, offsets, im_size, size):
- x, y = offsets
- im_h, im_w = im_size
- h, w = size
- canvas = np.ones((h, w, 3), dtype=np.float32)
- canvas *= np.array(self.fill_value, dtype=np.float32)
- canvas[y:y + im_h, x:x + im_w, :] = image.astype(np.float32)
- return canvas
 
  def __call__(self, im, im_info):
  im_h, im_w = im.shape[:2]
- if self.size:
- h, w = self.size
- assert (
- im_h <= h and im_w <= w
- ), '(h, w) of target size should be greater than (im_h, im_w)'
- else:
- h = int(np.ceil(im_h / self.size_divisor) * self.size_divisor)
- w = int(np.ceil(im_w / self.size_divisor) * self.size_divisor)
-
+ h, w = self.size
  if h == im_h and w == im_w:
  im = im.astype(np.float32)
  return im, im_info
 
- if self.pad_mode == -1:
- offset_x, offset_y = self.offsets
- elif self.pad_mode == 0:
- offset_y, offset_x = 0, 0
- elif self.pad_mode == 1:
- offset_y, offset_x = (h - im_h) // 2, (w - im_w) // 2
- else:
- offset_y, offset_x = h - im_h, w - im_w
-
- offsets, im_size, size = [offset_x, offset_y], [im_h, im_w], [h, w]
- im = self.apply_image(im, offsets, im_size, size)
-
- if self.pad_mode == 0:
- return im, im_info
-
+ canvas = np.ones((h, w, 3), dtype=np.float32)
+ canvas *= np.array(self.fill_value, dtype=np.float32)
+ canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
+ im = canvas
  return im, im_info