Datum transform

include/caffe/data_transformer.hpp

@@ -62,6 +62,7 @@ class DataTransformer {
    */
   void Transform(const vector<cv::Mat> & mat_vector,
                 Blob<Dtype>* transformed_blob);
+
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a cv::Mat
@@ -87,6 +88,41 @@ class DataTransformer {
    */
   void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
 
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param datum
+   *    Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const Datum& datum);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<Datum> & datum_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param mat_vector
+   *    A vector of Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const cv::Mat& cv_img);
+
  protected:
    /**
    * @brief Generates a random integer from Uniform({0, 1, ..., n-1}).

src/caffe/data_transformer.cpp

@@ -125,10 +125,31 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
+  // If datum is encoded, decoded and transform the cv::image.
+  if (datum.encoded()) {
+    CHECK(!param_.force_color() && !param_.force_gray())
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // Transform the cv::image into blob.
+    return Transform(cv_img, transformed_blob);
+  } else {
+    if (param_.force_color() || param_.force_gray()) {
+      LOG(ERROR) << "force_color and force_gray only for encoded datum";
+    }
+  }
+
+  const int crop_size = param_.crop_size();
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
 
+  // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -139,8 +160,6 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   CHECK_LE(width, datum_width);
   CHECK_GE(num, 1);
 
-  const int crop_size = param_.crop_size();
-
   if (crop_size) {
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
@@ -196,10 +215,12 @@ void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
   const int img_channels = cv_img.channels();
   const int img_height = cv_img.rows;
   const int img_width = cv_img.cols;
 
+  // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -212,7 +233,6 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
 
   CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
 
-  const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
@@ -297,11 +317,23 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
                                        Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
   const int input_num = input_blob->num();
   const int input_channels = input_blob->channels();
   const int input_height = input_blob->height();
   const int input_width = input_blob->width();
 
+  if (transformed_blob->count() == 0) {
+    // Initialize transformed_blob with the right shape.
+    if (crop_size) {
+      transformed_blob->Reshape(input_num, input_channels,
+                                crop_size, crop_size);
+    } else {
+      transformed_blob->Reshape(input_num, input_channels,
+                                input_height, input_width);
+    }
+  }
+
   const int num = transformed_blob->num();
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
@@ -313,7 +345,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   CHECK_GE(input_height, height);
   CHECK_GE(input_width, width);
 
-  const int crop_size = param_.crop_size();
+
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
@@ -395,6 +427,82 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   }
 }
 
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
+  if (datum.encoded()) {
+    CHECK(!param_.force_color() && !param_.force_gray())
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // InferBlobShape using the cv::image.
+    return InferBlobShape(cv_img);
+  }
+
+  const int crop_size = param_.crop_size();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
+  // Check dimensions.
+  CHECK_GT(datum_channels, 0);
+  CHECK_GE(datum_height, crop_size);
+  CHECK_GE(datum_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = datum_channels;
+  shape[2] = (crop_size)? crop_size: datum_height;
+  shape[3] = (crop_size)? crop_size: datum_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<Datum> & datum_vector) {
+  const int num = datum_vector.size();
+  CHECK_GT(num, 0) << "There is no datum to in the vector";
+  // Use first datum in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(datum_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {
+  const int crop_size = param_.crop_size();
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+  // Check dimensions.
+  CHECK_GT(img_channels, 0);
+  CHECK_GE(img_height, crop_size);
+  CHECK_GE(img_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = img_channels;
+  shape[2] = (crop_size)? crop_size: img_height;
+  shape[3] = (crop_size)? crop_size: img_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<cv::Mat> & mat_vector) {
+  const int num = mat_vector.size();
+  CHECK_GT(num, 0) << "There is no cv_img to in the vector";
+  // Use first cv_img in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(mat_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
   const bool needs_rand = param_.mirror() ||

src/caffe/layers/base_data_layer.cpp

@@ -21,11 +21,11 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   } else {
     output_labels_ = true;
   }
-  // The subclasses should setup the size of bottom and top
-  DataLayerSetUp(bottom, top);
   data_transformer_.reset(
       new DataTransformer<Dtype>(transform_param_, this->phase_));
   data_transformer_->InitRand();
+  // The subclasses should setup the size of bottom and top
+  DataLayerSetUp(bottom, top);
 }
 
 template <typename Dtype>
@@ -63,13 +63,15 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
   JoinPrefetchThread();
   DLOG(INFO) << "Thread joined";
   // Reshape to loaded data.
-  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
-      this->prefetch_data_.height(), this->prefetch_data_.width());
+  top[0]->ReshapeLike(prefetch_data_);
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              top[0]->mutable_cpu_data());
   DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(prefetch_label_);
+    // Copy the labels.
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
                top[1]->mutable_cpu_data());
   }

src/caffe/layers/base_data_layer.cu

@@ -10,12 +10,14 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
   // First, join the thread
   JoinPrefetchThread();
   // Reshape to loaded data.
-  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
-      this->prefetch_data_.height(), this->prefetch_data_.width());
+  top[0]->ReshapeLike(this->prefetch_data_);
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       top[0]->mutable_gpu_data());
   if (this->output_labels_) {
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(prefetch_label_);
+    // Copy the labels.
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
         top[1]->mutable_gpu_data());
   }