C++ Blob::CopyFrom方法代码示例

void CaffeMobile::putImage(AndroidBitmapInfo* info, void* pixels, const vector<Blob<float>*>& resImage) {
	Blob<float> * srcBlob = *resImage.data();

	LOG(DEBUG) << "srcBlob received";

	vector<int> shape = {1, 3, (int) info->width, (int) info->height };

	LOG(DEBUG) << "shape configured";

	Blob<float>* imgBlob = new Blob<float>();
	LOG(DEBUG) << "Blob created";

	imgBlob->Reshape(shape);
	LOG(DEBUG) << "imgBlob reshaped";

	imgBlob->CopyFrom(*srcBlob, false, true);
	LOG(DEBUG) << "imgBlob copied";

	int size = imgBlob->count();
	LOG(DEBUG) << "imgBlob size is: " << size;

	/*Partially from https://github.com/ruckus/android-image-filter-ndk*/

	uint32_t* pixelRow;
	int ix, iy, red, green, blue;

	for(iy = 0; iy < (int) info->height; iy++){

		pixelRow = (uint32_t*) pixels;

		for(ix =0; ix < (int) info->width; ix++){
			red = (int) clip(imgBlob->data_at(0,0,iy,ix), 0, 255);
			green = (int) clip(imgBlob->data_at(0,1,iy,ix), 0, 255);
			blue = (int) clip(imgBlob->data_at(0,2,iy,ix), 0, 255);

			pixelRow[ix] =
					((red << 16) & 0x00FF0000) |
					((green << 8) & 0x0000FF00) |
					(blue & 0x000000FF);
		}

		pixels = (char*)pixels + info->stride;
	}

	LOG(DEBUG) << "before return putImage " << size;

	return;
}

TYPED_TEST(DeconvolutionLayerTest, TestNDAgainst2D) {
  typedef typename TypeParam::Dtype Dtype;
  const int kernel_h = 11;
  const int kernel_w = 13;
  vector<int> bottom_shape(4);
  bottom_shape[0] = 15;
  bottom_shape[1] = 12;
  bottom_shape[2] = kernel_h * 2;
  bottom_shape[3] = kernel_w * 2;
  FillerParameter filler_param;
  GaussianFiller<Dtype> filler(filler_param);
  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
    filler.Fill(this->blob_bottom_vec_[i]);
  }
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->set_num_output(18);
  convolution_param->set_bias_term(false);
  convolution_param->set_group(6);
  convolution_param->set_kernel_h(kernel_h);
  convolution_param->set_kernel_w(kernel_w);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  Blob<Dtype> weights;
  Blob<Dtype> top_diff;
  // Shape and fill weights and top_diff.
  bool copy_diff;
  bool reshape;
  {
    DeconvolutionLayer<Dtype> layer(layer_param);
    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    top_diff.ReshapeLike(*this->blob_top_);
    filler.Fill(&top_diff);
    ASSERT_EQ(1, layer.blobs().size());
    copy_diff = false; reshape = true;
    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
  }
  vector<bool> propagate_down(1, true);
  Blob<Dtype> result_2d;
  Blob<Dtype> backward_result_2d;
  Blob<Dtype> backward_weight_result_2d;
  // Test with 2D im2col
  {
    caffe_set(this->blob_top_->count(), Dtype(0),
              this->blob_top_->mutable_cpu_data());
    caffe_set(this->blob_bottom_->count(), Dtype(0),
              this->blob_bottom_->mutable_cpu_diff());
    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
    // Do SetUp and Forward; save Forward result in result_2d.
    convolution_param->set_force_nd_im2col(false);
    DeconvolutionLayer<Dtype> layer_2d(layer_param);
    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    ASSERT_EQ(1, layer_2d.blobs().size());
    copy_diff = false; reshape = false;
    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
    copy_diff = false; reshape = true;
    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
    // Copy pre-generated top diff into actual top diff;
    // do Backward and save result in backward_result_2d.
    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
    caffe_copy(top_diff.count(), top_diff.cpu_data(),
               this->blob_top_->mutable_cpu_diff());
    layer_2d.Backward(this->blob_top_vec_, propagate_down,
                      this->blob_bottom_vec_);
    copy_diff = true; reshape = true;
    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
  }
  Blob<Dtype> result_nd;
  Blob<Dtype> backward_result_nd;
  Blob<Dtype> backward_weight_result_nd;
  // Test with ND im2col
  {
    caffe_set(this->blob_top_->count(), Dtype(0),
              this->blob_top_->mutable_cpu_data());
    caffe_set(this->blob_bottom_->count(), Dtype(0),
              this->blob_bottom_->mutable_cpu_diff());
    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
    // Do SetUp and Forward; save Forward result in result_nd.
    convolution_param->set_force_nd_im2col(true);
    DeconvolutionLayer<Dtype> layer_nd(layer_param);
    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    ASSERT_EQ(1, layer_nd.blobs().size());
    copy_diff = false; reshape = false;
    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
    copy_diff = false; reshape = true;
    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
    // Copy pre-generated top diff into actual top diff;
    // do Backward and save result in backward_result_nd.
    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
    caffe_copy(top_diff.count(), top_diff.cpu_data(),
               this->blob_top_->mutable_cpu_diff());
    layer_nd.Backward(this->blob_top_vec_, propagate_down,
                      this->blob_bottom_vec_);
    copy_diff = true; reshape = true;
    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
//.........这里部分代码省略.........