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);
}
ASSERT_EQ(result_nd.count(), result_2d.count());
for (int i = 0; i < result_2d.count(); ++i) {
EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
}
ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
for (int i = 0; i < backward_result_2d.count(); ++i) {
EXPECT_EQ(backward_result_2d.cpu_diff()[i],
backward_result_nd.cpu_diff()[i]);
}
ASSERT_EQ(backward_weight_result_nd.count(),
backward_weight_result_2d.count());
for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
backward_weight_result_nd.cpu_diff()[i]);
}
}