輸入圖層類型：ImageData中的窗口caffe cpp給出空白輸出

Question

我正在使用cpp在windows中使用caffe進行圖像分割問題。我正在使用「Imagedata」輸入類型來訓練網絡，但在測試時我得到了空白輸出。任何人都可以幫助我分析這個問題。

********** solver.prototxt *************** 

test_initialization: false 
base_lr: 0.01 
display: 51 
max_iter: 50000 
lr_policy: "step" 
gamma: 0.1 
momentum: 0.9 
weight_decay: 0.0001 
stepsize: 4069 
snapshot: 10000 
snapshot_prefix: "snapshot" 
solver_mode: GPU 
net: "train.prototxt" 
solver_type: SGD 

File_Triangle.txt和File_label_triangle.txt具有圖像位置和虛標籤的絕對路徑。 e.g d：\在CPP

shared_ptr<Net<float> > net_; 
net_.reset(new Net<float>("train.prototxt", caffe::Phase::TRAIN)); 
Caffe::set_mode(Caffe::GPU); 
caffe::SolverParameter solver_param; 
caffe::ReadSolverParamsFromTextFileOrDie("solver.prototxt", &solver_param); 
boost::shared_ptr<caffe::Solver<float> > solver(caffe::SolverRegistry<float>::CreateSolver(solver_param)); 
solver->Solve(); 

00000032.png 0

**************** train.prototxt ******************** 

layer { 
    name: "data" 
    type: "ImageData" 
    top: "data" 
    top: "xx" 
    include { 
    phase: TRAIN 
    } 
    image_data_param { 
    source: "File_triangle.txt" 
    batch_size: 1 
    new_height: 32 
    new_width: 32 
    is_color: False 
} 

} 

layer { 
    name: "label" 
    type: "ImageData" 
    top: "label" 
    top: "yy" 
    image_data_param { 
    source: "File_label_triangle.txt" 
    batch_size: 1 
    new_height: 32 
    new_width: 32 
    is_color: False 
} 
    include { 
    phase: TRAIN 
    } 
} 


layer { 
    name: "conv1" 
    type: "Convolution" 
    bottom: "data" 
    top: "conv1" 
    param { 
    lr_mult: 1.0 
    } 
    param { 
    lr_mult: 0.10000000149 
    } 
    convolution_param { 
    num_output: 32 
    pad: 1 
    kernel_size: 3 
    stride: 1 
    weight_filler { 
     type: "gaussian" 
     std: 0.0010000000475 
    } 
    bias_filler { 
     type: "constant" 
     value: 0.0 
    } 
    } 
} 
layer { 
    name: "relu1" 
    type: "ReLU" 
    bottom: "conv1" 
    top: "conv1" 
} 
layer { 
    name: "conv2" 
    type: "Convolution" 
    bottom: "conv1" 
    top: "conv2" 
    param { 
    lr_mult: 1.0 
    } 
    param { 
    lr_mult: 0.10000000149 
    } 
    convolution_param { 
    num_output: 1024 
    pad: 0 
    kernel_size: 16 
    stride: 16 
    weight_filler { 
     type: "gaussian" 
     std: 0.0010000000475 
    } 
    bias_filler { 
     type: "constant" 
     value: 0.0 
    } 
    } 
} 
layer { 
    name: "relu2" 
    type: "ReLU" 
    bottom: "conv2" 
    top: "conv2" 
} 
layer { 
    name: "upsample" 
    type: "Deconvolution" 
    bottom: "conv2" 
    top: "upsample" 
    param { 
    lr_mult: 1.0 
    } 
    convolution_param { 
    num_output: 1 
    pad: 0 
    kernel_size: 16 
    stride: 16 
    bias_filler { 
     type: "constant" 
     value: 128.0 
    } 
    } 
} 
layer { 
    name: "lossL1" 
    type: "SmoothL1Loss" 
    bottom: "upsample" 
    bottom: "label" 
    top: "lossL1" 
    loss_weight: 1.0 
} 

的代碼段用於訓練我使用.caffemodel測試網絡訓練。

******************** test.prototxt ********************** 

layer { 
    name: "data" 
    type: "Input" 
    top: "data" 
    input_param { shape: { dim: 1 dim: 1 dim: 32 dim: 32 } } 
} 

layer { 
    name: "conv1" 
    type: "Convolution" 
    bottom: "data" 
    top: "conv1" 
    param { 
    lr_mult: 1.0 
    } 
    param { 
    lr_mult: 0.10000000149 
    } 
    convolution_param { 
    num_output: 32 
    pad: 1 
    kernel_size: 3 
    stride: 1 
    weight_filler { 
     type: "gaussian" 
     std: 0.0010000000475 
    } 
    bias_filler { 
     type: "constant" 
     value: 0.0 
    } 
    } 
} 
layer { 
    name: "relu1" 
    type: "ReLU" 
    bottom: "conv1" 
    top: "conv1" 
} 
layer { 
    name: "conv2" 
    type: "Convolution" 
    bottom: "conv1" 
    top: "conv2" 
    param { 
    lr_mult: 1.0 
    } 
    param { 
    lr_mult: 0.10000000149 
    } 
    convolution_param { 
    num_output: 1024 
    pad: 0 
    kernel_size: 16 
    stride: 16 
    weight_filler { 
     type: "gaussian" 
     std: 0.0010000000475 
    } 
    bias_filler { 
     type: "constant" 
     value: 0.0 
    } 
    } 
} 
layer { 
    name: "relu2" 
    type: "ReLU" 
    bottom: "conv2" 
    top: "conv2" 
} 
layer { 
    name: "upsample" 
    type: "Deconvolution" 
    bottom: "conv2" 
    top: "upsample" 
    param { 
    lr_mult: 1.0 
    } 
    convolution_param { 
    num_output: 1 
    pad: 0 
    kernel_size: 16 
    stride: 16 
    bias_filler { 
     type: "constant" 
     value: 128.0 
    } 
    } 
} 

用於測試的代碼片段。

Caffe::set_mode(Caffe::GPU); 

boost::shared_ptr<caffe::Net<float> > net_; 
net_.reset(new Net<float>("test.prototxt", caffe::TEST)); 

net_->CopyTrainedLayersFrom("snapshot_iter_50000.caffemodel"); 

cv::Mat matInput = cv::imread("input image path"); 

matInput.convertTo(matInput, CV_32F); 
int height = matInput.rows; 
int width = matInput.cols; 

Blob<float>* input_layer = net_->input_blobs()[0]; 
float* input_data = input_layer->mutable_cpu_data(); 
int layer_index = height * width; 
for (size_t i = 0; i < height; i++) 
{ 
    for (size_t j = 0; j < width; j++) 
    { 
     input_data[i*width + j] = matInput.at<float>(i, j); 
    } 

} 

net_->Forward(); 

const shared_ptr<Blob<float> >& concat_blob = net_->blob_by_name("upsample"); 
const float* concat_out = concat_blob->cpu_data(); 

cv::Mat matout(height, width, CV_8UC1); 
for (size_t i = 0; i < height*width; i++) 
{ 
    matout.data[i] = concat_out[i]; 
} 

cv::imwrite(output_str, matout); 

Answer 1

我得到了問題。網絡正在給出適當的輸出，但錯誤在於傾銷。網絡以浮點形式提供輸出（即在上採樣層），並且它不處於標準化形式。下面的修改是給出適當的輸出。

const shared_ptr<Blob<float> >& concat_blob = net_->blob_by_name("upsample"); 
const float* concat_out = concat_blob->cpu_data(); 

cv::Mat matout(height, width, CV_32FC1); 
for (int i = 0; i < height; i++) 
{ 
    for (int j = 0; j < width; j++) 
    { 
     matout.at<float>(i, j) = (float)(concat_out[i*width + j]); 
    } 
} 
cv::normalize(matout, matout, 0, 255, CV_MINMAX); 
cv::imwrite("output image path", matout);