COMPMID-657 - Add PPMAccessor and TopNPredictionsAccessor to GoogleNet

Change-Id: Ib6f2f9e73043d2c59b2698c243fb1a9f51c526e9
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94363
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

5 files changed, 143 insertions, 134 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 1d041997e3..b2a5be647f 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -42,72 +42,6 @@ using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 using namespace arm_compute::logging;
 
-/** Generates appropriate accessor according to the specified path
- *
- * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
- *
- * @param[in] path      Path to the data files
- * @param[in] data_file Relative path to the data files from path
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
-{
-    if(path.empty())
-    {
-        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
-    }
-    else
-    {
-        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
-    }
-}
-
-/** Generates appropriate input accessor according to the specified ppm_path
- *
- * @note If ppm_path is empty will generate a DummyAccessor else will generate a PPMAccessor
- *
- * @param[in] ppm_path Path to PPM file
- * @param[in] mean_r   Red mean value to be subtracted from red channel
- * @param[in] mean_g   Green mean value to be subtracted from green channel
- * @param[in] mean_b   Blue mean value to be subtracted from blue channel
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_input_accessor(const std::string &ppm_path, float mean_r, float mean_g, float mean_b)
-{
-    if(ppm_path.empty())
-    {
-        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
-    }
-    else
-    {
-        return arm_compute::support::cpp14::make_unique<PPMAccessor>(ppm_path, true, mean_r, mean_g, mean_b);
-    }
-}
-
-/** Generates appropriate output accessor according to the specified labels_path
- *
- * @note If labels_path is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor
- *
- * @param[in]  labels_path   Path to labels text file
- * @param[in]  top_n         (Optional) Number of output classes to print
- * @param[out] output_stream (Optional) Output stream
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_output_accessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout)
-{
-    if(labels_path.empty())
-    {
-        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
-    }
-    else
-    {
-        return arm_compute::support::cpp14::make_unique<TopNPredictionsAccessor>(labels_path, top_n, output_stream);
-    }
-}
-
 /** Example demonstrating how to implement AlexNet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
@@ -166,8 +100,8 @@ void main_graph_alexnet(int argc, const char **argv)
           // Layer 1
           << ConvolutionLayer(
               11U, 11U, 96U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
               PadStrideInfo(4, 4, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
@@ -176,8 +110,8 @@ void main_graph_alexnet(int argc, const char **argv)
           << ConvolutionMethodHint::DIRECT
           << ConvolutionLayer(
               5U, 5U, 256U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
               PadStrideInfo(1, 1, 2, 2), 2)
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
@@ -185,42 +119,42 @@ void main_graph_alexnet(int argc, const char **argv)
           // Layer 3
           << ConvolutionLayer(
               3U, 3U, 384U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
               PadStrideInfo(1, 1, 1, 1))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           // Layer 4
           << ConvolutionLayer(
               3U, 3U, 384U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
               PadStrideInfo(1, 1, 1, 1), 2)
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           // Layer 5
           << ConvolutionLayer(
               3U, 3U, 256U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
               PadStrideInfo(1, 1, 1, 1), 2)
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
           // Layer 6
           << FullyConnectedLayer(
               4096U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           // Layer 7
           << FullyConnectedLayer(
               4096U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           // Layer 8
           << FullyConnectedLayer(
               1000U,
-              get_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
-              get_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
           // Softmax
           << SoftmaxLayer()
           << Tensor(get_output_accessor(label, 5));
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index 0e82c1e85d..354a2a39e4 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -42,27 +42,6 @@
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
-/** Generates appropriate accessor according to the specified path
- *
- * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
- *
- * @param path       Path to the data files
- * @param data_file  Relative path to the data files from path
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
-{
-    if(path.empty())
-    {
-        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
-    }
-    else
-    {
-        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
-    }
-}
-
 BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
                                unsigned int a_filt,
                                std::tuple<unsigned int, unsigned int> b_filters,
@@ -73,36 +52,36 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &&param
     SubGraph    i_a;
     i_a << ConvolutionLayer(
             1U, 1U, a_filt,
-            get_accessor(data_path, total_path + "1x1_w.npy"),
-            get_accessor(data_path, total_path + "1x1_b.npy"),
+            get_weights_accessor(data_path, total_path + "1x1_w.npy"),
+            get_weights_accessor(data_path, total_path + "1x1_b.npy"),
             PadStrideInfo(1, 1, 0, 0))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
     SubGraph i_b;
     i_b << ConvolutionLayer(
             1U, 1U, std::get<0>(b_filters),
-            get_accessor(data_path, total_path + "3x3_reduce_w.npy"),
-            get_accessor(data_path, total_path + "3x3_reduce_b.npy"),
+            get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"),
+            get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
             PadStrideInfo(1, 1, 0, 0))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
         << ConvolutionLayer(
             3U, 3U, std::get<1>(b_filters),
-            get_accessor(data_path, total_path + "3x3_w.npy"),
-            get_accessor(data_path, total_path + "3x3_b.npy"),
+            get_weights_accessor(data_path, total_path + "3x3_w.npy"),
+            get_weights_accessor(data_path, total_path + "3x3_b.npy"),
             PadStrideInfo(1, 1, 1, 1))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
     SubGraph i_c;
     i_c << ConvolutionLayer(
             1U, 1U, std::get<0>(c_filters),
-            get_accessor(data_path, total_path + "5x5_reduce_w.npy"),
-            get_accessor(data_path, total_path + "5x5_reduce_b.npy"),
+            get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"),
+            get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
             PadStrideInfo(1, 1, 0, 0))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
         << ConvolutionLayer(
             5U, 5U, std::get<1>(c_filters),
-            get_accessor(data_path, total_path + "5x5_w.npy"),
-            get_accessor(data_path, total_path + "5x5_b.npy"),
+            get_weights_accessor(data_path, total_path + "5x5_w.npy"),
+            get_weights_accessor(data_path, total_path + "5x5_b.npy"),
             PadStrideInfo(1, 1, 2, 2))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
@@ -110,8 +89,8 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &&param
     i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
         << ConvolutionLayer(
             1U, 1U, d_filt,
-            get_accessor(data_path, total_path + "pool_proj_w.npy"),
-            get_accessor(data_path, total_path + "pool_proj_b.npy"),
+            get_weights_accessor(data_path, total_path + "pool_proj_w.npy"),
+            get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
             PadStrideInfo(1, 1, 0, 0))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
@@ -121,32 +100,44 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &&param
 /** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
 void main_graph_googlenet(int argc, const char **argv)
 {
-    std::string  data_path;   /** Path to the trainable data */
-    unsigned int batches = 4; /** Number of batches */
+    std::string data_path; /* Path to the trainable data */
+    std::string image;     /* Image data */
+    std::string label;     /* Label data */
+
+    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
     // Parse arguments
     if(argc < 2)
     {
         // Print help
-        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n";
         std::cout << "No data folder provided: using random values\n\n";
     }
     else if(argc == 2)
     {
         //Do something with argv[1]
         data_path = argv[1];
-        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
-        std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n";
+        std::cout << "No image provided: using random values\n";
+    }
+    else if(argc == 3)
+    {
+        data_path = argv[1];
+        image     = argv[2];
+        std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n";
+        std::cout << "No text file with labels provided: skipping output accessor\n";
     }
     else
     {
-        //Do something with argv[1] and argv[2]
         data_path = argv[1];
-        batches   = std::strtol(argv[2], nullptr, 0);
+        image     = argv[2];
+        label     = argv[3];
     }
 
     // Check if OpenCL is available and initialize the scheduler
@@ -158,25 +149,26 @@ void main_graph_googlenet(int argc, const char **argv)
     Graph graph;
 
     graph << TargetHint::OPENCL
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, batches), 1, DataType::F32), DummyAccessor())
+          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                    get_input_accessor(image, mean_r, mean_g, mean_b))
           << ConvolutionLayer(
               7U, 7U, 64U,
-              get_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
-              get_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
               PadStrideInfo(2, 2, 3, 3))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
           << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
           << ConvolutionLayer(
               1U, 1U, 64U,
-              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
-              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << ConvolutionLayer(
               3U, 3U, 192U,
-              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
-              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
               PadStrideInfo(1, 1, 1, 1))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
@@ -195,10 +187,10 @@ void main_graph_googlenet(int argc, const char **argv)
           << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
           << FullyConnectedLayer(
               1000U,
-              get_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
-              get_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
           << SoftmaxLayer()
-          << Tensor(DummyAccessor());
+          << Tensor(get_output_accessor(label, 5));
 
     graph.run();
 }
@@ -206,7 +198,7 @@ void main_graph_googlenet(int argc, const char **argv)
 /** Main program for Googlenet
  *
  * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
 int main(int argc, const char **argv)
 {
diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp
index 15767632c8..9a1ed3d7d9 100644
--- a/utils/GraphUtils.cpp
+++ b/utils/GraphUtils.cpp
@@ -98,6 +98,9 @@ bool PPMAccessor::access_tensor(ITensor &tensor)
     // Open PPM file
     ppm.open(_ppm_path);
 
+    ARM_COMPUTE_ERROR_ON_MSG(ppm.width() != tensor.info()->dimension(0) || ppm.height() != tensor.info()->dimension(1),
+                             "Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d].", ppm.width(), ppm.height(), tensor.info()->dimension(0), tensor.info()->dimension(1));
+
     // Fill the tensor with the PPM content (BGR)
     ppm.fill_planar_tensor(tensor, _bgr);
 
diff --git a/utils/GraphUtils.h b/utils/GraphUtils.h
index 39b3f115bd..d7d5cd6778 100644
--- a/utils/GraphUtils.h
+++ b/utils/GraphUtils.h
@@ -175,6 +175,72 @@ public:
 private:
     const std::string _filename;
 };
+
+/** Generates appropriate weights accessor according to the specified path
+ *
+ * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
+ *
+ * @param[in] path      Path to the data files
+ * @param[in] data_file Relative path to the data files from path
+ *
+ * @return An appropriate tensor accessor
+ */
+inline std::unique_ptr<graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file)
+{
+    if(path.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
+    }
+}
+
+/** Generates appropriate input accessor according to the specified ppm_path
+ *
+ * @note If ppm_path is empty will generate a DummyAccessor else will generate a PPMAccessor
+ *
+ * @param[in] ppm_path Path to PPM file
+ * @param[in] mean_r   Red mean value to be subtracted from red channel
+ * @param[in] mean_g   Green mean value to be subtracted from green channel
+ * @param[in] mean_b   Blue mean value to be subtracted from blue channel
+ *
+ * @return An appropriate tensor accessor
+ */
+inline std::unique_ptr<graph::ITensorAccessor> get_input_accessor(const std::string &ppm_path, float mean_r, float mean_g, float mean_b)
+{
+    if(ppm_path.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<PPMAccessor>(ppm_path, true, mean_r, mean_g, mean_b);
+    }
+}
+
+/** Generates appropriate output accessor according to the specified labels_path
+ *
+ * @note If labels_path is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor
+ *
+ * @param[in]  labels_path   Path to labels text file
+ * @param[in]  top_n         (Optional) Number of output classes to print
+ * @param[out] output_stream (Optional) Output stream
+ *
+ * @return An appropriate tensor accessor
+ */
+inline std::unique_ptr<graph::ITensorAccessor> get_output_accessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout)
+{
+    if(labels_path.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<TopNPredictionsAccessor>(labels_path, top_n, output_stream);
+    }
+}
 } // namespace graph_utils
 } // namespace arm_compute
 
diff --git a/utils/Utils.h b/utils/Utils.h
index 1f3d971917..28382f47e4 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -410,6 +410,20 @@ public:
         }
     }
 
+    /** Return the width of the currently open PPM file.
+     */
+    unsigned int width() const
+    {
+        return _width;
+    }
+
+    /** Return the height of the currently open PPM file.
+     */
+    unsigned int height() const
+    {
+        return _height;
+    }
+
 private:
     std::ifstream _fs;
     unsigned int  _width, _height;