Compute Library
 21.02
graph_srcnn955.cpp
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24 #include "arm_compute/graph.h"
27 #include "utils/GraphUtils.h"
28 #include "utils/Utils.h"
29 
30 using namespace arm_compute::utils;
31 using namespace arm_compute::graph::frontend;
32 using namespace arm_compute::graph_utils;
33 
34 /** Example demonstrating how to implement SRCNN 9-5-5 network using the Compute Library's graph API */
35 class GraphSRCNN955Example : public Example
36 {
37 public:
38  GraphSRCNN955Example()
39  : cmd_parser(), common_opts(cmd_parser), model_input_width(nullptr), model_input_height(nullptr), common_params(), graph(0, "SRCNN955")
40  {
41  model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 300);
42  model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 300);
43 
44  // Add model id option
45  model_input_width->set_help("Input image width.");
46  model_input_height->set_help("Input image height.");
47  }
48  GraphSRCNN955Example(const GraphSRCNN955Example &) = delete;
49  GraphSRCNN955Example &operator=(const GraphSRCNN955Example &) = delete;
50  ~GraphSRCNN955Example() override = default;
51  bool do_setup(int argc, char **argv) override
52  {
53  // Parse arguments
54  cmd_parser.parse(argc, argv);
55  cmd_parser.validate();
56 
57  // Consume common parameters
58  common_params = consume_common_graph_parameters(common_opts);
59 
60  // Return when help menu is requested
61  if(common_params.help)
62  {
63  cmd_parser.print_help(argv[0]);
64  return false;
65  }
66 
67  // Get input image width and height
68  const unsigned int image_width = model_input_width->value();
69  const unsigned int image_height = model_input_height->value();
70 
71  // Print parameter values
72  std::cout << common_params << std::endl;
73  std::cout << "Image width: " << image_width << std::endl;
74  std::cout << "Image height: " << image_height << std::endl;
75 
76  // Get trainable parameters data path
77  const std::string data_path = common_params.data_path;
78  const std::string model_path = "/cnn_data/srcnn955_model/";
79 
80  // Create a preprocessor object
81  std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
82 
83  // Create input descriptor
84  const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
85  TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
86 
87  // Set weights trained layout
88  const DataLayout weights_layout = DataLayout::NCHW;
89 
90  graph << common_params.target
91  << common_params.fast_math_hint
92  << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */))
94  9U, 9U, 64U,
95  get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
96  get_weights_accessor(data_path, "conv1_biases.npy"),
97  PadStrideInfo(1, 1, 4, 4))
98  .set_name("conv1/convolution")
99  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu")
100  << ConvolutionLayer(
101  5U, 5U, 32U,
102  get_weights_accessor(data_path, "conv2_weights.npy", weights_layout),
103  get_weights_accessor(data_path, "conv2_biases.npy"),
104  PadStrideInfo(1, 1, 2, 2))
105  .set_name("conv2/convolution")
106  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/Relu")
107  << ConvolutionLayer(
108  5U, 5U, 3U,
109  get_weights_accessor(data_path, "conv3_weights.npy", weights_layout),
110  get_weights_accessor(data_path, "conv3_biases.npy"),
111  PadStrideInfo(1, 1, 2, 2))
112  .set_name("conv3/convolution")
113  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3/Relu")
114  << OutputLayer(std::make_unique<DummyAccessor>(0));
115 
116  // Finalize graph
117  GraphConfig config;
118  config.num_threads = common_params.threads;
119  config.use_tuner = common_params.enable_tuner;
120  config.tuner_mode = common_params.tuner_mode;
121  config.tuner_file = common_params.tuner_file;
122  config.mlgo_file = common_params.mlgo_file;
123  config.convert_to_uint8 = (common_params.data_type == DataType::QASYMM8);
124 
125  graph.finalize(common_params.target, config);
126 
127  return true;
128  }
129 
130  void do_run() override
131  {
132  // Run graph
133  graph.run();
134  }
135 
136 private:
137  CommandLineParser cmd_parser;
138  CommonGraphOptions common_opts;
139  SimpleOption<unsigned int> *model_input_width{ nullptr };
140  SimpleOption<unsigned int> *model_input_height{ nullptr };
141  CommonGraphParams common_params;
142  Stream graph;
143 };
144 
145 /** Main program for SRCNN 9-5-5
146  *
147  * Model is based on:
148  * http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html
149  * "Image Super-Resolution Using Deep Convolutional Networks"
150  * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
151  *
152  * @note To list all the possible arguments execute the binary appended with the --help option
153  *
154  * @param[in] argc Number of arguments
155  * @param[in] argv Arguments
156  */
157 int main(int argc, char **argv)
158 {
159  return arm_compute::utils::run_example<GraphSRCNN955Example>(argc, argv);
160 }
Graph configuration structure Device target types.
Definition: Types.h:80
CLTunerMode tuner_mode
Tuner mode to be used by the CL tuner.
Definition: Types.h:87
std::unique_ptr< graph::ITensorAccessor > get_input_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true)
Generates appropriate input accessor according to the specified graph parameters. ...
Definition: GraphUtils.h:497
bool convert_to_uint8
Convert graph to a synthetic uint8 graph.
Definition: Types.h:86
void consume_common_graph_parameters(CommonGraphValidateOptions &options, CommonParams &common_params)
Consumes the consume_common_graph_parameters graph options and creates a structure containing any inf...
Includes all the Graph headers at once.
Common command line options used to configure the graph examples.
Class to parse command line arguments.
std::string mlgo_file
Filename to load MLGO heuristics from.
Definition: Types.h:90
std::string tuner_file
File to load/store tuning values from.
Definition: Types.h:89
quantized, asymmetric fixed-point 8-bit number unsigned
Abstract Example class.
Definition: Utils.h:78
const T & value() const
Get the option value.
Definition: SimpleOption.h:112
Num samples, channels, height, width.
TensorShape permute_shape(TensorShape tensor_shape, DataLayout in_data_layout, DataLayout out_data_layout)
Permutes a given tensor shape given the input and output data layout.
Definition: GraphUtils.h:664
TensorDescriptor & set_layout(DataLayout data_layout)
Sets tensor descriptor data layout.
Structure holding all the common graph parameters.
bool use_tuner
Use a tuner in tunable backends.
Definition: Types.h:85
std::unique_ptr< graph::ITensorAccessor > get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout=DataLayout::NCHW)
Generates appropriate weights accessor according to the specified path.
Definition: GraphUtils.h:475
int num_threads
Number of threads to use (thread capable backends), if 0 the backend will auto-initialize, if -1 the backend will stay as it is.
Definition: Types.h:88
Stream frontend class to construct simple graphs in a stream fashion.
Definition: Stream.h:45
int main(int argc, char **argv)
Main program for SRCNN 9-5-5.
DataLayout
[DataLayout enum definition]
Definition: Types.h:120
ILayer & set_name(std::string name)
Sets the name of the layer.
Definition: ILayer.h:55
void set_help(std::string help)
Set the help message for the option.
Definition: Option.h:125