Compute Library
 21.02
graph_lenet.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 LeNet's network using the Compute Library's graph API */
35 class GraphLenetExample : public Example
36 {
37 public:
38  GraphLenetExample()
39  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet")
40  {
41  }
42  bool do_setup(int argc, char **argv) override
43  {
44  // Parse arguments
45  cmd_parser.parse(argc, argv);
46  cmd_parser.validate();
47 
48  // Consume common parameters
49  common_params = consume_common_graph_parameters(common_opts);
50 
51  // Return when help menu is requested
52  if(common_params.help)
53  {
54  cmd_parser.print_help(argv[0]);
55  return false;
56  }
57 
58  // Checks
59  ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
60 
61  // Print parameter values
62  std::cout << common_params << std::endl;
63 
64  // Get trainable parameters data path
65  std::string data_path = common_params.data_path;
66  unsigned int batches = 4; /** Number of batches */
67 
68  // Create input descriptor
69  const auto operation_layout = common_params.data_layout;
70  const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, operation_layout);
71  TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
72 
73  // Set weights trained layout
74  const DataLayout weights_layout = DataLayout::NCHW;
75 
76  //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
77  graph << common_params.target
78  << common_params.fast_math_hint
79  << InputLayer(input_descriptor, get_input_accessor(common_params))
81  5U, 5U, 20U,
82  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy", weights_layout),
83  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
84  PadStrideInfo(1, 1, 0, 0))
85  .set_name("conv1")
86  << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
88  5U, 5U, 50U,
89  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout),
90  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
91  PadStrideInfo(1, 1, 0, 0))
92  .set_name("conv2")
93  << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
95  500U,
96  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout),
97  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
98  .set_name("ip1")
101  10U,
102  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy", weights_layout),
103  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
104  .set_name("ip2")
105  << SoftmaxLayer().set_name("prob")
106  << OutputLayer(get_output_accessor(common_params));
107 
108  // Finalize graph
109  GraphConfig config;
110  config.num_threads = common_params.threads;
111  config.use_tuner = common_params.enable_tuner;
112  config.tuner_mode = common_params.tuner_mode;
113  config.tuner_file = common_params.tuner_file;
114  config.mlgo_file = common_params.mlgo_file;
115 
116  graph.finalize(common_params.target, config);
117 
118  return true;
119  }
120  void do_run() override
121  {
122  // Run graph
123  graph.run();
124  }
125 
126 private:
127  CommandLineParser cmd_parser;
128  CommonGraphOptions common_opts;
129  CommonGraphParams common_params;
130  Stream graph;
131 };
132 
133 /** Main program for LeNet
134  *
135  * @note To list all the possible arguments execute the binary appended with the --help option
136  *
137  * @param[in] argc Number of arguments
138  * @param[in] argv Arguments
139  */
140 int main(int argc, char **argv)
141 {
142  return arm_compute::utils::run_example<GraphLenetExample>(argc, argv);
143 }
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
unsigned int batches
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
#define ARM_COMPUTE_EXIT_ON_MSG(cond, msg)
If the condition is true, the given message is printed and program exits.
Definition: Error.h:379
Abstract Example class.
Definition: Utils.h:78
int main(int argc, char **argv)
Main program for LeNet.
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
bool is_data_type_quantized_asymmetric(DataType dt)
Check if a given data type is of asymmetric quantized type.
Definition: Utils.h:1190
TensorDescriptor & set_layout(DataLayout data_layout)
Sets tensor descriptor data layout.
Structure holding all the common graph parameters.
std::unique_ptr< graph::ITensorAccessor > get_output_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &output_stream=std::cout)
Generates appropriate output accessor according to the specified graph parameters.
Definition: GraphUtils.h:543
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
DataLayout
[DataLayout enum definition]
Definition: Types.h:120
ILayer & set_name(std::string name)
Sets the name of the layer.
Definition: ILayer.h:55