Release 18.03

1 files changed, 579 insertions, 0 deletions

diff --git a/src/armnnTfParser/test/FullyConnected.cpp b/src/armnnTfParser/test/FullyConnected.cpp
new file mode 100644
index 0000000000..2a7b4951b7
--- /dev/null
+++ b/src/armnnTfParser/test/FullyConnected.cpp
@@ -0,0 +1,579 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#include <boost/test/unit_test.hpp>
+#include "armnnTfParser/ITfParser.hpp"
+#include "ParserPrototxtFixture.hpp"
+#include "Runtime.hpp"
+#include "Network.hpp"
+#include "Graph.hpp"
+
+BOOST_AUTO_TEST_SUITE(TensorflowParser)
+
+// In Tensorflow fully connected layers are expressed as a MatMul followed by an Add.
+// The TfParser must detect this case and convert them to a FullyConnected layer.
+struct FullyConnectedFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+    FullyConnectedFixture()
+    {
+        // input = tf.placeholder(tf.float32, [1, 1], "input")
+        // weights = tf.constant([2], tf.float32, [1, 1])
+        // matmul = tf.matmul(input, weights)
+        // bias = tf.constant([1], tf.float32)
+        // output = tf.add(matmul, bias, name="output")
+        m_Prototext = R"(
+node {
+  name: "input"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 1
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 2.0
+      }
+    }
+  }
+}
+node {
+  name: "MatMul"
+  op: "MatMul"
+  input: "input"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "transpose_a"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "transpose_b"
+    value {
+      b: false
+    }
+  }
+}
+node {
+  name: "Const_1"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "output"
+  op: "Add"
+  input: "MatMul"
+  input: "Const_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+        )";
+        SetupSingleInputSingleOutput({ 1, 1 }, "input", "output");
+    }
+};
+
+BOOST_FIXTURE_TEST_CASE(FullyConnected, FullyConnectedFixture)
+{
+    RunTest<1>({ 3 }, { 7 });
+}
+
+// Similar to FullyConnectedFixture, but this time the MatMul's output is used by two Adds. This should result
+// in two FullyConnected layers being created.
+//      I
+//      |
+//      M -- C
+//     / \'
+// C-- A  A -- C
+//     \ /
+//      A
+struct MatMulUsedInTwoFcFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+    MatMulUsedInTwoFcFixture()
+    {
+        m_Prototext = R"(
+node {
+  name: "input"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 1
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 2.0
+      }
+    }
+  }
+}
+node {
+  name: "MatMul"
+  op: "MatMul"
+  input: "input"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "transpose_a"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "transpose_b"
+    value {
+      b: false
+    }
+  }
+}
+node {
+  name: "Const_1"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        float_val: 5.0
+      }
+    }
+  }
+}
+node {
+  name: "Const_2"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        float_val: 15.0
+      }
+    }
+  }
+}
+node {
+  name: "Add"
+  op: "Add"
+  input: "MatMul"
+  input: "Const_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "Add_1"
+  op: "Add"
+  input: "MatMul"
+  input: "Const_2"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "output"
+  op: "Add"
+  input: "Add"
+  input: "Add_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+        )";
+        SetupSingleInputSingleOutput({ 1, 1 }, "input", "output");
+    }
+};
+
+BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFc, MatMulUsedInTwoFcFixture)
+{
+    RunTest<1>({ 3 }, { 32 });
+    // Ideally we would check here that the armnn network has 5 layers:
+    //  Input, 2 x FullyConnected (biased), Add and Output.
+    // This would make sure the parser hasn't incorrectly added some unconnected layers corresponding to the MatMul
+}
+
+// Similar to MatMulUsedInTwoFc, but this time the Adds are 'staggered' (see diagram), which means that only one
+// FullyConnected layer can be created (the other should just be an Add).
+//        I
+//        |
+//        M -- C1
+//       / \'
+// C2 -- A  |
+//       \ /
+//        A
+struct MatMulUsedInTwoFcStaggeredFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+    MatMulUsedInTwoFcStaggeredFixture()
+    {
+        // input = tf.placeholder(tf.float32, shape=[1,1], name = "input")
+        // const1 = tf.constant([17], tf.float32, [1,1])
+        // mul = tf.matmul(input, const1)
+        // const2 = tf.constant([7], tf.float32, [1])
+        // fc = tf.add(mul, const2)
+        // output = tf.add(mul, fc, name="output")
+        m_Prototext = R"(
+node {
+  name: "input"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 1
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 17.0
+      }
+    }
+  }
+}
+node {
+  name: "MatMul"
+  op: "MatMul"
+  input: "input"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "transpose_a"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "transpose_b"
+    value {
+      b: false
+    }
+  }
+}
+node {
+  name: "Const_1"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        float_val: 7.0
+      }
+    }
+  }
+}
+node {
+  name: "Add"
+  op: "Add"
+  input: "MatMul"
+  input: "Const_1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "output"
+  op: "Add"
+  input: "MatMul"
+  input: "Add"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+        )";
+        SetupSingleInputSingleOutput({ 1, 1 }, "input", "output");
+    }
+};
+
+BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFcStaggered, MatMulUsedInTwoFcStaggeredFixture)
+{
+    RunTest<1>({ 2 }, { 75 });
+    // Ideally we would check here that the armnn network has 5 layers:
+    //   Input, FullyConnected (biased), FullyConnected (non biased), Add and Output.
+}
+
+// A MatMul in isolation, not connected to an add. Should result in a non-biased FullyConnected layer.
+struct MatMulFixture : public ParserPrototxtFixture<armnnTfParser::ITfParser>
+{
+    MatMulFixture()
+    {
+        // input = tf.placeholder(tf.float32, shape = [1, 1], name = "input")
+        // const = tf.constant([17], tf.float32, [1, 1])
+        //  output = tf.matmul(input, const, name = "output")
+        m_Prototext = R"(
+node {
+  name: "input"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 1
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 1
+          }
+        }
+        float_val: 17.0
+      }
+    }
+  }
+}
+node {
+  name: "output"
+  op: "MatMul"
+  input: "input"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "transpose_a"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "transpose_b"
+    value {
+      b: false
+    }
+  }
+}
+        )";
+        SetupSingleInputSingleOutput({ 1, 1 }, "input", "output");
+    }
+};
+
+BOOST_FIXTURE_TEST_CASE(MatMul, MatMulFixture)
+{
+    RunTest<1>({ 2 }, { 34 });
+}
+
+BOOST_AUTO_TEST_SUITE_END()