[TFtest] Add TF_SMALL_NET_0003 test (#4093)

This commit adds TF_SMALL_NET_0003 test which sequentially performs
Conv2d - FusedBatchNorm

Signed-off-by: Seok NamKoong <sk.namkoong@samsung.com>

3 files changed, 260 insertions, 0 deletions

diff --git a/res/TensorFlowTests/TF_SMALL_NET_0003/test.info b/res/TensorFlowTests/TF_SMALL_NET_0003/test.info
new file mode 100644
index 000000000..ee7d13cd1
--- /dev/null
+++ b/res/TensorFlowTests/TF_SMALL_NET_0003/test.info
@@ -0,0 +1,2 @@
+input, Placeholder:0, TF_FLOAT, [1, 3, 3, 5]
+output, FusedBatchNorm:0, TF_FLOAT, [1, 3, 3, 1]
diff --git a/res/TensorFlowTests/TF_SMALL_NET_0003/test.pbtxt b/res/TensorFlowTests/TF_SMALL_NET_0003/test.pbtxt
new file mode 100644
index 000000000..cf6b1a7db
--- /dev/null
+++ b/res/TensorFlowTests/TF_SMALL_NET_0003/test.pbtxt
@@ -0,0 +1,244 @@
+# Small Conv2D + FusedBatchNorm network
+node {
+  name: "Placeholder"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1 # N
+        }
+        dim {
+          size: 3 # H
+        }
+        dim {
+          size: 3 # W
+        }
+        dim {
+          size: 5 # C
+        }
+      }
+    }
+  }
+}
+node {
+  name: "Const"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 2 # H
+          }
+          dim {
+            size: 2 # W
+          }
+          dim {
+            size: 5 # I
+          }
+          dim {
+            size: 1 # O
+          }
+        }
+        float_val: 1.0
+      }
+    }
+  }
+}
+node {
+  name: "Conv2D"
+  op: "Conv2D"
+  input: "Placeholder"
+  input: "Const"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "data_format"
+    value {
+      s: "NHWC"
+    }
+  }
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "use_cudnn_on_gpu"
+    value {
+      b: true
+    }
+  }
+}
+node {
+  name: "scale"
+  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: "offset"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        float_val: 0.0
+      }
+    }
+  }
+}
+node {
+  name: "FusedBatchNorm/mean"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+        }
+        float_val: 0.0
+      }
+    }
+  }
+}
+node {
+  name: "FusedBatchNorm/variance"
+  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: "FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "Conv2D"
+  input: "scale"
+  input: "offset"
+  input: "FusedBatchNorm/mean"
+  input: "FusedBatchNorm/variance"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "data_format"
+    value {
+      s: "NHWC"
+    }
+  }
+  attr {
+    key: "epsilon"
+    value {
+      f: 0.0010000000474974513
+    }
+  }
+  attr {
+    key: "is_training"
+    value {
+      b: false
+    }
+  }
+}
diff --git a/res/TensorFlowTests/TF_SMALL_NET_0003/test.py b/res/TensorFlowTests/TF_SMALL_NET_0003/test.py
new file mode 100644
index 000000000..8a8e9c8c3
--- /dev/null
+++ b/res/TensorFlowTests/TF_SMALL_NET_0003/test.py
@@ -0,0 +1,14 @@
+# TF_SMALL_NET_0003/test.pbtxt is create with below script
+
+# Version info
+#  - Tensorflow : 1.13.1 
+#  - Python : 3.5.2
+
+import tensorflow as tf
+
+input = tf.placeholder(tf.float32, [1, 3, 3, 5])
+filter = tf.constant(1.0, shape=[2, 2, 5, 1])
+conv = tf.nn.conv2d(input, filter=filter, strides=[1, 1, 1, 1], padding='SAME')
+fbn = tf.nn.fused_batch_norm(conv, scale=[1.0], offset=[0.0], mean=[0.0], variance=[1.0], is_training=False)
+
+print(tf.get_default_graph().as_graph_def())