1 files changed, 64 insertions, 0 deletions
diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/concat_float_4D_axis3_1_nnfw.mod.py b/runtimes/tests/neural_networks_test/specs/V1_0/concat_float_4D_axis3_1_nnfw.mod.py
new file mode 100644
index 000000000..39080a3dc
--- /dev/null
+++ b/runtimes/tests/neural_networks_test/specs/V1_0/concat_float_4D_axis3_1_nnfw.mod.py
@@ -0,0 +1,64 @@
+#
+# Copyright (C) 2017 The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# model
+model = Model()
+i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 3, 2}") # input tensor 0
+i2 = Input("op2", "TENSOR_FLOAT32", "{1, 2, 3, 2}") # input tensor 1
+i3 = Input("op3", "TENSOR_FLOAT32", "{1, 2, 3, 2}") # input tensor 2
+axis0 = Int32Scalar("axis0", 3)
+r = Output("result", "TENSOR_FLOAT32", "{1, 2, 3, 6}") # output
+model = model.Operation("CONCATENATION", i1, i2, i3, axis0).To(r)
+
+# Example 1.
+input0 = {i1: [-0.03203143, -0.0334147 , -0.02527265,  0.04576106,  0.08869292,
+                0.06428383, -0.06473722, -0.21933985, -0.05541003, -0.24157837,
+               -0.16328812, -0.04581105],
+          i2: [-0.0569439 , -0.15872048,  0.02965238, -0.12761882, -0.00185435,
+               -0.03297619,  0.03581043, -0.12603407,  0.05999133,  0.00290503,
+                0.1727029 ,  0.03342071],
+          i3: [ 0.10992613,  0.09185287,  0.16433905, -0.00059073, -0.01480746,
+                0.0135175 ,  0.07129054, -0.15095694, -0.04579685, -0.13260484,
+               -0.10045543,  0.0647094 ]}
+output0 = {r: [-0.03203143, -0.0334147 , -0.0569439 , -0.15872048,  0.10992613,
+                0.09185287, -0.02527265,  0.04576106,  0.02965238, -0.12761882,
+                0.16433905, -0.00059073,  0.08869292,  0.06428383, -0.00185435,
+               -0.03297619, -0.01480746,  0.0135175 , -0.06473722, -0.21933985,
+                0.03581043, -0.12603407,  0.07129054, -0.15095694, -0.05541003,
+               -0.24157837,  0.05999133,  0.00290503, -0.04579685, -0.13260484,
+               -0.16328812, -0.04581105,  0.1727029 ,  0.03342071, -0.10045543,
+                0.0647094 ]}
+
+# Instantiate an example
+Example((input0, output0))
+
+
+'''
+# The above data was generated with the code below:
+
+with tf.Session() as sess:
+
+    t1 = tf.random_normal([1, 2, 3, 2], stddev=0.1, dtype=tf.float32)
+    t2 = tf.random_normal([1, 2, 3, 2], stddev=0.1, dtype=tf.float32)
+    t3 = tf.random_normal([1, 2, 3, 2], stddev=0.1, dtype=tf.float32)
+    c1 = tf.concat([t1, t2, t3], axis=3)
+
+    print(c1) # print shape
+    print( sess.run([tf.reshape(t1, [12]),
+                     tf.reshape(t2, [12]),
+                     tf.reshape(t3, [12]),
+                     tf.reshape(c1, [1*2*3*(2*3)])]))
+'''