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-rw-r--r--res/TensorFlowTests/TF_SMALL_NET_0003/test.info2
-rw-r--r--res/TensorFlowTests/TF_SMALL_NET_0003/test.pbtxt244
-rw-r--r--res/TensorFlowTests/TF_SMALL_NET_0003/test.py14
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())