[res] Add some test networks (#13970)

This commit adds some test networks.

ONE-DCO-1.0-Signed-off-by: seongwoo <[email protected]>

res/TensorFlowLiteRecipes/Conv2D_007/test.recipe

@@ -0,0 +1,83 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+}
+operand {
+  name: "ker_1"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias_1"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm_1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: SAME
+    stride_w: 2
+    stride_h: 2
+    activation: RELU
+  }
+  input: "ifm"
+  input: "ker_1"
+  input: "bias_1"
+  output: "ofm_1"
+}
+operand {
+  name: "ker_2"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias_2"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm_2"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: SAME
+    stride_w: 2
+    stride_h: 2
+    activation: RELU
+  }
+  input: "ofm_1"
+  input: "ker_2"
+  input: "bias_2"
+  output: "ofm_2"
+}
+input: "ifm"
+output: "ofm_2"

res/TensorFlowLiteRecipes/FullyConnected_010/test.recipe

@@ -0,0 +1,77 @@
+operand {
+  name: "in"
+  type: FLOAT32
+  shape { dim: 1 dim: 16 }
+}
+operand {
+  name: "weight"
+  type: FLOAT32
+  shape { dim: 16 dim: 16 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 16 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "out"
+  type: FLOAT32
+  shape { dim: 1 dim: 16 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: RELU
+  }
+  input: "in"
+  input: "weight"
+  input: "bias"
+  output: "out"
+}
+operand {
+  name: "weight_2"
+  type: FLOAT32
+  shape { dim: 4 dim: 16 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias_2"
+  type: FLOAT32
+  shape { dim: 4 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "out_2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: RELU
+  }
+  input: "out"
+  input: "weight_2"
+  input: "bias_2"
+  output: "out_2"
+}
+input: "in"
+output: "out_2"

res/TensorFlowLiteRecipes/Net_Conv_Gelu_000/test.recipe

@@ -0,0 +1,78 @@
+operand {
+  name: "Placeholder"
+  type: FLOAT32
+  shape { dim: 1 dim: 16 dim: 16 dim: 3 }
+}
+operand {
+  name: "Conv2D_1"
+  type: FLOAT32
+  shape { dim: 3 dim: 3 dim: 3 dim: 3 }
+  filler { tag: "gaussian" arg: "0.0" arg: "0.1" }
+}
+operand {
+  name: "Conv2D_2"
+  type: FLOAT32
+  shape { dim: 3 }
+  filler { tag: "gaussian" arg: "0.0" arg: "0.1" }
+}
+operand {
+  name: "Conv2D_21"
+  type: FLOAT32
+  shape { dim: 3 dim: 3 dim: 3 dim: 3 }
+  filler { tag: "gaussian" arg: "0.0" arg: "0.1" }
+}
+operand {
+  name: "Conv2D_11"
+  type: FLOAT32
+  shape { dim: 1 dim: 16 dim: 16 dim: 3 }
+}
+operand {
+  name: "Gelu"
+  type: FLOAT32
+  shape { dim: 1 dim: 16 dim: 16 dim: 3 }
+}
+operand {
+  name: "Conv2D_22"
+  type: FLOAT32
+  shape { dim: 1 dim: 16 dim: 16 dim: 3 }
+}
+operation {
+  type: "Conv2D"
+  input: "Placeholder"
+  input: "Conv2D_1"
+  input: "Conv2D_2"
+  output: "Conv2D_11"
+  conv2d_options {
+    padding: SAME
+    stride_w: 1
+    stride_h: 1
+    activation: NONE
+    dilation_w_factor: 1
+    dilation_h_factor: 1
+  }
+}
+operation {
+  type: "Gelu"
+  gelu_options {
+    approximate: false
+  }
+  input: "Conv2D_11"
+  output: "Gelu"
+}
+operation {
+  type: "Conv2D"
+  input: "Gelu"
+  input: "Conv2D_21"
+  input: "Conv2D_2"
+  output: "Conv2D_22"
+  conv2d_options {
+    padding: SAME
+    stride_w: 1
+    stride_h: 1
+    activation: NONE
+    dilation_w_factor: 1
+    dilation_h_factor: 1
+  }
+}
+input: "Placeholder"
+output: "Conv2D_22"

res/TensorFlowLiteRecipes/Net_Conv_Pad_000/test.recipe

@@ -0,0 +1,107 @@
+# Conv - Pad - Conv
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+}
+operand {
+  name: "ker_1"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias_1"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm_1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: SAME
+    stride_w: 2
+    stride_h: 2
+    activation: RELU
+  }
+  input: "ifm"
+  input: "ker_1"
+  input: "bias_1"
+  output: "ofm_1"
+}
+operand {
+  name: "pad"
+  type: INT32
+  shape { dim: 4 dim: 2 }
+  filler {
+    tag: "explicit"
+    arg: "0" arg: "0"
+    arg: "2" arg: "2"
+    arg: "2" arg: "2"
+    arg: "0" arg: "0"
+  }
+}
+operand {
+  name: "pad_ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+}
+operation {
+  type: "Pad"
+  input: "ofm_1"
+  input: "pad"
+  output: "pad_ofm"
+}
+operand {
+  name: "ker_2"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias_2"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm_2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: SAME
+    stride_w: 2
+    stride_h: 2
+    activation: RELU
+  }
+  input: "pad_ofm"
+  input: "ker_2"
+  input: "bias_2"
+  output: "ofm_2"
+}
+input: "ifm"
+output: "ofm_2"