1 files changed, 0 insertions, 99 deletions
diff --git a/tests/nnapi/specs/V1_2/sub_v1_2.mod.py b/tests/nnapi/specs/V1_2/sub_v1_2.mod.py
deleted file mode 100644
index 86299762d..000000000
--- a/tests/nnapi/specs/V1_2/sub_v1_2.mod.py
+++ /dev/null
@@ -1,99 +0,0 @@
-#
-# Copyright (C) 2018 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.
-#
-
-import random
-
-random.seed(0)
-
-# FLOAT32 and FLOAT16
-input0 = Input("input0", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
-input1 = Input("input1", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
-activation = Int32Scalar("act", 0)
-output0 = Output("output0", "TENSOR_FLOAT32",  "{1, 2, 2, 1}")
-
-model = Model().Operation("SUB", input0, input1, activation).To(output0)
-
-Example({
-    input0: [2.0, -4.0, 8.0, -16.0],
-    input1: [2.0, -2.0, -4.0, 4.0],
-    output0: [0.0, -2.0, 12.0, -20.0],
-}).AddVariations("float16").AddAllActivations(output0, activation)
-
-
-# QUANT8_ASYMM
-shape = "{2, 4, 16, 2}, 0.5, 0"
-input0 = Input("input0", "TENSOR_QUANT8_ASYMM", shape)
-input1 = Input("input1", "TENSOR_QUANT8_ASYMM", shape)
-activation = 0
-output0 = Output("output0", "TENSOR_QUANT8_ASYMM", shape)
-
-model = Model("quant8").Operation("SUB", input0, input1, activation).To(output0)
-
-input0_values = list(range(256))
-input1_values = list(input0_values)
-random.shuffle(input1_values)
-output_values = [max(0, a - b) for a, b in zip(input0_values, input1_values)]
-
-Example({
-    input0: input0_values,
-    input1: input1_values,
-    output0: output_values,
-})
-
-# SUB of data type TENSOR_FLOAT32 is introduced in V1_1.
-Example.SetVersion("V1_1", "sub_v1_2_none", "sub_v1_2_relu", "sub_v1_2_relu1", "sub_v1_2_relu6")
-
-
-# SUB, zero-sized input
-
-# Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates.
-p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores
-p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi
-o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
-o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
-tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out
-tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out
-model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3,  -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2)
-
-# Use ROI_ALIGN op to convert into zero-sized feature map.
-layout = BoolScalar("layout", False) # NHWC
-i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 2}")
-zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 2}")
-model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)
-
-# SUB op with numBatches = 0.
-i2 = Parameter("op", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [1, 2, 3, 4]) # weights
-o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 2}") # out
-model = model.Operation("SUB", zero_sized, i2, 0).To(o3)
-
-quant8 = DataTypeConverter().Identify({
-    p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
-    p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
-    o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
-    tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
-    i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
-    zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
-    i2: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
-    o3: ("TENSOR_QUANT8_ASYMM", 0.1, 128)
-})
-
-# Create test case with dummy values.
-Example({
-    i1: [1, 2],
-    o1: [0],
-    o2: [0],
-    o3: [0],
-}).AddVariations("relaxed", quant8, "float16")