1 files changed, 0 insertions, 69 deletions
diff --git a/tests/nnapi/specs/V1_2/quantize.mod.py b/tests/nnapi/specs/V1_2/quantize.mod.py
deleted file mode 100644
index a42624dce..000000000
--- a/tests/nnapi/specs/V1_2/quantize.mod.py
+++ /dev/null
@@ -1,69 +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 numpy as np
-
-num_values = 300
-values = list(np.linspace(-10, 10, num_values))
-
-for input_type in ["TENSOR_FLOAT32", "TENSOR_FLOAT16"]:
-  for scale, offset in [(1.0, 0),
-                        (1.0, 1),
-                        (0.01, 120),
-                        (10.0, 120)]:
-    input0 = Input("input0", input_type, "{%d}" % num_values)
-    output0 = Output("output0", input_type, "{%d}" % num_values)
-
-    model = Model().Operation("QUANTIZE", input0).To(output0)
-
-    quantizeOutput = DataTypeConverter().Identify({
-        output0: ["TENSOR_QUANT8_ASYMM", scale, offset],
-    })
-
-    Example({
-        input0: values,
-        output0: values,
-    }).AddVariations(quantizeOutput, includeDefault=False)
-
-
-# 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, 1}")
-zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 1}")
-model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)
-
-# QUANTIZE op with numBatches = 0.
-o3 = Output("out", "TENSOR_QUANT8_ASYMM", "{0, 2, 2, 1}, 0.1f, 128") # out
-model = model.Operation("QUANTIZE", zero_sized).To(o3)
-
-# Create test case with dummy values.
-Example({
-    i1: [1],
-    o1: [0],
-    o2: [0],
-    o3: [0],
-}).AddVariations("relaxed", "float16")