1 files changed, 0 insertions, 134 deletions
diff --git a/nn/runtime/test/specs/V1_3_cts_only/while_fib_dynamic.mod.py b/nn/runtime/test/specs/V1_3_cts_only/while_fib_dynamic.mod.py
deleted file mode 100644
index c69d732c0..000000000
--- a/nn/runtime/test/specs/V1_3_cts_only/while_fib_dynamic.mod.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#
-# Copyright (C) 2020 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: given n >= 2, produces [fib(1), ..., fib(n)].
-#
-# i = 2
-# fib = [1, 1]                         # shape=[1, i + 1]
-# while i < n:
-#     last2 = fib[0:1, (i - 2):i]      # shape=[1, 2]
-#     new_fib = matmul(last2, [1; 1])  # shape=[1, 1]
-#     fib = append(fib, new_fib)       # shape=[1, i + 2]
-#     i = i + 1
-
-FibType = ["TENSOR_FLOAT32", []]  # Unknown rank.
-CounterType = ["TENSOR_INT32", [1]]
-BoolType = ["TENSOR_BOOL8", [1]]
-
-Quant8Params = ["TENSOR_QUANT8_ASYMM", 1.0, 0]
-Quant8SignedParams = ["TENSOR_QUANT8_ASYMM_SIGNED", 1.0, 0]
-
-def MakeConditionModel():
-  fib = Input("fib", FibType)
-  i = Input("i", CounterType)
-  n = Input("n", CounterType)
-  out = Output("out", BoolType)
-  model = Model()
-  model.IdentifyInputs(fib, i, n)
-  model.IdentifyOutputs(out)
-  model.Operation("LESS", i, n).To(out)
-
-  quant8 = DataTypeConverter().Identify({
-      fib: Quant8Params,
-  })
-  quant8_signed = DataTypeConverter().Identify({
-      fib: Quant8SignedParams,
-  })
-
-  return SubgraphReference("cond", model), quant8, quant8_signed
-
-def MakeBodyModel():
-  fib = Input("fib", FibType)
-  i = Input("i", CounterType)
-  n = Input("n", CounterType)
-  fib_out = Output("fib_out", FibType)
-  i_out = Output("i_out", CounterType)
-  matrix = Parameter("matrix", ["TENSOR_FLOAT32", [1, 2]], [1, 1])
-  zero_bias = Parameter("zero_bias", ["TENSOR_FLOAT32", [1]], [0])
-  i_minus_2 = Internal("i_minus_2", ["TENSOR_INT32", [1]])
-  slice_start = Internal("slice_start", ["TENSOR_INT32", [2]])
-  slice_size = Parameter("slice_size", ["TENSOR_INT32", [2]], [1, 2])
-  last2 = Internal("last2", ["TENSOR_FLOAT32", [1, 2]])
-  new_fib = Internal("new_fib", ["TENSOR_FLOAT32", [1, 1]])
-  model = Model()
-  model.IdentifyInputs(fib, i, n)
-  model.IdentifyOutputs(fib_out, i_out)
-  model.Operation("ADD", i, [-2], 0).To(i_minus_2)
-  model.Operation("MUL", i_minus_2, [0, 1], 0).To(slice_start)
-  model.Operation("SLICE", fib, slice_start, slice_size).To(last2)
-  model.Operation("FULLY_CONNECTED", last2, matrix, zero_bias, 0).To(new_fib)
-  model.Operation("CONCATENATION", fib, new_fib, 1).To(fib_out)
-  model.Operation("ADD", i, [1], 0).To(i_out)
-
-  quant8 = DataTypeConverter().Identify({
-      fib_out: Quant8Params,
-      fib: Quant8Params,
-      last2: Quant8Params,
-      matrix: Quant8Params,
-      new_fib: Quant8Params,
-      zero_bias: ["TENSOR_INT32", 1.0, 0],
-  })
-  quant8_signed = DataTypeConverter().Identify({
-      fib_out: Quant8SignedParams,
-      fib: Quant8SignedParams,
-      last2: Quant8SignedParams,
-      matrix: Quant8SignedParams,
-      new_fib: Quant8SignedParams,
-      zero_bias: ["TENSOR_INT32", 1.0, 0],
-  })
-
-  return SubgraphReference("body", model), quant8, quant8_signed
-
-def Test(n_data, fib_data, add_unused_output=False):
-  n = Input("n", CounterType)
-  fib_out = Output("fib_out", ["TENSOR_FLOAT32", [1, len(fib_data)]])  # Known dimensions.
-  cond, cond_quant8, cond_quant8_signed = MakeConditionModel()
-  body, body_quant8, body_quant8_signed = MakeBodyModel()
-  fib_init = Parameter("fib_init", ["TENSOR_FLOAT32", [1, 2]], [1, 1])
-  i_init = [2]
-  outputs = [fib_out]
-  if add_unused_output:
-    i_out = Internal("i_out", CounterType)  # Unused.
-    outputs.append(i_out)
-  model = Model().Operation("WHILE", cond, body, fib_init, i_init, n).To(outputs)
-
-  quant8 = DataTypeConverter().Identify({
-      fib_init: Quant8Params,
-      fib_out: Quant8Params,
-      cond: cond_quant8,
-      body: body_quant8,
-  })
-  quant8_signed = DataTypeConverter().Identify({
-      fib_init: Quant8SignedParams,
-      fib_out: Quant8SignedParams,
-      cond: cond_quant8_signed,
-      body: body_quant8_signed,
-  })
-
-  name = "n_{}".format(n_data)
-  if add_unused_output:
-    name += "_unused_output"
-  example = Example({n: [n_data], fib_out: fib_data}, name=name)
-  example.AddVariations("relaxed", "float16", quant8, quant8_signed)
-  example.AddVariations(AllOutputsAsInternalCoverter())
-
-for use_shm_for_weights in [False, True]:
-  Configuration.use_shm_for_weights = use_shm_for_weights
-  Test(n_data=2, fib_data=[1, 1], add_unused_output=True)
-  Test(n_data=3, fib_data=[1, 1, 2], add_unused_output=True)
-  Test(n_data=4, fib_data=[1, 1, 2, 3], add_unused_output=True)
-  Test(n_data=5, fib_data=[1, 1, 2, 3, 5])
-  Test(n_data=6, fib_data=[1, 1, 2, 3, 5, 8])