| # |
| # 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") |