69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
#
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# Copyright (C) 2018 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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num_values = 300
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values = list(np.linspace(-10, 10, num_values))
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for input_type in ["TENSOR_FLOAT32", "TENSOR_FLOAT16"]:
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for scale, offset in [(1.0, 0),
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(1.0, 1),
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(0.01, 120),
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(10.0, 120)]:
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input0 = Input("input0", input_type, "{%d}" % num_values)
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output0 = Output("output0", input_type, "{%d}" % num_values)
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model = Model().Operation("QUANTIZE", input0).To(output0)
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quantizeOutput = DataTypeConverter().Identify({
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output0: ["TENSOR_QUANT8_ASYMM", scale, offset],
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})
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Example({
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input0: values,
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output0: values,
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}).AddVariations(quantizeOutput, includeDefault=False)
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# Zero-sized input
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# Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates.
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p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores
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p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi
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o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
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o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
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tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out
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tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out
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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)
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# Use ROI_ALIGN op to convert into zero-sized feature map.
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layout = BoolScalar("layout", False) # NHWC
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i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 1}")
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zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 1}")
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model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)
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# QUANTIZE op with numBatches = 0.
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o3 = Output("out", "TENSOR_QUANT8_ASYMM", "{0, 2, 2, 1}, 0.1f, 128") # out
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model = model.Operation("QUANTIZE", zero_sized).To(o3)
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Example({
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i1: [1],
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o1: [],
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o2: [],
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o3: [],
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}).AddVariations("relaxed", "float16")
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