62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
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# model
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model = Model()
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sparseData = Input("sparseData", "TENSOR_FLOAT32", "{210}")
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traversalOrder = Parameter("traversalOrder", "TENSOR_INT32", "{4}", [0, 1, 2, 3])
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blockMap = Parameter("blockMap", "TENSOR_INT32", "{2}", [0, 1])
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dimFormat = Parameter("dimFormat", "TENSOR_INT32", "{4}", [0, 0, 0, 0])
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dimensions = Parameter("dimensions", "TENSOR_INT32", "{4}", [2, 3, 5, 7])
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d0ArrSegments = Parameter("d0ArrSegments", "TENSOR_INT32", "{0}", [])
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d0ArrIndices = Parameter("d0ArrIndices", "TENSOR_INT32", "{0}", [])
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d1ArrSegments = Parameter("d1ArrSegments", "TENSOR_INT32", "{0}", [])
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d1ArrIndices = Parameter("d1ArrIndices", "TENSOR_INT32", "{0}", [])
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d2ArrSegments = Parameter("d2ArrSegments", "TENSOR_INT32", "{0}", [])
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d2ArrIndices = Parameter("d2ArrIndices", "TENSOR_INT32", "{0}", [])
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d3ArrSegments = Parameter("d3ArrSegments", "TENSOR_INT32", "{0}", [])
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d3ArrIndices = Parameter("d3ArrIndices", "TENSOR_INT32", "{0}", [])
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denseOut = Output("denseOut", "TENSOR_FLOAT32", "{10, 21}")
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model = model.Operation("DENSIFY", sparseData, traversalOrder, blockMap,
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dimFormat, dimensions, d0ArrSegments, d0ArrIndices, d1ArrSegments,
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d1ArrIndices, d2ArrSegments, d2ArrIndices, d3ArrSegments,
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d3ArrIndices).To(denseOut)
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inputData = [0.0] * 210
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inputData[0:22:7] = [11.0, 13.0, 17.0, 19.0]
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# Example 1. Input in operand 0,
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input0 = {sparseData: # input 0
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inputData}
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outputData = [0.0] * 210
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outputData[0:64:21] = [11.0, 13.0, 17.0, 19.0]
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output0 = {denseOut: # output 0
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outputData}
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quant8_symm = DataTypeConverter().Identify({
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sparseData: ("TENSOR_QUANT8_SYMM", 2.0),
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denseOut: ("TENSOR_QUANT8_SYMM", 2.0)
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})
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quant8_asymm = DataTypeConverter().Identify({
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sparseData: ("TENSOR_QUANT8_ASYMM", 0.5, 4),
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denseOut: ("TENSOR_QUANT8_ASYMM", 0.5, 4)
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})
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quant8_asymm_signed = DataTypeConverter().Identify({
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sparseData: ("TENSOR_QUANT8_ASYMM_SIGNED", 2.5, -9),
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denseOut: ("TENSOR_QUANT8_ASYMM_SIGNED", 2.5, -9)
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})
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quant16_symm = DataTypeConverter().Identify({
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sparseData: ("TENSOR_QUANT16_SYMM", 3.0),
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denseOut: ("TENSOR_QUANT16_SYMM", 3.0)
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})
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quant16_asymm = DataTypeConverter().Identify({
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sparseData: ("TENSOR_QUANT16_ASYMM", 2.0, 4),
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denseOut: ("TENSOR_QUANT16_ASYMM", 2.0, 4)
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})
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# Instantiate an example
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Example((input0, output0)).AddVariations("relaxed", "float16", "int32", quant8_symm,
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quant8_asymm, quant8_asymm_signed, quant16_symm,
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quant16_asymm)
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