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#!/usr/bin/python
# Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
#
# 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.
from operator_wrapping import Operator
from tensor_printer import TensorPrinter
from option_printer import OptionPrinter
from perf_predictor import PerfPredictor
class ConfigSaver(object):
def __init__(self, file_name, operator):
self.file_name = file_name
self.operator = operator
# Set self.verbose to 2 level to print more information
self.verbose = 2
self.op_idx = operator.operator_idx
self.op_name = operator.opcode_str
self.f = open(file_name, 'at')
def __del__(self):
self.f.close()
def SaveInfo(self):
self.f.write("[{}]\n".format(self.op_idx))
if (self.op_name == 'CONV_2D'):
self.SaveConv2DInputs()
else:
self.SaveInputs()
self.SaveOutputs()
self.SaveAttributes()
self.f.write('\n')
def SaveConv2DInputs(self):
if (len(self.operator.inputs) != 3):
raise AssertionError('Conv2D input count should be 3')
inputs = self.operator.inputs[0]
weights = self.operator.inputs[1]
bias = self.operator.inputs[2]
self.f.write("input: {}\n".format(
TensorPrinter(self.verbose, inputs).GetShapeString()))
self.f.write("input_type: {}\n".format(inputs.type_name))
self.f.write("weights: {}\n".format(
TensorPrinter(self.verbose, weights).GetShapeString()))
self.f.write("weights_type: {}\n".format(weights.type_name))
self.f.write("bias: {}\n".format(
TensorPrinter(self.verbose, bias).GetShapeString()))
self.f.write("bias_type: {}\n".format(bias.type_name))
def SaveInputs(self):
total = len(self.operator.inputs)
self.f.write("input_counts: {}\n".format(total))
for idx in range(total):
tensor = self.operator.inputs[idx]
input_shape_str = TensorPrinter(self.verbose, tensor).GetShapeString()
self.f.write("input{}: {}\n".format(idx, input_shape_str))
self.f.write("input{}_type: {}\n".format(idx, tensor.type_name))
def SaveOutputs(self):
total = len(self.operator.outputs)
self.f.write("output_counts: {}\n".format(total))
for idx in range(total):
tensor = self.operator.outputs[idx]
output_shape_str = TensorPrinter(self.verbose, tensor).GetShapeString()
self.f.write("output{}: {}\n".format(idx, output_shape_str))
self.f.write("output{}_type: {}\n".format(idx, tensor.type_name))
def SaveFilter(self):
self.f.write("filter_w: {}\n".format(self.operator.options.FilterWidth()))
self.f.write("filter_h: {}\n".format(self.operator.options.FilterHeight()))
def SaveStride(self):
self.f.write("stride_w: {}\n".format(self.operator.options.StrideW()))
self.f.write("stride_h: {}\n".format(self.operator.options.StrideH()))
def SaveDilation(self):
self.f.write("dilation_w: {}\n".format(self.operator.options.DilationWFactor()))
self.f.write("dilation_h: {}\n".format(self.operator.options.DilationHFactor()))
def SavePadding(self):
if self.operator.options.Padding() == 0:
self.f.write("padding: SAME\n")
elif self.operator.options.Padding() == 1:
self.f.write("padding: VALID\n")
def SaveFusedAct(self):
if self.operator.fused_activation is not "NONE":
self.f.write("fused_act: {}\n".format(self.operator.fused_activation))
def SaveAttributes(self):
# operator option
# Some operations does not have option. In such case no option is printed
option_str = OptionPrinter(self.verbose, self.op_name,
self.operator.options).GetOptionString()
if self.op_name == 'AVERAGE_POOL_2D' or self.op_name == 'MAX_POOL_2D':
self.SaveFilter()
self.SaveStride()
self.SavePadding()
elif self.op_name == 'CONV_2D':
self.SaveStride()
self.SaveDilation()
self.SavePadding()
elif self.op_name == 'TRANSPOSE_CONV':
self.SaveStride()
self.SavePadding()
elif self.op_name == 'DEPTHWISE_CONV_2D':
self.SaveStride()
self.SaveDilation()
self.SavePadding()
self.f.write("depthmultiplier: {}\n".format(
self.operator.options.DepthMultiplier()))
self.SaveFusedAct()
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