1c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# 2c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# Copyright (C) 2017 The Android Open Source Project 3c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# 4c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# Licensed under the Apache License, Version 2.0 (the "License"); 5c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# you may not use this file except in compliance with the License. 6c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# You may obtain a copy of the License at 7c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# 8c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# http://www.apache.org/licenses/LICENSE-2.0 9c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# 10c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# Unless required by applicable law or agreed to in writing, software 11c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# distributed under the License is distributed on an "AS IS" BASIS, 12c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# See the License for the specific language governing permissions and 14c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# limitations under the License. 15c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# 16c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang 17c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangmodel = Model() 18bee07f73a5f998a2dd6dc581e7776557c21f9684Miao Wangi1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 4}") # depth_in = 4 19297c580a2d2da2839d936437bf4e3a4c64034950I-Jui (Ray) Sungf1 = Parameter("op2", "TENSOR_FLOAT32", "{1, 2, 2, 4}", [.25, 0, 10, 100, .25, 1, 20, 100, .25, 0, 30, 100, .25, 1, 40, 100]) # depth_out = 4 20297c580a2d2da2839d936437bf4e3a4c64034950I-Jui (Ray) Sungb1 = Parameter("op3", "TENSOR_FLOAT32", "{4}", [600000, 700000, 800000, 900000]) # depth_out = 4 21c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangpad0 = Int32Scalar("pad0", 0) 22c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangact = Int32Scalar("act", 0) 23c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangstride = Int32Scalar("stride", 1) 24c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangcm = Int32Scalar("channelMultiplier", 1) 25c96b526122230f552e5bbb96d4bd0497f50c5c91gfanoutput = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 4}") 26c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang 27c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangmodel = model.Operation("DEPTHWISE_CONV_2D", 28c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang i1, f1, b1, 29c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang pad0, pad0, pad0, pad0, 30c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang stride, stride, 31c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang cm, act).To(output) 32c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang 33c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# Example 1. Input in operand 0, 34c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwanginput0 = { 35c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang i1: [ # input 0 36bee07f73a5f998a2dd6dc581e7776557c21f9684Miao Wang 10, 21, 100, 0, 37bee07f73a5f998a2dd6dc581e7776557c21f9684Miao Wang 10, 22, 200, 0, 38bee07f73a5f998a2dd6dc581e7776557c21f9684Miao Wang 10, 23, 300, 0, 39bee07f73a5f998a2dd6dc581e7776557c21f9684Miao Wang 10, 24, 400, 0], 40c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang } 41c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# (i1 (conv) f1) + b1 42c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangoutput0 = {output: # output 0 43c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang [600010, 700046, 830000, 900000]} 44c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang 45c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwang# Instantiate an example 46c715e9dcaab69a0817fd79ebcfb7011c53ff0c13leozwangExample((input0, output0)) 47