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committedSep 25, 2019
Fork the project ck-tensorlow program
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‎.ckr.json

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{
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"data_uoa": "ck-tensorflow-codereef",
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"data_uid": "c8d8309660ad5f7f",
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"data_alias": "ck-tensorflow-codereef",
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"data_name": "ck-tensorflow-codereef",
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"dict": {
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"shared": "git",
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"url": "https://github.com/code-reef/ck-tensorflow-codereef"
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}
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}

‎.cm/alias-a-program

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b0ac08fe1d3c2615

‎.cm/alias-u-b0ac08fe1d3c2615

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program

‎.gitignore

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*.pyc
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tmp*
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__pycache__
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preprocessed
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‎program/.cm/alias-a-tensorflow-codereef

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c316abb5c4c42939

‎program/.cm/alias-u-c316abb5c4c42939

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tensorflow-codereef

‎program/tensorflow-codereef/.cm/desc.json

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{}

‎program/tensorflow-codereef/.cm/info.json

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{
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"backup_data_uid": "c316abb5c4c42939",
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"backup_module_uid": "b0ac08fe1d3c2615",
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"backup_module_uoa": "program",
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"control": {
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"author": "cTuning foundation",
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"author_email": "admin@cTuning.org",
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"author_webpage": "http://cTuning.org",
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"copyright": "See CK COPYRIGHT.txt for copyright details",
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"engine": "CK",
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"iso_datetime": "2019-09-25T16:25:47.200447",
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"license": "See CK LICENSE.txt for licensing details",
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"version": [
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"1",
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"10",
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"3"
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]
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},
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"data_name": "tensorflow-codereef"
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}

‎program/tensorflow-codereef/.cm/meta.json

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{
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"backup_data_uid": "c316abb5c4c42939",
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"data_name": "tensorflow-codereef",
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"no_compile": "yes",
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"no_target_file": "yes",
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"print_files_after_run": [
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"stderr2.log",
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"stderr.log"
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],
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"process_in_tmp": "yes",
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"program": "yes",
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"run_cmds": {
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"classify": {
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"dataset_tags": [
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"image",
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"jpeg",
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"dataset"
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],
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"ignore_return_code": "no",
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"run_deps": {
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"lib-tensorflow": {
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"local": "yes",
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"name": "TensorFlow library",
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"no_tags": "vsrc",
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"skip_default": "yes",
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"skip_pruning_by_other_deps": "yes",
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"sort": 10,
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"tags": "lib,tensorflow"
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},
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"tensorflow-model": {
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"local": "yes",
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"name": "TensorFlow model (net and weights)",
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"sort": 20,
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"tags": "tensorflowmodel,native"
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}
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},
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"run_time": {
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"run_cmd_main": "$<<CK_ENV_COMPILER_PYTHON_FILE>>$ ../classify.py --model_dir=$<<CK_ENV_MODEL_TENSORFLOW>>$ --image_file=$#dataset_path#$$#dataset_filename#$",
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"run_cmd_out1": "stderr.log",
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"run_cmd_out2": "stderr2.log",
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"run_output_files": [
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"stderr.log",
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"stderr2.log"
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]
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}
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},
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"classify_ck_ai_api": {
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"ignore_return_code": "no",
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"run_deps": {
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"lib-tensorflow": {
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"local": "yes",
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"name": "TensorFlow library",
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"no_tags": "vsrc",
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"skip_default": "yes",
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"skip_pruning_by_other_deps": "yes",
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"sort": 10,
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"tags": "lib,tensorflow"
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},
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"tensorflow-model": {
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"local": "yes",
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"name": "TensorFlow model (net and weights)",
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"sort": 20,
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"tags": "tensorflowmodel,native"
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}
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},
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"run_time": {
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"run_cmd_main": "$<<CK_ENV_COMPILER_PYTHON_FILE>>$ ../classify.py --model_dir=$<<CK_ENV_MODEL_TENSORFLOW>>$ --image_file=$<<CK_AI_API_IMAGE_FILE>>$",
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"run_cmd_out1": "stderr.log",
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"run_cmd_out2": "stderr2.log",
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"run_output_files": [
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"stderr.log",
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"stderr2.log"
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]
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}
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},
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"ipython": {
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"ignore_return_code": "yes",
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"run_deps": {
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"lib-tensorflow": {
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"local": "yes",
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"name": "TensorFlow library",
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"skip_default": "yes",
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"skip_pruning_by_other_deps": "yes",
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"sort": 10,
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"tags": "lib,tensorflow"
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}
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},
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"run_time": {
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"run_cmd_main": "$<<CK_PYTHON_IPYTHON_BIN_FULL>>$"
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}
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},
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"time_cpu": {
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"dataset_tags": [
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"benchmark",
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"tensorflow",
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"dataset",
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"vcpu"
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],
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"ignore_return_code": "no",
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"run_deps": {
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"lib-tensorflow": {
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"local": "yes",
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"name": "TensorFlow library",
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"skip_default": "yes",
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"skip_pruning_by_other_deps": "yes",
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"sort": 10,
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"tags": "lib,tensorflow-cpu"
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}
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},
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"run_time": {
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"fine_grain_timer_file": "tmp-ck-timer.json",
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"run_cmd_main": "$<<CK_ENV_COMPILER_PYTHON_FILE>>$ $#dataset_path#$$#dataset_filename#$ --batch_size=$<<BATCH_SIZE>>$ --num_batches=$<<NUM_BATCHES>>$",
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"run_output_files": [
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"stderr.log",
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"stderr2.log",
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"tmp-ck-timer.json"
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]
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}
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},
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"time_cuda": {
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"dataset_tags": [
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"benchmark",
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"tensorflow",
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"dataset",
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"vcuda"
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],
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"ignore_return_code": "no",
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"run_deps": {
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"lib-tensorflow": {
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"local": "yes",
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"name": "TensorFlow library",
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"skip_default": "yes",
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"skip_pruning_by_other_deps": "yes",
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"sort": 10,
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"tags": "lib,tensorflow-cuda"
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}
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},
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"run_time": {
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"fine_grain_timer_file": "tmp-ck-timer.json",
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"run_cmd_main": "$<<CK_ENV_COMPILER_PYTHON_FILE>>$ $#dataset_path#$$#dataset_filename#$ --batch_size=$<<BATCH_SIZE>>$ --num_batches=$<<NUM_BATCHES>>$",
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"run_output_files": [
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"stderr.log",
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"stderr2.log",
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"tmp-ck-timer.json"
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]
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}
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}
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},
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"run_vars": {
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"BATCH_SIZE": 5,
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"NUM_BATCHES": 5
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},
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"tags": [
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"tensorflow-classification",
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"ck-ai-image-classification",
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"demo"
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]
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}

‎program/tensorflow-codereef/LICENSE

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Copyright 2015 The TensorFlow Authors. All rights reserved.
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‎program/tensorflow-codereef/classify.py

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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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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,
11+
# 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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"""Simple image classification with Inception.
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Run image classification with Inception trained on ImageNet 2012 Challenge data
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set.
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This program creates a graph from a saved GraphDef protocol buffer,
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and runs inference on an input JPEG image. It outputs human readable
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strings of the top 5 predictions along with their probabilities.
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Change the --image_file argument to any jpg image to compute a
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classification of that image.
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Please see the tutorial and website for a detailed description of how
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to use this script to perform image recognition.
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https://tensorflow.org/tutorials/image_recognition/
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Modified by Grigori Fursin to support unified Collective Knowledge API:
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* https://github.com/ctuning/ck
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* http://cKnowledge.org/ai
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os.path
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import re
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import sys
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import tarfile
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import numpy as np
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from six.moves import urllib
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import tensorflow as tf
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FLAGS = tf.app.flags.FLAGS
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# classify_image_graph_def.pb:
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# Binary representation of the GraphDef protocol buffer.
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# imagenet_synset_to_human_label_map.txt:
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# Map from synset ID to a human readable string.
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# imagenet_2012_challenge_label_map_proto.pbtxt:
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# Text representation of a protocol buffer mapping a label to synset ID.
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tf.app.flags.DEFINE_string(
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'model_dir', '/tmp/imagenet',
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"""Path to classify_image_graph_def.pb, """
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"""imagenet_synset_to_human_label_map.txt, and """
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"""imagenet_2012_challenge_label_map_proto.pbtxt.""")
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tf.app.flags.DEFINE_string('image_file', '',
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"""Absolute path to image file.""")
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tf.app.flags.DEFINE_integer('num_top_predictions', 5,
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"""Display this many predictions.""")
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# pylint: disable=line-too-long
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#DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
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# pylint: enable=line-too-long
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class NodeLookup(object):
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"""Converts integer node ID's to human readable labels."""
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def __init__(self,
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label_lookup_path=None,
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uid_lookup_path=None):
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if not label_lookup_path:
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label_lookup_path = os.path.join(
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FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
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if not uid_lookup_path:
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uid_lookup_path = os.path.join(
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FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
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self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
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def load(self, label_lookup_path, uid_lookup_path):
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"""Loads a human readable English name for each softmax node.
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Args:
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label_lookup_path: string UID to integer node ID.
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uid_lookup_path: string UID to human-readable string.
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Returns:
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dict from integer node ID to human-readable string.
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"""
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if not tf.gfile.Exists(uid_lookup_path):
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tf.logging.fatal('File does not exist %s', uid_lookup_path)
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if not tf.gfile.Exists(label_lookup_path):
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tf.logging.fatal('File does not exist %s', label_lookup_path)
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# Loads mapping from string UID to human-readable string
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proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
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uid_to_human = {}
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p = re.compile(r'[n\d]*[ \S,]*')
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for line in proto_as_ascii_lines:
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parsed_items = p.findall(line)
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uid = parsed_items[0]
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human_string = parsed_items[2]
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uid_to_human[uid] = human_string
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# Loads mapping from string UID to integer node ID.
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node_id_to_uid = {}
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proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
117+
for line in proto_as_ascii:
118+
if line.startswith(' target_class:'):
119+
target_class = int(line.split(': ')[1])
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if line.startswith(' target_class_string:'):
121+
target_class_string = line.split(': ')[1]
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node_id_to_uid[target_class] = target_class_string[1:-2]
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# Loads the final mapping of integer node ID to human-readable string
125+
node_id_to_name = {}
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for key, val in node_id_to_uid.items():
127+
if val not in uid_to_human:
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tf.logging.fatal('Failed to locate: %s', val)
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name = uid_to_human[val]
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node_id_to_name[key] = name
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return node_id_to_name
133+
134+
def id_to_string(self, node_id):
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if node_id not in self.node_lookup:
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return ''
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return self.node_lookup[node_id]
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139+
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def create_graph():
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"""Creates a graph from saved GraphDef file and returns a saver."""
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# Creates graph from saved graph_def.pb.
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with tf.gfile.FastGFile(os.path.join(
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FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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_ = tf.import_graph_def(graph_def, name='')
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149+
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def run_inference_on_image(image):
151+
"""Runs inference on an image.
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153+
Args:
154+
image: Image file name.
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156+
Returns:
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Nothing
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"""
159+
if not tf.gfile.Exists(image):
160+
tf.logging.fatal('File does not exist %s', image)
161+
image_data = tf.gfile.FastGFile(image, 'rb').read()
162+
163+
# Creates graph from saved GraphDef.
164+
create_graph()
165+
166+
with tf.Session() as sess:
167+
# Some useful tensors:
168+
# 'softmax:0': A tensor containing the normalized prediction across
169+
# 1000 labels.
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# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
171+
# float description of the image.
172+
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
173+
# encoding of the image.
174+
# Runs the softmax tensor by feeding the image_data as input to the graph.
175+
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
176+
predictions = sess.run(softmax_tensor,
177+
{'DecodeJpeg/contents:0': image_data})
178+
predictions = np.squeeze(predictions)
179+
180+
# Creates node ID --> English string lookup.
181+
node_lookup = NodeLookup()
182+
183+
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
184+
185+
print("---------- Prediction for "+image+" ----------")
186+
for node_id in top_k:
187+
human_string = node_lookup.id_to_string(node_id)
188+
score = predictions[node_id]
189+
# print('%s (score = %.5f)' % (human_string, score))
190+
print('%.5f - "%s"' % (score,human_string))
191+
192+
def main(_):
193+
import time
194+
195+
t1=time.time()
196+
run_inference_on_image(FLAGS.image_file)
197+
tt=time.time()-t1
198+
199+
# print ("")
200+
# print ("Elapsed time: "+("%.2f"%tt)+" sec.")
201+
202+
if __name__ == '__main__':
203+
tf.app.run()

‎program/tensorflow-codereef/classify.py.orig

Lines changed: 203 additions & 0 deletions
Original file line numberDiff line numberDiff line change
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1+
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2+
#
3+
# Licensed under the Apache License, Version 2.0 (the "License");
4+
# you may not use this file except in compliance with the License.
5+
# You may obtain a copy of the License at
6+
#
7+
# http://www.apache.org/licenses/LICENSE-2.0
8+
#
9+
# Unless required by applicable law or agreed to in writing, software
10+
# distributed under the License is distributed on an "AS IS" BASIS,
11+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12+
# See the License for the specific language governing permissions and
13+
# limitations under the License.
14+
# ==============================================================================
15+
16+
"""Simple image classification with Inception.
17+
18+
Run image classification with Inception trained on ImageNet 2012 Challenge data
19+
set.
20+
21+
This program creates a graph from a saved GraphDef protocol buffer,
22+
and runs inference on an input JPEG image. It outputs human readable
23+
strings of the top 5 predictions along with their probabilities.
24+
25+
Change the --image_file argument to any jpg image to compute a
26+
classification of that image.
27+
28+
Please see the tutorial and website for a detailed description of how
29+
to use this script to perform image recognition.
30+
31+
https://tensorflow.org/tutorials/image_recognition/
32+
33+
Modified by Grigori Fursin to support unified Collective Knowledge API:
34+
* https://github.com/ctuning/ck
35+
* http://cKnowledge.org/ai
36+
37+
"""
38+
39+
from __future__ import absolute_import
40+
from __future__ import division
41+
from __future__ import print_function
42+
43+
import os.path
44+
import re
45+
import sys
46+
import tarfile
47+
48+
import numpy as np
49+
from six.moves import urllib
50+
import tensorflow as tf
51+
52+
FLAGS = tf.app.flags.FLAGS
53+
54+
# classify_image_graph_def.pb:
55+
# Binary representation of the GraphDef protocol buffer.
56+
# imagenet_synset_to_human_label_map.txt:
57+
# Map from synset ID to a human readable string.
58+
# imagenet_2012_challenge_label_map_proto.pbtxt:
59+
# Text representation of a protocol buffer mapping a label to synset ID.
60+
tf.app.flags.DEFINE_string(
61+
'model_dir', '/tmp/imagenet',
62+
"""Path to classify_image_graph_def.pb, """
63+
"""imagenet_synset_to_human_label_map.txt, and """
64+
"""imagenet_2012_challenge_label_map_proto.pbtxt.""")
65+
tf.app.flags.DEFINE_string('image_file', '',
66+
"""Absolute path to image file.""")
67+
tf.app.flags.DEFINE_integer('num_top_predictions', 5,
68+
"""Display this many predictions.""")
69+
70+
# pylint: disable=line-too-long
71+
#DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
72+
# pylint: enable=line-too-long
73+
74+
75+
class NodeLookup(object):
76+
"""Converts integer node ID's to human readable labels."""
77+
78+
def __init__(self,
79+
label_lookup_path=None,
80+
uid_lookup_path=None):
81+
if not label_lookup_path:
82+
label_lookup_path = os.path.join(
83+
FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
84+
if not uid_lookup_path:
85+
uid_lookup_path = os.path.join(
86+
FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
87+
self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
88+
89+
def load(self, label_lookup_path, uid_lookup_path):
90+
"""Loads a human readable English name for each softmax node.
91+
92+
Args:
93+
label_lookup_path: string UID to integer node ID.
94+
uid_lookup_path: string UID to human-readable string.
95+
96+
Returns:
97+
dict from integer node ID to human-readable string.
98+
"""
99+
if not tf.gfile.Exists(uid_lookup_path):
100+
tf.logging.fatal('File does not exist %s', uid_lookup_path)
101+
if not tf.gfile.Exists(label_lookup_path):
102+
tf.logging.fatal('File does not exist %s', label_lookup_path)
103+
104+
# Loads mapping from string UID to human-readable string
105+
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
106+
uid_to_human = {}
107+
p = re.compile(r'[n\d]*[ \S,]*')
108+
for line in proto_as_ascii_lines:
109+
parsed_items = p.findall(line)
110+
uid = parsed_items[0]
111+
human_string = parsed_items[2]
112+
uid_to_human[uid] = human_string
113+
114+
# Loads mapping from string UID to integer node ID.
115+
node_id_to_uid = {}
116+
proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
117+
for line in proto_as_ascii:
118+
if line.startswith(' target_class:'):
119+
target_class = int(line.split(': ')[1])
120+
if line.startswith(' target_class_string:'):
121+
target_class_string = line.split(': ')[1]
122+
node_id_to_uid[target_class] = target_class_string[1:-2]
123+
124+
# Loads the final mapping of integer node ID to human-readable string
125+
node_id_to_name = {}
126+
for key, val in node_id_to_uid.items():
127+
if val not in uid_to_human:
128+
tf.logging.fatal('Failed to locate: %s', val)
129+
name = uid_to_human[val]
130+
node_id_to_name[key] = name
131+
132+
return node_id_to_name
133+
134+
def id_to_string(self, node_id):
135+
if node_id not in self.node_lookup:
136+
return ''
137+
return self.node_lookup[node_id]
138+
139+
140+
def create_graph():
141+
"""Creates a graph from saved GraphDef file and returns a saver."""
142+
# Creates graph from saved graph_def.pb.
143+
with tf.gfile.FastGFile(os.path.join(
144+
FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
145+
graph_def = tf.GraphDef()
146+
graph_def.ParseFromString(f.read())
147+
_ = tf.import_graph_def(graph_def, name='')
148+
149+
150+
def run_inference_on_image(image):
151+
"""Runs inference on an image.
152+
153+
Args:
154+
image: Image file name.
155+
156+
Returns:
157+
Nothing
158+
"""
159+
if not tf.gfile.Exists(image):
160+
tf.logging.fatal('File does not exist %s', image)
161+
image_data = tf.gfile.FastGFile(image, 'rb').read()
162+
163+
# Creates graph from saved GraphDef.
164+
create_graph()
165+
166+
with tf.Session() as sess:
167+
# Some useful tensors:
168+
# 'softmax:0': A tensor containing the normalized prediction across
169+
# 1000 labels.
170+
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
171+
# float description of the image.
172+
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
173+
# encoding of the image.
174+
# Runs the softmax tensor by feeding the image_data as input to the graph.
175+
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
176+
predictions = sess.run(softmax_tensor,
177+
{'DecodeJpeg/contents:0': image_data})
178+
predictions = np.squeeze(predictions)
179+
180+
# Creates node ID --> English string lookup.
181+
node_lookup = NodeLookup()
182+
183+
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
184+
185+
print("---------- Prediction for "+image+" ----------")
186+
for node_id in top_k:
187+
human_string = node_lookup.id_to_string(node_id)
188+
score = predictions[node_id]
189+
# print('%s (score = %.5f)' % (human_string, score))
190+
print('%.5f - "%s"' % (score,human_string))
191+
192+
def main(_):
193+
import time
194+
195+
t1=time.time()
196+
run_inference_on_image(FLAGS.image_file)
197+
tt=time.time()-t1
198+
199+
# print ("")
200+
# print ("Elapsed time: "+("%.2f"%tt)+" sec.")
201+
202+
if __name__ == '__main__':
203+
tf.app.run()

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