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Re-use per-worker vision tensors as means for optimized vision benchmark #59

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Merged
merged 1 commit into from
Mar 12, 2021
Merged

Re-use per-worker vision tensors as means for optimized vision benchmark #59

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10 changes: 5 additions & 5 deletions cmd/aibench_run_inference_redisai_vision/main.go
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Original file line number Diff line number Diff line change
Expand Up @@ -40,8 +40,8 @@ var (
)

// Vars only for git sha and diff handling
var GitSHA1 string = ""
var GitDirty string = "0"
var GitSHA1 = ""
var GitDirty = "0"

func AibenchGitSHA1() string {
return GitSHA1
Expand All @@ -51,7 +51,7 @@ func AibenchGitDirty() (dirty bool) {
dirty = false
dirtyLines, err := strconv.Atoi(GitDirty)
if err == nil {
dirty = (dirtyLines != 0)
dirty = dirtyLines != 0
}
return
}
Expand Down Expand Up @@ -279,8 +279,8 @@ func (p *Processor) ProcessInferenceQuery(q []byte, isWarm bool, workerNum int,
if isWarm && p.opts.showExplain {
return nil, nil
}
tensorName := fmt.Sprintf("imageTensor:{w%d-i%d}", workerNum, queryNumber)
outputTensorName := fmt.Sprintf("classificationTensor:{w%d-i%d}", workerNum, queryNumber)
tensorName := fmt.Sprintf("imageTensor:{w%d}", workerNum)
outputTensorName := fmt.Sprintf("classificationTensor:{w%d}", workerNum)
tensorValues := q
pos := rand.Int31n(int32(len(p.pclient)))
var err error
Expand Down