chore: #22 Added code to run through
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@ -18,6 +18,7 @@ func HandleModels (handle *Handle) {
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model_classes.HandleList(handle)
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// Train endpoints
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handleRun(handle)
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models_train.HandleTrainEndpoints(handle)
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}
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@ -70,19 +70,20 @@ func handleRun(handle *Handle) {
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}
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definitions_rows, err := handle.Db.Query("select id from model_definition where model_id=$1;", model.Id)
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if !definitions_rows.Next() {
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// TODO improve this
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return ErrorCode(nil, 400, c.AddMap(nil))
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if err != nil {
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return Error500(err)
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}
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defer definitions_rows.Close()
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if !definitions_rows.Next() {
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return Error500(nil)
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// TODO improve this
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fmt.Printf("Could not find definition\n")
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return ErrorCode(nil, 400, c.AddMap(nil))
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}
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var def_id string
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if err = definitions_rows.Scan(&def_id); err != nil {
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return Error500(nil)
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return Error500(err)
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}
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// TODO create a database table with tasks
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@ -97,18 +98,21 @@ func handleRun(handle *Handle) {
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img_file.Write(file)
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root := tg.NewRoot()
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tf_img := image.Read(root, path.Join(run_path, "img.png"), 1)
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tf_model := tg.LoadModel(path.Join("savedData", model.Id, "defs", def_id, "model"), []string{"serve"}, nil)
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tf_img := image.Read(root, path.Join(run_path, "img.png"), 3)
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tf_img_tensor, err := tf.NewTensor(tf_img.Value())
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batch := tg.Batchify(root, []tf.Output{tf_img.Value()})
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exec_results := tg.Exec(root, []tf.Output{batch}, nil, &tf.SessionOptions{})
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inputImage, err:= tf.NewTensor(exec_results[0].Value())
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if err != nil {
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return Error500(err)
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}
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tf_model := tg.LoadModel(path.Join("savedData", model.Id, "defs", def_id, "model"), []string{"serve"}, nil)
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results := tf_model.Exec([]tf.Output{
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tf_model.Op("StatefulPartitionedCall", 0),
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}, map[tf.Output]*tf.Tensor{
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tf_model.Op("serving_default_inputs_input", 0): tf_img_tensor,
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tf_model.Op("serving_default_rescaling_input", 0): inputImage,
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})
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predictions := results[0]
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@ -303,7 +303,7 @@
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Image File
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</span>
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</button>
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<input id="file" name="file" type="file" required accept="application/zip" />
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<input id="file" name="file" type="file" required accept="image/png" />
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</div>
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</fieldset>
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<button>
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