Docx4J.bind
(
lc_docxPackage,
new ByteArrayInputStream( ic_data ),
ai_flags
);
Docx4J.save( lc_docxPackage, ac_os );
现在我知道问题出现了：
class RequestProcess(dxmodels.WorkflowEnabled):
def __init__(self):
lang = self.customer.user.customerprofile.language
@xworkflows.on_enter_state('documents')
def on_documents(self, *args, **kwargs):
self.update_timestamps()
ctx = {'request': self}
EmailFromTemplate('request-required-documents', extra_context=ctx, lang=lang)\
.send_to(self.customer.user)
@xworkflows.on_enter_state('validation')
def on_validation(self, *args, **kwargs):
self.update_timestamps()
validate_requirements_for_request(self)
@xworkflows.on_enter_state('signature')
def on_signature(self, *args, **kwargs):
self.customer_product.lock()
self.contract = create_contract(self)
self.update_timestamps()
ctx = {'request': self}
EmailFromTemplate('product-request-accepted', extra_context=ctx, lang=lang)\
.send_to(self.customer.user)
class Meta:
abstract = True
因为
def create_request_from_external(obj):
...
req = Request(**{
'state': 'documents',
'request_type': get_request_type(obj.customer),
'customer': obj.customer,
'created_by': get_system_user(),
'entered_documents_at': datetime.datetime.now(),
})
req.save()
obj.request = req
obj.save()
create_customer_product(req, obj.product)
audit.log('system',
verb='created',
target=obj.customer.user,
action_object=req,
message=_('Customer created for %s') % obj)
# Build initial requirements state
validate_requirements_for_request(req)
return req
我有以下错误class Request(RequestProcess, FolderLinkMixin, TimeStampedModel):
。我不想在每个方法中添加TypeError: __init__() got an unexpected keyword argument 'customerd。我只想在变量中实例化一次。我该怎么办才能修好它？我是否需要直接在课程lang = self.customer.user.customerprofile.language或Request
例如，如果我编写一个方法，删除RequestProcess方法并注释其他方法，它就可以正常工作。
__init__@xworkflows.on_enter_state('deposit')
def on_deposit(self, *args, **kwargs):
self.update_timestamps()
self.customer_product.lock()
create_loan_from_request(self)
ctx = {'request': self, 'deposit_date': self.loan.product.deposit_date}
EmailFromTemplate('deposit-notice', extra_context=ctx, lang=self.customer.user.customerprofile.language)\
.send_to(self.customer.user)
答案 0 :(得分：1)
问题是您已将模型的public interface Classifier {
public class Recognition{
private final String id;
private final String title;
private final Float confidence;
private RectF location;
public Recognition(
final String id, final String title, final Float confidence, final RectF location){
this.id = id;
this.title = title;
this.confidence = confidence;
this.location = location;
}
public String getId(){return id;}
public String getTitle(){return title;}
public Float getConfidence(){return confidence;}
public RectF getLocation(){return location;}
public void setLocation(RectF location){this.location = location;}
public String toString(){
String resultString = "";
if (id != null) {
resultString += "[" + id + "] ";
}
if (title != null) {
resultString += title + " ";
}
if (confidence != null) {
resultString += String.format("(%.1f%%) ", confidence * 100.0f);
}
if (location != null) {
resultString += location + " ";
}
return resultString.trim();
}
}
List<Recognition> recognizeImage(Bitmap bitmap);
void enableStatLogging(final boolean debug);
String getStatString();
void close();
}
方法替换为不接受任何参数的方法：
public class TensorFlowImageClassifier implements Classifier {
private static final String TAG = "TensorFlowImageClassifier";
private static final int MAX_RESULTS = 3;
private static final float THRESHOLD = 0.1f;
private String inputName;
private String outputName;
private int inputSize;
private int imageMean;
private float imageStd;
private Vector<String> labels = new Vector<String>();
private int[] intValues;
private float[] floatValues;
private float[] outputs;
private String[] outputNames;
private boolean logStats = false;
private TensorFlowInferenceInterface inferenceInterface;
private TensorFlowImageClassifier() {}
/*
assetManager : assets 로드하는데 사용
modelFilename : pb 파일
labelFilename : txt 파일
inputSize : 정사각형 길이, inputSize * inputSize
imageMean : image values 평균값
imageStd : image values 표준값?
inputName : image input 노드 레이블
outputName : output 노드 레이블
*/
public static Classifier create(
AssetManager assetManager, String modelFilename, String labelFilename, int inputSize, int imageMean, float imageStd, String inputName, String outputName){
TensorFlowImageClassifier c = new TensorFlowImageClassifier();
c.inputName = inputName;
c.outputName = outputName;
String actualFilename = labelFilename.split("file:///android_asset/")[1];
Log.d(TAG, "reading labels from : " + actualFilename);
BufferedReader br = null;
try {
br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));
String line;
while((line = br.readLine()) != null){
c.labels.add(line);
}
br.close();
} catch (IOException e) {
throw new RuntimeException("failed reading labels" , e);
}
c.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename);
final Operation operation = c.inferenceInterface.graphOperation(outputName);
final int numClasses = (int)operation.output(0).shape().size(1);
Log.d(TAG, "reading " + c.labels.size() + " labels, size of output layers : " + numClasses);
c.inputSize = inputSize;
c.imageMean = imageMean;
c.imageStd = imageStd;
c.outputNames = new String[]{outputName};
c.intValues = new int[inputSize * inputSize];
c.floatValues = new float[inputSize * inputSize * 3];
c.outputs = new float[numClasses];
return c;
}
@RequiresApi(api = Build.VERSION_CODES.JELLY_BEAN_MR2)
public List<Recognition> recognizeImage(final Bitmap bitmap){
beginSection("recognizeImage");
beginSection("preprocessBitmap");
bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
for(int i = 0; i < intValues.length; i++){
final int val = intValues[i];
floatValues[i*3+0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
floatValues[i*3+1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
floatValues[i*3+2] = ((val & 0xFF) - imageMean) / imageStd;
}
endSection();
beginSection("feed");
inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3);
endSection();
beginSection("run");
inferenceInterface.run(outputNames, logStats);
endSection();
beginSection("fetch");
inferenceInterface.fetch(outputName, outputs);
endSection();
PriorityQueue<Recognition> pq = new PriorityQueue<Recognition>(
3,
new Comparator<Recognition>(){
public int compare(Recognition lhs, Recognition rhs){
return Float.compare(rhs.getConfidence(), lhs.getConfidence());
}
}
);
for(int i = 0; i < outputs.length; ++i){
if(outputs[i] > THRESHOLD){
pq.add(
new Recognition("" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));
}
}
final ArrayList<Recognition> recognitions = new ArrayList<Recognition>();
int recognitionSize = Math.min(pq.size(), MAX_RESULTS);
for(int i = 0; i < recognitionSize; ++i){
recognitions.add(pq.poll());
}
endSection();
return recognitions;
}
public void enableStatLogging(boolean logStats){this.logStats = logStats;}
public String getStatString(){return inferenceInterface.getStatString();}
public void close(){inferenceInterface.close();}
}
另请注意，__init__仅适用于class RequestProcess(dxmodels.WorkflowEnabled):
def __init__(self):
lang = self.customer.user.customerprofile.language
方法，因为您未设置lang，所以无法在其他地方访问它。
我会尽可能避免覆盖模型的__init__方法。在这种情况下，我认为最好定义一个属性。
self.lang在模型方法中，您可以访问__init__。