def inference(image_path, model_path, batch_size, sc):
imageDF = NNImageReader.readImages(image_path,
sc,
resizeH=300,
resizeW=300,
image_codec=1)
getName = udf(lambda row: row[0], StringType())
transformer = ChainedPreprocessing([
RowToImageFeature(),
ImageResize(256, 256),
ImageCenterCrop(224, 224),
ImageChannelNormalize(123.0, 117.0, 104.0),
ImageMatToTensor(),
ImageFeatureToTensor()
])
model = Model.loadModel(model_path)
est = Estimator.from_bigdl(model=model, feature_preprocessing=transformer)
predictionDF = est.predict(data=imageDF,
batch_size=batch_size,
feature_cols="image").withColumn(
"name", getName(col("image")))
return predictionDF
def inference(image_path, model_path, batch_size, sc):
imageDF = NNImageReader.readImages(image_path,
sc,
resizeH=300,
resizeW=300,
image_codec=1)
getName = udf(lambda row: row[0], StringType())
transformer = ChainedPreprocessing([
RowToImageFeature(),
ImageResize(256, 256),
ImageCenterCrop(224, 224),
ImageChannelNormalize(123.0, 117.0, 104.0),
ImageMatToTensor(),
ImageFeatureToTensor()
])
model = Model.loadModel(model_path)
classifier_model = NNClassifierModel(model, transformer)\
.setFeaturesCol("image").setBatchSize(batch_size)
predictionDF = classifier_model.transform(imageDF).withColumn(
"name", getName(col("image")))
return predictionDF
def _load(self, path):
return Model.loadModel(path, bigdl_type=self.bigdl_type)