bigdl_transformations_resnet_layers.py

# Create a chained transformer that resizes, crops and normalizes each image in the dataframe
transformer = ChainedPreprocessing(
    [RowToImageFeature(), ImageResize(256, 256), ImageCenterCrop(224, 224),
     ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(), ImageFeatureToTensor()])

# Load pre-trained Resnet-50 that was downloaded earlier and give the column to pick features from
preTrainedNNModel = NNModel(Model.loadModel(model_path), transformer) \
    .setFeaturesCol("image") \
    .setPredictionCol("embedding")

# Print all layers in Resnet-50
for layer in preTrainedNNModel.model.layers:
    print(layer.name())