import bentoml
from bentoml.adapters import TfTensorInput
from bentoml.frameworks.tensorflow import TensorflowSavedModelArtifact
@bentoml.env(infer_pip_packages=True)
@bentoml.artifacts([TensorflowSavedModelArtifact('model')])
class Tensorflow2Classifier(bentoml.BentoService):
@bentoml.api(input=TfTensorInput())
def predict(self, tensor):
return self.artifacts.model(tensor)
import bentoml
from bentoml.adapters import JsonInput, TfTensorInput
from bentoml.frameworks.tensorflow import TensorflowSavedModelArtifact
@bentoml.env(infer_pip_packages=True)
@bentoml.artifacts([
TensorflowSavedModelArtifact('model1'),
TensorflowSavedModelArtifact('model2'),
TensorflowSavedModelArtifact('model3'),
])
class Tensorflow2Classifier(bentoml.BentoService):
@bentoml.api(input=TfTensorInput(), batch=True)
def predict1(self, tensor):
return self.artifacts.model1(tensor)
@bentoml.api(input=TfTensorInput(), batch=True)
def predict2(self, tensor):
return self.artifacts.model2(tensor)
@bentoml.api(input=JsonInput(), batch=True)
def predict3(self, jsons):
import tensorflow as tf
tensor = tf.ragged.constant(jsons, dtype=tf.float64)
return self.artifacts.model3(tensor)
import bentoml
import io
import tensorflow as tf
import numpy as np
from config import InferenceConfig, INPUT_IMAGE, INPUT_IMAGE_META, INPUT_ANCHORS
from docparser import stage2_structure_parser
from docparser.utils.data_utils import DocsDataset
from docparser.utils.eval_utils import convert_bbox_list_to_save_format
from util import ForwardModel
tf.compat.v1.enable_eager_execution() # required
@bentoml.env(pip_packages=['tensorflow'])
@bentoml.artifacts([TensorflowSavedModelArtifact('trackable')])
class DocparserService(bentoml.BentoService):
def __init__(self):
super().__init__()
model_config = InferenceConfig()
self.preprocess_obj = ForwardModel(model_config)
def preprocess(self, image):
images = np.expand_dims(image, axis=0)[:,:,:,:3]
molded_images, image_metas, windows = self.preprocess_obj.mold_inputs(images)
molded_images = molded_images.astype(np.float32)
image_shape = molded_images[0].shape
for g in molded_images[1:]:
assert g.shape == image_shape, \
"After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes."