miscellaneous/distributed_tensorflow_mask_rcnn/container-serving/resources/predict.py

import glob
import os
import sys

sys.path.append("/tensorpack/examples/FasterRCNN")

from itertools import groupby
from threading import Lock

import cv2
import numpy as np
from config import config as cfg
from config import finalize_configs
from dataset import register_coco
from eval import DetectionResult, predict_image
from modeling.generalized_rcnn import ResNetFPNModel
from tensorpack.predict.base import OfflinePredictor
from tensorpack.predict.config import PredictConfig
from tensorpack.tfutils.sessinit import get_model_loader


class MaskRCNNService:

    lock = Lock()
    predictor = None

    # class method to load trained model and create an offline predictor
    @classmethod
    def get_predictor(cls):
        """load trained model"""

        with cls.lock:
            # check if model is already loaded
            if cls.predictor:
                return cls.predictor

            # create a mask r-cnn model
            mask_rcnn_model = ResNetFPNModel()

            try:
                model_dir = os.environ["SM_MODEL_DIR"]
            except KeyError:
                model_dir = "/opt/ml/model"

            try:
                resnet_arch = os.environ["RESNET_ARCH"]
            except KeyError:
                resnet_arch = "resnet50"

            # file path to previoulsy trained mask r-cnn model
            latest_trained_model = ""
            model_search_path = os.path.join(model_dir, "model-*.index")
            for model_file in glob.glob(model_search_path):
                if model_file > latest_trained_model:
                    latest_trained_model = model_file

            trained_model = latest_trained_model[:-6]
            print(f"Using model: {trained_model}")

            cfg.MODE_FPN = True
            cfg.MODE_MASK = True
            if resnet_arch == "resnet101":
                cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 23, 3]
            else:
                cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3]

            cfg_prefix = "CONFIG__"
            for key, value in dict(os.environ).items():
                if key.startswith(cfg_prefix):
                    attr_name = key[len(cfg_prefix) :]
                    attr_name = attr_name.replace("__", ".")
                    value = eval(value)
                    print(f"update config: {attr_name}={value}")
                    nested_var = cfg
                    attr_list = attr_name.split(".")
                    for attr in attr_list[0:-1]:
                        nested_var = getattr(nested_var, attr)
                    setattr(nested_var, attr_list[-1], value)

            cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
            cfg.DATA.BASEDIR = "/data"
            cfg.DATA.TRAIN = "coco_train2017"
            cfg.DATA.VAL = "coco_val2017"
            register_coco(cfg.DATA.BASEDIR)
            finalize_configs(is_training=False)

            # Create an inference model
            # PredictConfig takes a model, input tensors and output tensors
            input_tensors = mask_rcnn_model.get_inference_tensor_names()[0]
            output_tensors = mask_rcnn_model.get_inference_tensor_names()[1]

            cls.predictor = OfflinePredictor(
                PredictConfig(
                    model=mask_rcnn_model,
                    session_init=get_model_loader(trained_model),
                    input_names=input_tensors,
                    output_names=output_tensors,
                )
            )
            return cls.predictor

    # class method to predict
    @classmethod
    def predict(cls, img=None, img_id=None):

        detection_results = predict_image(img, cls.predictor)
        print(detection_results)

        predictions = {"img_id": str(img_id)}

        annotations = []
        for dr in detection_results:
            a = {}
            b = dr.box.tolist()
            a["bbox"] = [int(b[0]), int(b[1]), int(b[2] - b[0]), int(b[3] - b[1])]
            category_id = dr.class_id

            a["category_id"] = int(category_id)
            a["category_name"] = cfg.DATA.CLASS_NAMES[int(category_id)]
            rle = cls.binary_mask_to_rle(dr.mask)
            a["segmentation"] = rle
            annotations.append(a)

        predictions["annotations"] = annotations
        print(predictions)

        return predictions

    @classmethod
    def binary_mask_to_rle(cls, binary_mask):
        rle = {"counts": [], "size": list(binary_mask.shape)}
        counts = rle.get("counts")
        for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order="C"))):
            if i == 0 and value == 1:
                counts.append(0)
            counts.append(len(list(elements)))
        return rle


# create predictor
MaskRCNNService.get_predictor()

import base64
import json
import tempfile

from flask import Flask, Response, request

app = Flask(__name__)


@app.route("/ping", methods=["GET"])
def health_check():
    """Determine if the container is working and healthy. In this sample container, we declare
    it healthy if we can load the model successfully and crrate a predictor."""
    health = MaskRCNNService.get_predictor() is not None  # You can insert a health check here

    status = 200 if health else 404
    return Response(response="\n", status=status, mimetype="application/json")


@app.route("/invocations", methods=["POST"])
def inference():
    if not request.is_json:
        result = {"error": "Content type is not application/json"}
        print(result)
        return Response(response=result, status=415, mimetype="application/json")

    try:
        content = request.get_json()
        img_id = content["img_id"]
        with tempfile.NamedTemporaryFile() as fh:
            ing_data_string = content["img_data"]
            img_data_bytes = bytearray(ing_data_string, encoding="utf-8")
            fh.write(base64.decodebytes(img_data_bytes))
            fh.seek(0)
            img = cv2.imread(fh.name, cv2.IMREAD_COLOR)
            fh.close()
            pred = MaskRCNNService.predict(img=img, img_id=img_id)

            return Response(response=json.dumps(pred), status=200, mimetype="application/json")
    except Exception as e:
        print(str(e))
        result = {"error": "Internal server error"}
        return Response(response=result, status=500, mimetype="application/json")