OPIXray_grpc_image_client.py

#!/usr/bin/env python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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from functools import partial
import argparse
import numpy as np
import time
import os
import sys

import tritonclient.grpc as grpcclient
from tritonclient.utils import InferenceServerException

sys.path.append("./OPIXray/DOAM")
import torch
from OPIXray.DOAM.layers.functions.detection import Detect
from OPIXray.DOAM.detection_draw import draw_with_coordinate
sys.path.append("./OPIXray/DOAM/utils")
from OPIXray.DOAM.utils.predict_struct import result_struct
from OPIXray.DOAM.data import OPIXray_CLASSES
from PIL import Image
import cv2

def preprocess(img, h, w):
    """
    Pre-process an image to meet the size, type and format
    requirements specified by the parameters.
    """
    resized_img = cv2.resize(img,(w, h))
    #  RGB2BGR
    resized = np.array(resized_img)
    resized = resized.transpose(2, 0, 1)[np.newaxis,::].astype(np.float32)
    print(resized.shape,resized.dtype)
    return resized


def file_paser(FLAGS,h=300,w=300):
    filenames = []
    if os.path.isdir(FLAGS.image_filename):
        filenames = [
            os.path.join(FLAGS.image_filename, f)
            for f in os.listdir(FLAGS.image_filename)
            if os.path.isfile(os.path.join(FLAGS.image_filename, f))
        ]
    else:
        filenames = [
            FLAGS.image_filename,
        ]

    filenames.sort()

    image_data = []
    og_ims = []
    for filename in filenames:
        img = cv2.imread(filename)
        og_ims.append(img)
        image_data.append(preprocess(img, h, w,))
    return image_data,og_ims


def plot_result(detections,og_im,h=954,w=1225,classes=OPIXray_CLASSES):
    all_boxes = [[[] for _ in range(1)]
                 for _ in range(len(classes) + 1)]
    class_correct_scores, class_coordinate_dict = result_struct(detections, h, w, all_boxes=all_boxes, OPIXray_CLASSES=OPIXray_CLASSES)
    print(class_coordinate_dict)
    draw_with_coordinate(class_correct_scores, class_coordinate_dict,og_im)

if __name__ == '__main__':
    # python OPIXray_grpc_image_client.py  -u 192.168.8.187:8001 -m opi
    parser = argparse.ArgumentParser()
    parser.add_argument('-v',
                        '--verbose',
                        action="store_true",
                        required=False,
                        default=False,
                        help='Enable verbose output')
    parser.add_argument('-m',
                        '--model-name',
                        type=str,
                        required=True,
                        help='Name of model')
    parser.add_argument('-u',
                        '--url',
                        type=str,
                        required=False,
                        default='localhost:8001',
                        help='Inference server URL. Default is localhost:8001.')
    parser.add_argument('-t',
                        '--client-timeout',
                        type=float,
                        required=False,
                        default=None,
                        help='Client timeout in seconds. Default is None.')
    parser.add_argument('image_filename',
                        type=str,
                        nargs='?',
                        default=None,
                        help='Input image / Input folder.')

    FLAGS = parser.parse_args()
    try:
        triton_client = grpcclient.InferenceServerClient(url=FLAGS.url,
                                                         verbose=FLAGS.verbose)
    except Exception as e:
        print("context creation failed: " + str(e))
        sys.exit()

    model_name = FLAGS.model_name

    # Infer
    inputs = []
    outputs = []
    inputs.append(grpcclient.InferInput('modelInput', [1,3, 300,300], "FP32"))
    # inputs.append(grpcclient.InferInput('INPUT1', [1, 16], "INT32"))

    # Create the data for the two input tensors. Initialize the first
    # to unique integers and the second to all ones.

    # input0_data = np.arange(start=0, stop=16, dtype=np.int32)
    # input0_data = np.expand_dims(input0_data, axis=0)

    image_data,og_ims = file_paser(FLAGS)
    # input0_data = np.ones(shape=(1,3,300,300), dtype=np.float32)
    input0_data = image_data[0]
    # Initialize the data

    inputs[0].set_data_from_numpy(input0_data)

    outputs.append(grpcclient.InferRequestedOutput('modelOutput'))
    outputs.append(grpcclient.InferRequestedOutput('407'))
    outputs.append(grpcclient.InferRequestedOutput('408'))

    # Define the callback function. Note the last two parameters should be
    # result and error. InferenceServerClient would povide the results of an
    # inference as grpcclient.InferResult in result. For successful
    # inference, error will be None, otherwise it will be an object of
    # tritonclientutils.InferenceServerException holding the error details
    def callback(user_data, result, error):
        if error:
            user_data.append(error)
        else:
            user_data.append(result)

    # list to hold the results of inference.
    user_data = []

    # Inference call
    triton_client.async_infer(model_name=model_name,
                              inputs=inputs,
                              callback=partial(callback, user_data),
                              outputs=outputs,
                              client_timeout=FLAGS.client_timeout)
    start1 = time.time()
    # Wait until the results are available in user_data
    time_out = 10
    while ((len(user_data) == 0) and time_out > 0):
        time_out = time_out - .1
        time.sleep(.1)



    # Display and validate the available results
    print((len(user_data)))
    if ((len(user_data) == 1)):
        # Check for the errors
        if type(user_data[0]) == InferenceServerException:
            print(user_data[0])
            sys.exit(1)

        # Validate the values by matching with already computed expected
        # values.
        # outputs.append(grpcclient.InferRequestedOutput('264'))
        # outputs.append(grpcclient.InferRequestedOutput('modelOutput'))
        # outputs.append(grpcclient.InferRequestedOutput('406'))

        output0_data = torch.from_numpy(user_data[0].as_numpy('modelOutput'))
        output1_data = torch.from_numpy(user_data[0].as_numpy('407'))
        output2_data = torch.from_numpy(user_data[0].as_numpy('408'))
        print(output0_data.shape,output1_data.shape,output2_data.shape)
        # exit(0)
        detect = Detect(6, 0, 200, 0.01, 0.45)
        result = detect.forward(output0_data,output1_data,output2_data).data

        print(time.time() - start1, "s")
        plot_result(result,og_im=og_ims[0])
        # for i in range(16):
        #     print(
        #         str(input0_data[0][i]) + " + " + str(input1_data[0][i]) +
        #         " = " + str(output0_data[0][i]))
        #     print(
        #         str(input0_data[0][i]) + " - " + str(input1_data[0][i]) +
        #         " = " + str(output1_data[0][i]))
        #     if (input0_data[0][i] + input1_data[0][i]) != output0_data[0][i]:
        #         print("sync infer error: incorrect sum")
        #         sys.exit(1)
        #     if (input0_data[0][i] - input1_data[0][i]) != output1_data[0][i]:
        #         print("sync infer error: incorrect difference")
        #         sys.exit(1)
        print("PASS: Async infer")