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rest_serving_client.py
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#
# Copyright (c) 2019 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np
#import classes
import datetime
import argparse
import json
import requests
from client_utils import print_statistics
def create_request(img, request_format):
signature = "serving_default"
if request_format == "row_name":
instances = []
for i in range(0, img.shape[0], 1):
instances.append({args['input_name']: img[i].tolist()})
data_obj = {"signature_name": signature, "instances": instances}
elif request_format == "row_noname":
data_obj = {"signature_name": signature, 'instances': img.tolist()}
elif request_format == "column_name":
data_obj = {"signature_name": signature,
'inputs': {args['input_name']: img.tolist()}}
elif request_format == "column_noname":
data_obj = {"signature_name": signature, 'inputs': img.tolist()}
else:
print("invalid request format defined")
exit(1)
data_json = json.dumps(data_obj)
return data_json
parser = argparse.ArgumentParser(description='Sends requests via TensorFlow Serving RESTfull API using images in numpy format. '
'It displays performance statistics and optionally the model accuracy')
parser.add_argument('--images_numpy_path', required=True, help='numpy in shape [n,w,h,c] or [n,c,h,w]')
parser.add_argument('--labels_numpy_path', required=False, help='numpy in shape [n,1] - can be used to check model accuracy')
parser.add_argument('--rest_url', required=False, default='http://localhost', help='Specify url to REST API service. default: http://localhost')
parser.add_argument('--rest_port', required=False, default=5555, help='Specify port to REST API service. default: 5555')
parser.add_argument('--input_name', required=False, default='input', help='Specify input tensor name. default: input')
parser.add_argument('--output_name', required=False, default='resnet_v1_50/predictions/Reshape_1',
help='Specify output name. default: resnet_v1_50/predictions/Reshape_1')
parser.add_argument('--transpose_input', choices=["False", "True"], default="True",
help='Set to False to skip NHWC>NCHW or NCHW>NHWC input transposing. default: True',
dest="transpose_input")
parser.add_argument('--transpose_method', choices=["nchw2nhwc", "nhwc2nchw"], default="nhwc2nchw",
help="How the input transposition should be executed: nhwc2nchw or nhwc2nchw",
dest="transpose_method")
parser.add_argument('--iterations', default=0,
help='Number of requests iterations, as default use number of images in numpy memmap. default: 0 (consume all frames)',
dest='iterations', type=int)
# If input numpy file has too few frames according to the value of iterations and the batch size, it will be
# duplicated to match requested number of frames
parser.add_argument('--batchsize', default=1,
help='Number of images in a single request. default: 1',
dest='batchsize')
parser.add_argument('--model_name', default='resnet', help='Define model name, must be same as is in service. default: resnet',
dest='model_name')
parser.add_argument('--request_format', default='row_noname', help='Request format according to TF Serving API: row_noname,row_name,column_noname,column_name',
choices=["row_noname", "row_name", "column_noname", "column_name"], dest='request_format')
parser.add_argument('--model_version', help='Model version to be used. Default: LATEST',
type=int, dest='model_version')
args = vars(parser.parse_args())
processing_times = np.zeros((0), int)
# optional preprocessing depending on the model
imgs = np.load(args['images_numpy_path'], mmap_mode='r', allow_pickle=False)
imgs = imgs - np.min(imgs) # Normalization 0-255
imgs = imgs / np.ptp(imgs) * 255 # Normalization 0-255
# imgs = imgs[:,:,:,::-1] # RGB to BGR
imgs = imgs.astype(np.uint8)
print('Image data range:', np.amin(imgs), ':', np.amax(imgs))
# optional preprocessing depending on the model
if args.get('labels_numpy_path') is not None:
lbs = np.load(args['labels_numpy_path'], mmap_mode='r', allow_pickle=False)
matched_count = 0
total_executed = 0
batch_size = int(args.get('batchsize'))
while batch_size >= imgs.shape[0]:
imgs = np.append(imgs, imgs, axis=0)
if args.get('labels_numpy_path') is not None:
lbs = np.append(lbs, lbs, axis=0)
iterations = int((imgs.shape[0]//batch_size) if not (args.get('iterations') or args.get('iterations') != 0) else args.get('iterations'))
print('Start processing:')
print('\tModel name: {}'.format(args.get('model_name')))
print('\tIterations: {}'.format(iterations))
print('\tImages numpy path: {}'.format(args.get('images_numpy_path')))
if args.get('transpose_input') == "True":
if args.get('transpose_method') == "nhwc2nchw":
imgs = imgs.transpose((0, 3, 1, 2))
if args.get('transpose_method') == "nchw2nhwc":
imgs = imgs.transpose((0, 2, 3, 1))
print('\tImages in shape: {}\n'.format(imgs.shape))
iteration = 0
while iteration <= iterations:
for x in range(0, imgs.shape[0] - batch_size + 1, batch_size):
iteration += 1
if iteration > iterations:
break
img = imgs[x:(x + batch_size)]
if args.get('labels_numpy_path') is not None:
lb = lbs[x:(x + batch_size)]
data_json = create_request(img, args.get('request_format'))
version = ""
if args.get('model_version') is not None:
version = "/versions/{}".format(args.get('model_version'))
start_time = datetime.datetime.now()
result = requests.post("https://clpvt0s6x.paperspacegradient.com/model-serving/deqj8z1ium0gi1:predict", data=data_json)
end_time = datetime.datetime.now()
try:
result_dict = json.loads(result.text)
except ValueError:
print("The server response is not json format: {}",format(result.text))
exit(1)
if "error" in result_dict:
print('Server returned error: {}'.format(result_dict))
exit(1)
if "outputs" in result_dict: # is column format
keyname = "outputs"
if type(result_dict[keyname]) is dict:
if args['output_name'] not in result_dict[keyname]:
print("Invalid output name", args['output_name'])
print("Available outputs:")
for Y in result_dict[keyname]:
print(Y)
exit(1)
output = result_dict[keyname][args['output_name']]
else:
output = result_dict[keyname]
elif "predictions" in result_dict: # is row format
keyname = "predictions"
if type(result_dict[keyname][0]) is dict: # are multiple outputs
output = []
for row in result_dict[keyname]: # iternate over all results in the batch
output.append(row[args['output_name']])
else:
output = result_dict[keyname]
else:
print("Missing required response in {}".format(result_dict))
exit(1)
duration = (end_time - start_time).total_seconds() * 1000
processing_times = np.append(processing_times, np.array([int(duration)]))
# print(output)
nu = np.array(output) # numpy array with inference results
print("output shape: {}".format(nu.shape))
# for object classification models show imagenet class
print('Iteration {}; Processing time: {:.2f} ms; speed {:.2f} fps'.format(iteration, round(np.average(duration), 2),
round(1000 * batch_size / np.average(duration), 2)))
# # # Comment out this section for non imagenet datasets
# # print("imagenet top results in a single batch:")
# for i in range(nu.shape[0]):
# single_result = nu[[i], ...]
# ma = np.argmax(single_result)
# mark_message = ""
# if args.get('labels_numpy_path') is not None:
# total_executed += 1
# if ma == lb[i]:
# matched_count += 1
# mark_message = "; Correct match."
# else:
# mark_message = "; Incorrect match. Should be {} {}".format(lb[i], classes.imagenet_classes[lb[i]])
# print("\t", i, classes.imagenet_classes[ma], ma, mark_message)
# Comment out this section for non imagenet datasets
print_statistics(processing_times, batch_size)
# if args.get('labels_numpy_path') is not None:
# print('Classification accuracy: {:.2f}'.format(100*matched_count/total_executed))