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grpc_serving_client.py
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#
# Copyright (c) 2018-2021 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 grpc
import numpy as np
from tensorflow import make_tensor_proto, make_ndarray
import classes
import datetime
import argparse
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
from client_utils import print_statistics, prepare_certs
parser = argparse.ArgumentParser(description='Sends requests via TFS gRPC 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('--grpc_address',required=False, default='localhost', help='Specify url to grpc service. default:localhost')
parser.add_argument('--grpc_port',required=False, default=9000, help='Specify port to grpc service. default: 9000')
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 nchw2nhwc",
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('--pipeline_name', default='', help='Define pipeline name, must be same as is in service',
dest='pipeline_name')
parser.add_argument('--dag-batch-size-auto', default=False, action='store_true', help='add demultiplexer dimension at front', dest='dag-batch-size-auto')
parser.add_argument('--tls', default=False, action='store_true', help='use TLS communication with gRPC endpoint')
parser.add_argument('--server_cert', required=False, help='Path to server certificate')
parser.add_argument('--client_cert', required=False, help='Path to client certificate')
parser.add_argument('--client_key', required=False, help='Path to client key')
args = vars(parser.parse_args())
address = "{}:{}".format(args['grpc_address'],args['grpc_port'])
if args.get('tls'):
server_ca_cert, client_key, client_cert = prepare_certs(server_cert=args['server_cert'],
client_key=args['client_key'],
client_ca=args['client_cert'])
creds = grpc.ssl_channel_credentials(root_certificates=server_ca_cert,
private_key=client_key, certificate_chain=client_cert)
channel = grpc.secure_channel(address, creds)
else:
channel = grpc.insecure_channel(address)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
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
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('\tNumpy file shape: {}\n'.format(imgs.shape))
iteration = 0
is_pipeline_request = bool(args.get('pipeline_name'))
while iteration <= iterations:
for x in range(0, imgs.shape[0] - batch_size + 1, batch_size):
iteration += 1
if iteration > iterations: break
request = predict_pb2.PredictRequest()
request.model_spec.name = args.get('pipeline_name') if is_pipeline_request else args.get('model_name')
img = imgs[x:(x + batch_size)]
if args.get('labels_numpy_path') is not None:
lb = lbs[x:(x + batch_size)]
if args.get('dag-batch-size-auto'):
newShape = img.shape[0:1] + (1,) + img.shape[1:]
request.inputs[args['input_name']].CopyFrom(make_tensor_proto(img, shape=newShape))
else:
request.inputs[args['input_name']].CopyFrom(make_tensor_proto(img, shape=(img.shape)))
start_time = datetime.datetime.now()
result = stub.Predict(request, 10.0) # result includes a dictionary with all model outputs
end_time = datetime.datetime.now()
if args['output_name'] not in result.outputs:
print("Invalid output name", args['output_name'])
print("Available outputs:")
for Y in result.outputs:
print(Y)
exit(1)
duration = (end_time - start_time).total_seconds() * 1000
processing_times = np.append(processing_times,np.array([int(duration)]))
output = make_ndarray(result.outputs[args['output_name']])
nu = np.array(output)
# 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]):
if is_pipeline_request:
# shape (1,)
print("response shape", output.shape)
ma = nu[0] - 1 # indexes needs to be shifted left due to 1x1001 shape
else:
# shape (1,1000)
single_result = nu[[i],...]
offset = 0
if nu.shape[1] == 1001:
offset = 1
ma = np.argmax(single_result) - offset
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))