def main(_):
t1 = t.time()
host, port = FLAGS.server.split(':')
channel = grpc.insecure_channel(FLAGS.server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
# request = predict_pb2.PredictRequest()
predict = []
image_data = img_data(image_path)
imag_data = []
for i in range(len(image_data)):
imag_data.append(image_data[i].numpy().tolist())
t2 = t.time()
for h in range(0, 5):
request = model_name(h)
t3 = t.time()
# Build a batch of images.
request.inputs['input_1'].CopyFrom(
tf.make_tensor_proto(imag_data,
shape=[len(image_data), 224, 224, 3]))
result_future = stub.Predict.future(request, 10.0) # 10 secs timeout
result = result_future.result()
t4 = t.time()
classes = result.outputs['output_1'].float_val
prediction = []
for i in range(0, 80):
prediction.append(classes[i])
if len(prediction) % 16 == 0:
classification = np.argmax(prediction)
predict.append(classification)
prediction = []
print(predict)
fine_label = get_label(max(predict, key=predict.count))
print(fine_label, t4 - t2, t2 - t1, t4 - t1)
def prediction():
t1 = t.time()
predict = []
img = img_data(image_path)
# print(img[1])
print(t.time() - t1)
t2 = t.time()
for a in range(0, 1):
SERVER_URL = url(a)
for i in range(0, 5):
j = json.dumps(img[i].numpy().tolist())
predict_request = '{"instances":%s}' % j
# print(t.time()-t1)
# print(predict_request)
start_time = t.time()
response = requests.post(SERVER_URL, data=predict_request)
print(t.time() - start_time)
# print(type(response))
# t2=t.time()
prediction = response.json()['predictions'][0]
# print(t.time()-t2)
classification = np.argmax(prediction)
# print(t.time()-t2)
predict.append(classification)
# print(t.time()-t2)
print(predict)
fine_label = get_label(max(predict, key=predict.count))
print(t.time() - t1, t.time() - t2)
print(fine_label)
def main():
# Build a batch of images.
image_data = img_data(image_path)
imag_data = []
for i in range(len(image_data)):
imag_data.append(image_data[i].numpy().tolist())
with tf.io.TFRecordWriter("tf_serving_warmup_requests") as writer:
request = predict_pb2.PredictRequest()
request.model_spec.name = 'model1'
request.model_spec.signature_name = 'serving_default'
request.inputs['input_1'].CopyFrom(
tf.make_tensor_proto(imag_data, shape=[len(imag_data),224,224,3]))
log = prediction_log_pb2.PredictionLog(
predict_log=prediction_log_pb2.PredictLog(request=request))
writer.write(log.SerializeToString())
# GPU settings
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
t1=t.time()
# load the model
# model = get_model()
# model.load_weights(save_model_dir)
model = tf.saved_model.load(save_model_dir)
#image data
predict=[]
probabilit=[]
img=img_data(image_path)
#Take the category with the most predicted results
# for i in range(len(img)):
# image=tf.expand_dims(img[i],0)
# predictions = model(image, training=False)
# probabilities=tf.nn.softmax(predictions)
# label = np.argmax(predictions,1)
# probability=np.max(tf.keras.backend.eval((tf.math.top_k(probabilities,1)).values))
# predict.append(int(label))
# print(max(predict, key=predict.count))
# print('time:',(t.time()-t1))