def predict_object():
if arguments.gpu:
device = rai.Device.gpu
else:
device = rai.Device.cpu
con = rai.Client(host=arguments.host, port=arguments.port)
tf_model_path = 'models/tensorflow/imagenet/resnet50.pb'
script_path = 'models/tensorflow/imagenet/data_processing_script.txt'
img_path = 'images/x.png'
class_idx = json.load(open("data/imagenet_classes.json"))
image = io.imread(img_path)
tf_model = load_model(tf_model_path)
script = load_script(script_path)
out1 = con.modelset('imagenet_model',
rai.Backend.tf,
device,
inputs=['images'],
outputs=['output'],
data=tf_model)
out2 = con.scriptset('imagenet_script', device, script)
a = time.time()
tensor = rai.BlobTensor.from_numpy(image)
con.tensorset('image', tensor)
out4 = con.scriptrun('imagenet_script', 'pre_process_3ch', 'image',
'temp1')
out5 = con.modelrun('imagenet_model', 'temp1', 'temp2')
out6 = con.scriptrun('imagenet_script', 'post_process', 'temp2', 'out')
final = con.tensorget('out', as_type=rai.BlobTensor)
ind = final.to_numpy().item()
return class_idx[str(ind)]
if arguments.gpu:
device = 'gpu'
else:
device = 'cpu'
con = rai.Client(host=arguments.host, port=arguments.port)
pt_model_path = '../models/pytorch/imagenet/resnet50.pt'
script_path = '../models/pytorch/imagenet/data_processing_script.txt'
img_path = '../data/cat.jpg'
class_idx = json.load(open("../data/imagenet_classes.json"))
image = io.imread(img_path)
pt_model = ml2rt.load_model(pt_model_path)
script = ml2rt.load_script(script_path)
out1 = con.modelset('imagenet_model', 'torch', device, pt_model)
out2 = con.scriptset('imagenet_script', device, script)
a = time.time()
out3 = con.tensorset('image', image)
out4 = con.scriptrun('imagenet_script', 'pre_process_3ch', 'image', 'temp1')
out5 = con.modelrun('imagenet_model', 'temp1', 'temp2')
out6 = con.scriptrun('imagenet_script', 'post_process', 'temp2', 'out')
final = con.tensorget('out')
ind = final[0]
print(ind, class_idx[str(ind)])
print(time.time() - a)
15: "person",
16: "pottedplant",
17: "sheep",
18: "sofa",
19: "train",
20: "tvmonitor"
}
if arguments.gpu:
device = 'gpu'
else:
device = 'cpu'
con = rai.Client(host=arguments.host, port=arguments.port)
model = ml2rt.load_model('../models/tensorflow/tinyyolo/tinyyolo.pb')
script = ml2rt.load_script(
'../models/tensorflow/tinyyolo/yolo_boxes_script.py')
con.modelset('yolo', 'tf', device, model, inputs=['input'], outputs=['output'])
con.scriptset('yolo-post', device, script)
img_jpg = Image.open('../data/sample_dog_416.jpg')
# normalize
img = np.array(img_jpg).astype(np.float32)
img = np.expand_dims(img, axis=0)
img /= 256.0
con.tensorset('in', img)
con.modelrun('yolo', 'in', 'out')
con.scriptrun('yolo-post', 'boxes_from_tf', inputs='out', outputs='boxes')
boxes = con.tensorget('boxes')
import numpy as np
from redisai import Client, DType, Device, Backend
import ml2rt
client = Client()
client.tensorset('x', [2, 3], dtype=DType.float)
t = client.tensorget('x')
print(t.value)
model = ml2rt.load_model('test/testdata/graph.pb')
tensor1 = np.array([2, 3], dtype=np.float)
client.tensorset('a', tensor1)
client.tensorset('b', (12, 10), dtype=np.float)
client.modelset('m',
Backend.tf,
Device.cpu,
inputs=['a', 'b'],
outputs='mul',
data=model)
client.modelrun('m', ['a', 'b'], ['mul'])
print(client.tensorget('mul'))
# Try with a script
script = ml2rt.load_script('test/testdata/script.txt')
client.scriptset('ket', Device.cpu, script)
client.scriptrun('ket', 'bar', inputs=['a', 'b'], outputs='c')
device = rai.Device.gpu
else:
device = rai.Device.cpu
con = rai.Client(host=arguments.host, port=arguments.port)
tf_model_path = '../models/tensorflow/imagenet/resnet50.pb'
script_path = '../models/tensorflow/imagenet/data_processing_script.txt'
img_path = '../data/cat.jpg'
class_idx = json.load(open("../data/imagenet_classes.json"))
image = io.imread(img_path)
tf_model = load_model(tf_model_path)
script = load_script(script_path)
out1 = con.modelset('imagenet_model',
rai.Backend.tf,
device,
inputs=['images'],
outputs=['output'],
data=tf_model)
out2 = con.scriptset('imagenet_script', device, script)
a = time.time()
tensor = rai.BlobTensor.from_numpy(image)
con.tensorset('image', tensor)
out4 = con.scriptrun('imagenet_script', 'pre_process_3ch', 'image', 'temp1')
out5 = con.modelrun('imagenet_model', 'temp1', 'temp2')
out6 = con.scriptrun('imagenet_script', 'post_process', 'temp2', 'out')
final = con.tensorget('out', as_type=rai.BlobTensor)
def test_ScriptLoad(self):
dirname = os.path.dirname(__file__)
path = f'{dirname}/testdata/script.txt'
load_script(path)