blob: e9805805d44469463ec00865bd3b85834446642a [file] [log] [blame]
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under th
import os
import numpy as np
from PIL import Image
from singa import device
from singa import tensor
from singa import sonnx
import onnx
from utils import download_model, check_exist_or_download
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
def preprocess(img):
input_shape = (1, 1, 64, 64)
img = img.resize((64, 64), Image.ANTIALIAS)
img_data = np.array(img).astype(np.float32)
img_data = np.resize(img_data, input_shape)
return img_data
def get_image_labe():
labels = [
'neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust',
'fear', 'contempt'
# download image
image_url = ''
img =
return img, labels
class MyModel(sonnx.SONNXModel):
def __init__(self, onnx_model):
super(MyModel, self).__init__(onnx_model)
def forward(self, *x):
y = super(MyModel, self).forward(*x)
return y[0]
def train_one_batch(self, x, y):
if __name__ == "__main__":
url = ''
download_dir = '/tmp/'
model_path = os.path.join(download_dir, 'emotion_ferplus', 'model.onnx')"onnx load model...")
onnx_model = onnx.load(model_path)
# inference"preprocessing...")
img, labels = get_image_labe()
img = preprocess(img)
# sg_ir = sonnx.prepare(onnx_model) # run without graph
# y =[img])"model compling...")
dev = device.create_cuda_gpu()
x = tensor.PlaceHolder(img.shape, device=dev)
model = MyModel(onnx_model)
model.compile([x], is_train=False, use_graph=True, sequential=True)
# verifty the test
# from utils import load_dataset
# inputs, ref_outputs = load_dataset(os.path.join('/tmp', 'emotion_ferplus', 'test_data_set_0'))
# x_batch = tensor.Tensor(device=dev, data=inputs[0])
# outputs =[x_batch])
# for ref_o, o in zip(ref_outputs, outputs):
# np.testing.assert_almost_equal(ref_o, tensor.to_numpy(o), 4)"model running...")
x = tensor.Tensor(device=dev, data=img)
y = model.forward(x)"postprocessing...")
y = tensor.softmax(y)
scores = tensor.to_numpy(y)
scores = np.squeeze(scores)
a = np.argsort(scores)[::-1]
for i in a[0:5]:'class=%s ; probability=%f' % (labels[i], scores[i]))