examples/onnx/tiny_yolov2.py

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# to you under the Apache License, Version 2.0 (the
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import os
import numpy as np
from PIL import Image, ImageDraw

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)-15s %(message)s')


def preprocess(img):
    img = np.array(img).astype(np.float32)
    img = np.rollaxis(img, 2, 0)
    img = np.expand_dims(img, axis=0)
    return img


def get_image():
    image_url = 'https://raw.githubusercontent.com/simo23/tinyYOLOv2/master/person.jpg'
    img = Image.open(check_exist_or_download(image_url))
    img = img.resize((416, 416))
    return img


def postprcess(out):
    numClasses = 20
    anchors = [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52]

    def sigmoid(x, derivative=False):
        return x * (1 - x) if derivative else 1 / (1 + np.exp(-x))

    def softmax(x):
        scoreMatExp = np.exp(np.asarray(x))
        return scoreMatExp / scoreMatExp.sum(0)

    clut = [(0, 0, 0), (255, 0, 0), (255, 0, 255), (0, 0, 255), (0, 255, 0),
            (0, 255, 128), (128, 255, 0), (128, 128, 0), (0, 128, 255),
            (128, 0, 128), (255, 0, 128), (128, 0, 255), (255, 128, 128),
            (128, 255, 128), (255, 255, 0), (255, 128, 128), (128, 128, 255),
            (255, 128, 128), (128, 255, 128)]
    label = [
        "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
        "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
        "pottedplant", "sheep", "sofa", "train", "tvmonitor"
    ]

    img = get_image()
    draw = ImageDraw.Draw(img)

    for cy in range(13):
        for cx in range(13):
            for b in range(5):
                channel = b * (numClasses + 5)
                tx = out[channel][cy][cx]
                ty = out[channel + 1][cy][cx]
                tw = out[channel + 2][cy][cx]
                th = out[channel + 3][cy][cx]
                tc = out[channel + 4][cy][cx]
                x = (float(cx) + sigmoid(tx)) * 32
                y = (float(cy) + sigmoid(ty)) * 32

                w = np.exp(tw) * 32 * anchors[2 * b]
                h = np.exp(th) * 32 * anchors[2 * b + 1]

                confidence = sigmoid(tc)

                classes = np.zeros(numClasses)
                for c in range(0, numClasses):
                    classes[c] = out[channel + 5 + c][cy][cx]

                classes = softmax(classes)
                detectedClass = classes.argmax()
                if 0.5 < classes[detectedClass] * confidence:
                    color = clut[detectedClass]
                    x = x - w / 2
                    y = y - h / 2
                    draw.line((x, y, x + w, y), fill=color)
                    draw.line((x, y, x, y + h), fill=color)
                    draw.line((x + w, y, x + w, y + h), fill=color)
                    draw.line((x, y + h, x + w, y + h), fill=color)
                    draw.text((x, y), label[detectedClass], fill=color)
                    logging.info("bounding box: (%.2f, %.2f, %.2f, %.2f)" %
                                 (x, y, x + w, y + h))
                    logging.info('class=%s ; probability=%f' %
                                 (label[detectedClass],
                                  classes[detectedClass] * confidence))
    img.save("result.png")


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):
        pass


if __name__ == "__main__":

    url = 'https://onnxzoo.blob.core.windows.net/models/opset_8/tiny_yolov2/tiny_yolov2.tar.gz'
    download_dir = '/tmp/'
    model_path = os.path.join(download_dir, 'tiny_yolov2', 'Model.onnx')

    logging.info("onnx load model...")
    download_model(url)
    onnx_model = onnx.load(model_path)

    # inference
    logging.info("preprocessing...")
    img = get_image()
    img = preprocess(img)
    # sg_ir = sonnx.prepare(onnx_model) # run without graph
    # y = sg_ir.run([img])

    logging.info("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', 'tiny_yolov2', 'test_data_set_0'))
    # x_batch = tensor.Tensor(device=dev, data=inputs[0])
    # outputs = sg_ir.run([x_batch])
    # for ref_o, o in zip(ref_outputs, outputs):
    #     np.testing.assert_almost_equal(ref_o, tensor.to_numpy(o), 4)

    logging.info("model running...")
    x = tensor.Tensor(device=dev, data=img)
    y = model.forward(x)

    logging.info("postprocessing...")
    out = tensor.to_numpy(y)[0]
    postprcess(out)