examples/onnx/superresolution.py

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import os
import numpy as np
from PIL import Image
from resizeimage import resizeimage

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 = resizeimage.resize_cover(img, [224, 224], validate=False)
    img_ycbcr = img.convert('YCbCr')
    img_y_0, img_cb, img_cr = img_ycbcr.split()
    img_ndarray = np.asarray(img_y_0)
    img_4 = np.expand_dims(np.expand_dims(img_ndarray, axis=0), axis=0)
    img_5 = img_4.astype(np.float32) / 255.0
    return img_5, img_cb, img_cr


def get_image():
    # download image
    image_url = 'https://s3.amazonaws.com/model-server/inputs/kitten.jpg'
    img = Image.open(check_exist_or_download(image_url))
    return img


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://github.com/onnx/models/raw/master/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.tar.gz'
    download_dir = '/tmp/'
    model_path = os.path.join(download_dir, 'super_resolution',
                              'super_resolution.onnx')

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

    # preprocess
    logging.info("preprocessing...")
    img = get_image()
    img_y, img_cb, img_cr = 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_y.shape, device=dev)
    model = MyModel(onnx_model)
    model.compile([x], is_train=False, use_graph=True, sequential=True)

    # inference
    logging.info("model running...")
    x_batch = tensor.Tensor(device=dev, data=img_y)
    img_y = model.forward(x_batch)
    array_img_y = tensor.to_numpy(img_y)
    img_out_y = Image.fromarray(np.uint8((array_img_y[0] * 255.0).clip(0,
                                                                       255)[0]),
                                mode='L')

    # postprocess
    logging.info("postprocessing...")
    final_img = Image.merge("YCbCr", [
        img_out_y,
        img_cb.resize(img_out_y.size, Image.BICUBIC),
        img_cr.resize(img_out_y.size, Image.BICUBIC),
    ]).convert("RGB")
    final_img.show()