初めに
今回はチュートリアルを実行したのみ
環境(GPUなし)
Windows10 Pro 64bit GPUなし Python 3.8.2
mxnet, gluoncvのインストール
pip install mxnet pip install gluoncv --pre
その他のパッケージはインストール不要
バージョンの確認(pip freeze)
certifi==2020.6.20 chardet==3.0.4 cycler==0.10.0 gluoncv==0.8.0b20200730 graphviz==0.8.4 idna==2.6 kiwisolver==1.2.0 matplotlib==3.3.0 mxnet==1.6.0 numpy==1.16.6 Pillow==7.2.0 portalocker==1.7.1 pyparsing==3.0.0a2 python-dateutil==2.8.1 pywin32==228 requests==2.18.4 scipy==1.5.2 six==1.15.0 tqdm==4.48.0 urllib3==1.22
実行ファイル
import numpy as np import mxnet as mx from mxnet.gluon.data.vision import transforms import gluoncv ctx = mx.cpu(0) transform_fn = transforms.Compose([ transforms.Resize((640,192)), transforms.ToTensor(), ]) url = 'https://raw.githubusercontent.com/KuangHaofei/GluonCV_Test/master/monodepthv2/tutorials/test_img.png' filename = 'test_img.png' gluoncv.utils.download(url, filename) img = mx.image.imread(filename) original_width, original_height = img.shape[1], img.shape[0] img = transform_fn(img) img = img.expand_dims(0).as_in_context(ctx) model = gluoncv.model_zoo.get_model('monodepth2_resnet18_kitti_stereo_640x192', root='./model', pretrained_base=False, ctx=ctx, pretrained=True) outputs = model.predict(img) disp = outputs[("disp", 0)] disp_resized = mx.nd.contrib.BilinearResize2D(disp, height=original_height, width=original_width) import matplotlib as mpl from matplotlib import cm from matplotlib import pyplot as plt disp_resized_np = disp_resized.squeeze().as_in_context(mx.cpu()).asnumpy() vmax = np.percentile(disp_resized_np, 95) normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax) mapper = cm.ScalarMappable(norm=normalizer, cmap='magma') colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8) plt.axis('off') plt.imshow(colormapped_im) plt.show()