Delete weights/graphcast/README.md

weights/graphcast/README.md

@@ -1,54 +0,0 @@
----
-license: mit
-language:
-- en
-- zh
-metrics:
-- accuracy
-tags:
-- climate
----
-
-# OpenCastKit: an open-source solutions of global data-driven high-resolution weather forecasting
-
-This is an open-source solutions of global data-driven high-resolution weather forecasting, implemented and improved by [High-Flyer AI](https://www.high-flyer.cn/). It can compare with the ECMWF Integrated Forecasting System (IFS).
-
-See also: [Github repository](https://github.com/HFAiLab/OpenCastKit) and [High-flyer AI's blog](https://www.high-flyer.cn/blog/opencast/)
-
-Several cases:
-
-![Typhoon track comparison](./pic/wind_small.gif)
-
-![Water vapour comparison](./pic/precipitation_small.gif)
-
-For more cases about FourCastNet/GraphCast prediction, please have a look at [HF-Earth](https://www.high-flyer.cn/hf-earth/), a daily updated demo released by [High-Flyer AI](https://www.high-flyer.cn/en/).
-
-## Inference
-
-### FourCastNet
-
-You can load the weights `backbone.pt` and `precipitation.pt` to generate weather predictions, as shown in the following pseudocode. The complete code is released at `./infer2img.py`.
-
-```python
-import xarray as xr
-import cartopy.crs as ccrs
-from afnonet import AFNONet   # download the code from https://github.com/HFAiLab/OpenCastKit/blob/master/model/afnonet.py
-
-backbone_model = AFNONet(img_size=[720, 1440], in_chans=20, out_chans=20, norm_layer=partial(nn.LayerNorm, eps=1e-6))
-backbone_model.load('./weights/fourcastnet/backbone.pt')
-precip_model = AFNONet(img_size=[720, 1440], in_chans=20, out_chans=1, norm_layer=partial(nn.LayerNorm, eps=1e-6))
-precip_model.load('./weights/fourcastnet/precipitation.pt')
-
-input_x = get_data('2023-01-01 00:00:00')
-
-pred_x = backbone_model(input_x)    # input Xt, output Xt+1
-pred_p = precip_model(pred_x)       # input Xt+1, output Pt+1
-
-plot_data = xr.Dataset([pred_x, pred_p])
-ax = plt.axes(projection=ccrs.PlateCarree())
-plot_data.plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False, add_labels=False, rasterized=True)
-ax.coastlines(resolution='110m')
-plt.savefig('img.png')
-```
-
-FourCastNet can predict 7 surface variables, plus 5 atmospheric variables at each of 3 or 4 pressure levels, for 21 variables total. The details of these variables follow the [paper](https://arxiv.org/abs/2202.11214).