#BSD 3-Clause License # #Copyright (c) 2022, FourCastNet authors #All rights reserved. # #Redistribution and use in source and binary forms, with or without #modification, are permitted provided that the following conditions are met: # #1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # #2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # #3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # #THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" #AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE #DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE #FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL #DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR #SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, #OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE #OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # #The code was authored by the following people: # #Jaideep Pathak - NVIDIA Corporation #Shashank Subramanian - NERSC, Lawrence Berkeley National Laboratory #Peter Harrington - NERSC, Lawrence Berkeley National Laboratory #Sanjeev Raja - NERSC, Lawrence Berkeley National Laboratory #Ashesh Chattopadhyay - Rice University #Morteza Mardani - NVIDIA Corporation #Thorsten Kurth - NVIDIA Corporation #David Hall - NVIDIA Corporation #Zongyi Li - California Institute of Technology, NVIDIA Corporation #Kamyar Azizzadenesheli - Purdue University #Pedram Hassanzadeh - Rice University #Karthik Kashinath - NVIDIA Corporation #Animashree Anandkumar - California Institute of Technology, NVIDIA Corporation import logging import glob from types import new_class import torch import torch.nn as nn import torch.nn.functional as F import random import numpy as np import torch from torch.utils.data import DataLoader, Dataset from torch.utils.data.distributed import DistributedSampler from torch import Tensor import h5py import math import torchvision.transforms.functional as TF import matplotlib import matplotlib.pyplot as plt class PeriodicPad2d(nn.Module): """ pad longitudinal (left-right) circular and pad latitude (top-bottom) with zeros """ def __init__(self, pad_width): super(PeriodicPad2d, self).__init__() self.pad_width = pad_width def forward(self, x): # pad left and right circular out = F.pad(x, (self.pad_width, self.pad_width, 0, 0), mode="circular") # pad top and bottom zeros out = F.pad(out, (0, 0, self.pad_width, self.pad_width), mode="constant", value=0) return out def reshape_fields(img, inp_or_tar, crop_size_x, crop_size_y,rnd_x, rnd_y, params, y_roll, train, normalize=True, orog=None, add_noise=False): #Takes in np array of size (n_history+1, c, h, w) and returns torch tensor of size ((n_channels*(n_history+1), crop_size_x, crop_size_y) if len(np.shape(img)) ==3: img = np.expand_dims(img, 0) img = img[:, :, 0:720] #remove last pixel n_history = np.shape(img)[0] - 1 img_shape_x = np.shape(img)[-2] img_shape_y = np.shape(img)[-1] n_channels = np.shape(img)[1] #this will either be N_in_channels or N_out_channels channels = params.in_channels if inp_or_tar =='inp' else params.out_channels means = np.load(params.global_means_path)[:, channels] stds = np.load(params.global_stds_path)[:, channels] if crop_size_x == None: crop_size_x = img_shape_x if crop_size_y == None: crop_size_y = img_shape_y if normalize: if params.normalization == 'minmax': raise Exception("minmax not supported. Use zscore") elif params.normalization == 'zscore': img -=means img /=stds if params.add_grid: if inp_or_tar == 'inp': if params.gridtype == 'linear': assert params.N_grid_channels == 2, "N_grid_channels must be set to 2 for gridtype linear" x = np.meshgrid(np.linspace(-1, 1, img_shape_x)) y = np.meshgrid(np.linspace(-1, 1, img_shape_y)) grid_x, grid_y = np.meshgrid(y, x) grid = np.stack((grid_x, grid_y), axis = 0) elif params.gridtype == 'sinusoidal': assert params.N_grid_channels == 4, "N_grid_channels must be set to 4 for gridtype sinusoidal" x1 = np.meshgrid(np.sin(np.linspace(0, 2*np.pi, img_shape_x))) x2 = np.meshgrid(np.cos(np.linspace(0, 2*np.pi, img_shape_x))) y1 = np.meshgrid(np.sin(np.linspace(0, 2*np.pi, img_shape_y))) y2 = np.meshgrid(np.cos(np.linspace(0, 2*np.pi, img_shape_y))) grid_x1, grid_y1 = np.meshgrid(y1, x1) grid_x2, grid_y2 = np.meshgrid(y2, x2) grid = np.expand_dims(np.stack((grid_x1, grid_y1, grid_x2, grid_y2), axis = 0), axis = 0) img = np.concatenate((img, grid), axis = 1 ) if params.orography and inp_or_tar == 'inp': img = np.concatenate((img, np.expand_dims(orog, axis = (0,1) )), axis = 1) n_channels += 1 if params.roll: img = np.roll(img, y_roll, axis = -1) if train and (crop_size_x or crop_size_y): img = img[:,:,rnd_x:rnd_x+crop_size_x, rnd_y:rnd_y+crop_size_y] if inp_or_tar == 'inp': img = np.reshape(img, (n_channels*(n_history+1), crop_size_x, crop_size_y)) elif inp_or_tar == 'tar': if params.two_step_training: img = np.reshape(img, (n_channels*2, crop_size_x, crop_size_y)) else: img = np.reshape(img, (n_channels, crop_size_x, crop_size_y)) if add_noise: img = img + np.random.normal(0, scale=params.noise_std, size=img.shape) return torch.as_tensor(img) def reshape_precip(img, inp_or_tar, crop_size_x, crop_size_y,rnd_x, rnd_y, params, y_roll, train, normalize=True): if len(np.shape(img)) ==2: img = np.expand_dims(img, 0) img = img[:,:720,:] img_shape_x = img.shape[-2] img_shape_y = img.shape[-1] n_channels = 1 if crop_size_x == None: crop_size_x = img_shape_x if crop_size_y == None: crop_size_y = img_shape_y if normalize: eps = params.precip_eps img = np.log1p(img/eps) if params.add_grid: if inp_or_tar == 'inp': if params.gridtype == 'linear': assert params.N_grid_channels == 2, "N_grid_channels must be set to 2 for gridtype linear" x = np.meshgrid(np.linspace(-1, 1, img_shape_x)) y = np.meshgrid(np.linspace(-1, 1, img_shape_y)) grid_x, grid_y = np.meshgrid(y, x) grid = np.stack((grid_x, grid_y), axis = 0) elif params.gridtype == 'sinusoidal': assert params.N_grid_channels == 4, "N_grid_channels must be set to 4 for gridtype sinusoidal" x1 = np.meshgrid(np.sin(np.linspace(0, 2*np.pi, img_shape_x))) x2 = np.meshgrid(np.cos(np.linspace(0, 2*np.pi, img_shape_x))) y1 = np.meshgrid(np.sin(np.linspace(0, 2*np.pi, img_shape_y))) y2 = np.meshgrid(np.cos(np.linspace(0, 2*np.pi, img_shape_y))) grid_x1, grid_y1 = np.meshgrid(y1, x1) grid_x2, grid_y2 = np.meshgrid(y2, x2) grid = np.expand_dims(np.stack((grid_x1, grid_y1, grid_x2, grid_y2), axis = 0), axis = 0) img = np.concatenate((img, grid), axis = 1 ) if params.roll: img = np.roll(img, y_roll, axis = -1) if train and (crop_size_x or crop_size_y): img = img[:,rnd_x:rnd_x+crop_size_x, rnd_y:rnd_y+crop_size_y] img = np.reshape(img, (n_channels, crop_size_x, crop_size_y)) return torch.as_tensor(img) def vis_precip(fields): pred, tar = fields fig, ax = plt.subplots(1, 2, figsize=(24,12)) ax[0].imshow(pred, cmap="coolwarm") ax[0].set_title("tp pred") ax[1].imshow(tar, cmap="coolwarm") ax[1].set_title("tp tar") fig.tight_layout() return fig