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import torch
import numpy as np
import torch.nn as nn
from set_convs import convDeepSet
from unet_wrap_padding import Unet
from vit import *
from models import *
hadisd_publisher_shifts = {
"tas": 273.15,
"u": 0.0,
"v": 0.0,
"psl": 0.0,
"ws": 0.0,
}
hadisd_publisher_scales = {
"tas": 10,
"u": 10,
"v": 10,
"psl": 100,
"ws": 10.0,
}
def hadisd_normalisation_factors(var: str):
path = "/home/azureuser/aux_data/norm_factors/"
return {
"mean": np.load(path + f"mean_hadisd_{var}.npy"),
"std": np.load(path + f"std_hadisd_{var}.npy"),
}
def unnormalise_hadisd_var(x, var):
factors = hadisd_normalisation_factors(var)
hadisd_shift = hadisd_publisher_shifts[var]
hadisd_scale = hadisd_publisher_scales[var]
return hadisd_shift + hadisd_scale * (factors["mean"] + factors["std"] * x)
class DownscalingRmseLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, target, output, prev_step, fix_sigma=None, expand=True):
target = torch.flatten(target.to(output.device))
output = torch.flatten(output)
tmp = torch.isnan(target)
clean_target = target[~tmp]
clean_output = output[~tmp]
return torch.mean((clean_target - clean_output) ** 2)
class ConvCNPWeatherOnToOff(nn.Module):
"""
ConvCNP for decoder
"""
def __init__(
self,
in_channels,
out_channels,
int_channels,
device,
res,
data_path="../data/",
mode="end_to_end",
decoder=None,
film=False,
):
super().__init__()
# Setup
self.device = device
self.in_channels = in_channels
self.out_channels = out_channels
self.int_channels = int_channels
self.decoder = decoder
self.int_x = 256
self.int_y = 128
self.mode = mode
self.film = film
# Load lon-lat of internal discretisation
self.era5_x = (
torch.from_numpy(
np.load(data_path + "grid_lon_lat/era5_x_{}.npy".format(res))
).float()
/ 360
)
self.era5_y = (
torch.from_numpy(
np.load(data_path + "grid_lon_lat/era5_y_{}.npy".format(res))
).float()
/ 360
)
# Setup setconv
self.sc_out = convDeepSet(
0.001, "OnToOff", density_channel=False, device=self.device
)
if self.mode not in ["downscaling", "end_to_end"]:
unet_out_channels = out_channels
else:
unet_out_channels = int_channels
# UNet backbone
if self.decoder == "base":
self.decoder_lr = Unet(
in_channels=in_channels,
out_channels=unet_out_channels,
div_factor=1,
film=film,
)
else:
raise Exception(f"Expected to use base decoder, but got {self.decoder}")
# Postprocessing MLP
self.mlp = DownscalingMLP(
in_channels=24 + 9,
out_channels=1,
h_channels=64,
h_layers=2,
)
def forward(self, task, film_index):
x = task["y_context"]
batch_size = x.shape[0]
# UNet backbone
x = self.decoder_lr(x, film_index=task["lt"])
# Transform to station predictions with setconv
num_channels = x.shape[3]
x = x.permute(0, 3, 1, 2)
assert list(x.shape) == [batch_size, num_channels, 240, 121]
x_target = task["x_target"]
num_stations = x_target.shape[2]
x = self.sc_out(
x_in=task["x_context"],
wt=x,
x_out=[x_target[:, 0, :], x_target[:, 1, :]],
)
assert x.shape[0] == batch_size
assert x.shape[2] == num_stations
# Concatenate auxiliary data at stations
alt_target = task["alt_target"]
assert torch.isnan(alt_target).sum() == 0
assert alt_target.shape[0] == batch_size
assert alt_target.shape[2] == num_stations
aux_time = task["aux_time"].squeeze(-1).repeat(1, 1, num_stations)
assert aux_time.shape[0] == batch_size
assert aux_time.shape[2] == num_stations
x = torch.cat([x, alt_target, x_target, aux_time], dim=1).permute(0, 2, 1)
assert x.shape[0] == batch_size
assert x.shape[1] == num_stations
tmp = self.mlp(x)
assert list(tmp.shape) == [batch_size, num_stations, 1]
y_hat = tmp.squeeze(-1)
assert list(y_hat.shape) == [batch_size, num_stations]
return y_hat
class ResidualBlock(nn.Module):
def __init__(self, n_channels):
super().__init__()
self.block = nn.Sequential(nn.Linear(n_channels, n_channels), nn.ReLU())
def forward(self, x):
return self.block(x) + x
class DownscalingMLP(nn.Module):
"""
MLP for handling auxiliary data at station locations
"""
def __init__(self, in_channels, out_channels, h_channels, h_layers):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(in_channels, h_channels),
*[ResidualBlock(h_channels) for _ in range(h_layers)],
nn.Linear(h_channels, out_channels),
)
def forward(self, x):
return self.mlp(x)
def find_epoch(results_dir: str) -> int:
losses = np.load(results_dir + "losses_0.npy")
epoch = np.argmin(losses)
return epoch
def load_config(results_dir: str) -> dict:
with open(results_dir + "config.pkl", "rb") as f:
return pickle.load(f)
def load_model(results_dir, device, config_dir=None, epoch=None):
"""
Load the ConvCNPWeatherOnToOff to generate forecasts
"""
config_dir = config_dir or results_dir
config = load_config(config_dir)
epoch = epoch or find_epoch(results_dir)
model = ConvCNPWeatherOnToOff(
in_channels=config["in_channels"],
out_channels=config["out_channels"],
int_channels=config["int_channels"],
device=device,
res=config["res"],
decoder=config["decoder"],
mode=config["mode"],
film=False,
)
full_state_dict = torch.load(results_dir + f"epoch_{epoch}", map_location=device)
state_dict = full_state_dict["model_state_dict"]
state_dict = {k[7:]: v for k, v in zip(state_dict.keys(), state_dict.values())}
model.load_state_dict(state_dict)
model = model.to(device)
model.eval()
return model
def load_model_e2e(results_dir, lead_time, device):
"""
Load the full E2E model to generate forecasts
"""
config = load_config(results_dir)
epoch = find_epoch(results_dir)
model = ConvCNPWeatherE2E(
device="cuda",
lead_time=lead_time,
se_model_path="../results/assimilation/all_v4_vit_ps3",
forecast_model_path="../results/forecast/vit_fnl_randomlt_mlp",
sf_model_path="../results/downscaling_NEW",
)
full_state_dict = torch.load(results_dir + f"epoch_{epoch}", map_location=device)
state_dict = full_state_dict["model_state_dict"]
state_dict = {k[7:]: v for k, v in zip(state_dict.keys(), state_dict.values())}
model.load_state_dict(state_dict)
model = model.to(device)
model.eval()
return model
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