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import torch
import torch.nn as nn
import numpy as np
from models import ConvCNPWeather
from misc_downscaling_functionality import ConvCNPWeatherOnToOff
class ConvCNPWeatherE2E(nn.Module):
"""
Complete Aardvark weather model. This chains together the trained encoder,
processor and decoder modules to create complete forecasts. It can be finetuned
end to end to optimise predictions for a specific variable and location.
"""
def __init__(
self,
device,
lead_time,
se_model_path,
forecast_model_path,
sf_model_path,
return_gridded=False,
aux_data_path=None,
):
super().__init__()
self.device = device
self.lead_time = lead_time
self.return_gridded = return_gridded
# Load encoder
self.se_model = self.load_se_model(se_model_path)
# Load processor
self.forecast_model = nn.ModuleList(
[
self.load_forecast_model(forecast_model_path, l + 1)
for l in range(lead_time)
]
)
# Load decoder
self.sf_model = self.load_sf_model(sf_model_path, lead_time)
self.decoder = None
# Setup normalisation factors
self.forecast_input_means = (
self.to_tensor(np.load(aux_data_path + "norm_factors/mean_4u_1.npy"))
.unsqueeze(0)
.unsqueeze(0)
.unsqueeze(0)
)
self.forecast_input_stds = (
self.to_tensor(np.load(aux_data_path + "norm_factors/std_4u_1.npy"))
.unsqueeze(0)
.unsqueeze(0)
.unsqueeze(0)
)
self.forecast_pred_diff_means = (
self.to_tensor(np.load(aux_data_path + "norm_factors/mean_diff_4u_1.npy"))
.unsqueeze(0)
.unsqueeze(0)
.unsqueeze(0)
)
self.forecast_pred_diff_stds = (
self.to_tensor(np.load(aux_data_path + "norm_factors/std_diff_4u_1.npy"))
.unsqueeze(0)
.unsqueeze(0)
.unsqueeze(0)
)
def to_tensor(self, arr):
return torch.from_numpy(arr).float().to(self.device)
def load_se_model(self, se_model_path):
"""
Load the trained encoder module
"""
with open(se_model_path + "/config.pkl", "rb") as handle:
forecast_config = pickle.load(handle)
model = ConvCNPWeather(
in_channels=forecast_config["in_channels"],
out_channels=forecast_config["out_channels"],
int_channels=forecast_config["int_channels"],
device="cuda",
res=forecast_config["res"],
gnp=bool(0),
decoder=forecast_config["decoder"],
mode=forecast_config["mode"],
film=bool(0),
)
best_epoch = np.argmin(np.load("{}/losses_0.npy".format(se_model_path)))
state_dict = torch.load(
"{}/epoch_{}".format(se_model_path, best_epoch),
map_location="cuda",
)["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("cuda")
return model
def load_forecast_model(self, forecast_model_path, lead_time):
"""
Load the trained processor module
"""
with open(forecast_model_path + "/config.pkl", "rb") as handle:
forecast_config = pickle.load(handle)
model = ConvCNPWeather(
in_channels=forecast_config["in_channels"],
out_channels=forecast_config["out_channels"],
int_channels=forecast_config["int_channels"],
device="cuda",
res=forecast_config["res"],
gnp=bool(0),
decoder=forecast_config["decoder"],
mode=forecast_config["mode"],
film=False,
)
state_dict = torch.load(
f"{forecast_model_path}/forecast_{lead_time}/epoch_0",
map_location="cuda",
)["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("cuda")
return model
def load_sf_model(self, sf_model_path, lead_time):
"""
Load the trained decoder module
"""
with open(sf_model_path + "config.pkl", "rb") as handle:
config = pickle.load(handle)
model = ConvCNPWeatherOnToOff(
in_channels=config["in_channels"],
out_channels=config["out_channels"],
int_channels=config["int_channels"],
device="cuda",
res=config["res"],
decoder=config["decoder"],
mode=config["mode"],
film=False,
)
best_epoch = np.argmin(
np.load("{}/lt_{}/losses_0.npy".format(sf_model_path, lead_time))
)
full_state_dict = torch.load(
sf_model_path + f"/lt_{lead_time}/epoch_{best_epoch}", map_location="cuda"
)
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("cuda")
model.eval()
return model
def process_se_output(self, task, x):
"""
Reshape and normalise encoder output for input to processor
"""
task["forecast"]["y_context"][:, :24, ...] = x.permute(0, 3, 2, 1)
if self.return_gridded:
return task, x.permute(0, 3, 2, 1)
return task
def process_forecast_output(self, task, x, last=False):
"""
Reshape and normalise processor output for input to decoder
"""
base_context = task["forecast"]["y_context"][:, :-11, ...].permute(0, 2, 3, 1)
base_context = (
base_context * self.forecast_input_stds + self.forecast_input_means
).permute(0, 3, 2, 1)
x = self.forecast_pred_diff_means + x * self.forecast_pred_diff_stds
unnorm_x = x + base_context.permute(0, 2, 3, 1)
forecast = unnorm_x
x = (unnorm_x - self.forecast_input_means) / self.forecast_input_stds
task["downscaling"]["y_context"][:, :24, ...] = x.permute(0, 3, 2, 1)
task["forecast"]["y_context"] = torch.cat(
[
x.permute(0, 3, 2, 1),
task["forecast"]["y_context"][:, 24:, ...],
],
axis=1,
)
if self.return_gridded:
return task, forecast
return task
def forward(self, task, film_index=None):
"""
Produce a forecast
"""
# Generate initial state
x = self.se_model(task["assimilation"], film_index=None)
if self.return_gridded:
task, initial_state = self.process_se_output(task, x)
else:
task = self.process_se_output(task, x)
# Generate forecast
for lt in range(self.lead_time):
x = self.forecast_model[lt](task["forecast"], film_index=None)
if self.return_gridded:
task, forecast = self.process_forecast_output(task, x)
else:
task = self.process_forecast_output(task, x)
# Generate station forecast
x = self.sf_model(task["downscaling"], film_index=None)
if self.return_gridded:
initial_state = (
initial_state.permute(0, 3, 2, 1) * self.forecast_input_stds
+ self.forecast_input_means
)
return x, forecast, initial_state
return x
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