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Don't upload big maps #707

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Don't upload big maps #707

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27 changes: 15 additions & 12 deletions fme/downscaling/aggregators/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -534,6 +534,8 @@ def __init__(
self._name = ensure_trailing_slash(name)
self._mean_target = Mean(batch_mean)
self._mean_prediction = Mean(batch_mean)
# corresponds to 40 x 80 deg at 3km resolution, in comparison CONUS is 28x65 deg
self.max_img_upload_size = 3276800

@torch.no_grad()
def record_batch(self, target: TensorMapping, prediction: TensorMapping) -> None:
Expand Down Expand Up @@ -583,19 +585,20 @@ def get_relative_mean(target, prediction):
metrics = {}
spectra = {}
for var_name in target.keys():
gap = torch.full(
(target[var_name].shape[-2], self.gap_width),
float(target[var_name].min()),
device=target[var_name].device,
)
maps[f"maps/{self._name}full-field/{var_name}"] = torch.cat(
(prediction[var_name], gap, target[var_name]), dim=1
)
maps[f"maps/{self._name}log10_relative_mean/{var_name}"] = relative[
var_name
]
error = prediction[var_name] - target[var_name]
maps[f"maps/{self._name}error/{var_name}"] = error
if target[var_name].nelement() < self.max_img_upload_size:
gap = torch.full(
(target[var_name].shape[-2], self.gap_width),
float(target[var_name].min()),
device=target[var_name].device,
)
maps[f"maps/{self._name}full-field/{var_name}"] = torch.cat(
(prediction[var_name], gap, target[var_name]), dim=1
)
maps[f"maps/{self._name}log10_relative_mean/{var_name}"] = relative[
var_name
]
maps[f"maps/{self._name}error/{var_name}"] = error
metrics[f"metrics/{self._name}bias/{var_name}"] = error.mean()

spectra_prefix = ensure_trailing_slash(f"power_spectrum_of_{self._name}")
Expand Down