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loss.py
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loss.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ["FilterMSELoss", "FilterHuberLoss"]
class FilterMSELoss(nn.Module):
def __init__(self, **kwargs):
super(FilterMSELoss, self).__init__()
print('FilterMSELoss')
def forward(self, pred, gold, raw, col_names):
# Remove bad input
cond1 = raw[:, :, :, col_names["Patv"]] < 0
cond2 = raw[:, :, :, col_names["Pab1"]] > 89
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Pab2"]] > 89)
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Pab3"]] > 89)
cond2 = torch.logical_or(cond2,
raw[:, :, :, col_names["Wdir"]] < -180)
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Wdir"]] > 180)
cond2 = torch.logical_or(cond2,
raw[:, :, :, col_names["Ndir"]] < -720)
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Ndir"]] > 720)
cond2 = torch.logical_or(cond2, cond1)
cond3 = raw[:, :, :, col_names["Patv"]] == 0
cond3 = torch.logical_and(cond3,
raw[:, :, :, col_names["Wspd"]] > 2.5)
cond3 = torch.logical_or(cond3, cond2)
cond = torch.logical_not(cond3)
cond = cond.float()
# cond = torch.cast(cond, "float32")
return torch.mean(F.mse_loss(pred, gold, reduction='none') * cond)
class FilterHuberLoss(nn.Module):
def __init__(self, delta=5, **kwargs):
super(FilterHuberLoss, self).__init__()
self.delta = delta
print('FilterHuberLoss', 'delta = {}'.format(self.delta))
def forward(self, pred, gold, raw, col_names):
# Remove bad input
cond1 = raw[:, :, :, col_names["Patv"]] < 0
cond2 = raw[:, :, :, col_names["Pab1"]] > 89
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Pab2"]] > 89)
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Pab3"]] > 89)
cond2 = torch.logical_or(cond2,
raw[:, :, :, col_names["Wdir"]] < -180)
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Wdir"]] > 180)
cond2 = torch.logical_or(cond2,
raw[:, :, :, col_names["Ndir"]] < -720)
cond2 = torch.logical_or(cond2, raw[:, :, :, col_names["Ndir"]] > 720)
cond2 = torch.logical_or(cond2, cond1)
cond3 = raw[:, :, :, col_names["Patv"]] == 0
cond3 = torch.logical_and(cond3,
raw[:, :, :, col_names["Wspd"]] > 2.5)
cond3 = torch.logical_or(cond3, cond2)
cond = torch.logical_not(cond3)
cond = cond.float()
# cond = torch.cast(cond, "float32")
return torch.mean(F.smooth_l1_loss(pred, gold, reduction='none', beta=self.delta) * cond)