criterions.py

# Copyright (c) 2021-2022, NVIDIA CORPORATION. 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
import numpy as np

class QuantileLoss(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.register_buffer('q', torch.tensor(config.quantiles))

    def forward(self, predictions, targets):
        diff = predictions - targets
        ql = (1-self.q)*F.relu(diff) + self.q*F.relu(-diff)
        losses = ql.view(-1, ql.shape[-1]).mean(0)
        return losses

def qrisk(pred, tgt, quantiles):
    if isinstance(pred, torch.Tensor):
        pred = pred.detach().cpu().numpy()
    if isinstance(tgt, torch.Tensor):
        tgt = tgt.detach().cpu().numpy()
    diff = pred - tgt
    ql = (1-quantiles)*np.clip(diff,0, float('inf')) + quantiles*np.clip(-diff,0, float('inf'))
    losses = ql.reshape(-1, ql.shape[-1])
    normalizer = np.abs(tgt).mean()
    risk = 2 * losses / normalizer
    return risk.mean(0)