# Copyright (c) 2019 Horizon Robotics. 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.
"""Various function/classes related to loss computation."""

import gin
import torch
import torch.nn.functional as F


@gin.configurable
def element_wise_huber_loss(x, y):
    """Elementwise Huber loss.

    Args:
        x (Tensor): label
        y (Tensor): prediction
    Returns:
        loss (Tensor)
    """
    return F.smooth_l1_loss(y, x, reduction="none")


@gin.configurable
def element_wise_squared_loss(x, y):
    """Elementwise squared loss.

    Args:
        x (Tensor): label
        y (Tensor): prediction
    Returns:
        loss (Tensor)
    """
    return F.mse_loss(y, x, reduction="none")

# Copyright (c) 2019 Horizon Robotics. 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.
"""Various function/classes related to loss computation."""

import gin
import torch
import torch.nn.functional as F


@gin.configurable
def element_wise_huber_loss(x, y):
    """Elementwise Huber loss.

    Args:
        x (Tensor): label
        y (Tensor): prediction
    Returns:
        loss (Tensor)
    """
    return F.smooth_l1_loss(y, x, reduction="none")


@gin.configurable
def element_wise_squared_loss(x, y):
    """Elementwise squared loss.

    Args:
        x (Tensor): label
        y (Tensor): prediction
    Returns:
        loss (Tensor)
    """
    return F.mse_loss(y, x, reduction="none")

# Copyright (c) 2019 Horizon Robotics. 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. Various function/classes related to loss computation. Elementwise Huber loss.      Args:         x (Tensor): label         y (Tensor): prediction     Returns:         loss (Tensor) Elementwise squared loss.      Args:         x (Tensor): label         y (Tensor): prediction     Returns:         loss (Tensor)