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_smooth_l1_loss.py
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_smooth_l1_loss.py
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# Copyright 2022 The FastEstimator 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.
# ==============================================================================
from typing import TypeVar
import tensorflow as tf
import torch
from fastestimator.backend._reduce_mean import reduce_mean
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor)
def smooth_l1_loss(y_true: Tensor, y_pred: Tensor, beta: float = 1.0) -> Tensor:
"""Calculate Smooth L1 Loss between two tensors.
This method can be used with TensorFlow tensors:
```python
true = tf.constant([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])
pred = tf.constant([[0.1,0.9,0.05,0.05], [0.1,0.2,0.0,0.7], [0.0,0.15,0.8,0.05], [1.0,0.0,0.0,0.0]])
Smooth_L1 = fe.backend.smooth_l1_loss(y_pred=pred, y_true=true, loss_type='smooth', beta=0.65) #[0.0048, 0.0269, 0.0125, 0.0000]
true = tf.constant([[1], [3], [2], [0]])
pred = tf.constant([[2.0], [0.0], [2.0], [1.0]])
Smooth_L1 = fe.backend.smooth_l1_loss(y_pred=pred, y_true=true, loss_type='smooth', beta=0.65) #[0.6750, 2.6750, 0.0000, 0.6750]
```
This method can be used with PyTorch tensors:
```python
true = torch.tensor([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])
pred = torch.tensor([[0.1,0.9,0.05,0.05], [0.1,0.2,0.0,0.7], [0.0,0.15,0.8,0.05], [1.0,0.0,0.0,0.0]])
Smooth_L1 = fe.backend.smooth_l1_loss(y_pred=pred, y_true=true, loss_type='smooth', beta=0.65) #[0.0048, 0.0269, 0.0125, 0.0000]
true = torch.tensor([[1], [3], [2], [0]])
pred = torch.tensor([[2.0], [0.0], [2.0], [1.0]])
Smooth_L1 = fe.backend.smooth_l1_loss(y_pred=pred, y_true=true, loss_type='smooth', beta=0.65) #[0.6750, 2.6750, 0.0000, 0.6750]
```
Args:
y_true: Ground truth class labels with a shape like (batch) or (batch, n_classes). dtype: int, float16, float32.
y_pred: Prediction score for each class, with a shape like y_true. dtype: float32 or float16.
beta: Threshold factor. Needs to be a positive number. dtype: float16 or float32.
Returns:
Smooth L1 between `y_true` and `y_pred` wrt beta.
Raises:
ValueError: If `y_pred` is an unacceptable data type.
ValueError: If beta is less than 1 for Smooth L1 loss.
"""
if beta <= 0:
raise ValueError("Beta cannot be less than or equal to 0")
if tf.is_tensor(y_pred):
y_true = tf.cast(y_true, y_pred.dtype)
regression_diff = tf.math.abs(y_true - y_pred) # |y - f(x)|
regression_loss = tf.where(tf.math.less(regression_diff, beta),
0.5 * tf.math.pow(regression_diff, 2) / beta,
regression_diff - 0.5 * beta)
smooth_mae = reduce_mean(regression_loss, axis=-1)
elif isinstance(y_pred, torch.Tensor):
smooth_mae = reduce_mean(
torch.nn.SmoothL1Loss(reduction="none", beta=beta)(y_pred, y_true), axis=-1)
else:
raise ValueError("Unrecognized tensor type {}".format(type(y_pred)))
return smooth_mae