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point_cross_entropy_loss.py
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point_cross_entropy_loss.py
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# Copyright (c) 2021 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 paddle
from paddle import nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
@manager.LOSSES.add_component
class PointCrossEntropyLoss(nn.Layer):
"""
Implements the point cross entropy loss function.
The original article refers to
Kirillov A, Wu Y, He K, et al. "PointRend: Image Segmentation As Rendering."
(https://arxiv.org/abs/1912.08193).
Args:
weight (tuple|list|ndarray|Tensor, optional): A manual rescaling weight
given to each class. Its length must be equal to the number of classes.
Default ``None``.
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient. Default ``255``.
top_k_percent_pixels (float, optional): the value lies in [0.0, 1.0]. When its value < 1.0, only compute the loss for
the top k percent pixels (e.g., the top 20% pixels). This is useful for hard pixel mining. Default ``1.0``.
data_format (str, optional): The tensor format to use, 'NCHW' or 'NHWC'. Default ``'NCHW'``.
"""
def __init__(self,
weight=None,
ignore_index=255,
top_k_percent_pixels=1.0,
data_format='NCHW',
align_corners=False):
super(PointCrossEntropyLoss, self).__init__()
if weight is not None:
weight = paddle.to_tensor(weight, dtype='float32')
self.weight = weight
self.ignore_index = ignore_index
self.top_k_percent_pixels = top_k_percent_pixels
self.EPS = 1e-8
self.data_format = data_format
self.align_corners = align_corners
def forward(self, logits, label, semantic_weights=None):
"""
Forward computation.
Args:
logits (Tensor): Logit tensor, the data type is float32, float64. Shape is
(logit,points). logit'shape: [N, C, point_num]. logit'shape:[N, point_num, 2], where C is number of classes.
label (Tensor): Label tensor, the data type is int64. Shape is (N), where each
value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
(N, D1, D2,..., Dk), k >= 1.
semantic_weights (Tensor, optional): Weights about loss for each pixels, shape is the same as label. Default: None.
"""
# for loss
logit, points = logits # [N, C, point_num],[N, point_num, 2]
label = label.unsqueeze(1) # [N,1,H,W]
label = point_sample(
label.astype('float32'),
points,
mode='nearest',
align_corners=self.align_corners) # [N, 1, point_num]
label = paddle.squeeze(label, axis=1).astype('int64') # [N, xx]
channel_axis = 1 if self.data_format == 'NCHW' else -1
if self.weight is not None and logit.shape[channel_axis] != len(
self.weight):
raise ValueError(
'The number of weights = {} must be the same as the number of classes = {}.'
.format(len(self.weight), logit.shape[1]))
logit = paddle.transpose(logit, [0, 2, 1])
no_ignore_label = label
#no_ignore_label[label==self.ignore_index] = 0
loss = F.cross_entropy(logit,
no_ignore_label,
ignore_index=self.ignore_index,
reduction='none')
mask = label != self.ignore_index
mask = paddle.cast(mask, 'float32')
loss = loss * mask
if semantic_weights is not None:
loss = loss * semantic_weights
if self.weight is not None:
_one_hot = F.one_hot(label, logit.shape[-1])
_one_hot_weight = _one_hot * self.weight
loss = loss * _one_hot_weight.argmax(-1)
coef = paddle.sum(_one_hot_weight, axis=-1)
#coef = paddle.ones_like(label)
else:
coef = paddle.ones_like(label)
label.stop_gradient = True
mask.stop_gradient = True
if self.top_k_percent_pixels == 1.0:
avg_loss = paddle.mean(loss) / (
paddle.mean(mask * coef.astype(mask.dtype)) self.EPS)
return avg_loss
loss = loss.reshape((-1, ))
top_k_pixels = int(self.top_k_percent_pixels * loss.numel())
loss, indices = paddle.topk(loss, top_k_pixels)
coef = coef.reshape((-1, ))
coef = paddle.gather(coef, indices)
coef.stop_gradient = True
return loss.mean() / (paddle.mean(coef) self.EPS)
def point_sample(input, points, align_corners=False, **kwargs):
"""A wrapper around :func:`grid_sample` to support 3D point_coords tensors
Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to
lie inside ``[0, 1] x [0, 1]`` square.
Args:
input (Tensor): Feature map, shape (N, C, H, W).
points (Tensor): Image based absolute point coordinates (normalized),
range [0, 1] x [0, 1], shape (N, P, 2) or (N, Hgrid, Wgrid, 2).
align_corners (bool): Whether align_corners. Default: False
Returns:
Tensor: Features of `point` on `input`, shape (N, C, P) or
(N, C, Hgrid, Wgrid).
"""
def denormalize(grid):
"""Denormalize input grid from range [0, 1] to [-1, 1]
Args:
grid (Tensor): The grid to be denormalize, range [0, 1].
Returns:
Tensor: Denormalized grid, range [-1, 1].
"""
return grid * 2.0 - 1.0
add_dim = False
if points.dim() == 3:
add_dim = True
points = paddle.unsqueeze(points, axis=2) # [2, 2048, 1, 2]
output = F.grid_sample(input,
denormalize(points),
align_corners=align_corners,
**kwargs)
if add_dim:
output = paddle.squeeze(output, axis=3)
return output