/
point_cross_entropy_loss.py
160 lines (138 loc) · 6.29 KB
/
point_cross_entropy_loss.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
# 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) + 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