Spaces:
Sleeping
Sleeping
File size: 6,582 Bytes
251e479 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', [
'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward',
'softmax_focal_loss_forward', 'softmax_focal_loss_backward'
])
class SigmoidFocalLossFunction(Function):
@staticmethod
def symbolic(g, input, target, gamma, alpha, weight, reduction):
return g.op(
'mmcv::MMCVSigmoidFocalLoss',
input,
target,
gamma_f=gamma,
alpha_f=alpha,
weight_f=weight,
reduction_s=reduction)
@staticmethod
def forward(ctx,
input,
target,
gamma=2.0,
alpha=0.25,
weight=None,
reduction='mean'):
assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor))
assert input.dim() == 2
assert target.dim() == 1
assert input.size(0) == target.size(0)
if weight is None:
weight = input.new_empty(0)
else:
assert weight.dim() == 1
assert input.size(1) == weight.size(0)
ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2}
assert reduction in ctx.reduction_dict.keys()
ctx.gamma = float(gamma)
ctx.alpha = float(alpha)
ctx.reduction = ctx.reduction_dict[reduction]
output = input.new_zeros(input.size())
ext_module.sigmoid_focal_loss_forward(
input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha)
if ctx.reduction == ctx.reduction_dict['mean']:
output = output.sum() / input.size(0)
elif ctx.reduction == ctx.reduction_dict['sum']:
output = output.sum()
ctx.save_for_backward(input, target, weight)
return output
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
input, target, weight = ctx.saved_tensors
grad_input = input.new_zeros(input.size())
ext_module.sigmoid_focal_loss_backward(
input,
target,
weight,
grad_input,
gamma=ctx.gamma,
alpha=ctx.alpha)
grad_input *= grad_output
if ctx.reduction == ctx.reduction_dict['mean']:
grad_input /= input.size(0)
return grad_input, None, None, None, None, None
sigmoid_focal_loss = SigmoidFocalLossFunction.apply
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha, weight=None, reduction='mean'):
super(SigmoidFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.register_buffer('weight', weight)
self.reduction = reduction
def forward(self, input, target):
return sigmoid_focal_loss(input, target, self.gamma, self.alpha,
self.weight, self.reduction)
def __repr__(self):
s = self.__class__.__name__
s += f'(gamma={self.gamma}, '
s += f'alpha={self.alpha}, '
s += f'reduction={self.reduction})'
return s
class SoftmaxFocalLossFunction(Function):
@staticmethod
def symbolic(g, input, target, gamma, alpha, weight, reduction):
return g.op(
'mmcv::MMCVSoftmaxFocalLoss',
input,
target,
gamma_f=gamma,
alpha_f=alpha,
weight_f=weight,
reduction_s=reduction)
@staticmethod
def forward(ctx,
input,
target,
gamma=2.0,
alpha=0.25,
weight=None,
reduction='mean'):
assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor))
assert input.dim() == 2
assert target.dim() == 1
assert input.size(0) == target.size(0)
if weight is None:
weight = input.new_empty(0)
else:
assert weight.dim() == 1
assert input.size(1) == weight.size(0)
ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2}
assert reduction in ctx.reduction_dict.keys()
ctx.gamma = float(gamma)
ctx.alpha = float(alpha)
ctx.reduction = ctx.reduction_dict[reduction]
channel_stats, _ = torch.max(input, dim=1)
input_softmax = input - channel_stats.unsqueeze(1).expand_as(input)
input_softmax.exp_()
channel_stats = input_softmax.sum(dim=1)
input_softmax /= channel_stats.unsqueeze(1).expand_as(input)
output = input.new_zeros(input.size(0))
ext_module.softmax_focal_loss_forward(
input_softmax,
target,
weight,
output,
gamma=ctx.gamma,
alpha=ctx.alpha)
if ctx.reduction == ctx.reduction_dict['mean']:
output = output.sum() / input.size(0)
elif ctx.reduction == ctx.reduction_dict['sum']:
output = output.sum()
ctx.save_for_backward(input_softmax, target, weight)
return output
@staticmethod
def backward(ctx, grad_output):
input_softmax, target, weight = ctx.saved_tensors
buff = input_softmax.new_zeros(input_softmax.size(0))
grad_input = input_softmax.new_zeros(input_softmax.size())
ext_module.softmax_focal_loss_backward(
input_softmax,
target,
weight,
buff,
grad_input,
gamma=ctx.gamma,
alpha=ctx.alpha)
grad_input *= grad_output
if ctx.reduction == ctx.reduction_dict['mean']:
grad_input /= input_softmax.size(0)
return grad_input, None, None, None, None, None
softmax_focal_loss = SoftmaxFocalLossFunction.apply
class SoftmaxFocalLoss(nn.Module):
def __init__(self, gamma, alpha, weight=None, reduction='mean'):
super(SoftmaxFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.register_buffer('weight', weight)
self.reduction = reduction
def forward(self, input, target):
return softmax_focal_loss(input, target, self.gamma, self.alpha,
self.weight, self.reduction)
def __repr__(self):
s = self.__class__.__name__
s += f'(gamma={self.gamma}, '
s += f'alpha={self.alpha}, '
s += f'reduction={self.reduction})'
return s
|