零基础入门语义分割-地表建筑物识别 Task4 评价函数与损失函数 -学习笔记

import numpy as np
def dice(output, target):
    '''计算Dice系数'''
    smooth = 1e-6 # 避免0为除数
    intersection = (output * target).sum()
    return (2. * intersection + smooth) / (output.sum() + target.sum() + smooth)
# 生成随机两个矩阵测试
target = np.random.randint(0, 2, (3, 3))
output = np.random.randint(0, 2, (3, 3))
d = dice(output, target)
# ----------------------------
target = array([[1, 0, 0],
            [0, 1, 1],
          [0, 0, 1]])
output = array([[1, 0, 1],
            [0, 1, 0],
            [0, 0, 0]])
d = 0.5714286326530524

dice = dice_fn(y_pred.sigmoid(), y_true)

def iou_score(output, target):
    '''计算IoU指标'''
    intersection = np.logical_and(target, output) 
    union = np.logical_or(target, output) 
    return np.sum(intersection) / np.sum(union)
# 生成随机两个矩阵测试
target = np.random.randint(0, 2, (3, 3))
output = np.random.randint(0, 2, (3, 3))
d = iou_score(output, target)
# ----------------------------
target = array([[1, 0, 0],
            [0, 1, 1],
          [0, 0, 1]])
output = array([[1, 0, 1],
            [0, 1, 0],
            [0, 0, 0]])
d = 0.4

import torch
import torch.nn as nn
bce = nn.BCELoss()
bce_sig = nn.BCEWithLogitsLoss()
input = torch.randn(5, 1, requires_grad=True)
target = torch.empty(5, 1).random_(2)
pre = nn.Sigmoid()(input)
loss_bce = bce(pre, target)
loss_bce_sig = bce_sig(input, target)
# ------------------------
input = tensor([[-0.2296],
            [-0.6389],
            [-0.2405],
            [ 1.3451],
            [ 0.7580]], requires_grad=True)
output = tensor([[1.],
             [0.],
             [0.],
             [1.],
             [1.]])
pre = tensor([[0.4428],
            [0.3455],
            [0.4402],
            [0.7933],
            [0.6809]], grad_fn=<SigmoidBackward>)
print(loss_bce)
tensor(0.4869, grad_fn=<BinaryCrossEntropyBackward>)
print(loss_bce_sig)
tensor(0.4869, grad_fn=<BinaryCrossEntropyWithLogitsBackward>)

def loss_fn(y_pred, y_true):
     bce = bce_fn(y_pred, y_true)
     dice = dice_fn(y_pred.sigmoid(), y_true)
     return (0.8 * bce + 0.2 * dice,dice)

bce_fn = nn.BCEWithLogitsLoss(weight=torch.tensor([5]).to(DEVICE))

import torch.nn as nn
import torch
import torch.nn.functional as F
class FocalLoss(nn.Module):
    def __init__(self, alpha=1, gamma=2, logits=False, reduce=True):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.logits = logits  # 如果BEC带logits则损失函数在计算BECloss之前会自动计算softmax/sigmoid将其映射到[0,1]
        self.reduce = reduce
    def forward(self, inputs, targets):
        if self.logits:
            BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduce=False)
        else:
            BCE_loss = F.binary_cross_entropy(inputs, targets, reduce=False)
        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1-pt)**self.gamma * BCE_loss
        if self.reduce:
            return torch.mean(F_loss)
        else:
            return F_loss
# ------------------------
FL1 = FocalLoss(logits=False)
FL2 = FocalLoss(logits=True)
inputs = torch.randn(5, 1, requires_grad=True)
targets = torch.empty(5, 1).random_(2)
pre = nn.Sigmoid()(inputs)
f_loss_1 = FL1(pre, targets)
f_loss_2 = FL2(inputs, targets)
# ------------------------
print('inputs:', inputs)
inputs: tensor([[-1.3521],
        [ 0.4975],
        [-1.0178],
        [-0.3859],
        [-0.2923]], requires_grad=True)
print('targets:', targets)
targets: tensor([[1.],
        [1.],
        [0.],
        [1.],
        [1.]])
print('pre:', pre)
pre: tensor([[0.2055],
        [0.6219],
        [0.2655],
        [0.4047],
        [0.4274]], grad_fn=<SigmoidBackward>)
print('f_loss_1:', f_loss_1)
f_loss_1: tensor(0.3375, grad_fn=<MeanBackward0>)
print('f_loss_2', f_loss_2)
f_loss_2 tensor(0.3375, grad_fn=<MeanBackward0>)

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
try:
    from itertools import  ifilterfalse
except ImportError: # py3k
    from itertools import  filterfalse as ifilterfalse
# --------------------------- MULTICLASS LOSSES ---------------------------
def lovasz_softmax(probas, labels, classes='present', per_image=False, ignore=None):
    """
    Multi-class Lovasz-Softmax loss
      probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1).
              Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
      labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
      per_image: compute the loss per image instead of per batch
      ignore: void class labels
    """
    if per_image:
        loss = mean(lovasz_softmax_flat(*flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes)
                          for prob, lab in zip(probas, labels))
    else:
        loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore), classes=classes)
    return loss
def lovasz_softmax_flat(probas, labels, classes='present'):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
    """
    if probas.numel() == 0:
        # only void pixels, the gradients should be 0
        return probas * 0.
    C = probas.size(1)
    losses = []
    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
    for c in class_to_sum:
        fg = (labels == c).float() # foreground for class c
        if (classes is 'present' and fg.sum() == 0):
            continue
        if C == 1:
            if len(classes) > 1:
                raise ValueError('Sigmoid output possible only with 1 class')
            class_pred = probas[:, 0]
        else:
            class_pred = probas[:, c]
        errors = (Variable(fg) - class_pred).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
    return mean(losses)
def flatten_probas(probas, labels, ignore=None):
    """
    Flattens predictions in the batch
    """
    if probas.dim() == 3:
        # assumes output of a sigmoid layer
        B, H, W = probas.size()
        probas = probas.view(B, 1, H, W)
    B, C, H, W = probas.size()
    probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C)  # B * H * W, C = P, C
    labels = labels.view(-1)
    if ignore is None:
        return probas, labels
    valid = (labels != ignore)
    vprobas = probas[valid.nonzero().squeeze()]
    vlabels = labels[valid]
    return vprobas, vlabels
def xloss(logits, labels, ignore=None):
    """
    Cross entropy loss
    """
    return F.cross_entropy(logits, Variable(labels), ignore_index=255)
# --------------------------- HELPER FUNCTIONS ---------------------------
def isnan(x):
    return x != x
def mean(l, ignore_nan=False, empty=0):
    """
    nanmean compatible with generators.
    """
    l = iter(l)
    if ignore_nan:
        l = ifilterfalse(isnan, l)
    try:
        n = 1
        acc = next(l)
    except StopIteration:
        if empty == 'raise':
            raise ValueError('Empty mean')
        return empty
    for n, v in enumerate(l, 2):
        acc += v
    if n == 1:
        return acc
    return acc / n