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51e0ad4 | 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 | import matplotlib.pyplot as plt
import torch
from torch.nn import functional as F
import os
import shutil
# import logging
import cv2
import numpy as np
from PIL import Image
import sys
import time
import pandas as pd
import pdb
from torchvision import transforms
def metric_s_for_null(pred):
num_seg, T, H, W = pred.shape
pred = pred.view(num_seg*T, H, W)
assert len(pred.shape) == 3
N = pred.size(0)
num_pixels = pred.view(-1).shape[0]
temp_pred = torch.sigmoid(pred)
pred = (temp_pred > 0.5).int()
x = torch.sum(pred.view(-1))
s = torch.sqrt(x / num_pixels)
return s
def mask_iou(pred, target, eps=1e-7, size_average=True):
r"""
param:
pred: size [N x H x W]
target: size [N x H x W]
output:
iou: size [1] (size_average=True) or [N] (size_average=False)
"""
# return mask_iou_224(pred, target, eps=1e-7)
num_ref, T, H, W = pred.shape
pred = pred.view(num_ref*T, H, W)
target = target.view(num_ref*T, H, W)
assert len(pred.shape) == 3 and pred.shape == target.shape
N = pred.size(0)
# 像素数
num_pixels = pred.size(-1) * pred.size(-2)
# gt是否是纯黑
no_obj_flag = (target.sum(2).sum(1) == 0)
# 会把pred进行sigmoid变为01之间的概率
temp_pred = torch.sigmoid(pred)
# 通过阈值变成01矩阵
pred = (temp_pred > 0.4).int()
# 交集
inter = (pred * target).sum(2).sum(1)
# 并集
union = torch.max(pred, target).sum(2).sum(1)
inter_no_obj = ((1 - target) * (1 - pred)).sum(2).sum(1)
inter[no_obj_flag] = inter_no_obj[no_obj_flag]
union[no_obj_flag] = num_pixels
iou = torch.sum(inter / (union + eps)) / N
return iou.item()
def _eval_pr(y_pred, y, num, device='cuda'):
if device.startswith('cuda'):
prec, recall = torch.zeros(num).to(y_pred.device), torch.zeros(num).to(y_pred.device)
# 0到1 生成num个阈值
thlist = torch.linspace(0, 1 - 1e-10, num).to(y_pred.device)
else:
prec, recall = torch.zeros(num), torch.zeros(num)
thlist = torch.linspace(0, 1 - 1e-10, num)
for i in range(num):
y_temp = (y_pred >= thlist[i]).float()
# 计算 True Positives（TP）
tp = (y_temp * y).sum()
# 一个是交集除以 预测面积 一个是除以真实面积
prec[i], recall[i] = tp / (y_temp.sum() + 1e-20), tp / (y.sum() + 1e-20)
return prec, recall
def Eval_Fmeasure(pred, gt, measure_path, pr_num=255, device='cuda'):
r"""
param:
pred: size [N x H x W]
gt: size [N x H x W]
output:
iou: size [1] (size_average=True) or [N] (size_average=False)
"""
num_ref, T, H, W = pred.shape
pred = pred.view(num_ref*T, H, W)
gt = gt.view(num_ref*T, H, W)
assert len(pred.shape) == 3
# sigmoid转为01之间的
pred = torch.sigmoid(pred)
N = pred.size(0)
beta2 = 0.3
avg_f, img_num = 0.0, 0
score = torch.zeros(pr_num)
for img_id in range(N):
if torch.mean(gt[img_id]) == 0.0:
continue
prec, recall = _eval_pr(pred[img_id], gt[img_id], pr_num, device=device)
f_score = (1 + beta2) * prec * recall / (beta2 * prec + recall)
f_score[f_score != f_score] = 0 # for Nan
avg_f += f_score
img_num += 1
score = avg_f / img_num
return score.max().item()
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