mirror of
https://github.com/xuebinqin/DIS.git
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75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
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import os
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# os.environ['CUDA_VISIBLE_DEVICES'] = '2'
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from skimage import io, transform
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import torch
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import torchvision
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from torch.autograd import Variable
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import Dataset, DataLoader
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from torchvision import transforms, utils
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import torch.optim as optim
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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import glob
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def mae_torch(pred,gt):
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h,w = gt.shape[0:2]
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sumError = torch.sum(torch.absolute(torch.sub(pred.float(), gt.float())))
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maeError = torch.divide(sumError,float(h)*float(w)*255.0+1e-4)
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return maeError
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def f1score_torch(pd,gt):
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# print(gt.shape)
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gtNum = torch.sum((gt>128).float()*1) ## number of ground truth pixels
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pp = pd[gt>128]
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nn = pd[gt<=128]
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pp_hist =torch.histc(pp,bins=255,min=0,max=255)
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nn_hist = torch.histc(nn,bins=255,min=0,max=255)
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pp_hist_flip = torch.flipud(pp_hist)
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nn_hist_flip = torch.flipud(nn_hist)
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pp_hist_flip_cum = torch.cumsum(pp_hist_flip, dim=0)
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nn_hist_flip_cum = torch.cumsum(nn_hist_flip, dim=0)
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precision = (pp_hist_flip_cum)/(pp_hist_flip_cum + nn_hist_flip_cum + 1e-4)#torch.divide(pp_hist_flip_cum,torch.sum(torch.sum(pp_hist_flip_cum, nn_hist_flip_cum), 1e-4))
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recall = (pp_hist_flip_cum)/(gtNum + 1e-4)
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f1 = (1+0.3)*precision*recall/(0.3*precision+recall + 1e-4)
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return torch.reshape(precision,(1,precision.shape[0])),torch.reshape(recall,(1,recall.shape[0])),torch.reshape(f1,(1,f1.shape[0]))
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def f1_mae_torch(pred, gt, valid_dataset, idx, mybins, hypar):
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import time
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tic = time.time()
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if(len(gt.shape)>2):
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gt = gt[:,:,0]
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pre, rec, f1 = f1score_torch(pred,gt)
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mae = mae_torch(pred,gt)
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# hypar["valid_out_dir"] = hypar["valid_out_dir"]+"-eval" ###
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if(hypar["valid_out_dir"]!=""):
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if(not os.path.exists(hypar["valid_out_dir"])):
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os.mkdir(hypar["valid_out_dir"])
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dataset_folder = os.path.join(hypar["valid_out_dir"],valid_dataset.dataset["data_name"][idx])
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if(not os.path.exists(dataset_folder)):
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os.mkdir(dataset_folder)
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io.imsave(os.path.join(dataset_folder,valid_dataset.dataset["im_name"][idx]+".png"),pred.cpu().data.numpy().astype(np.uint8))
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print(valid_dataset.dataset["im_name"][idx]+".png")
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print("time for evaluation : ", time.time()-tic)
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return pre.cpu().data.numpy(), rec.cpu().data.numpy(), f1.cpu().data.numpy(), mae.cpu().data.numpy()
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