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all_test_acc_nocrf.py
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'''
2024.5.20: add accuracy assessment
'''
import os
import argparse
import torch
import torch.nn as nn
from torch.utils import data
from utils.tools import *
import torch.backends.cudnn as cudnn
# import pydensecrf.densecrf as dcrf
# from pydensecrf.utils import unary_from_softmax
from allconfig import get_arguments, get_model, get_testloader, init_seeds
import cv2
from glob import glob
# RGB band
imagenet_mean = np.array([0.485, 0.456, 0.406])*255
imagenet_std = np.array([0.229, 0.224, 0.225])*255
def pv2rgb(mask, palette, num_classes):
h, w = mask.shape[:2] # H W C
mask_rgb = np.zeros(shape=(h, w, 3), dtype=np.uint8)
mask_convert = mask[np.newaxis, :, :]
# num_classes = len(palette)
for i in range(num_classes):
mask_rgb[np.all(mask_convert == i, axis=0)] = palette[i]
return mask_rgb
def main():
# Fixed
cudnn.enabled = True
cudnn.benchmark = True
init_seeds()
# Setup parameters
args = get_arguments()
model = get_model(args)
test_loader = get_testloader(args)
# test_loader = get_valloader(args)
# Setup restore path of trained weights
restore_weight= glob(os.path.join(args.restore_path,
'*_batch'+repr(args.restore_iter)+'*.pth'))
print(args.restore_path)
print(restore_weight)
if len(restore_weight)!=1:
return
else:
restore_weight = restore_weight[0]
restore_name = os.path.basename(restore_weight)[:-4] # remove suffix
savetxtfile = os.path.join(args.restore_path, f'Test_{restore_name}.txt')
f = open(savetxtfile, 'w')
# save args
argsDict = args.__dict__
f.writelines('------------------ start ------------------' + '\n')
for eachArg, value in argsDict.items():
f.writelines(eachArg + ' : ' + str(value) + '\n')
f.writelines('------------------- end -------------------')
f.flush()
w, h = map(int, args.input_size_test.split(','))
input_size_test = (w, h)
# save path
predpath = os.path.join(args.restore_path, 'pred')
os.makedirs(predpath, exist_ok=True)
# Loading model weights
saved_state_dict = torch.load(restore_weight)
model.load_state_dict(saved_state_dict, strict=True) # add strict false
model.eval()
model.cuda()
interp_test = nn.Upsample(size=(input_size_test[1], input_size_test[0]), mode='bilinear')
TP_all = np.zeros((args.num_classes, 1))
FP_all = np.zeros((args.num_classes, 1))
TN_all = np.zeros((args.num_classes, 1))
FN_all = np.zeros((args.num_classes, 1))
n_valid_sample_all = 0
F1 = np.zeros((args.num_classes, 1))
IoU = np.zeros((args.num_classes, 1))
TP_all_crf = np.zeros((args.num_classes, 1))
FP_all_crf = np.zeros((args.num_classes, 1))
TN_all_crf = np.zeros((args.num_classes, 1))
FN_all_crf = np.zeros((args.num_classes, 1))
n_valid_sample_all_crf = 0
F1_crf = np.zeros((args.num_classes, 1))
IoU_crf = np.zeros((args.num_classes, 1))
for index, batch in enumerate(test_loader):
image, label,_, name = batch
label = label.squeeze().numpy()
img_size = image.shape[2:]
block_size = input_size_test
min_overlap = 40
# crop the test images into 128×128 patches
y_end,x_end = np.subtract(img_size, block_size)
x = np.linspace(0, x_end, int(np.ceil(x_end/np.float64(block_size[1]-min_overlap)))+1, endpoint=True).astype('int')
y = np.linspace(0, y_end, int(np.ceil(y_end/np.float64(block_size[0]-min_overlap)))+1, endpoint=True).astype('int')
test_pred = np.zeros(img_size)
test_porb = np.zeros((args.num_classes,image.shape[2],image.shape[3]))
for j in range(len(x)):
for k in range(len(y)):
r_start,c_start = (y[k],x[j])
r_end,c_end = (r_start+block_size[0],c_start+block_size[1])
image_part = image[0,:,r_start:r_end, c_start:c_end].unsqueeze(0).cuda()
with torch.no_grad():
# pb,pe = model(image_part)
pb = model(image_part)
if not isinstance(pb, torch.Tensor):
# src_output = [torch.softmax(i, dim=1) for i in pb]
# src_output = torch.stack(src_output, dim=0) # B N C H W
# src_output = torch.mean(src_output, dim=0) # N C H W
# src_output = interp_test(src_output)
src_output = interp_test((nn.functional.softmax(pb[0],dim=1)+nn.functional.softmax(pb[1],dim=1))/2)
else:
src_output = interp_test(torch.softmax(pb, dim=1))
pred = torch.argmax(src_output.detach(), 1)
pred = pred.squeeze().data.cpu().numpy()
src_output = src_output.cpu().detach().numpy().squeeze()
if (j==0)and(k==0):
test_pred[r_start:r_end, c_start:c_end] = pred
test_porb[:,r_start:r_end, c_start:c_end] = src_output
elif (j==0)and(k!=0):
test_pred[r_start+int(min_overlap/2):r_end, c_start:c_end] = pred[int(min_overlap/2):,:]
test_porb[:,r_start+int(min_overlap/2):r_end, c_start:c_end] = src_output[:,int(min_overlap/2):,:]
elif (j!=0)and(k==0):
test_pred[r_start:r_end, c_start+int(min_overlap/2):c_end] = pred[:,int(min_overlap/2):]
test_porb[:,r_start:r_end, c_start+int(min_overlap/2):c_end] = src_output[:,:,int(min_overlap/2):]
elif (j!=0)and(k!=0):
test_pred[r_start+int(min_overlap/2):r_end, c_start+int(min_overlap/2):c_end] = pred[int(min_overlap/2):,int(min_overlap/2):]
test_porb[:,r_start+int(min_overlap/2):r_end, c_start+int(min_overlap/2):c_end] = src_output[:,int(min_overlap/2):,int(min_overlap/2):]
#print(index+1, '/', len(test_loader), ': Testing ', name)
TP,FP,TN,FN,n_valid_sample = eval_image(test_pred.reshape(-1),label.reshape(-1),args.num_classes)
TP_all += TP
FP_all += FP
TN_all += TN
FN_all += FN
n_valid_sample_all += n_valid_sample
test_pred = np.asarray(test_pred, dtype=np.uint8)
output_col = pv2rgb(test_pred, args.palette, args.num_classes)
# plt.imsave(,output_col)
cv2.imwrite('%s/%s_pred.png' % (predpath, name[0].split('.')[0]), output_col)
# CRF
'''
# convert img to original version
image = np.moveaxis(image[0].cpu().numpy(),0,-1) # H W C
image = (image*imagenet_std+imagenet_mean).astype('uint8')
im = np.ascontiguousarray(image)
unary = unary_from_softmax(test_porb)
unary = np.ascontiguousarray(unary)
d = dcrf.DenseCRF2D(img_size[1], img_size[0], args.num_classes)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=3, compat=3)
d.addPairwiseBilateral(sxy=80, srgb=13, rgbim=im, compat=10)
Q = d.inference(5)
test_pred_crf = np.argmax(Q, axis=0).reshape((img_size[0], img_size[1]))
TP,FP,TN,FN,n_valid_sample = eval_image(test_pred_crf.reshape(-1),label.reshape(-1),args.num_classes)
TP_all_crf += TP
FP_all_crf += FP
TN_all_crf += TN
FN_all_crf += FN
n_valid_sample_all_crf += n_valid_sample
test_pred_crf = np.asarray(test_pred_crf, dtype=np.uint8)
# output_col_crf = index2bgr_z(test_pred_crf)
output_col_crf = pv2rgb(test_pred_crf, args.palette, args.num_classes)
# plt.imsave('%s/%s_CRGNet_crf.png' % (args.snapshot_dir, name[0].split('.')[0]),output_col_crf)
cv2.imwrite('%s/%s_pred_crf.png' % (predpath, name[0].split('.')[0]), output_col_crf)
'''
OA = np.sum(TP_all)*1.0 / n_valid_sample_all
for i in range(args.num_classes):
P = TP_all[i]*1.0 / (TP_all[i] + FP_all[i] + args.epsilon)
R = TP_all[i]*1.0 / (TP_all[i] + FN_all[i] + args.epsilon)
F1[i] = 2.0*P*R / (P + R + args.epsilon)
IoU[i] = TP_all[i]*1.0 / (TP_all[i] + FP_all[i] + FN_all[i] + args.epsilon)
for i in range(args.num_classes):
f.write('===>' + args.name_classes[i] + ': %.2f\n'%(float(F1[i]) * 100))
print('===>' + args.name_classes[i] + ': %.2f'%(float(F1[i]) * 100))
mF1 = np.mean(F1)
mIoU = np.mean(IoU)
f.write('===> mean F1: %.2f mean IoU: %.2f OA: %.2f\n'%(mF1*100,mIoU*100,OA*100))
print('===> mean F1: %.2f mean IoU: %.2f OA: %.2f'%(mF1*100,mIoU*100,OA*100))
'''
OA = np.sum(TP_all_crf)*1.0 / n_valid_sample_all_crf
for i in range(args.num_classes):
P = TP_all_crf[i]*1.0 / (TP_all_crf[i] + FP_all_crf[i] + args.epsilon)
R = TP_all_crf[i]*1.0 / (TP_all_crf[i] + FN_all_crf[i] + args.epsilon)
F1_crf[i] = 2.0*P*R / (P + R + args.epsilon)
IoU_crf[i] = TP_all_crf[i]*1.0 / (TP_all_crf[i] + FP_all_crf[i] + FN_all_crf[i] + args.epsilon)
for i in range(args.num_classes):
f.write('===>' + args.name_classes[i] + ': %.2f\n'%(float(F1_crf[i]) * 100))
print('===>' + args.name_classes[i] + ': %.2f'%(float(F1_crf[i]) * 100))
mF1 = np.mean(F1_crf)
mIoU = np.mean(IoU_crf)
f.write('===> mean F1: %.2f mean IoU: %.2f OA: %.2f\n'%(mF1*100,mIoU*100,OA*100))
print('===> mean F1: %.2f mean IoU: %.2f OA: %.2f'%(mF1*100,mIoU*100,OA*100))
'''
f.close()
# rename
newtxtfile = os.path.join(args.restore_path, f'Test_{restore_name[:-8]}'+'_'+repr(int(mF1*10000))+'.txt')
os.rename(savetxtfile, newtxtfile)
if __name__ == '__main__':
main()