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evaluation_multi.py
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evaluation_multi.py
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import json
import os.path as osp
from PIL import Image
import torch.nn as nn
from torch.autograd import Variable
import torch
import numpy as np
from data import CreateTrgDataLoader
from model import CreateModel
import os
from options.test_options import TestOptions
import scipy.io as sio
# color coding of semantic classes
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156, 190, 153, 153, 153, 153, 153, 250, 170, 30,
220, 220, 0, 107, 142, 35, 152, 251, 152, 70, 130, 180, 220, 20, 60, 255, 0, 0, 0, 0, 142, 0, 0, 70,
0, 60, 100, 0, 80, 100, 0, 0, 230, 119, 11, 32]
zero_pad = 256 * 3 - len(palette)
for i in range(zero_pad):
palette.append(0)
def colorize_mask(mask):
# mask: numpy array of the mask
new_mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
new_mask.putpalette(palette)
return new_mask
def fast_hist(a, b, n): #a: label(flatten), b:prediction, n: num_class
k = (a>=0) & (a<n)
return np.bincount( n*a[k].astype(int)+b[k], minlength=n**2 ).reshape(n, n)
def per_class_iu(hist):
return np.diag(hist) / ( hist.sum(1)+hist.sum(0)-np.diag(hist) ) # nominator= intersection part(diagonal) of confusion matrix, denominator = union part of confusion matrix, hist here seems to be the confusion matrix
def label_mapping(input, mapping):
output = np.copy(input)
for ind in range(len(mapping)):
output[ input==mapping[ind][0] ] = mapping[ind][1]
return np.array(output, dtype=np.int64)
def compute_mIoU( gt_dir, pred_dir, devkit_dir='', restore_from='' ):
with open( osp.join(devkit_dir, 'info.json'),'r' ) as fp:
info = json.load(fp)
num_classes = np.int(info['classes'])
print('Num classes', num_classes)
name_classes = np.array(info['label'], dtype=np.str)
mapping = np.array( info['label2train'],dtype=np.int )
hist = np.zeros( (num_classes, num_classes) )
image_path_list = osp.join( devkit_dir, 'val.txt')
label_path_list = osp.join( devkit_dir, 'label.txt')
gt_imgs = open(label_path_list, 'r').read().splitlines() #store image-id by image-id
gt_imgs = [osp.join(gt_dir, x) for x in gt_imgs]
pred_imgs = open(image_path_list, 'r').read().splitlines()
pred_imgs = [osp.join(pred_dir, x.split('/')[-1]) for x in pred_imgs]
##TODO code above: from val.txt and label.txt extract the image id to evaluate with corresponding labels.
## and store paths to access those images and labels for validations and predictive network output(image, pred_imgs)
for ind in range(len(gt_imgs)):
pred = np.array(Image.open(pred_imgs[ind]))
label = np.array(Image.open(gt_imgs[ind]))
label = label_mapping(label, mapping)
if len(label.flatten()) != len(pred.flatten()): #size doesn't match
print('Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format( len(label.flatten()), len(pred.flatten()), gt_imgs[ind], pred_imgs[ind] ))
continue
hist += fast_hist(label.flatten(), pred.flatten(), num_classes)
if ind > 0 and ind % 10 == 0:
with open(restore_from+'_mIoU.txt', 'a') as f:
f.write( '{:d} / {:d}: {:0.2f}\n'.format(ind, len(gt_imgs), 100*np.mean(per_class_iu(hist))) )
print('{:d} / {:d}: {:0.2f}'.format(ind, len(gt_imgs), 100*np.mean(per_class_iu(hist))))
hist2 = np.zeros((19, 19))
for i in range(19):
hist2[i] = hist[i]/np.sum(hist[i])
mIoUs = per_class_iu(hist)
for ind_class in range(num_classes):
with open(restore_from+'_mIoU.txt', 'a') as f:
f.write('===>'+name_classes[ind_class]+':\t' + str(round(mIoUs[ind_class]*100,2)) + '\n')
print('===>'+name_classes[ind_class]+':\t' + str(round(mIoUs[ind_class]*100,2)))
with open(restore_from+'_mIoU.txt', 'a') as f:
f.write('===> mIoU: ' + str(round(np.nanmean(mIoUs)*100,2)) + '\n')
print('===> mIoU19: ' + str(round( np.nanmean(mIoUs)*100,2 )))
print('===> mIoU16: ' + str(round( np.mean(mIoUs[[0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18]])*100,2 )))
print('===> mIoU13: ' + str(round( np.mean(mIoUs[[0, 1, 2, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18]])*100,2 )))
def main():
opt = TestOptions()
args = opt.initialize()
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
if not os.path.exists(args.save):
os.makedirs(args.save)
args.restore_from = args.restore_opt1
model1 = CreateModel(args)
model1.eval()
model1.cuda()
args.restore_from = args.restore_opt2
model2 = CreateModel(args)
model2.eval()
model2.cuda()
args.restore_from = args.restore_opt3
model3 = CreateModel(args)
model3.eval()
model3.cuda()
targetloader = CreateTrgDataLoader(args)
# change the mean for different dataset other than CS
IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32)
IMG_MEAN = torch.reshape( torch.from_numpy(IMG_MEAN), (1,3,1,1) )
mean_img = torch.zeros(1, 1)
# ------------------------------------------------- #
# compute scores and save them
with torch.no_grad():
for index, batch in enumerate(targetloader):
if index % 100 == 0:
print( '%d processd' % index )
image, _, name = batch # 1. get image
# create mean image
if mean_img.shape[-1] < 2:
B, C, H, W = image.shape
mean_img = IMG_MEAN.repeat(B,1,H,W) # 2. get mean image
image = image.clone() - mean_img # 3, image - mean_img
image = Variable(image).cuda()
# forward
output1 = model1(image)
output1 = nn.functional.softmax(output1, dim=1)
output2 = model2(image)
output2 = nn.functional.softmax(output2, dim=1)
output3 = model3(image)
output3 = nn.functional.softmax(output3, dim=1)
a, b = 0.3333, 0.3333
output = a*output1 + b*output2 + (1.0-a-b)*output3
output = nn.functional.interpolate(output, (1024, 2048), mode='bilinear', align_corners=True).cpu().data[0].numpy()
#output = nn.functional.upsample( output, (1024, 2048), mode='bilinear', align_corners=True).cpu().data[0].numpy()
output = output.transpose(1,2,0)
output_nomask = np.asarray( np.argmax(output, axis=2), dtype=np.uint8 )
output_col = colorize_mask(output_nomask)
output_nomask = Image.fromarray(output_nomask)
name = name[0].split('/')[-1]
output_nomask.save( '%s/%s' % (args.save, name) ) # image = (height, width)
output_col.save( '%s/%s_color.png' % (args.save, name.split('.')[0]) )
# scores computed and saved
# ------------------------------------------------- #
compute_mIoU( args.gt_dir, args.save, args.devkit_dir, args.restore_from )
if __name__ == '__main__':
main()