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test_coco.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
@author: liuyaqi
"""
import torch
import torch.nn as nn
import cv2
import numpy as np
import os
import dmac_vgg_skip as dmac_vgg
from utils import load_pairs_csv,imreadtonumpy
from utils import fast_hist,get_NMM,get_MCC
class SoftmaxMask(nn.Module):
def __init__(self):
super(SoftmaxMask,self).__init__()
self.softmax = nn.Softmax2d()
def forward(self,x):
x = self.softmax(x)
return x[:,1,:,:]
class Test_COCO(object):
def __init__(self, args):
"""
data prepare
"""
self.data_path = args.data_path
self.list_path = args.list_path
self.test_num = args.test_num
pair_list_full = load_pairs_csv(os.path.join(self.data_path,self.list_path))
if len(pair_list_full) < self.test_num:
print 'The test number is larger than the data length!'
return
self.pair_list = pair_list_full[:self.test_num]
"""
model initialization
"""
self.model_path = args.model_path
self.gpu = args.gpu_idx
self.nolabel = args.nolabel
self.input_scale = args.input_scale
self.loc = dmac_vgg.DMAC_VGG(self.nolabel, self.gpu, self.input_scale)
loc_saved_state_dict = torch.load(self.model_path)
self.loc.load_state_dict(loc_saved_state_dict)
self.loc.cuda(self.gpu)
"""
visualization options
"""
self.vis_flag = args.vis_flag
self.vis_base_path = args.vis_base_path
self.vis_task_path = args.vis_task_path
if self.vis_flag == True:
if not os.path.exists(self.vis_base_path):
os.makedirs(self.vis_base_path)
print 'mkdir '+self.vis_base_path
else:
print self.vis_base_path + ' exist!'
if not os.path.exists(os.path.join(self.vis_base_path,self.vis_task_path)):
os.makedirs(os.path.join(self.vis_base_path,self.vis_task_path))
print 'mkdir ' + os.path.join(self.vis_base_path,self.vis_task_path)
else:
print os.path.join(self.vis_base_path,self.vis_task_path) + ' exist!'
os.makedirs(os.path.join(self.vis_base_path,self.vis_task_path))
print 'mkdir ' + os.path.join(self.vis_base_path,self.vis_task_path)
"""
score options
"""
self.score_save_path = args.score_save_path
self.score_save_filename = args.score_save_filename
if not os.path.exists(self.score_save_path):
os.makedirs(self.score_save_path)
print 'mkdir ' + self.score_save_path
self.NMM1 = 0
self.NMM2 = 0
self.NMM = 0
self.MCC1 = 0
self.MCC2 = 0
self.MCC = 0
self.iou1 = 0
self.iou2 = 0
self.iou = 0
self.interp256 = nn.UpsamplingBilinear2d(size=(self.input_scale, self.input_scale))
self.softmax_mask = SoftmaxMask()
def test(self):
self.loc.eval()
print self.list_path + ' evaluation begin!'
print 'Total number ' + str(len(self.pair_list))
count = 0
for piece in self.pair_list:
img1,_ = imreadtonumpy(self.data_path,piece[0],self.input_scale)
img2,_ = imreadtonumpy(self.data_path,piece[1],self.input_scale)
label_tmp = int(piece[2])
if label_tmp == 1:
gt_temp = cv2.imread(os.path.join(self.data_path,piece[3]))[:,:,0]
gt_temp[gt_temp == 255] = 1
gt_temp = cv2.resize(gt_temp,(self.input_scale,self.input_scale) , interpolation = cv2.INTER_NEAREST)
gt1 = gt_temp
gt_temp = cv2.imread(os.path.join(self.data_path,piece[4]))[:,:,0]
gt_temp[gt_temp == 255] = 1
gt_temp = cv2.resize(gt_temp,(self.input_scale,self.input_scale) , interpolation = cv2.INTER_NEAREST)
gt2 = gt_temp
else:
gt1 = np.zeros((self.input_scale,self.input_scale))
gt2 = np.zeros((self.input_scale,self.input_scale))
image1 = torch.from_numpy(img1[np.newaxis, :].transpose(0,3,1,2)).float().cuda(self.gpu)
image2 = torch.from_numpy(img2[np.newaxis, :].transpose(0,3,1,2)).float().cuda(self.gpu)
output = self.loc(image1, image2)
output0 = self.interp256(output[0]).cpu().data[0].numpy()
output0 = output0[:,:self.input_scale,:self.input_scale]
output1 = self.interp256(output[1]).cpu().data[0].numpy()
output1 = output1[:,:self.input_scale,:self.input_scale]
output0 = output0.transpose(1,2,0)
output0 = np.argmax(output0,axis = 2)
output1 = output1.transpose(1,2,0)
output1 = np.argmax(output1,axis = 2)
hist1 = fast_hist(gt1.flatten(),output0.flatten(),self.nolabel).astype(float)
hist2 = fast_hist(gt2.flatten(),output1.flatten(),self.nolabel).astype(float)
self.NMM1 += get_NMM(hist1,gt1)
self.NMM2 += get_NMM(hist2,gt2)
self.MCC1 += get_MCC(hist1)
self.MCC2 += get_MCC(hist2)
iou1_tmp = np.diag(hist1) / (hist1.sum(1) + hist1.sum(0) - np.diag(hist1))
iou2_tmp = np.diag(hist2) / (hist2.sum(1) + hist2.sum(0) - np.diag(hist2))
self.iou1 += iou1_tmp[1]
self.iou2 += iou2_tmp[1]
if self.vis_flag == True:
self.vis_fun(piece,img1,img2,gt1,gt2,output)
print 'item ' + str(count) + ' processed!'
count += 1
self.iou = (self.iou1 + self.iou2) / float(count * 2)
self.iou1 = self.iou1 / float(count)
self.iou2 = self.iou2 / float(count)
self.NMM = (self.NMM1 + self.NMM2) / float(count * 2)
self.NMM1 = self.NMM1 / float(count)
self.NMM2 = self.NMM2 / float(count)
self.MCC = (self.MCC1 + self.MCC2) / float(count * 2)
self.MCC1 = self.MCC1 / float(count)
self.MCC2 = self.MCC2 / float(count)
self.printscores()
def printscores(self):
self.iou1 = ("%.4f" % self.iou1)
self.iou2 = ("%.4f" % self.iou2)
self.iou = ("%.4f" % self.iou)
self.NMM1 = ("%.4f" % self.NMM1)
self.NMM2 = ("%.4f" % self.NMM2)
self.NMM = ("%.4f" % self.NMM)
self.MCC1 = ("%.4f" % self.MCC1)
self.MCC2 = ("%.4f" % self.MCC2)
self.MCC = ("%.4f" % self.MCC)
print "iou 1 = ",self.iou1
print "iou 2 = ",self.iou2
print "iou avg = ",self.iou
print "NMM 1 = ",self.NMM1
print "NMM 2 = ",self.NMM2
print "NMM avg = ",self.NMM
print "MCC 1 = ",self.MCC1
print "MCC 2 = ",self.MCC2
print "MCC avg = ",self.MCC
save_file = os.path.join(self.score_save_path,self.score_save_filename)
file_out = open(save_file, 'w')
headers = 'iou1,' + 'iou2,' + 'iou,' \
+ 'NMM1,' + 'NMM2,' + 'NMM,' \
+ 'MCC1,' + 'MCC2,' + 'MCC\n'
file_out.write(headers)
write_line = self.iou1 + ',' + self.iou2 + ',' + self.iou + ',' \
+ self.NMM1 + ',' + self.NMM2 + ',' + self.NMM + ',' \
+ self.MCC1 + ',' + self.MCC2 + ',' + self.MCC + '\n'
file_out.write(write_line)
file_out.close()
def vis_fun(self,piece,img1,img2,gt1,gt2,output):
piece_0 = piece[0].split('/')
piece_1 = piece[1].split('/')
p_0_len = len(piece_0)
p_1_len = len(piece_1)
piece_0_ = piece_0[p_0_len-1].split('.')
piece_1_ = piece_1[p_1_len-1].split('.')
piece_dir = os.path.join(self.vis_base_path, self.vis_task_path, piece_0_[0] + '_' + piece_1_[0])
if not os.path.exists(piece_dir):
os.makedirs(piece_dir)
print 'mkdir '+ piece_dir
else:
print piece_dir + ' exist!'
img1[:,:,0] = img1[:,:,0] + 104.008
img1[:,:,1] = img1[:,:,1] + 116.669
img1[:,:,2] = img1[:,:,2] + 122.675
img2[:,:,0] = img2[:,:,0] + 104.008
img2[:,:,1] = img2[:,:,1] + 116.669
img2[:,:,2] = img2[:,:,2] + 122.675
gt1 = np.uint8(gt1 * 255)
gt2 = np.uint8(gt2 * 255)
output0 = self.softmax_mask(self.interp256(output[0])).cpu().data[0].numpy()
output1 = self.softmax_mask(self.interp256(output[1])).cpu().data[0].numpy()
output0 = np.uint8(output0 * 255)
output1 = np.uint8(output1 * 255)
mask0 = cv2.applyColorMap(output0,cv2.COLORMAP_JET)
mask1 = cv2.applyColorMap(output1,cv2.COLORMAP_JET)
cv2.imwrite(os.path.join(piece_dir,'img_1.jpg'), img1)
cv2.imwrite(os.path.join(piece_dir,'img_2.jpg'), img2)
cv2.imwrite(os.path.join(piece_dir,'img_1_gt.png'), gt1)
cv2.imwrite(os.path.join(piece_dir,'img_2_gt.png'), gt2)
cv2.imwrite(os.path.join(piece_dir,'img_1_mask.png'), output0)
cv2.imwrite(os.path.join(piece_dir,'img_2_mask.png'), output1)
cv2.imwrite(os.path.join(piece_dir,'img_1_colormask.png'), mask0)
cv2.imwrite(os.path.join(piece_dir,'img_2_colormask.png'), mask1)
"""
The parameters for testing.
"""
class ArgsLocal:
pass
args = ArgsLocal()
args.data_path = os.getcwd()
args.list_path = os.path.join(args.data_path,'val2014_combine_fore_easy.csv')
args.test_num = 3000
args.model_path = 'DMAC-adv.pth'
args.gpu_idx = 0
args.nolabel = 2
args.input_scale = 256
args.vis_flag = False
args.vis_base_path = 'vis'
args.vis_task_path = 'coco_combine_6_easy'
args.score_save_path = 'scores'
args.score_save_filename = 'val2014_combine_fore_easy_dmac_adv.csv'
tc = Test_COCO(args)
tc.test()