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tester.py
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import os.path as osp
import torch
import timeit
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torchvision.utils import save_image
# from torchvision import transforms
from networks import get_model
from utils import *
# from PIL import Image
import time
import cv2
from tqdm import tqdm
from metrics import SegMetric
from convert2CSV import convert_2_masks, mask2csv2
# def make_dataset(dir):
# images = []
# assert osp.isdir(dir), '%s is not a valid directory' % dir
# f = dir.split('/')[-1].split('_')[-1]
# print(dir, len([name for name in os.listdir(dir)
# if osp.isfile(osp.join(dir, name))]))
# for i in range(len([name for name in os.listdir(dir) if osp.isfile(osp.join(dir, name))])):
# img = str(i) + '.jpg'
# path = osp.join(dir, img)
# images.append(path)
# return images
class Tester(object):
def __init__(self, data_loader, config):
# Data loader
self.data_loader = data_loader
# Model hyper-parameters
self.imsize = config.imsize
self.parallel = config.parallel
self.classes = config.classes
self.pretrained_model = config.pretrained_model # int type
self.model_path = config.model_path
self.arch = config.arch
# self.test_size = config.test_size
self.batch_size = config.batch_size
self.test_colorful = config.test_colorful
self.test_color_label_path = osp.join(config.test_color_label_path, self.arch)
self.test_pred_label_path = osp.join(config.test_pred_label_path, self.arch)
self.build_model()
def test(self):
time_meter = AverageMeter()
if os.path.exists("mask.csv"):
os.remove("mask.csv")
print("remove mask.csv file")
# Model loading
print("Load Model From:",self.model_path)
self.G.load_state_dict(torch.load(self.model_path))
self.G.eval()
metrics = SegMetric(n_classes=self.classes)
metrics.reset()
index = 0
for index, (images, labels) in enumerate(tqdm(self.data_loader, desc='Testing Data')):
images = images.cuda()
labels = labels.cuda()
size = labels.size()
h, w = size[1], size[2]
torch.cuda.synchronize()
tic = time.perf_counter()
with torch.no_grad():
outputs = self.G(images)
# Whether or not multi branch?
if self.arch == 'CE2P' or 'FaceParseNet' in self.arch:
outputs = outputs[0][-1]
outputs = F.interpolate(outputs, (h, w), mode='bilinear', align_corners=True)
pred = outputs.data.max(1)[1].cpu().numpy() # Matrix index
gt = labels.cpu().numpy()
metrics.update(gt, pred)
for b in range(pred.shape[0]):
if index == 0 and b == 0:
header = True
else:
header = False
mask_dict = convert_2_masks(pred[b])
mask2csv2(mask_dict, image_id = index * pred.shape[0] + b, header=header)
torch.cuda.synchronize()
time_meter.update(time.perf_counter() - tic)
if self.test_colorful: # Whether color the test results to png files
# labels_predict_color = generate_label(outputs, self.imsize)
labels = labels[:, :, :].view(size[0], 1, size[1], size[2])
oneHot_size = (size[0], self.classes, size[1], size[2])
labels_real = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
labels_real = labels_real.scatter_(1, labels.data.long().cuda(), 1.0)
labels_predict_plain = generate_label_plain(outputs, self.imsize)
compare_predict_color = generate_compare_results(images, labels_real, outputs, self.imsize)
for k in range(self.batch_size):
# save_image(labels_predict_color[k], osp.join(self.test_color_label_path, str(index * self.batch_size + k) +'.png'))
cv2.imwrite(osp.join(self.test_pred_label_path, str(index * self.batch_size + k) +'.png'), labels_predict_plain[k])
save_image(compare_predict_color[k], osp.join(self.test_color_label_path, str(index * self.batch_size + k) +'.png'))
print("----------------- Runtime Performance ------------------")
print('Total %d batches (%d images) tested.' % (index + 1, (index+1)*images.size(0)))
print("Inference Time per image: {:.4f}s".format(time_meter.average() / images.size(0)))
print("Inference FPS: {:.2f}".format(images.size(0) / time_meter.average()))
score = metrics.get_scores()[0]
class_iou = metrics.get_scores()[1]
print("----------------- Total Performance --------------------")
for k, v in score.items():
print(k, v)
print("----------------- Class IoU Performance ----------------")
facial_names = ['background', 'skin', 'nose', 'eyeglass', 'left_eye', 'right_eye', 'left_brow', 'right_brow',
'left_ear', 'right_ear', 'mouth', 'upper_lip', 'lower_lip', 'hair', 'hat', 'earring', 'necklace',
'neck', 'cloth']
for i in range(self.classes):
print(facial_names[i] + "\t: {}".format(str(class_iou[i])))
print("--------------------------------------------------------")
def test_unseen(self):
time_meter = AverageMeter()
if os.path.exists("mask.csv"):
os.remove("mask.csv")
print("remove mask.csv file")
# Model loading
print("Load Model From:",self.model_path)
self.G.load_state_dict(torch.load(self.model_path))
self.G.eval()
metrics = SegMetric(n_classes=self.classes)
metrics.reset()
index = 0
for index, images in enumerate(tqdm(self.data_loader, desc='Testing Data')):
images = images.cuda()
h, w = images.shape[2], images.shape[3]
torch.cuda.synchronize()
tic = time.perf_counter()
with torch.no_grad():
outputs = self.G(images)
# Whether or not multi branch?
if self.arch == 'CE2P' or 'FaceParseNet' in self.arch:
outputs = outputs[0][-1]
outputs = F.interpolate(outputs, (h, w), mode='bilinear', align_corners=True)
pred = outputs.data.max(1)[1].cpu().numpy() # Matrix index
for b in range(pred.shape[0]):
if index == 0 and b == 0:
header = True
else:
header = False
mask_dict = convert_2_masks(pred[b], mode='unseen')
mask2csv2(mask_dict, image_id = index * pred.shape[0] + b, header=header)
torch.cuda.synchronize()
time_meter.update(time.perf_counter() - tic)
if self.test_colorful: # Whether color the test results to png files
oneHot_size = (1, self.classes, 512, 512)
labels_real = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
labels_predict_plain = generate_label_plain(outputs, self.imsize)
compare_predict_color = generate_compare_results(images, labels_real, outputs, self.imsize)
for k in range(self.batch_size):
cv2.imwrite(osp.join(self.test_pred_label_path, str(index * self.batch_size + k) +'.png'), labels_predict_plain[k])
save_image(compare_predict_color[k], osp.join(self.test_color_label_path, str(index * self.batch_size + k) +'.png'))
def build_model(self):
self.G = get_model(self.arch, pretrained=False).cuda()
if self.parallel:
self.G = nn.DataParallel(self.G)