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test.py
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test.py
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#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : test.py
# Author : Bowen Pan
# Email : [email protected]
# Date : 09/25/2018
#
# Distributed under terms of the MIT license.
"""
"""
from utils import Foo
from models import VPNModel
from datasets import OVMDataset
from opts import parser
from transform import *
import torchvision
import torch
from torch import nn
import os
import time
import cv2
import dominate
from dominate.tags import *
mean_rgb = [0.485, 0.456, 0.406]
std_rgb = [0.229, 0.224, 0.225]
def main():
global args, web_path, best_prec1
best_prec1 = 0
args = parser.parse_args()
network_config = Foo(
encoder=args.encoder,
decoder=args.decoder,
fc_dim=args.fc_dim,
num_views=args.n_views,
num_class=94,
transform_type=args.transform_type,
output_size=args.label_resolution,
)
val_dataset = OVMDataset(args.data_root, args.eval_list,
transform=torchvision.transforms.Compose([
Stack(roll=True),
ToTorchFormatTensor(div=True),
GroupNormalize(mean_rgb, std_rgb)
]),
num_views=network_config.num_views, input_size=args.input_resolution,
label_size=args.segSize, use_mask=args.use_mask, use_depth=args.use_depth, is_train=False)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size,
num_workers=args.num_workers, shuffle=False,
pin_memory=True
)
mapper = VPNModel(network_config)
mapper = nn.DataParallel(mapper.cuda())
if args.weights:
if os.path.isfile(args.weights):
print(("=> loading checkpoint '{}'".format(args.weights)))
checkpoint = torch.load(args.weights)
args.start_epoch = checkpoint['epoch']
mapper.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.weights)))
criterion = nn.NLLLoss(weight=None, size_average=True)
eval(val_loader, mapper, criterion)
web_path = os.path.join(args.visualize, args.store_name)
if os.path.isdir(web_path):
pass
else:
os.makedirs(web_path)
with dominate.document(title=web_path) as web:
for step in range(len(val_loader)):
if step % args.print_freq == 0:
h2('Step {}'.format(step*args.batch_size))
with table(border = 1, style = 'table-layout: fixed;'):
with tr():
for i in range(args.test_views):
path = 'Step-{}-{}.png'.format(step * args.batch_size, i)
with td(style='word-wrap: break-word;', halign='center', valign='top'):
img(style='width:128px', src=path)
path = 'Step-{}-pred.png'.format(step * args.batch_size)
with td(style='word-wrap: break-word;', halign='center', valign='top'):
img(style='width:128px', src=path)
path = 'Step-{}-gt.png'.format(step * args.batch_size)
with td(style='word-wrap: break-word;', halign='center', valign='top'):
img(style='width:128px', src=path)
with open(os.path.join(web_path, 'index.html'), 'w') as fp:
fp.write(web.render())
def eval(val_loader, mapper, criterion):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
mapper.eval()
end = time.time()
web_path = os.path.join(args.visualize, args.store_name)
if os.path.isdir(web_path):
pass
else:
os.makedirs(web_path)
prec_stat = {}
for i in range(args.num_class):
prec_stat[str(i)] = {'intersec': 0, 'union': 0, 'all': 0}
with open('./metadata/colormap_coarse.csv') as f:
lines = f.readlines()
cat = []
for line in lines:
line = line.rstrip()
cat.append(line)
cat = cat[1:]
label_dic = {}
for i, value in enumerate(cat):
key = str(i)
label_dic[key] = [int(x) for x in value.split(',')[1:]]
for step, (rgb_stack, target, rgb_origin, OverMaskOrigin) in enumerate(val_loader):
data_time.update(time.time() - end)
with torch.no_grad():
input_rgb_var = torch.autograd.variable(rgb_stack).cuda()
_, output = mapper(x=input_rgb_var, return_feat=True)
target_var = target.cuda()
target_var = target_var.view(-1)
upsample = output.view(-1, args.label_resolution, args.label_resolution, args.num_class).transpose(3,2).transpose(2,1).contiguous()
upsample = nn.functional.upsample(upsample, size=args.segSize, mode='bilinear', align_corners=False)
upsample = nn.functional.softmax(upsample, dim=1)
output = torch.log(upsample.transpose(1,2).transpose(2,3).contiguous().view(-1, args.num_class))
_, pred = upsample.data.topk(1, 1, True, True)
pred = pred.squeeze(1)
loss = criterion(output, target_var)
losses.update(loss.data[0], input_rgb_var.size(0))
prec_stat = count_mean_accuracy(output.data, target_var.data, prec_stat)
prec1 = accuracy(output.data, target_var.data, topk=(1,))[0]
batch_time.update(time.time() - end)
end = time.time()
if step % args.print_freq == 0:
output = ('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
step + 1, len(val_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1))
print(output)
pred = np.uint8(pred.cpu()[0])
predMask = np.uint8(np.zeros((args.segSize, args.segSize, 3)))
for i, _ in enumerate(pred):
for j, _ in enumerate(pred[0]):
key = str(pred[i][j])
predMask[i,j] = label_dic[key]
predMask = cv2.resize(predMask[:, :, ::-1], (256, 256), interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(web_path, 'Step-{}-pred.png'.format(step * args.batch_size, i)), predMask)
gtMask = OverMaskOrigin[0].cpu().numpy()
gtMask = cv2.resize(gtMask, (256, 256), interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(web_path, 'Step-{}-gt.png'.format(step * args.batch_size, i)), gtMask)
rgb = rgb_origin.cpu().numpy()[0]
for i in range(args.test_views):
cv2.imwrite(os.path.join(web_path, 'Step-{}-{}.png'.format(step * args.batch_size, i)), cv2.resize(rgb[(i + args.view_bias) % 8], (256, 256), interpolation=cv2.INTER_NEAREST))
sum_acc = 0
counted_cat = 0
sum_iou = 0
for key in prec_stat:
if int(prec_stat[key]['all']) != 0:
acc = prec_stat[key]['intersec'] / (prec_stat[key]['all'] + 1e-10)
iou = prec_stat[key]['intersec'] / (prec_stat[key]['union'] + 1e-10)
sum_acc += acc
sum_iou += iou
counted_cat += 1
mean_acc = sum_acc / counted_cat
mean_iou = sum_iou / counted_cat
output = ('Testing Results: Prec@1 {top1.avg:.3f} Mean Prec@1 {meantop:.3f} Mean IoU {meaniou:.3f} Loss {loss.avg:.5f}'
.format(top1=top1, loss=losses, meantop=mean_acc, meaniou=mean_iou))
print(output)
output_best = '\nBest Prec@1 of: %.3f' % (best_prec1)
print(output_best)
return top1.avg
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def count_mean_accuracy(output, target, prec_stat):
_, pred = output.topk(1, 1, True, True)
pred = pred.squeeze(1)
for key in prec_stat.keys():
label = int(key)
pred_map = np.uint8(pred.cpu().numpy() == label)
target_map = np.uint8(target.cpu().numpy() == label)
intersection_t = pred_map * (pred_map == target_map)
union_t = pred_map + target_map - intersection_t
prec_stat[key]['intersec'] += np.sum(intersection_t)
prec_stat[key]['union'] += np.sum(union_t)
prec_stat[key]['all'] += np.sum(target_map)
return prec_stat
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__=='__main__':
main()