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test.py
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r""" testing code """
import argparse
import os
import torch.nn as nn
import numpy as np
import torch
import cv2
from skimage import filters
from PIL import Image
from model.sccnet import SCCNetwork
from common.logger import Logger, AverageMeter
from common.vis import Visualizer
from common.evaluation import Evaluator
from common import utils
from data.dataset import FSSDataset
import torchvision.transforms.functional as FF
import torch.nn.functional as F
from torchmetrics.classification import BinaryJaccardIndex
def eigen2img_multi(eigen):
img = eigen.numpy()
threshs = filters.threshold_multiotsu(img)
return img > threshs[1]
def eigen2img_adp(eigen):
minv, maxv = eigen.min(), eigen.max()
eigen = (eigen - minv) / (1e-6 + maxv - minv)
uint_img = (eigen.numpy() * 255).astype('uint8')
return cv2.adaptiveThreshold(uint_img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 9, 2)
def eigen2img_merge(eigen):
mask = eigen2img_multi(eigen)
detail = np.asarray(eigen2img_adp(eigen))
return np.logical_and(mask, detail)
def test(model, dataloader, nshot, args):
r""" Test HSNet """
# Freeze randomness during testing for reproducibility
utils.fix_randseed(0)
average_meter = AverageMeter(dataloader.dataset)
iou = BinaryJaccardIndex().cuda()
for idx, batch in enumerate(dataloader):
# 1. forward pass
batch = utils.to_cuda(batch)
pred_mask = model.module.predict_mask_nshot(batch, nshot=nshot)
assert pred_mask.size() == batch['query_mask'].size()
if args.fuse:
b = pred_mask.size()[0]
for i in range(b):
img_name = batch['query_name'][i]
file_name = os.path.join(args.eigen_path, f'{img_name}.pth')
eigen = torch.load(file_name)
if args.perfect:
best_iou = 0
best_eigen_mask = None
for j in range(4):
eigen_img = eigen2img_merge(eigen['eigenvectors'][j + 1].resize(64, 64))
eigen_mask = torch.tensor(eigen_img).int()
eigen_mask = F.interpolate(
eigen_mask.unsqueeze(0).unsqueeze(0).float(), (256, 256),
mode='bilinear',
align_corners=True).cuda().int().squeeze()
tiou = iou(batch['query_mask'][i], eigen_mask).cpu().item()
if tiou > best_iou:
best_iou = tiou
best_eigen_mask = eigen_mask
if best_iou > 0.1 and best_eigen_mask is not None:
pred_mask[i, :, :] = torch.logical_or(pred_mask[i, :, :], best_eigen_mask)
else:
eigen_img = eigen2img_merge(eigen['eigenvectors'][1].resize(64, 64))
eigen_mask = torch.tensor(eigen_img).int()
eigen_mask = F.interpolate(
eigen_mask.unsqueeze(0).unsqueeze(0).float(), (256, 256),
mode='bilinear',
align_corners=True).cuda().int().squeeze()
tiou = iou(pred_mask[i, :, :], eigen_mask).cpu().item()
if tiou > 0.1:
pred_mask[i, :, :] = torch.logical_or(pred_mask[i, :, :], eigen_mask)
if len(args.seg_path) > 0:
b = pred_mask.size()[0]
for i in range(b):
img_name = batch['query_name'][i]
cls_id = batch['class_id'][i]
file_name = os.path.join(args.seg_path, f'{img_name}__{cls_id}.png')
img = FF.to_pil_image(pred_mask[i, :, :].int())
img.save(file_name)
# 2. Evaluate prediction
area_inter, area_union = Evaluator.classify_prediction(pred_mask.clone(), batch)
average_meter.update(area_inter, area_union, batch['class_id'], loss=None)
average_meter.write_process(idx, len(dataloader), epoch=-1, write_batch_idx=1)
# Visualize predictions
if Visualizer.visualize:
Visualizer.visualize_prediction_batch(batch['support_imgs'], batch['support_masks'],
batch['query_img'], batch['query_mask'],
pred_mask, batch['class_id'], idx,
area_inter[1].float() / area_union[1].float())
# Write evaluation results
average_meter.write_result('Test', 0)
miou, fb_iou = average_meter.compute_iou()
return miou, fb_iou
if __name__ == '__main__':
# Arguments parsing
parser = argparse.ArgumentParser(description='SCCNet Pytorch Implementation')
parser.add_argument('--datapath', type=str, default='../Datasets_HSN')
parser.add_argument('--benchmark', type=str, default='pascal', choices=['pascal', 'isaid', 'dlrsd'])
parser.add_argument('--logpath', type=str, default='')
parser.add_argument('--bsz', type=int, default=1)
parser.add_argument('--img_size', type=int, default=400)
parser.add_argument('--nworker', type=int, default=0)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--fold', type=int, default=0, choices=[0, 1, 2, 3])
parser.add_argument('--nshot', type=int, default=1)
parser.add_argument('--backbone', type=str, default='resnet101', choices=['vgg16', 'resnet50', 'resnet101'])
parser.add_argument('--visualize', action='store_true')
parser.add_argument('--use_original_imgsize', action='store_true')
parser.add_argument('--seg_path', type=str, default='')
parser.add_argument('--eigen_path', type=str, default='')
parser.add_argument('--fuse', type=bool, default=False)
parser.add_argument('--perfect', type=bool, default=False)
args = parser.parse_args()
Logger.initialize(args, training=False)
# Model initialization
model = SCCNetwork(args.backbone, args.use_original_imgsize)
model.eval()
Logger.log_params(model)
# Device setup
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Logger.info('# available GPUs: %d' % torch.cuda.device_count())
model = nn.DataParallel(model)
model.to(device)
# Load trained model
if args.load == '': raise Exception('Pretrained model not specified.')
model.load_state_dict(torch.load(args.load))
# Helper classes (for testing) initialization
Evaluator.initialize()
Visualizer.initialize(args.visualize)
# Dataset initialization
FSSDataset.initialize(img_size=args.img_size, datapath=args.datapath, use_original_imgsize=args.use_original_imgsize)
dataloader_test = FSSDataset.build_dataloader(args.benchmark, args.bsz, args.nworker, args.fold, 'test', aug=False, shot=args.nshot)
# Test HSNet
with torch.no_grad():
test_miou, test_fb_iou = test(model, dataloader_test, args.nshot, args)
Logger.info('Fold %d mIoU: %5.2f \t FB-IoU: %5.2f' % (args.fold, test_miou.item(), test_fb_iou.item()))
Logger.info('==================== Finished Testing ====================')