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test.py
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import argparse
import numpy as np
from util.util import load_by_path
from PIL import Image
from models.DAHRN.CDNet import BASECDNet
from grad_cam import CDGradCamUtil, CDModelOutputWrapper
# model = BASECDNet('hrnet18')
# saved_path = '../DTHRCDNet/checkpoints/SYSU_rf_basecdn_hrnet18_c1_hybrid_SCAT_0913/CUBECDN_test_60_F1_1_0.83629_net_F.pth'
# model = load_by_path(model, saved_path)
# model = CDModelOutputWrapper(model)
# # target_layer = [model.model.cube.cubes[0].stageY.branches[0]]
# # target_layer = [model.model.cbuilder.decoder1]
# target_layer = [model.model.sca]
# cam = CDGradCamUtil(model,target_layer,gradcam='GradCAM', use_cuda=True, branch='cd')
# img1 = Image.open('images/00080_A.png')
# img2 = Image.open('images/00080_B.png')
# grad_gray = cam(img1, img2)
# cam.cd_save_cam()
import torchvision.transforms as transforms
from util.util import mkdir
from util.util import save_images
from models import create_model
def transform():
transform_list = []
transform_list += [transforms.ToTensor()]
transform_list += [transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]
return transforms.Compose(transform_list)
def val(opt):
image_1_path = opt.image1_path
image_2_path = opt.image2_path
A_img = Image.open(image_1_path).convert('RGB')
B_img = Image.open(image_2_path).convert('RGB')
trans = transform()
A = trans(A_img).unsqueeze(0)
B = trans(B_img).unsqueeze(0)
# dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
save_path = opt.results_dir
mkdir(save_path)
model.eval()
data = {}
data['A']= A
data['B'] = B
data['A_paths'] = [image_1_path]
model.set_input(data) # unpack data from data loader
pred = model.test(val=False) # run inference return pred
img_path = [image_1_path] # get image paths
save_images(pred, save_path, img_path)
def make_opt(opt):
opt.image1_path = 'checkpoints/bisai_dahrn_1008/val_133_F1_1_0.92568/val_9_1_A.png'
opt.image2_path = 'checkpoints/bisai_dahrn_1008/val_133_F1_1_0.92568/val_9_1_B.png'
opt.model='DAHRN'
opt.epoch='133_F1_1_0.92568'
opt.encoder='hrnet18'
opt.name='bisai_dahrn_1008'
opt.cube_num=1
opt.criterion=''
opt.depth=4
opt.Block='basic'
opt.hrblocks='base'
opt.output_stride=32
opt.bilinear=True
opt.fuse=False
return opt
if __name__ == '__main__':
# 从外界调用方式：
# python test.py --image1_path [path-to-img1] --image2_path [path-to-img2] --results_dir [path-to-result_dir]
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--image1_path', type=str, default='./samples/A/test_2_0000_0000.png',
help='path to images A')
parser.add_argument('--image2_path', type=str, default='./samples/B/test_2_0000_0000.png',
help='path to images B')
parser.add_argument('--results_dir', type=str, default='./samples/output/', help='saves results here.')
parser.add_argument('--name', type=str, default='test',
help='name of the experiment. It decides where to store samples and models')
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
# model parameters
parser.add_argument('--model', type=str, default='DAHRN', help='chooses which model to use. [CDF0 | CDFA]')
parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB ')
parser.add_argument('--arch', type=str, default='mynet3', help='feature extractor architecture | mynet3')
parser.add_argument('--n_class', type=int, default=2, help='# of output pred channels: 2 for num of classes')
# dataset parameters
parser.add_argument('--dataset_mode', type=str, default='changedetection',
help='chooses how datasets are loaded. [changedetection | json]')
parser.add_argument('--istest', type=bool, default=True, help='True for the case without label')
parser.add_argument('--serial_batches', action='store_true',
help='if true, takes images in order to make batches, otherwise takes them randomly')
parser.add_argument('--num_threads', default=0, type=int, help='# threads for loading data')
parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
parser.add_argument('--load_size', type=int, default=256, help='scale images to this size')
parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
parser.add_argument('--max_dataset_size', type=int, default=float("inf"),
help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
parser.add_argument('--preprocess', type=str, default='',
help='scaling and cropping of images at load time [resize_and_crop | none]')
parser.add_argument('--no_flip', type=bool, default=True,
help='if specified, do not flip(left-right) the images for data augmentation')
parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
parser.add_argument('--epoch', type=str, default='',
help='which epoch to load? set to latest to use latest cached model')
parser.add_argument('--load_iter', type=int, default='0',
help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
parser.add_argument('--isTrain', type=bool, default=False, help='is or not')
parser.add_argument('--num_test', type=int, default=np.inf, help='how many test images to run')
opt = parser.parse_args()
opt = make_opt(opt)
val(opt)