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basnet_test.py
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import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
import cv2
# import torch.optim as optim
from data import test_dataset
import numpy as np
import onnx
from PIL import Image
import glob
import time
from data import get_loader
from data_loader import RescaleT
from data_loader import CenterCrop
from data_loader import ToTensor
from data_loader import ToTensorLab, OtherTrans
from data_loader import SalObjDataset
from model import BASNet
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
def normPRED(d):
ma = torch.max(d)
mi = torch.min(d)
dn = (d-mi)/(ma-mi)
return dn
def overlay(image, mask):
mask_3 = np.tile(np.expand_dims(mask, axis=-1), (1, 1, 3))
olay = np.multiply(image, mask_3)
return olay
def save_output(image_name, pred, d_dir, o_dir):
pred = pred.squeeze()
pred = pred.cpu().data.numpy()
th = 0.1
pred[pred > th] = 1
pred[pred <= th] = 0
img_name = image_name.split("/")[-1]
image = io.imread(image_name)
mask = transform.resize(pred, (image.shape[0],image.shape[1]), anti_aliasing=False, mode = 'constant', order=0)
mask = np.tile(np.expand_dims(mask, axis=-1), (1, 1, 3))
#kernel = np.ones((3, 3), np.uint8)
#mask = cv2.erode(mask, kernel, iterations=4)
olay = image * mask
#pb_np = np.array(imo)
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1,len(bbb)):
imidx = imidx + "." + bbb[i]
io.imsave(o_dir+imidx+'.jpg', olay)
io.imsave(d_dir + imidx + '.jpg', mask)
if __name__ == '__main__':
# --------- 1. get image path and name ---------
image_dir = '/home/hypevr/Desktop/data_0616/xy/2/image/'#'/media/hypevr/KEY/tonaci_selected/'#'./test_data/test_images/'
prediction_dir = '/home/hypevr/Desktop/data_0616/xy/2/mask/'#'/media/hypev/KEY/tonaci_selected_masks/'
olay_dir = '/home/hypevr/Desktop/data_0616/xy/2/olay/'#'/media/hypevr/KEY/tonaci_selected_olay/'
model_dir = './saved_models/basnet_bsi_human2_fr0.2_pb_0.2/basnet_209.pth' #refine/
plate_dir = '/home/hypevr/Desktop/data_0616/xy/2/back'
img_name_list = glob.glob(image_dir + '*.jpg')
# --------- 2. dataloader ---------
#1. dataload
##test_salobj_dataset = SalObjDataset(img_name_list = img_name_list, lbl_name_list = [],transform=transforms.Compose([RescaleT(352), ToTensorLab(flag=0)])) #,OtherTrans()
#test_salobj_dataloader = DataLoader(test_salobj_dataset, batch_size=1,shuffle=False,num_workers=1)
# test_salobj_dataloader = get_loader(image_dir, prediction_dir, batchsize=1,
# trainsize=416)
test_loader = test_dataset(image_dir, image_dir, 352, True)
# --------- 3. model define ---------
print("...load BASNet...")
net = BASNet(3, 1)
#net = nn.DataParallel(net)
net.load_state_dict(torch.load(model_dir)) #, map_location='cuda:0'
net.cuda()
net.eval()
scriptedmodel = torch.jit.script(net)
torch.jit.save(scriptedmodel, 'scripted_BASNet_57.pt')
x = torch.ones((1, 3, 352, 352)).cuda()
torch.onnx.export(net, x, "basnet.onnx", opset_version=11)
onnx_model = onnx.load("basnet.onnx")
onnx.checker.check_model(onnx_model)
#net.eval()
#example = torch.rand(1, 3, 256, 256).cuda()
#traced_script_module = torch.jit.trace(net, example)
#traced_script_module.save("traced_model_BASNet.pt")
net = torch.load('scripted_BASNet_57.pt')
net.eval()
# --------- 4. inference for each image ---------
for i in range(test_loader.size):
image_orig, inputs_test, gt, name = test_loader.load_data()
##inputs_test = data_test[0]
inputs_test = inputs_test.type(torch.FloatTensor)
image_resized = inputs_test.numpy()[0, :, :, :].transpose((1, 2, 0))
#io.imsave('after_resize.png', inputs_test.numpy()[0, :, :, :].transpose((1, 2, 0)))
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
start = time.time()
d1 = net(inputs_test)#,d2,d3,d4,d5,d6,d7,d8 ,, d2, d3, d4, d5, d6, d7, d8
#print(d1)
torch.cuda.synchronize()
#d1 = d1.cpu()
print(time.time()-start)
pred = normPRED(d1)
#pred = overlay(image_resized, pred.squeeze().cpu().data.numpy())
# save results to test_results folder
save_output(image_dir+name, pred,prediction_dir, olay_dir)
#del d1,d2,d3,d4,d5,d6,d7,d8