/
tester.py
127 lines (100 loc) · 4.75 KB
/
tester.py
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import os
import cv2
import logging
import numpy as np
import torch
from time import time
import utils
from utils import CONFIG
import networks
from utils import comput_sad_loss, compute_connectivity_error, \
compute_gradient_loss, compute_mse_loss
class Tester(object):
def __init__(self, test_dataloader):
self.test_dataloader = test_dataloader
self.logger = logging.getLogger("Logger")
self.model_config = CONFIG.model
self.test_config = CONFIG.test
self.log_config = CONFIG.log
self.data_config = CONFIG.data
self.build_model()
self.resume_step = None
utils.print_network(self.G, CONFIG.version)
if self.test_config.checkpoint:
self.logger.info('Resume checkpoint: {}'.format(self.test_config.checkpoint))
self.restore_model(self.test_config.checkpoint)
def build_model(self):
self.G = networks.get_generator(encoder=self.model_config.arch.encoder, decoder=self.model_config.arch.decoder)
if not self.test_config.cpu:
self.G.cuda()
def restore_model(self, resume_checkpoint):
"""
Restore the trained generator and discriminator.
:param resume_checkpoint: File name of checkpoint
:return:
"""
pth_path = os.path.join(self.log_config.checkpoint_path, '{}.pth'.format(resume_checkpoint))
print('model path: ', pth_path)
checkpoint = torch.load(pth_path)
self.G.load_state_dict(utils.remove_prefix_state_dict(checkpoint['state_dict']), strict=True)
def test(self):
self.G = self.G.eval()
mse_loss = 0
sad_loss = 0
conn_loss = 0
grad_loss = 0
test_num = 0
test_time = 0
img_count = 0
with torch.no_grad():
for image_dict in self.test_dataloader:
image, alpha, trimap = image_dict['image'], image_dict['alpha'], image_dict['trimap']
guidancemap = image_dict['guidancemap']
alpha_shape, name = image_dict['alpha_shape'], image_dict['image_name']
if CONFIG.test.guidancemap_phase == "trimap":
inpmap = trimap
else:
inpmap = guidancemap
if not self.test_config.cpu:
image = image.cuda()
alpha = alpha.cuda()
inpmap = inpmap.cuda()
start = time()
alpha_pred, _ = self.G(image, inpmap)
end = time()
inference_time = end - start
test_time+=inference_time
img_count+=1
print(inference_time, img_count)
if self.model_config.trimap_channel == 3:
trimap = trimap.argmax(dim=1, keepdim=True)
# alpha_pred[trimap == 2] = 1
# alpha_pred[trimap == 0] = 0
trimap[trimap==2] = 255
trimap[trimap==1] = 128
for cnt in range(image.shape[0]):
h, w = alpha_shape
test_alpha = alpha[cnt, 0, ...].data.cpu().numpy() * 255
test_pred = alpha_pred[cnt, 0, ...].data.cpu().numpy() * 255
test_pred = test_pred.astype(np.uint8)
test_trimap = trimap[cnt, 0, ...].data.cpu().numpy()
test_pred = test_pred[:h, :w]
test_trimap = test_trimap[:h, :w]
if self.test_config.alpha_path is not None:
cv2.imwrite(os.path.join(self.test_config.alpha_path, os.path.splitext(name[cnt])[0] + ".png"),
test_pred)
mse_loss += compute_mse_loss(test_pred, test_alpha, test_trimap)
print(name, comput_sad_loss(test_pred, test_alpha, test_trimap)[0])
sad_loss += comput_sad_loss(test_pred, test_alpha, test_trimap)[0]
if not self.test_config.fast_eval:
conn_loss += compute_connectivity_error(test_pred, test_alpha, test_trimap, 0.1)
grad_loss += compute_gradient_loss(test_pred, test_alpha, test_trimap)
test_num += 1
self.logger.info("TEST NUM: \t\t {}".format(test_num))
self.logger.info("MSE: \t\t {}".format(mse_loss / test_num))
self.logger.info("SAD: \t\t {}".format(sad_loss / test_num))
if not self.test_config.fast_eval:
self.logger.info("GRAD: \t\t {}".format(grad_loss / test_num))
self.logger.info("CONN: \t\t {}".format(conn_loss / test_num))
self.logger.info("time: \t\t {}".format(test_time))
self.logger.info("time_per_image: \t\t {}".format(test_time/ test_num))