def __init__(self, trainlist, validlist, meanpath, model,
optimizer, weight_decay=0.0001, gpu=-1):
super(Trainer, self).__init__(meanpath, model, gpu)
self.trainset = loader.load_image_list(trainlist)
self.validset = loader.load_image_list(validlist)
self.optimizer = optimizer
self.wd_rate = weight_decay
optimizer.setup(model)
print('set up')
def setup_class(clazz):
clazz.root = 'resources/test_train'
imglist = os.path.join(clazz.root, 'image-list3.txt')
clazz.dataset = loader.load_image_list(imglist)
meanpath = os.path.join(clazz.root, 'mean.npy')
model = FrgNet64()
clazz.hypernet = NetSet(meanpath, model)
def partition_data(self, imglist, outdir, batchsize=100, confidence=1.0, gpu=-1):
dataset = loader.load_image_list(imglist)
correct_count = 0
wrong_count = 0
for i in six.moves.range(1, len(dataset), batchsize):
mini_dataset = dataset[i:i+batchsize]
x_batch, y_batch = self.create_minibatch(mini_dataset)
confs = self.model.calc_confidence(x_batch)
labels = self.calc_max_label(confs.data)
for idx, tuple in enumerate(labels):
label, conf = tuple
path, ans = mini_dataset[idx]
if label == ans:
if conf < confidence:
print('correct: path={}, confidence={}'.format(path, conf))
save_path = os.path.join(os.path.join(outdir, 'yes'), str(label))
if not os.path.exists(save_path):
os.makedirs(save_path)
correct_count += 1
shutil.copyfile(path, os.path.join(save_path, str(conf) + '-' + str(correct_count) + '.png'))
else:
print('wrong: path={}, confidence={}'.format(path, conf))
save_path = os.path.join(os.path.join(outdir, 'no'), str(label))
if not os.path.exists(save_path):
os.makedirs(save_path)
wrong_count += 1
shutil.copyfile(path, os.path.join(save_path, str(conf) + '-' + str(wrong_count) + '.png'))
print('correct={}, wrong={}, accuracy rate={}'.format(
correct_count, wrong_count, correct_count / len(dataset)))
def tile_sample_images_from_list(listpath, outpath, size, label=-1, w=16, h=9): imglist = loader.load_image_list(listpath) imglist = list(filter(lambda x: True if(label < 0) else x[1] == label, imglist)) imglist = list(map(lambda x: x[0], imglist)) tile_sample_images(imglist, outpath, size, w=w, h=h)
def test_load_image_list4(self):
""" load_image_list case4: image list contains a line \
that has more than 2 values. """
filepath = 'resources/test_util/image-list4.txt'
xs = loader.load_image_list(filepath)
def test_load_image_list3(self):
""" load_image_list case3: image list contains more than 2 lines, \
and an empty line. """
filepath = 'resources/test_util/image-list3.txt'
xs = loader.load_image_list(filepath)
assert xs == [('test1.png', 0), ('test2.png', 1)]
def test_load_image_list2(self):
""" load_image_list case2: image list contains 1 line. """
filepath = 'resources/test_util/image-list2.txt'
xs = loader.load_image_list(filepath)
assert xs == [('test1.png', 0)]
def test_load_image_list1(self):
""" load_image_list case1: image list contains no lines. """
filepath = 'resources/test_util/image-list1.txt'
xs = loader.load_image_list(filepath)
assert xs == []
