@testing.parameterize(*testing.product_dict([
{
'size': (48, 96),
'scaled_size': (32, 64),
'y_offset': 8,
'x_offset': 16
},
{
'size': (16, 68),
'scaled_size': (16, 32),
'y_offset': 0,
'x_offset': 18
},
{
'size': (24, 16),
'scaled_size': (8, 16),
'y_offset': 8,
'x_offset': 0
},
{
'size': (47, 97),
'scaled_size': (32, 64),
'y_offset': 7,
'x_offset': 16
},
], [
{
'fill': 128
},
{
'fill': (104, 117, 123)
},
{
'fill': np.random.uniform(255, size=(3, 1, 1))
},
], [
{
'interpolation': PIL.Image.NEAREST
},
{
'interpolation': PIL.Image.BILINEAR
},
{
'interpolation': PIL.Image.BICUBIC
},
{
'interpolation': PIL.Image.LANCZOS
},
]))
@testing.parameterize(*(testing.product_dict([
{
'pick': 'prob',
'shapes': (1, 200),
'n_class': 200
},
{
'pick': 'res5',
'shapes': (1, 2048, 7, 7),
'n_class': None
},
{
'pick': ['res2', 'conv1'],
'shapes': ((1, 256, 56, 56), (1, 64, 112, 112)),
'n_class': None
},
], [
{
'model_class': ResNet50
},
{
'model_class': ResNet101
},
{
'model_class': ResNet152
},
], [{
'arch': 'fb'
}, {
'arch': 'he'
}])))
from chainercv.utils import testing
@testing.parameterize(*(testing.product_dict([
{
'pick': 'softmax',
'shapes': (1, 200),
'n_class': 200
},
{
'pick': 'conv1',
'shapes': (1, 1280, 7, 7),
'n_class': None
},
{
'pick': ['expanded_conv_2', 'conv'],
'shapes': ((1, 24, 56, 56), (1, 32, 112, 112)),
'n_class': None
},
], [
{
'model_class': MobileNetV2
},
], [
{
'arch': 'tf'
},
])))
class TestMobileNetCall(unittest.TestCase):
def setUp(self):
self.link = self.model_class(n_class=self.n_class,
@testing.parameterize(*testing.product_dict(
[
{
'iterable': tuple
},
{
'iterable': list
},
],
[
{
'keys': 'item1',
'func': lambda in_data: 'transformed_' + in_data[1],
'expected_sample': 'transformed_item1(3)',
},
{
'keys': ('item1', ),
'func': lambda in_data: ('transformed_' + in_data[1], ),
'expected_sample': ('transformed_item1(3)', ),
},
{
'keys': ('item0', 'item2'),
'func':
lambda in_data:
('transformed_' + in_data[0], 'transformed_' + in_data[2]),
'expected_sample':
('transformed_item0(3)', 'transformed_item2(3)'),
},
],
))
class TestTransformDataset(unittest.TestCase):
from chainer.testing import attr
from chainer import Variable
from chainercv.links import SEResNeXt101
from chainercv.links import SEResNeXt50
from chainercv.utils import testing
@testing.parameterize(*(
testing.product_dict(
[
{'pick': 'prob', 'shapes': (1, 200), 'n_class': 200},
{'pick': 'res5',
'shapes': (1, 2048, 7, 7), 'n_class': None},
{'pick': ['res2', 'conv1'],
'shapes': ((1, 256, 56, 56), (1, 64, 112, 112)), 'n_class': None},
],
[
{'model_class': SEResNeXt50},
{'model_class': SEResNeXt101},
],
)
))
class TestSEResNeXtCall(unittest.TestCase):
def setUp(self):
self.link = self.model_class(
n_class=self.n_class, pretrained_model=None)
self.link.pick = self.pick
def check_call(self):