def test_zero_image(self): """ Test on zero-value image if cause any division by zero """ X = as_floatX(np.zeros((5, 32 * 32 * 3))) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = LeCunLCN(img_shape=[32, 32]) dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert isfinite(result)
def test_zero_image(self): """ Test on zero-value image if cause any division by zero """ X = as_floatX(np.zeros((5, 32 * 32 * 3))) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = LeCunLCN(img_shape=[32, 32]) dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert isfinite(result)
def test_channel(self): """ Test if works fine withe different number of channel as argument """ rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(5, 32 * 32 * 3)) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = LeCunLCN(img_shape=[32, 32], channels=[1, 2]) dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert isfinite(result)
def test_channel(self): """ Test if works fine withe different number of channel as argument """ rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(5, 32 * 32 * 3)) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = LeCunLCN(img_shape=[32, 32], channels=[1, 2]) dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert isfinite(result)
def test_rgb_yuv(): """ Test on a random image if the per-processor loads and works without anyerror and doesn't result in any nan or inf values """ rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(5, 32 * 32 * 3)) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = RGB_YUV() dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert isfinite(result)
def test_rgb_yuv(): """ Test on a random image if the per-processor loads and works without anyerror and doesn't result in any nan or inf values """ rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(5, 32 * 32 * 3)) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = RGB_YUV() dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert isfinite(result)
def test_random_image(self): """ Test on a random image if the per-processor loads and works without anyerror and doesn't result in any nan or inf values """ rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(5, 32 * 32 * 3)) axes = ["b", 0, 1, "c"] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = LeCunLCN(img_shape=[32, 32]) dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert not np.any(np.isnan(result)) assert not np.any(np.isinf(result))
def test_random_image(self): """ Test on a random image if the per-processor loads and works without anyerror and doesn't result in any nan or inf values """ rng = np.random.RandomState([1, 2, 3]) X = as_floatX(rng.randn(5, 32 * 32 * 3)) axes = ['b', 0, 1, 'c'] view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes) dataset = DenseDesignMatrix(X=X, view_converter=view_converter) dataset.axes = axes preprocessor = LeCunLCN(img_shape=[32, 32]) dataset.apply_preprocessor(preprocessor) result = dataset.get_design_matrix() assert not np.any(np.isnan(result)) assert not np.any(np.isinf(result))