1 files changed, 0 insertions, 67 deletions
diff --git a/supervised/datautils.py b/supervised/datautils.py
deleted file mode 100644
index 843f669..0000000
--- a/supervised/datautils.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np
-import torch
-from torchvision.transforms import transforms
-
-
-def color_distortion(s=1.0):
-    # s is the strength of color distortion.
-    color_jitter = transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s)
-    rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
-    rnd_gray = transforms.RandomGrayscale(p=0.2)
-    color_distort = transforms.Compose([
-        rnd_color_jitter,
-        rnd_gray
-    ])
-    return color_distort
-
-
-class Clip(object):
-    def __call__(self, x):
-        return torch.clamp(x, 0, 1)
-
-
-class RandomGaussianBlur(object):
-    """
-        PyTorch version of
-        https://github.com/google-research/simclr/blob/244e7128004c5fd3c7805cf3135c79baa6c3bb96/data_util.py#L311
-    """
-
-    def gaussian_blur(self, image, sigma):
-        image = image.reshape(1, 3, 224, 224)
-        radius = np.int(self.kernel_size / 2)
-        kernel_size = radius * 2 + 1
-        x = np.arange(-radius, radius + 1)
-
-        blur_filter = np.exp(
-            -np.power(x, 2.0) / (2.0 * np.power(np.float(sigma), 2.0)))
-        blur_filter /= np.sum(blur_filter)
-
-        conv1 = torch.nn.Conv2d(3, 3, kernel_size=(kernel_size, 1), groups=3,
-                                padding=[kernel_size // 2, 0], bias=False)
-        conv1.weight = torch.nn.Parameter(torch.Tensor(np.tile(
-            blur_filter.reshape(kernel_size, 1, 1, 1), 3
-        ).transpose([3, 2, 0, 1])))
-
-        conv2 = torch.nn.Conv2d(3, 3, kernel_size=(1, kernel_size), groups=3,
-                                padding=[0, kernel_size // 2], bias=False)
-        conv2.weight = torch.nn.Parameter(torch.Tensor(np.tile(
-            blur_filter.reshape(kernel_size, 1, 1, 1), 3
-        ).transpose([3, 2, 1, 0])))
-
-        res = conv2(conv1(image))
-        assert res.shape == image.shape
-        return res[0]
-
-    def __init__(self, kernel_size, sigma_range=(0.1, 2), p=0.5):
-        self.kernel_size = kernel_size
-        self.sigma_range = sigma_range
-        self.p = p
-
-    def __call__(self, img):
-        with torch.no_grad():
-            assert isinstance(img, torch.Tensor)
-            if np.random.uniform() < self.p:
-                return self.gaussian_blur(
-                    img, sigma=np.random.uniform(*self.sigma_range)
-                )
-            return img