new project from v0.0.1

scepter/modules/transform/utils.py

@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import cv2
+import numpy as np
+import torch
+from packaging import version
+from PIL import Image
+from torchvision.version import __version__ as tv_version
+
+try:
+    import accimage
+except ImportError:
+    accimage = None
+
+
+def is_pil_image(img):
+    if accimage is not None:
+        return isinstance(img, (Image.Image, accimage.Image))
+    else:
+        return isinstance(img, Image.Image)
+
+
+def is_cv2_image(img):
+    return isinstance(img, np.ndarray) and img.dtype == np.uint8
+
+
+def is_tensor(t):
+    return isinstance(t, torch.Tensor)
+
+
+INPUT_PIL_TYPE_WARNING = 'input should be PIL Image'
+INPUT_CV2_TYPE_WARNING = 'input should be cv2 image(uint8 np.ndarray)'
+INPUT_TENSOR_TYPE_WARNING = 'input should be tensor(uint8 np.ndarray)'
+
+# Recommend to use nn.Module backend to transform
+TORCHVISION_CAPABILITY = version.parse(tv_version) >= version.parse('0.8.0')
+
+BACKEND_TORCHVISION = 'torchvision'
+BACKEND_PILLOW = 'pillow'
+BACKEND_CV2 = 'cv2'
+
+# Recommend to use InterpolationMode since torchvision 0.9.0
+INTERPOLATION_MODE_CAPABILITY = version.parse(tv_version) >= version.parse(
+    '0.9.0')
+if INTERPOLATION_MODE_CAPABILITY:
+    from torchvision.transforms.functional import InterpolationMode
+else:
+    import warnings
+
+    warnings.filterwarnings('ignore', message='Default upsampling behavior.*')
+INTERPOLATION_STYLE = {
+    'bilinear':
+    Image.BILINEAR
+    if not INTERPOLATION_MODE_CAPABILITY else InterpolationMode('bilinear'),
+    'nearest':
+    Image.NEAREST
+    if not INTERPOLATION_MODE_CAPABILITY else InterpolationMode('nearest'),
+    'bicubic':
+    Image.BICUBIC
+    if not INTERPOLATION_MODE_CAPABILITY else InterpolationMode('bicubic'),
+}
+INTERPOLATION_STYLE_CV2 = {
+    'bilinear': cv2.INTER_LINEAR,
+    'nearest': cv2.INTER_NEAREST,
+    'bicubic': cv2.INTER_CUBIC,
+}