jwyang commited on
Commit
5a189a4
1 Parent(s): 6fb88a6

remove pil_interp

Browse files
Files changed (1) hide show
  1. focalnet.py +4 -5
focalnet.py CHANGED
@@ -15,7 +15,6 @@ from timm.models.registry import register_model
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  from torchvision import transforms
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  from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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  from timm.data import create_transform
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- from timm.data.transforms import _pil_interp
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  class Mlp(nn.Module):
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  def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
@@ -499,14 +498,14 @@ def build_transforms(img_size, center_crop=False):
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  if center_crop:
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  size = int((256 / 224) * img_size)
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  t.append(
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- transforms.Resize(size, interpolation=_pil_interp('bicubic'))
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  )
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  t.append(
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  transforms.CenterCrop(img_size)
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  )
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  else:
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  t.append(
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- transforms.Resize(img_size, interpolation=_pil_interp('bicubic'))
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  )
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  t.append(transforms.ToTensor())
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  t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
@@ -517,14 +516,14 @@ def build_transforms4display(img_size, center_crop=False):
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  if center_crop:
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  size = int((256 / 224) * img_size)
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  t.append(
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- transforms.Resize(size, interpolation=_pil_interp('bicubic'))
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  )
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  t.append(
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  transforms.CenterCrop(img_size)
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  )
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  else:
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  t.append(
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- transforms.Resize(img_size, interpolation=_pil_interp('bicubic'))
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  )
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  t.append(transforms.ToTensor())
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  return transforms.Compose(t)
 
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  from torchvision import transforms
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  from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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  from timm.data import create_transform
 
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  class Mlp(nn.Module):
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  def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
 
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  if center_crop:
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  size = int((256 / 224) * img_size)
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  t.append(
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+ transforms.Resize(size)
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  )
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  t.append(
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  transforms.CenterCrop(img_size)
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  )
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  else:
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  t.append(
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+ transforms.Resize(img_size)
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  )
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  t.append(transforms.ToTensor())
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  t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
 
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  if center_crop:
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  size = int((256 / 224) * img_size)
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  t.append(
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+ transforms.Resize(size)
520
  )
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  t.append(
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  transforms.CenterCrop(img_size)
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  )
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  else:
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  t.append(
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+ transforms.Resize(img_size)
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  )
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  t.append(transforms.ToTensor())
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  return transforms.Compose(t)