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Update code to Python 3.7 compliance #5125

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Merged
merged 3 commits into from
Dec 27, 2021
+5 −5
Merged

Update code to Python 3.7 compliance #5125

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a22c2e3
Upgrade to py37
oke-aditya Dec 6, 2021
f307ac2
Merge branch 'main' of https://github.com/pytorch/vision into up_py37
oke-aditya Dec 25, 2021
9e6a4ad
Update
oke-aditya Dec 25, 2021
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4 changes: 2 additions & 2 deletions torchvision/datasets/_optical_flow.py
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Expand Up @@ -338,10 +338,10 @@ def __init__(self, root, split="train", pass_name="clean", camera="left", transf
directions = ("into_future", "into_past")
for pass_name, camera, direction in itertools.product(passes, cameras, directions):
image_dirs = sorted(glob(str(root / pass_name / split / "*/*")))
image_dirs = sorted([Path(image_dir) / camera for image_dir in image_dirs])
image_dirs = sorted(Path(image_dir) / camera for image_dir in image_dirs)

flow_dirs = sorted(glob(str(root / "optical_flow" / split / "*/*")))
flow_dirs = sorted([Path(flow_dir) / direction / camera for flow_dir in flow_dirs])
flow_dirs = sorted(Path(flow_dir) / direction / camera for flow_dir in flow_dirs)

if not image_dirs or not flow_dirs:
raise FileNotFoundError(
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2 changes: 1 addition & 1 deletion torchvision/datasets/food101.py
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Expand Up @@ -51,7 +51,7 @@ def __init__(

self._labels = []
self._image_files = []
with open(self._meta_folder / f"{split}.json", "r") as f:
with open(self._meta_folder / f"{split}.json") as f:
metadata = json.loads(f.read())

self.classes = sorted(metadata.keys())
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2 changes: 1 addition & 1 deletion torchvision/io/_video_opt.py
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Expand Up @@ -71,7 +71,7 @@ def _validate_pts(pts_range: Tuple[int, int]) -> None:
assert (
pts_range[0] <= pts_range[1]
), """Start pts should not be smaller than end pts, got
start pts: {0:d} and end pts: {1:d}""".format(
start pts: {:d} and end pts: {:d}""".format(
pts_range[0],
pts_range[1],
)
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2 changes: 1 addition & 1 deletion torchvision/models/optical_flow/raft.py
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Expand Up @@ -74,7 +74,7 @@ class BottleneckBlock(nn.Module):
"""Slightly modified BottleNeck block (extra relu and biases)"""

def __init__(self, in_channels, out_channels, *, norm_layer, stride=1):
super(BottleneckBlock, self).__init__()
super().__init__()

# See note in ResidualBlock for the reason behind bias=True
self.convnormrelu1 = ConvNormActivation(
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