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Misc lint fixes #1020

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Jun 14, 2019
Merged

Misc lint fixes #1020

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2 changes: 1 addition & 1 deletion references/detection/coco_utils.py
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Expand Up @@ -195,7 +195,7 @@ def convert_to_coco_api(ds):


def get_coco_api_from_dataset(dataset):
for i in range(10):
for _ in range(10):
if isinstance(dataset, torchvision.datasets.CocoDetection):
break
if isinstance(dataset, torch.utils.data.Subset):
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2 changes: 1 addition & 1 deletion references/detection/group_by_aspect_ratio.py
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Expand Up @@ -101,7 +101,7 @@ def __len__(self):
collate_fn=lambda x: x[0])
aspect_ratios = []
with tqdm(total=len(dataset)) as pbar:
for i, (img, _) in enumerate(data_loader):
for _i, (img, _) in enumerate(data_loader):
pbar.update(1)
height, width = img.shape[-2:]
aspect_ratio = float(height) / float(width)
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@fmassa Shouldn't aspect_ratio be width/height?

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I think the important thing in here is for it to be consistent between all the implementations, and I don't think it will change anything in this particular case.

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Well, it just looks weid to understand because it is different from other definition.

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which definition?

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Something like this and this, and also aspect_ratio = w / h is commonly defined in other places like computer graphics. Anyway, it doesn't affect the behavior in your code.

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I believe we can change this definition here without breaking the rest of the code. I would be willing to merge a PR that do this change.

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@fmassa Please take a look at #1194

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2 changes: 0 additions & 2 deletions references/detection/train.py
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Expand Up @@ -9,8 +9,6 @@
import torchvision.models.detection
import torchvision.models.detection.mask_rcnn

from torchvision import transforms

from coco_utils import get_coco, get_coco_kp

from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
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4 changes: 3 additions & 1 deletion test/test_ops.py
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Expand Up @@ -14,7 +14,9 @@ def setUpClass(cls):
cls.dtype = torch.float64

def slow_roi_pooling(self, x, rois, pool_h, pool_w, spatial_scale=1,
device=torch.device('cpu'), dtype=torch.float64):
device=None, dtype=torch.float64):
if device is None:
device = torch.device("cpu")
c = x.size(1)
y = torch.zeros(rois.size(0), c, pool_h, pool_w, dtype=dtype, device=device)

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4 changes: 2 additions & 2 deletions test/test_transforms.py
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Expand Up @@ -141,7 +141,7 @@ def test_randomresized_params(self):
img = to_pil_image(img)
size = 100
epsilon = 0.05
for i in range(10):
for _ in range(10):
scale_min = round(random.random(), 2)
scale_range = (scale_min, scale_min + round(random.random(), 2))
aspect_min = max(round(random.random(), 2), epsilon)
Expand All @@ -153,7 +153,7 @@ def test_randomresized_params(self):
aspect_ratio_obtained == 1.0)

def test_randomperspective(self):
for i in range(10):
for _ in range(10):
height = random.randint(24, 32) * 2
width = random.randint(24, 32) * 2
img = torch.ones(3, height, width)
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