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add tunable crop scale and aspect ratio in RandomSizedCrop #343

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Merged
merged 2 commits into from Nov 23, 2017
Merged

add tunable crop scale and aspect ratio in RandomSizedCrop #343

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df029ab
add tunable crop scale and aspect ratio in
qinzheng93 Nov 23, 2017
b575f91
fix whitespace around arithmetic operators
qinzheng93 Nov 23, 2017
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14 changes: 9 additions & 5 deletions torchvision/transforms/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -323,19 +323,23 @@ def __call__(self, img):
class RandomResizedCrop(object):
"""Crop the given PIL Image to random size and aspect ratio.

A crop of random size of (0.08 to 1.0) of the original size and a random
aspect ratio of 3/4 to 4/3 of the original aspect ratio is made. This crop
A crop of random size (default: of 0.08 to 1.0) of the original size and a random
aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.

Args:
size: expected output size of each edge
scale: range of size of the origin size cropped
ratio: range of aspect ratio of the origin aspect ratio cropped
interpolation: Default: PIL.Image.BILINEAR
"""

def __init__(self, size, interpolation=Image.BILINEAR):
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):
self.size = (size, size)
self.interpolation = interpolation
self.scale = scale
self.ratio = ratio

@staticmethod
def get_params(img):
Expand All @@ -350,8 +354,8 @@ def get_params(img):
"""
for attempt in range(10):
area = img.size[0] * img.size[1]
target_area = random.uniform(0.08, 1.0) * area
aspect_ratio = random.uniform(3. / 4, 4. / 3)
target_area = random.uniform(*self.scale) * area
aspect_ratio = random.uniform(*self.ratio)

w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
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