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Add float support to ColorJitter #2672

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Add float support to ColorJitter #2672

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126c594
Add float support to ColorJitter
vballoli Sep 13, 2020
2b0c8a0
Fixed indent
vballoli Sep 13, 2020
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16 changes: 13 additions & 3 deletions torchvision/transforms/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import random
import warnings
from collections.abc import Sequence
from typing import Tuple, List, Optional
from typing import Tuple, List, Optional, Union

import torch
from PIL import Image
Expand Down Expand Up @@ -1018,8 +1018,9 @@ def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
clip_first_on_zero=False)

@staticmethod
@torch.jit.unused
def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
def _check_input(value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
if isinstance(value, numbers.Number):
if value < 0:
raise ValueError("If {} is a single number, it must be non negative.".format(name))
Expand All @@ -1040,7 +1041,8 @@ def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_firs

@staticmethod
@torch.jit.unused
def get_params(brightness, contrast, saturation, hue):
def get_params(brightness: Union[float, tuple], contrast: Union[float, tuple],
saturation: Union[float, tuple], hue: Union[float, tuple]):
"""Get a randomized transform to be applied on image.

Arguments are same as that of __init__.
Expand All @@ -1052,18 +1054,26 @@ def get_params(brightness, contrast, saturation, hue):
transforms = []

if brightness is not None:
if isinstance(brightness, float):
ColorJitter._check_input(brightness, 'brightness')
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We are using the returned value of _check_input, so this doesn't actually do anything IMO.

Could we instead just do

brightness = ColorJitter._check_input(brightness, 'brightness')

without the conditional on float?

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Yeah that definitely makes sense, not sure how I missed that.

brightness_factor = random.uniform(brightness[0], brightness[1])
transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

if contrast is not None:
if isinstance(contrast, float):
ColorJitter._check_input(contrast, 'contrast')
contrast_factor = random.uniform(contrast[0], contrast[1])
transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

if saturation is not None:
if isinstance(saturation, float):
ColorJitter._check_input(saturation, 'saturation')
saturation_factor = random.uniform(saturation[0], saturation[1])
transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

if hue is not None:
if isinstance(hue, float):
ColorJitter._check_input(hue, 'hue')
hue_factor = random.uniform(hue[0], hue[1])
transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))

Expand Down