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未验证 提交 fc71459f 编写于 作者: D David Nicolas 提交者： GitHub
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[xdoctest][task 167-170] reformat example code with google style in... 

[xdoctest][task 167-170] reformat example code with google style in /paddle/vision/datasets/*; test=docs_preview (#56906)

* reformat example code with google style

* udpate

* update

* add timeout for dataset download

* update cifar timeout

* update cifar timeout and fix an output

* update cifar timeout

* add a blank line

---------
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>
上级 8aaceba5
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Showing with 386 addition and 389 deletion
python/paddle/amp/__init__.py
浏览文件 @ fc71459f
......@@ -55,8 +55,9 @@ def is_float16_supported(device=None):
.. code-block:: python
import paddle
paddle.amp.is_float16_supported() # True or False
>>> import paddle
>>> paddle.amp.is_float16_supported() # True or False
False
"""
device = (
......@@ -81,8 +82,9 @@ def is_bfloat16_supported(device=None):
.. code-block:: python
import paddle
paddle.amp.is_bfloat16_supported() # True or False
>>> import paddle
>>> paddle.amp.is_bfloat16_supported() # True or False
True
"""
device = (
......
python/paddle/vision/datasets/cifar.py
浏览文件 @ fc71459f
......@@ -61,46 +61,46 @@ class Cifar10(Dataset):
.. code-block:: python
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import Cifar10
cifar10 = Cifar10()
print(len(cifar10))
# 50000
for i in range(5): # only show first 5 images
img, label = cifar10[i]
# do something with img and label
print(type(img), img.size, label)
# <class 'PIL.Image.Image'> (32, 32) 6
transform = T.Compose(
[
T.Resize(64),
T.ToTensor(),
T.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True,
),
]
)
cifar10_test = Cifar10(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(cifar10_test))
# 10000
>>> # doctest: +TIMEOUT(60)
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import Cifar10
>>> cifar10 = Cifar10()
>>> print(len(cifar10))
50000
>>> for i in range(5): # only show first 5 images
... img, label = cifar10[i]
... # do something with img and label
... print(type(img), img.size, label)
... # <class 'PIL.Image.Image'> (32, 32) 6
>>> transform = T.Compose(
... [
... T.Resize(64),
... T.ToTensor(),
... T.Normalize(
... mean=[0.5, 0.5, 0.5],
... std=[0.5, 0.5, 0.5],
... to_rgb=True,
... ),
... ]
... )
>>> cifar10_test = Cifar10(
... mode="test",
... transform=transform, # apply transform to every image
... backend="cv2", # use OpenCV as image transform backend
... )
>>> print(len(cifar10_test))
10000
>>> for img, label in itertools.islice(iter(cifar10_test), 5): # only show first 5 images
... # do something with img and label
... print(type(img), img.shape, label)
... # <class 'paddle.Tensor'> [3, 64, 64] 3
for img, label in itertools.islice(iter(cifar10_test), 5): # only show first 5 images
# do something with img and label
print(type(img), img.shape, label)
# <class 'paddle.Tensor'> [3, 64, 64] 3
"""
def __init__(
......@@ -210,46 +210,47 @@ class Cifar100(Cifar10):
.. code-block:: python
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import Cifar100
cifar100 = Cifar100()
print(len(cifar100))
# 50000
for i in range(5): # only show first 5 images
img, label = cifar100[i]
# do something with img and label
print(type(img), img.size, label)
# <class 'PIL.Image.Image'> (32, 32) 19
transform = T.Compose(
[
T.Resize(64),
T.ToTensor(),
T.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True,
),
]
)
cifar100_test = Cifar100(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(cifar100_test))
# 10000
>>> # doctest: +TIMEOUT(60)
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import Cifar100
>>> cifar100 = Cifar100()
>>> print(len(cifar100))
50000
>>> for i in range(5): # only show first 5 images
... img, label = cifar100[i]
... # do something with img and label
... print(type(img), img.size, label)
... # <class 'PIL.Image.Image'> (32, 32) 19
>>> transform = T.Compose(
... [
... T.Resize(64),
... T.ToTensor(),
... T.Normalize(
... mean=[0.5, 0.5, 0.5],
... std=[0.5, 0.5, 0.5],
... to_rgb=True,
... ),
... ]
... )
>>> cifar100_test = Cifar100(
... mode="test",
... transform=transform, # apply transform to every image
... backend="cv2", # use OpenCV as image transform backend
... )
>>> print(len(cifar100_test))
10000
>>> for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images
... # do something with img and label
... print(type(img), img.shape, label)
... # <class 'paddle.Tensor'> [3, 64, 64] 49
for img, label in itertools.islice(iter(cifar100_test), 5): # only show first 5 images
# do something with img and label
print(type(img), img.shape, label)
# <class 'paddle.Tensor'> [3, 64, 64] 49
"""
def __init__(
......
python/paddle/vision/datasets/flowers.py
浏览文件 @ fc71459f
......@@ -65,46 +65,44 @@ class Flowers(Dataset):
.. code-block:: python
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import Flowers
flowers = Flowers()
print(len(flowers))
# 6149
for i in range(5): # only show first 5 images
img, label = flowers[i]
# do something with img and label
print(type(img), img.size, label)
# <class 'PIL.JpegImagePlugin.JpegImageFile'> (523, 500) [1]
transform = T.Compose(
[
T.Resize(64),
T.ToTensor(),
T.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True,
),
]
)
flowers_test = Flowers(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(flowers_test))
# 1020
for img, label in itertools.islice(iter(flowers_test), 5): # only show first 5 images
# do something with img and label
print(type(img), img.shape, label)
# <class 'paddle.Tensor'> [3, 64, 96] [1]
>>> # doctest: +TIMEOUT(60)
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import Flowers
>>> flowers = Flowers()
>>> print(len(flowers))
6149
>>> for i in range(5): # only show first 5 images
... img, label = flowers[i]
... # do something with img and label
... print(type(img), img.size, label)
... # <class 'PIL.JpegImagePlugin.JpegImageFile'> (523, 500) [1]
>>> transform = T.Compose(
... [
... T.Resize(64),
... T.ToTensor(),
... T.Normalize(
... mean=[0.5, 0.5, 0.5],
... std=[0.5, 0.5, 0.5],
... to_rgb=True,
... ),
... ]
... )
>>> flowers_test = Flowers(
... mode="test",
... transform=transform, # apply transform to every image
... backend="cv2", # use OpenCV as image transform backend
... )
>>> print(len(flowers_test))
1020
>>> for img, label in itertools.islice(iter(flowers_test), 5): # only show first 5 images
... # do something with img and label
... print(type(img), img.shape, label)
... # <class 'paddle.Tensor'> [3, 64, 96] [1]
"""
def __init__(
......
python/paddle/vision/transforms/transforms.py
浏览文件 @ fc71459f
......@@ -91,16 +91,16 @@ class Compose:
.. code-block:: python
from paddle.vision.datasets import Flowers
from paddle.vision.transforms import Compose, ColorJitter, Resize
transform = Compose([ColorJitter(), Resize(size=608)])
flowers = Flowers(mode='test', transform=transform)
for i in range(10):
sample = flowers[i]
print(sample[0].size, sample[1])
>>> from paddle.vision.datasets import Flowers
>>> from paddle.vision.transforms import Compose, ColorJitter, Resize
>>> transform = Compose([ColorJitter(), Resize(size=608)])
>>> flowers = Flowers(mode='test', transform=transform)
>>> for i in range(3):
... sample = flowers[i]
... print(sample[0].size, sample[1])
(916, 608) [1]
(758, 608) [1]
(811, 608) [1]
"""
def __init__(self, transforms):
......@@ -166,72 +166,72 @@ class BaseTransform:
.. code-block:: python
import numpy as np
from PIL import Image
import paddle.vision.transforms.functional as F
from paddle.vision.transforms import BaseTransform
def _get_image_size(img):
if F._is_pil_image(img):
return img.size
elif F._is_numpy_image(img):
return img.shape[:2][::-1]
else:
raise TypeError("Unexpected type {}".format(type(img)))
class CustomRandomFlip(BaseTransform):
def __init__(self, prob=0.5, keys=None):
super().__init__(keys)
self.prob = prob
def _get_params(self, inputs):
image = inputs[self.keys.index('image')]
params = {}
params['flip'] = np.random.random() < self.prob
params['size'] = _get_image_size(image)
return params
def _apply_image(self, image):
if self.params['flip']:
return F.hflip(image)
return image
# if you only want to transform image, do not need to rewrite this function
def _apply_coords(self, coords):
if self.params['flip']:
w = self.params['size'][0]
coords[:, 0] = w - coords[:, 0]
return coords
# if you only want to transform image, do not need to rewrite this function
def _apply_boxes(self, boxes):
idxs = np.array([(0, 1), (2, 1), (0, 3), (2, 3)]).flatten()
coords = np.asarray(boxes).reshape(-1, 4)[:, idxs].reshape(-1, 2)
coords = self._apply_coords(coords).reshape((-1, 4, 2))
minxy = coords.min(axis=1)
maxxy = coords.max(axis=1)
trans_boxes = np.concatenate((minxy, maxxy), axis=1)
return trans_boxes
# if you only want to transform image, do not need to rewrite this function
def _apply_mask(self, mask):
if self.params['flip']:
return F.hflip(mask)
return mask
# create fake inputs
fake_img = Image.fromarray((np.random.rand(400, 500, 3) * 255.).astype('uint8'))
fake_boxes = np.array([[2, 3, 200, 300], [50, 60, 80, 100]])
fake_mask = fake_img.convert('L')
# only transform for image:
flip_transform = CustomRandomFlip(1.0)
converted_img = flip_transform(fake_img)
# transform for image, boxes and mask
flip_transform = CustomRandomFlip(1.0, keys=('image', 'boxes', 'mask'))
(converted_img, converted_boxes, converted_mask) = flip_transform((fake_img, fake_boxes, fake_mask))
print('converted boxes', converted_boxes)
>>> import numpy as np
>>> from PIL import Image
>>> import paddle.vision.transforms.functional as F
>>> from paddle.vision.transforms import BaseTransform
>>> def _get_image_size(img):
... if F._is_pil_image(img):
... return img.size
... elif F._is_numpy_image(img):
... return img.shape[:2][::-1]
... else:
... raise TypeError("Unexpected type {}".format(type(img)))
...
>>> class CustomRandomFlip(BaseTransform):
... def __init__(self, prob=0.5, keys=None):
... super().__init__(keys)
... self.prob = prob
...
... def _get_params(self, inputs):
... image = inputs[self.keys.index('image')]
... params = {}
... params['flip'] = np.random.random() < self.prob
... params['size'] = _get_image_size(image)
... return params
...
... def _apply_image(self, image):
... if self.params['flip']:
... return F.hflip(image)
... return image
...
... # if you only want to transform image, do not need to rewrite this function
... def _apply_coords(self, coords):
... if self.params['flip']:
... w = self.params['size'][0]
... coords[:, 0] = w - coords[:, 0]
... return coords
...
... # if you only want to transform image, do not need to rewrite this function
... def _apply_boxes(self, boxes):
... idxs = np.array([(0, 1), (2, 1), (0, 3), (2, 3)]).flatten()
... coords = np.asarray(boxes).reshape(-1, 4)[:, idxs].reshape(-1, 2)
... coords = self._apply_coords(coords).reshape((-1, 4, 2))
... minxy = coords.min(axis=1)
... maxxy = coords.max(axis=1)
... trans_boxes = np.concatenate((minxy, maxxy), axis=1)
... return trans_boxes
...
... # if you only want to transform image, do not need to rewrite this function
... def _apply_mask(self, mask):
... if self.params['flip']:
... return F.hflip(mask)
... return mask
...
>>> # create fake inputs
>>> fake_img = Image.fromarray((np.random.rand(400, 500, 3) * 255.).astype('uint8'))
>>> fake_boxes = np.array([[2, 3, 200, 300], [50, 60, 80, 100]])
>>> fake_mask = fake_img.convert('L')
>>> # only transform for image:
>>> flip_transform = CustomRandomFlip(1.0)
>>> converted_img = flip_transform(fake_img)
>>> # transform for image, boxes and mask
>>> flip_transform = CustomRandomFlip(1.0, keys=('image', 'boxes', 'mask'))
>>> (converted_img, converted_boxes, converted_mask) = flip_transform((fake_img, fake_boxes, fake_mask))
>>> converted_boxes
array([[300, 3, 498, 300],
[420, 60, 450, 100]])
"""
......@@ -319,23 +319,18 @@ class ToTensor(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
import paddle.vision.transforms as T
import paddle.vision.transforms.functional as F
fake_img = Image.fromarray((np.random.rand(4, 5, 3) * 255.).astype(np.uint8))
transform = T.ToTensor()
tensor = transform(fake_img)
print(tensor.shape)
# [3, 4, 5]
print(tensor.dtype)
# paddle.float32
>>> import numpy as np
>>> from PIL import Image
>>> import paddle.vision.transforms as T
>>> import paddle.vision.transforms.functional as F
>>> fake_img = Image.fromarray((np.random.rand(4, 5, 3) * 255.).astype(np.uint8))
>>> transform = T.ToTensor()
>>> tensor = transform(fake_img)
>>> print(tensor.shape)
[3, 4, 5]
>>> print(tensor.dtype)
paddle.float32
"""
def __init__(self, data_format='CHW', keys=None):
......@@ -389,21 +384,19 @@ class Resize(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import Resize
fake_img = Image.fromarray((np.random.rand(256, 300, 3) * 255.).astype(np.uint8))
transform = Resize(size=224)
converted_img = transform(fake_img)
print(converted_img.size)
# (262, 224)
transform = Resize(size=(200,150))
converted_img = transform(fake_img)
print(converted_img.size)
# (150, 200)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import Resize
>>> fake_img = Image.fromarray((np.random.rand(256, 300, 3) * 255.).astype(np.uint8))
>>> transform = Resize(size=224)
>>> converted_img = transform(fake_img)
>>> print(converted_img.size)
(262, 224)
>>> transform = Resize(size=(200,150))
>>> converted_img = transform(fake_img)
>>> print(converted_img.size)
(150, 200)
"""
def __init__(self, size, interpolation='bilinear', keys=None):
......@@ -456,16 +449,15 @@ class RandomResizedCrop(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import RandomResizedCrop
transform = RandomResizedCrop(224)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import RandomResizedCrop
fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
print(fake_img.size)
>>> transform = RandomResizedCrop(224)
>>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(224, 224)
"""
......@@ -643,16 +635,16 @@ class CenterCrop(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import CenterCrop
transform = CenterCrop(224)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import CenterCrop
fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> transform = CenterCrop(224)
>>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(224, 224)
fake_img = transform(fake_img)
print(fake_img.size)
"""
def __init__(self, size, keys=None):
......@@ -684,16 +676,15 @@ class RandomHorizontalFlip(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import RandomHorizontalFlip
transform = RandomHorizontalFlip(0.5)
fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import RandomHorizontalFlip
fake_img = transform(fake_img)
print(fake_img.size)
>>> transform = RandomHorizontalFlip(0.5)
>>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(320, 300)
"""
def __init__(self, prob=0.5, keys=None):
......@@ -738,16 +729,14 @@ class RandomVerticalFlip(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import RandomVerticalFlip
transform = RandomVerticalFlip()
fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
print(fake_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import RandomVerticalFlip
>>> transform = RandomVerticalFlip()
>>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(320, 300)
"""
......@@ -800,20 +789,21 @@ class Normalize(BaseTransform):
.. code-block:: python
:name: code-example
import paddle
from paddle.vision.transforms import Normalize
normalize = Normalize(mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
data_format='HWC')
fake_img = paddle.rand([300,320,3]).numpy() * 255.
fake_img = normalize(fake_img)
print(fake_img.shape)
# (300, 320, 3)
print(fake_img.max(), fake_img.min())
# 0.99999905 -0.999974
>>> import paddle
>>> from paddle.vision.transforms import Normalize
>>> paddle.seed(2023)
>>> normalize = Normalize(mean=[127.5, 127.5, 127.5],
... std=[127.5, 127.5, 127.5],
... data_format='HWC')
...
>>> fake_img = paddle.rand([300,320,3]).numpy() * 255.
>>> fake_img = normalize(fake_img)
>>> print(fake_img.shape)
(300, 320, 3)
>>> print(fake_img.max(), fake_img.min())
0.99999464 -0.9999929
"""
......@@ -860,16 +850,15 @@ class Transpose(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import Transpose
transform = Transpose()
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import Transpose
fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
print(fake_img.shape)
>>> transform = Transpose()
>>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.shape)
(3, 300, 320)
"""
......@@ -908,15 +897,19 @@ class BrightnessTransform(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import BrightnessTransform
transform = BrightnessTransform(0.4)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import BrightnessTransform
>>> np.random.seed(2023)
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
>>> transform = BrightnessTransform(0.4)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> print(fake_img.load()[1,1])
(60, 169, 34)
>>> # doctest: +SKIP('random sample in Brightness function')
>>> fake_img = transform(fake_img)
>>> print(fake_img.load()[1,1])
(68, 192, 38)
"""
......@@ -951,15 +944,15 @@ class ContrastTransform(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import ContrastTransform
transform = ContrastTransform(0.4)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import ContrastTransform
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
>>> transform = ContrastTransform(0.4)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(224, 224)
"""
......@@ -996,16 +989,15 @@ class SaturationTransform(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import SaturationTransform
transform = SaturationTransform(0.4)
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import SaturationTransform
>>> transform = SaturationTransform(0.4)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(224, 224)
"""
def __init__(self, value, keys=None):
......@@ -1039,15 +1031,15 @@ class HueTransform(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import HueTransform
transform = HueTransform(0.4)
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import HueTransform
fake_img = transform(fake_img)
>>> transform = HueTransform(0.4)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(224, 224)
"""
......@@ -1090,15 +1082,15 @@ class ColorJitter(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import ColorJitter
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import ColorJitter
transform = ColorJitter(0.4, 0.4, 0.4, 0.4)
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
fake_img = transform(fake_img)
>>> transform = ColorJitter(0.4, 0.4, 0.4, 0.4)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(224, 224)
"""
......@@ -1197,15 +1189,17 @@ class RandomCrop(BaseTransform):
.. code-block:: python
:name: code-example1
import paddle
from paddle.vision.transforms import RandomCrop
transform = RandomCrop(224)
>>> import paddle
>>> from paddle.vision.transforms import RandomCrop
>>> transform = RandomCrop(224)
fake_img = paddle.randint(0, 255, shape=(3, 324,300), dtype = 'int32')
print(fake_img.shape) # [3, 324, 300]
>>> fake_img = paddle.randint(0, 255, shape=(3, 324,300), dtype = 'int32')
>>> print(fake_img.shape)
[3, 324, 300]
crop_img = transform(fake_img)
print(crop_img.shape) # [3, 224, 224]
>>> crop_img = transform(fake_img)
>>> print(crop_img.shape)
[3, 224, 224]
"""
def __init__(
......@@ -1313,16 +1307,15 @@ class Pad(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import Pad
transform = Pad(2)
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import Pad
fake_img = transform(fake_img)
print(fake_img.size)
>>> transform = Pad(2)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(228, 228)
"""
def __init__(self, padding, fill=0, padding_mode='constant', keys=None):
......@@ -1429,15 +1422,14 @@ class RandomAffine(BaseTransform):
.. code-block:: python
import paddle
from paddle.vision.transforms import RandomAffine
>>> import paddle
>>> from paddle.vision.transforms import RandomAffine
transform = RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 0.5], shear=[-10, 10])
fake_img = paddle.randn((3, 256, 300)).astype(paddle.float32)
fake_img = transform(fake_img)
print(fake_img.shape)
>>> transform = RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 0.5], shear=[-10, 10])
>>> fake_img = paddle.randn((3, 256, 300)).astype(paddle.float32)
>>> fake_img = transform(fake_img)
>>> print(fake_img.shape)
[3, 256, 300]
"""
def __init__(
......@@ -1583,16 +1575,15 @@ class RandomRotation(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import RandomRotation
transform = RandomRotation(90)
fake_img = Image.fromarray((np.random.rand(200, 150, 3) * 255.).astype(np.uint8))
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import RandomRotation
fake_img = transform(fake_img)
print(fake_img.size)
>>> transform = RandomRotation(90)
>>> fake_img = Image.fromarray((np.random.rand(200, 150, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(150, 200)
"""
def __init__(
......@@ -1683,15 +1674,14 @@ class RandomPerspective(BaseTransform):
.. code-block:: python
import paddle
from paddle.vision.transforms import RandomPerspective
>>> import paddle
>>> from paddle.vision.transforms import RandomPerspective
transform = RandomPerspective(prob=1.0, distortion_scale=0.9)
fake_img = paddle.randn((3, 200, 150)).astype(paddle.float32)
fake_img = transform(fake_img)
print(fake_img.shape)
>>> transform = RandomPerspective(prob=1.0, distortion_scale=0.9)
>>> fake_img = paddle.randn((3, 200, 150)).astype(paddle.float32)
>>> fake_img = transform(fake_img)
>>> print(fake_img.shape)
[3, 200, 150]
"""
def __init__(
......@@ -1806,16 +1796,15 @@ class Grayscale(BaseTransform):
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import Grayscale
transform = Grayscale()
fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import Grayscale
fake_img = transform(fake_img)
print(np.array(fake_img).shape)
>>> transform = Grayscale()
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(np.array(fake_img).shape)
(224, 224)
"""
def __init__(self, num_output_channels=1, keys=None):
......@@ -1861,13 +1850,20 @@ class RandomErasing(BaseTransform):
.. code-block:: python
import paddle
>>> import paddle
fake_img = paddle.randn((3, 10, 10)).astype(paddle.float32)
transform = paddle.vision.transforms.RandomErasing()
result = transform(fake_img)
>>> fake_img = paddle.randn((1, 5, 5)).astype(paddle.float32)
>>> transform = paddle.vision.transforms.RandomErasing()
>>> result = transform(fake_img)
>>> # doctest: +SKIP('random sample')
>>> print(result)
Tensor(shape=[1, 5, 5], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[-0.22141267, -0.71004093, 1.71224928, 2.99622107, -0.82959402],
[ 0.36916021, -0.25601348, 0.86669374, 1.27504587, -0.56462914],
[-0.45704395, -0.87613666, 1.12195814, -0.87974882, 0.04902615],
[-0.91549885, -0.15066874, 1.26381516, 0. , 0. ],
[ 0.87887472, -1.59914243, -0.73970413, 0. , 0. ]]])
print(result)
"""
def __init__(
......
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