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未验证 提交 a277fe4d 编写于 作者: D David Nicolas 提交者： GitHub
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[Doctest]fix No.163,164,166, test=docs_preview (#56527)

上级 0213a19c
隐藏空白更改
内联 并排
Showing with 247 addition and 278 deletion
python/paddle/vision/models/squeezenet.py
浏览文件 @ a277fe4d
......@@ -90,20 +90,20 @@ class SqueezeNet(nn.Layer):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import SqueezeNet
>>> import paddle
>>> from paddle.vision.models import SqueezeNet
# build v1.0 model
model = SqueezeNet(version='1.0')
>>> # build v1.0 model
>>> model = SqueezeNet(version='1.0')
# build v1.1 model
# model = SqueezeNet(version='1.1')
>>> # build v1.1 model
>>> # model = SqueezeNet(version='1.1')
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
def __init__(self, version, num_classes=1000, with_pool=True):
......@@ -233,20 +233,20 @@ def squeezenet1_0(pretrained=False, **kwargs):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import squeezenet1_0
>>> import paddle
>>> from paddle.vision.models import squeezenet1_0
# build model
model = squeezenet1_0()
>>> # build model
>>> model = squeezenet1_0()
# build model and load imagenet pretrained weight
# model = squeezenet1_0(pretrained=True)
>>> # build model and load imagenet pretrained weight
>>> # model = squeezenet1_0(pretrained=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
return _squeezenet('squeezenet1_0', '1.0', pretrained, **kwargs)
......@@ -267,19 +267,19 @@ def squeezenet1_1(pretrained=False, **kwargs):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import squeezenet1_1
>>> import paddle
>>> from paddle.vision.models import squeezenet1_1
# build model
model = squeezenet1_1()
>>> # build model
>>> model = squeezenet1_1()
# build model and load imagenet pretrained weight
# model = squeezenet1_1(pretrained=True)
>>> # build model and load imagenet pretrained weight
>>> # model = squeezenet1_1(pretrained=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
return _squeezenet('squeezenet1_1', '1.1', pretrained, **kwargs)
python/paddle/vision/models/vgg.py
浏览文件 @ a277fe4d
......@@ -46,21 +46,21 @@ class VGG(nn.Layer):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import VGG
from paddle.vision.models.vgg import make_layers
>>> import paddle
>>> from paddle.vision.models import VGG
>>> from paddle.vision.models.vgg import make_layers
vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
>>> vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
features = make_layers(vgg11_cfg)
>>> features = make_layers(vgg11_cfg)
vgg11 = VGG(features)
>>> vgg11 = VGG(features)
x = paddle.rand([1, 3, 224, 224])
out = vgg11(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = vgg11(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
def __init__(self, features, num_classes=1000, with_pool=True):
......@@ -212,20 +212,20 @@ def vgg11(pretrained=False, batch_norm=False, **kwargs):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import vgg11
>>> import paddle
>>> from paddle.vision.models import vgg11
# build model
model = vgg11()
>>> # build model
>>> model = vgg11()
# build vgg11 model with batch_norm
model = vgg11(batch_norm=True)
>>> # build vgg11 model with batch_norm
>>> model = vgg11(batch_norm=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
model_name = 'vgg11'
if batch_norm:
......@@ -249,20 +249,20 @@ def vgg13(pretrained=False, batch_norm=False, **kwargs):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import vgg13
>>> import paddle
>>> from paddle.vision.models import vgg13
# build model
model = vgg13()
>>> # build model
>>> model = vgg13()
# build vgg13 model with batch_norm
model = vgg13(batch_norm=True)
>>> # build vgg13 model with batch_norm
>>> model = vgg13(batch_norm=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
model_name = 'vgg13'
if batch_norm:
......@@ -286,20 +286,20 @@ def vgg16(pretrained=False, batch_norm=False, **kwargs):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import vgg16
>>> import paddle
>>> from paddle.vision.models import vgg16
# build model
model = vgg16()
>>> # build model
>>> model = vgg16()
# build vgg16 model with batch_norm
model = vgg16(batch_norm=True)
>>> # build vgg16 model with batch_norm
>>> model = vgg16(batch_norm=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
model_name = 'vgg16'
if batch_norm:
......@@ -323,20 +323,20 @@ def vgg19(pretrained=False, batch_norm=False, **kwargs):
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import vgg19
>>> import paddle
>>> from paddle.vision.models import vgg19
# build model
model = vgg19()
>>> # build model
>>> model = vgg19()
# build vgg19 model with batch_norm
model = vgg19(batch_norm=True)
>>> # build vgg19 model with batch_norm
>>> model = vgg19(batch_norm=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)
print(out.shape)
# [1, 1000]
>>> print(out.shape)
[1, 1000]
"""
model_name = 'vgg19'
if batch_norm:
......
python/paddle/vision/transforms/functional.py
浏览文件 @ a277fe4d
......@@ -70,16 +70,14 @@ def to_tensor(pic, data_format='CHW'):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
tensor = F.to_tensor(fake_img)
print(tensor.shape)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> tensor = F.to_tensor(fake_img)
>>> print(tensor.shape)
[3, 256, 300]
"""
if not (
......@@ -127,21 +125,18 @@ def resize(img, size, interpolation='bilinear'):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
converted_img = F.resize(fake_img, 224)
print(converted_img.size)
# (262, 224)
converted_img = F.resize(fake_img, (200, 150))
print(converted_img.size)
# (150, 200)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> converted_img = F.resize(fake_img, 224)
>>> print(converted_img.size)
(262, 224)
>>> converted_img = F.resize(fake_img, (200, 150))
>>> print(converted_img.size)
(150, 200)
"""
if not (
_is_pil_image(img) or _is_numpy_image(img) or _is_tensor_image(img)
......@@ -196,19 +191,18 @@ def pad(img, padding, fill=0, padding_mode='constant'):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
padded_img = F.pad(fake_img, padding=1)
print(padded_img.size)
padded_img = F.pad(fake_img, padding=(2, 1))
print(padded_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> padded_img = F.pad(fake_img, padding=1)
>>> print(padded_img.size)
(302, 258)
>>> padded_img = F.pad(fake_img, padding=(2, 1))
>>> print(padded_img.size)
(304, 258)
"""
if not (
_is_pil_image(img) or _is_numpy_image(img) or _is_tensor_image(img)
......@@ -244,16 +238,14 @@ def crop(img, top, left, height, width):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
cropped_img = F.crop(fake_img, 56, 150, 200, 100)
print(cropped_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> cropped_img = F.crop(fake_img, 56, 150, 200, 100)
>>> print(cropped_img.size)
(100, 200)
"""
if not (
......@@ -287,16 +279,14 @@ def center_crop(img, output_size):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
cropped_img = F.center_crop(fake_img, (150, 100))
print(cropped_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> cropped_img = F.center_crop(fake_img, (150, 100))
>>> print(cropped_img.size)
(100, 150)
"""
if not (
_is_pil_image(img) or _is_numpy_image(img) or _is_tensor_image(img)
......@@ -327,16 +317,14 @@ def hflip(img):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
flpped_img = F.hflip(fake_img)
print(flpped_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> flipped_img = F.hflip(fake_img)
>>> print(flipped_img.size)
(300, 256)
"""
if not (
......@@ -368,16 +356,14 @@ def vflip(img):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
flpped_img = F.vflip(fake_img)
print(flpped_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> flipped_img = F.vflip(fake_img)
>>> print(flipped_img.size)
(300, 256)
"""
if not (
......@@ -411,20 +397,26 @@ def adjust_brightness(img, brightness_factor):
Examples:
.. code-block:: python
:name: code-example1
:name: code-example1
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> np.random.seed(2023)
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> print(fake_img.size)
(300, 256)
>>> print(fake_img.load()[1,1])
(61, 155, 171)
>>> converted_img = F.adjust_brightness(fake_img, 0.5)
>>> print(converted_img.size)
(300, 256)
>>> print(converted_img.load()[1,1])
(30, 77, 85)
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
print(fake_img.size) # (300, 256)
print(fake_img.load()[1,1]) # (95, 127, 202)
converted_img = F.adjust_brightness(fake_img, 0.5)
print(converted_img.size) # (300, 256)
print(converted_img.load()[1,1]) # (47, 63, 101)
"""
......@@ -460,16 +452,14 @@ def adjust_contrast(img, contrast_factor):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
converted_img = F.adjust_contrast(fake_img, 0.4)
print(converted_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> converted_img = F.adjust_contrast(fake_img, 0.4)
>>> print(converted_img.size)
(300, 256)
"""
if not (
_is_pil_image(img) or _is_numpy_image(img) or _is_tensor_image(img)
......@@ -503,16 +493,14 @@ def adjust_saturation(img, saturation_factor):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
converted_img = F.adjust_saturation(fake_img, 0.4)
print(converted_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> converted_img = F.adjust_saturation(fake_img, 0.4)
>>> print(converted_img.size)
(300, 256)
"""
if not (
......@@ -556,16 +544,14 @@ def adjust_hue(img, hue_factor):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
converted_img = F.adjust_hue(fake_img, 0.4)
print(converted_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> converted_img = F.adjust_hue(fake_img, 0.4)
>>> print(converted_img.size)
(300, 256)
"""
if not (
......@@ -657,13 +643,12 @@ def affine(
Examples:
.. code-block:: python
import paddle
from paddle.vision.transforms import functional as F
fake_img = paddle.randn((3, 256, 300)).astype(paddle.float32)
affined_img = F.affine(fake_img, 45, translate=[0.2, 0.2], scale=0.5, shear=[-10, 10])
print(affined_img.shape)
>>> import paddle
>>> from paddle.vision.transforms import functional as F
>>> fake_img = paddle.randn((3, 256, 300)).astype(paddle.float32)
>>> affined_img = F.affine(fake_img, 45, translate=[0.2, 0.2], scale=0.5, shear=[-10, 10])
>>> print(affined_img.shape)
[3, 256, 300]
"""
if not (
......@@ -789,16 +774,14 @@ def rotate(
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
rotated_img = F.rotate(fake_img, 90)
print(rotated_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> rotated_img = F.rotate(fake_img, 90)
>>> print(rotated_img.size)
(300, 256)
"""
if not (
......@@ -897,16 +880,14 @@ def perspective(img, startpoints, endpoints, interpolation='nearest', fill=0):
Examples:
.. code-block:: python
import paddle
from paddle.vision.transforms import functional as F
fake_img = paddle.randn((3, 256, 300)).astype(paddle.float32)
startpoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
endpoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
perspectived_img = F.perspective(fake_img, startpoints, endpoints)
print(perspectived_img.shape)
>>> import paddle
>>> from paddle.vision.transforms import functional as F
>>> fake_img = paddle.randn((3, 256, 300)).astype(paddle.float32)
>>> startpoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
>>> endpoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
>>> perspectived_img = F.perspective(fake_img, startpoints, endpoints)
>>> print(perspectived_img.shape)
[3, 256, 300]
"""
if not (
......@@ -946,16 +927,14 @@ def to_grayscale(img, num_output_channels=1):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
gray_img = F.to_grayscale(fake_img)
print(gray_img.size)
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> gray_img = F.to_grayscale(fake_img)
>>> print(gray_img.size)
(300, 256)
"""
if not (
......@@ -993,19 +972,16 @@ def normalize(img, mean, std, data_format='CHW', to_rgb=False):
Examples:
.. code-block:: python
import numpy as np
from PIL import Image
from paddle.vision.transforms import functional as F
fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
fake_img = Image.fromarray(fake_img)
mean = [127.5, 127.5, 127.5]
std = [127.5, 127.5, 127.5]
normalized_img = F.normalize(fake_img, mean, std, data_format='HWC')
print(normalized_img.max(), normalized_img.min())
>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import functional as F
>>> fake_img = (np.random.rand(256, 300, 3) * 255.).astype('uint8')
>>> fake_img = Image.fromarray(fake_img)
>>> mean = [127.5, 127.5, 127.5]
>>> std = [127.5, 127.5, 127.5]
>>> normalized_img = F.normalize(fake_img, mean, std, data_format='HWC')
>>> print(normalized_img.max(), normalized_img.min())
0.99215686 -1.0
"""
......@@ -1039,35 +1015,28 @@ def erase(img, i, j, h, w, v, inplace=False):
Examples:
.. code-block:: python
import paddle
fake_img = paddle.randn((3, 2, 4)).astype(paddle.float32)
print(fake_img)
#Tensor(shape=[3, 2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[ 0.02169025, -0.97859967, -1.39175487, -1.07478464],
# [ 0.20654772, 1.74624777, 0.32268861, -0.13857445]],
#
# [[-0.14993843, 1.10793507, -0.40056887, -1.94395220],
# [ 0.41686651, 0.44551995, -0.09356714, -0.60898107]],
#
# [[-0.24998808, -1.47699273, -0.88838995, 0.42629015],
# [ 0.56948012, -0.96200180, 0.53355658, 3.20450878]]])
values = paddle.zeros((1,1,1), dtype=paddle.float32)
result = paddle.vision.transforms.erase(fake_img, 0, 1, 1, 2, values)
print(result)
#Tensor(shape=[3, 2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[ 0.02169025, 0. , 0. , -1.07478464],
# [ 0.20654772, 1.74624777, 0.32268861, -0.13857445]],
#
# [[-0.14993843, 0. , 0. , -1.94395220],
# [ 0.41686651, 0.44551995, -0.09356714, -0.60898107]],
#
# [[-0.24998808, 0. , 0. , 0.42629015],
# [ 0.56948012, -0.96200180, 0.53355658, 3.20450878]]])
>>> import paddle
>>> paddle.seed(2023)
>>> fake_img = paddle.randn((3, 2, 4)).astype(paddle.float32)
>>> print(fake_img)
Tensor(shape=[3, 2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.06132207, 1.11349595, 0.41906244, -0.24858207],
[-1.85169315, -1.50370061, 1.73954511, 0.13331604]],
[[ 1.66359663, -0.55764782, -0.59911072, -0.57773495],
[-1.03176904, -0.33741450, -0.29695082, -1.50258386]],
[[ 0.67233968, -1.07747352, 0.80170447, -0.06695852],
[-1.85003340, -0.23008066, 0.65083790, 0.75387722]]])
>>> values = paddle.zeros((1,1,1), dtype=paddle.float32)
>>> result = paddle.vision.transforms.erase(fake_img, 0, 1, 1, 2, values)
>>> print(result)
Tensor(shape=[3, 2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.06132207, 0. , 0. , -0.24858207],
[-1.85169315, -1.50370061, 1.73954511, 0.13331604]],
[[ 1.66359663, 0. , 0. , -0.57773495],
[-1.03176904, -0.33741450, -0.29695082, -1.50258386]],
[[ 0.67233968, 0. , 0. , -0.06695852],
[-1.85003340, -0.23008066, 0.65083790, 0.75387722]]])
"""
if _is_tensor_image(img):
......
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