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Make vision datasets return PIL.Image as default #28264

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Oct 30, 2020
Merged

Make vision datasets return PIL.Image as default #28264

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8c6b19c
return pil image as default
LielinJiang Oct 20, 2020
a6944d2
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
LielinJiang Oct 20, 2020
9ba2174
make vision dataset return PIL.Image as default
LielinJiang Oct 27, 2020
48c8300
fix code style
LielinJiang Oct 27, 2020
3173b11
fix coverage and sample code
LielinJiang Oct 27, 2020
ffa5575
fix code style
LielinJiang Oct 27, 2020
90fa769
fix sample code, test=document_fix
LielinJiang Oct 27, 2020
adc0719
update comment, test=document_fix
LielinJiang Oct 28, 2020
108a788
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
LielinJiang Oct 28, 2020
908ad8c
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
LielinJiang Oct 30, 2020
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23 changes: 19 additions & 4 deletions python/paddle/hapi/callbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,12 +296,17 @@ class ProgBarLogger(Callback):
.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

train_dataset = paddle.vision.datasets.MNIST(mode='train')
transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)

lenet = paddle.vision.LeNet()
model = paddle.Model(lenet,
Expand Down Expand Up @@ -432,12 +437,17 @@ class ModelCheckpoint(Callback):
.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

train_dataset = paddle.vision.datasets.MNIST(mode='train')
transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)

lenet = paddle.vision.LeNet()
model = paddle.Model(lenet,
Expand Down Expand Up @@ -484,13 +494,18 @@ class VisualDL(Callback):
.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

train_dataset = paddle.vision.datasets.MNIST(mode='train')
eval_dataset = paddle.vision.datasets.MNIST(mode='test')
transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
eval_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)

net = paddle.vision.LeNet()
model = paddle.Model(net, inputs, labels)
Expand Down
45 changes: 36 additions & 9 deletions python/paddle/hapi/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -836,6 +836,7 @@ class Model(object):

import paddle
import paddle.nn as nn
import paddle.vision.transforms as T
from paddle.static import InputSpec

device = paddle.set_device('cpu') # or 'gpu'
Expand All @@ -857,7 +858,11 @@ class Model(object):
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy())

data = paddle.vision.datasets.MNIST(mode='train')
transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
data = paddle.vision.datasets.MNIST(mode='train', transform=transform)
model.fit(data, epochs=2, batch_size=32, verbose=1)
"""

Expand Down Expand Up @@ -1066,6 +1071,7 @@ def save(self, path, training=True):

import paddle
import paddle.nn as nn
import paddle.vision.transforms as T
from paddle.static import InputSpec

class Mnist(nn.Layer):
Expand All @@ -1092,7 +1098,13 @@ def forward(self, x):
optim = paddle.optimizer.SGD(learning_rate=1e-3,
parameters=model.parameters())
model.prepare(optim, paddle.nn.CrossEntropyLoss())
data = paddle.vision.datasets.MNIST(mode='train')

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
data = paddle.vision.datasets.MNIST(mode='train', transform=transform)

model.fit(data, epochs=1, batch_size=32, verbose=0)
model.save('checkpoint/test') # save for training
model.save('inference_model', False) # save for inference
Expand Down Expand Up @@ -1352,14 +1364,19 @@ def fit(
.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

dynamic = True
device = paddle.set_device('cpu') # or 'gpu'
paddle.disable_static(device) if dynamic else None

train_dataset = paddle.vision.datasets.MNIST(mode='train')
val_dataset = paddle.vision.datasets.MNIST(mode='test')

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)

input = InputSpec([None, 1, 28, 28], 'float32', 'image')
label = InputSpec([None, 1], 'int64', 'label')
Expand All @@ -1385,16 +1402,21 @@ def fit(
.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

dynamic = True
device = paddle.set_device('cpu') # or 'gpu'
paddle.disable_static(device) if dynamic else None

train_dataset = paddle.vision.datasets.MNIST(mode='train')

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
train_loader = paddle.io.DataLoader(train_dataset,
places=device, batch_size=64)
val_dataset = paddle.vision.datasets.MNIST(mode='test')
val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
val_loader = paddle.io.DataLoader(val_dataset,
places=device, batch_size=64)

Expand Down Expand Up @@ -1521,10 +1543,15 @@ def evaluate(
.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

# declarative mode
val_dataset = paddle.vision.datasets.MNIST(mode='test')
transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)

input = InputSpec([-1, 1, 28, 28], 'float32', 'image')
label = InputSpec([None, 1], 'int64', 'label')
Expand Down
8 changes: 6 additions & 2 deletions python/paddle/tests/test_callbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
from paddle.static import InputSpec
from paddle.vision.models import LeNet
from paddle.hapi.callbacks import config_callbacks
import paddle.vision.transforms as T


class TestCallbacks(unittest.TestCase):
Expand Down Expand Up @@ -112,8 +113,11 @@ def test_visualdl_callback(self):
inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

train_dataset = paddle.vision.datasets.MNIST(mode='train')
eval_dataset = paddle.vision.datasets.MNIST(mode='test')
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = paddle.vision.datasets.MNIST(
mode='train', transform=transform)
eval_dataset = paddle.vision.datasets.MNIST(
mode='test', transform=transform)

net = paddle.vision.LeNet()
model = paddle.Model(net, inputs, labels)
Expand Down
54 changes: 46 additions & 8 deletions python/paddle/tests/test_dataset_cifar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,10 +27,11 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 50000)
data, label = cifar[idx]
data = np.array(data)
self.assertTrue(len(data.shape) == 3)
self.assertTrue(data.shape[0] == 3)
self.assertTrue(data.shape[2] == 3)
self.assertTrue(data.shape[1] == 32)
self.assertTrue(data.shape[2] == 32)
self.assertTrue(data.shape[0] == 32)
self.assertTrue(0 <= int(label) <= 9)


Expand All @@ -43,12 +44,30 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 10000)
data, label = cifar[idx]
data = np.array(data)
self.assertTrue(len(data.shape) == 3)
self.assertTrue(data.shape[0] == 3)
self.assertTrue(data.shape[2] == 3)
self.assertTrue(data.shape[1] == 32)
self.assertTrue(data.shape[2] == 32)
self.assertTrue(data.shape[0] == 32)
self.assertTrue(0 <= int(label) <= 9)

# test cv2 backend
cifar = Cifar10(mode='test', backend='cv2')
self.assertTrue(len(cifar) == 10000)

# traversal whole dataset may cost a
# long time, randomly check 1 sample
idx = np.random.randint(0, 10000)
data, label = cifar[idx]
self.assertTrue(len(data.shape) == 3)
self.assertTrue(data.shape[2] == 3)
self.assertTrue(data.shape[1] == 32)
self.assertTrue(data.shape[0] == 32)
self.assertTrue(0 <= int(label) <= 99)

with self.assertRaises(ValueError):
cifar = Cifar10(mode='test', backend=1)


class TestCifar100Train(unittest.TestCase):
def test_main(self):
Expand All @@ -59,10 +78,11 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 50000)
data, label = cifar[idx]
data = np.array(data)
self.assertTrue(len(data.shape) == 3)
self.assertTrue(data.shape[0] == 3)
self.assertTrue(data.shape[2] == 3)
self.assertTrue(data.shape[1] == 32)
self.assertTrue(data.shape[2] == 32)
self.assertTrue(data.shape[0] == 32)
self.assertTrue(0 <= int(label) <= 99)


Expand All @@ -75,12 +95,30 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 10000)
data, label = cifar[idx]
data = np.array(data)
self.assertTrue(len(data.shape) == 3)
self.assertTrue(data.shape[0] == 3)
self.assertTrue(data.shape[2] == 3)
self.assertTrue(data.shape[1] == 32)
self.assertTrue(data.shape[2] == 32)
self.assertTrue(data.shape[0] == 32)
self.assertTrue(0 <= int(label) <= 99)

# test cv2 backend
cifar = Cifar100(mode='test', backend='cv2')
self.assertTrue(len(cifar) == 10000)

# traversal whole dataset may cost a
# long time, randomly check 1 sample
idx = np.random.randint(0, 10000)
data, label = cifar[idx]
self.assertTrue(len(data.shape) == 3)
self.assertTrue(data.shape[2] == 3)
self.assertTrue(data.shape[1] == 32)
self.assertTrue(data.shape[0] == 32)
self.assertTrue(0 <= int(label) <= 99)

with self.assertRaises(ValueError):
cifar = Cifar100(mode='test', backend=1)


if __name__ == '__main__':
unittest.main()
24 changes: 24 additions & 0 deletions python/paddle/tests/test_dataset_voc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,9 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 3)
image, label = voc2012[idx]
image = np.array(image)
label = np.array(label)

self.assertTrue(len(image.shape) == 3)
self.assertTrue(len(label.shape) == 2)

Expand All @@ -45,6 +48,9 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 1)
image, label = voc2012[idx]
image = np.array(image)
label = np.array(label)

self.assertTrue(len(image.shape) == 3)
self.assertTrue(len(label.shape) == 2)

Expand All @@ -58,9 +64,27 @@ def test_main(self):
# long time, randomly check 1 sample
idx = np.random.randint(0, 1)
image, label = voc2012[idx]
image = np.array(image)
label = np.array(label)

self.assertTrue(len(image.shape) == 3)
self.assertTrue(len(label.shape) == 2)

# test cv2 backend
voc2012 = VOC2012(mode='test', backend='cv2')
self.assertTrue(len(voc2012) == 2)

# traversal whole dataset may cost a
# long time, randomly check 1 sample
idx = np.random.randint(0, 1)
image, label = voc2012[idx]

self.assertTrue(len(image.shape) == 3)
self.assertTrue(len(label.shape) == 2)

with self.assertRaises(ValueError):
voc2012 = VOC2012(mode='test', backend=1)


if __name__ == '__main__':
unittest.main()
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