Add vision api for hapi (#24404)

* add vision

* fix predict, test=develop

* add unittest for vision apis, test=develop

* fix typos

* add hapi models api, test=develop

* fix code format, test=develop

* fix typos, test=develop

* fix sample code import, test=develop

* fix sample codes, test=develop

* add decompress, test=develop

* rm darknet, test=develop

* rm debug code, test=develop

python/paddle/incubate/hapi/download.py

@@ -22,6 +22,8 @@ import os.path as osp
 import shutil
 import requests
 import hashlib
+import tarfile
+import zipfile
 import time
 from collections import OrderedDict
 from paddle.fluid.dygraph.parallel import ParallelEnv
@@ -166,6 +168,11 @@ def get_path_from_url(url, root_dir, md5sum=None, check_exist=True):
         else:
             while not os.path.exists(fullpath):
                 time.sleep(1)
+
+    if ParallelEnv().local_rank == 0:
+        if tarfile.is_tarfile(fullpath) or zipfile.is_zipfile(fullpath):
+            fullpath = _decompress(fullpath)
+
     return fullpath
 
 
@@ -233,3 +240,101 @@ def _md5check(fullname, md5sum=None):
                     "{}(base)".format(fullname, calc_md5sum, md5sum))
         return False
     return True
+
+
+def _decompress(fname):
+    """
+    Decompress for zip and tar file
+    """
+    logger.info("Decompressing {}...".format(fname))
+
+    # For protecting decompressing interupted,
+    # decompress to fpath_tmp directory firstly, if decompress
+    # successed, move decompress files to fpath and delete
+    # fpath_tmp and remove download compress file.
+
+    if tarfile.is_tarfile(fname):
+        uncompressed_path = _uncompress_file_tar(fname)
+    elif zipfile.is_zipfile(fname):
+        uncompressed_path = _uncompress_file_zip(fname)
+    else:
+        raise TypeError("Unsupport compress file type {}".format(fname))
+
+    return uncompressed_path
+
+
+def _uncompress_file_zip(filepath):
+    files = zipfile.ZipFile(filepath, 'r')
+    file_list = files.namelist()
+
+    file_dir = os.path.dirname(filepath)
+
+    if _is_a_single_file(file_list):
+        rootpath = file_list[0]
+        uncompressed_path = os.path.join(file_dir, rootpath)
+
+        for item in file_list:
+            files.extract(item, file_dir)
+
+    elif _is_a_single_dir(file_list):
+        rootpath = os.path.splitext(file_list[0])[0].split(os.sep)[-1]
+        uncompressed_path = os.path.join(file_dir, rootpath)
+
+        for item in file_list:
+            files.extract(item, file_dir)
+
+    else:
+        rootpath = os.path.splitext(filepath)[0].split(os.sep)[-1]
+        uncompressed_path = os.path.join(file_dir, rootpath)
+        if not os.path.exists(uncompressed_path):
+            os.makedirs(uncompressed_path)
+        for item in file_list:
+            files.extract(item, os.path.join(file_dir, rootpath))
+
+    files.close()
+
+    return uncompressed_path
+
+
+def _uncompress_file_tar(filepath, mode="r:*"):
+    files = tarfile.open(filepath, mode)
+    file_list = files.getnames()
+
+    file_dir = os.path.dirname(filepath)
+
+    if _is_a_single_file(file_list):
+        rootpath = file_list[0]
+        uncompressed_path = os.path.join(file_dir, rootpath)
+        for item in file_list:
+            files.extract(item, file_dir)
+    elif _is_a_single_dir(file_list):
+        rootpath = os.path.splitext(file_list[0])[0].split(os.sep)[-1]
+        uncompressed_path = os.path.join(file_dir, rootpath)
+        for item in file_list:
+            files.extract(item, file_dir)
+    else:
+        rootpath = os.path.splitext(filepath)[0].split(os.sep)[-1]
+        uncompressed_path = os.path.join(file_dir, rootpath)
+        if not os.path.exists(uncompressed_path):
+            os.makedirs(uncompressed_path)
+
+        for item in file_list:
+            files.extract(item, os.path.join(file_dir, rootpath))
+
+    files.close()
+
+    return uncompressed_path
+
+
+def _is_a_single_file(file_list):
+    if len(file_list) == 1 and file_list[0].find(os.sep) < -1:
+        return True
+    return False
+
+
+def _is_a_single_dir(file_list):
+    file_name = file_list[0].split(os.sep)[0]
+    for i in range(1, len(file_list)):
+        if file_name != file_list[i].split(os.sep)[0]:
+            return False
+    return True

python/paddle/incubate/hapi/metrics.py

@@ -42,7 +42,7 @@ class Metric(object):
         m.accumulate()
         
     Advanced usage for :code:`add_metric_op`
-    Metric calculating con be accelerate by calucateing metric states
+    Metric calculation can be accelerated by calculating metric states
     from model outputs and labels by Paddle OPs in :code:`add_metric_op`,
     metric states will be fetch as numpy array and call :code:`update`
     with states in numpy format.

python/paddle/incubate/hapi/model.py

@@ -1633,7 +1633,12 @@ class Model(fluid.dygraph.Layer):
                 for k, v in zip(self._metrics_name(), metrics):
                     logs[k] = v
             else:
-                outs = getattr(self, mode + '_batch')(data)
+                if self._inputs is not None:
+                    outs = getattr(self,
+                                   mode + '_batch')(data[:len(self._inputs)])
+                else:
+                    outs = getattr(self, mode + '_batch')(data)
+
                 outputs.append(outs)
 
             logs['step'] = step

python/paddle/incubate/hapi/tests/test_download.py

@@ -45,6 +45,18 @@ class TestDownload(unittest.TestCase):
             url = 'https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0t.pdparams'
             self.download(url, None)
 
+    def test_download_and_uncompress(self):
+        urls = [
+            "https://paddle-hapi.bj.bcebos.com/unittest/files.tar",
+            "https://paddle-hapi.bj.bcebos.com/unittest/files.zip",
+            "https://paddle-hapi.bj.bcebos.com/unittest/single_dir.tar",
+            "https://paddle-hapi.bj.bcebos.com/unittest/single_dir.zip",
+            "https://paddle-hapi.bj.bcebos.com/unittest/single_file.tar",
+            "https://paddle-hapi.bj.bcebos.com/unittest/single_file.zip",
+        ]
+        for url in urls:
+            self.download(url, None)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/incubate/hapi/tests/test_transforms.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# when test, you should add hapi root path to the PYTHONPATH,
+# export PYTHONPATH=PATH_TO_HAPI:$PYTHONPATH
+import unittest
+import os
+import tempfile
+import cv2
+import shutil
+import numpy as np
+
+from paddle.incubate.hapi.datasets import DatasetFolder
+from paddle.incubate.hapi.vision.transforms import transforms
+
+
+class TestTransforms(unittest.TestCase):
+    def setUp(self):
+        self.data_dir = tempfile.mkdtemp()
+        for i in range(2):
+            sub_dir = os.path.join(self.data_dir, 'class_' + str(i))
+            if not os.path.exists(sub_dir):
+                os.makedirs(sub_dir)
+            for j in range(2):
+                if j == 0:
+                    fake_img = (np.random.random(
+                        (280, 350, 3)) * 255).astype('uint8')
+                else:
+                    fake_img = (np.random.random(
+                        (400, 300, 3)) * 255).astype('uint8')
+                cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
+
+    def tearDown(self):
+        shutil.rmtree(self.data_dir)
+
+    def do_transform(self, trans):
+        dataset_folder = DatasetFolder(self.data_dir, transform=trans)
+
+        for _ in dataset_folder:
+            pass
+
+    def test_trans_all(self):
+        normalize = transforms.Normalize(
+            mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
+        trans = transforms.Compose([
+            transforms.RandomResizedCrop(224), transforms.GaussianNoise(),
+            transforms.ColorJitter(
+                brightness=0.4, contrast=0.4, saturation=0.4,
+                hue=0.4), transforms.RandomHorizontalFlip(),
+            transforms.Permute(mode='CHW'), normalize
+        ])
+
+        self.do_transform(trans)
+
+    def test_trans_resize(self):
+        trans = transforms.Compose([
+            transforms.Resize(300, [0, 1]),
+            transforms.RandomResizedCrop((280, 280)),
+            transforms.Resize(280, [0, 1]),
+            transforms.Resize((256, 200)),
+            transforms.Resize((180, 160)),
+            transforms.CenterCrop(128),
+            transforms.CenterCrop((128, 128)),
+        ])
+        self.do_transform(trans)
+
+    def test_trans_centerCrop(self):
+        trans = transforms.Compose([
+            transforms.CenterCropResize(224),
+            transforms.CenterCropResize(128, 160),
+        ])
+        self.do_transform(trans)
+
+    def test_flip(self):
+        trans = transforms.Compose([
+            transforms.RandomHorizontalFlip(1.0),
+            transforms.RandomHorizontalFlip(0.0),
+            transforms.RandomVerticalFlip(0.0),
+            transforms.RandomVerticalFlip(1.0),
+        ])
+        self.do_transform(trans)
+
+    def test_color_jitter(self):
+        trans = transforms.BatchCompose([
+            transforms.BrightnessTransform(0.0),
+            transforms.HueTransform(0.0),
+            transforms.SaturationTransform(0.0),
+            transforms.ContrastTransform(0.0),
+        ])
+        self.do_transform(trans)
+
+    def test_exception(self):
+        trans = transforms.Compose([transforms.Resize(-1)])
+
+        trans_batch = transforms.BatchCompose([transforms.Resize(-1)])
+
+        with self.assertRaises(Exception):
+            self.do_transform(trans)
+
+        with self.assertRaises(Exception):
+            self.do_transform(trans_batch)
+
+        with self.assertRaises(ValueError):
+            transforms.ContrastTransform(-1.0)
+
+        with self.assertRaises(ValueError):
+            transforms.SaturationTransform(-1.0),
+
+        with self.assertRaises(ValueError):
+            transforms.HueTransform(-1.0)
+
+        with self.assertRaises(ValueError):
+            transforms.BrightnessTransform(-1.0)
+
+    def test_info(self):
+        str(transforms.Compose([transforms.Resize((224, 224))]))
+        str(transforms.BatchCompose([transforms.Resize((224, 224))]))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/incubate/hapi/tests/test_vision_models.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+
+import paddle.incubate.hapi.vision.models as models
+from paddle.incubate.hapi.model import Input
+
+
+class TestVisonModels(unittest.TestCase):
+    def models_infer(self, arch, pretrained=False, batch_norm=False):
+
+        x = np.array(np.random.random((2, 3, 224, 224)), dtype=np.float32)
+        if batch_norm:
+            model = models.__dict__[arch](pretrained=pretrained,
+                                          batch_norm=True)
+        else:
+            model = models.__dict__[arch](pretrained=pretrained)
+        inputs = [Input([None, 3, 224, 224], 'float32', name='image')]
+
+        model.prepare(inputs=inputs)
+
+        model.test_batch(x)
+
+    def test_mobilenetv2_pretrained(self):
+        self.models_infer('mobilenet_v2', pretrained=True)
+
+    def test_mobilenetv1(self):
+        self.models_infer('mobilenet_v1')
+
+    def test_vgg11(self):
+        self.models_infer('vgg11')
+
+    def test_vgg13(self):
+        self.models_infer('vgg13')
+
+    def test_vgg16(self):
+        self.models_infer('vgg16')
+
+    def test_vgg16_bn(self):
+        self.models_infer('vgg16', batch_norm=True)
+
+    def test_vgg19(self):
+        self.models_infer('vgg19')
+
+    def test_resnet18(self):
+        self.models_infer('resnet18')
+
+    def test_resnet34(self):
+        self.models_infer('resnet34')
+
+    def test_resnet50(self):
+        self.models_infer('resnet50')
+
+    def test_resnet101(self):
+        self.models_infer('resnet101')
+
+    def test_resnet152(self):
+        self.models_infer('resnet152')
+
+    def test_lenet(self):
+        lenet = models.__dict__['LeNet']()
+
+        inputs = [Input([None, 1, 28, 28], 'float32', name='x')]
+        lenet.prepare(inputs=inputs)
+
+        x = np.array(np.random.random((2, 1, 28, 28)), dtype=np.float32)
+        lenet.test_batch(x)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/incubate/hapi/vision/__init__.py

@@ -13,6 +13,9 @@
 # limitations under the License.
 
 from . import models
+from . import transforms
 from .models import *
+from .transforms import *
 
-__all__ = models.__all__
+__all__ = models.__all__ \
+        + transforms.__all__

python/paddle/incubate/hapi/vision/models/__init__.py

@@ -12,7 +12,20 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
+from . import resnet
+from . import vgg
+from . import mobilenetv1
+from . import mobilenetv2
 from . import lenet
+
+from .resnet import *
+from .mobilenetv1 import *
+from .mobilenetv2 import *
+from .vgg import *
 from .lenet import *
 
-__all__ = lenet.__all__
+__all__ = resnet.__all__ \
+        + vgg.__all__ \
+        + mobilenetv1.__all__ \
+        + mobilenetv2.__all__ \
+        + lenet.__all__

python/paddle/incubate/hapi/vision/models/mobilenetv1.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+
+from ...model import Model
+from ...download import get_weights_path_from_url
+
+__all__ = ['MobileNetV1', 'mobilenet_v1']
+
+model_urls = {
+    'mobilenetv1_1.0':
+    ('https://paddle-hapi.bj.bcebos.com/models/mobilenet_v1_x1.0.pdparams',
+     'bf0d25cb0bed1114d9dac9384ce2b4a6')
+}
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 filter_size,
+                 num_filters,
+                 stride,
+                 padding,
+                 channels=None,
+                 num_groups=1,
+                 act='relu',
+                 use_cudnn=True,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            act=None,
+            use_cudnn=use_cudnn,
+            param_attr=ParamAttr(
+                initializer=MSRA(), name=self.full_name() + "_weights"),
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(name=self.full_name() + "_bn" + "_scale"),
+            bias_attr=ParamAttr(name=self.full_name() + "_bn" + "_offset"),
+            moving_mean_name=self.full_name() + "_bn" + '_mean',
+            moving_variance_name=self.full_name() + "_bn" + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class DepthwiseSeparable(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters1,
+                 num_filters2,
+                 num_groups,
+                 stride,
+                 scale,
+                 name=None):
+        super(DepthwiseSeparable, self).__init__()
+
+        self._depthwise_conv = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=int(num_filters1 * scale),
+            filter_size=3,
+            stride=stride,
+            padding=1,
+            num_groups=int(num_groups * scale),
+            use_cudnn=False)
+
+        self._pointwise_conv = ConvBNLayer(
+            num_channels=int(num_filters1 * scale),
+            filter_size=1,
+            num_filters=int(num_filters2 * scale),
+            stride=1,
+            padding=0)
+
+    def forward(self, inputs):
+        y = self._depthwise_conv(inputs)
+        y = self._pointwise_conv(y)
+        return y
+
+
+class MobileNetV1(Model):
+    """MobileNetV1 model from
+    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_.
+
+    Args:
+        scale (float): scale of channels in each layer. Default: 1.0.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool): use pool before the last fc layer or not. Default: True.
+        classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import MobileNetV1
+
+            model = MobileNetV1()
+    """
+
+    def __init__(self,
+                 scale=1.0,
+                 num_classes=1000,
+                 with_pool=True,
+                 classifier_activation='softmax'):
+        super(MobileNetV1, self).__init__()
+        self.scale = scale
+        self.dwsl = []
+        self.num_classes = num_classes
+        self.with_pool = with_pool
+
+        self.conv1 = ConvBNLayer(
+            num_channels=3,
+            filter_size=3,
+            channels=3,
+            num_filters=int(32 * scale),
+            stride=2,
+            padding=1)
+
+        dws21 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(32 * scale),
+                num_filters1=32,
+                num_filters2=64,
+                num_groups=32,
+                stride=1,
+                scale=scale),
+            name="conv2_1")
+        self.dwsl.append(dws21)
+
+        dws22 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(64 * scale),
+                num_filters1=64,
+                num_filters2=128,
+                num_groups=64,
+                stride=2,
+                scale=scale),
+            name="conv2_2")
+        self.dwsl.append(dws22)
+
+        dws31 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(128 * scale),
+                num_filters1=128,
+                num_filters2=128,
+                num_groups=128,
+                stride=1,
+                scale=scale),
+            name="conv3_1")
+        self.dwsl.append(dws31)
+
+        dws32 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(128 * scale),
+                num_filters1=128,
+                num_filters2=256,
+                num_groups=128,
+                stride=2,
+                scale=scale),
+            name="conv3_2")
+        self.dwsl.append(dws32)
+
+        dws41 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(256 * scale),
+                num_filters1=256,
+                num_filters2=256,
+                num_groups=256,
+                stride=1,
+                scale=scale),
+            name="conv4_1")
+        self.dwsl.append(dws41)
+
+        dws42 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(256 * scale),
+                num_filters1=256,
+                num_filters2=512,
+                num_groups=256,
+                stride=2,
+                scale=scale),
+            name="conv4_2")
+        self.dwsl.append(dws42)
+
+        for i in range(5):
+            tmp = self.add_sublayer(
+                sublayer=DepthwiseSeparable(
+                    num_channels=int(512 * scale),
+                    num_filters1=512,
+                    num_filters2=512,
+                    num_groups=512,
+                    stride=1,
+                    scale=scale),
+                name="conv5_" + str(i + 1))
+            self.dwsl.append(tmp)
+
+        dws56 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(512 * scale),
+                num_filters1=512,
+                num_filters2=1024,
+                num_groups=512,
+                stride=2,
+                scale=scale),
+            name="conv5_6")
+        self.dwsl.append(dws56)
+
+        dws6 = self.add_sublayer(
+            sublayer=DepthwiseSeparable(
+                num_channels=int(1024 * scale),
+                num_filters1=1024,
+                num_filters2=1024,
+                num_groups=1024,
+                stride=1,
+                scale=scale),
+            name="conv6")
+        self.dwsl.append(dws6)
+
+        if with_pool:
+            self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
+
+        if num_classes > -1:
+            self.out = Linear(
+                int(1024 * scale),
+                num_classes,
+                act=classifier_activation,
+                param_attr=ParamAttr(
+                    initializer=MSRA(), name=self.full_name() + "fc7_weights"),
+                bias_attr=ParamAttr(name="fc7_offset"))
+
+    def forward(self, inputs):
+        y = self.conv1(inputs)
+        for dws in self.dwsl:
+            y = dws(y)
+
+        if self.with_pool:
+            y = self.pool2d_avg(y)
+
+        if self.num_classes > 0:
+            y = fluid.layers.reshape(y, shape=[-1, 1024])
+            y = self.out(y)
+        return y
+
+
+def _mobilenet(arch, pretrained=False, **kwargs):
+    model = MobileNetV1(**kwargs)
+    if pretrained:
+        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
+            arch)
+        weight_path = get_weights_path_from_url(model_urls[arch][0],
+                                                model_urls[arch][1])
+        assert weight_path.endswith(
+            '.pdparams'), "suffix of weight must be .pdparams"
+        model.load(weight_path)
+
+    return model
+
+
+def mobilenet_v1(pretrained=False, scale=1.0, **kwargs):
+    """MobileNetV1
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+        scale: (float): scale of channels in each layer. Default: 1.0.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import mobilenet_v1
+
+            # build model
+            model = mobilenet_v1()
+
+            # build model and load imagenet pretrained weight
+            # model = mobilenet_v1(pretrained=True)
+
+            # build mobilenet v1 with scale=0.5
+            model = mobilenet_v1(scale=0.5)
+    """
+    model = _mobilenet(
+        'mobilenetv1_' + str(scale), pretrained, scale=scale, **kwargs)
+    return model

python/paddle/incubate/hapi/vision/models/mobilenetv2.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+
+from ...model import Model
+from ...download import get_weights_path_from_url
+
+__all__ = ['MobileNetV2', 'mobilenet_v2']
+
+model_urls = {
+    'mobilenetv2_1.0':
+    ('https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparams',
+     '8ff74f291f72533f2a7956a4efff9d88')
+}
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 filter_size,
+                 num_filters,
+                 stride,
+                 padding,
+                 channels=None,
+                 num_groups=1,
+                 use_cudnn=True):
+        super(ConvBNLayer, self).__init__()
+
+        tmp_param = ParamAttr(name=self.full_name() + "_weights")
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            act=None,
+            use_cudnn=use_cudnn,
+            param_attr=tmp_param,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(
+            num_filters,
+            param_attr=ParamAttr(name=self.full_name() + "_bn" + "_scale"),
+            bias_attr=ParamAttr(name=self.full_name() + "_bn" + "_offset"),
+            moving_mean_name=self.full_name() + "_bn" + '_mean',
+            moving_variance_name=self.full_name() + "_bn" + '_variance')
+
+    def forward(self, inputs, if_act=True):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        if if_act:
+            y = fluid.layers.relu6(y)
+        return y
+
+
+class InvertedResidualUnit(fluid.dygraph.Layer):
+    def __init__(
+            self,
+            num_channels,
+            num_in_filter,
+            num_filters,
+            stride,
+            filter_size,
+            padding,
+            expansion_factor, ):
+        super(InvertedResidualUnit, self).__init__()
+        num_expfilter = int(round(num_in_filter * expansion_factor))
+        self._expand_conv = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_expfilter,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            num_groups=1)
+
+        self._bottleneck_conv = ConvBNLayer(
+            num_channels=num_expfilter,
+            num_filters=num_expfilter,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            num_groups=num_expfilter,
+            use_cudnn=False)
+
+        self._linear_conv = ConvBNLayer(
+            num_channels=num_expfilter,
+            num_filters=num_filters,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            num_groups=1)
+
+    def forward(self, inputs, ifshortcut):
+        y = self._expand_conv(inputs, if_act=True)
+        y = self._bottleneck_conv(y, if_act=True)
+        y = self._linear_conv(y, if_act=False)
+        if ifshortcut:
+            y = fluid.layers.elementwise_add(inputs, y)
+        return y
+
+
+class InvresiBlocks(fluid.dygraph.Layer):
+    def __init__(self, in_c, t, c, n, s):
+        super(InvresiBlocks, self).__init__()
+
+        self._first_block = InvertedResidualUnit(
+            num_channels=in_c,
+            num_in_filter=in_c,
+            num_filters=c,
+            stride=s,
+            filter_size=3,
+            padding=1,
+            expansion_factor=t)
+
+        self._inv_blocks = []
+        for i in range(1, n):
+            tmp = self.add_sublayer(
+                sublayer=InvertedResidualUnit(
+                    num_channels=c,
+                    num_in_filter=c,
+                    num_filters=c,
+                    stride=1,
+                    filter_size=3,
+                    padding=1,
+                    expansion_factor=t),
+                name=self.full_name() + "_" + str(i + 1))
+            self._inv_blocks.append(tmp)
+
+    def forward(self, inputs):
+        y = self._first_block(inputs, ifshortcut=False)
+        for inv_block in self._inv_blocks:
+            y = inv_block(y, ifshortcut=True)
+        return y
+
+
+class MobileNetV2(Model):
+    """MobileNetV2 model from
+    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
+
+    Args:
+        scale (float): scale of channels in each layer. Default: 1.0.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool): use pool before the last fc layer or not. Default: True.
+        classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import MobileNetV2
+
+            model = MobileNetV2()
+    """
+
+    def __init__(self,
+                 scale=1.0,
+                 num_classes=1000,
+                 with_pool=True,
+                 classifier_activation='softmax'):
+        super(MobileNetV2, self).__init__()
+        self.scale = scale
+        self.num_classes = num_classes
+        self.with_pool = with_pool
+
+        bottleneck_params_list = [
+            (1, 16, 1, 1),
+            (6, 24, 2, 2),
+            (6, 32, 3, 2),
+            (6, 64, 4, 2),
+            (6, 96, 3, 1),
+            (6, 160, 3, 2),
+            (6, 320, 1, 1),
+        ]
+
+        self._conv1 = ConvBNLayer(
+            num_channels=3,
+            num_filters=int(32 * scale),
+            filter_size=3,
+            stride=2,
+            padding=1)
+
+        self._invl = []
+        i = 1
+        in_c = int(32 * scale)
+        for layer_setting in bottleneck_params_list:
+            t, c, n, s = layer_setting
+            i += 1
+            tmp = self.add_sublayer(
+                sublayer=InvresiBlocks(
+                    in_c=in_c, t=t, c=int(c * scale), n=n, s=s),
+                name='conv' + str(i))
+            self._invl.append(tmp)
+            in_c = int(c * scale)
+
+        self._out_c = int(1280 * scale) if scale > 1.0 else 1280
+        self._conv9 = ConvBNLayer(
+            num_channels=in_c,
+            num_filters=self._out_c,
+            filter_size=1,
+            stride=1,
+            padding=0)
+
+        if with_pool:
+            self._pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
+
+        if num_classes > 0:
+            tmp_param = ParamAttr(name=self.full_name() + "fc10_weights")
+            self._fc = Linear(
+                self._out_c,
+                num_classes,
+                act=classifier_activation,
+                param_attr=tmp_param,
+                bias_attr=ParamAttr(name="fc10_offset"))
+
+    def forward(self, inputs):
+        y = self._conv1(inputs, if_act=True)
+        for inv in self._invl:
+            y = inv(y)
+        y = self._conv9(y, if_act=True)
+
+        if self.with_pool:
+            y = self._pool2d_avg(y)
+        if self.num_classes > 0:
+            y = fluid.layers.reshape(y, shape=[-1, self._out_c])
+            y = self._fc(y)
+        return y
+
+
+def _mobilenet(arch, pretrained=False, **kwargs):
+    model = MobileNetV2(**kwargs)
+    if pretrained:
+        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
+            arch)
+        weight_path = get_weights_path_from_url(model_urls[arch][0],
+                                                model_urls[arch][1])
+        assert weight_path.endswith(
+            '.pdparams'), "suffix of weight must be .pdparams"
+        model.load(weight_path)
+
+    return model
+
+
+def mobilenet_v2(pretrained=False, scale=1.0, **kwargs):
+    """MobileNetV2
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+        scale: (float): scale of channels in each layer. Default: 1.0.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import mobilenet_v2
+
+            # build model
+            model = mobilenet_v2()
+
+            # build model and load imagenet pretrained weight
+            # model = mobilenet_v2(pretrained=True)
+
+            # build mobilenet v2 with scale=0.5
+            model = mobilenet_v2(scale=0.5)
+    """
+    model = _mobilenet(
+        'mobilenetv2_' + str(scale), pretrained, scale=scale, **kwargs)
+    return model

python/paddle/incubate/hapi/vision/models/resnet.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle.fluid as fluid
+
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.container import Sequential
+
+from ...model import Model
+from ...download import get_weights_path_from_url
+
+__all__ = [
+    'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'
+]
+
+model_urls = {
+    'resnet18': ('https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams',
+                 '0ba53eea9bc970962d0ef96f7b94057e'),
+    'resnet34': ('https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams',
+                 '46bc9f7c3dd2e55b7866285bee91eff3'),
+    'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams',
+                 '5ce890a9ad386df17cf7fe2313dca0a1'),
+    'resnet101': ('https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams',
+                  'fb07a451df331e4b0bb861ed97c3a9b9'),
+    'resnet152': ('https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams',
+                  'f9c700f26d3644bb76ad2226ed5f5713'),
+}
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        x = self._conv(inputs)
+        x = self._batch_norm(x)
+
+        return x
+
+
+class BasicBlock(fluid.dygraph.Layer):
+    """residual block of resnet18 and resnet34
+    """
+    expansion = 1
+
+    def __init__(self, num_channels, num_filters, stride, shortcut=True):
+        super(BasicBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=3,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = short + conv1
+
+        return fluid.layers.relu(y)
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    """residual block of resnet50, resnet101 amd resnet152
+    """
+
+    expansion = 4
+
+    def __init__(self, num_channels, num_filters, stride, shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * self.expansion,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * self.expansion,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * self.expansion
+
+    def forward(self, inputs):
+        x = self.conv0(inputs)
+        conv1 = self.conv1(x)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        x = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        return fluid.layers.relu(x)
+
+
+class ResNet(Model):
+    """ResNet model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        Block (BasicBlock|BottleneckBlock): block module of model.
+        depth (int): layers of resnet, default: 50.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool): use pool before the last fc layer or not. Default: True.
+        classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import ResNet
+            from paddle.incubate.hapi.vision.models.resnet import BottleneckBlock, BasicBlock
+
+            resnet50 = ResNet(BottleneckBlock, 50)
+
+            resnet18 = ResNet(BasicBlock, 18)
+
+    """
+
+    def __init__(self,
+                 Block,
+                 depth=50,
+                 num_classes=1000,
+                 with_pool=True,
+                 classifier_activation='softmax'):
+        super(ResNet, self).__init__()
+
+        self.num_classes = num_classes
+        self.with_pool = with_pool
+
+        layer_config = {
+            18: [2, 2, 2, 2],
+            34: [3, 4, 6, 3],
+            50: [3, 4, 6, 3],
+            101: [3, 4, 23, 3],
+            152: [3, 8, 36, 3],
+        }
+        assert depth in layer_config.keys(), \
+            "supported depth are {} but input layer is {}".format(
+                layer_config.keys(), depth)
+
+        layers = layer_config[depth]
+
+        in_channels = 64
+        out_channels = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
+        self.pool = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.layers = []
+        for idx, num_blocks in enumerate(layers):
+            blocks = []
+            shortcut = False
+            for b in range(num_blocks):
+                if b == 1:
+                    in_channels = out_channels[idx] * Block.expansion
+                block = Block(
+                    num_channels=in_channels,
+                    num_filters=out_channels[idx],
+                    stride=2 if b == 0 and idx != 0 else 1,
+                    shortcut=shortcut)
+                blocks.append(block)
+                shortcut = True
+            layer = self.add_sublayer("layer_{}".format(idx),
+                                      Sequential(*blocks))
+            self.layers.append(layer)
+
+        if with_pool:
+            self.global_pool = Pool2D(
+                pool_size=7, pool_type='avg', global_pooling=True)
+
+        if num_classes > 0:
+            stdv = 1.0 / math.sqrt(out_channels[-1] * Block.expansion * 1.0)
+            self.fc_input_dim = out_channels[-1] * Block.expansion * 1 * 1
+            self.fc = Linear(
+                self.fc_input_dim,
+                num_classes,
+                act=classifier_activation,
+                param_attr=fluid.param_attr.ParamAttr(
+                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        x = self.conv(inputs)
+        x = self.pool(x)
+        for layer in self.layers:
+            x = layer(x)
+
+        if self.with_pool:
+            x = self.global_pool(x)
+
+        if self.num_classes > -1:
+            x = fluid.layers.reshape(x, shape=[-1, self.fc_input_dim])
+            x = self.fc(x)
+        return x
+
+
+def _resnet(arch, Block, depth, pretrained, **kwargs):
+    model = ResNet(Block, depth, **kwargs)
+    if pretrained:
+        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
+            arch)
+        weight_path = get_weights_path_from_url(model_urls[arch][0],
+                                                model_urls[arch][1])
+        assert weight_path.endswith(
+            '.pdparams'), "suffix of weight must be .pdparams"
+        model.load(weight_path)
+    return model
+
+
+def resnet18(pretrained=False, **kwargs):
+    """ResNet 18-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import resnet18
+
+            # build model
+            model = resnet18()
+
+            # build model and load imagenet pretrained weight
+            # model = resnet18(pretrained=True)
+    """
+    return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
+
+
+def resnet34(pretrained=False, **kwargs):
+    """ResNet 34-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import resnet34
+
+            # build model
+            model = resnet34()
+
+            # build model and load imagenet pretrained weight
+            # model = resnet34(pretrained=True)
+    """
+    return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
+
+
+def resnet50(pretrained=False, **kwargs):
+    """ResNet 50-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import resnet50
+
+            # build model
+            model = resnet50()
+
+            # build model and load imagenet pretrained weight
+            # model = resnet50(pretrained=True)
+    """
+    return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
+
+
+def resnet101(pretrained=False, **kwargs):
+    """ResNet 101-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import resnet101
+
+            # build model
+            model = resnet101()
+
+            # build model and load imagenet pretrained weight
+            # model = resnet101(pretrained=True)
+    """
+    return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
+
+
+def resnet152(pretrained=False, **kwargs):
+    """ResNet 152-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import resnet152
+
+            # build model
+            model = resnet152()
+
+            # build model and load imagenet pretrained weight
+            # model = resnet152(pretrained=True)
+    """
+    return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)

python/paddle/incubate/hapi/vision/models/vgg.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.container import Sequential
+
+from ...model import Model
+from ...download import get_weights_path_from_url
+
+__all__ = [
+    'VGG',
+    'vgg11',
+    'vgg13',
+    'vgg16',
+    'vgg19',
+]
+
+model_urls = {
+    'vgg16': ('https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams',
+              'c788f453a3b999063e8da043456281ee')
+}
+
+
+class Classifier(fluid.dygraph.Layer):
+    def __init__(self, num_classes, classifier_activation='softmax'):
+        super(Classifier, self).__init__()
+        self.linear1 = Linear(512 * 7 * 7, 4096)
+        self.linear2 = Linear(4096, 4096)
+        self.linear3 = Linear(4096, num_classes, act=classifier_activation)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = fluid.layers.relu(x)
+        x = fluid.layers.dropout(x, 0.5)
+        x = self.linear2(x)
+        x = fluid.layers.relu(x)
+        x = fluid.layers.dropout(x, 0.5)
+        out = self.linear3(x)
+        return out
+
+
+class VGG(Model):
+    """VGG model from
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
+
+    Args:
+        features (fluid.dygraph.Layer): vgg features create by function make_layers.
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import VGG
+            from paddle.incubate.hapi.vision.models.vgg import make_layers
+
+            vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
+
+            features = make_layers(vgg11_cfg)
+
+            vgg11 = VGG(features)
+
+    """
+
+    def __init__(self,
+                 features,
+                 num_classes=1000,
+                 classifier_activation='softmax'):
+        super(VGG, self).__init__()
+        self.features = features
+        self.num_classes = num_classes
+
+        if num_classes > 0:
+            classifier = Classifier(num_classes, classifier_activation)
+            self.classifier = self.add_sublayer("classifier",
+                                                Sequential(classifier))
+
+    def forward(self, x):
+        x = self.features(x)
+
+        if self.num_classes > 0:
+            x = fluid.layers.flatten(x, 1)
+            x = self.classifier(x)
+        return x
+
+
+def make_layers(cfg, batch_norm=False):
+    layers = []
+    in_channels = 3
+
+    for v in cfg:
+        if v == 'M':
+            layers += [Pool2D(pool_size=2, pool_stride=2)]
+        else:
+            if batch_norm:
+                conv2d = Conv2D(in_channels, v, filter_size=3, padding=1)
+                layers += [conv2d, BatchNorm(v, act='relu')]
+            else:
+                conv2d = Conv2D(
+                    in_channels, v, filter_size=3, padding=1, act='relu')
+                layers += [conv2d]
+            in_channels = v
+    return Sequential(*layers)
+
+
+cfgs = {
+    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'B':
+    [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'D': [
+        64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512,
+        512, 512, 'M'
+    ],
+    'E': [
+        64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512,
+        'M', 512, 512, 512, 512, 'M'
+    ],
+}
+
+
+def _vgg(arch, cfg, batch_norm, pretrained, **kwargs):
+    model = VGG(make_layers(
+        cfgs[cfg], batch_norm=batch_norm),
+                num_classes=1000,
+                **kwargs)
+
+    if pretrained:
+        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
+            arch)
+        weight_path = get_weights_path_from_url(model_urls[arch][0],
+                                                model_urls[arch][1])
+        assert weight_path.endswith(
+            '.pdparams'), "suffix of weight must be .pdparams"
+        model.load(weight_path)
+
+    return model
+
+
+def vgg11(pretrained=False, batch_norm=False, **kwargs):
+    """VGG 11-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+        batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import vgg11
+
+            # build model
+            model = vgg11()
+
+            # build vgg11 model with batch_norm
+            model = vgg11(batch_norm=True)
+    """
+    model_name = 'vgg11'
+    if batch_norm:
+        model_name += ('_bn')
+    return _vgg(model_name, 'A', batch_norm, pretrained, **kwargs)
+
+
+def vgg13(pretrained=False, batch_norm=False, **kwargs):
+    """VGG 13-layer model
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+        batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import vgg13
+
+            # build model
+            model = vgg13()
+
+            # build vgg13 model with batch_norm
+            model = vgg13(batch_norm=True)
+    """
+    model_name = 'vgg13'
+    if batch_norm:
+        model_name += ('_bn')
+    return _vgg(model_name, 'B', batch_norm, pretrained, **kwargs)
+
+
+def vgg16(pretrained=False, batch_norm=False, **kwargs):
+    """VGG 16-layer model 
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+        batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import vgg16
+
+            # build model
+            model = vgg16()
+
+            # build vgg16 model with batch_norm
+            model = vgg16(batch_norm=True)
+    """
+    model_name = 'vgg16'
+    if batch_norm:
+        model_name += ('_bn')
+    return _vgg(model_name, 'D', batch_norm, pretrained, **kwargs)
+
+
+def vgg19(pretrained=False, batch_norm=False, **kwargs):
+    """VGG 19-layer model 
+    
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+        batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.incubate.hapi.vision.models import vgg19
+
+            # build model
+            model = vgg19()
+
+            # build vgg19 model with batch_norm
+            model = vgg19(batch_norm=True)
+    """
+    model_name = 'vgg19'
+    if batch_norm:
+        model_name += ('_bn')
+    return _vgg(model_name, 'E', batch_norm, pretrained, **kwargs)

python/paddle/incubate/hapi/vision/transforms/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import transforms
+from . import functional
+
+from .transforms import *
+from .functional import *
+
+__all__ = transforms.__all__ \
+        + functional.__all__

python/paddle/incubate/hapi/vision/transforms/functional.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import collections
+import random
+
+import cv2
+import numpy as np
+
+if sys.version_info < (3, 3):
+    Sequence = collections.Sequence
+    Iterable = collections.Iterable
+else:
+    Sequence = collections.abc.Sequence
+    Iterable = collections.abc.Iterable
+
+__all__ = ['flip', 'resize']
+
+
+def flip(image, code):
+    """
+    Accordding to the code (the type of flip), flip the input image
+
+    Args:
+        image: Input image, with (H, W, C) shape
+        code: Code that indicates the type of flip.
+            -1 : Flip horizontally and vertically
+            0 : Flip vertically
+            1 : Flip horizontally
+
+    Examples:
+        .. code-block:: python
+
+            import numpy as np
+            from paddle.incubate.hapi.vision.transforms import functional as F
+
+            fake_img = np.random.rand(224, 224, 3)
+
+            # flip horizontally and vertically
+            F.flip(fake_img, -1)
+
+            # flip vertically
+            F.flip(fake_img, 0)
+
+            # flip horizontally
+            F.flip(fake_img, 1)
+    """
+    return cv2.flip(image, flipCode=code)
+
+
+def resize(img, size, interpolation=cv2.INTER_LINEAR):
+    """
+    resize the input data to given size
+
+    Args:
+        input: Input data, could be image or masks, with (H, W, C) shape
+        size: Target size of input data, with (height, width) shape.
+        interpolation: Interpolation method.
+
+    Examples:
+        .. code-block:: python
+
+            import numpy as np
+            from paddle.incubate.hapi.vision.transforms import functional as F
+
+            fake_img = np.random.rand(256, 256, 3)
+
+            F.resize(fake_img, 224)
+
+            F.resize(fake_img, (200, 150))
+    """
+
+    if isinstance(interpolation, Sequence):
+        interpolation = random.choice(interpolation)
+
+    if isinstance(size, int):
+        h, w = img.shape[:2]
+        if (w <= h and w == size) or (h <= w and h == size):
+            return img
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+            return cv2.resize(img, (ow, oh), interpolation=interpolation)
+        else:
+            oh = size
+            ow = int(size * w / h)
+            return cv2.resize(img, (ow, oh), interpolation=interpolation)
+    else:
+        return cv2.resize(img, size[::-1], interpolation=interpolation)

python/paddle/incubate/hapi/vision/transforms/transforms.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import division
+
+import math
+import sys
+import random
+import cv2
+
+import numpy as np
+import numbers
+import types
+import collections
+import warnings
+import traceback
+
+from . import functional as F
+
+if sys.version_info < (3, 3):
+    Sequence = collections.Sequence
+    Iterable = collections.Iterable
+else:
+    Sequence = collections.abc.Sequence
+    Iterable = collections.abc.Iterable
+
+__all__ = [
+    "Compose",
+    "BatchCompose",
+    "Resize",
+    "RandomResizedCrop",
+    "CenterCropResize",
+    "CenterCrop",
+    "RandomHorizontalFlip",
+    "RandomVerticalFlip",
+    "Permute",
+    "Normalize",
+    "GaussianNoise",
+    "BrightnessTransform",
+    "SaturationTransform",
+    "ContrastTransform",
+    "HueTransform",
+    "ColorJitter",
+]
+
+
+class Compose(object):
+    """
+    Composes several transforms together use for composing list of transforms
+    together for a dataset transform.
+
+    Args:
+        transforms (list): List of transforms to compose.
+
+    Returns:
+        A compose object which is callable, __call__ for this Compose
+        object will call each given :attr:`transforms` sequencely.
+
+    Examples:
+    
+        .. code-block:: python
+
+            from paddle.incubate.hapi.datasets import Flowers
+            from paddle.incubate.hapi.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].shape, sample[1])
+
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, *data):
+        for f in self.transforms:
+            try:
+                # multi-fileds in a sample
+                if isinstance(data, Sequence):
+                    data = f(*data)
+                # single field in a sample, call transform directly
+                else:
+                    data = f(data)
+            except Exception as e:
+                stack_info = traceback.format_exc()
+                print("fail to perform transform [{}] with error: "
+                      "{} and stack:\n{}".format(f, e, str(stack_info)))
+                raise e
+        return data
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string
+
+
+class BatchCompose(object):
+    """Composes several batch transforms together
+
+    Args:
+        transforms (list): List of transforms to compose.
+                           these transforms perform on batch data.
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+            from paddle.io import DataLoader
+
+            from paddle.incubate.hapi.model import set_device
+            from paddle.incubate.hapi.datasets import Flowers
+            from paddle.incubate.hapi.vision.transforms import Compose, BatchCompose, Resize
+
+            class NormalizeBatch(object):
+                def __init__(self,
+                            mean=[0.485, 0.456, 0.406],
+                            std=[0.229, 0.224, 0.225],
+                            scale=True,
+                            channel_first=True):
+
+                    self.mean = mean
+                    self.std = std
+                    self.scale = scale
+                    self.channel_first = channel_first
+                    if not (isinstance(self.mean, list) and isinstance(self.std, list) and
+                            isinstance(self.scale, bool)):
+                        raise TypeError("{}: input type is invalid.".format(self))
+                    from functools import reduce
+                    if reduce(lambda x, y: x * y, self.std) == 0:
+                        raise ValueError('{}: std is invalid!'.format(self))
+
+                def __call__(self, samples):
+                    for i in range(len(samples)):
+                        samples[i] = list(samples[i])
+                        im = samples[i][0]
+                        im = im.astype(np.float32, copy=False)
+                        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+                        std = np.array(self.std)[np.newaxis, np.newaxis, :]
+                        if self.scale:
+                            im = im / 255.0
+                        im -= mean
+                        im /= std
+                        if self.channel_first:
+                            im = im.transpose((2, 0, 1))
+                        samples[i][0] = im
+                    return samples
+
+            transform = Compose([Resize((500, 500))])
+            flowers_dataset = Flowers(mode='test', transform=transform)
+
+            device = set_device('cpu')
+
+            collate_fn = BatchCompose([NormalizeBatch()])
+            loader = DataLoader(
+                        flowers_dataset,
+                        batch_size=4,
+                        places=device,
+                        return_list=True,
+                        collate_fn=collate_fn)
+
+            for data in loader:
+                # do something
+                break
+    """
+
+    def __init__(self, transforms=[]):
+        self.transforms = transforms
+
+    def __call__(self, data):
+        for f in self.transforms:
+            try:
+                data = f(data)
+            except Exception as e:
+                stack_info = traceback.format_exc()
+                print("fail to perform batch transform [{}] with error: "
+                      "{} and stack:\n{}".format(f, e, str(stack_info)))
+                raise e
+
+        # sample list to batch data
+        batch = list(zip(*data))
+
+        return batch
+
+
+class Resize(object):
+    """Resize the input Image to the given size.
+
+    Args:
+        size (int|list|tuple): Desired output size. If size is a sequence like
+            (h, w), output size will be matched to this. If size is an int,
+            smaller edge of the image will be matched to this number.
+            i.e, if height > width, then image will be rescaled to
+            (size * height / width, size)
+        interpolation (int): Interpolation mode of resize. Default: cv2.INTER_LINEAR.
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import Resize
+
+            transform = Resize(size=224)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, size, interpolation=cv2.INTER_LINEAR):
+        assert isinstance(size, int) or (isinstance(size, Iterable) and
+                                         len(size) == 2)
+        self.size = size
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        return F.resize(img, self.size, self.interpolation)
+
+
+class RandomResizedCrop(object):
+    """Crop the input data to random size and aspect ratio.
+    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 1.33) of the original aspect ratio is made.
+    After applying crop transfrom, the input data will be resized to given size.
+
+    Args:
+        output_size (int|list|tuple): Target size of output image, with (height, width) shape.
+        scale (list|tuple): Range of size of the origin size cropped. Default: (0.08, 1.0)
+        ratio (list|tuple): Range of aspect ratio of the origin aspect ratio cropped. Default: (0.75, 1.33)
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import RandomResizedCrop
+
+            transform = RandomResizedCrop(224)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self,
+                 output_size,
+                 scale=(0.08, 1.0),
+                 ratio=(3. / 4, 4. / 3),
+                 interpolation=cv2.INTER_LINEAR):
+        if isinstance(output_size, int):
+            self.output_size = (output_size, output_size)
+        else:
+            self.output_size = output_size
+        assert (scale[0] <= scale[1]), "scale should be of kind (min, max)"
+        assert (ratio[0] <= ratio[1]), "ratio should be of kind (min, max)"
+        self.scale = scale
+        self.ratio = ratio
+        self.interpolation = interpolation
+
+    def _get_params(self, image, attempts=10):
+        height, width, _ = image.shape
+        area = height * width
+
+        for _ in range(attempts):
+            target_area = np.random.uniform(*self.scale) * area
+            log_ratio = tuple(math.log(x) for x in self.ratio)
+            aspect_ratio = math.exp(np.random.uniform(*log_ratio))
+
+            w = int(round(math.sqrt(target_area * aspect_ratio)))
+            h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if 0 < w <= width and 0 < h <= height:
+                x = np.random.randint(0, width - w + 1)
+                y = np.random.randint(0, height - h + 1)
+                return x, y, w, h
+
+        # Fallback to central crop
+        in_ratio = float(width) / float(height)
+        if in_ratio < min(self.ratio):
+            w = width
+            h = int(round(w / min(self.ratio)))
+        elif in_ratio > max(self.ratio):
+            h = height
+            w = int(round(h * max(self.ratio)))
+        else:  # whole image
+            w = width
+            h = height
+        x = (width - w) // 2
+        y = (height - h) // 2
+        return x, y, w, h
+
+    def __call__(self, img):
+        x, y, w, h = self._get_params(img)
+        cropped_img = img[y:y + h, x:x + w]
+        return F.resize(cropped_img, self.output_size, self.interpolation)
+
+
+class CenterCropResize(object):
+    """Crops to center of image with padding then scales size.
+
+    Args:
+        size (int|list|tuple): Target size of output image, with (height, width) shape.
+        crop_padding (int): Center crop with the padding. Default: 32.
+        interpolation (int): Interpolation mode of resize. Default: cv2.INTER_LINEAR.
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import CenterCropResize
+
+            transform = CenterCropResize(224)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, size, crop_padding=32, interpolation=cv2.INTER_LINEAR):
+        if isinstance(size, int):
+            self.size = (size, size)
+        else:
+            self.size = size
+        self.crop_padding = crop_padding
+        self.interpolation = interpolation
+
+    def _get_params(self, img):
+        h, w = img.shape[:2]
+        size = min(self.size)
+        c = int(size / (size + self.crop_padding) * min((h, w)))
+        x = (h + 1 - c) // 2
+        y = (w + 1 - c) // 2
+        return c, x, y
+
+    def __call__(self, img):
+        c, x, y = self._get_params(img)
+        cropped_img = img[x:x + c, y:y + c, :]
+        return F.resize(cropped_img, self.size, self.interpolation)
+
+
+class CenterCrop(object):
+    """Crops the given the input data at the center.
+
+    Args:
+        output_size: Target size of output image, with (height, width) shape.
+    
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import CenterCrop
+
+            transform = CenterCrop(224)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, output_size):
+        if isinstance(output_size, int):
+            self.output_size = (output_size, output_size)
+        else:
+            self.output_size = output_size
+
+    def _get_params(self, img):
+        th, tw = self.output_size
+        h, w, _ = img.shape
+        assert th <= h and tw <= w, "output size is bigger than image size"
+        x = int(round((w - tw) / 2.0))
+        y = int(round((h - th) / 2.0))
+        return x, y
+
+    def __call__(self, img):
+        x, y = self._get_params(img)
+        th, tw = self.output_size
+        return img[y:y + th, x:x + tw]
+
+
+class RandomHorizontalFlip(object):
+    """Horizontally flip the input data randomly with a given probability.
+
+    Args:
+        prob (float): Probability of the input data being flipped. Default: 0.5
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import RandomHorizontalFlip
+
+            transform = RandomHorizontalFlip(224)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, prob=0.5):
+        self.prob = prob
+
+    def __call__(self, img):
+        if np.random.random() < self.prob:
+            return F.flip(img, code=1)
+        return img
+
+
+class RandomVerticalFlip(object):
+    """Vertically flip the input data randomly with a given probability.
+
+    Args:
+        prob (float): Probability of the input data being flipped. Default: 0.5
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import RandomVerticalFlip
+
+            transform = RandomVerticalFlip(224)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, prob=0.5):
+        self.prob = prob
+
+    def __call__(self, img):
+        if np.random.random() < self.prob:
+            return F.flip(img, code=0)
+        return img
+
+
+class Normalize(object):
+    """Normalize the input data with mean and standard deviation.
+    Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels,
+    this transform will normalize each channel of the input data.
+    ``output[channel] = (input[channel] - mean[channel]) / std[channel]``
+
+    Args:
+        mean (int|float|list): Sequence of means for each channel.
+        std (int|float|list): Sequence of standard deviations for each channel.
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import Normalize
+
+            normalize = Normalize(mean=[0.5, 0.5, 0.5], 
+                                std=[0.5, 0.5, 0.5])
+
+            fake_img = np.random.rand(3, 500, 500).astype('float32')
+
+            fake_img = normalize(fake_img)
+            print(fake_img.shape)
+    
+    """
+
+    def __init__(self, mean=0.0, std=1.0):
+        if isinstance(mean, numbers.Number):
+            mean = [mean, mean, mean]
+
+        if isinstance(std, numbers.Number):
+            mean = [std, std, std]
+
+        self.mean = np.array(mean, dtype=np.float32).reshape(len(mean), 1, 1)
+        self.std = np.array(std, dtype=np.float32).reshape(len(std), 1, 1)
+
+    def __call__(self, img):
+        return (img - self.mean) / self.std
+
+
+class Permute(object):
+    """Change input data to a target mode.
+    For example, most transforms use HWC mode image,
+    while the Neural Network might use CHW mode input tensor.
+    Input image should be HWC mode and an instance of numpy.ndarray. 
+
+    Args:
+        mode (str): Output mode of input. Default: "CHW".
+        to_rgb (bool): Convert 'bgr' image to 'rgb'. Default: True.
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import Permute
+
+            transform = Permute()
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, mode="CHW", to_rgb=True):
+        assert mode in [
+            "CHW"
+        ], "Only support 'CHW' mode, but received mode: {}".format(mode)
+        self.mode = mode
+        self.to_rgb = to_rgb
+
+    def __call__(self, img):
+        if self.to_rgb:
+            img = img[..., ::-1]
+        if self.mode == "CHW":
+            return img.transpose((2, 0, 1))
+        return img
+
+
+class GaussianNoise(object):
+    """Add random gaussian noise to the input data.
+    Gaussian noise is generated with given mean and std.
+
+    Args:
+        mean (float): Gaussian mean used to generate noise.
+        std (float): Gaussian standard deviation used to generate noise.
+    
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import GaussianNoise
+
+            transform = GaussianNoise()
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, mean=0.0, std=1.0):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.std = np.array(std, dtype=np.float32)
+
+    def __call__(self, img):
+        dtype = img.dtype
+        noise = np.random.normal(self.mean, self.std, img.shape) * 255
+        img = img + noise.astype(np.float32)
+        return np.clip(img, 0, 255).astype(dtype)
+
+
+class BrightnessTransform(object):
+    """Adjust brightness of the image.
+
+    Args:
+        value (float): How much to adjust the brightness. Can be any
+            non negative number. 0 gives the original image
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import BrightnessTransform
+
+            transform = BrightnessTransform(0.4)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, value):
+        if value < 0:
+            raise ValueError("brightness value should be non-negative")
+        self.value = value
+
+    def __call__(self, img):
+        if self.value == 0:
+            return img
+
+        dtype = img.dtype
+        img = img.astype(np.float32)
+        alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value)
+        img = img * alpha
+        return img.clip(0, 255).astype(dtype)
+
+
+class ContrastTransform(object):
+    """Adjust contrast of the image.
+
+    Args:
+        value (float): How much to adjust the contrast. Can be any
+            non negative number. 0 gives the original image
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import ContrastTransform
+
+            transform = ContrastTransform(0.4)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, value):
+        if value < 0:
+            raise ValueError("contrast value should be non-negative")
+        self.value = value
+
+    def __call__(self, img):
+        if self.value == 0:
+            return img
+
+        dtype = img.dtype
+        img = img.astype(np.float32)
+        alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value)
+        img = img * alpha + cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).mean() * (
+            1 - alpha)
+        return img.clip(0, 255).astype(dtype)
+
+
+class SaturationTransform(object):
+    """Adjust saturation of the image.
+
+    Args:
+        value (float): How much to adjust the saturation. Can be any
+            non negative number. 0 gives the original image
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import SaturationTransform
+
+            transform = SaturationTransform(0.4)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+        
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, value):
+        if value < 0:
+            raise ValueError("saturation value should be non-negative")
+        self.value = value
+
+    def __call__(self, img):
+        if self.value == 0:
+            return img
+
+        dtype = img.dtype
+        img = img.astype(np.float32)
+        alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value)
+        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        gray_img = gray_img[..., np.newaxis]
+        img = img * alpha + gray_img * (1 - alpha)
+        return img.clip(0, 255).astype(dtype)
+
+
+class HueTransform(object):
+    """Adjust hue of the image.
+
+    Args:
+        value (float): How much to adjust the hue. Can be any number
+            between 0 and 0.5, 0 gives the original image
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import HueTransform
+
+            transform = HueTransform(0.4)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, value):
+        if value < 0 or value > 0.5:
+            raise ValueError("hue value should be in [0.0, 0.5]")
+        self.value = value
+
+    def __call__(self, img):
+        if self.value == 0:
+            return img
+
+        dtype = img.dtype
+        img = img.astype(np.uint8)
+        hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL)
+        h, s, v = cv2.split(hsv_img)
+
+        alpha = np.random.uniform(-self.value, self.value)
+        h = h.astype(np.uint8)
+        # uint8 addition take cares of rotation across boundaries
+        with np.errstate(over="ignore"):
+            h += np.uint8(alpha * 255)
+        hsv_img = cv2.merge([h, s, v])
+        return cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR_FULL).astype(dtype)
+
+
+class ColorJitter(object):
+    """Randomly change the brightness, contrast, saturation and hue of an image.
+
+    Args:
+        brightness: How much to jitter brightness.
+            Chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
+            or the given [min, max]. Should be non negative numbers.
+        contrast: How much to jitter contrast.
+            Chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
+            or the given [min, max]. Should be non negative numbers.
+        saturation: How much to jitter saturation.
+            Chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
+            or the given [min, max]. Should be non negative numbers.
+        hue: How much to jitter hue.
+            Chosen uniformly from [-hue, hue] or the given [min, max].
+            Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
+
+    Examples:
+    
+        .. code-block:: python
+
+            import numpy as np
+
+            from paddle.incubate.hapi.vision.transforms import ColorJitter
+
+            transform = ColorJitter(0.4)
+
+            fake_img = np.random.rand(500, 500, 3).astype('float32')
+
+            fake_img = transform(fake_img)
+            print(fake_img.shape)
+    """
+
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
+        transforms = []
+        if brightness != 0:
+            transforms.append(BrightnessTransform(brightness))
+        if contrast != 0:
+            transforms.append(ContrastTransform(contrast))
+        if saturation != 0:
+            transforms.append(SaturationTransform(saturation))
+        if hue != 0:
+            transforms.append(HueTransform(hue))
+
+        random.shuffle(transforms)
+        self.transforms = Compose(transforms)
+
+    def __call__(self, img):
+        return self.transforms(img)

python/setup.py.in

@@ -182,6 +182,7 @@ packages=['paddle',
           'paddle.incubate.hapi.datasets',
           'paddle.incubate.hapi.vision',
           'paddle.incubate.hapi.vision.models',
+          'paddle.incubate.hapi.vision.transforms',
           'paddle.io',
           'paddle.nn',
           'paddle.nn.functional',