update paddle.io

bmn/reader.py

@@ -22,7 +22,7 @@ import sys
 sys.path.append('../')
 
 from distributed import DistributedBatchSampler
-from paddle.fluid.io import Dataset, DataLoader
+from paddle.io import Dataset, DataLoader
 
 logger = logging.getLogger(__name__)
 

cyclegan/data.py

@@ -30,7 +30,7 @@ IMAGES_ROOT = "./data/" + DATASET + "/"
 import paddle.fluid as fluid
 
 
-class Cityscapes(fluid.io.Dataset):
+class Cityscapes(paddle.io.Dataset):
     def __init__(self, root_path, file_path, mode='train', return_name=False):
         self.root_path = root_path
         self.file_path = file_path

cyclegan/train.py

@@ -86,13 +86,13 @@ def main():
     if FLAGS.resume:
         g.load(FLAGS.resume)
 
-    loader_A = fluid.io.DataLoader(
+    loader_A = paddle.io.DataLoader(
         data.DataA(),
         places=place,
         shuffle=True,
         return_list=True,
         batch_size=FLAGS.batch_size)
-    loader_B = fluid.io.DataLoader(
+    loader_B = paddle.io.DataLoader(
         data.DataB(),
         places=place,
         shuffle=True,

distributed.py

@@ -23,7 +23,7 @@ import numpy as np
 from paddle import fluid
 from paddle.fluid.layers import collective
 from paddle.fluid.dygraph.parallel import ParallelEnv, ParallelStrategy
-from paddle.fluid.io import BatchSampler
+from paddle.io import BatchSampler
 
 _parallel_context_initialized = False
 
@@ -39,7 +39,7 @@ class DistributedBatchSampler(BatchSampler):
         Dataset is assumed to be of constant size.
         
     Args:
-        data_source: this could be a `fluid.io.Dataset` implement
+        data_source: this could be a `paddle.io.Dataset` implement
                      or other python object which implemented
                      `__len__` for BatchSampler to get sample
                      number of data source.

image_classification/main.py

@@ -32,7 +32,7 @@ from imagenet_dataset import ImageNetDataset
 from distributed import DistributedBatchSampler
 from paddle.fluid.dygraph.parallel import ParallelEnv
 from metrics import Accuracy
-from paddle.fluid.io import BatchSampler, DataLoader
+from paddle.io import BatchSampler, DataLoader
 
 
 def make_optimizer(step_per_epoch, parameter_list=None):

mnist.py

@@ -24,7 +24,7 @@ import numpy as np
 from paddle import fluid
 from paddle.fluid.optimizer import Momentum
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
-from paddle.fluid.io import MNIST as MnistDataset
+from .vision.datasets import MNIST as MnistDataset
 
 from model import Model, CrossEntropy, Input, set_device
 from metrics import Accuracy

model.py

@@ -32,7 +32,7 @@ from paddle.fluid.dygraph.parallel import ParallelEnv
 from paddle.fluid.layers.utils import flatten
 from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
 from paddle.fluid.incubate.fleet.base import role_maker
-from paddle.fluid.io import DataLoader, Dataset
+from paddle.io import DataLoader, Dataset
 
 from distributed import DistributedBatchSampler, _all_gather, prepare_distributed_context, _parallel_context_initialized
 from metrics import Metric
@@ -913,11 +913,11 @@ class Model(fluid.dygraph.Layer):
         FIXME: add more comments and usage
         Args:
             train_data (Dataset|DataLoader): An iterable data loader is used for 
-                train. An instance of paddle.fluid.io.Dataset or 
-                paddle.fluid.io.Dataloader is recomended.
+                train. An instance of paddle paddle.io.Dataset or 
+                paddle.io.Dataloader is recomended.
             eval_data (Dataset|DataLoader): An iterable data loader is used for
                 evaluation at the end of epoch. If None, will not do evaluation. 
-                An instance of paddle.fluid.io.Dataset or paddle.fluid.io.Dataloader 
+                An instance of paddle.io.Dataset or paddle.io.Dataloader 
                 is recomended.
             batch_size (int): Integer number. The batch size of train_data and eval_data. 
                 When train_data and eval_data are both the instance of Dataloader, this 
@@ -1041,8 +1041,8 @@ class Model(fluid.dygraph.Layer):
         FIXME: add more comments and usage
         Args:
             eval_data (Dataset|DataLoader): An iterable data loader is used for
-                evaluation. An instance of paddle.fluid.io.Dataset or 
-                paddle.fluid.io.Dataloader is recomended.
+                evaluation. An instance of paddle.io.Dataset or 
+                paddle.io.Dataloader is recomended.
             batch_size (int): Integer number. The batch size of train_data and eval_data. 
                 When train_data and eval_data are both the instance of Dataloader, this 
                 parameter will be ignored.
@@ -1116,7 +1116,7 @@ class Model(fluid.dygraph.Layer):
         FIXME: add more comments and usage
         Args:
             test_data (Dataset|DataLoader): An iterable data loader is used for
-                predict. An instance of paddle.fluid.io.Dataset or paddle.fluid.io.Dataloader 
+                predict. An instance of paddle.io.Dataset or paddle.io.Dataloader 
                 is recomended.
             batch_size (int): Integer number. The batch size of train_data and eval_data. 
                 When train_data and eval_data are both the instance of Dataloader, this 
@@ -1177,8 +1177,8 @@ class Model(fluid.dygraph.Layer):
         """
         Args:
             eval_data (Dataset|DataLoader|None): An iterable data loader is used for 
-                eval. An instance of paddle.fluid.io.Dataset or 
-                paddle.fluid.io.Dataloader is recomended. 
+                eval. An instance of paddle.io.Dataset or 
+                paddle.io.Dataloader is recomended. 
         """
         assert isinstance(
             eval_data,

models/darknet.py

@@ -21,7 +21,7 @@ from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
 from model import Model
 from .download import get_weights_path
 
-__all__ = ['DarkNet53', 'ConvBNLayer', 'darknet53']
+__all__ = ['DarkNet', 'ConvBNLayer', 'darknet53']
 
 # {num_layers: (url, md5)}
 pretrain_infos = {
@@ -136,9 +136,17 @@ class LayerWarp(fluid.dygraph.Layer):
 DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
 
 
-class DarkNet53(Model):
+class DarkNet(Model):
+    """DarkNet model from
+    `"YOLOv3: An Incremental Improvement" <https://arxiv.org/abs/1804.02767>`_
+
+    Args:
+        num_layers (int): layer number of DarkNet, only 53 supported currently, default: 53.
+        ch_in (int): channel number of input data, default 3.
+    """
+
     def __init__(self, num_layers=53, ch_in=3):
-        super(DarkNet53, self).__init__()
+        super(DarkNet, self).__init__()
         assert num_layers in DarkNet_cfg.keys(), \
             "only support num_layers in {} currently" \
             .format(DarkNet_cfg.keys())
@@ -188,7 +196,7 @@ class DarkNet53(Model):
 
 
 def _darknet(num_layers=53, input_channels=3, pretrained=True):
-    model = DarkNet53(num_layers, input_channels)
+    model = DarkNet(num_layers, input_channels)
     if pretrained:
         assert num_layers in pretrain_infos.keys(), \
                 "DarkNet{} do not have pretrained weights now, " \
@@ -201,4 +209,11 @@ def _darknet(num_layers=53, input_channels=3, pretrained=True):
 
 
 def darknet53(input_channels=3, pretrained=True):
+    """DarkNet 53-layer model
+    
+    Args:
+        input_channels (bool): channel number of input data, default 3. 
+        pretrained (bool): If True, returns a model pre-trained on ImageNet,
+            default True.
+    """
     return _darknet(53, input_channels, pretrained)

models/tsm.py

@@ -201,4 +201,12 @@ def _tsm_resnet(num_layers, seg_num=8, num_classes=400, pretrained=True):
 
 
 def tsm_resnet50(seg_num=8, num_classes=400, pretrained=True):
+    """TSM model with 50-layer ResNet as backbone
+    
+    Args:
+        seg_num (int): segment number of each video sample. Default 8.
+        num_classes (int): video class number. Default 400.
+        pretrained (bool): If True, returns a model with pre-trained model
+            on COCO, default True
+    """
     return _tsm_resnet(50, seg_num, num_classes, pretrained)

models/yolov3.py

@@ -88,6 +88,20 @@ class YoloDetectionBlock(fluid.dygraph.Layer):
 
 
 class YOLOv3(Model):
+    """YOLOv3 model from
+    `"YOLOv3: An Incremental Improvement" <https://arxiv.org/abs/1804.02767>`_
+
+    Args:
+        num_classes (int): class number, default 80.
+        model_mode (str): 'train', 'eval', 'test' mode, network structure
+            will be diffrent in the output layer and data, in 'train' mode,
+            no output layer append, in 'eval' and 'test', output feature
+            map will be decode to predictions by 'fluid.layers.yolo_box',
+            in 'eval' mode, return feature maps and predictions, in 'test'
+            mode, only return predictions. Default 'train'.
+
+    """
+
     def __init__(self, num_classes=80, model_mode='train'):
         super(YOLOv3, self).__init__()
         self.num_classes = num_classes
@@ -245,4 +259,17 @@ def _yolov3_darknet(num_layers=53, num_classes=80,
 
 
 def yolov3_darknet53(num_classes=80, model_mode='train', pretrained=True):
+    """YOLOv3 model with 53-layer DarkNet as backbone
+    
+    Args:
+        num_classes (int): class number, default 80.
+        model_mode (str): 'train', 'eval', 'test' mode, network structure
+            will be diffrent in the output layer and data, in 'train' mode,
+            no output layer append, in 'eval' and 'test', output feature
+            map will be decode to predictions by 'fluid.layers.yolo_box',
+            in 'eval' mode, return feature maps and predictions, in 'test'
+            mode, only return predictions. Default 'train'.
+        pretrained (bool): If True, returns a model with pre-trained model
+            on COCO, default True
+    """
     return _yolov3_darknet(53, num_classes, model_mode, pretrained)

sequence_tagging/reader.py

@@ -168,13 +168,13 @@ def create_lexnet_data_generator(args, reader, file_name, place, mode="train"):
 
 def create_dataloader(generator, place, feed_list=None):
     if not feed_list:
-        data_loader = fluid.io.DataLoader.from_generator(
+        data_loader = paddle.io.DataLoader.from_generator(
             capacity=50,
             use_double_buffer=True,
             iterable=True,
             return_list=True)
     else:
-        data_loader = fluid.io.DataLoader.from_generator(
+        data_loader = paddle.io.DataLoader.from_generator(
             feed_list=feed_list,
             capacity=50,
             use_double_buffer=True,

transformer/predict.py

@@ -22,7 +22,7 @@ from functools import partial
 import numpy as np
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.io import DataLoader
+from paddle.io import DataLoader
 from paddle.fluid.layers.utils import flatten
 
 from utils.configure import PDConfig

transformer/reader.py

@@ -22,7 +22,7 @@ from functools import partial
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-from paddle.fluid.io import BatchSampler, DataLoader, Dataset
+from paddle.io import BatchSampler, DataLoader, Dataset
 
 
 def create_data_loader(args, device):

transformer/train.py

@@ -21,7 +21,7 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 import numpy as np
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.io import DataLoader
+from paddle.io import DataLoader
 
 from utils.configure import PDConfig
 from utils.check import check_gpu, check_version

tsm/kinetics_dataset.py

@@ -26,7 +26,7 @@ except ImportError:
     import pickle
     from io import BytesIO
 
-from paddle.fluid.io import Dataset
+from paddle.io import Dataset
 
 import logging
 logger = logging.getLogger(__name__)

vision/__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.

vision/datasets/__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 .folder import *
+from .mnist import *
+from .flowers import *
+from .coco import *

yolov3/coco.py → vision/datasets/coco.py

@@ -20,7 +20,7 @@ import cv2
 import numpy as np
 from pycocotools.coco import COCO
 
-from paddle.fluid.io import Dataset
+from paddle.io import Dataset
 
 import logging
 logger = logging.getLogger(__name__)

vision/datasets/flowers.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 print_function
+
+import os
+import io
+import tarfile
+import numpy as np
+import scipy.io as scio
+from PIL import Image
+
+from paddle.io import Dataset
+from .utils import _check_exists_and_download
+
+__all__ = ["Flowers"]
+
+DATA_URL = 'http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgz'
+LABEL_URL = 'http://paddlemodels.bj.bcebos.com/flowers/imagelabels.mat'
+SETID_URL = 'http://paddlemodels.bj.bcebos.com/flowers/setid.mat'
+DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
+LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
+SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
+
+# In official 'readme', tstid is the flag of test data
+# and trnid is the flag of train data. But test data is more than train data.
+# So we exchange the train data and test data.
+MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': "valid"}
+
+
+class Flowers(Dataset):
+    """
+    Implement of flowers dataset
+
+    Args:
+        data_file(str): path to data file, can be set None if
+            :attr:`download` is True. Default None
+        label_file(str): path to label file, can be set None if
+            :attr:`download` is True. Default None
+        setid_file(str): path to subset index file, can be set
+            None if :attr:`download` is True. Default None
+        mode(str): 'train', 'valid' or 'test' mode. Default 'train'.
+        download(bool): whether auto download mnist dataset if
+            :attr:`image_path`/:attr:`label_path` unset. Default
+            True
+
+    Examples:
+        
+        .. code-block:: python
+
+            from hapi.vision.datasets import Flowers
+
+            flowers = Flowers(mode='test')
+
+            for i in range(len(flowers)):
+                sample = flowers[i]
+                print(sample[0].shape, sample[1])
+
+    """
+
+    def __init__(self,
+                 data_file=None,
+                 label_file=None,
+                 setid_file=None,
+                 mode='train',
+                 transform=None,
+                 target_transform=None,
+                 download=True):
+        assert mode.lower() in ['train', 'valid', 'test'], \
+                "mode should be 'train', 'valid' or 'test', but got {}".format(mode)
+        self.flag = MODE_FLAG_MAP[mode.lower()]
+
+        self.data_file = data_file
+        if self.data_file is None:
+            assert download, "data_file not set and auto download disabled"
+            self.data_file = _check_exists_and_download(
+                data_file, DATA_URL, DATA_MD5, 'flowers', download)
+
+        self.label_file = label_file
+        if self.label_file is None:
+            assert download, "label_file not set and auto download disabled"
+            self.label_file = _check_exists_and_download(
+                label_file, LABEL_URL, LABEL_MD5, 'flowers', download)
+
+        self.setid_file = setid_file
+        if self.setid_file is None:
+            assert download, "setid_file not set and auto download disabled"
+            self.setid_file = _check_exists_and_download(
+                setid_file, SETID_URL, SETID_MD5, 'flowers', download)
+
+        self.transform = transform
+        self.target_transform = target_transform
+
+        # read dataset into memory
+        self._load_anno()
+
+    def _load_anno(self):
+        self.name2mem = {}
+        self.data_tar = tarfile.open(self.data_file)
+        for ele in self.data_tar.getmembers():
+            self.name2mem[ele.name] = ele
+
+        self.labels = scio.loadmat(self.label_file)['labels'][0]
+        self.indexes = scio.loadmat(self.setid_file)[self.flag][0]
+
+    def __getitem__(self, idx):
+        index = self.indexes[idx]
+        label = np.array([self.labels[index - 1]])
+        img_name = "jpg/image_%05d.jpg" % index
+        img_ele = self.name2mem[img_name]
+        image = self.data_tar.extractfile(img_ele).read()
+        image = np.array(Image.open(io.BytesIO(image)))
+
+        if self.transform is not None:
+            image = self.transform(image)
+        if self.target_transform is not None:
+            label = self.target_transform(label)
+
+        return image, label
+
+    def __len__(self):
+        return len(self.indexes)

datasets/folder.py → vision/datasets/folder.py

@@ -16,7 +16,9 @@ import os
 import sys
 import cv2
 
-from paddle.fluid.io import Dataset
+from paddle.io import Dataset
+
+__all__ = ["DatasetFolder"]
 
 
 def has_valid_extension(filename, extensions):

vision/datasets/mnist.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 print_function
+
+import os
+import gzip
+import struct
+import numpy as np
+
+import paddle.dataset.common
+from paddle.io import Dataset
+from .utils import _check_exists_and_download
+
+__all__ = ["MNIST"]
+
+URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
+TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
+TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
+TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
+TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
+TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
+TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
+TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
+TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
+
+
+class MNIST(Dataset):
+    """
+    Implement of MNIST dataset
+
+    Args:
+        image_path(str): path to image file, can be set None if
+            :attr:`download` is True. Default None
+        label_path(str): path to label file, can be set None if
+            :attr:`download` is True. Default None
+        mode(str): 'train' or 'test' mode. Default 'train'.
+        download(bool): whether auto download mnist dataset if
+            :attr:`image_path`/:attr:`label_path` unset. Default
+            True
+
+    Returns:
+        Dataset: MNIST Dataset.
+
+    Examples:
+        
+        .. code-block:: python
+
+            from hapi.vision.datasets import MNIST
+
+            mnist = MNIST(mode='test')
+
+            for i in range(len(mnist)):
+                sample = mnist[i]
+                print(sample[0].shape, sample[1])
+
+    """
+
+    def __init__(self,
+                 image_path=None,
+                 label_path=None,
+                 mode='train',
+                 transform=None,
+                 target_transform=None,
+                 download=True):
+        assert mode.lower() in ['train', 'test'], \
+                "mode should be 'train' or 'test', but got {}".format(mode)
+        self.mode = mode.lower()
+
+        self.image_path = image_path
+        if self.image_path is None:
+            assert download, "image_path not set and auto download disabled"
+            image_url = TRAIN_IMAGE_URL if mode == 'train' else TEST_IMAGE_URL
+            image_md5 = TRAIN_IMAGE_MD5 if mode == 'train' else TEST_IMAGE_MD5
+            self.image_path = _check_exists_and_download(
+                image_path, image_url, image_md5, 'mnist', download)
+
+        self.label_path = label_path
+        if self.label_path is None:
+            assert download, "label_path not set and auto download disabled"
+            label_url = TRAIN_LABEL_URL if mode == 'train' else TEST_LABEL_URL
+            label_md5 = TRAIN_LABEL_MD5 if mode == 'train' else TEST_LABEL_MD5
+            self.label_path = _check_exists_and_download(
+                label_path, label_url, label_md5, 'mnist', download)
+
+        self.transform = transform
+        self.target_transform = target_transform
+
+        # read dataset into memory
+        self._parse_dataset()
+
+    def _parse_dataset(self, buffer_size=100):
+        self.images = []
+        self.labels = []
+        with gzip.GzipFile(self.image_path, 'rb') as image_file:
+            img_buf = image_file.read()
+            with gzip.GzipFile(self.label_path, 'rb') as label_file:
+                lab_buf = label_file.read()
+
+                step_label = 0
+                offset_img = 0
+                # read from Big-endian
+                # get file info from magic byte
+                # image file : 16B
+                magic_byte_img = '>IIII'
+                magic_img, image_num, rows, cols = struct.unpack_from(
+                    magic_byte_img, img_buf, offset_img)
+                offset_img += struct.calcsize(magic_byte_img)
+
+                offset_lab = 0
+                # label file : 8B
+                magic_byte_lab = '>II'
+                magic_lab, label_num = struct.unpack_from(magic_byte_lab,
+                                                          lab_buf, offset_lab)
+                offset_lab += struct.calcsize(magic_byte_lab)
+
+                while True:
+                    if step_label >= label_num:
+                        break
+                    fmt_label = '>' + str(buffer_size) + 'B'
+                    labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
+                    offset_lab += struct.calcsize(fmt_label)
+                    step_label += buffer_size
+
+                    fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
+                    images_temp = struct.unpack_from(fmt_images, img_buf,
+                                                     offset_img)
+                    images = np.reshape(images_temp, (buffer_size, rows *
+                                                      cols)).astype('float32')
+                    offset_img += struct.calcsize(fmt_images)
+
+                    images = images / 255.0
+                    images = images * 2.0
+                    images = images - 1.0
+
+                    for i in range(buffer_size):
+                        self.images.append(images[i, :])
+                        self.labels.append(np.array([labels[i]]))
+
+    def __getitem__(self, idx):
+        image, label = self.images[idx], self.labels[idx]
+        if self.transform is not None:
+            image = self.transform(image)
+        if self.target_transform is not None:
+            label = self.target_transform(label)
+        return image, label
+
+    def __len__(self):
+        return len(self.labels)

vision/datasets/utils.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 print_function
+
+import os
+import paddle.dataset.common
+
+
+def _check_exists_and_download(path, url, md5, module_name, download=True):
+    if path and os.path.exists(path):
+        return path
+
+    if download:
+        return paddle.dataset.common.download(url, module_name, md5)
+    else:
+        raise FileNotFoundError(
+            '{} not exists and auto download disabled'.format(path))

transform/__init__.py → vision/transforms/__init__.py

@@ -13,3 +13,5 @@
 # limitations under the License.
 
 from .transforms import *
+from .functional import *
+from .detection_transforms import *

yolov3/transforms.py → vision/transforms/detection_transforms.py

@@ -19,48 +19,18 @@ import cv2
 import traceback
 import numpy as np
 
-import logging
-logger = logging.getLogger(__name__)
-
-__all__ = ['ColorDistort', 'RandomExpand', 'RandomCrop', 'RandomFlip',
-           'NormalizeBox', 'PadBox', 'RandomShape', 'NormalizeImage',
-           'BboxXYXY2XYWH', 'ResizeImage', 'Compose', 'BatchCompose']
-
-
-class Compose(object):
-    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()
-                logger.info("fail to perform transform [{}] with error: "
-                        "{} and stack:\n{}".format(f, e, str(stack_info)))
-                raise e
-        return data
-
-
-class BatchCompose(object):
-    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()
-                logger.info("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
+__all__ = [
+    'ColorDistort',
+    'RandomExpand',
+    'RandomCrop',
+    'RandomFlip',
+    'NormalizeBox',
+    'PadBox',
+    'RandomShape',
+    'NormalizeImage',
+    'BboxXYXY2XYWH',
+    'ResizeImage',
+]
 
 
 class ColorDistort(object):

transform/transforms.py → vision/transforms/transforms.py

@@ -24,6 +24,7 @@ import numbers
 import types
 import collections
 import warnings
+import traceback
 
 from . import functional as F
 
@@ -34,6 +35,7 @@ else:
 
 __all__ = [
     "Compose",
+    "BatchCompose",
     "Resize",
     "RandomResizedCrop",
     "CenterCropResize",
@@ -62,10 +64,16 @@ class Compose(object):
     def __init__(self, transforms):
         self.transforms = transforms
 
-    def __call__(self, img):
-        for t in self.transforms:
-            img = t(img)
-        return img
+    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 transform [{}] with error: "
+                        "{} and stack:\n{}".format(f, e, str(stack_info)))
+                raise e
+        return data
 
     def __repr__(self):
         format_string = self.__class__.__name__ + '('
@@ -76,6 +84,33 @@ class Compose(object):
         return format_string
 
 
+class BatchCompose(object):
+    """Composes several batch transforms together
+
+    Args:
+        transforms (list of ``Transform`` objects): list of transforms to compose.
+                                            these transforms perform on batch data.
+
+    """
+    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 PIL Image to the given size.
 

yolov3/infer.py

@@ -22,7 +22,7 @@ from PIL import Image
 
 from paddle import fluid
 from paddle.fluid.optimizer import Momentum
-from paddle.fluid.io import DataLoader
+from paddle.io import DataLoader
 
 from model import Model, Input, set_device
 from models import yolov3_darknet53, YoloLoss

yolov3/main.py

@@ -23,15 +23,15 @@ import numpy as np
 
 from paddle import fluid
 from paddle.fluid.optimizer import Momentum
-from paddle.fluid.io import DataLoader
+from paddle.io import DataLoader
 
 from model import Model, Input, set_device
 from distributed import DistributedBatchSampler
 from models import yolov3_darknet53, YoloLoss
 
 from coco_metric import COCOMetric
-from coco import COCODataset
-from transforms import *
+from vision.datasets import COCODataset
+from vision.transforms import *
 
 NUM_MAX_BOXES = 50