Merge branch 'master' of https://github.com/PaddlePaddle/hapi into yolov3

datasets/folder.py

+import os
+import sys
+import cv2
+
+from paddle.fluid.io import Dataset
+
+
+def has_valid_extension(filename, extensions):
+    """Checks if a file is an allowed extension.
+
+    Args:
+        filename (string): path to a file
+        extensions (tuple of strings): extensions to consider (lowercase)
+
+    Returns:
+        bool: True if the filename ends with one of given extensions
+    """
+    return filename.lower().endswith(extensions)
+
+
+def make_dataset(dir, class_to_idx, extensions=None, is_valid_file=None):
+    images = []
+    dir = os.path.expanduser(dir)
+    if not ((extensions is None) ^ (is_valid_file is None)):
+        raise ValueError(
+            "Both extensions and is_valid_file cannot be None or not None at the same time"
+        )
+    if extensions is not None:
+
+        def is_valid_file(x):
+            return has_valid_extension(x, extensions)
+
+    for target in sorted(class_to_idx.keys()):
+        d = os.path.join(dir, target)
+        if not os.path.isdir(d):
+            continue
+        for root, _, fnames in sorted(os.walk(d, followlinks=True)):
+            for fname in sorted(fnames):
+                path = os.path.join(root, fname)
+                if is_valid_file(path):
+                    item = (path, class_to_idx[target])
+                    images.append(item)
+
+    return images
+
+
+class DatasetFolder(Dataset):
+    """A generic data loader where the samples are arranged in this way:
+
+        root/class_a/1.ext
+        root/class_a/2.ext
+        root/class_a/3.ext
+
+        root/class_b/123.ext
+        root/class_b/456.ext
+        root/class_b/789.ext
+
+    Args:
+        root (string): Root directory path.
+        loader (callable, optional): A function to load a sample given its path.
+        extensions (tuple[string], optional): A list of allowed extensions.
+            both extensions and is_valid_file should not be passed.
+        transform (callable, optional): A function/transform that takes in
+            a sample and returns a transformed version.
+        target_transform (callable, optional): A function/transform that takes
+            in the target and transforms it.
+        is_valid_file (callable, optional): A function that takes path of a file
+            and check if the file is a valid file (used to check of corrupt files)
+            both extensions and is_valid_file should not be passed.
+
+     Attributes:
+        classes (list): List of the class names.
+        class_to_idx (dict): Dict with items (class_name, class_index).
+        samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
+    """
+
+    def __init__(self,
+                 root,
+                 loader=None,
+                 extensions=None,
+                 transform=None,
+                 target_transform=None,
+                 is_valid_file=None):
+        self.root = root
+        if extensions is None:
+            extensions = IMG_EXTENSIONS
+        classes, class_to_idx = self._find_classes(self.root)
+        samples = make_dataset(self.root, class_to_idx, extensions,
+                               is_valid_file)
+        if len(samples) == 0:
+            raise (RuntimeError(
+                "Found 0 files in subfolders of: " + self.root + "\n"
+                "Supported extensions are: " + ",".join(extensions)))
+
+        self.loader = cv2_loader if loader is None else loader
+        self.extensions = extensions
+
+        self.classes = classes
+        self.class_to_idx = class_to_idx
+        self.samples = samples
+        self.targets = [s[1] for s in samples]
+
+    def _find_classes(self, dir):
+        """
+        Finds the class folders in a dataset.
+
+        Args:
+            dir (string): Root directory path.
+
+        Returns:
+            tuple: (classes, class_to_idx) where classes are relative to (dir), 
+                    and class_to_idx is a dictionary.
+
+        """
+        if sys.version_info >= (3, 5):
+            # Faster and available in Python 3.5 and above
+            classes = [d.name for d in os.scandir(dir) if d.is_dir()]
+        else:
+            classes = [
+                d for d in os.listdir(dir)
+                if os.path.isdir(os.path.join(dir, d))
+            ]
+        classes.sort()
+        class_to_idx = {classes[i]: i for i in range(len(classes))}
+        return classes, class_to_idx
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (sample, target) where target is class_index of the target class.
+        """
+        path, target = self.samples[index]
+        sample = self.loader(path)
+        if self.transform is not None:
+            sample = self.transform(sample)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return sample, target
+
+    def __len__(self):
+        return len(self.samples)
+
+
+IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
+                  '.tiff', '.webp')
+
+
+def cv2_loader(path):
+    return cv2.imread(path)

image_classification/README.MD

+# 高级api图像分类
+
+## 数据集准备
+在开始训练前，请确保已经下载解压好[ImageNet数据集](http://image-net.org/download)，并放在合适的目录下，准备好的数据集的目录结构如下所示：
+
+```bash
+/path/to/imagenet
+    train
+         n01440764
+              xxx.jpg
+              ...
+         n01443537
+              xxx.jpg
+              ...
+         ...
+    val
+         n01440764
+              xxx.jpg
+              ...
+         n01443537
+              xxx.jpg
+              ...
+         ...
+```
+
+
+## 训练
+### 单卡训练
+执行如下命令进行训练
+```bash
+python -u main.py --arch resnet50 /path/to/imagenet -d
+```
+
+### 多卡训练
+执行如下命令进行训练
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch main.py --arch resnet50 -d /path/to/imagenet
+```
+
+## 预测
+
+### 单卡预测
+执行如下命令进行预测
+```bash
+python -u main.py --arch resnet50 -d --evaly-only /path/to/imagenet 
+```
+
+### 多卡预测
+执行如下命令进行多卡预测
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch main.py --arch resnet50 --evaly-only /path/to/imagenet
+```
+
+
+## 参数说明
+
+
+* **arch**: 要训练或预测的模型名称
+* **device**: 训练使用的设备，'gpu'或'cpu'，默认值：'gpu'
+* **dynamic**: 是否使用动态图模式训练
+* **epoch**: 训练的轮数，默认值：120
+* **learning-rate**: 学习率，默认值：0.1
+* **batch-size**: 每张卡的batch size，默认值：64
+* **output-dir**: 模型文件保存的文件夹，默认值：'output'
+* **num-workers**: dataloader的进程数，默认值：4
+* **resume**: 恢复训练的模型路径，默认值：None
+* **eval-only**: 仅仅进行预测，默认值：False
+
+
+## 模型
+
+| 模型 | top1 acc | top5 acc |
+| --- | --- | --- |
+| ResNet50 | 76.28 | 93.04 | 

image_classification/imagenet_dataset.py

+import os
+import cv2
+import math
+import random
+import numpy as np
+
+from datasets.folder import DatasetFolder
+
+
+def center_crop_resize(img):
+    h, w = img.shape[:2]
+    c = int(224 / 256 * min((h, w)))
+    i = (h + 1 - c) // 2
+    j = (w + 1 - c) // 2
+    img = img[i:i + c, j:j + c, :]
+    return cv2.resize(img, (224, 224), 0, 0, cv2.INTER_LINEAR)
+
+
+def random_crop_resize(img):
+    height, width = img.shape[:2]
+    area = height * width
+
+    for attempt in range(10):
+        target_area = random.uniform(0.08, 1.) * area
+        log_ratio = (math.log(3 / 4), math.log(4 / 3))
+        aspect_ratio = math.exp(random.uniform(*log_ratio))
+
+        w = int(round(math.sqrt(target_area * aspect_ratio)))
+        h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+        if w <= width and h <= height:
+            i = random.randint(0, height - h)
+            j = random.randint(0, width - w)
+            img = img[i:i + h, j:j + w, :]
+            return cv2.resize(img, (224, 224), 0, 0, cv2.INTER_LINEAR)
+
+    return center_crop_resize(img)
+
+
+def random_flip(img):
+    if np.random.randint(0, 2) == 1:
+        img = img[:, ::-1, :]
+    return img
+
+
+def normalize_permute(img):
+    # transpose and convert to RGB from BGR
+    img = img.astype(np.float32).transpose((2, 0, 1))[::-1, ...]
+    mean = np.array([123.675, 116.28, 103.53], dtype=np.float32)
+    std = np.array([58.395, 57.120, 57.375], dtype=np.float32)
+    invstd = 1. / std
+    for v, m, s in zip(img, mean, invstd):
+        v.__isub__(m).__imul__(s)
+    return img
+
+
+def compose(functions):
+    def process(sample):
+        img, label = sample
+        for fn in functions:
+            img = fn(img)
+        return img, label
+
+    return process
+
+
+class ImageNetDataset(DatasetFolder):
+    def __init__(self, path, mode='train'):
+        super(ImageNetDataset, self).__init__(path)
+        self.mode = mode
+        if self.mode == 'train':
+            self.transform = compose([
+                cv2.imread, random_crop_resize, random_flip, normalize_permute
+            ])
+        else:
+            self.transform = compose(
+                [cv2.imread, center_crop_resize, normalize_permute])
+
+    def __getitem__(self, idx):
+        img, label = self.samples[idx]
+        return self.transform((img, [label]))
+
+    def __len__(self):
+        return len(self.samples)

image_classification/main.py

+# Copyright (c) 2019 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 argparse
+import contextlib
+import os
+import sys
+sys.path.append('../')
+
+import time
+import math
+import numpy as np
+import models
+import paddle.fluid as fluid
+
+from model import CrossEntropy, Input, set_device
+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
+
+
+def make_optimizer(step_per_epoch, parameter_list=None):
+    base_lr = FLAGS.lr
+    momentum = 0.9
+    weight_decay = 1e-4
+
+    boundaries = [step_per_epoch * e for e in [30, 60, 80]]
+    values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
+    learning_rate = fluid.layers.piecewise_decay(
+        boundaries=boundaries, values=values)
+    learning_rate = fluid.layers.linear_lr_warmup(
+        learning_rate=learning_rate,
+        warmup_steps=5 * step_per_epoch,
+        start_lr=0.,
+        end_lr=base_lr)
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate,
+        momentum=momentum,
+        regularization=fluid.regularizer.L2Decay(weight_decay),
+        parameter_list=parameter_list)
+    return optimizer
+
+
+def main():
+    device = set_device(FLAGS.device)
+    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+
+    model = models.__dict__[FLAGS.arch](pretrained=FLAGS.eval_only and
+                                        not FLAGS.resume)
+
+    if FLAGS.resume is not None:
+        model.load(FLAGS.resume)
+
+    inputs = [Input([None, 3, 224, 224], 'float32', name='image')]
+    labels = [Input([None, 1], 'int64', name='label')]
+
+    train_dataset = ImageNetDataset(
+        os.path.join(FLAGS.data, 'train'), mode='train')
+    val_dataset = ImageNetDataset(os.path.join(FLAGS.data, 'val'), mode='val')
+
+    optim = make_optimizer(
+        np.ceil(
+            len(train_dataset) * 1. / FLAGS.batch_size / ParallelEnv().nranks),
+        parameter_list=model.parameters())
+
+    model.prepare(optim, CrossEntropy(), Accuracy(topk=(1, 5)), inputs, labels)
+
+    if FLAGS.eval_only:
+        model.evaluate(
+            val_dataset,
+            batch_size=FLAGS.batch_size,
+            num_workers=FLAGS.num_workers)
+        return
+
+    output_dir = os.path.join(FLAGS.output_dir, FLAGS.arch,
+                              time.strftime('%Y-%m-%d-%H-%M',
+                                            time.localtime()))
+    if ParallelEnv().local_rank == 0 and not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    model.fit(train_dataset,
+              val_dataset,
+              batch_size=FLAGS.batch_size,
+              epochs=FLAGS.epoch,
+              save_dir=output_dir,
+              num_workers=FLAGS.num_workers)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser("Resnet Training on ImageNet")
+    parser.add_argument(
+        'data',
+        metavar='DIR',
+        help='path to dataset '
+        '(should have subdirectories named "train" and "val"')
+    parser.add_argument(
+        "--arch", type=str, default='resnet50', help="model name")
+    parser.add_argument(
+        "--device", type=str, default='gpu', help="device to run, cpu or gpu")
+    parser.add_argument(
+        "-d", "--dynamic", action='store_true', help="enable dygraph mode")
+    parser.add_argument(
+        "-e", "--epoch", default=90, type=int, help="number of epoch")
+    parser.add_argument(
+        '--lr',
+        '--learning-rate',
+        default=0.1,
+        type=float,
+        metavar='LR',
+        help='initial learning rate')
+    parser.add_argument(
+        "-b", "--batch-size", default=64, type=int, help="batch size")
+    parser.add_argument(
+        "-n", "--num-workers", default=4, type=int, help="dataloader workers")
+    parser.add_argument(
+        "--output-dir", type=str, default='output', help="save dir")
+    parser.add_argument(
+        "-r",
+        "--resume",
+        default=None,
+        type=str,
+        help="checkpoint path to resume")
+    parser.add_argument(
+        "--eval-only", action='store_true', help="enable dygraph mode")
+    FLAGS = parser.parse_args()
+    assert FLAGS.data, "error: must provide data path"
+    main()

models/__init__.py

+from .resnet import *

models/download.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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import os.path as osp
+import shutil
+import requests
+import tqdm
+import hashlib
+import time
+
+from paddle.fluid.dygraph.parallel import ParallelEnv
+
+import logging
+logger = logging.getLogger(__name__)
+
+__all__ = ['get_weights_path']
+
+WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/hapi/weights")
+
+DOWNLOAD_RETRY_LIMIT = 3
+
+
+def get_weights_path(url, md5sum=None):
+    """Get weights path from WEIGHT_HOME, if not exists,
+    download it from url.
+    """
+    path, _ = get_path(url, WEIGHTS_HOME, md5sum)
+    return path
+
+
+def map_path(url, root_dir):
+    # parse path after download under root_dir
+    fname = osp.split(url)[-1]
+    fpath = fname
+    return osp.join(root_dir, fpath)
+
+
+def get_path(url, root_dir, md5sum=None, check_exist=True):
+    """ Download from given url to root_dir.
+    if file or directory specified by url is exists under
+    root_dir, return the path directly, otherwise download
+    from url and decompress it, return the path.
+
+    url (str): download url
+    root_dir (str): root dir for downloading, it should be
+                    WEIGHTS_HOME or DATASET_HOME
+    md5sum (str): md5 sum of download package
+    """
+    # parse path after download to decompress under root_dir
+    fullpath = map_path(url, root_dir)
+
+    exist_flag = False
+    if osp.exists(fullpath) and check_exist and _md5check(fullpath, md5sum):
+        exist_flag = True
+        if ParallelEnv().local_rank == 0:
+            logger.info("Found {}".format(fullpath))
+    else:
+        if ParallelEnv().local_rank == 0:
+            fullpath = _download(url, root_dir, md5sum)
+        else:
+            while not os.path.exists(fullpath):
+                time.sleep(1)
+    return fullpath, exist_flag
+
+
+def _download(url, path, md5sum=None):
+    """
+    Download from url, save to path.
+
+    url (str): download url
+    path (str): download to given path
+    """
+    if not osp.exists(path):
+        os.makedirs(path)
+
+    fname = osp.split(url)[-1]
+    fullname = osp.join(path, fname)
+    retry_cnt = 0
+
+    while not (osp.exists(fullname) and _md5check(fullname, md5sum)):
+        if retry_cnt < DOWNLOAD_RETRY_LIMIT:
+            retry_cnt += 1
+        else:
+            raise RuntimeError("Download from {} failed. "
+                               "Retry limit reached".format(url))
+        if ParallelEnv().local_rank == 0:
+            logger.info("Downloading {} from {}".format(fname, url))
+
+        req = requests.get(url, stream=True)
+        if req.status_code != 200:
+            raise RuntimeError("Downloading from {} failed with code "
+                               "{}!".format(url, req.status_code))
+
+        # For protecting download interupted, download to
+        # tmp_fullname firstly, move tmp_fullname to fullname
+        # after download finished
+        tmp_fullname = fullname + "_tmp"
+        total_size = req.headers.get('content-length')
+        with open(tmp_fullname, 'wb') as f:
+            if total_size:
+                for chunk in tqdm.tqdm(
+                        req.iter_content(chunk_size=1024),
+                        total=(int(total_size) + 1023) // 1024,
+                        unit='KB'):
+                    f.write(chunk)
+            else:
+                for chunk in req.iter_content(chunk_size=1024):
+                    if chunk:
+                        f.write(chunk)
+        shutil.move(tmp_fullname, fullname)
+
+    return fullname
+
+
+def _md5check(fullname, md5sum=None):
+    if md5sum is None:
+        return True
+    if ParallelEnv().local_rank == 0:
+        logger.info("File {} md5 checking...".format(fullname))
+    md5 = hashlib.md5()
+    with open(fullname, 'rb') as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            md5.update(chunk)
+    calc_md5sum = md5.hexdigest()
+
+    if calc_md5sum != md5sum:
+        if ParallelEnv().local_rank == 0:
+            logger.info("File {} md5 check failed, {}(calc) != "
+                        "{}(base)".format(fullname, calc_md5sum, md5sum))
+        return False
+    return True

models/resnet.py

+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle.fluid as fluid
+
+from paddle.fluid.layer_helper import LayerHelper
+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
+
+__all__ = ['ResNet', 'resnet50', 'resnet101', 'resnet152']
+
+model_urls = {
+    'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams',
+                 '0884c9087266496c41c60d14a96f8530')
+}
+
+
+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 BottleneckBlock(fluid.dygraph.Layer):
+    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 * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    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)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(x)
+        # return fluid.layers.relu(x)
+
+
+class ResNet(Model):
+    def __init__(self, Block, depth=50, num_classes=1000):
+        super(ResNet, self).__init__()
+
+        layer_config = {
+            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]
+        num_in = [64, 256, 512, 1024]
+        num_out = [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):
+                block = Block(
+                    num_channels=num_in[idx] if b == 0 else num_out[idx] * 4,
+                    num_filters=num_out[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)
+
+        self.global_pool = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+        self.fc_input_dim = num_out[-1] * 4 * 1 * 1
+        self.fc = Linear(
+            self.fc_input_dim,
+            num_classes,
+            act='softmax',
+            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)
+        x = self.global_pool(x)
+        x = fluid.layers.reshape(x, shape=[-1, self.fc_input_dim])
+        x = self.fc(x)
+        return x
+
+
+def _resnet(arch, Block, depth, pretrained):
+    model = ResNet(Block, depth)
+    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(model_urls[arch][0],
+                                       model_urls[arch][1])
+        assert weight_path.endswith(
+            '.pdparams'), "suffix of weight must be .pdparams"
+        model.load(weight_path[:-9])
+    return model
+
+
+def resnet50(pretrained=False):
+    return _resnet('resnet50', BottleneckBlock, 50, pretrained)
+
+
+def resnet101(pretrained=False):
+    return _resnet('resnet101', BottleneckBlock, 101, pretrained)
+
+
+def resnet152(pretrained=False):
+    return _resnet('resnet152', BottleneckBlock, 152, pretrained)

progressbar.py

@@ -107,7 +107,7 @@ class ProgressBar(object):
                 eta = time_per_unit * (self._num - current_num)
                 if eta > 3600:
                     eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) //
-                                                60, eta % 60)
+                                                   60, eta % 60)
                 elif eta > 60:
                     eta_format = '%d:%02d' % (eta // 60, eta % 60)
                 else:
@@ -148,7 +148,7 @@ class ProgressBar(object):
                         else:
                             info += ' %.4e' % v
                     elif isinstance(v, np.ndarray) and \
-                        isinstance(v.size, 1) and \
+                        v.size == 1 and \
                         isinstance(v.dtype, (np.float32, np.float64)):
                         if abs(v[0]) > 1e-3:
                             info += ' %.4f' % v[0]