Add example for DeepFool.

examples/model_security/model_attacks/cv/yolov3_darknet53/coco_attack_deepfool.py

@@ -0,0 +1,157 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""generate adversarial example for yolov3_darknet53 by DeepFool"""
+import os
+import argparse
+import datetime
+import numpy as np
+
+from mindspore import Tensor
+from mindspore.nn import Cell
+from mindspore.ops import operations as P
+from mindspore.context import ParallelMode
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+import mindspore as ms
+
+from mindarmour.adv_robustness.attacks import DeepFool
+
+from src.yolo import YOLOV3DarkNet53
+from src.logger import get_logger
+from src.yolo_dataset import create_yolo_dataset
+from src.config import ConfigYOLOV3DarkNet53
+
+
+def parse_args():
+    """Parse arguments."""
+    parser = argparse.ArgumentParser('mindspore coco testing')
+
+    # device related
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+
+    parser.add_argument('--data_dir', type=str, default='', help='train data dir')
+    parser.add_argument('--pretrained', default='', type=str, help='model_path, local pretrained model to load')
+    parser.add_argument('--samples_num', default=1, type=int, help='Number of sample to be generated.')
+    parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
+    parser.add_argument('--testing_shape', type=str, default='', help='shape for test ')
+
+    args, _ = parser.parse_known_args()
+
+    args.data_root = os.path.join(args.data_dir, 'val2014')
+    args.annFile = os.path.join(args.data_dir, 'annotations/instances_val2014.json')
+
+    return args
+
+
+def conver_testing_shape(args):
+    """Convert testing shape to list."""
+    testing_shape = [int(args.testing_shape), int(args.testing_shape)]
+    return testing_shape
+
+
+class SolveOutput(Cell):
+    """Solve output of the target network to adapt DeepFool."""
+    def __init__(self, network):
+        super(SolveOutput, self).__init__()
+        self._network = network
+        self._reshape = P.Reshape()
+
+    def construct(self, image, input_shape):
+        prediction = self._network(image, input_shape)
+        output_big = prediction[0]
+        output_big = self._reshape(output_big, (output_big.shape[0], -1, 85))
+        output_big_boxes = output_big[:, :, 0: 5]
+        output_big_logits = output_big[:, :, 5:]
+        return output_big_boxes, output_big_logits
+
+
+def test():
+    """The function of eval."""
+    args = parse_args()
+
+    devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True, device_id=devid)
+
+    # logger
+    args.outputs_dir = os.path.join(args.log_path,
+                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+    rank_id = int(os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0
+    args.logger = get_logger(args.outputs_dir, rank_id)
+
+    context.reset_auto_parallel_context()
+    parallel_mode = ParallelMode.STAND_ALONE
+    context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=1)
+
+    args.logger.info('Creating Network....')
+    network = SolveOutput(YOLOV3DarkNet53(is_training=False))
+
+    data_root = args.data_root
+    ann_file = args.annFile
+
+    args.logger.info(args.pretrained)
+    if os.path.isfile(args.pretrained):
+        param_dict = load_checkpoint(args.pretrained)
+        param_dict_new = {}
+        for key, values in param_dict.items():
+            if key.startswith('moments.'):
+                continue
+            elif key.startswith('yolo_network.'):
+                param_dict_new[key[13:]] = values
+            else:
+                param_dict_new[key] = values
+        load_param_into_net(network, param_dict_new)
+        args.logger.info('load_model {} success'.format(args.pretrained))
+    else:
+        args.logger.info('{} not exists or not a pre-trained file'.format(args.pretrained))
+        assert FileNotFoundError('{} not exists or not a pre-trained file'.format(args.pretrained))
+        exit(1)
+
+    config = ConfigYOLOV3DarkNet53()
+    if args.testing_shape:
+        config.test_img_shape = conver_testing_shape(args)
+
+    ds, data_size = create_yolo_dataset(data_root, ann_file, is_training=False, batch_size=1,
+                                        max_epoch=1, device_num=1, rank=rank_id, shuffle=False,
+                                        config=config)
+
+    args.logger.info('testing shape : {}'.format(config.test_img_shape))
+    args.logger.info('totol {} images to eval'.format(data_size))
+
+    network.set_train(False)
+    # build attacker
+    attack = DeepFool(network, num_classes=80, model_type='detection', reserve_ratio=0.9, bounds=(0, 1))
+    input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
+
+    args.logger.info('Start inference....')
+    batch_num = args.samples_num
+    adv_example = []
+    for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):
+        if i >= batch_num:
+            break
+        image = data["image"]
+        image_shape = data["image_shape"]
+
+        gt_boxes, gt_logits = network(image, input_shape)
+        gt_boxes, gt_logits = gt_boxes.asnumpy(), gt_logits.asnumpy()
+        gt_labels = np.argmax(gt_logits, axis=2)
+
+        adv_img = attack.generate((image.asnumpy(), image_shape.asnumpy()), (gt_boxes, gt_labels))
+        adv_example.append(adv_img)
+    np.save('adv_example.npy', adv_example)
+
+
+if __name__ == "__main__":
+    test()

examples/model_security/model_attacks/cv/yolov3_darknet53/src/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================

examples/model_security/model_attacks/cv/yolov3_darknet53/src/config.py

@@ -0,0 +1,68 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Config parameters for Darknet based yolov3_darknet53 models."""
+
+
+class ConfigYOLOV3DarkNet53:
+    """
+    Config parameters for the yolov3_darknet53.
+
+    Examples:
+        ConfigYOLOV3DarkNet53()
+    """
+    # train_param
+    # data augmentation related
+    hue = 0.1
+    saturation = 1.5
+    value = 1.5
+    jitter = 0.3
+
+    resize_rate = 1
+    multi_scale = [[320, 320],
+                   [352, 352],
+                   [384, 384],
+                   [416, 416],
+                   [448, 448],
+                   [480, 480],
+                   [512, 512],
+                   [544, 544],
+                   [576, 576],
+                   [608, 608]
+                   ]
+
+    num_classes = 80
+    max_box = 50
+
+    backbone_input_shape = [32, 64, 128, 256, 512]
+    backbone_shape = [64, 128, 256, 512, 1024]
+    backbone_layers = [1, 2, 8, 8, 4]
+
+    # confidence under ignore_threshold means no object when training
+    ignore_threshold = 0.7
+
+    # h->w
+    anchor_scales = [(10, 13),
+                     (16, 30),
+                     (33, 23),
+                     (30, 61),
+                     (62, 45),
+                     (59, 119),
+                     (116, 90),
+                     (156, 198),
+                     (373, 326)]
+    out_channel = 255
+
+    # test_param
+    test_img_shape = [416, 416]

examples/model_security/model_attacks/cv/yolov3_darknet53/src/convert_weight.py

@@ -0,0 +1,80 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Convert weight to mindspore ckpt."""
+import os
+import argparse
+import numpy as np
+from mindspore.train.serialization import save_checkpoint
+from mindspore import Tensor
+
+from src.yolo import YOLOV3DarkNet53
+
+def load_weight(weights_file):
+    """Loads pre-trained weights."""
+    if not os.path.isfile(weights_file):
+        raise ValueError(f'"{weights_file}" is not a valid weight file.')
+    with open(weights_file, 'rb') as fp:
+        np.fromfile(fp, dtype=np.int32, count=5)
+        return np.fromfile(fp, dtype=np.float32)
+
+
+def build_network():
+    """Build YOLOv3 network."""
+    network = YOLOV3DarkNet53(is_training=True)
+    params = network.get_parameters()
+    params = [p for p in params if 'backbone' in p.name]
+    return params
+
+
+def convert(weights_file, output_file):
+    """Conver weight to mindspore ckpt."""
+    params = build_network()
+    weights = load_weight(weights_file)
+    index = 0
+    param_list = []
+    for i in range(0, len(params), 5):
+        weight = params[i]
+        mean = params[i+1]
+        var = params[i+2]
+        gamma = params[i+3]
+        beta = params[i+4]
+        beta_data = weights[index: index+beta.size()].reshape(beta.shape)
+        index += beta.size()
+        gamma_data = weights[index: index+gamma.size()].reshape(gamma.shape)
+        index += gamma.size()
+        mean_data = weights[index: index+mean.size()].reshape(mean.shape)
+        index += mean.size()
+        var_data = weights[index: index + var.size()].reshape(var.shape)
+        index += var.size()
+        weight_data = weights[index: index+weight.size()].reshape(weight.shape)
+        index += weight.size()
+
+        param_list.append({'name': weight.name, 'type': weight.dtype, 'shape': weight.shape,
+                           'data': Tensor(weight_data)})
+        param_list.append({'name': mean.name, 'type': mean.dtype, 'shape': mean.shape, 'data': Tensor(mean_data)})
+        param_list.append({'name': var.name, 'type': var.dtype, 'shape': var.shape, 'data': Tensor(var_data)})
+        param_list.append({'name': gamma.name, 'type': gamma.dtype, 'shape': gamma.shape, 'data': Tensor(gamma_data)})
+        param_list.append({'name': beta.name, 'type': beta.dtype, 'shape': beta.shape, 'data': Tensor(beta_data)})
+
+    save_checkpoint(param_list, output_file)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="yolov3 weight convert.")
+    parser.add_argument("--input_file", type=str, default="./darknet53.conv.74", help="input file path.")
+    parser.add_argument("--output_file", type=str, default="./ackbone_darknet53.ckpt", help="output file path.")
+    args_opt = parser.parse_args()
+
+    convert(args_opt.input_file, args_opt.output_file)

examples/model_security/model_attacks/cv/yolov3_darknet53/src/darknet.py

@@ -0,0 +1,212 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""DarkNet model."""
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+
+# pylint: disable=locally-disables, missing-docstring
+
+
+def conv_block(in_channels,
+               out_channels,
+               kernel_size,
+               stride,
+               dilation=1):
+    """Get a conv2d batchnorm and relu layer"""
+    pad_mode = 'same'
+    padding = 0
+
+    return nn.SequentialCell(
+        [nn.Conv2d(in_channels,
+                   out_channels,
+                   kernel_size=kernel_size,
+                   stride=stride,
+                   padding=padding,
+                   dilation=dilation,
+                   pad_mode=pad_mode),
+         nn.BatchNorm2d(out_channels, momentum=0.1),
+         nn.ReLU()]
+    )
+
+
+class ResidualBlock(nn.Cell):
+    """
+    DarkNet V1 residual block definition.
+
+    Args:
+        in_channels: Integer. Input channel.
+        out_channels: Integer. Output channel.
+
+    Returns:
+        Tensor, output tensor.
+    Examples:
+        ResidualBlock(3, 208)
+    """
+    expansion = 4
+
+    def __init__(self,
+                 in_channels,
+                 out_channels):
+
+        super(ResidualBlock, self).__init__()
+        out_chls = out_channels//2
+        self.conv1 = conv_block(in_channels, out_chls, kernel_size=1, stride=1)
+        self.conv2 = conv_block(out_chls, out_channels, kernel_size=3, stride=1)
+        self.add = P.TensorAdd()
+
+    def construct(self, x):
+        identity = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+        out = self.add(out, identity)
+
+        return out
+
+
+class DarkNet(nn.Cell):
+    """
+    DarkNet V1 network.
+
+    Args:
+        block: Cell. Block for network.
+        layer_nums: List. Numbers of different layers.
+        in_channels: Integer. Input channel.
+        out_channels: Integer. Output channel.
+        detect: Bool. Whether detect or not. Default:False.
+
+    Returns:
+        Tuple, tuple of output tensor,(f1,f2,f3,f4,f5).
+
+    Examples:
+        DarkNet(ResidualBlock,
+               [1, 2, 8, 8, 4],
+               [32, 64, 128, 256, 512],
+               [64, 128, 256, 512, 1024],
+               100)
+    """
+    def __init__(self,
+                 block,
+                 layer_nums,
+                 in_channels,
+                 out_channels,
+                 detect=False):
+        super(DarkNet, self).__init__()
+
+        self.outchannel = out_channels[-1]
+        self.detect = detect
+
+        if not len(layer_nums) == len(in_channels) == len(out_channels) == 5:
+            raise ValueError("the length of layer_num, inchannel, outchannel list must be 5!")
+        self.conv0 = conv_block(3,
+                                in_channels[0],
+                                kernel_size=3,
+                                stride=1)
+        self.conv1 = conv_block(in_channels[0],
+                                out_channels[0],
+                                kernel_size=3,
+                                stride=2)
+        self.layer1 = self._make_layer(block,
+                                       layer_nums[0],
+                                       in_channel=out_channels[0],
+                                       out_channel=out_channels[0])
+        self.conv2 = conv_block(in_channels[1],
+                                out_channels[1],
+                                kernel_size=3,
+                                stride=2)
+        self.layer2 = self._make_layer(block,
+                                       layer_nums[1],
+                                       in_channel=out_channels[1],
+                                       out_channel=out_channels[1])
+        self.conv3 = conv_block(in_channels[2],
+                                out_channels[2],
+                                kernel_size=3,
+                                stride=2)
+        self.layer3 = self._make_layer(block,
+                                       layer_nums[2],
+                                       in_channel=out_channels[2],
+                                       out_channel=out_channels[2])
+        self.conv4 = conv_block(in_channels[3],
+                                out_channels[3],
+                                kernel_size=3,
+                                stride=2)
+        self.layer4 = self._make_layer(block,
+                                       layer_nums[3],
+                                       in_channel=out_channels[3],
+                                       out_channel=out_channels[3])
+        self.conv5 = conv_block(in_channels[4],
+                                out_channels[4],
+                                kernel_size=3,
+                                stride=2)
+        self.layer5 = self._make_layer(block,
+                                       layer_nums[4],
+                                       in_channel=out_channels[4],
+                                       out_channel=out_channels[4])
+
+    def _make_layer(self, block, layer_num, in_channel, out_channel):
+        """
+        Make Layer for DarkNet.
+
+        :param block: Cell. DarkNet block.
+        :param layer_num: Integer. Layer number.
+        :param in_channel: Integer. Input channel.
+        :param out_channel: Integer. Output channel.
+
+        Examples:
+            _make_layer(ConvBlock, 1, 128, 256)
+        """
+        layers = []
+        darkblk = block(in_channel, out_channel)
+        layers.append(darkblk)
+
+        for _ in range(1, layer_num):
+            darkblk = block(out_channel, out_channel)
+            layers.append(darkblk)
+
+        return nn.SequentialCell(layers)
+
+    def construct(self, x):
+        c1 = self.conv0(x)
+        c2 = self.conv1(c1)
+        c3 = self.layer1(c2)
+        c4 = self.conv2(c3)
+        c5 = self.layer2(c4)
+        c6 = self.conv3(c5)
+        c7 = self.layer3(c6)
+        c8 = self.conv4(c7)
+        c9 = self.layer4(c8)
+        c10 = self.conv5(c9)
+        c11 = self.layer5(c10)
+        if self.detect:
+            return c7, c9, c11
+
+        return c11
+
+    def get_out_channels(self):
+        return self.outchannel
+
+
+def darknet53():
+    """
+    Get DarkNet53 neural network.
+
+    Returns:
+        Cell, cell instance of DarkNet53 neural network.
+
+    Examples:
+        darknet53()
+    """
+    return DarkNet(ResidualBlock, [1, 2, 8, 8, 4],
+                   [32, 64, 128, 256, 512],
+                   [64, 128, 256, 512, 1024])

examples/model_security/model_attacks/cv/yolov3_darknet53/src/distributed_sampler.py

@@ -0,0 +1,60 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Yolo dataset distributed sampler."""
+from __future__ import division
+import math
+import numpy as np
+
+
+class DistributedSampler:
+    """Distributed sampler."""
+    def __init__(self, dataset_size, num_replicas=None, rank=None, shuffle=True):
+        if num_replicas is None:
+            print("***********Setting world_size to 1 since it is not passed in ******************")
+            num_replicas = 1
+        if rank is None:
+            print("***********Setting rank to 0 since it is not passed in ******************")
+            rank = 0
+        self.dataset_size = dataset_size
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = int(math.ceil(dataset_size * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.shuffle = shuffle
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        if self.shuffle:
+            indices = np.random.RandomState(seed=self.epoch).permutation(self.dataset_size)
+            # np.array type. number from 0 to len(dataset_size)-1, used as index of dataset
+            indices = indices.tolist()
+            self.epoch += 1
+            # change to list type
+        else:
+            indices = list(range(self.dataset_size))
+
+        # add extra samples to make it evenly divisible
+        indices += indices[:(self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples

examples/model_security/model_attacks/cv/yolov3_darknet53/src/initializer.py

@@ -0,0 +1,204 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Parameter init."""
+import math
+from functools import reduce
+import numpy as np
+from mindspore.common import initializer as init
+from mindspore.common.initializer import Initializer as MeInitializer
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+import mindspore.nn as nn
+from .util import load_backbone
+
+def calculate_gain(nonlinearity, param=None):
+    r"""Return the recommended gain value for the given nonlinearity function.
+    The values are as follows:
+
+    ================= ====================================================
+    nonlinearity      gain
+    ================= ====================================================
+    Linear / Identity :math:`1`
+    Conv{1,2,3}D      :math:`1`
+    Sigmoid           :math:`1`
+    Tanh              :math:`\frac{5}{3}`
+    ReLU              :math:`\sqrt{2}`
+    Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
+    ================= ====================================================
+
+    Args:
+        nonlinearity: the non-linear function (`nn.functional` name)
+        param: optional parameter for the non-linear function
+
+    Examples:
+        >>> gain = nn.init.calculate_gain('leaky_relu', 0.2)  # leaky_relu with negative_slope=0.2
+    """
+    linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
+    if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
+        return 1
+    if nonlinearity == 'tanh':
+        return 5.0 / 3
+    if nonlinearity == 'relu':
+        return math.sqrt(2.0)
+    if nonlinearity == 'leaky_relu':
+        if param is None:
+            negative_slope = 0.01
+        elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
+            # True/False are instances of int, hence check above
+            negative_slope = param
+        else:
+            raise ValueError("negative_slope {} not a valid number".format(param))
+        return math.sqrt(2.0 / (1 + negative_slope ** 2))
+
+    raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
+
+
+def _assignment(arr, num):
+    """Assign the value of 'num' and 'arr'."""
+    if arr.shape == ():
+        arr = arr.reshape((1))
+        arr[:] = num
+        arr = arr.reshape(())
+    else:
+        if isinstance(num, np.ndarray):
+            arr[:] = num[:]
+        else:
+            arr[:] = num
+    return arr
+
+
+def _calculate_correct_fan(array, mode):
+    mode = mode.lower()
+    valid_modes = ['fan_in', 'fan_out']
+    if mode not in valid_modes:
+        raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
+
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(array)
+    return fan_in if mode == 'fan_in' else fan_out
+
+
+def kaiming_uniform_(arr, a=0, mode='fan_in', nonlinearity='leaky_relu'):
+    r"""Fills the input `Tensor` with values according to the method
+    described in `Delving deep into rectifiers: Surpassing human-level
+    performance on ImageNet classification` - He, K. et al. (2015), using a
+    uniform distribution. The resulting tensor will have values sampled from
+    :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
+
+    .. math::
+        \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
+
+    Also known as He initialization.
+
+    Args:
+        tensor: an n-dimensional `Tensor`
+        a: the negative slope of the rectifier used after this layer (only
+        used with ``'leaky_relu'``)
+        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
+            preserves the magnitude of the variance of the weights in the
+            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
+            backwards pass.
+        nonlinearity: the non-linear function (`nn.functional` name),
+            recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
+
+    Examples:
+        >>> w = np.empty(3, 5)
+        >>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')
+    """
+    fan = _calculate_correct_fan(arr, mode)
+    gain = calculate_gain(nonlinearity, a)
+    std = gain / math.sqrt(fan)
+    bound = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
+    return np.random.uniform(-bound, bound, arr.shape)
+
+
+def _calculate_fan_in_and_fan_out(arr):
+    """Calculate fan in and fan out."""
+    dimensions = len(arr.shape)
+    if dimensions < 2:
+        raise ValueError("Fan in and fan out can not be computed for array with fewer than 2 dimensions")
+
+    num_input_fmaps = arr.shape[1]
+    num_output_fmaps = arr.shape[0]
+    receptive_field_size = 1
+    if dimensions > 2:
+        receptive_field_size = reduce(lambda x, y: x * y, arr.shape[2:])
+    fan_in = num_input_fmaps * receptive_field_size
+    fan_out = num_output_fmaps * receptive_field_size
+
+    return fan_in, fan_out
+
+
+class KaimingUniform(MeInitializer):
+    """Kaiming uniform initializer."""
+    def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
+        super(KaimingUniform, self).__init__()
+        self.a = a
+        self.mode = mode
+        self.nonlinearity = nonlinearity
+
+    def _initialize(self, arr):
+        tmp = kaiming_uniform_(arr, self.a, self.mode, self.nonlinearity)
+        _assignment(arr, tmp)
+
+
+def default_recurisive_init(custom_cell):
+    """Initialize parameter."""
+    for _, cell in custom_cell.cells_and_names():
+        if isinstance(cell, nn.Conv2d):
+            cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
+                                                  cell.weight.shape,
+                                                  cell.weight.dtype))
+            if cell.bias is not None:
+                fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight)
+                bound = 1 / math.sqrt(fan_in)
+                cell.bias.set_data(init.initializer(init.Uniform(bound),
+                                                    cell.bias.shape,
+                                                    cell.bias.dtype))
+        elif isinstance(cell, nn.Dense):
+            cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
+                                                  cell.weight.shape,
+                                                  cell.weight.dtype))
+            if cell.bias is not None:
+                fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight)
+                bound = 1 / math.sqrt(fan_in)
+                cell.bias.set_data(init.initializer(init.Uniform(bound),
+                                                    cell.bias.shape,
+                                                    cell.bias.dtype))
+        elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
+            pass
+
+def load_yolov3_params(args, network):
+    """Load yolov3 darknet parameter from checkpoint."""
+    if args.pretrained_backbone:
+        network = load_backbone(network, args.pretrained_backbone, args)
+        args.logger.info('load pre-trained backbone {} into network'.format(args.pretrained_backbone))
+    else:
+        args.logger.info('Not load pre-trained backbone, please be careful')
+
+    if args.resume_yolov3:
+        param_dict = load_checkpoint(args.resume_yolov3)
+        param_dict_new = {}
+        for key, values in param_dict.items():
+            if key.startswith('moments.'):
+                continue
+            elif key.startswith('yolo_network.'):
+                param_dict_new[key[13:]] = values
+                args.logger.info('in resume {}'.format(key))
+            else:
+                param_dict_new[key] = values
+                args.logger.info('in resume {}'.format(key))
+
+        args.logger.info('resume finished')
+        load_param_into_net(network, param_dict_new)
+        args.logger.info('load_model {} success'.format(args.resume_yolov3))

examples/model_security/model_attacks/cv/yolov3_darknet53/src/logger.py

@@ -0,0 +1,80 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Custom Logger."""
+import os
+import sys
+import logging
+from datetime import datetime
+
+
+class LOGGER(logging.Logger):
+    """
+    Logger.
+
+    Args:
+         logger_name: String. Logger name.
+         rank: Integer. Rank id.
+    """
+    def __init__(self, logger_name, rank=0):
+        super(LOGGER, self).__init__(logger_name)
+        self.rank = rank
+        if rank % 8 == 0:
+            console = logging.StreamHandler(sys.stdout)
+            console.setLevel(logging.INFO)
+            formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+            console.setFormatter(formatter)
+            self.addHandler(console)
+
+    def setup_logging_file(self, log_dir, rank=0):
+        """Setup logging file."""
+        self.rank = rank
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir, exist_ok=True)
+        log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
+        self.log_fn = os.path.join(log_dir, log_name)
+        fh = logging.FileHandler(self.log_fn)
+        fh.setLevel(logging.INFO)
+        formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+        fh.setFormatter(formatter)
+        self.addHandler(fh)
+
+    def info(self, msg, *args, **kwargs):
+        if self.isEnabledFor(logging.INFO):
+            self._log(logging.INFO, msg, args, **kwargs)
+
+    def save_args(self, args):
+        self.info('Args:')
+        args_dict = vars(args)
+        for key in args_dict.keys():
+            self.info('--> %s: %s', key, args_dict[key])
+        self.info('')
+
+    def important_info(self, msg, *args, **kwargs):
+        if self.isEnabledFor(logging.INFO) and self.rank == 0:
+            line_width = 2
+            important_msg = '\n'
+            important_msg += ('*'*70 + '\n')*line_width
+            important_msg += ('*'*line_width + '\n')*2
+            important_msg += '*'*line_width + ' '*8 + msg + '\n'
+            important_msg += ('*'*line_width + '\n')*2
+            important_msg += ('*'*70 + '\n')*line_width
+            self.info(important_msg, *args, **kwargs)
+
+
+def get_logger(path, rank):
+    """Get Logger."""
+    logger = LOGGER('yolov3_darknet53', rank)
+    logger.setup_logging_file(path, rank)
+    return logger

examples/model_security/model_attacks/cv/yolov3_darknet53/src/loss.py

@@ -0,0 +1,70 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""YOLOV3 loss."""
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+
+
+class XYLoss(nn.Cell):
+    """Loss for x and y."""
+    def __init__(self):
+        super(XYLoss, self).__init__()
+        self.cross_entropy = P.SigmoidCrossEntropyWithLogits()
+        self.reduce_sum = P.ReduceSum()
+
+    def construct(self, object_mask, box_loss_scale, predict_xy, true_xy):
+        xy_loss = object_mask * box_loss_scale * self.cross_entropy(predict_xy, true_xy)
+        xy_loss = self.reduce_sum(xy_loss, ())
+        return xy_loss
+
+
+class WHLoss(nn.Cell):
+    """Loss for w and h."""
+    def __init__(self):
+        super(WHLoss, self).__init__()
+        self.square = P.Square()
+        self.reduce_sum = P.ReduceSum()
+
+    def construct(self, object_mask, box_loss_scale, predict_wh, true_wh):
+        wh_loss = object_mask * box_loss_scale * 0.5 * P.Square()(true_wh - predict_wh)
+        wh_loss = self.reduce_sum(wh_loss, ())
+        return wh_loss
+
+
+class ConfidenceLoss(nn.Cell):
+    """Loss for confidence."""
+    def __init__(self):
+        super(ConfidenceLoss, self).__init__()
+        self.cross_entropy = P.SigmoidCrossEntropyWithLogits()
+        self.reduce_sum = P.ReduceSum()
+
+    def construct(self, object_mask, predict_confidence, ignore_mask):
+        confidence_loss = self.cross_entropy(predict_confidence, object_mask)
+        confidence_loss = object_mask * confidence_loss + (1 - object_mask) * confidence_loss * ignore_mask
+        confidence_loss = self.reduce_sum(confidence_loss, ())
+        return confidence_loss
+
+
+class ClassLoss(nn.Cell):
+    """Loss for classification."""
+    def __init__(self):
+        super(ClassLoss, self).__init__()
+        self.cross_entropy = P.SigmoidCrossEntropyWithLogits()
+        self.reduce_sum = P.ReduceSum()
+
+    def construct(self, object_mask, predict_class, class_probs):
+        class_loss = object_mask * self.cross_entropy(predict_class, class_probs)
+        class_loss = self.reduce_sum(class_loss, ())
+        return class_loss

examples/model_security/model_attacks/cv/yolov3_darknet53/src/lr_scheduler.py

@@ -0,0 +1,182 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Learning rate scheduler."""
+import math
+from collections import Counter
+
+import numpy as np
+
+# pylint: disable=locally-disables, invalid-name
+
+
+def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
+    """Linear learning rate."""
+    lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
+    lr = float(init_lr) + lr_inc * current_step
+    return lr
+
+
+def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
+    """Warmup step learning rate."""
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+    milestones = lr_epochs
+    milestones_steps = []
+    for milestone in milestones:
+        milestones_step = milestone * steps_per_epoch
+        milestones_steps.append(milestones_step)
+
+    lr_each_step = []
+    lr = base_lr
+    milestones_steps_counter = Counter(milestones_steps)
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            lr = lr * gamma**milestones_steps_counter[i]
+        lr_each_step.append(lr)
+
+    return np.array(lr_each_step).astype(np.float32)
+
+
+def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
+    return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
+
+
+def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
+    lr_epochs = []
+    for i in range(1, max_epoch):
+        if i % epoch_size == 0:
+            lr_epochs.append(i)
+    return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
+
+
+def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
+    """Cosine annealing learning rate."""
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+
+    lr_each_step = []
+    for i in range(total_steps):
+        last_epoch = i // steps_per_epoch
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
+        lr_each_step.append(lr)
+
+    return np.array(lr_each_step).astype(np.float32)
+
+
+def warmup_cosine_annealing_lr_V2(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
+    """Cosine annealing learning rate V2."""
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+
+    last_lr = 0
+    last_epoch_V1 = 0
+
+    T_max_V2 = int(max_epoch*1/3)
+
+    lr_each_step = []
+    for i in range(total_steps):
+        last_epoch = i // steps_per_epoch
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            if i < total_steps*2/3:
+                lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
+                last_lr = lr
+                last_epoch_V1 = last_epoch
+            else:
+                base_lr = last_lr
+                last_epoch = last_epoch-last_epoch_V1
+                lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max_V2)) / 2
+
+        lr_each_step.append(lr)
+    return np.array(lr_each_step).astype(np.float32)
+
+
+def warmup_cosine_annealing_lr_sample(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
+    """Warmup cosine annealing learning rate."""
+    start_sample_epoch = 60
+    step_sample = 2
+    tobe_sampled_epoch = 60
+    end_sampled_epoch = start_sample_epoch + step_sample*tobe_sampled_epoch
+    max_sampled_epoch = max_epoch+tobe_sampled_epoch
+    T_max = max_sampled_epoch
+
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    total_sampled_steps = int(max_sampled_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+
+    lr_each_step = []
+
+    for i in range(total_sampled_steps):
+        last_epoch = i // steps_per_epoch
+        if last_epoch in range(start_sample_epoch, end_sampled_epoch, step_sample):
+            continue
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
+        lr_each_step.append(lr)
+
+    assert total_steps == len(lr_each_step)
+    return np.array(lr_each_step).astype(np.float32)
+
+
+def get_lr(args):
+    """generate learning rate."""
+    if args.lr_scheduler == 'exponential':
+        lr = warmup_step_lr(args.lr,
+                            args.lr_epochs,
+                            args.steps_per_epoch,
+                            args.warmup_epochs,
+                            args.max_epoch,
+                            gamma=args.lr_gamma,
+                            )
+    elif args.lr_scheduler == 'cosine_annealing':
+        lr = warmup_cosine_annealing_lr(args.lr,
+                                        args.steps_per_epoch,
+                                        args.warmup_epochs,
+                                        args.max_epoch,
+                                        args.T_max,
+                                        args.eta_min)
+    elif args.lr_scheduler == 'cosine_annealing_V2':
+        lr = warmup_cosine_annealing_lr_V2(args.lr,
+                                           args.steps_per_epoch,
+                                           args.warmup_epochs,
+                                           args.max_epoch,
+                                           args.T_max,
+                                           args.eta_min)
+    elif args.lr_scheduler == 'cosine_annealing_sample':
+        lr = warmup_cosine_annealing_lr_sample(args.lr,
+                                               args.steps_per_epoch,
+                                               args.warmup_epochs,
+                                               args.max_epoch,
+                                               args.T_max,
+                                               args.eta_min)
+    else:
+        raise NotImplementedError(args.lr_scheduler)
+    return lr

examples/model_security/model_attacks/cv/yolov3_darknet53/src/transforms.py

@@ -0,0 +1,595 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Preprocess dataset."""
+import random
+import threading
+import copy
+
+import numpy as np
+from PIL import Image
+import cv2
+
+# pylint: disable=locally-disables, unused-argument, invalid-name
+
+
+def _rand(a=0., b=1.):
+    return np.random.rand() * (b - a) + a
+
+
+def bbox_iou(bbox_a, bbox_b, offset=0):
+    """Calculate Intersection-Over-Union(IOU) of two bounding boxes.
+
+    Parameters
+    ----------
+    bbox_a : numpy.ndarray
+        An ndarray with shape :math:`(N, 4)`.
+    bbox_b : numpy.ndarray
+        An ndarray with shape :math:`(M, 4)`.
+    offset : float or int, default is 0
+        The ``offset`` is used to control the whether the width(or height) is computed as
+        (right - left + ``offset``).
+        Note that the offset must be 0 for normalized bboxes, whose ranges are in ``[0, 1]``.
+
+    Returns
+    -------
+    numpy.ndarray
+        An ndarray with shape :math:`(N, M)` indicates IOU between each pairs of
+        bounding boxes in `bbox_a` and `bbox_b`.
+
+    """
+    if bbox_a.shape[1] < 4 or bbox_b.shape[1] < 4:
+        raise IndexError("Bounding boxes axis 1 must have at least length 4")
+
+    tl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2])
+    br = np.minimum(bbox_a[:, None, 2:4], bbox_b[:, 2:4])
+
+    area_i = np.prod(br - tl + offset, axis=2) * (tl < br).all(axis=2)
+    area_a = np.prod(bbox_a[:, 2:4] - bbox_a[:, :2] + offset, axis=1)
+    area_b = np.prod(bbox_b[:, 2:4] - bbox_b[:, :2] + offset, axis=1)
+    return area_i / (area_a[:, None] + area_b - area_i)
+
+
+def statistic_normalize_img(img, statistic_norm):
+    """Statistic normalize images."""
+    # img: RGB
+    if isinstance(img, Image.Image):
+        img = np.array(img)
+    img = img/255.
+    mean = np.array([0.485, 0.456, 0.406])
+    std = np.array([0.229, 0.224, 0.225])
+    if statistic_norm:
+        img = (img - mean) / std
+    return img
+
+
+def get_interp_method(interp, sizes=()):
+    """
+    Get the interpolation method for resize functions.
+    The major purpose of this function is to wrap a random interp method selection
+    and a auto-estimation method.
+
+    Note:
+        When shrinking an image, it will generally look best with AREA-based
+        interpolation, whereas, when enlarging an image, it will generally look best
+        with Bicubic or Bilinear.
+
+    Args:
+        interp (int): Interpolation method for all resizing operations.
+
+            - 0: Nearest Neighbors Interpolation.
+            - 1: Bilinear interpolation.
+            - 2: Bicubic interpolation over 4x4 pixel neighborhood.
+            - 3: Nearest Neighbors. Originally it should be Area-based, as we cannot find Area-based,
+              so we use NN instead. Area-based (resampling using pixel area relation).
+              It may be a preferred method for image decimation, as it gives moire-free results.
+              But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default).
+            - 4: Lanczos interpolation over 8x8 pixel neighborhood.
+            - 9: Cubic for enlarge, area for shrink, bilinear for others.
+            - 10: Random select from interpolation method mentioned above.
+
+        sizes (tuple): Format should like (old_height, old_width, new_height, new_width),
+            if None provided, auto(9) will return Area(2) anyway. Default: ()
+
+    Returns:
+        int, interp method from 0 to 4.
+    """
+    if interp == 9:
+        if sizes:
+            assert len(sizes) == 4
+            oh, ow, nh, nw = sizes
+            if nh > oh and nw > ow:
+                return 2
+            if nh < oh and nw < ow:
+                return 0
+            return 1
+        return 2
+    if interp == 10:
+        return random.randint(0, 4)
+    if interp not in (0, 1, 2, 3, 4):
+        raise ValueError('Unknown interp method %d' % interp)
+    return interp
+
+
+def pil_image_reshape(interp):
+    """Reshape pil image."""
+    reshape_type = {
+        0: Image.NEAREST,
+        1: Image.BILINEAR,
+        2: Image.BICUBIC,
+        3: Image.NEAREST,
+        4: Image.LANCZOS,
+    }
+    return reshape_type[interp]
+
+
+def _preprocess_true_boxes(true_boxes, anchors, in_shape, num_classes,
+                           max_boxes, label_smooth, label_smooth_factor=0.1):
+    """Preprocess annotation boxes."""
+    anchors = np.array(anchors)
+    num_layers = anchors.shape[0] // 3
+    anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+    true_boxes = np.array(true_boxes, dtype='float32')
+    input_shape = np.array(in_shape, dtype='int32')
+    boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
+    # trans to box center point
+    boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
+    # input_shape is [h, w]
+    true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
+    true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
+    # true_boxes [x, y, w, h]
+
+    grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
+    # grid_shape [h, w]
+    y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]),
+                        5 + num_classes), dtype='float32') for l in range(num_layers)]
+    # y_true [gridy, gridx]
+    anchors = np.expand_dims(anchors, 0)
+    anchors_max = anchors / 2.
+    anchors_min = -anchors_max
+    valid_mask = boxes_wh[..., 0] > 0
+
+    wh = boxes_wh[valid_mask]
+    if wh.size > 0:
+        wh = np.expand_dims(wh, -2)
+        boxes_max = wh / 2.
+        boxes_min = -boxes_max
+
+        intersect_min = np.maximum(boxes_min, anchors_min)
+        intersect_max = np.minimum(boxes_max, anchors_max)
+        intersect_wh = np.maximum(intersect_max - intersect_min, 0.)
+        intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
+        box_area = wh[..., 0] * wh[..., 1]
+        anchor_area = anchors[..., 0] * anchors[..., 1]
+        iou = intersect_area / (box_area + anchor_area - intersect_area)
+
+        best_anchor = np.argmax(iou, axis=-1)
+        for t, n in enumerate(best_anchor):
+            for l in range(num_layers):
+                if n in anchor_mask[l]:
+                    i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32')  # grid_y
+                    j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32')  # grid_x
+
+                    k = anchor_mask[l].index(n)
+                    c = true_boxes[t, 4].astype('int32')
+                    y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4]
+                    y_true[l][j, i, k, 4] = 1.
+
+                    # lable-smooth
+                    if label_smooth:
+                        sigma = label_smooth_factor/(num_classes-1)
+                        y_true[l][j, i, k, 5:] = sigma
+                        y_true[l][j, i, k, 5+c] = 1-label_smooth_factor
+                    else:
+                        y_true[l][j, i, k, 5 + c] = 1.
+
+    # pad_gt_boxes for avoiding dynamic shape
+    pad_gt_box0 = np.zeros(shape=[max_boxes, 4], dtype=np.float32)
+    pad_gt_box1 = np.zeros(shape=[max_boxes, 4], dtype=np.float32)
+    pad_gt_box2 = np.zeros(shape=[max_boxes, 4], dtype=np.float32)
+
+    mask0 = np.reshape(y_true[0][..., 4:5], [-1])
+    gt_box0 = np.reshape(y_true[0][..., 0:4], [-1, 4])
+    # gt_box [boxes, [x,y,w,h]]
+    gt_box0 = gt_box0[mask0 == 1]
+    # gt_box0: get all boxes which have object
+    pad_gt_box0[:gt_box0.shape[0]] = gt_box0
+    # gt_box0.shape[0]: total number of boxes in gt_box0
+    # top N of pad_gt_box0 is real box, and after are pad by zero
+
+    mask1 = np.reshape(y_true[1][..., 4:5], [-1])
+    gt_box1 = np.reshape(y_true[1][..., 0:4], [-1, 4])
+    gt_box1 = gt_box1[mask1 == 1]
+    pad_gt_box1[:gt_box1.shape[0]] = gt_box1
+
+    mask2 = np.reshape(y_true[2][..., 4:5], [-1])
+    gt_box2 = np.reshape(y_true[2][..., 0:4], [-1, 4])
+
+    gt_box2 = gt_box2[mask2 == 1]
+    pad_gt_box2[:gt_box2.shape[0]] = gt_box2
+    return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2
+
+
+def _reshape_data(image, image_size):
+    """Reshape image."""
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    ori_w, ori_h = image.size
+    ori_image_shape = np.array([ori_w, ori_h], np.int32)
+    # original image shape fir:H sec:W
+    h, w = image_size
+    interp = get_interp_method(interp=9, sizes=(ori_h, ori_w, h, w))
+    image = image.resize((w, h), pil_image_reshape(interp))
+    image_data = statistic_normalize_img(image, statistic_norm=True)
+    if len(image_data.shape) == 2:
+        image_data = np.expand_dims(image_data, axis=-1)
+        image_data = np.concatenate([image_data, image_data, image_data], axis=-1)
+    image_data = image_data.astype(np.float32)
+    return image_data, ori_image_shape
+
+
+def color_distortion(img, hue, sat, val, device_num):
+    """Color distortion."""
+    hue = _rand(-hue, hue)
+    sat = _rand(1, sat) if _rand() < .5 else 1 / _rand(1, sat)
+    val = _rand(1, val) if _rand() < .5 else 1 / _rand(1, val)
+    if device_num != 1:
+        cv2.setNumThreads(1)
+    x = cv2.cvtColor(img, cv2.COLOR_RGB2HSV_FULL)
+    x = x / 255.
+    x[..., 0] += hue
+    x[..., 0][x[..., 0] > 1] -= 1
+    x[..., 0][x[..., 0] < 0] += 1
+    x[..., 1] *= sat
+    x[..., 2] *= val
+    x[x > 1] = 1
+    x[x < 0] = 0
+    x = x * 255.
+    x = x.astype(np.uint8)
+    image_data = cv2.cvtColor(x, cv2.COLOR_HSV2RGB_FULL)
+    return image_data
+
+
+def filp_pil_image(img):
+    return img.transpose(Image.FLIP_LEFT_RIGHT)
+
+
+def convert_gray_to_color(img):
+    if len(img.shape) == 2:
+        img = np.expand_dims(img, axis=-1)
+        img = np.concatenate([img, img, img], axis=-1)
+    return img
+
+
+def _is_iou_satisfied_constraint(min_iou, max_iou, box, crop_box):
+    iou = bbox_iou(box, crop_box)
+    return min_iou <= iou.min() and max_iou >= iou.max()
+
+
+def _choose_candidate_by_constraints(max_trial, input_w, input_h, image_w, image_h, jitter, box, use_constraints):
+    """Choose candidate by constraints."""
+    if use_constraints:
+        constraints = (
+            (0.1, None),
+            (0.3, None),
+            (0.5, None),
+            (0.7, None),
+            (0.9, None),
+            (None, 1),
+        )
+    else:
+        constraints = (
+            (None, None),
+        )
+    # add default candidate
+    candidates = [(0, 0, input_w, input_h)]
+    for constraint in constraints:
+        min_iou, max_iou = constraint
+        min_iou = -np.inf if min_iou is None else min_iou
+        max_iou = np.inf if max_iou is None else max_iou
+
+        for _ in range(max_trial):
+            # box_data should have at least one box
+            new_ar = float(input_w) / float(input_h) * _rand(1 - jitter, 1 + jitter) / _rand(1 - jitter, 1 + jitter)
+            scale = _rand(0.25, 2)
+
+            if new_ar < 1:
+                nh = int(scale * input_h)
+                nw = int(nh * new_ar)
+            else:
+                nw = int(scale * input_w)
+                nh = int(nw / new_ar)
+
+            dx = int(_rand(0, input_w - nw))
+            dy = int(_rand(0, input_h - nh))
+
+            if box.size > 0:
+                t_box = copy.deepcopy(box)
+                t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(image_w) + dx
+                t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(image_h) + dy
+
+                crop_box = np.array((0, 0, input_w, input_h))
+                if not _is_iou_satisfied_constraint(min_iou, max_iou, t_box, crop_box[np.newaxis]):
+                    continue
+                else:
+                    candidates.append((dx, dy, nw, nh))
+            else:
+                raise Exception("!!! annotation box is less than 1")
+    return candidates
+
+
+def _correct_bbox_by_candidates(candidates, input_w, input_h, image_w,
+                                image_h, flip, box, box_data, allow_outside_center):
+    """Calculate correct boxes."""
+    while candidates:
+        if len(candidates) > 1:
+            # ignore default candidate which do not crop
+            candidate = candidates.pop(np.random.randint(1, len(candidates)))
+        else:
+            candidate = candidates.pop(np.random.randint(0, len(candidates)))
+        dx, dy, nw, nh = candidate
+        t_box = copy.deepcopy(box)
+        t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(image_w) + dx
+        t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(image_h) + dy
+        if flip:
+            t_box[:, [0, 2]] = input_w - t_box[:, [2, 0]]
+
+        if allow_outside_center:
+            pass
+        else:
+            t_box = t_box[np.logical_and((t_box[:, 0] + t_box[:, 2])/2. >= 0., (t_box[:, 1] + t_box[:, 3])/2. >= 0.)]
+            t_box = t_box[np.logical_and((t_box[:, 0] + t_box[:, 2]) / 2. <= input_w,
+                                         (t_box[:, 1] + t_box[:, 3]) / 2. <= input_h)]
+
+        # recorrect x, y for case x,y < 0 reset to zero, after dx and dy, some box can smaller than zero
+        t_box[:, 0:2][t_box[:, 0:2] < 0] = 0
+        # recorrect w,h not higher than input size
+        t_box[:, 2][t_box[:, 2] > input_w] = input_w
+        t_box[:, 3][t_box[:, 3] > input_h] = input_h
+        box_w = t_box[:, 2] - t_box[:, 0]
+        box_h = t_box[:, 3] - t_box[:, 1]
+        # discard invalid box: w or h smaller than 1 pixel
+        t_box = t_box[np.logical_and(box_w > 1, box_h > 1)]
+
+        if t_box.shape[0] > 0:
+            # break if number of find t_box
+            box_data[: len(t_box)] = t_box
+            return box_data, candidate
+    raise Exception('all candidates can not satisfied re-correct bbox')
+
+
+def _data_aug(image, box, jitter, hue, sat, val, image_input_size, max_boxes,
+              anchors, num_classes, max_trial=10, device_num=1):
+    """Crop an image randomly with bounding box constraints.
+
+        This data augmentation is used in training of
+        Single Shot Multibox Detector [#]_. More details can be found in
+        data augmentation section of the original paper.
+        .. [#] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy,
+           Scott Reed, Cheng-Yang Fu, Alexander C. Berg.
+           SSD: Single Shot MultiBox Detector. ECCV 2016."""
+
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+
+    image_w, image_h = image.size
+    input_h, input_w = image_input_size
+
+    np.random.shuffle(box)
+    if len(box) > max_boxes:
+        box = box[:max_boxes]
+    flip = _rand() < .5
+    box_data = np.zeros((max_boxes, 5))
+
+    candidates = _choose_candidate_by_constraints(use_constraints=False,
+                                                  max_trial=max_trial,
+                                                  input_w=input_w,
+                                                  input_h=input_h,
+                                                  image_w=image_w,
+                                                  image_h=image_h,
+                                                  jitter=jitter,
+                                                  box=box)
+    box_data, candidate = _correct_bbox_by_candidates(candidates=candidates,
+                                                      input_w=input_w,
+                                                      input_h=input_h,
+                                                      image_w=image_w,
+                                                      image_h=image_h,
+                                                      flip=flip,
+                                                      box=box,
+                                                      box_data=box_data,
+                                                      allow_outside_center=True)
+    dx, dy, nw, nh = candidate
+    interp = get_interp_method(interp=10)
+    image = image.resize((nw, nh), pil_image_reshape(interp))
+    # place image, gray color as back graoud
+    new_image = Image.new('RGB', (input_w, input_h), (128, 128, 128))
+    new_image.paste(image, (dx, dy))
+    image = new_image
+
+    if flip:
+        image = filp_pil_image(image)
+
+    image = np.array(image)
+
+    image = convert_gray_to_color(image)
+
+    image_data = color_distortion(image, hue, sat, val, device_num)
+    image_data = statistic_normalize_img(image_data, statistic_norm=True)
+
+    image_data = image_data.astype(np.float32)
+
+    return image_data, box_data
+
+
+def preprocess_fn(image, box, config, input_size, device_num):
+    """Preprocess data function."""
+    config_anchors = config.anchor_scales
+    anchors = np.array([list(x) for x in config_anchors])
+    max_boxes = config.max_box
+    num_classes = config.num_classes
+    jitter = config.jitter
+    hue = config.hue
+    sat = config.saturation
+    val = config.value
+    image, anno = _data_aug(image, box, jitter=jitter, hue=hue, sat=sat, val=val,
+                            image_input_size=input_size, max_boxes=max_boxes,
+                            num_classes=num_classes, anchors=anchors, device_num=device_num)
+    return image, anno
+
+
+def reshape_fn(image, img_id, config):
+    input_size = config.test_img_shape
+    image, ori_image_shape = _reshape_data(image, image_size=input_size)
+    return image, ori_image_shape, img_id
+
+
+class MultiScaleTrans:
+    """Multi scale transform."""
+    def __init__(self, config, device_num):
+        self.config = config
+        self.seed = 0
+        self.size_list = []
+        self.resize_rate = config.resize_rate
+        self.dataset_size = config.dataset_size
+        self.size_dict = {}
+        self.seed_num = int(1e6)
+        self.seed_list = self.generate_seed_list(seed_num=self.seed_num)
+        self.resize_count_num = int(np.ceil(self.dataset_size / self.resize_rate))
+        self.device_num = device_num
+        self.anchor_scales = config.anchor_scales
+        self.num_classes = config.num_classes
+        self.max_box = config.max_box
+        self.label_smooth = config.label_smooth
+        self.label_smooth_factor = config.label_smooth_factor
+
+    def generate_seed_list(self, init_seed=1234, seed_num=int(1e6), seed_range=(1, 1000)):
+        seed_list = []
+        random.seed(init_seed)
+        for _ in range(seed_num):
+            seed = random.randint(seed_range[0], seed_range[1])
+            seed_list.append(seed)
+        return seed_list
+
+    def __call__(self, imgs, annos, x1, x2, x3, x4, x5, x6, batchInfo):
+        epoch_num = batchInfo.get_epoch_num()
+        size_idx = int(batchInfo.get_batch_num() / self.resize_rate)
+        seed_key = self.seed_list[(epoch_num * self.resize_count_num + size_idx) % self.seed_num]
+        ret_imgs = []
+        ret_annos = []
+
+        bbox1 = []
+        bbox2 = []
+        bbox3 = []
+        gt1 = []
+        gt2 = []
+        gt3 = []
+
+        if self.size_dict.get(seed_key, None) is None:
+            random.seed(seed_key)
+            new_size = random.choice(self.config.multi_scale)
+            self.size_dict[seed_key] = new_size
+        seed = seed_key
+
+        input_size = self.size_dict[seed]
+        for img, anno in zip(imgs, annos):
+            img, anno = preprocess_fn(img, anno, self.config, input_size, self.device_num)
+            ret_imgs.append(img.transpose(2, 0, 1).copy())
+            bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
+                _preprocess_true_boxes(true_boxes=anno, anchors=self.anchor_scales, in_shape=img.shape[0:2],
+                                       num_classes=self.num_classes, max_boxes=self.max_box,
+                                       label_smooth=self.label_smooth, label_smooth_factor=self.label_smooth_factor)
+            bbox1.append(bbox_true_1)
+            bbox2.append(bbox_true_2)
+            bbox3.append(bbox_true_3)
+            gt1.append(gt_box1)
+            gt2.append(gt_box2)
+            gt3.append(gt_box3)
+            ret_annos.append(0)
+        return np.array(ret_imgs), np.array(ret_annos), np.array(bbox1), np.array(bbox2), np.array(bbox3), \
+               np.array(gt1), np.array(gt2), np.array(gt3)
+
+
+def thread_batch_preprocess_true_box(annos, config, input_shape, result_index, batch_bbox_true_1, batch_bbox_true_2,
+                                     batch_bbox_true_3, batch_gt_box1, batch_gt_box2, batch_gt_box3):
+    """Preprocess true box for multi-thread."""
+    i = 0
+    for anno in annos:
+        bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
+            _preprocess_true_boxes(true_boxes=anno, anchors=config.anchor_scales, in_shape=input_shape,
+                                   num_classes=config.num_classes, max_boxes=config.max_box,
+                                   label_smooth=config.label_smooth, label_smooth_factor=config.label_smooth_factor)
+        batch_bbox_true_1[result_index + i] = bbox_true_1
+        batch_bbox_true_2[result_index + i] = bbox_true_2
+        batch_bbox_true_3[result_index + i] = bbox_true_3
+        batch_gt_box1[result_index + i] = gt_box1
+        batch_gt_box2[result_index + i] = gt_box2
+        batch_gt_box3[result_index + i] = gt_box3
+        i = i + 1
+
+
+def batch_preprocess_true_box(annos, config, input_shape):
+    """Preprocess true box with multi-thread."""
+    batch_bbox_true_1 = []
+    batch_bbox_true_2 = []
+    batch_bbox_true_3 = []
+    batch_gt_box1 = []
+    batch_gt_box2 = []
+    batch_gt_box3 = []
+    threads = []
+
+    step = 4
+    for index in range(0, len(annos), step):
+        for _ in range(step):
+            batch_bbox_true_1.append(None)
+            batch_bbox_true_2.append(None)
+            batch_bbox_true_3.append(None)
+            batch_gt_box1.append(None)
+            batch_gt_box2.append(None)
+            batch_gt_box3.append(None)
+        step_anno = annos[index: index + step]
+        t = threading.Thread(target=thread_batch_preprocess_true_box,
+                             args=(step_anno, config, input_shape, index, batch_bbox_true_1, batch_bbox_true_2,
+                                   batch_bbox_true_3, batch_gt_box1, batch_gt_box2, batch_gt_box3))
+        t.start()
+        threads.append(t)
+
+    for t in threads:
+        t.join()
+
+    return np.array(batch_bbox_true_1), np.array(batch_bbox_true_2), np.array(batch_bbox_true_3), \
+           np.array(batch_gt_box1), np.array(batch_gt_box2), np.array(batch_gt_box3)
+
+
+def batch_preprocess_true_box_single(annos, config, input_shape):
+    """Preprocess true boxes."""
+    batch_bbox_true_1 = []
+    batch_bbox_true_2 = []
+    batch_bbox_true_3 = []
+    batch_gt_box1 = []
+    batch_gt_box2 = []
+    batch_gt_box3 = []
+    for anno in annos:
+        bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
+            _preprocess_true_boxes(true_boxes=anno, anchors=config.anchor_scales, in_shape=input_shape,
+                                   num_classes=config.num_classes, max_boxes=config.max_box,
+                                   label_smooth=config.label_smooth, label_smooth_factor=config.label_smooth_factor)
+        batch_bbox_true_1.append(bbox_true_1)
+        batch_bbox_true_2.append(bbox_true_2)
+        batch_bbox_true_3.append(bbox_true_3)
+        batch_gt_box1.append(gt_box1)
+        batch_gt_box2.append(gt_box2)
+        batch_gt_box3.append(gt_box3)
+
+    return np.array(batch_bbox_true_1), np.array(batch_bbox_true_2), np.array(batch_bbox_true_3), \
+           np.array(batch_gt_box1), np.array(batch_gt_box2), np.array(batch_gt_box3)

examples/model_security/model_attacks/cv/yolov3_darknet53/src/util.py

@@ -0,0 +1,187 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Util class or function."""
+from mindspore.train.serialization import load_checkpoint
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+
+from .yolo import YoloLossBlock
+
+
+class AverageMeter:
+    """Computes and stores the average and current value"""
+
+    def __init__(self, name, fmt=':f', tb_writer=None):
+        self.name = name
+        self.fmt = fmt
+        self.reset()
+        self.tb_writer = tb_writer
+        self.cur_step = 1
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+        if self.tb_writer is not None:
+            self.tb_writer.add_scalar(self.name, self.val, self.cur_step)
+        self.cur_step += 1
+
+    def __str__(self):
+        fmtstr = '{name}:{avg' + self.fmt + '}'
+        return fmtstr.format(**self.__dict__)
+
+
+def load_backbone(net, ckpt_path, args):
+    """Load darknet53 backbone checkpoint."""
+    param_dict = load_checkpoint(ckpt_path)
+    yolo_backbone_prefix = 'feature_map.backbone'
+    darknet_backbone_prefix = 'network.backbone'
+    find_param = []
+    not_found_param = []
+    net.init_parameters_data()
+    for name, cell in net.cells_and_names():
+        if name.startswith(yolo_backbone_prefix):
+            name = name.replace(yolo_backbone_prefix, darknet_backbone_prefix)
+            if isinstance(cell, (nn.Conv2d, nn.Dense)):
+                darknet_weight = '{}.weight'.format(name)
+                darknet_bias = '{}.bias'.format(name)
+                if darknet_weight in param_dict:
+                    cell.weight.set_data(param_dict[darknet_weight].data)
+                    find_param.append(darknet_weight)
+                else:
+                    not_found_param.append(darknet_weight)
+                if darknet_bias in param_dict:
+                    cell.bias.set_data(param_dict[darknet_bias].data)
+                    find_param.append(darknet_bias)
+                else:
+                    not_found_param.append(darknet_bias)
+            elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
+                darknet_moving_mean = '{}.moving_mean'.format(name)
+                darknet_moving_variance = '{}.moving_variance'.format(name)
+                darknet_gamma = '{}.gamma'.format(name)
+                darknet_beta = '{}.beta'.format(name)
+                if darknet_moving_mean in param_dict:
+                    cell.moving_mean.set_data(param_dict[darknet_moving_mean].data)
+                    find_param.append(darknet_moving_mean)
+                else:
+                    not_found_param.append(darknet_moving_mean)
+                if darknet_moving_variance in param_dict:
+                    cell.moving_variance.set_data(param_dict[darknet_moving_variance].data)
+                    find_param.append(darknet_moving_variance)
+                else:
+                    not_found_param.append(darknet_moving_variance)
+                if darknet_gamma in param_dict:
+                    cell.gamma.set_data(param_dict[darknet_gamma].data)
+                    find_param.append(darknet_gamma)
+                else:
+                    not_found_param.append(darknet_gamma)
+                if darknet_beta in param_dict:
+                    cell.beta.set_data(param_dict[darknet_beta].data)
+                    find_param.append(darknet_beta)
+                else:
+                    not_found_param.append(darknet_beta)
+
+    args.logger.info('================found_param {}========='.format(len(find_param)))
+    args.logger.info(find_param)
+    args.logger.info('================not_found_param {}========='.format(len(not_found_param)))
+    args.logger.info(not_found_param)
+    args.logger.info('=====load {} successfully ====='.format(ckpt_path))
+
+    return net
+
+
+def default_wd_filter(x):
+    """default weight decay filter."""
+    parameter_name = x.name
+    if parameter_name.endswith('.bias'):
+        # all bias not using weight decay
+        return False
+    if parameter_name.endswith('.gamma'):
+        # bn weight bias not using weight decay, be carefully for now x not include BN
+        return False
+    if parameter_name.endswith('.beta'):
+        # bn weight bias not using weight decay, be carefully for now x not include BN
+        return False
+
+    return True
+
+
+def get_param_groups(network):
+    """Param groups for optimizer."""
+    decay_params = []
+    no_decay_params = []
+    for x in network.trainable_params():
+        parameter_name = x.name
+        if parameter_name.endswith('.bias'):
+            # all bias not using weight decay
+            no_decay_params.append(x)
+        elif parameter_name.endswith('.gamma'):
+            # bn weight bias not using weight decay, be carefully for now x not include BN
+            no_decay_params.append(x)
+        elif parameter_name.endswith('.beta'):
+            # bn weight bias not using weight decay, be carefully for now x not include BN
+            no_decay_params.append(x)
+        else:
+            decay_params.append(x)
+
+    return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
+
+
+class ShapeRecord:
+    """Log image shape."""
+    def __init__(self):
+        self.shape_record = {
+            320: 0,
+            352: 0,
+            384: 0,
+            416: 0,
+            448: 0,
+            480: 0,
+            512: 0,
+            544: 0,
+            576: 0,
+            608: 0,
+            'total': 0
+        }
+
+    def set(self, shape):
+        if len(shape) > 1:
+            shape = shape[0]
+        shape = int(shape)
+        self.shape_record[shape] += 1
+        self.shape_record['total'] += 1
+
+    def show(self, logger):
+        for key in self.shape_record:
+            rate = self.shape_record[key] / float(self.shape_record['total'])
+            logger.info('shape {}: {:.2f}%'.format(key, rate*100))
+
+
+def keep_loss_fp32(network):
+    """Keep loss of network with float32"""
+    for _, cell in network.cells_and_names():
+        if isinstance(cell, (YoloLossBlock,)):
+            cell.to_float(mstype.float32)

examples/model_security/model_attacks/cv/yolov3_darknet53/src/yolo.py

@@ -0,0 +1,441 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""YOLOv3 based on DarkNet."""
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore.common.tensor import Tensor
+from mindspore import context
+from mindspore.context import ParallelMode
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
+from mindspore.communication.management import get_group_size
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+
+from src.darknet import DarkNet, ResidualBlock
+from src.config import ConfigYOLOV3DarkNet53
+from src.loss import XYLoss, WHLoss, ConfidenceLoss, ClassLoss
+
+# pylint: disable=locally-disables, missing-docstring, invalid-name
+
+
+def _conv_bn_relu(in_channel,
+                  out_channel,
+                  ksize,
+                  stride=1,
+                  padding=0,
+                  dilation=1,
+                  alpha=0.1,
+                  momentum=0.9,
+                  eps=1e-5,
+                  pad_mode="same"):
+    """Get a conv2d batchnorm and relu layer"""
+    return nn.SequentialCell(
+        [nn.Conv2d(in_channel,
+                   out_channel,
+                   kernel_size=ksize,
+                   stride=stride,
+                   padding=padding,
+                   dilation=dilation,
+                   pad_mode=pad_mode),
+         nn.BatchNorm2d(out_channel, momentum=momentum, eps=eps),
+         nn.LeakyReLU(alpha)]
+    )
+
+
+class YoloBlock(nn.Cell):
+    """
+    YoloBlock for YOLOv3.
+
+    Args:
+        in_channels: Integer. Input channel.
+        out_chls: Interger. Middle channel.
+        out_channels: Integer. Output channel.
+
+    Returns:
+        Tuple, tuple of output tensor,(f1,f2,f3).
+
+    Examples:
+        YoloBlock(1024, 512, 255)
+
+    """
+    def __init__(self, in_channels, out_chls, out_channels):
+        super(YoloBlock, self).__init__()
+        out_chls_2 = out_chls*2
+
+        self.conv0 = _conv_bn_relu(in_channels, out_chls, ksize=1)
+        self.conv1 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
+
+        self.conv2 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
+        self.conv3 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
+
+        self.conv4 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
+        self.conv5 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
+
+        self.conv6 = nn.Conv2d(out_chls_2, out_channels, kernel_size=1, stride=1, has_bias=True)
+
+    def construct(self, x):
+        c1 = self.conv0(x)
+        c2 = self.conv1(c1)
+
+        c3 = self.conv2(c2)
+        c4 = self.conv3(c3)
+
+        c5 = self.conv4(c4)
+        c6 = self.conv5(c5)
+
+        out = self.conv6(c6)
+        return c5, out
+
+
+class YOLOv3(nn.Cell):
+    """
+     YOLOv3 Network.
+
+     Note:
+         backbone = darknet53
+
+     Args:
+         backbone_shape: List. Darknet output channels shape.
+         backbone: Cell. Backbone Network.
+         out_channel: Interger. Output channel.
+
+     Returns:
+         Tensor, output tensor.
+
+     Examples:
+         YOLOv3(backbone_shape=[64, 128, 256, 512, 1024]
+                backbone=darknet53(),
+                out_channel=255)
+     """
+    def __init__(self, backbone_shape, backbone, out_channel):
+        super(YOLOv3, self).__init__()
+        self.out_channel = out_channel
+        self.backbone = backbone
+        self.backblock0 = YoloBlock(backbone_shape[-1], out_chls=backbone_shape[-2], out_channels=out_channel)
+
+        self.conv1 = _conv_bn_relu(in_channel=backbone_shape[-2], out_channel=backbone_shape[-2]//2, ksize=1)
+        self.backblock1 = YoloBlock(in_channels=backbone_shape[-2]+backbone_shape[-3],
+                                    out_chls=backbone_shape[-3],
+                                    out_channels=out_channel)
+
+        self.conv2 = _conv_bn_relu(in_channel=backbone_shape[-3], out_channel=backbone_shape[-3]//2, ksize=1)
+        self.backblock2 = YoloBlock(in_channels=backbone_shape[-3]+backbone_shape[-4],
+                                    out_chls=backbone_shape[-4],
+                                    out_channels=out_channel)
+        self.concat = P.Concat(axis=1)
+
+    def construct(self, x):
+        # input_shape of x is (batch_size, 3, h, w)
+        # feature_map1 is (batch_size, backbone_shape[2], h/8, w/8)
+        # feature_map2 is (batch_size, backbone_shape[3], h/16, w/16)
+        # feature_map3 is (batch_size, backbone_shape[4], h/32, w/32)
+        img_hight = P.Shape()(x)[2]
+        img_width = P.Shape()(x)[3]
+        feature_map1, feature_map2, feature_map3 = self.backbone(x)
+        con1, big_object_output = self.backblock0(feature_map3)
+
+        con1 = self.conv1(con1)
+        ups1 = P.ResizeNearestNeighbor((img_hight / 16, img_width / 16))(con1)
+        con1 = self.concat((ups1, feature_map2))
+        con2, medium_object_output = self.backblock1(con1)
+
+        con2 = self.conv2(con2)
+        ups2 = P.ResizeNearestNeighbor((img_hight / 8, img_width / 8))(con2)
+        con3 = self.concat((ups2, feature_map1))
+        _, small_object_output = self.backblock2(con3)
+
+        return big_object_output, medium_object_output, small_object_output
+
+
+class DetectionBlock(nn.Cell):
+    """
+     YOLOv3 detection Network. It will finally output the detection result.
+
+     Args:
+         scale: Character.
+         config: ConfigYOLOV3DarkNet53, Configuration instance.
+         is_training: Bool, Whether train or not, default True.
+
+     Returns:
+         Tuple, tuple of output tensor,(f1,f2,f3).
+
+     Examples:
+         DetectionBlock(scale='l',stride=32)
+     """
+
+    def __init__(self, scale, config=ConfigYOLOV3DarkNet53(), is_training=True):
+        super(DetectionBlock, self).__init__()
+        self.config = config
+        if scale == 's':
+            idx = (0, 1, 2)
+        elif scale == 'm':
+            idx = (3, 4, 5)
+        elif scale == 'l':
+            idx = (6, 7, 8)
+        else:
+            raise KeyError("Invalid scale value for DetectionBlock")
+        self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
+        self.num_anchors_per_scale = 3
+        self.num_attrib = 4+1+self.config.num_classes
+        self.lambda_coord = 1
+
+        self.sigmoid = nn.Sigmoid()
+        self.reshape = P.Reshape()
+        self.tile = P.Tile()
+        self.concat = P.Concat(axis=-1)
+        self.conf_training = is_training
+
+    def construct(self, x, input_shape):
+        num_batch = P.Shape()(x)[0]
+        grid_size = P.Shape()(x)[2:4]
+
+        # Reshape and transpose the feature to [n, grid_size[0], grid_size[1], 3, num_attrib]
+        prediction = P.Reshape()(x, (num_batch,
+                                     self.num_anchors_per_scale,
+                                     self.num_attrib,
+                                     grid_size[0],
+                                     grid_size[1]))
+        prediction = P.Transpose()(prediction, (0, 3, 4, 1, 2))
+
+        range_x = range(grid_size[1])
+        range_y = range(grid_size[0])
+        grid_x = P.Cast()(F.tuple_to_array(range_x), ms.float32)
+        grid_y = P.Cast()(F.tuple_to_array(range_y), ms.float32)
+        # Tensor of shape [grid_size[0], grid_size[1], 1, 1] representing the coordinate of x/y axis for each grid
+        # [batch, gridx, gridy, 1, 1]
+        grid_x = self.tile(self.reshape(grid_x, (1, 1, -1, 1, 1)), (1, grid_size[0], 1, 1, 1))
+        grid_y = self.tile(self.reshape(grid_y, (1, -1, 1, 1, 1)), (1, 1, grid_size[1], 1, 1))
+        # Shape is [grid_size[0], grid_size[1], 1, 2]
+        grid = self.concat((grid_x, grid_y))
+
+        box_xy = prediction[:, :, :, :, :2]
+        box_wh = prediction[:, :, :, :, 2:4]
+        box_confidence = prediction[:, :, :, :, 4:5]
+        box_probs = prediction[:, :, :, :, 5:]
+
+        # gridsize1 is x
+        # gridsize0 is y
+        box_xy = (self.sigmoid(box_xy) + grid) / P.Cast()(F.tuple_to_array((grid_size[1], grid_size[0])), ms.float32)
+        # box_wh is w->h
+        box_wh = P.Exp()(box_wh) * self.anchors / input_shape
+        box_confidence = self.sigmoid(box_confidence)
+        box_probs = self.sigmoid(box_probs)
+
+        if self.conf_training:
+            return grid, prediction, box_xy, box_wh
+        return self.concat((box_xy, box_wh, box_confidence, box_probs))
+
+
+class Iou(nn.Cell):
+    """Calculate the iou of boxes"""
+    def __init__(self):
+        super(Iou, self).__init__()
+        self.min = P.Minimum()
+        self.max = P.Maximum()
+
+    def construct(self, box1, box2):
+        # box1: pred_box [batch, gx, gy, anchors, 1,      4] ->4: [x_center, y_center, w, h]
+        # box2: gt_box   [batch, 1,  1,  1,       maxbox, 4]
+        # convert to topLeft and rightDown
+        box1_xy = box1[:, :, :, :, :, :2]
+        box1_wh = box1[:, :, :, :, :, 2:4]
+        box1_mins = box1_xy - box1_wh / F.scalar_to_array(2.0) # topLeft
+        box1_maxs = box1_xy + box1_wh / F.scalar_to_array(2.0) # rightDown
+
+        box2_xy = box2[:, :, :, :, :, :2]
+        box2_wh = box2[:, :, :, :, :, 2:4]
+        box2_mins = box2_xy - box2_wh / F.scalar_to_array(2.0)
+        box2_maxs = box2_xy + box2_wh / F.scalar_to_array(2.0)
+
+        intersect_mins = self.max(box1_mins, box2_mins)
+        intersect_maxs = self.min(box1_maxs, box2_maxs)
+        intersect_wh = self.max(intersect_maxs - intersect_mins, F.scalar_to_array(0.0))
+        # P.squeeze: for effiecient slice
+        intersect_area = P.Squeeze(-1)(intersect_wh[:, :, :, :, :, 0:1]) * \
+                         P.Squeeze(-1)(intersect_wh[:, :, :, :, :, 1:2])
+        box1_area = P.Squeeze(-1)(box1_wh[:, :, :, :, :, 0:1]) * P.Squeeze(-1)(box1_wh[:, :, :, :, :, 1:2])
+        box2_area = P.Squeeze(-1)(box2_wh[:, :, :, :, :, 0:1]) * P.Squeeze(-1)(box2_wh[:, :, :, :, :, 1:2])
+        iou = intersect_area / (box1_area + box2_area - intersect_area)
+        # iou : [batch, gx, gy, anchors, maxboxes]
+        return iou
+
+
+class YoloLossBlock(nn.Cell):
+    """
+    Loss block cell of YOLOV3 network.
+    """
+    def __init__(self, scale, config=ConfigYOLOV3DarkNet53()):
+        super(YoloLossBlock, self).__init__()
+        self.config = config
+        if scale == 's':
+            # anchor mask
+            idx = (0, 1, 2)
+        elif scale == 'm':
+            idx = (3, 4, 5)
+        elif scale == 'l':
+            idx = (6, 7, 8)
+        else:
+            raise KeyError("Invalid scale value for DetectionBlock")
+        self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
+        self.ignore_threshold = Tensor(self.config.ignore_threshold, ms.float32)
+        self.concat = P.Concat(axis=-1)
+        self.iou = Iou()
+        self.reduce_max = P.ReduceMax(keep_dims=False)
+        self.xy_loss = XYLoss()
+        self.wh_loss = WHLoss()
+        self.confidenceLoss = ConfidenceLoss()
+        self.classLoss = ClassLoss()
+
+    def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box, input_shape):
+        # prediction : origin output from yolo
+        # pred_xy: (sigmoid(xy)+grid)/grid_size
+        # pred_wh: (exp(wh)*anchors)/input_shape
+        # y_true : after normalize
+        # gt_box: [batch, maxboxes, xyhw] after normalize
+
+        object_mask = y_true[:, :, :, :, 4:5]
+        class_probs = y_true[:, :, :, :, 5:]
+
+        grid_shape = P.Shape()(prediction)[1:3]
+        grid_shape = P.Cast()(F.tuple_to_array(grid_shape[::-1]), ms.float32)
+
+        pred_boxes = self.concat((pred_xy, pred_wh))
+        true_xy = y_true[:, :, :, :, :2] * grid_shape - grid
+        true_wh = y_true[:, :, :, :, 2:4]
+        true_wh = P.Select()(P.Equal()(true_wh, 0.0),
+                             P.Fill()(P.DType()(true_wh),
+                                      P.Shape()(true_wh), 1.0),
+                             true_wh)
+        true_wh = P.Log()(true_wh / self.anchors * input_shape)
+        # 2-w*h for large picture, use small scale, since small obj need more precise
+        box_loss_scale = 2 - y_true[:, :, :, :, 2:3] * y_true[:, :, :, :, 3:4]
+
+        gt_shape = P.Shape()(gt_box)
+        gt_box = P.Reshape()(gt_box, (gt_shape[0], 1, 1, 1, gt_shape[1], gt_shape[2]))
+
+        # add one more dimension for broadcast
+        iou = self.iou(P.ExpandDims()(pred_boxes, -2), gt_box)
+        # gt_box is x,y,h,w after normalize
+        # [batch, grid[0], grid[1], num_anchor, num_gt]
+        best_iou = self.reduce_max(iou, -1)
+        # [batch, grid[0], grid[1], num_anchor]
+
+        # ignore_mask IOU too small
+        ignore_mask = best_iou < self.ignore_threshold
+        ignore_mask = P.Cast()(ignore_mask, ms.float32)
+        ignore_mask = P.ExpandDims()(ignore_mask, -1)
+        # ignore_mask backpro will cause a lot maximunGrad and minimumGrad time consume.
+        # so we turn off its gradient
+        ignore_mask = F.stop_gradient(ignore_mask)
+
+        xy_loss = self.xy_loss(object_mask, box_loss_scale, prediction[:, :, :, :, :2], true_xy)
+        wh_loss = self.wh_loss(object_mask, box_loss_scale, prediction[:, :, :, :, 2:4], true_wh)
+        confidence_loss = self.confidenceLoss(object_mask, prediction[:, :, :, :, 4:5], ignore_mask)
+        class_loss = self.classLoss(object_mask, prediction[:, :, :, :, 5:], class_probs)
+        loss = xy_loss + wh_loss + confidence_loss + class_loss
+        batch_size = P.Shape()(prediction)[0]
+        return loss / batch_size
+
+
+class YOLOV3DarkNet53(nn.Cell):
+    """
+    Darknet based YOLOV3 network.
+
+    Args:
+        is_training: Bool. Whether train or not.
+
+    Returns:
+        Cell, cell instance of Darknet based YOLOV3 neural network.
+
+    Examples:
+        YOLOV3DarkNet53(True)
+    """
+
+    def __init__(self, is_training):
+        super(YOLOV3DarkNet53, self).__init__()
+        self.config = ConfigYOLOV3DarkNet53()
+
+        # YOLOv3 network
+        self.feature_map = YOLOv3(backbone=DarkNet(ResidualBlock, self.config.backbone_layers,
+                                                   self.config.backbone_input_shape,
+                                                   self.config.backbone_shape,
+                                                   detect=True),
+                                  backbone_shape=self.config.backbone_shape,
+                                  out_channel=self.config.out_channel)
+
+        # prediction on the default anchor boxes
+        self.detect_1 = DetectionBlock('l', is_training=is_training)
+        self.detect_2 = DetectionBlock('m', is_training=is_training)
+        self.detect_3 = DetectionBlock('s', is_training=is_training)
+
+    def construct(self, x, input_shape):
+        big_object_output, medium_object_output, small_object_output = self.feature_map(x)
+        output_big = self.detect_1(big_object_output, input_shape)
+        output_me = self.detect_2(medium_object_output, input_shape)
+        output_small = self.detect_3(small_object_output, input_shape)
+        # big is the final output which has smallest feature map
+        return output_big, output_me, output_small
+
+
+class YoloWithLossCell(nn.Cell):
+    """YOLOV3 loss."""
+    def __init__(self, network):
+        super(YoloWithLossCell, self).__init__()
+        self.yolo_network = network
+        self.config = ConfigYOLOV3DarkNet53()
+        self.loss_big = YoloLossBlock('l', self.config)
+        self.loss_me = YoloLossBlock('m', self.config)
+        self.loss_small = YoloLossBlock('s', self.config)
+
+    def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2, input_shape):
+        yolo_out = self.yolo_network(x, input_shape)
+        loss_l = self.loss_big(*yolo_out[0], y_true_0, gt_0, input_shape)
+        loss_m = self.loss_me(*yolo_out[1], y_true_1, gt_1, input_shape)
+        loss_s = self.loss_small(*yolo_out[2], y_true_2, gt_2, input_shape)
+        return loss_l + loss_m + loss_s
+
+
+class TrainingWrapper(nn.Cell):
+    """Training wrapper."""
+    def __init__(self, network, optimizer, sens=1.0):
+        super(TrainingWrapper, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_grad()
+        self.weights = optimizer.parameters
+        self.optimizer = optimizer
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
+        self.sens = sens
+        self.reducer_flag = False
+        self.grad_reducer = None
+        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
+        if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
+            self.reducer_flag = True
+        if self.reducer_flag:
+            mean = context.get_auto_parallel_context("gradients_mean")
+            if auto_parallel_context().get_device_num_is_set():
+                degree = context.get_auto_parallel_context("device_num")
+            else:
+                degree = get_group_size()
+            self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
+
+    def construct(self, *args):
+        weights = self.weights
+        loss = self.network(*args)
+        sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
+        grads = self.grad(self.network, weights)(*args, sens)
+        if self.reducer_flag:
+            grads = self.grad_reducer(grads)
+        return F.depend(loss, self.optimizer(grads))

examples/model_security/model_attacks/cv/yolov3_darknet53/src/yolo_dataset.py

@@ -0,0 +1,192 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""YOLOV3 dataset."""
+import os
+
+import multiprocessing
+import cv2
+from PIL import Image
+from pycocotools.coco import COCO
+import mindspore.dataset as de
+import mindspore.dataset.vision.c_transforms as CV
+
+from src.distributed_sampler import DistributedSampler
+from src.transforms import reshape_fn, MultiScaleTrans
+
+# pylint: disable=locally-disables, invalid-name
+
+
+min_keypoints_per_image = 10
+
+
+def _has_only_empty_bbox(anno):
+    return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno)
+
+
+def _count_visible_keypoints(anno):
+    return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno)
+
+
+def has_valid_annotation(anno):
+    """Check annotation file."""
+    # if it's empty, there is no annotation
+    if not anno:
+        return False
+    # if all boxes have close to zero area, there is no annotation
+    if _has_only_empty_bbox(anno):
+        return False
+    # keypoints task have a slight different critera for considering
+    # if an annotation is valid
+    if "keypoints" not in anno[0]:
+        return True
+    # for keypoint detection tasks, only consider valid images those
+    # containing at least min_keypoints_per_image
+    if _count_visible_keypoints(anno) >= min_keypoints_per_image:
+        return True
+    return False
+
+
+class COCOYoloDataset:
+    """YOLOV3 Dataset for COCO."""
+    def __init__(self, root, ann_file, remove_images_without_annotations=True,
+                 filter_crowd_anno=True, is_training=True):
+        self.coco = COCO(ann_file)
+        self.root = root
+        self.img_ids = list(sorted(self.coco.imgs.keys()))
+        self.filter_crowd_anno = filter_crowd_anno
+        self.is_training = is_training
+
+        # filter images without any annotations
+        if remove_images_without_annotations:
+            img_ids = []
+            for img_id in self.img_ids:
+                ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
+                anno = self.coco.loadAnns(ann_ids)
+                if has_valid_annotation(anno):
+                    img_ids.append(img_id)
+            self.img_ids = img_ids
+
+        self.categories = {cat["id"]: cat["name"] for cat in self.coco.cats.values()}
+
+        self.cat_ids_to_continuous_ids = {
+            v: i for i, v in enumerate(self.coco.getCatIds())
+        }
+        self.continuous_ids_cat_ids = {
+            v: k for k, v in self.cat_ids_to_continuous_ids.items()
+        }
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            (img, target) (tuple): target is a dictionary contains "bbox", "segmentation" or "keypoints",
+                generated by the image's annotation. img is a PIL image.
+        """
+        coco = self.coco
+        img_id = self.img_ids[index]
+        img_path = coco.loadImgs(img_id)[0]["file_name"]
+        img = Image.open(os.path.join(self.root, img_path)).convert("RGB")
+        if not self.is_training:
+            return img, img_id
+
+        ann_ids = coco.getAnnIds(imgIds=img_id)
+        target = coco.loadAnns(ann_ids)
+        # filter crowd annotations
+        if self.filter_crowd_anno:
+            annos = [anno for anno in target if anno["iscrowd"] == 0]
+        else:
+            annos = [anno for anno in target]
+
+        target = {}
+        boxes = [anno["bbox"] for anno in annos]
+        target["bboxes"] = boxes
+
+        classes = [anno["category_id"] for anno in annos]
+        classes = [self.cat_ids_to_continuous_ids[cl] for cl in classes]
+        target["labels"] = classes
+
+        bboxes = target['bboxes']
+        labels = target['labels']
+        out_target = []
+        for bbox, label in zip(bboxes, labels):
+            tmp = []
+            # convert to [x_min y_min x_max y_max]
+            bbox = self._convetTopDown(bbox)
+            tmp.extend(bbox)
+            tmp.append(int(label))
+            # tmp [x_min y_min x_max y_max, label]
+            out_target.append(tmp)
+        return img, out_target, [], [], [], [], [], []
+
+    def __len__(self):
+        return len(self.img_ids)
+
+    def _convetTopDown(self, bbox):
+        x_min = bbox[0]
+        y_min = bbox[1]
+        w = bbox[2]
+        h = bbox[3]
+        return [x_min, y_min, x_min+w, y_min+h]
+
+
+def create_yolo_dataset(image_dir, anno_path, batch_size, max_epoch, device_num, rank,
+                        config=None, is_training=True, shuffle=True):
+    """Create dataset for YOLOV3."""
+    cv2.setNumThreads(0)
+
+    if is_training:
+        filter_crowd = True
+        remove_empty_anno = True
+    else:
+        filter_crowd = False
+        remove_empty_anno = False
+
+    yolo_dataset = COCOYoloDataset(root=image_dir, ann_file=anno_path, filter_crowd_anno=filter_crowd,
+                                   remove_images_without_annotations=remove_empty_anno, is_training=is_training)
+    distributed_sampler = DistributedSampler(len(yolo_dataset), device_num, rank, shuffle=shuffle)
+    hwc_to_chw = CV.HWC2CHW()
+
+    config.dataset_size = len(yolo_dataset)
+    cores = multiprocessing.cpu_count()
+    num_parallel_workers = int(cores / device_num)
+    if is_training:
+        multi_scale_trans = MultiScaleTrans(config, device_num)
+        dataset_column_names = ["image", "annotation", "bbox1", "bbox2", "bbox3",
+                                "gt_box1", "gt_box2", "gt_box3"]
+        if device_num != 8:
+            ds = de.GeneratorDataset(yolo_dataset, column_names=dataset_column_names,
+                                     num_parallel_workers=min(32, num_parallel_workers),
+                                     sampler=distributed_sampler)
+            ds = ds.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=dataset_column_names,
+                          num_parallel_workers=min(32, num_parallel_workers), drop_remainder=True)
+        else:
+            ds = de.GeneratorDataset(yolo_dataset, column_names=dataset_column_names, sampler=distributed_sampler)
+            ds = ds.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=dataset_column_names,
+                          num_parallel_workers=min(8, num_parallel_workers), drop_remainder=True)
+    else:
+        ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "img_id"],
+                                 sampler=distributed_sampler)
+        compose_map_func = (lambda image, img_id: reshape_fn(image, img_id, config))
+        ds = ds.map(operations=compose_map_func, input_columns=["image", "img_id"],
+                    output_columns=["image", "image_shape", "img_id"],
+                    column_order=["image", "image_shape", "img_id"],
+                    num_parallel_workers=8)
+        ds = ds.map(operations=hwc_to_chw, input_columns=["image"], num_parallel_workers=8)
+        ds = ds.batch(batch_size, drop_remainder=True)
+    ds = ds.repeat(max_epoch)
+
+    return ds, len(yolo_dataset)