updata deeplabv3 CI

tests/st/networks/models/deeplabv3/src/__init__.py

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+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Init DeepLabv3."""
+from .deeplabv3 import ASPP, DeepLabV3, deeplabv3_resnet50
+from .backbone import *
+
+__all__ = [
+    "ASPP", "DeepLabV3", "deeplabv3_resnet50"
+]
+
+__all__.extend(backbone.__all__)

tests/st/networks/models/deeplabv3/src/backbone/__init__.py

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+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Init backbone."""
+from .resnet_deeplab import Subsample, DepthwiseConv2dNative, SpaceToBatch, BatchToSpace, ResNetV1, \
+    RootBlockBeta, resnet50_dl
+
+__all__ = [
+    "Subsample", "DepthwiseConv2dNative", "SpaceToBatch", "BatchToSpace", "ResNetV1", "RootBlockBeta", "resnet50_dl"
+]

tests/st/networks/models/deeplabv3/src/backbone/resnet_deeplab.py

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+# 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.
+# ============================================================================
+"""ResNet based DeepLab."""
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.initializer import initializer
+from mindspore._checkparam import twice
+from mindspore.common.parameter import Parameter
+
+
+def _conv_bn_relu(in_channel,
+                  out_channel,
+                  ksize,
+                  stride=1,
+                  padding=0,
+                  dilation=1,
+                  pad_mode="pad",
+                  use_batch_statistics=False):
+    """Get a conv2d -> batchnorm -> 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, use_batch_statistics=use_batch_statistics),
+         nn.ReLU()]
+    )
+
+
+def _deep_conv_bn_relu(in_channel,
+                       channel_multiplier,
+                       ksize,
+                       stride=1,
+                       padding=0,
+                       dilation=1,
+                       pad_mode="pad",
+                       use_batch_statistics=False):
+    """Get a spacetobatch -> conv2d -> batchnorm -> relu -> batchtospace layer"""
+    return nn.SequentialCell(
+        [DepthwiseConv2dNative(in_channel,
+                               channel_multiplier,
+                               kernel_size=ksize,
+                               stride=stride,
+                               padding=padding,
+                               dilation=dilation,
+                               pad_mode=pad_mode),
+         nn.BatchNorm2d(channel_multiplier * in_channel, use_batch_statistics=use_batch_statistics),
+         nn.ReLU()]
+    )
+
+
+def _stob_deep_conv_btos_bn_relu(in_channel,
+                                 channel_multiplier,
+                                 ksize,
+                                 space_to_batch_block_shape,
+                                 batch_to_space_block_shape,
+                                 paddings,
+                                 crops,
+                                 stride=1,
+                                 padding=0,
+                                 dilation=1,
+                                 pad_mode="pad",
+                                 use_batch_statistics=False):
+    """Get a spacetobatch -> conv2d -> batchnorm -> relu -> batchtospace layer"""
+    return nn.SequentialCell(
+        [SpaceToBatch(space_to_batch_block_shape, paddings),
+         DepthwiseConv2dNative(in_channel,
+                               channel_multiplier,
+                               kernel_size=ksize,
+                               stride=stride,
+                               padding=padding,
+                               dilation=dilation,
+                               pad_mode=pad_mode),
+         BatchToSpace(batch_to_space_block_shape, crops),
+         nn.BatchNorm2d(channel_multiplier * in_channel, use_batch_statistics=use_batch_statistics),
+         nn.ReLU()]
+    )
+
+
+def _stob_conv_btos_bn_relu(in_channel,
+                            out_channel,
+                            ksize,
+                            space_to_batch_block_shape,
+                            batch_to_space_block_shape,
+                            paddings,
+                            crops,
+                            stride=1,
+                            padding=0,
+                            dilation=1,
+                            pad_mode="pad",
+                            use_batch_statistics=False):
+    """Get a spacetobatch -> conv2d -> batchnorm -> relu -> batchtospace layer"""
+    return nn.SequentialCell([SpaceToBatch(space_to_batch_block_shape, paddings),
+                              nn.Conv2d(in_channel,
+                                        out_channel,
+                                        kernel_size=ksize,
+                                        stride=stride,
+                                        padding=padding,
+                                        dilation=dilation,
+                                        pad_mode=pad_mode),
+                              BatchToSpace(batch_to_space_block_shape, crops),
+                              nn.BatchNorm2d(out_channel, use_batch_statistics=use_batch_statistics),
+                              nn.ReLU()]
+                             )
+
+
+def _make_layer(block,
+                in_channels,
+                out_channels,
+                num_blocks,
+                stride=1,
+                rate=1,
+                multi_grads=None,
+                output_stride=None,
+                g_current_stride=2,
+                g_rate=1):
+    """Make layer for DeepLab-ResNet network."""
+    if multi_grads is None:
+        multi_grads = [1] * num_blocks
+    # (stride == 2, num_blocks == 4 --> strides == [1, 1, 1, 2])
+    strides = [1] * (num_blocks - 1) + [stride]
+    blocks = []
+    if output_stride is not None:
+        if output_stride % 4 != 0:
+            raise ValueError('The output_stride needs to be a multiple of 4.')
+        output_stride //= 4
+    for i_stride, _ in enumerate(strides):
+        if output_stride is not None and g_current_stride > output_stride:
+            raise ValueError('The target output_stride cannot be reached.')
+        if output_stride is not None and g_current_stride == output_stride:
+            b_rate = g_rate
+            b_stride = 1
+            g_rate *= strides[i_stride]
+        else:
+            b_rate = rate
+            b_stride = strides[i_stride]
+            g_current_stride *= strides[i_stride]
+        blocks.append(block(in_channels=in_channels,
+                            out_channels=out_channels,
+                            stride=b_stride,
+                            rate=b_rate,
+                            multi_grad=multi_grads[i_stride]))
+        in_channels = out_channels
+    layer = nn.SequentialCell(blocks)
+    return layer, g_current_stride, g_rate
+
+
+class Subsample(nn.Cell):
+    """
+    Subsample for DeepLab-ResNet.
+    Args:
+        factor (int): Sample factor.
+    Returns:
+        Tensor, the sub sampled tensor.
+    Examples:
+        >>> Subsample(2)
+    """
+    def __init__(self, factor):
+        super(Subsample, self).__init__()
+        self.factor = factor
+        self.pool = nn.MaxPool2d(kernel_size=1,
+                                 stride=factor)
+
+    def construct(self, x):
+        if self.factor == 1:
+            return x
+        return self.pool(x)
+
+
+class SpaceToBatch(nn.Cell):
+    def __init__(self, block_shape, paddings):
+        super(SpaceToBatch, self).__init__()
+        self.space_to_batch = P.SpaceToBatch(block_shape, paddings)
+        self.bs = block_shape
+        self.pd = paddings
+
+    def construct(self, x):
+        return self.space_to_batch(x)
+
+
+class BatchToSpace(nn.Cell):
+    def __init__(self, block_shape, crops):
+        super(BatchToSpace, self).__init__()
+        self.batch_to_space = P.BatchToSpace(block_shape, crops)
+        self.bs = block_shape
+        self.cr = crops
+
+    def construct(self, x):
+        return self.batch_to_space(x)
+
+
+class _DepthwiseConv2dNative(nn.Cell):
+    """Depthwise Conv2D Cell."""
+    def __init__(self,
+                 in_channels,
+                 channel_multiplier,
+                 kernel_size,
+                 stride,
+                 pad_mode,
+                 padding,
+                 dilation,
+                 group,
+                 weight_init):
+        super(_DepthwiseConv2dNative, self).__init__()
+        self.in_channels = in_channels
+        self.channel_multiplier = channel_multiplier
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.pad_mode = pad_mode
+        self.padding = padding
+        self.dilation = dilation
+        self.group = group
+        if not (isinstance(in_channels, int) and in_channels > 0):
+            raise ValueError('Attr \'in_channels\' of \'DepthwiseConv2D\' Op passed '
+                             + str(in_channels) + ', should be a int and greater than 0.')
+        if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \
+            (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
+                kernel_size[0] < 1 or kernel_size[1] < 1:
+            raise ValueError('Attr \'kernel_size\' of \'DepthwiseConv2D\' Op passed '
+                             + str(self.kernel_size) + ', should be a int or tuple and equal to or greater than 1.')
+        self.weight = Parameter(initializer(weight_init, [1, in_channels // group, *kernel_size]),
+                                name='weight')
+
+    def construct(self, *inputs):
+        """Must be overridden by all subclasses."""
+        raise NotImplementedError
+
+
+class DepthwiseConv2dNative(_DepthwiseConv2dNative):
+    """Depthwise Conv2D Cell."""
+    def __init__(self,
+                 in_channels,
+                 channel_multiplier,
+                 kernel_size,
+                 stride=1,
+                 pad_mode='same',
+                 padding=0,
+                 dilation=1,
+                 group=1,
+                 weight_init='normal'):
+        kernel_size = twice(kernel_size)
+        super(DepthwiseConv2dNative, self).__init__(
+            in_channels,
+            channel_multiplier,
+            kernel_size,
+            stride,
+            pad_mode,
+            padding,
+            dilation,
+            group,
+            weight_init)
+        self.depthwise_conv2d_native = P.DepthwiseConv2dNative(channel_multiplier=self.channel_multiplier,
+                                                               kernel_size=self.kernel_size,
+                                                               mode=3,
+                                                               pad_mode=self.pad_mode,
+                                                               pad=self.padding,
+                                                               stride=self.stride,
+                                                               dilation=self.dilation,
+                                                               group=self.group)
+
+    def set_strategy(self, strategy):
+        self.depthwise_conv2d_native.set_strategy(strategy)
+        return self
+
+    def construct(self, x):
+        return self.depthwise_conv2d_native(x, self.weight)
+
+
+class BottleneckV1(nn.Cell):
+    """
+    ResNet V1 BottleneckV1 block definition.
+    Args:
+        in_channels (int): Input channel.
+        out_channels (int): Output channel.
+        stride (int): Stride size for the initial convolutional layer. Default: 1.
+        rate (int): Rate for convolution. Default: 1.
+        multi_grad (int): Employ a rate within network. Default: 1.
+    Returns:
+        Tensor, the ResNet unit's output.
+    Examples:
+        >>> BottleneckV1(3,256,stride=2)
+    """
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 use_batch_statistics=False,
+                 use_batch_to_stob_and_btos=False):
+        super(BottleneckV1, self).__init__()
+        expansion = 4
+        mid_channels = out_channels // expansion
+        self.conv_bn1 = _conv_bn_relu(in_channels,
+                                      mid_channels,
+                                      ksize=1,
+                                      stride=1,
+                                      use_batch_statistics=use_batch_statistics)
+        self.conv_bn2 = _conv_bn_relu(mid_channels,
+                                      mid_channels,
+                                      ksize=3,
+                                      stride=stride,
+                                      padding=1,
+                                      dilation=1,
+                                      use_batch_statistics=use_batch_statistics)
+        if use_batch_to_stob_and_btos:
+            self.conv_bn2 = _stob_conv_btos_bn_relu(mid_channels,
+                                                    mid_channels,
+                                                    ksize=3,
+                                                    stride=stride,
+                                                    padding=0,
+                                                    dilation=1,
+                                                    space_to_batch_block_shape=2,
+                                                    batch_to_space_block_shape=2,
+                                                    paddings=[[2, 3], [2, 3]],
+                                                    crops=[[0, 1], [0, 1]],
+                                                    pad_mode="valid",
+                                                    use_batch_statistics=use_batch_statistics)
+
+        self.conv3 = nn.Conv2d(mid_channels,
+                               out_channels,
+                               kernel_size=1,
+                               stride=1)
+        self.bn3 = nn.BatchNorm2d(out_channels, use_batch_statistics=use_batch_statistics)
+        if in_channels != out_channels:
+            conv = nn.Conv2d(in_channels,
+                             out_channels,
+                             kernel_size=1,
+                             stride=stride)
+            bn = nn.BatchNorm2d(out_channels, use_batch_statistics=use_batch_statistics)
+            self.downsample = nn.SequentialCell([conv, bn])
+        else:
+            self.downsample = Subsample(stride)
+        self.add = P.TensorAdd()
+        self.relu = nn.ReLU()
+        self.Reshape = P.Reshape()
+
+    def construct(self, x):
+        out = self.conv_bn1(x)
+        out = self.conv_bn2(out)
+        out = self.bn3(self.conv3(out))
+        out = self.add(out, self.downsample(x))
+        out = self.relu(out)
+        return out
+
+
+class BottleneckV2(nn.Cell):
+    """
+    ResNet V2 Bottleneck variance V2 block definition.
+    Args:
+        in_channels (int): Input channel.
+        out_channels (int): Output channel.
+        stride (int): Stride size for the initial convolutional layer. Default: 1.
+    Returns:
+        Tensor, the ResNet unit's output.
+    Examples:
+        >>> BottleneckV2(3,256,stride=2)
+    """
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 use_batch_statistics=False,
+                 use_batch_to_stob_and_btos=False,
+                 dilation=1):
+        super(BottleneckV2, self).__init__()
+        expansion = 4
+        mid_channels = out_channels // expansion
+        self.conv_bn1 = _conv_bn_relu(in_channels,
+                                      mid_channels,
+                                      ksize=1,
+                                      stride=1,
+                                      use_batch_statistics=use_batch_statistics)
+        self.conv_bn2 = _conv_bn_relu(mid_channels,
+                                      mid_channels,
+                                      ksize=3,
+                                      stride=stride,
+                                      padding=1,
+                                      dilation=dilation,
+                                      use_batch_statistics=use_batch_statistics)
+        if use_batch_to_stob_and_btos:
+            self.conv_bn2 = _stob_conv_btos_bn_relu(mid_channels,
+                                                    mid_channels,
+                                                    ksize=3,
+                                                    stride=stride,
+                                                    padding=0,
+                                                    dilation=1,
+                                                    space_to_batch_block_shape=2,
+                                                    batch_to_space_block_shape=2,
+                                                    paddings=[[2, 3], [2, 3]],
+                                                    crops=[[0, 1], [0, 1]],
+                                                    pad_mode="valid",
+                                                    use_batch_statistics=use_batch_statistics)
+        self.conv3 = nn.Conv2d(mid_channels,
+                               out_channels,
+                               kernel_size=1,
+                               stride=1)
+        self.bn3 = nn.BatchNorm2d(out_channels, use_batch_statistics=use_batch_statistics)
+        if in_channels != out_channels:
+            conv = nn.Conv2d(in_channels,
+                             out_channels,
+                             kernel_size=1,
+                             stride=stride)
+            bn = nn.BatchNorm2d(out_channels, use_batch_statistics=use_batch_statistics)
+            self.downsample = nn.SequentialCell([conv, bn])
+        else:
+            self.downsample = Subsample(stride)
+        self.add = P.TensorAdd()
+        self.relu = nn.ReLU()
+
+    def construct(self, x):
+        out = self.conv_bn1(x)
+        out = self.conv_bn2(out)
+        out = self.bn3(self.conv3(out))
+        out = self.add(out, x)
+        out = self.relu(out)
+        return out
+
+
+class BottleneckV3(nn.Cell):
+    """
+    ResNet V1 Bottleneck variance V1 block definition.
+    Args:
+        in_channels (int): Input channel.
+        out_channels (int): Output channel.
+        stride (int): Stride size for the initial convolutional layer. Default: 1.
+    Returns:
+        Tensor, the ResNet unit's output.
+    Examples:
+        >>> BottleneckV3(3,256,stride=2)
+    """
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 use_batch_statistics=False):
+        super(BottleneckV3, self).__init__()
+        expansion = 4
+        mid_channels = out_channels // expansion
+        self.conv_bn1 = _conv_bn_relu(in_channels,
+                                      mid_channels,
+                                      ksize=1,
+                                      stride=1,
+                                      use_batch_statistics=use_batch_statistics)
+        self.conv_bn2 = _conv_bn_relu(mid_channels,
+                                      mid_channels,
+                                      ksize=3,
+                                      stride=stride,
+                                      padding=1,
+                                      dilation=1,
+                                      use_batch_statistics=use_batch_statistics)
+        self.conv3 = nn.Conv2d(mid_channels,
+                               out_channels,
+                               kernel_size=1,
+                               stride=1)
+        self.bn3 = nn.BatchNorm2d(out_channels, use_batch_statistics=use_batch_statistics)
+
+        if in_channels != out_channels:
+            conv = nn.Conv2d(in_channels,
+                             out_channels,
+                             kernel_size=1,
+                             stride=stride)
+            bn = nn.BatchNorm2d(out_channels, use_batch_statistics=use_batch_statistics)
+            self.downsample = nn.SequentialCell([conv, bn])
+        else:
+            self.downsample = Subsample(stride)
+        self.downsample = Subsample(stride)
+        self.add = P.TensorAdd()
+        self.relu = nn.ReLU()
+
+    def construct(self, x):
+        out = self.conv_bn1(x)
+        out = self.conv_bn2(out)
+        out = self.bn3(self.conv3(out))
+        out = self.add(out, self.downsample(x))
+        out = self.relu(out)
+        return out
+
+
+class ResNetV1(nn.Cell):
+    """
+    ResNet V1 for DeepLab.
+    Args:
+    Returns:
+        Tuple, output tensor tuple, (c2,c5).
+    Examples:
+        >>> ResNetV1(False)
+    """
+    def __init__(self, fine_tune_batch_norm=False):
+        super(ResNetV1, self).__init__()
+        self.layer_root = nn.SequentialCell(
+            [RootBlockBeta(fine_tune_batch_norm),
+             nn.MaxPool2d(kernel_size=(3, 3),
+                          stride=(2, 2),
+                          pad_mode='same')])
+        self.layer1_1 = BottleneckV1(128, 256, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer1_2 = BottleneckV2(256, 256, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer1_3 = BottleneckV3(256, 256, stride=2, use_batch_statistics=fine_tune_batch_norm)
+        self.layer2_1 = BottleneckV1(256, 512, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer2_2 = BottleneckV2(512, 512, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer2_3 = BottleneckV2(512, 512, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer2_4 = BottleneckV3(512, 512, stride=2, use_batch_statistics=fine_tune_batch_norm)
+        self.layer3_1 = BottleneckV1(512, 1024, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer3_2 = BottleneckV2(1024, 1024, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer3_3 = BottleneckV2(1024, 1024, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer3_4 = BottleneckV2(1024, 1024, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer3_5 = BottleneckV2(1024, 1024, stride=1, use_batch_statistics=fine_tune_batch_norm)
+        self.layer3_6 = BottleneckV2(1024, 1024, stride=1, use_batch_statistics=fine_tune_batch_norm)
+
+        self.layer4_1 = BottleneckV1(1024, 2048, stride=1, use_batch_to_stob_and_btos=True,
+                                     use_batch_statistics=fine_tune_batch_norm)
+        self.layer4_2 = BottleneckV2(2048, 2048, stride=1, use_batch_to_stob_and_btos=True,
+                                     use_batch_statistics=fine_tune_batch_norm)
+        self.layer4_3 = BottleneckV2(2048, 2048, stride=1, use_batch_to_stob_and_btos=True,
+                                     use_batch_statistics=fine_tune_batch_norm)
+
+    def construct(self, x):
+        x = self.layer_root(x)
+        x = self.layer1_1(x)
+        c2 = self.layer1_2(x)
+        x = self.layer1_3(c2)
+        x = self.layer2_1(x)
+        x = self.layer2_2(x)
+        x = self.layer2_3(x)
+        x = self.layer2_4(x)
+        x = self.layer3_1(x)
+        x = self.layer3_2(x)
+        x = self.layer3_3(x)
+        x = self.layer3_4(x)
+        x = self.layer3_5(x)
+        x = self.layer3_6(x)
+
+        x = self.layer4_1(x)
+        x = self.layer4_2(x)
+        c5 = self.layer4_3(x)
+        return c2, c5
+
+
+class RootBlockBeta(nn.Cell):
+    """
+    ResNet V1 beta root block definition.
+    Returns:
+        Tensor, the block unit's output.
+    Examples:
+        >>> RootBlockBeta()
+    """
+    def __init__(self, fine_tune_batch_norm=False):
+        super(RootBlockBeta, self).__init__()
+        self.conv1 = _conv_bn_relu(3, 64, ksize=3, stride=2, padding=0, pad_mode="valid",
+                                   use_batch_statistics=fine_tune_batch_norm)
+        self.conv2 = _conv_bn_relu(64, 64, ksize=3, stride=1, padding=0, pad_mode="same",
+                                   use_batch_statistics=fine_tune_batch_norm)
+        self.conv3 = _conv_bn_relu(64, 128, ksize=3, stride=1, padding=0, pad_mode="same",
+                                   use_batch_statistics=fine_tune_batch_norm)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        return x
+
+
+def resnet50_dl(fine_tune_batch_norm=False):
+    return ResNetV1(fine_tune_batch_norm)

tests/st/networks/models/deeplabv3/src/config.py

@@ -0,0 +1,38 @@
+# 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.
+# ============================================================================
+"""
+network config setting, will be used in train.py and evaluation.py
+"""
+from easydict import EasyDict as ed
+
+config = ed({
+    "learning_rate": 0.0014,
+    "weight_decay": 0.00005,
+    "momentum": 0.97,
+    "crop_size": 513,
+    "eval_scales": [0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+    "atrous_rates": None,
+    "image_pyramid": None,
+    "output_stride": 16,
+    "fine_tune_batch_norm": False,
+    "ignore_label": 255,
+    "decoder_output_stride": None,
+    "seg_num_classes": 21,
+    "epoch_size": 6,
+    "batch_size": 2,
+    "enable_save_ckpt": True,
+    "save_checkpoint_steps": 10000,
+    "save_checkpoint_num": 1
+})

tests/st/networks/models/deeplabv3/src/deeplabv3.py

@@ -0,0 +1,457 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""DeepLabv3."""
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from .backbone.resnet_deeplab import _conv_bn_relu, resnet50_dl, _deep_conv_bn_relu, \
+    DepthwiseConv2dNative, SpaceToBatch, BatchToSpace
+
+
+class ASPPSampleBlock(nn.Cell):
+    """ASPP sample block."""
+    def __init__(self, feature_shape, scale_size, output_stride):
+        super(ASPPSampleBlock, self).__init__()
+        sample_h = (feature_shape[0] * scale_size + 1) / output_stride + 1
+        sample_w = (feature_shape[1] * scale_size + 1) / output_stride + 1
+        self.sample = P.ResizeBilinear((int(sample_h), int(sample_w)), align_corners=True)
+
+    def construct(self, x):
+        return self.sample(x)
+
+
+class ASPP(nn.Cell):
+    """
+    ASPP model for DeepLabv3.
+
+    Args:
+        channel (int): Input channel.
+        depth (int): Output channel.
+        feature_shape (list): The shape of feature,[h,w].
+        scale_sizes (list): Input scales for multi-scale feature extraction.
+        atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+        output_stride (int): 'The ratio of input to output spatial resolution.'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ASPP(channel=2048,256,[14,14],[1],[6],16)
+    """
+    def __init__(self, channel, depth, feature_shape, scale_sizes,
+                 atrous_rates, output_stride, fine_tune_batch_norm=False):
+        super(ASPP, self).__init__()
+        self.aspp0 = _conv_bn_relu(channel,
+                                   depth,
+                                   ksize=1,
+                                   stride=1,
+                                   use_batch_statistics=fine_tune_batch_norm)
+        self.atrous_rates = []
+        if atrous_rates is not None:
+            self.atrous_rates = atrous_rates
+            self.aspp_pointwise = _conv_bn_relu(channel,
+                                                depth,
+                                                ksize=1,
+                                                stride=1,
+                                                use_batch_statistics=fine_tune_batch_norm)
+            self.aspp_depth_depthwiseconv = DepthwiseConv2dNative(channel,
+                                                                  channel_multiplier=1,
+                                                                  kernel_size=3,
+                                                                  stride=1,
+                                                                  dilation=1,
+                                                                  pad_mode="valid")
+            self.aspp_depth_bn = nn.BatchNorm2d(1 * channel, use_batch_statistics=fine_tune_batch_norm)
+            self.aspp_depth_relu = nn.ReLU()
+            self.aspp_depths = []
+            self.aspp_depth_spacetobatchs = []
+            self.aspp_depth_batchtospaces = []
+
+            for scale_size in scale_sizes:
+                aspp_scale_depth_size = np.ceil((feature_shape[0]*scale_size)/16)
+                if atrous_rates is None:
+                    break
+                for rate in atrous_rates:
+                    padding = 0
+                    for j in range(100):
+                        padded_size = rate * j
+                        if padded_size >= aspp_scale_depth_size + 2 * rate:
+                            padding = padded_size - aspp_scale_depth_size - 2 * rate
+                            break
+                    paddings = [[rate, rate + int(padding)],
+                                [rate, rate + int(padding)]]
+                    self.aspp_depth_spacetobatch = SpaceToBatch(rate, paddings)
+                    self.aspp_depth_spacetobatchs.append(self.aspp_depth_spacetobatch)
+                    crops = [[0, int(padding)], [0, int(padding)]]
+                    self.aspp_depth_batchtospace = BatchToSpace(rate, crops)
+                    self.aspp_depth_batchtospaces.append(self.aspp_depth_batchtospace)
+            self.aspp_depths = nn.CellList(self.aspp_depths)
+            self.aspp_depth_spacetobatchs = nn.CellList(self.aspp_depth_spacetobatchs)
+            self.aspp_depth_batchtospaces = nn.CellList(self.aspp_depth_batchtospaces)
+
+        self.global_pooling = nn.AvgPool2d(kernel_size=(int(feature_shape[0]), int(feature_shape[1])))
+        self.global_poolings = []
+        for scale_size in scale_sizes:
+            pooling_h = np.ceil((feature_shape[0]*scale_size)/output_stride)
+            pooling_w = np.ceil((feature_shape[0]*scale_size)/output_stride)
+            self.global_poolings.append(nn.AvgPool2d(kernel_size=(int(pooling_h), int(pooling_w))))
+        self.global_poolings = nn.CellList(self.global_poolings)
+        self.conv_bn = _conv_bn_relu(channel,
+                                     depth,
+                                     ksize=1,
+                                     stride=1,
+                                     use_batch_statistics=fine_tune_batch_norm)
+        self.samples = []
+        for scale_size in scale_sizes:
+            self.samples.append(ASPPSampleBlock(feature_shape, scale_size, output_stride))
+        self.samples = nn.CellList(self.samples)
+        self.feature_shape = feature_shape
+        self.concat = P.Concat(axis=1)
+
+    def construct(self, x, scale_index=0):
+        aspp0 = self.aspp0(x)
+        aspp1 = self.global_poolings[scale_index](x)
+        aspp1 = self.conv_bn(aspp1)
+        aspp1 = self.samples[scale_index](aspp1)
+        output = self.concat((aspp1, aspp0))
+
+        for i in range(len(self.atrous_rates)):
+            aspp_i = self.aspp_depth_spacetobatchs[i + scale_index * len(self.atrous_rates)](x)
+            aspp_i = self.aspp_depth_depthwiseconv(aspp_i)
+            aspp_i = self.aspp_depth_batchtospaces[i + scale_index * len(self.atrous_rates)](aspp_i)
+            aspp_i = self.aspp_depth_bn(aspp_i)
+            aspp_i = self.aspp_depth_relu(aspp_i)
+            aspp_i = self.aspp_pointwise(aspp_i)
+            output = self.concat((output, aspp_i))
+        return output
+
+
+class DecoderSampleBlock(nn.Cell):
+    """Decoder sample block."""
+    def __init__(self, feature_shape, scale_size=1.0, decoder_output_stride=4):
+        super(DecoderSampleBlock, self).__init__()
+        sample_h = (feature_shape[0] * scale_size + 1) / decoder_output_stride + 1
+        sample_w = (feature_shape[1] * scale_size + 1) / decoder_output_stride + 1
+        self.sample = P.ResizeBilinear((int(sample_h), int(sample_w)), align_corners=True)
+
+    def construct(self, x):
+        return self.sample(x)
+
+
+class Decoder(nn.Cell):
+    """
+    Decode module for DeepLabv3.
+    Args:
+        low_level_channel (int): Low level input channel
+        channel (int): Input channel.
+        depth (int): Output channel.
+        feature_shape (list): 'Input image shape, [N,C,H,W].'
+        scale_sizes (list): 'Input scales for multi-scale feature extraction.'
+        decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+    Returns:
+        Tensor, output tensor.
+    Examples:
+        >>> Decoder(256, 100, [56,56])
+    """
+    def __init__(self,
+                 low_level_channel,
+                 channel,
+                 depth,
+                 feature_shape,
+                 scale_sizes,
+                 decoder_output_stride,
+                 fine_tune_batch_norm):
+        super(Decoder, self).__init__()
+        self.feature_projection = _conv_bn_relu(low_level_channel, 48, ksize=1, stride=1,
+                                                pad_mode="same", use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_depth0 = _deep_conv_bn_relu(channel + 48,
+                                                 channel_multiplier=1,
+                                                 ksize=3,
+                                                 stride=1,
+                                                 pad_mode="same",
+                                                 dilation=1,
+                                                 use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_pointwise0 = _conv_bn_relu(channel + 48,
+                                                depth,
+                                                ksize=1,
+                                                stride=1,
+                                                use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_depth1 = _deep_conv_bn_relu(depth,
+                                                 channel_multiplier=1,
+                                                 ksize=3,
+                                                 stride=1,
+                                                 pad_mode="same",
+                                                 dilation=1,
+                                                 use_batch_statistics=fine_tune_batch_norm)
+        self.decoder_pointwise1 = _conv_bn_relu(depth,
+                                                depth,
+                                                ksize=1,
+                                                stride=1,
+                                                use_batch_statistics=fine_tune_batch_norm)
+        self.depth = depth
+        self.concat = P.Concat(axis=1)
+        self.samples = []
+        for scale_size in scale_sizes:
+            self.samples.append(DecoderSampleBlock(feature_shape, scale_size, decoder_output_stride))
+        self.samples = nn.CellList(self.samples)
+
+    def construct(self, x, low_level_feature, scale_index):
+        low_level_feature = self.feature_projection(low_level_feature)
+        low_level_feature = self.samples[scale_index](low_level_feature)
+        x = self.samples[scale_index](x)
+        output = self.concat((x, low_level_feature))
+        output = self.decoder_depth0(output)
+        output = self.decoder_pointwise0(output)
+        output = self.decoder_depth1(output)
+        output = self.decoder_pointwise1(output)
+        return output
+
+
+class SingleDeepLabV3(nn.Cell):
+    """
+    DeepLabv3 Network.
+    Args:
+        num_classes (int): Class number.
+        feature_shape (list): Input image shape, [N,C,H,W].
+        backbone (Cell): Backbone Network.
+        channel (int): Resnet output channel.
+        depth (int): ASPP block depth.
+        scale_sizes (list): Input scales for multi-scale feature extraction.
+        atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+        decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+        output_stride (int): 'The ratio of input to output spatial resolution.'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+    Returns:
+        Tensor, output tensor.
+    Examples:
+        >>> SingleDeepLabV3(num_classes=10,
+            >>>           feature_shape=[1,3,224,224],
+            >>>           backbone=resnet50_dl(),
+            >>>           channel=2048,
+            >>>           depth=256)
+            >>>           scale_sizes=[1.0])
+            >>>           atrous_rates=[6])
+            >>>           decoder_output_stride=4)
+            >>>           output_stride=16)
+        """
+
+    def __init__(self,
+                 num_classes,
+                 feature_shape,
+                 backbone,
+                 channel,
+                 depth,
+                 scale_sizes,
+                 atrous_rates,
+                 decoder_output_stride,
+                 output_stride,
+                 fine_tune_batch_norm=False):
+        super(SingleDeepLabV3, self).__init__()
+        self.num_classes = num_classes
+        self.channel = channel
+        self.depth = depth
+        self.scale_sizes = []
+        for scale_size in np.sort(scale_sizes):
+            self.scale_sizes.append(scale_size)
+        self.net = backbone
+        self.aspp = ASPP(channel=self.channel,
+                         depth=self.depth,
+                         feature_shape=[feature_shape[2],
+                                        feature_shape[3]],
+                         scale_sizes=self.scale_sizes,
+                         atrous_rates=atrous_rates,
+                         output_stride=output_stride,
+                         fine_tune_batch_norm=fine_tune_batch_norm)
+        self.aspp.add_flags(loop_can_unroll=True)
+        atrous_rates_len = 0
+        if atrous_rates is not None:
+            atrous_rates_len = len(atrous_rates)
+        self.fc1 = _conv_bn_relu(depth * (2 + atrous_rates_len), depth,
+                                 ksize=1,
+                                 stride=1,
+                                 use_batch_statistics=fine_tune_batch_norm)
+        self.fc2 = nn.Conv2d(depth,
+                             num_classes,
+                             kernel_size=1,
+                             stride=1,
+                             has_bias=True)
+        self.upsample = P.ResizeBilinear((int(feature_shape[2]),
+                                          int(feature_shape[3])),
+                                         align_corners=True)
+        self.samples = []
+        for scale_size in self.scale_sizes:
+            self.samples.append(SampleBlock(feature_shape, scale_size))
+        self.samples = nn.CellList(self.samples)
+        self.feature_shape = [float(feature_shape[0]), float(feature_shape[1]), float(feature_shape[2]),
+                              float(feature_shape[3])]
+
+        self.pad = P.Pad(((0, 0), (0, 0), (1, 1), (1, 1)))
+        self.dropout = nn.Dropout(keep_prob=0.9)
+        self.shape = P.Shape()
+        self.decoder_output_stride = decoder_output_stride
+        if decoder_output_stride is not None:
+            self.decoder = Decoder(low_level_channel=depth,
+                                   channel=depth,
+                                   depth=depth,
+                                   feature_shape=[feature_shape[2],
+                                                  feature_shape[3]],
+                                   scale_sizes=self.scale_sizes,
+                                   decoder_output_stride=decoder_output_stride,
+                                   fine_tune_batch_norm=fine_tune_batch_norm)
+
+    def construct(self, x, scale_index=0):
+        x = (2.0 / 255.0) * x - 1.0
+        x = self.pad(x)
+        low_level_feature, feature_map = self.net(x)
+        for scale_size in self.scale_sizes:
+            if scale_size * self.feature_shape[2] + 1.0 >= self.shape(x)[2] - 2:
+                output = self.aspp(feature_map, scale_index)
+                output = self.fc1(output)
+                if self.decoder_output_stride is not None:
+                    output = self.decoder(output, low_level_feature, scale_index)
+                output = self.fc2(output)
+                output = self.samples[scale_index](output)
+                return output
+            scale_index += 1
+        return feature_map
+
+
+class SampleBlock(nn.Cell):
+    """Sample block."""
+    def __init__(self,
+                 feature_shape,
+                 scale_size=1.0):
+        super(SampleBlock, self).__init__()
+        sample_h = np.ceil(float(feature_shape[2]) * scale_size)
+        sample_w = np.ceil(float(feature_shape[3]) * scale_size)
+        self.sample = P.ResizeBilinear((int(sample_h), int(sample_w)), align_corners=True)
+
+    def construct(self, x):
+        return self.sample(x)
+
+
+class DeepLabV3(nn.Cell):
+    """DeepLabV3 model."""
+    def __init__(self, num_classes, feature_shape, backbone, channel, depth, infer_scale_sizes, atrous_rates,
+                 decoder_output_stride, output_stride, fine_tune_batch_norm, image_pyramid):
+        super(DeepLabV3, self).__init__()
+        self.infer_scale_sizes = []
+        if infer_scale_sizes is not None:
+            self.infer_scale_sizes = infer_scale_sizes
+
+        self.infer_scale_sizes = infer_scale_sizes
+        if image_pyramid is None:
+            image_pyramid = [1.0]
+
+        self.image_pyramid = image_pyramid
+        scale_sizes = []
+        for pyramid in image_pyramid:
+            scale_sizes.append(pyramid)
+        for scale in infer_scale_sizes:
+            scale_sizes.append(scale)
+        self.samples = []
+        for scale_size in scale_sizes:
+            self.samples.append(SampleBlock(feature_shape, scale_size))
+        self.samples = nn.CellList(self.samples)
+        self.deeplabv3 = SingleDeepLabV3(num_classes=num_classes,
+                                         feature_shape=feature_shape,
+                                         backbone=resnet50_dl(fine_tune_batch_norm),
+                                         channel=channel,
+                                         depth=depth,
+                                         scale_sizes=scale_sizes,
+                                         atrous_rates=atrous_rates,
+                                         decoder_output_stride=decoder_output_stride,
+                                         output_stride=output_stride,
+                                         fine_tune_batch_norm=fine_tune_batch_norm)
+        self.softmax = P.Softmax(axis=1)
+        self.concat = P.Concat(axis=2)
+        self.expand_dims = P.ExpandDims()
+        self.reduce_mean = P.ReduceMean()
+        self.sample_common = P.ResizeBilinear((int(feature_shape[2]),
+                                               int(feature_shape[3])),
+                                              align_corners=True)
+
+    def construct(self, x):
+        logits = ()
+        if self.training:
+            if len(self.image_pyramid) >= 1:
+                if self.image_pyramid[0] == 1:
+                    logits = self.deeplabv3(x)
+                else:
+                    x1 = self.samples[0](x)
+                    logits = self.deeplabv3(x1)
+                    logits = self.sample_common(logits)
+                logits = self.expand_dims(logits, 2)
+                for i in range(len(self.image_pyramid) - 1):
+                    x_i = self.samples[i + 1](x)
+                    logits_i = self.deeplabv3(x_i)
+                    logits_i = self.sample_common(logits_i)
+                    logits_i = self.expand_dims(logits_i, 2)
+                    logits = self.concat((logits, logits_i))
+            logits = self.reduce_mean(logits, 2)
+            return logits
+        if len(self.infer_scale_sizes) >= 1:
+            infer_index = len(self.image_pyramid)
+            x1 = self.samples[infer_index](x)
+            logits = self.deeplabv3(x1)
+            logits = self.sample_common(logits)
+            logits = self.softmax(logits)
+            logits = self.expand_dims(logits, 2)
+            for i in range(len(self.infer_scale_sizes) - 1):
+                x_i = self.samples[i + 1 + infer_index](x)
+                logits_i = self.deeplabv3(x_i)
+                logits_i = self.sample_common(logits_i)
+                logits_i = self.softmax(logits_i)
+                logits_i = self.expand_dims(logits_i, 2)
+                logits = self.concat((logits, logits_i))
+        logits = self.reduce_mean(logits, 2)
+        return logits
+
+
+def deeplabv3_resnet50(num_classes, feature_shape, image_pyramid,
+                       infer_scale_sizes, atrous_rates=None, decoder_output_stride=None,
+                       output_stride=16, fine_tune_batch_norm=False):
+    """
+    ResNet50 based DeepLabv3 network.
+
+    Args:
+        num_classes (int): Class number.
+        feature_shape (list): Input image shape, [N,C,H,W].
+        image_pyramid (list): Input scales for multi-scale feature extraction.
+        atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+        infer_scale_sizes (list): 'The scales to resize images for inference.
+        decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+        output_stride (int): 'The ratio of input to output spatial resolution.'
+        fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+
+    Returns:
+        Cell, cell instance of ResNet50 based DeepLabv3 neural network.
+
+    Examples:
+        >>> deeplabv3_resnet50(100, [1,3,224,224],[1.0],[1.0])
+    """
+    return DeepLabV3(num_classes=num_classes,
+                     feature_shape=feature_shape,
+                     backbone=resnet50_dl(fine_tune_batch_norm),
+                     channel=2048,
+                     depth=256,
+                     infer_scale_sizes=infer_scale_sizes,
+                     atrous_rates=atrous_rates,
+                     decoder_output_stride=decoder_output_stride,
+                     output_stride=output_stride,
+                     fine_tune_batch_norm=fine_tune_batch_norm,
+                     image_pyramid=image_pyramid)

tests/st/networks/models/deeplabv3/src/ei_dataset.py

@@ -0,0 +1,84 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Process Dataset."""
+import abc
+import os
+import time
+
+from .utils.adapter import get_raw_samples, read_image
+
+
+class BaseDataset:
+    """
+    Create dataset.
+
+    Args:
+        data_url (str): The path of data.
+        usage (str): Whether to use train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+    def __init__(self, data_url, usage):
+        self.data_url = data_url
+        self.usage = usage
+        self.cur_index = 0
+        self.samples = []
+        _s_time = time.time()
+        self._load_samples()
+        _e_time = time.time()
+        print(f"load samples success~, time cost = {_e_time - _s_time}")
+
+    def __getitem__(self, item):
+        sample = self.samples[item]
+        return self._next_data(sample)
+
+    def __len__(self):
+        return len(self.samples)
+
+    @staticmethod
+    def _next_data(sample):
+        image_path = sample[0]
+        mask_image_path = sample[1]
+
+        image = read_image(image_path)
+        mask_image = read_image(mask_image_path)
+        return [image, mask_image]
+
+    @abc.abstractmethod
+    def _load_samples(self):
+        pass
+
+
+class HwVocRawDataset(BaseDataset):
+    """
+    Create dataset with raw data.
+
+    Args:
+        data_url (str): The path of data.
+        usage (str): Whether to use train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+    def __init__(self, data_url, usage="train"):
+        super().__init__(data_url, usage)
+
+    def _load_samples(self):
+        try:
+            self.samples = get_raw_samples(os.path.join(self.data_url, self.usage))
+        except Exception as e:
+            print("load HwVocRawDataset failed!!!")
+            raise e

tests/st/networks/models/deeplabv3/src/losses.py

@@ -0,0 +1,63 @@
+# 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.
+# ============================================================================
+"""OhemLoss."""
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+
+
+class OhemLoss(nn.Cell):
+    """Ohem loss cell."""
+    def __init__(self, num, ignore_label):
+        super(OhemLoss, self).__init__()
+        self.mul = P.Mul()
+        self.shape = P.Shape()
+        self.one_hot = nn.OneHot(-1, num, 1.0, 0.0)
+        self.squeeze = P.Squeeze()
+        self.num = num
+        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean()
+        self.select = P.Select()
+        self.reshape = P.Reshape()
+        self.cast = P.Cast()
+        self.not_equal = P.NotEqual()
+        self.equal = P.Equal()
+        self.reduce_sum = P.ReduceSum(keep_dims=False)
+        self.fill = P.Fill()
+        self.transpose = P.Transpose()
+        self.ignore_label = ignore_label
+        self.loss_weight = 1.0
+
+    def construct(self, logits, labels):
+        logits = self.transpose(logits, (0, 2, 3, 1))
+        logits = self.reshape(logits, (-1, self.num))
+        labels = F.cast(labels, mstype.int32)
+        labels = self.reshape(labels, (-1,))
+        one_hot_labels = self.one_hot(labels)
+        losses = self.cross_entropy(logits, one_hot_labels)[0]
+        weights = self.cast(self.not_equal(labels, self.ignore_label), mstype.float32) * self.loss_weight
+        weighted_losses = self.mul(losses, weights)
+        loss = self.reduce_sum(weighted_losses, (0,))
+        zeros = self.fill(mstype.float32, self.shape(weights), 0.0)
+        ones = self.fill(mstype.float32, self.shape(weights), 1.0)
+        present = self.select(self.equal(weights, zeros), zeros, ones)
+        present = self.reduce_sum(present, (0,))
+
+        zeros = self.fill(mstype.float32, self.shape(present), 0.0)
+        min_control = self.fill(mstype.float32, self.shape(present), 1.0)
+        present = self.select(self.equal(present, zeros), min_control, present)
+        loss = loss / present
+        return loss

tests/st/networks/models/deeplabv3/src/md_dataset.py

@@ -0,0 +1,116 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Dataset module."""
+from PIL import Image
+import mindspore.dataset as de
+import mindspore.dataset.transforms.vision.c_transforms as C
+import numpy as np
+
+from .ei_dataset import HwVocRawDataset
+from .utils import custom_transforms as tr
+
+
+class DataTransform:
+    """Transform dataset for DeepLabV3."""
+
+    def __init__(self, args, usage):
+        self.args = args
+        self.usage = usage
+
+    def __call__(self, image, label):
+        if self.usage == "train":
+            return self._train(image, label)
+        if self.usage == "eval":
+            return self._eval(image, label)
+        return None
+
+    def _train(self, image, label):
+        """
+        Process training data.
+
+        Args:
+            image (list): Image data.
+            label (list): Dataset label.
+        """
+        image = Image.fromarray(image)
+        label = Image.fromarray(label)
+
+        rsc_tr = tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size)
+        image, label = rsc_tr(image, label)
+
+        rhf_tr = tr.RandomHorizontalFlip()
+        image, label = rhf_tr(image, label)
+
+        image = np.array(image).astype(np.float32)
+        label = np.array(label).astype(np.float32)
+
+        return image, label
+
+    def _eval(self, image, label):
+        """
+        Process eval data.
+
+        Args:
+            image (list): Image data.
+            label (list): Dataset label.
+        """
+        image = Image.fromarray(image)
+        label = Image.fromarray(label)
+
+        fsc_tr = tr.FixScaleCrop(crop_size=self.args.crop_size)
+        image, label = fsc_tr(image, label)
+
+        image = np.array(image).astype(np.float32)
+        label = np.array(label).astype(np.float32)
+
+        return image, label
+
+
+def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train", shuffle=True):
+    """
+    Create Dataset for DeepLabV3.
+
+    Args:
+        args (dict): Train parameters.
+        data_url (str): Dataset path.
+        epoch_num (int): Epoch of dataset (default=1).
+        batch_size (int): Batch size of dataset (default=1).
+        usage (str): Whether is use to train or eval (default='train').
+
+    Returns:
+        Dataset.
+    """
+    # create iter dataset
+    dataset = HwVocRawDataset(data_url, usage=usage)
+    dataset_len = len(dataset)
+
+    # wrapped with GeneratorDataset
+    dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=None)
+    dataset.set_dataset_size(dataset_len)
+    dataset = dataset.map(input_columns=["image", "label"], operations=DataTransform(args, usage=usage))
+
+    channelswap_op = C.HWC2CHW()
+    dataset = dataset.map(input_columns="image", operations=channelswap_op)
+
+    # 1464 samples / batch_size 8 = 183 batches
+    # epoch_num is num of steps
+    # 3658 steps / 183 = 20 epochs
+    if usage == "train" and shuffle:
+        dataset = dataset.shuffle(1464)
+    dataset = dataset.batch(batch_size, drop_remainder=(usage == "train"))
+    dataset = dataset.repeat(count=epoch_num)
+    dataset.map_model = 4
+
+    return dataset

tests/st/networks/models/deeplabv3/src/miou_precision.py

@@ -0,0 +1,72 @@
+# 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.
+# ============================================================================
+"""mIou."""
+import numpy as np
+from mindspore.nn.metrics.metric import Metric
+
+
+def confuse_matrix(target, pred, n):
+    k = (target >= 0) & (target < n)
+    return np.bincount(n * target[k].astype(int) + pred[k], minlength=n ** 2).reshape(n, n)
+
+
+def iou(hist):
+    denominator = hist.sum(1) + hist.sum(0) - np.diag(hist)
+    res = np.diag(hist) / np.where(denominator > 0, denominator, 1)
+    res = np.sum(res) / np.count_nonzero(denominator)
+    return res
+
+
+class MiouPrecision(Metric):
+    """Calculate miou precision."""
+    def __init__(self, num_class=21):
+        super(MiouPrecision, self).__init__()
+        if not isinstance(num_class, int):
+            raise TypeError('num_class should be integer type, but got {}'.format(type(num_class)))
+        if num_class < 1:
+            raise ValueError('num_class must be at least 1, but got {}'.format(num_class))
+        self._num_class = num_class
+        self._mIoU = []
+        self.clear()
+
+    def clear(self):
+        self._hist = np.zeros((self._num_class, self._num_class))
+        self._mIoU = []
+
+    def update(self, *inputs):
+        if len(inputs) != 2:
+            raise ValueError('Need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
+        predict_in = self._convert_data(inputs[0])
+        label_in = self._convert_data(inputs[1])
+        if predict_in.shape[1] != self._num_class:
+            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
+                             'classes'.format(self._num_class, predict_in.shape[1]))
+        pred = np.argmax(predict_in, axis=1)
+        label = label_in
+        if len(label.flatten()) != len(pred.flatten()):
+            print('Skipping: len(gt) = {:d}, len(pred) = {:d}'.format(len(label.flatten()), len(pred.flatten())))
+            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
+                             'classes'.format(self._num_class, predict_in.shape[1]))
+        self._hist = confuse_matrix(label.flatten(), pred.flatten(), self._num_class)
+        mIoUs = iou(self._hist)
+        self._mIoU.append(mIoUs)
+
+    def eval(self):
+        """
+        Computes the mIoU categorical accuracy.
+        """
+        mIoU = np.nanmean(self._mIoU)
+        print('mIoU = {}'.format(mIoU))
+        return mIoU

tests/st/networks/models/deeplabv3/src/utils/__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.
+# ============================================================================

tests/st/networks/models/deeplabv3/src/utils/adapter.py

@@ -0,0 +1,67 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Adapter dataset."""
+import fnmatch
+import io
+import os
+
+import numpy as np
+from PIL import Image
+
+from ..utils import file_io
+
+
+def get_raw_samples(data_url):
+    """
+    Get dataset from raw data.
+
+    Args:
+        data_url (str): Dataset path.
+
+    Returns:
+        list, a file list.
+    """
+    def _list_files(dir_path, pattern):
+        full_files = []
+        _, _, files = next(file_io.walk(dir_path))
+        for f in files:
+            if fnmatch.fnmatch(f.lower(), pattern.lower()):
+                full_files.append(os.path.join(dir_path, f))
+        return full_files
+
+    img_files = _list_files(os.path.join(data_url, "Images"), "*.jpg")
+    seg_files = _list_files(os.path.join(data_url, "SegmentationClassRaw"), "*.png")
+
+    files = []
+    for img_file in img_files:
+        _, file_name = os.path.split(img_file)
+        name, _ = os.path.splitext(file_name)
+        seg_file = os.path.join(data_url, "SegmentationClassRaw", ".".join([name, "png"]))
+        if seg_file in seg_files:
+            files.append([img_file, seg_file])
+    return files
+
+
+def read_image(img_path):
+    """
+    Read image from file.
+
+    Args:
+        img_path (str): image path.
+    """
+    img = file_io.read(img_path.strip(), binary=True)
+    data = io.BytesIO(img)
+    img = Image.open(data)
+    return np.array(img)

tests/st/networks/models/deeplabv3/src/utils/custom_transforms.py

@@ -0,0 +1,149 @@
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""Random process dataset."""
+import random
+
+import numpy as np
+from PIL import Image, ImageOps, ImageFilter
+
+
+class Normalize:
+    """Normalize a tensor image with mean and standard deviation.
+    Args:
+        mean (tuple): means for each channel.
+        std (tuple): standard deviations for each channel.
+    """
+
+    def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, img, mask):
+        img = np.array(img).astype(np.float32)
+        mask = np.array(mask).astype(np.float32)
+        img = ((img - self.mean) / self.std).astype(np.float32)
+
+        return img, mask
+
+
+class RandomHorizontalFlip:
+    """Randomly decide whether to horizontal flip."""
+    def __call__(self, img, mask):
+        if random.random() < 0.5:
+            img = img.transpose(Image.FLIP_LEFT_RIGHT)
+            mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
+
+        return img, mask
+
+
+class RandomRotate:
+    """
+    Randomly decide whether to rotate.
+
+    Args:
+        degree (float): The degree of rotate.
+    """
+    def __init__(self, degree):
+        self.degree = degree
+
+    def __call__(self, img, mask):
+        rotate_degree = random.uniform(-1 * self.degree, self.degree)
+        img = img.rotate(rotate_degree, Image.BILINEAR)
+        mask = mask.rotate(rotate_degree, Image.NEAREST)
+
+        return img, mask
+
+
+class RandomGaussianBlur:
+    """Randomly decide whether to filter image with gaussian blur."""
+    def __call__(self, img, mask):
+        if random.random() < 0.5:
+            img = img.filter(ImageFilter.GaussianBlur(
+                radius=random.random()))
+
+        return img, mask
+
+
+class RandomScaleCrop:
+    """Randomly decide whether to scale and crop image."""
+    def __init__(self, base_size, crop_size, fill=0):
+        self.base_size = base_size
+        self.crop_size = crop_size
+        self.fill = fill
+
+    def __call__(self, img, mask):
+        # random scale (short edge)
+        short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0))
+        w, h = img.size
+        if h > w:
+            ow = short_size
+            oh = int(1.0 * h * ow / w)
+        else:
+            oh = short_size
+            ow = int(1.0 * w * oh / h)
+        img = img.resize((ow, oh), Image.BILINEAR)
+        mask = mask.resize((ow, oh), Image.NEAREST)
+        # pad crop
+        if short_size < self.crop_size:
+            padh = self.crop_size - oh if oh < self.crop_size else 0
+            padw = self.crop_size - ow if ow < self.crop_size else 0
+            img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
+            mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill)
+        # random crop crop_size
+        w, h = img.size
+        x1 = random.randint(0, w - self.crop_size)
+        y1 = random.randint(0, h - self.crop_size)
+        img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+        mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+
+        return img, mask
+
+
+class FixScaleCrop:
+    """Scale and crop image with fixing size."""
+    def __init__(self, crop_size):
+        self.crop_size = crop_size
+
+    def __call__(self, img, mask):
+        w, h = img.size
+        if w > h:
+            oh = self.crop_size
+            ow = int(1.0 * w * oh / h)
+        else:
+            ow = self.crop_size
+            oh = int(1.0 * h * ow / w)
+        img = img.resize((ow, oh), Image.BILINEAR)
+        mask = mask.resize((ow, oh), Image.NEAREST)
+        # center crop
+        w, h = img.size
+        x1 = int(round((w - self.crop_size) / 2.))
+        y1 = int(round((h - self.crop_size) / 2.))
+        img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+        mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
+
+        return img, mask
+
+
+class FixedResize:
+    """Resize image with fixing size."""
+    def __init__(self, size):
+        self.size = (size, size)
+
+    def __call__(self, img, mask):
+        assert img.size == mask.size
+
+        img = img.resize(self.size, Image.BILINEAR)
+        mask = mask.resize(self.size, Image.NEAREST)
+        return img, mask

tests/st/networks/models/deeplabv3/src/utils/file_io.py

@@ -0,0 +1,36 @@
+# 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.
+# ============================================================================
+"""File operation module."""
+import os
+
+
+def _is_obs(url):
+    return url.startswith("obs://") or url.startswith("s3://")
+
+
+def read(url, binary=False):
+    if _is_obs(url):
+        # TODO read cloud file.
+        return None
+
+    with open(url, "rb" if binary else "r") as f:
+        return f.read()
+
+
+def walk(url):
+    if _is_obs(url):
+        # TODO read cloud file.
+        return None
+    return os.walk(url)

tests/st/networks/models/deeplabv3/test_deeplabv3.py

@@ -0,0 +1,102 @@
+# 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.
+# ============================================================================
+"""train."""
+import argparse
+import time
+import pytest
+import numpy as np
+from mindspore import context, Tensor
+from mindspore.nn.optim.momentum import Momentum
+from mindspore import Model
+from mindspore.train.callback import Callback
+from src.md_dataset import create_dataset
+from src.losses import OhemLoss
+from src.deeplabv3 import deeplabv3_resnet50
+from src.config import config
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+#--train
+#--eval
+#  --Images
+#    --2008_001135.jpg
+#    --2008_001404.jpg
+#  --SegmentationClassRaw
+#    --2008_001135.png
+#    --2008_001404.png
+data_url = "/home/workspace/mindspore_dataset/voc/voc2012"
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+    Note:
+        if per_print_times is 0 do not print loss.
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+    def __init__(self, data_size, per_print_times=1):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0")
+        self.data_size = data_size
+        self._per_print_times = per_print_times
+        self.time = 1000
+        self.loss = 0
+    def epoch_begin(self, run_context):
+        self.epoch_time = time.time()
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        epoch_mseconds = (time.time() - self.epoch_time) * 1000
+        self.time = epoch_mseconds / self.data_size
+        self.loss = cb_params.net_outputs
+        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
+                                                           str(cb_params.net_outputs)))
+
+def model_fine_tune(train_net, fix_weight_layer):
+    for para in train_net.trainable_params():
+        para.set_parameter_data(Tensor(np.ones(para.data.shape).astype(np.float32) * 0.02))
+        if fix_weight_layer in para.name:
+            para.requires_grad = False
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_deeplabv3_1p():
+    start_time = time.time()
+    epoch_size = 100
+    args_opt = argparse.Namespace(base_size=513, crop_size=513, batch_size=2)
+    args_opt.base_size = config.crop_size
+    args_opt.crop_size = config.crop_size
+    args_opt.batch_size = config.batch_size
+    train_dataset = create_dataset(args_opt, data_url, epoch_size, config.batch_size,
+                                   usage="eval")
+    dataset_size = train_dataset.get_dataset_size()
+    callback = LossCallBack(dataset_size)
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                             infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                             decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                             fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
+    net.set_train()
+    model_fine_tune(net, 'layer')
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
+    model = Model(net, loss, opt)
+    model.train(epoch_size, train_dataset, callback)
+    print(time.time() - start_time)
+    print("expect loss: ", callback.loss)
+    print("expect time: ", callback.time)
+    expect_loss = 0.92
+    expect_time = 40
+    assert callback.loss.asnumpy() <= expect_loss
+    assert callback.time <= expect_time