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tests/st/networks/models/deeplabv3/src/__init__.py Normal file
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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__)

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tests/st/networks/models/deeplabv3/src/backbone/__init__.py Normal file
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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"
]

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tests/st/networks/models/deeplabv3/src/backbone/resnet_deeplab.py Normal file
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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)

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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.
# ============================================================================
"""
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
})

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tests/st/networks/models/deeplabv3/src/deeplabv3.py Normal file
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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.
# ============================================================================
"""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)

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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.
# ============================================================================
"""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

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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.
# ============================================================================
"""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

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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.
# ============================================================================
"""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

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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.
# ============================================================================
"""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

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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.
# ============================================================================

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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.
# ============================================================================
"""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)

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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.
# ============================================================================
"""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

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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.
# ============================================================================
"""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)

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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.
# ============================================================================
"""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