"""
MindSpore implementation of Xception.
Refer to Xception: Deep Learning with Depthwise Separable Convolutions.
"""
from mindspore import nn, ops, Tensor
import mindspore.common.initializer as init
from mindcv.models.registry import register_model
from mindcv.models.utils import load_pretrained
from mindcv.models.layers import GlobalAvgPooling
__all__ = [
'Xception',
'xception'
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'conv1', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'xception': _cfg(url='')
}
class SeparableConv2d(nn.Cell):
'''SeparableCon2d module of Xception'''
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int = 1,
stride: int = 1,
padding: int = 0):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, group=in_channels, pad_mode='pad',
padding=padding)
self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, pad_mode='valid')
def construct(self, x):
x = self.conv1(x)
x = self.pointwise(x)
return x
class Block(nn.Cell):
'''Basic module of Xception'''
def __init__(self,
in_filters: int,
out_filters: int,
reps: int,
strides: int = 1,
start_with_relu: bool = True,
grow_first: bool = True):
super().__init__()
if out_filters != in_filters or strides != 1:
self.skip = nn.Conv2d(in_filters, out_filters, 1, stride=strides, pad_mode='valid', has_bias=False)
self.skipbn = nn.BatchNorm2d(out_filters, momentum=0.9)
else:
self.skip = None
self.relu = nn.ReLU()
rep = []
filters = in_filters
if grow_first:
rep.append(nn.ReLU())
rep.append(SeparableConv2d(in_filters, out_filters, kernel_size=3, stride=1, padding=1))
rep.append(nn.BatchNorm2d(out_filters, momentum=0.9))
filters = out_filters
for _ in range(reps - 1):
rep.append(nn.ReLU())
rep.append(SeparableConv2d(filters, filters, kernel_size=3, stride=1, padding=1))
rep.append(nn.BatchNorm2d(filters, momentum=0.9))
if not grow_first:
rep.append(nn.ReLU())
rep.append(SeparableConv2d(in_filters, out_filters, kernel_size=3, stride=1, padding=1))
rep.append(nn.BatchNorm2d(out_filters, momentum=0.9))
if not start_with_relu:
rep = rep[1:]
else:
rep[0] = nn.ReLU()
if strides != 1:
rep.append(nn.MaxPool2d(3, strides, pad_mode="same"))
self.rep = nn.SequentialCell(*rep)
def construct(self, inp):
x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x = ops.add(x, skip)
return x
[文档]class Xception(nn.Cell):
r"""Xception model architecture from
`"Deep Learning with Depthwise Separable Convolutions" <https://arxiv.org/abs/1610.02357>`_.
Args:
num_classes (int) : number of classification classes. Default: 1000.
in_channels (int): number the channels of the input. Default: 3.
"""
def __init__(self,
num_classes: int = 1000,
in_channels: int = 3):
super().__init__()
self.num_classes = num_classes
blocks = []
self.conv1 = nn.Conv2d(in_channels, 32, 3, 2, pad_mode='valid')
self.bn1 = nn.BatchNorm2d(32, momentum=0.9)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(32, 64, 3, pad_mode='valid')
self.bn2 = nn.BatchNorm2d(64, momentum=0.9)
# Entry flow
blocks.append(Block(64, 128, 2, 2, start_with_relu=False, grow_first=True))
blocks.append(Block(128, 256, 2, 2, start_with_relu=True, grow_first=True))
blocks.append(Block(256, 728, 2, 2, start_with_relu=True, grow_first=True))
# Middle flow
for _ in range(8):
blocks.append(Block(728, 728, 3, 1, start_with_relu=True, grow_first=True))
# Exit flow
blocks.append(Block(728, 1024, 2, 2, start_with_relu=True, grow_first=False))
self.blocks = nn.SequentialCell(blocks)
self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
self.bn3 = nn.BatchNorm2d(1536, momentum=0.9)
self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
self.bn4 = nn.BatchNorm2d(2048, momentum=0.9)
self.pool = GlobalAvgPooling()
self.dropout = nn.Dropout()
self.classifier = nn.Dense(2048, num_classes)
self._initialize_weights()
def forward_features(self, x: Tensor) -> Tensor:
"""forward the backbone of Xception"""
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.blocks(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.relu(x)
return x
def forward_head(self, x: Tensor) -> Tensor:
x = self.pool(x)
x = self.dropout(x)
x = self.classifier(x)
return x
def construct(self, x: Tensor) -> Tensor:
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _initialize_weights(self) -> None:
"""Initialize weights for cells."""
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.set_data(
init.initializer(init.XavierUniform(),
cell.weight.shape, cell.weight.dtype))
elif isinstance(cell, nn.Dense):
cell.weight.set_data(init.initializer(init.Normal(0.01, 0), cell.weight.shape, cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer(init.Constant(0), cell.bias.shape, cell.weight.dtype))
@register_model
def xception(pretrained: bool = False, num_classes: int = 1000, in_channels: int = 3, **kwargs) -> Xception:
"""Get Xception model.
Refer to the base class `models.Xception` for more details."""
default_cfg = default_cfgs['xception']
model = Xception(num_classes=num_classes, in_channels=in_channels, **kwargs)
if pretrained:
load_pretrained(model, default_cfg, num_classes=num_classes, in_channels=in_channels)
return model