"""
MindSpore implementation of `GoogLeNet`.
Refer to Going deeper with convolutions.
"""
from typing import Union, Tuple
from mindspore import nn, ops, Tensor
import mindspore.common.initializer as init
from .utils import load_pretrained
from .registry import register_model
from .layers.pooling import GlobalAvgPooling
__all__ = [
'GoogLeNet',
'googlenet'
]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'first_conv': 'conv1.conv', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'googlenet': _cfg(url='')
}
class BasicConv2d(nn.Cell):
"""A block for combine conv and relu"""
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int = 1,
stride: int = 1,
padding: int = 0,
pad_mode: str = 'same'
) -> None:
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride,
padding=padding, pad_mode=pad_mode)
self.relu = nn.ReLU()
def construct(self, x: Tensor) -> Tensor:
x = self.conv(x)
x = self.relu(x)
return x
class Inception(nn.Cell):
"""Inception module of GoogLeNet."""
def __init__(self,
in_channels: int,
ch1x1: int,
ch3x3red: int,
ch3x3: int,
ch5x5red: int,
ch5x5: int,
pool_proj: int
) -> None:
super().__init__()
self.b1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
self.b2 = nn.SequentialCell([
BasicConv2d(in_channels, ch3x3red, kernel_size=1),
BasicConv2d(ch3x3red, ch3x3, kernel_size=3)
])
self.b3 = nn.SequentialCell([
BasicConv2d(in_channels, ch5x5red, kernel_size=1),
BasicConv2d(ch5x5red, ch5x5, kernel_size=5)
])
self.b4 = nn.SequentialCell([
nn.MaxPool2d(kernel_size=3, stride=1, pad_mode='same'),
BasicConv2d(in_channels, pool_proj, kernel_size=1)
])
def construct(self, x: Tensor) -> Tensor:
branch1 = self.b1(x)
branch2 = self.b2(x)
branch3 = self.b3(x)
branch4 = self.b4(x)
return ops.concat((branch1, branch2, branch3, branch4), axis=1)
class InceptionAux(nn.Cell):
"""Inception module for the aux classifier head"""
def __init__(self,
in_channels: int,
num_classes: int,
drop_rate: float = 0.7
) -> None:
super().__init__()
self.avg_pool = nn.AvgPool2d(kernel_size=5, stride=3)
self.conv = BasicConv2d(in_channels, 128, kernel_size=1)
self.fc1 = nn.Dense(2048, 1024)
self.fc2 = nn.Dense(1024, num_classes)
self.flatten = nn.Flatten()
self.relu = nn.ReLU()
self.dropout = nn.Dropout(1 - drop_rate)
def construct(self, x: Tensor) -> Tensor:
x = self.avg_pool(x)
x = self.conv(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc2(x)
return x
[文档]class GoogLeNet(nn.Cell):
r"""GoogLeNet (Inception v1) model architecture from
`"Going Deeper with Convolutions" <https://arxiv.org/abs/1409.4842>`_.
Args:
num_classes: number of classification classes. Default: 1000.
aux_logits: use auxiliary classifier or not. Default: False.
in_channels: number the channels of the input. Default: 3.
drop_rate: dropout rate of the layer before main classifier. Default: 0.2.
drop_rate_aux: dropout rate of the layer before auxiliary classifier. Default: 0.7.
"""
def __init__(self,
num_classes: int = 1000,
aux_logits: bool = False,
in_channels: int = 3,
drop_rate: float = 0.2,
drop_rate_aux: float = 0.7
) -> None:
super().__init__()
self.aux_logits = aux_logits
self.conv1 = BasicConv2d(in_channels, 64, kernel_size=7, stride=2)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.conv2 = BasicConv2d(64, 64, kernel_size=1)
self.conv3 = BasicConv2d(64, 192, kernel_size=3)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="same")
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
if self.aux_logits:
self.aux1 = InceptionAux(512, num_classes, drop_rate=drop_rate_aux)
self.aux2 = InceptionAux(528, num_classes, drop_rate=drop_rate_aux)
self.pool = GlobalAvgPooling()
self.dropout = nn.Dropout(keep_prob=1 - drop_rate)
self.classifier = nn.Dense(1024, num_classes)
self._initialize_weights()
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.TruncatedNormal(0.02), cell.weight.shape, cell.weight.dtype))
elif isinstance(cell, nn.Dense):
cell.weight.set_data(
init.initializer(init.TruncatedNormal(0.02), cell.weight.shape, cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(
init.initializer(init.TruncatedNormal(0.02), cell.bias.shape, cell.bias.dtype))
def construct(self, x: Tensor) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor]]:
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.maxpool2(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.maxpool3(x)
x = self.inception4a(x)
if self.aux_logits and self.training:
aux1 = self.aux1(x)
else:
aux1 = None
x = self.inception4b(x)
x = self.inception4c(x)
x = self.inception4d(x)
if self.aux_logits and self.training:
aux2 = self.aux2(x)
else:
aux2 = None
x = self.inception4e(x)
x = self.maxpool4(x)
x = self.inception5a(x)
x = self.inception5b(x)
x = self.pool(x)
x = self.dropout(x)
x = self.classifier(x)
if self.aux_logits and self.training:
return x, aux2, aux1
return x
@register_model
def googlenet(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> GoogLeNet:
"""Get GoogLeNet model.
Refer to the base class `models.GoogLeNet` for more details."""
default_cfg = default_cfgs['googlenet']
model = GoogLeNet(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