"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/models/squeezenet.py
"""
from typing import Any
import oneflow as flow
import oneflow.nn as nn
import oneflow.nn.init as init
from .utils import load_state_dict_from_url
from .registry import ModelCreator
__all__ = ["SqueezeNet", "squeezenet1_0", "squeezenet1_1"]
model_urls = {
"squeezenet1_0": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/SqueezeNet/squeezenet1_0.zip",
"squeezenet1_1": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/SqueezeNet/squeezenet1_1.zip",
}
class Fire(nn.Module):
def __init__(
self,
inplanes: int,
squeeze_planes: int,
expand1x1_planes: int,
expand3x3_planes: int,
) -> None:
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_activation = nn.ReLU(inplace=True)
self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1)
self.expand1x1_activation = nn.ReLU(inplace=True)
self.expand3x3 = nn.Conv2d(
squeeze_planes, expand3x3_planes, kernel_size=3, padding=1
)
self.expand3x3_activation = nn.ReLU(inplace=True)
def forward(self, x: flow.Tensor) -> flow.Tensor:
x = self.squeeze_activation(self.squeeze(x))
return flow.cat(
[
self.expand1x1_activation(self.expand1x1(x)),
self.expand3x3_activation(self.expand3x3(x)),
],
1,
)
class SqueezeNet(nn.Module):
def __init__(self, version: str = "1_0", num_classes: int = 1000) -> None:
super(SqueezeNet, self).__init__()
self.num_classes = num_classes
if version == "1_0":
self.features = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=7, stride=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(96, 16, 64, 64),
Fire(128, 16, 64, 64),
Fire(128, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(256, 32, 128, 128),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(512, 64, 256, 256),
)
elif version == "1_1":
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(64, 16, 64, 64),
Fire(128, 16, 64, 64),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(128, 32, 128, 128),
Fire(256, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
Fire(512, 64, 256, 256),
)
else:
# FIXME: Is this needed? SqueezeNet should only be called from the
# FIXME: squeezenet1_x() functions
# FIXME: This checking is not done for the other models
raise ValueError(
"Unsupported SqueezeNet version {version}:"
"1_0 or 1_1 expected".format(version=version)
)
# Final convolution is initialized differently from the rest
final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
self.classifier = nn.Sequential(
nn.Dropout(p=0.5),
final_conv,
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m is final_conv:
init.normal_(m.weight, mean=0.0, std=0.01)
else:
init.kaiming_uniform_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
def forward(self, x: flow.Tensor) -> flow.Tensor:
x = self.features(x)
x = self.classifier(x)
return flow.flatten(x, 1)
def _squeezenet(
version: str, pretrained: bool, progress: bool, **kwargs: Any
) -> SqueezeNet:
model = SqueezeNet(version, **kwargs)
if pretrained:
arch = "squeezenet" + version
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict)
return model
[docs]@ModelCreator.register_model
def squeezenet1_0(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> SqueezeNet:
"""
Constructs the SqueezeNet model.
.. note::
SqueezeNet model from the `SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size <https://arxiv.org/abs/1602.07360>`_ paper.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> squeezenet1_0 = flowvision.models.squeezenet1_0(pretrained=False, progress=True)
"""
return _squeezenet("1_0", pretrained, progress, **kwargs)
[docs]@ModelCreator.register_model
def squeezenet1_1(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> SqueezeNet:
"""
Constructs the SqueezeNet 1.1 model.
.. note::
SqueezeNet 1.1 model from the `SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size <https://arxiv.org/abs/1602.07360>`_ paper.
Note that SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> squeezenet1_1 = flowvision.models.squeezenet1_1(pretrained=False, progress=True)
"""
return _squeezenet("1_1", pretrained, progress, **kwargs)