from functools import partial
from typing import Any, Optional
import torch
import torch.nn as nn
import torch.nn.init as init
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_named_param, handle_legacy_interface
__all__ = ["SqueezeNet", "SqueezeNet1_0_Weights", "SqueezeNet1_1_Weights", "squeezenet1_0", "squeezenet1_1"]
class Fire(nn.Module):
def __init__(self, inplanes: int, squeeze_planes: int, expand1x1_planes: int, expand3x3_planes: int) -> None:
super().__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: torch.Tensor) -> torch.Tensor:
x = self.squeeze_activation(self.squeeze(x))
return torch.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, dropout: float = 0.5) -> None:
super().__init__()
_log_api_usage_once(self)
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(f"Unsupported SqueezeNet version {version}: 1_0 or 1_1 expected")
# 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=dropout), 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: torch.Tensor) -> torch.Tensor:
x = self.features(x)
x = self.classifier(x)
return torch.flatten(x, 1)
def _squeezenet(
version: str,
weights: Optional[WeightsEnum],
progress: bool,
**kwargs: Any,
) -> SqueezeNet:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = SqueezeNet(version, **kwargs)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
_COMMON_META = {
"categories": _IMAGENET_CATEGORIES,
"recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
}
class SqueezeNet1_0_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"min_size": (21, 21),
"num_params": 1248424,
"_metrics": {
"ImageNet-1K": {
"acc@1": 58.092,
"acc@5": 80.420,
}
},
"_ops": 0.819,
"_file_size": 4.778,
},
)
DEFAULT = IMAGENET1K_V1
class SqueezeNet1_1_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"min_size": (17, 17),
"num_params": 1235496,
"_metrics": {
"ImageNet-1K": {
"acc@1": 58.178,
"acc@5": 80.624,
}
},
"_ops": 0.349,
"_file_size": 4.729,
},
)
DEFAULT = IMAGENET1K_V1
@register_model()
@handle_legacy_interface(weights=("pretrained", SqueezeNet1_0_Weights.IMAGENET1K_V1))
def squeezenet1_0(
*, weights: Optional[SqueezeNet1_0_Weights] = None, progress: bool = True, **kwargs: Any
) -> SqueezeNet:
"""SqueezeNet model architecture from the `SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size
<https://arxiv.org/abs/1602.07360>`_ paper.
Args:
weights (:class:`~torchvision.models.SqueezeNet1_0_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.SqueezeNet1_0_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.squeezenet.SqueezeNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/squeezenet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.SqueezeNet1_0_Weights
:members:
"""
weights = SqueezeNet1_0_Weights.verify(weights)
return _squeezenet("1_0", weights, progress, **kwargs)
@register_model()
@handle_legacy_interface(weights=("pretrained", SqueezeNet1_1_Weights.IMAGENET1K_V1))
def squeezenet1_1(
*, weights: Optional[SqueezeNet1_1_Weights] = None, progress: bool = True, **kwargs: Any
) -> SqueezeNet:
"""SqueezeNet 1.1 model from the `official SqueezeNet repo
<https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
Args:
weights (:class:`~torchvision.models.SqueezeNet1_1_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.SqueezeNet1_1_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.squeezenet.SqueezeNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/squeezenet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.SqueezeNet1_1_Weights
:members:
"""
weights = SqueezeNet1_1_Weights.verify(weights)
return _squeezenet("1_1", weights, progress, **kwargs)