Source code for torchvision.models.squeezenet

from typing import Any

import torch
import torch.nn as nn
import torch.nn.init as init

from .._internally_replaced_utils import load_state_dict_from_url
from ..utils import _log_api_usage_once

__all__ = ["SqueezeNet", "squeezenet1_0", "squeezenet1_1"]

model_urls = {
    "squeezenet1_0": "https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth",
    "squeezenet1_1": "https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth",
}


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, 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


def squeezenet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
    r"""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.
    The required minimum input size of the model is 21x21.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _squeezenet("1_0", pretrained, progress, **kwargs)


def squeezenet1_1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
    r"""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.
    The required minimum input size of the model is 17x17.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _squeezenet("1_1", pretrained, progress, **kwargs)