Source code for torchvision.models.quantization.mobilenetv2

from functools import partial
from typing import Any, Optional, Union

from torch import Tensor
from torch import nn
from torch.ao.quantization import QuantStub, DeQuantStub
from torchvision.models.mobilenetv2 import InvertedResidual, MobileNetV2, MobileNet_V2_Weights

from ...ops.misc import Conv2dNormActivation
from ...transforms._presets import ImageClassification
from .._api import WeightsEnum, Weights
from .._meta import _IMAGENET_CATEGORIES
from .._utils import handle_legacy_interface, _ovewrite_named_param
from .utils import _fuse_modules, _replace_relu, quantize_model


__all__ = [
    "QuantizableMobileNetV2",
    "MobileNet_V2_QuantizedWeights",
    "mobilenet_v2",
]


class QuantizableInvertedResidual(InvertedResidual):
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        super().__init__(*args, **kwargs)
        self.skip_add = nn.quantized.FloatFunctional()

    def forward(self, x: Tensor) -> Tensor:
        if self.use_res_connect:
            return self.skip_add.add(x, self.conv(x))
        else:
            return self.conv(x)

    def fuse_model(self, is_qat: Optional[bool] = None) -> None:
        for idx in range(len(self.conv)):
            if type(self.conv[idx]) is nn.Conv2d:
                _fuse_modules(self.conv, [str(idx), str(idx + 1)], is_qat, inplace=True)


class QuantizableMobileNetV2(MobileNetV2):
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """
        MobileNet V2 main class

        Args:
           Inherits args from floating point MobileNetV2
        """
        super().__init__(*args, **kwargs)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x: Tensor) -> Tensor:
        x = self.quant(x)
        x = self._forward_impl(x)
        x = self.dequant(x)
        return x

    def fuse_model(self, is_qat: Optional[bool] = None) -> None:
        for m in self.modules():
            if type(m) is Conv2dNormActivation:
                _fuse_modules(m, ["0", "1", "2"], is_qat, inplace=True)
            if type(m) is QuantizableInvertedResidual:
                m.fuse_model(is_qat)


class MobileNet_V2_QuantizedWeights(WeightsEnum):
    IMAGENET1K_QNNPACK_V1 = Weights(
        url="https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            "num_params": 3504872,
            "min_size": (1, 1),
            "categories": _IMAGENET_CATEGORIES,
            "backend": "qnnpack",
            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2",
            "unquantized": MobileNet_V2_Weights.IMAGENET1K_V1,
            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 71.658,
                    "acc@5": 90.150,
                }
            },
            "_docs": """
                These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized
                weights listed below.
            """,
        },
    )
    DEFAULT = IMAGENET1K_QNNPACK_V1


@handle_legacy_interface(
    weights=(
        "pretrained",
        lambda kwargs: MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1
        if kwargs.get("quantize", False)
        else MobileNet_V2_Weights.IMAGENET1K_V1,
    )
)
def mobilenet_v2(
    *,
    weights: Optional[Union[MobileNet_V2_QuantizedWeights, MobileNet_V2_Weights]] = None,
    progress: bool = True,
    quantize: bool = False,
    **kwargs: Any,
) -> QuantizableMobileNetV2:
    """
    Constructs a MobileNetV2 architecture from
    `MobileNetV2: Inverted Residuals and Linear Bottlenecks
    <https://arxiv.org/abs/1801.04381>`_.

    .. note::
        Note that ``quantize = True`` returns a quantized model with 8 bit
        weights. Quantized models only support inference and run on CPUs.
        GPU inference is not yet supported.

    Args:
        weights (:class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
            pretrained weights for the model. See
            :class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` 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.
        quantize (bool, optional): If True, returns a quantized version of the model. Default is False.
        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/mobilenetv2.py>`_
            for more details about this class.
    .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights
        :members:
    .. autoclass:: torchvision.models.MobileNet_V2_Weights
        :members:
        :noindex:
    """
    weights = (MobileNet_V2_QuantizedWeights if quantize else MobileNet_V2_Weights).verify(weights)

    if weights is not None:
        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
        if "backend" in weights.meta:
            _ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
    backend = kwargs.pop("backend", "qnnpack")

    model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs)
    _replace_relu(model)
    if quantize:
        quantize_model(model, backend)

    if weights is not None:
        model.load_state_dict(weights.get_state_dict(progress=progress))

    return model


# The dictionary below is internal implementation detail and will be removed in v0.15
from .._utils import _ModelURLs
from ..mobilenetv2 import model_urls  # noqa: F401


quant_model_urls = _ModelURLs(
    {
        "mobilenet_v2_qnnpack": MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1.url,
    }
)