File size: 8,080 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
import warnings
from functools import partial
from typing import Any, Optional, Union
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
from ...transforms._presets import ImageClassification
from .._api import register_model, Weights, WeightsEnum
from .._meta import _IMAGENET_CATEGORIES
from .._utils import _ovewrite_named_param, handle_legacy_interface
from ..googlenet import BasicConv2d, GoogLeNet, GoogLeNet_Weights, GoogLeNetOutputs, Inception, InceptionAux
from .utils import _fuse_modules, _replace_relu, quantize_model
__all__ = [
"QuantizableGoogLeNet",
"GoogLeNet_QuantizedWeights",
"googlenet",
]
class QuantizableBasicConv2d(BasicConv2d):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.relu = nn.ReLU()
def forward(self, x: Tensor) -> Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
_fuse_modules(self, ["conv", "bn", "relu"], is_qat, inplace=True)
class QuantizableInception(Inception):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.cat = nn.quantized.FloatFunctional()
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return self.cat.cat(outputs, 1)
class QuantizableInceptionAux(InceptionAux):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.relu = nn.ReLU()
def forward(self, x: Tensor) -> Tensor:
# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
x = F.adaptive_avg_pool2d(x, (4, 4))
# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
x = self.conv(x)
# N x 128 x 4 x 4
x = torch.flatten(x, 1)
# N x 2048
x = self.relu(self.fc1(x))
# N x 1024
x = self.dropout(x)
# N x 1024
x = self.fc2(x)
# N x 1000 (num_classes)
return x
class QuantizableGoogLeNet(GoogLeNet):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__( # type: ignore[misc]
*args, blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], **kwargs
)
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x: Tensor) -> GoogLeNetOutputs:
x = self._transform_input(x)
x = self.quant(x)
x, aux1, aux2 = self._forward(x)
x = self.dequant(x)
aux_defined = self.training and self.aux_logits
if torch.jit.is_scripting():
if not aux_defined:
warnings.warn("Scripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple")
return GoogLeNetOutputs(x, aux2, aux1)
else:
return self.eager_outputs(x, aux2, aux1)
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
r"""Fuse conv/bn/relu modules in googlenet model
Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization.
Model is modified in place. Note that this operation does not change numerics
and the model after modification is in floating point
"""
for m in self.modules():
if type(m) is QuantizableBasicConv2d:
m.fuse_model(is_qat)
class GoogLeNet_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/googlenet_fbgemm-c81f6644.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
"num_params": 6624904,
"min_size": (15, 15),
"categories": _IMAGENET_CATEGORIES,
"backend": "fbgemm",
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
"unquantized": GoogLeNet_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 69.826,
"acc@5": 89.404,
}
},
"_ops": 1.498,
"_file_size": 12.618,
"_docs": """
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
weights listed below.
""",
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
@register_model(name="quantized_googlenet")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: GoogLeNet_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else GoogLeNet_Weights.IMAGENET1K_V1,
)
)
def googlenet(
*,
weights: Optional[Union[GoogLeNet_QuantizedWeights, GoogLeNet_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> QuantizableGoogLeNet:
"""GoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions <http://arxiv.org/abs/1409.4842>`__.
.. 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.GoogLeNet_QuantizedWeights` or :class:`~torchvision.models.GoogLeNet_Weights`, optional): The
pretrained weights for the model. See
:class:`~torchvision.models.quantization.GoogLeNet_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, return a quantized version of the model. Default is False.
**kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableGoogLeNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/googlenet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.quantization.GoogLeNet_QuantizedWeights
:members:
.. autoclass:: torchvision.models.GoogLeNet_Weights
:members:
:noindex:
"""
weights = (GoogLeNet_QuantizedWeights if quantize else GoogLeNet_Weights).verify(weights)
original_aux_logits = kwargs.get("aux_logits", False)
if weights is not None:
if "transform_input" not in kwargs:
_ovewrite_named_param(kwargs, "transform_input", True)
_ovewrite_named_param(kwargs, "aux_logits", True)
_ovewrite_named_param(kwargs, "init_weights", False)
_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", "fbgemm")
model = QuantizableGoogLeNet(**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, check_hash=True))
if not original_aux_logits:
model.aux_logits = False
model.aux1 = None # type: ignore[assignment]
model.aux2 = None # type: ignore[assignment]
else:
warnings.warn(
"auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
)
return model
|