Source code for torchvision.models.inception
from collections import namedtuple
import warnings
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from .._internally_replaced_utils import load_state_dict_from_url
from typing import Callable, Any, Optional, Tuple, List
__all__ = ['Inception3', 'inception_v3', 'InceptionOutputs', '_InceptionOutputs']
model_urls = {
# Inception v3 ported from TensorFlow
'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth',
}
InceptionOutputs = namedtuple('InceptionOutputs', ['logits', 'aux_logits'])
InceptionOutputs.__annotations__ = {'logits': Tensor, 'aux_logits': Optional[Tensor]}
# Script annotations failed with _GoogleNetOutputs = namedtuple ...
# _InceptionOutputs set here for backwards compat
_InceptionOutputs = InceptionOutputs
[docs]def inception_v3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> "Inception3":
r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
The required minimum input size of the model is 75x75.
.. note::
**Important**: In contrast to the other models the inception_v3 expects tensors with a size of
N x 3 x 299 x 299, so ensure your images are sized accordingly.
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
aux_logits (bool): If True, add an auxiliary branch that can improve training.
Default: *True*
transform_input (bool): If True, preprocesses the input according to the method with which it
was trained on ImageNet. Default: *False*
"""
if pretrained:
if 'transform_input' not in kwargs:
kwargs['transform_input'] = True
if 'aux_logits' in kwargs:
original_aux_logits = kwargs['aux_logits']
kwargs['aux_logits'] = True
else:
original_aux_logits = True
kwargs['init_weights'] = False # we are loading weights from a pretrained model
model = Inception3(**kwargs)
state_dict = load_state_dict_from_url(model_urls['inception_v3_google'],
progress=progress)
model.load_state_dict(state_dict)
if not original_aux_logits:
model.aux_logits = False
model.AuxLogits = None
return model
return Inception3(**kwargs)
class Inception3(nn.Module):
def __init__(
self,
num_classes: int = 1000,
aux_logits: bool = True,
transform_input: bool = False,
inception_blocks: Optional[List[Callable[..., nn.Module]]] = None,
init_weights: Optional[bool] = None
) -> None:
super(Inception3, self).__init__()
if inception_blocks is None:
inception_blocks = [
BasicConv2d, InceptionA, InceptionB, InceptionC,
InceptionD, InceptionE, InceptionAux
]
if init_weights is None:
warnings.warn('The default weight initialization of inception_v3 will be changed in future releases of '
'torchvision. If you wish to keep the old behavior (which leads to long initialization times'
' due to scipy/scipy#11299), please set init_weights=True.', FutureWarning)
init_weights = True
assert len(inception_blocks) == 7
conv_block = inception_blocks[0]
inception_a = inception_blocks[1]
inception_b = inception_blocks[2]
inception_c = inception_blocks[3]
inception_d = inception_blocks[4]
inception_e = inception_blocks[5]
inception_aux = inception_blocks[6]
self.aux_logits = aux_logits
self.transform_input = transform_input
self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)
self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Mixed_5b = inception_a(192, pool_features=32)
self.Mixed_5c = inception_a(256, pool_features=64)
self.Mixed_5d = inception_a(288, pool_features=64)
self.Mixed_6a = inception_b(288)
self.Mixed_6b = inception_c(768, channels_7x7=128)
self.Mixed_6c = inception_c(768, channels_7x7=160)
self.Mixed_6d = inception_c(768, channels_7x7=160)
self.Mixed_6e = inception_c(768, channels_7x7=192)
self.AuxLogits: Optional[nn.Module] = None
if aux_logits:
self.AuxLogits = inception_aux(768, num_classes)
self.Mixed_7a = inception_d(768)
self.Mixed_7b = inception_e(1280)
self.Mixed_7c = inception_e(2048)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout()
self.fc = nn.Linear(2048, num_classes)
if init_weights:
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
stddev = float(m.stddev) if hasattr(m, 'stddev') else 0.1 # type: ignore
torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=stddev, a=-2, b=2)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _transform_input(self, x: Tensor) -> Tensor:
if self.transform_input:
x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
return x
def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor]]:
# N x 3 x 299 x 299
x = self.Conv2d_1a_3x3(x)
# N x 32 x 149 x 149
x = self.Conv2d_2a_3x3(x)
# N x 32 x 147 x 147
x = self.Conv2d_2b_3x3(x)
# N x 64 x 147 x 147
x = self.maxpool1(x)
# N x 64 x 73 x 73
x = self.Conv2d_3b_1x1(x)
# N x 80 x 73 x 73
x = self.Conv2d_4a_3x3(x)
# N x 192 x 71 x 71
x = self.maxpool2(x)
# N x 192 x 35 x 35
x = self.Mixed_5b(x)
# N x 256 x 35 x 35
x = self.Mixed_5c(x)
# N x 288 x 35 x 35
x = self.Mixed_5d(x)
# N x 288 x 35 x 35
x = self.Mixed_6a(x)
# N x 768 x 17 x 17
x = self.Mixed_6b(x)
# N x 768 x 17 x 17
x = self.Mixed_6c(x)
# N x 768 x 17 x 17
x = self.Mixed_6d(x)
# N x 768 x 17 x 17
x = self.Mixed_6e(x)
# N x 768 x 17 x 17
aux: Optional[Tensor] = None
if self.AuxLogits is not None:
if self.training:
aux = self.AuxLogits(x)
# N x 768 x 17 x 17
x = self.Mixed_7a(x)
# N x 1280 x 8 x 8
x = self.Mixed_7b(x)
# N x 2048 x 8 x 8
x = self.Mixed_7c(x)
# N x 2048 x 8 x 8
# Adaptive average pooling
x = self.avgpool(x)
# N x 2048 x 1 x 1
x = self.dropout(x)
# N x 2048 x 1 x 1
x = torch.flatten(x, 1)
# N x 2048
x = self.fc(x)
# N x 1000 (num_classes)
return x, aux
@torch.jit.unused
def eager_outputs(self, x: Tensor, aux: Optional[Tensor]) -> InceptionOutputs:
if self.training and self.aux_logits:
return InceptionOutputs(x, aux)
else:
return x # type: ignore[return-value]
def forward(self, x: Tensor) -> InceptionOutputs:
x = self._transform_input(x)
x, aux = self._forward(x)
aux_defined = self.training and self.aux_logits
if torch.jit.is_scripting():
if not aux_defined:
warnings.warn("Scripted Inception3 always returns Inception3 Tuple")
return InceptionOutputs(x, aux)
else:
return self.eager_outputs(x, aux)
class InceptionA(nn.Module):
def __init__(
self,
in_channels: int,
pool_features: int,
conv_block: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InceptionA, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)
self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)
self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
def _forward(self, x: Tensor) -> List[Tensor]:
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return outputs
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionB(nn.Module):
def __init__(
self,
in_channels: int,
conv_block: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InceptionB, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
def _forward(self, x: Tensor) -> List[Tensor]:
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionC(nn.Module):
def __init__(
self,
in_channels: int,
channels_7x7: int,
conv_block: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InceptionC, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)
c7 = channels_7x7
self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
def _forward(self, x: Tensor) -> List[Tensor]:
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return outputs
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionD(nn.Module):
def __init__(
self,
in_channels: int,
conv_block: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InceptionD, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
def _forward(self, x: Tensor) -> List[Tensor]:
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch7x7x3 = self.branch7x7x3_1(x)
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch7x7x3, branch_pool]
return outputs
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionE(nn.Module):
def __init__(
self,
in_channels: int,
conv_block: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InceptionE, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
def _forward(self, x: Tensor) -> List[Tensor]:
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionAux(nn.Module):
def __init__(
self,
in_channels: int,
num_classes: int,
conv_block: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InceptionAux, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.conv0 = conv_block(in_channels, 128, kernel_size=1)
self.conv1 = conv_block(128, 768, kernel_size=5)
self.conv1.stddev = 0.01 # type: ignore[assignment]
self.fc = nn.Linear(768, num_classes)
self.fc.stddev = 0.001 # type: ignore[assignment]
def forward(self, x: Tensor) -> Tensor:
# N x 768 x 17 x 17
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# N x 768 x 5 x 5
x = self.conv0(x)
# N x 128 x 5 x 5
x = self.conv1(x)
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x
class BasicConv2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
**kwargs: Any
) -> None:
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x: Tensor) -> Tensor:
x = self.conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
