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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import build_conv_layer
from mmengine.model import BaseModule
from mmpretrain.registry import MODELS
from .base_backbone import BaseBackbone
class BasicConv2d(BaseModule):
"""A basic convolution block including convolution, batch norm and ReLU.
Args:
in_channels (int): The number of input channels.
out_channels (int): The number of output channels.
conv_cfg (dict, optional): The config of convolution layer.
Defaults to None, which means to use ``nn.Conv2d``.
init_cfg (dict, optional): The config of initialization.
Defaults to None.
**kwargs: Other keyword arguments of the convolution layer.
"""
def __init__(self,
in_channels: int,
out_channels: int,
conv_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = None,
**kwargs) -> None:
super().__init__(init_cfg=init_cfg)
self.conv = build_conv_layer(
conv_cfg, in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
self.relu = nn.ReLU(inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
x = self.conv(x)
x = self.bn(x)
return self.relu(x)
class InceptionA(BaseModule):
"""Type-A Inception block.
Args:
in_channels (int): The number of input channels.
pool_features (int): The number of channels in pooling branch.
conv_cfg (dict, optional): The convolution layer config in the
:class:`BasicConv2d` block. Defaults to None.
init_cfg (dict, optional): The config of initialization.
Defaults to None.
"""
def __init__(self,
in_channels: int,
pool_features: int,
conv_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = None):
super().__init__(init_cfg=init_cfg)
self.branch1x1 = BasicConv2d(
in_channels, 64, kernel_size=1, conv_cfg=conv_cfg)
self.branch5x5_1 = BasicConv2d(
in_channels, 48, kernel_size=1, conv_cfg=conv_cfg)
self.branch5x5_2 = BasicConv2d(
48, 64, kernel_size=5, padding=2, conv_cfg=conv_cfg)
self.branch3x3dbl_1 = BasicConv2d(
in_channels, 64, kernel_size=1, conv_cfg=conv_cfg)
self.branch3x3dbl_2 = BasicConv2d(
64, 96, kernel_size=3, padding=1, conv_cfg=conv_cfg)
self.branch3x3dbl_3 = BasicConv2d(
96, 96, kernel_size=3, padding=1, conv_cfg=conv_cfg)
self.branch_pool_downsample = nn.AvgPool2d(
kernel_size=3, stride=1, padding=1)
self.branch_pool = BasicConv2d(
in_channels, pool_features, kernel_size=1, conv_cfg=conv_cfg)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
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 = self.branch_pool_downsample(x)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
class InceptionB(BaseModule):
"""Type-B Inception block.
Args:
in_channels (int): The number of input channels.
conv_cfg (dict, optional): The convolution layer config in the
:class:`BasicConv2d` block. Defaults to None.
init_cfg (dict, optional): The config of initialization.
Defaults to None.
"""
def __init__(self,
in_channels: int,
conv_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = None):
super().__init__(init_cfg=init_cfg)
self.branch3x3 = BasicConv2d(
in_channels, 384, kernel_size=3, stride=2, conv_cfg=conv_cfg)
self.branch3x3dbl_1 = BasicConv2d(
in_channels, 64, kernel_size=1, conv_cfg=conv_cfg)
self.branch3x3dbl_2 = BasicConv2d(
64, 96, kernel_size=3, padding=1, conv_cfg=conv_cfg)
self.branch3x3dbl_3 = BasicConv2d(
96, 96, kernel_size=3, stride=2, conv_cfg=conv_cfg)
self.branch_pool = nn.MaxPool2d(kernel_size=3, stride=2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = self.branch_pool(x)
outputs = [branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
class InceptionC(BaseModule):
"""Type-C Inception block.
Args:
in_channels (int): The number of input channels.
channels_7x7 (int): The number of channels in 7x7 convolution branch.
conv_cfg (dict, optional): The convolution layer config in the
:class:`BasicConv2d` block. Defaults to None.
init_cfg (dict, optional): The config of initialization.
Defaults to None.
"""
def __init__(self,
in_channels: int,
channels_7x7: int,
conv_cfg: Optional[dict] = None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.branch1x1 = BasicConv2d(
in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
c7 = channels_7x7
self.branch7x7_1 = BasicConv2d(
in_channels, c7, kernel_size=1, conv_cfg=conv_cfg)
self.branch7x7_2 = BasicConv2d(
c7, c7, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
self.branch7x7_3 = BasicConv2d(
c7, 192, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
self.branch7x7dbl_1 = BasicConv2d(
in_channels, c7, kernel_size=1, conv_cfg=conv_cfg)
self.branch7x7dbl_2 = BasicConv2d(
c7, c7, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
self.branch7x7dbl_3 = BasicConv2d(
c7, c7, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
self.branch7x7dbl_4 = BasicConv2d(
c7, c7, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
self.branch7x7dbl_5 = BasicConv2d(
c7, 192, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
self.branch_pool_downsample = nn.AvgPool2d(
kernel_size=3, stride=1, padding=1)
self.branch_pool = BasicConv2d(
in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
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 = self.branch_pool_downsample(x)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)
class InceptionD(BaseModule):
"""Type-D Inception block.
Args:
in_channels (int): The number of input channels.
conv_cfg (dict, optional): The convolution layer config in the
:class:`BasicConv2d` block. Defaults to None.
init_cfg (dict, optional): The config of initialization.
Defaults to None.
"""
def __init__(self,
in_channels: int,
conv_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = None):
super().__init__(init_cfg=init_cfg)
self.branch3x3_1 = BasicConv2d(
in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
self.branch3x3_2 = BasicConv2d(
192, 320, kernel_size=3, stride=2, conv_cfg=conv_cfg)
self.branch7x7x3_1 = BasicConv2d(
in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
self.branch7x7x3_2 = BasicConv2d(
192, 192, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
self.branch7x7x3_3 = BasicConv2d(
192, 192, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
self.branch7x7x3_4 = BasicConv2d(
192, 192, kernel_size=3, stride=2, conv_cfg=conv_cfg)
self.branch_pool = nn.MaxPool2d(kernel_size=3, stride=2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
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 = self.branch_pool(x)
outputs = [branch3x3, branch7x7x3, branch_pool]
return torch.cat(outputs, 1)
class InceptionE(BaseModule):
"""Type-E Inception block.
Args:
in_channels (int): The number of input channels.
conv_cfg (dict, optional): The convolution layer config in the
:class:`BasicConv2d` block. Defaults to None.
init_cfg (dict, optional): The config of initialization.
Defaults to None.
"""
def __init__(self,
in_channels: int,
conv_cfg: Optional[dict] = None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.branch1x1 = BasicConv2d(
in_channels, 320, kernel_size=1, conv_cfg=conv_cfg)
self.branch3x3_1 = BasicConv2d(
in_channels, 384, kernel_size=1, conv_cfg=conv_cfg)
self.branch3x3_2a = BasicConv2d(
384, 384, kernel_size=(1, 3), padding=(0, 1), conv_cfg=conv_cfg)
self.branch3x3_2b = BasicConv2d(
384, 384, kernel_size=(3, 1), padding=(1, 0), conv_cfg=conv_cfg)
self.branch3x3dbl_1 = BasicConv2d(
in_channels, 448, kernel_size=1, conv_cfg=conv_cfg)
self.branch3x3dbl_2 = BasicConv2d(
448, 384, kernel_size=3, padding=1, conv_cfg=conv_cfg)
self.branch3x3dbl_3a = BasicConv2d(
384, 384, kernel_size=(1, 3), padding=(0, 1), conv_cfg=conv_cfg)
self.branch3x3dbl_3b = BasicConv2d(
384, 384, kernel_size=(3, 1), padding=(1, 0), conv_cfg=conv_cfg)
self.branch_pool_downsample = nn.AvgPool2d(
kernel_size=3, stride=1, padding=1)
self.branch_pool = BasicConv2d(
in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
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 = self.branch_pool_downsample(x)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
class InceptionAux(BaseModule):
"""The Inception block for the auxiliary classification branch.
Args:
in_channels (int): The number of input channels.
num_classes (int): The number of categroies.
conv_cfg (dict, optional): The convolution layer config in the
:class:`BasicConv2d` block. Defaults to None.
init_cfg (dict, optional): The config of initialization.
Defaults to use trunc normal with ``std=0.01`` for Conv2d layers
and use trunc normal with ``std=0.001`` for Linear layers..
"""
def __init__(self,
in_channels: int,
num_classes: int,
conv_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = [
dict(type='TruncNormal', layer='Conv2d', std=0.01),
dict(type='TruncNormal', layer='Linear', std=0.001)
]):
super().__init__(init_cfg=init_cfg)
self.downsample = nn.AvgPool2d(kernel_size=5, stride=3)
self.conv0 = BasicConv2d(
in_channels, 128, kernel_size=1, conv_cfg=conv_cfg)
self.conv1 = BasicConv2d(128, 768, kernel_size=5, conv_cfg=conv_cfg)
self.gap = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(768, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
# N x 768 x 17 x 17
x = self.downsample(x)
# 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 = self.gap(x)
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x
@MODELS.register_module()
class InceptionV3(BaseBackbone):
"""Inception V3 backbone.
A PyTorch implementation of `Rethinking the Inception Architecture for
Computer Vision <https://arxiv.org/abs/1512.00567>`_
This implementation is modified from
https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py.
Licensed under the BSD 3-Clause License.
Args:
num_classes (int): The number of categroies. Defaults to 1000.
aux_logits (bool): Whether to enable the auxiliary branch. If False,
the auxiliary logits output will be None. Defaults to False.
dropout (float): Dropout rate. Defaults to 0.5.
init_cfg (dict, optional): The config of initialization. Defaults
to use trunc normal with ``std=0.1`` for all Conv2d and Linear
layers and constant with ``val=1`` for all BatchNorm2d layers.
Example:
>>> import torch
>>> from mmpretrain.models import build_backbone
>>>
>>> inputs = torch.rand(2, 3, 299, 299)
>>> cfg = dict(type='InceptionV3', num_classes=100)
>>> backbone = build_backbone(cfg)
>>> aux_out, out = backbone(inputs)
>>> # The auxiliary branch is disabled by default.
>>> assert aux_out is None
>>> print(out.shape)
torch.Size([2, 100])
>>> cfg = dict(type='InceptionV3', num_classes=100, aux_logits=True)
>>> backbone = build_backbone(cfg)
>>> aux_out, out = backbone(inputs)
>>> print(aux_out.shape, out.shape)
torch.Size([2, 100]) torch.Size([2, 100])
"""
def __init__(
self,
num_classes: int = 1000,
aux_logits: bool = False,
dropout: float = 0.5,
init_cfg: Optional[dict] = [
dict(type='TruncNormal', layer=['Conv2d', 'Linear'], std=0.1),
dict(type='Constant', layer='BatchNorm2d', val=1)
],
) -> None:
super().__init__(init_cfg=init_cfg)
self.aux_logits = aux_logits
self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Mixed_5b = InceptionA(192, pool_features=32)
self.Mixed_5c = InceptionA(256, pool_features=64)
self.Mixed_5d = InceptionA(288, pool_features=64)
self.Mixed_6a = InceptionB(288)
self.Mixed_6b = InceptionC(768, channels_7x7=128)
self.Mixed_6c = InceptionC(768, channels_7x7=160)
self.Mixed_6d = InceptionC(768, channels_7x7=160)
self.Mixed_6e = InceptionC(768, channels_7x7=192)
self.AuxLogits: Optional[nn.Module] = None
if aux_logits:
self.AuxLogits = InceptionAux(768, num_classes)
self.Mixed_7a = InceptionD(768)
self.Mixed_7b = InceptionE(1280)
self.Mixed_7c = InceptionE(2048)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(p=dropout)
self.fc = nn.Linear(2048, num_classes)
def forward(
self,
x: torch.Tensor) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
"""Forward function."""
# 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[torch.Tensor] = None
if self.aux_logits and 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 aux, x
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