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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 mmcls.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 | |
| 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 mmcls.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 | |