502 lines
18 KiB
Python
502 lines
18 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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from mmcv.cnn import build_conv_layer
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from mmengine.model import BaseModule
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from mmcls.registry import MODELS
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from .base_backbone import BaseBackbone
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class BasicConv2d(BaseModule):
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"""A basic convolution block including convolution, batch norm and ReLU.
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Args:
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in_channels (int): The number of input channels.
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out_channels (int): The number of output channels.
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conv_cfg (dict, optional): The config of convolution layer.
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Defaults to None, which means to use ``nn.Conv2d``.
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init_cfg (dict, optional): The config of initialization.
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Defaults to None.
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**kwargs: Other keyword arguments of the convolution layer.
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"""
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def __init__(self,
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in_channels: int,
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out_channels: int,
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conv_cfg: Optional[dict] = None,
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init_cfg: Optional[dict] = None,
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**kwargs) -> None:
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super().__init__(init_cfg=init_cfg)
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self.conv = build_conv_layer(
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conv_cfg, in_channels, out_channels, bias=False, **kwargs)
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self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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x = self.conv(x)
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x = self.bn(x)
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return self.relu(x)
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class InceptionA(BaseModule):
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"""Type-A Inception block.
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Args:
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in_channels (int): The number of input channels.
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pool_features (int): The number of channels in pooling branch.
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conv_cfg (dict, optional): The convolution layer config in the
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:class:`BasicConv2d` block. Defaults to None.
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init_cfg (dict, optional): The config of initialization.
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Defaults to None.
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"""
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def __init__(self,
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in_channels: int,
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pool_features: int,
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conv_cfg: Optional[dict] = None,
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init_cfg: Optional[dict] = None):
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super().__init__(init_cfg=init_cfg)
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self.branch1x1 = BasicConv2d(
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in_channels, 64, kernel_size=1, conv_cfg=conv_cfg)
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self.branch5x5_1 = BasicConv2d(
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in_channels, 48, kernel_size=1, conv_cfg=conv_cfg)
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self.branch5x5_2 = BasicConv2d(
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48, 64, kernel_size=5, padding=2, conv_cfg=conv_cfg)
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self.branch3x3dbl_1 = BasicConv2d(
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in_channels, 64, kernel_size=1, conv_cfg=conv_cfg)
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self.branch3x3dbl_2 = BasicConv2d(
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64, 96, kernel_size=3, padding=1, conv_cfg=conv_cfg)
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self.branch3x3dbl_3 = BasicConv2d(
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96, 96, kernel_size=3, padding=1, conv_cfg=conv_cfg)
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self.branch_pool_downsample = nn.AvgPool2d(
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kernel_size=3, stride=1, padding=1)
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self.branch_pool = BasicConv2d(
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in_channels, pool_features, kernel_size=1, conv_cfg=conv_cfg)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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branch1x1 = self.branch1x1(x)
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branch5x5 = self.branch5x5_1(x)
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branch5x5 = self.branch5x5_2(branch5x5)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = self.branch_pool_downsample(x)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionB(BaseModule):
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"""Type-B Inception block.
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Args:
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in_channels (int): The number of input channels.
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conv_cfg (dict, optional): The convolution layer config in the
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:class:`BasicConv2d` block. Defaults to None.
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init_cfg (dict, optional): The config of initialization.
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Defaults to None.
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"""
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def __init__(self,
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in_channels: int,
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conv_cfg: Optional[dict] = None,
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init_cfg: Optional[dict] = None):
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super().__init__(init_cfg=init_cfg)
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self.branch3x3 = BasicConv2d(
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in_channels, 384, kernel_size=3, stride=2, conv_cfg=conv_cfg)
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self.branch3x3dbl_1 = BasicConv2d(
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in_channels, 64, kernel_size=1, conv_cfg=conv_cfg)
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self.branch3x3dbl_2 = BasicConv2d(
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64, 96, kernel_size=3, padding=1, conv_cfg=conv_cfg)
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self.branch3x3dbl_3 = BasicConv2d(
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96, 96, kernel_size=3, stride=2, conv_cfg=conv_cfg)
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self.branch_pool = nn.MaxPool2d(kernel_size=3, stride=2)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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branch3x3 = self.branch3x3(x)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = self.branch_pool(x)
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outputs = [branch3x3, branch3x3dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionC(BaseModule):
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"""Type-C Inception block.
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Args:
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in_channels (int): The number of input channels.
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channels_7x7 (int): The number of channels in 7x7 convolution branch.
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conv_cfg (dict, optional): The convolution layer config in the
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:class:`BasicConv2d` block. Defaults to None.
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init_cfg (dict, optional): The config of initialization.
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Defaults to None.
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"""
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def __init__(self,
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in_channels: int,
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channels_7x7: int,
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conv_cfg: Optional[dict] = None,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.branch1x1 = BasicConv2d(
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in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
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c7 = channels_7x7
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self.branch7x7_1 = BasicConv2d(
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in_channels, c7, kernel_size=1, conv_cfg=conv_cfg)
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self.branch7x7_2 = BasicConv2d(
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c7, c7, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
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self.branch7x7_3 = BasicConv2d(
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c7, 192, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
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self.branch7x7dbl_1 = BasicConv2d(
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in_channels, c7, kernel_size=1, conv_cfg=conv_cfg)
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self.branch7x7dbl_2 = BasicConv2d(
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c7, c7, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
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self.branch7x7dbl_3 = BasicConv2d(
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c7, c7, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
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self.branch7x7dbl_4 = BasicConv2d(
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c7, c7, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
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self.branch7x7dbl_5 = BasicConv2d(
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c7, 192, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
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self.branch_pool_downsample = nn.AvgPool2d(
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kernel_size=3, stride=1, padding=1)
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self.branch_pool = BasicConv2d(
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in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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branch1x1 = self.branch1x1(x)
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branch7x7 = self.branch7x7_1(x)
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branch7x7 = self.branch7x7_2(branch7x7)
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branch7x7 = self.branch7x7_3(branch7x7)
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branch7x7dbl = self.branch7x7dbl_1(x)
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branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
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branch_pool = self.branch_pool_downsample(x)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionD(BaseModule):
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"""Type-D Inception block.
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Args:
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in_channels (int): The number of input channels.
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conv_cfg (dict, optional): The convolution layer config in the
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:class:`BasicConv2d` block. Defaults to None.
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init_cfg (dict, optional): The config of initialization.
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Defaults to None.
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"""
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def __init__(self,
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in_channels: int,
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conv_cfg: Optional[dict] = None,
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init_cfg: Optional[dict] = None):
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super().__init__(init_cfg=init_cfg)
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self.branch3x3_1 = BasicConv2d(
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in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
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self.branch3x3_2 = BasicConv2d(
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192, 320, kernel_size=3, stride=2, conv_cfg=conv_cfg)
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self.branch7x7x3_1 = BasicConv2d(
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in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
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self.branch7x7x3_2 = BasicConv2d(
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192, 192, kernel_size=(1, 7), padding=(0, 3), conv_cfg=conv_cfg)
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self.branch7x7x3_3 = BasicConv2d(
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192, 192, kernel_size=(7, 1), padding=(3, 0), conv_cfg=conv_cfg)
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self.branch7x7x3_4 = BasicConv2d(
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192, 192, kernel_size=3, stride=2, conv_cfg=conv_cfg)
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self.branch_pool = nn.MaxPool2d(kernel_size=3, stride=2)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = self.branch3x3_2(branch3x3)
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branch7x7x3 = self.branch7x7x3_1(x)
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branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
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branch_pool = self.branch_pool(x)
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outputs = [branch3x3, branch7x7x3, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionE(BaseModule):
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"""Type-E Inception block.
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Args:
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in_channels (int): The number of input channels.
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conv_cfg (dict, optional): The convolution layer config in the
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:class:`BasicConv2d` block. Defaults to None.
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init_cfg (dict, optional): The config of initialization.
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Defaults to None.
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"""
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def __init__(self,
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in_channels: int,
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conv_cfg: Optional[dict] = None,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.branch1x1 = BasicConv2d(
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in_channels, 320, kernel_size=1, conv_cfg=conv_cfg)
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self.branch3x3_1 = BasicConv2d(
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in_channels, 384, kernel_size=1, conv_cfg=conv_cfg)
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self.branch3x3_2a = BasicConv2d(
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384, 384, kernel_size=(1, 3), padding=(0, 1), conv_cfg=conv_cfg)
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self.branch3x3_2b = BasicConv2d(
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384, 384, kernel_size=(3, 1), padding=(1, 0), conv_cfg=conv_cfg)
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self.branch3x3dbl_1 = BasicConv2d(
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in_channels, 448, kernel_size=1, conv_cfg=conv_cfg)
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self.branch3x3dbl_2 = BasicConv2d(
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448, 384, kernel_size=3, padding=1, conv_cfg=conv_cfg)
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self.branch3x3dbl_3a = BasicConv2d(
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384, 384, kernel_size=(1, 3), padding=(0, 1), conv_cfg=conv_cfg)
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self.branch3x3dbl_3b = BasicConv2d(
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384, 384, kernel_size=(3, 1), padding=(1, 0), conv_cfg=conv_cfg)
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self.branch_pool_downsample = nn.AvgPool2d(
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kernel_size=3, stride=1, padding=1)
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self.branch_pool = BasicConv2d(
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in_channels, 192, kernel_size=1, conv_cfg=conv_cfg)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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branch1x1 = self.branch1x1(x)
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = [
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self.branch3x3_2a(branch3x3),
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self.branch3x3_2b(branch3x3),
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]
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branch3x3 = torch.cat(branch3x3, 1)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = [
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self.branch3x3dbl_3a(branch3x3dbl),
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self.branch3x3dbl_3b(branch3x3dbl),
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]
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branch3x3dbl = torch.cat(branch3x3dbl, 1)
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branch_pool = self.branch_pool_downsample(x)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionAux(BaseModule):
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"""The Inception block for the auxiliary classification branch.
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Args:
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in_channels (int): The number of input channels.
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num_classes (int): The number of categroies.
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conv_cfg (dict, optional): The convolution layer config in the
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:class:`BasicConv2d` block. Defaults to None.
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init_cfg (dict, optional): The config of initialization.
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Defaults to use trunc normal with ``std=0.01`` for Conv2d layers
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and use trunc normal with ``std=0.001`` for Linear layers..
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"""
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def __init__(self,
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in_channels: int,
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num_classes: int,
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conv_cfg: Optional[dict] = None,
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init_cfg: Optional[dict] = [
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dict(type='TruncNormal', layer='Conv2d', std=0.01),
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dict(type='TruncNormal', layer='Linear', std=0.001)
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]):
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super().__init__(init_cfg=init_cfg)
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self.downsample = nn.AvgPool2d(kernel_size=5, stride=3)
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self.conv0 = BasicConv2d(
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in_channels, 128, kernel_size=1, conv_cfg=conv_cfg)
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self.conv1 = BasicConv2d(128, 768, kernel_size=5, conv_cfg=conv_cfg)
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self.gap = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(768, num_classes)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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# N x 768 x 17 x 17
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x = self.downsample(x)
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# N x 768 x 5 x 5
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x = self.conv0(x)
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# N x 128 x 5 x 5
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x = self.conv1(x)
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# N x 768 x 1 x 1
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# Adaptive average pooling
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x = self.gap(x)
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# N x 768 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 768
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x = self.fc(x)
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# N x 1000
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return x
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@MODELS.register_module()
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class InceptionV3(BaseBackbone):
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"""Inception V3 backbone.
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A PyTorch implementation of `Rethinking the Inception Architecture for
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Computer Vision <https://arxiv.org/abs/1512.00567>`_
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This implementation is modified from
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https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py.
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Licensed under the BSD 3-Clause License.
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Args:
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num_classes (int): The number of categroies. Defaults to 1000.
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aux_logits (bool): Whether to enable the auxiliary branch. If False,
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the auxiliary logits output will be None. Defaults to False.
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dropout (float): Dropout rate. Defaults to 0.5.
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init_cfg (dict, optional): The config of initialization. Defaults
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to use trunc normal with ``std=0.1`` for all Conv2d and Linear
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layers and constant with ``val=1`` for all BatchNorm2d layers.
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Example:
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>>> import torch
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>>> from mmcls.models import build_backbone
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>>>
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>>> inputs = torch.rand(2, 3, 299, 299)
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>>> cfg = dict(type='InceptionV3', num_classes=100)
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>>> backbone = build_backbone(cfg)
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>>> aux_out, out = backbone(inputs)
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>>> # The auxiliary branch is disabled by default.
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>>> assert aux_out is None
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>>> print(out.shape)
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torch.Size([2, 100])
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>>> cfg = dict(type='InceptionV3', num_classes=100, aux_logits=True)
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>>> backbone = build_backbone(cfg)
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>>> aux_out, out = backbone(inputs)
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>>> print(aux_out.shape, out.shape)
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torch.Size([2, 100]) torch.Size([2, 100])
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"""
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def __init__(
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self,
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num_classes: int = 1000,
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aux_logits: bool = False,
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dropout: float = 0.5,
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init_cfg: Optional[dict] = [
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dict(type='TruncNormal', layer=['Conv2d', 'Linear'], std=0.1),
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dict(type='Constant', layer='BatchNorm2d', val=1)
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],
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) -> None:
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super().__init__(init_cfg=init_cfg)
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self.aux_logits = aux_logits
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self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
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self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
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self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
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self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
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self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
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self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.Mixed_5b = InceptionA(192, pool_features=32)
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self.Mixed_5c = InceptionA(256, pool_features=64)
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self.Mixed_5d = InceptionA(288, pool_features=64)
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self.Mixed_6a = InceptionB(288)
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self.Mixed_6b = InceptionC(768, channels_7x7=128)
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self.Mixed_6c = InceptionC(768, channels_7x7=160)
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self.Mixed_6d = InceptionC(768, channels_7x7=160)
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self.Mixed_6e = InceptionC(768, channels_7x7=192)
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self.AuxLogits: Optional[nn.Module] = None
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if aux_logits:
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self.AuxLogits = InceptionAux(768, num_classes)
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self.Mixed_7a = InceptionD(768)
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self.Mixed_7b = InceptionE(1280)
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self.Mixed_7c = InceptionE(2048)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.dropout = nn.Dropout(p=dropout)
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self.fc = nn.Linear(2048, num_classes)
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|
|
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def forward(
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self,
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x: torch.Tensor) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
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"""Forward function."""
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|
# N x 3 x 299 x 299
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|
x = self.Conv2d_1a_3x3(x)
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|
# N x 32 x 149 x 149
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|
x = self.Conv2d_2a_3x3(x)
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|
# N x 32 x 147 x 147
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|
x = self.Conv2d_2b_3x3(x)
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|
# 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
|