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mmcls.models.backbones.convmixer 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence

import torch
import torch.nn as nn
from mmcv.cnn.bricks import (Conv2dAdaptivePadding, build_activation_layer,
from mmcv.utils import digit_version

from ..builder import BACKBONES
from .base_backbone import BaseBackbone

class Residual(nn.Module):

    def __init__(self, fn):
        self.fn = fn

    def forward(self, x):
        return self.fn(x) + x

[文档]@BACKBONES.register_module() class ConvMixer(BaseBackbone): """ConvMixer. . A PyTorch implementation of : `Patches Are All You Need? <>`_ Modified from the `official repo <>`_ and `timm <>`_. Args: arch (str | dict): The model's architecture. If string, it should be one of architecture in ``ConvMixer.arch_settings``. And if dict, it should include the following two keys: - embed_dims (int): The dimensions of patch embedding. - depth (int): Number of repetitions of ConvMixer Layer. - patch_size (int): The patch size. - kernel_size (int): The kernel size of depthwise conv layers. Defaults to '768/32'. in_channels (int): Number of input image channels. Defaults to 3. patch_size (int): The size of one patch in the patch embed layer. Defaults to 7. norm_cfg (dict): The config dict for norm layers. Defaults to ``dict(type='BN')``. act_cfg (dict): The config dict for activation after each convolution. Defaults to ``dict(type='GELU')``. out_indices (Sequence | int): Output from which stages. Defaults to -1, means the last stage. frozen_stages (int): Stages to be frozen (all param fixed). Defaults to 0, which means not freezing any parameters. init_cfg (dict, optional): Initialization config dict. """ arch_settings = { '768/32': { 'embed_dims': 768, 'depth': 32, 'patch_size': 7, 'kernel_size': 7 }, '1024/20': { 'embed_dims': 1024, 'depth': 20, 'patch_size': 14, 'kernel_size': 9 }, '1536/20': { 'embed_dims': 1536, 'depth': 20, 'patch_size': 7, 'kernel_size': 9 }, } def __init__(self, arch='768/32', in_channels=3, norm_cfg=dict(type='BN'), act_cfg=dict(type='GELU'), out_indices=-1, frozen_stages=0, init_cfg=None): super().__init__(init_cfg=init_cfg) if isinstance(arch, str): assert arch in self.arch_settings, \ f'Unavailable arch, please choose from ' \ f'({set(self.arch_settings)}) or pass a dict.' arch = self.arch_settings[arch] elif isinstance(arch, dict): essential_keys = { 'embed_dims', 'depth', 'patch_size', 'kernel_size' } assert isinstance(arch, dict) and essential_keys <= set(arch), \ f'Custom arch needs a dict with keys {essential_keys}' self.embed_dims = arch['embed_dims'] self.depth = arch['depth'] self.patch_size = arch['patch_size'] self.kernel_size = arch['kernel_size'] self.act = build_activation_layer(act_cfg) # check out indices and frozen stages if isinstance(out_indices, int): out_indices = [out_indices] assert isinstance(out_indices, Sequence), \ f'"out_indices" must by a sequence or int, ' \ f'get {type(out_indices)} instead.' for i, index in enumerate(out_indices): if index < 0: out_indices[i] = self.depth + index assert out_indices[i] >= 0, f'Invalid out_indices {index}' self.out_indices = out_indices self.frozen_stages = frozen_stages # Set stem layers self.stem = nn.Sequential( nn.Conv2d( in_channels, self.embed_dims, kernel_size=self.patch_size, stride=self.patch_size), self.act, build_norm_layer(norm_cfg, self.embed_dims)[1]) # Set conv2d according to torch version convfunc = nn.Conv2d if digit_version(torch.__version__) < digit_version('1.9.0'): convfunc = Conv2dAdaptivePadding # Repetitions of ConvMixer Layer self.stages = nn.Sequential(*[ nn.Sequential( Residual( nn.Sequential( convfunc( self.embed_dims, self.embed_dims, self.kernel_size, groups=self.embed_dims, padding='same'), self.act, build_norm_layer(norm_cfg, self.embed_dims)[1])), nn.Conv2d(self.embed_dims, self.embed_dims, kernel_size=1), self.act, build_norm_layer(norm_cfg, self.embed_dims)[1]) for _ in range(self.depth) ]) self._freeze_stages() def forward(self, x): x = self.stem(x) outs = [] for i, stage in enumerate(self.stages): x = stage(x) if i in self.out_indices: outs.append(x) # x = self.pooling(x).flatten(1) return tuple(outs) def train(self, mode=True): super(ConvMixer, self).train(mode) self._freeze_stages() def _freeze_stages(self): for i in range(self.frozen_stages): stage = self.stages[i] stage.eval() for param in stage.parameters(): param.requires_grad = False
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