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You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.

Source code for mmselfsup.models.backbones.vision_transformer

# Copyright (c) OpenMMLab. All rights reserved.
import math
from functools import reduce
from operator import mul

import torch.nn as nn
from mmcls.models.backbones import VisionTransformer as _VisionTransformer
from mmcls.models.utils import to_2tuple
from mmcv.cnn.bricks.transformer import PatchEmbed
from torch.nn.modules.batchnorm import _BatchNorm

from mmselfsup.models.utils import build_2d_sincos_position_embedding
from ..builder import BACKBONES


@BACKBONES.register_module()
class VisionTransformer(_VisionTransformer):
    """Vision Transformer.

    A pytorch implement of: `An Images is Worth 16x16 Words: Transformers for
    Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.

    Part of the code is modified from:
    `<https://github.com/facebookresearch/moco-v3/blob/main/vits.py>`_.

    Args:
        stop_grad_conv1 (bool, optional): whether to stop the gradient of
            convolution layer in `PatchEmbed`. Defaults to False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters. Defaults to -1.
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Defaults to False.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Defaults to None.
    """

    arch_zoo = {
        **dict.fromkeys(
            ['mocov3-s', 'mocov3-small'], {
                'embed_dims': 384,
                'num_layers': 12,
                'num_heads': 12,
                'feedforward_channels': 1536,
            }),
        **dict.fromkeys(
            ['b', 'base'], {
                'embed_dims': 768,
                'num_layers': 12,
                'num_heads': 12,
                'feedforward_channels': 3072
            }),
    }

    def __init__(self,
                 stop_grad_conv1=False,
                 frozen_stages=-1,
                 norm_eval=False,
                 init_cfg=None,
                 **kwargs):
        super(VisionTransformer, self).__init__(init_cfg=init_cfg, **kwargs)
        self.patch_size = kwargs['patch_size']
        self.frozen_stages = frozen_stages
        self.norm_eval = norm_eval
        self.init_cfg = init_cfg

        if isinstance(self.patch_embed, PatchEmbed):
            if stop_grad_conv1:
                self.patch_embed.projection.weight.requires_grad = False
                self.patch_embed.projection.bias.requires_grad = False

        self._freeze_stages()

    def init_weights(self):
        super(VisionTransformer, self).init_weights()

        if not (isinstance(self.init_cfg, dict)
                and self.init_cfg['type'] == 'Pretrained'):

            # Use fixed 2D sin-cos position embedding
            pos_emb = build_2d_sincos_position_embedding(
                patches_resolution=self.patch_resolution,
                embed_dims=self.embed_dims,
                cls_token=True)
            self.pos_embed.data.copy_(pos_emb)
            self.pos_embed.requires_grad = False

            # xavier_uniform initialization for PatchEmbed
            if isinstance(self.patch_embed, PatchEmbed):
                val = math.sqrt(
                    6. / float(3 * reduce(mul, to_2tuple(self.patch_size), 1) +
                               self.embed_dims))
                nn.init.uniform_(self.patch_embed.projection.weight, -val, val)
                nn.init.zeros_(self.patch_embed.projection.bias)

            # initialization for linear layers
            for name, m in self.named_modules():
                if isinstance(m, nn.Linear):
                    if 'qkv' in name:
                        # treat the weights of Q, K, V separately
                        val = math.sqrt(
                            6. /
                            float(m.weight.shape[0] // 3 + m.weight.shape[1]))
                        nn.init.uniform_(m.weight, -val, val)
                    else:
                        nn.init.xavier_uniform_(m.weight)
                    nn.init.zeros_(m.bias)
            nn.init.normal_(self.cls_token, std=1e-6)

    def _freeze_stages(self):
        """Freeze patch_embed layer, some parameters and stages."""
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False

            self.cls_token.requires_grad = False
            self.pos_embed.requires_grad = False

        for i in range(1, self.frozen_stages + 1):
            m = self.layers[i - 1]
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

            if i == (self.num_layers) and self.final_norm:
                for param in getattr(self, 'norm1').parameters():
                    param.requires_grad = False

    def train(self, mode=True):
        super(VisionTransformer, self).train(mode)
        self._freeze_stages()
        if mode and self.norm_eval:
            for m in self.modules():
                # trick: eval have effect on BatchNorm only
                if isinstance(m, _BatchNorm):
                    m.eval()