mmclassification/mmpretrain/models/necks/spark_neck.py

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

import torch
import torch.nn as nn
from mmengine.model import BaseModule

from mmpretrain.registry import MODELS
from ..utils import build_norm_layer


def is_pow2n(x):
    return x > 0 and (x & (x - 1) == 0)


class ConvBlock2x(BaseModule):
    """The definition of convolution block."""

    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 mid_channels: int,
                 norm_cfg: dict,
                 act_cfg: dict,
                 last_act: bool,
                 init_cfg: Optional[dict] = None) -> None:
        super().__init__(init_cfg=init_cfg)

        self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1, bias=False)
        self.norm1 = build_norm_layer(norm_cfg, mid_channels)
        self.activate1 = MODELS.build(act_cfg)

        self.conv2 = nn.Conv2d(mid_channels, out_channels, 3, 1, 1, bias=False)
        self.norm2 = build_norm_layer(norm_cfg, out_channels)
        self.activate2 = MODELS.build(act_cfg) if last_act else nn.Identity()

    def forward(self, x: torch.Tensor):
        out = self.conv1(x)
        out = self.norm1(out)
        out = self.activate1(out)

        out = self.conv2(out)
        out = self.norm2(out)
        out = self.activate2(out)
        return out


class DecoderConvModule(BaseModule):
    """The convolution module of decoder with upsampling."""

    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 mid_channels: int,
                 kernel_size: int = 4,
                 scale_factor: int = 2,
                 num_conv_blocks: int = 1,
                 norm_cfg: dict = dict(type='SyncBN'),
                 act_cfg: dict = dict(type='ReLU6'),
                 last_act: bool = True,
                 init_cfg: Optional[dict] = None):
        super().__init__(init_cfg=init_cfg)

        assert (kernel_size - scale_factor >= 0) and\
               (kernel_size - scale_factor) % 2 == 0,\
               f'kernel_size should be greater than or equal to scale_factor '\
               f'and (kernel_size - scale_factor) should be even numbers, '\
               f'while the kernel size is {kernel_size} and scale_factor is '\
               f'{scale_factor}.'

        padding = (kernel_size - scale_factor) // 2
        self.upsample = nn.ConvTranspose2d(
            in_channels,
            in_channels,
            kernel_size=kernel_size,
            stride=scale_factor,
            padding=padding,
            bias=True)

        conv_blocks_list = [
            ConvBlock2x(
                in_channels=in_channels,
                out_channels=out_channels,
                mid_channels=mid_channels,
                norm_cfg=norm_cfg,
                last_act=last_act,
                act_cfg=act_cfg) for _ in range(num_conv_blocks)
        ]
        self.conv_blocks = nn.Sequential(*conv_blocks_list)

    def forward(self, x):
        x = self.upsample(x)
        return self.conv_blocks(x)


@MODELS.register_module()
class SparKLightDecoder(BaseModule):
    """The decoder for SparK, which upsamples the feature maps.

    Args:
        feature_dim (int): The dimension of feature map.
        upsample_ratio (int): The ratio of upsample, equal to downsample_raito
            of the algorithm.
        mid_channels (int): The middle channel of `DecoderConvModule`. Defaults
            to 0.
        kernel_size (int): The kernel size of `ConvTranspose2d` in
            `DecoderConvModule`. Defaults to 4.
        scale_factor (int): The scale_factor of `ConvTranspose2d` in
            `DecoderConvModule`. Defaults to 2.
        num_conv_blocks (int): The number of convolution blocks in
            `DecoderConvModule`. Defaults to 1.
        norm_cfg (dict): Normalization config. Defaults to dict(type='SyncBN').
        act_cfg (dict): Activation config. Defaults to dict(type='ReLU6').
        last_act (bool): Whether apply the last activation in
            `DecoderConvModule`. Defaults to False.
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """

    def __init__(
        self,
        feature_dim: int,
        upsample_ratio: int,
        mid_channels: int = 0,
        kernel_size: int = 4,
        scale_factor: int = 2,
        num_conv_blocks: int = 1,
        norm_cfg: dict = dict(type='SyncBN'),
        act_cfg: dict = dict(type='ReLU6'),
        last_act: bool = False,
        init_cfg: Optional[dict] = [
            dict(type='Kaiming', layer=['Conv2d', 'ConvTranspose2d']),
            dict(type='TruncNormal', std=0.02, layer=['Linear']),
            dict(
                type='Constant',
                val=1,
                layer=['_BatchNorm', 'LayerNorm', 'SyncBatchNorm'])
        ],
    ):
        super().__init__(init_cfg=init_cfg)
        self.feature_dim = feature_dim

        assert is_pow2n(upsample_ratio)
        n = round(math.log2(upsample_ratio))
        channels = [feature_dim // 2**i for i in range(n + 1)]

        self.decoder = nn.ModuleList([
            DecoderConvModule(
                in_channels=c_in,
                out_channels=c_out,
                mid_channels=c_in if mid_channels == 0 else mid_channels,
                kernel_size=kernel_size,
                scale_factor=scale_factor,
                num_conv_blocks=num_conv_blocks,
                norm_cfg=norm_cfg,
                act_cfg=act_cfg,
                last_act=last_act)
            for (c_in, c_out) in zip(channels[:-1], channels[1:])
        ])
        self.proj = nn.Conv2d(
            channels[-1], 3, kernel_size=1, stride=1, bias=True)

    def forward(self, to_dec):
        x = 0
        for i, d in enumerate(self.decoder):
            if i < len(to_dec) and to_dec[i] is not None:
                x = x + to_dec[i]
            x = self.decoder[i](x)
        return self.proj(x)