openrec/modeling/encoders/convnextv2.py

# Copyright (c) Meta Platforms, Inc. and affiliates.

# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import trunc_normal_

from openrec.modeling.common import DropPath


class LayerNorm(nn.Module):
    """ LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
    with shape (batch_size, channels, height, width).
    """

    def __init__(self,
                 normalized_shape,
                 eps=1e-6,
                 data_format='channels_last'):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ['channels_last', 'channels_first']:
            raise NotImplementedError
        self.normalized_shape = (normalized_shape, )

    def forward(self, x):
        if self.data_format == 'channels_last':
            return F.layer_norm(x, self.normalized_shape, self.weight,
                                self.bias, self.eps)
        elif self.data_format == 'channels_first':
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x


class GRN(nn.Module):
    """ GRN (Global Response Normalization) layer
    """

    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))

    def forward(self, inputs, mask=None):
        x = inputs
        if mask is not None:
            x = x * (1. - mask)
        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (inputs * Nx) + self.beta + inputs


class Block(nn.Module):
    """ ConvNeXtV2 Block.

    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
    """

    def __init__(self, dim, drop_path=0.):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3,
                                groups=dim)  # depthwise conv
        self.norm = LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(
            dim,
            4 * dim)  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.grn = GRN(4 * dim)
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        input = x
        x = self.dwconv(x.contiguous())
        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.grn(x)
        x = self.pwconv2(x)
        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)
        return x


class ConvNeXtV2(nn.Module):
    """ ConvNeXt V2

    Args:
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
        drop_path_rate (float): Stochastic depth rate. Default: 0.
        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.

    """

    def __init__(
        self,
        in_channels=3,
        depths=[3, 3, 9, 3],
        dims=[96, 192, 384, 768],
        drop_path_rate=0.,
        strides=[(4, 4), (2, 2), (2, 2), (2, 2)],
        out_channels=256,
        last_stage=False,
        feat2d=False,
        **kwargs,
    ):
        super().__init__()
        self.strides = strides
        self.depths = depths
        self.downsample_layers = nn.ModuleList(
        )  # stem and 3 intermediate downsampling conv layers
        stem = nn.Sequential(
            nn.Conv2d(in_channels,
                      dims[0],
                      kernel_size=strides[0],
                      stride=strides[0]),
            LayerNorm(dims[0], eps=1e-6, data_format='channels_first'))
        self.downsample_layers.append(stem)
        for i in range(3):
            downsample_layer = nn.Sequential(
                LayerNorm(dims[i], eps=1e-6, data_format='channels_first'),
                nn.Conv2d(dims[i],
                          dims[i + 1],
                          kernel_size=strides[i + 1],
                          stride=strides[i + 1]),
            )
            self.downsample_layers.append(downsample_layer)

        self.stages = nn.ModuleList(
        )  # 4 feature resolution stages, each consisting of multiple residual blocks
        dp_rates = [
            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
        ]
        cur = 0
        for i in range(4):
            stage = nn.Sequential(*[
                Block(dim=dims[i], drop_path=dp_rates[cur + j])
                for j in range(depths[i])
            ])
            self.stages.append(stage)
            cur += depths[i]
        self.out_channels = dims[-1]
        self.last_stage = last_stage
        self.feat2d = feat2d
        if last_stage:
            self.out_channels = out_channels
            self.last_conv = nn.Linear(dims[-1], self.out_channels, bias=False)
            self.hardswish = nn.Hardswish()
            self.dropout = nn.Dropout(p=0.1)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, (nn.Conv2d, nn.Linear)) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
            if m.weight is not None:
                nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.SyncBatchNorm):
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
            if m.weight is not None:
                nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.BatchNorm2d):
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
            if m.weight is not None:
                nn.init.constant_(m.weight, 1.0)

    def no_weight_decay(self):
        return {}

    def forward(self, x):
        feats = []
        for i in range(4):
            x = self.downsample_layers[i](x)
            x = self.stages[i](x)
            feats.append(x)

        if self.last_stage:
            x = x.mean(2).transpose(1, 2)
            x = self.last_conv(x)
            x = self.hardswish(x)
            x = self.dropout(x)
            return x
        if self.feat2d:
            return x
        return feats