model_convnextv2.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in this directory. It is adapted (vendored, self-contained)
# from facebookresearch/ConvNeXt-V2 (models/convnextv2.py, models/utils.py):
# the timm and MinkowskiEngine dependencies are removed and the sparse
# (FCMAE pretraining) code paths dropped, so only the dense inference model
# remains. The architecture and parameter names are unchanged, so checkpoints
# trained with the upstream code load without modification.

import torch
import torch.nn as nn
import torch.nn.functional as F


def trunc_normal_(tensor, std=0.02):
    # No-op at inference: weights are loaded from a checkpoint, so the
    # init distribution is irrelevant. Kept only so _init_weights runs.
    return tensor


class LayerNorm(nn.Module):
    """LayerNorm supporting channels_last (N, H, W, C) or channels_first
    (N, C, H, W) layouts."""

    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)
        u = x.mean(1, keepdim=True)
        s = (x - u).pow(2).mean(1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        return self.weight[:, None, None] * x + self.bias[:, None, None]


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

    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, x):
        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x


class Block(nn.Module):
    """ConvNeXt V2 block. drop_path is unused at inference (always Identity)."""

    def __init__(self, dim, drop_path=0.0):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
        self.norm = LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, 4 * dim)
        self.act = nn.GELU()
        self.grn = GRN(4 * dim)
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.drop_path = nn.Identity()

    def forward(self, x):
        inp = x
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1)
        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)
        return inp + self.drop_path(x)


class ConvNeXtV2(nn.Module):
    def __init__(self, in_chans=3, num_classes=1000,
                 depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
                 drop_path_rate=0.0, head_init_scale=1.0):
        super().__init__()
        self.depths = depths
        self.downsample_layers = nn.ModuleList()
        stem = nn.Sequential(
            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
            LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
        )
        self.downsample_layers.append(stem)
        for i in range(3):
            self.downsample_layers.append(nn.Sequential(
                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
                nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
            ))

        self.stages = nn.ModuleList()
        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
        cur = 0
        for i in range(4):
            self.stages.append(nn.Sequential(
                *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])]
            ))
            cur += depths[i]

        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)
        self.head = nn.Linear(dims[-1], num_classes)
        self.apply(self._init_weights)
        self.head.weight.data.mul_(head_init_scale)
        self.head.bias.data.mul_(head_init_scale)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=0.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward_features(self, x):
        for i in range(4):
            x = self.downsample_layers[i](x)
            x = self.stages[i](x)
        return self.norm(x.mean([-2, -1]))

    def forward(self, x):
        return self.head(self.forward_features(x))


def convnextv2_nano(**kwargs):
    return ConvNeXtV2(depths=[2, 2, 8, 2], dims=[80, 160, 320, 640], **kwargs)


def convnextv2_tiny(**kwargs):
    return ConvNeXtV2(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)


def convnextv2_base(**kwargs):
    return ConvNeXtV2(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)


FACTORY = {
    "cv2n": convnextv2_nano,
    "cv2t": convnextv2_tiny,
    "cv2b": convnextv2_base,
}