MindEyeV2/src/autoencoder/convnext.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 timm.models.layers import trunc_normal_, DropPath


class Block(nn.Module):
    r""" ConvNeXt Block. There are two equivalent implementations:
    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
    We use (2) as we find it slightly faster in PyTorch
    
    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):
        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.pwconv2 = nn.Linear(4 * dim, dim)
        self.gamma = (
            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            if layer_scale_init_value > 0
            else None
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, x):
        input = x
        x = self.dwconv(x)
        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.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * 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 ConvNeXt(nn.Module):
    r""" ConvNeXt
        A PyTorch impl of : `A ConvNet for the 2020s`  -
          https://arxiv.org/pdf/2201.03545.pdf
    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.
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
    """

    def __init__(
        self,
        in_chans=3,
        num_classes=1000,
        depths=[3, 3, 9, 3],
        dims=[96, 192, 384, 768],
        drop_path_rate=0.1,
        layer_scale_init_value=0.0,
        head_init_scale=1.0,
    ):
        super().__init__()

        self.downsample_layers = (
            nn.ModuleList()
        )  # stem and 3 intermediate downsampling conv layers
        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):
            downsample_layer = 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.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],
                        layer_scale_init_value=layer_scale_init_value,
                    )
                    for j in range(depths[i])
                ]
            )
            self.stages.append(stage)
            cur += depths[i]

        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=0.02)
            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)
        x = x.flatten(2, 3).permute(0, 2, 1)
        x = self.norm(x)
        x_avg = x.mean(dim=1)
        return x, x_avg

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


class LayerNorm(nn.Module):
    r""" 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


def convnext_tiny(**kwargs):
    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
    return model, 768


def convnext_small(**kwargs):
    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
    return model, 768


def convnext_base(**kwargs):
    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
    return model, 1024


def convnext_large(**kwargs):
    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
    return model, 1536


def convnext_xlarge(**kwargs):
    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
    return model, 2048

def MLP(mlp, embedding, norm_layer):
    mlp_spec = f"{embedding}-{mlp}"
    layers = []
    f = list(map(int, mlp_spec.split("-")))
    for i in range(len(f) - 2):
        layers.append(nn.Linear(f[i], f[i + 1]))
        if norm_layer == "batch_norm":
            layers.append(nn.BatchNorm1d(f[i + 1]))
        elif norm_layer == "layer_norm":
            layers.append(nn.LayerNorm(f[i + 1]))
        layers.append(nn.ReLU(True))
    layers.append(nn.Linear(f[-2], f[-1], bias=False))
    return nn.Sequential(*layers)

class ConvnextXL(torch.nn.Module):
    def __init__(self, path):
        super().__init__()
        self.backbone, _ = convnext_xlarge()
        self.maps_projector = MLP('512-512-512', 2048, 'layer_norm')
        self.init_weights(path)

    def init_weights(self, path):
        sd = torch.load(path, map_location='cpu')['model']
        for k in list(sd.keys()):
            if 'module' in k:
                sd[k.replace('module.', '')] = sd[k]
                del sd[k]
        try:
            self.load_state_dict(sd, strict=False)
        except:
            pass
    
    def forward(self, x):
        p, _ = self.backbone(x)  # b, hw, 2048
        return p, self.maps_projector(p)


if __name__== "__main__":
    m = ConvnextXL('../train_logs/models/convnext_xlarge_alpha0.75_fullckpt.pth')
    preds = m(torch.randn(2, 3, 512, 512))