# 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))