# 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