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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.utils.checkpoint as checkpoint |
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from mmcv.cnn.bricks import DropPath |
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from mmdet.registry import MODELS |
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@MODELS.register_module() |
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class FocalNet(nn.Module): |
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def __init__( |
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self, |
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patch_size=4, |
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in_chans=3, |
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embed_dim=96, |
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depths=[2, 2, 6, 2], |
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mlp_ratio=4., |
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drop_rate=0., |
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drop_path_rate=0.3, |
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norm_layer=nn.LayerNorm, |
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patch_norm=True, |
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out_indices=[0, 1, 2, 3], |
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frozen_stages=-1, |
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focal_levels=[3, 3, 3, 3], |
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focal_windows=[3, 3, 3, 3], |
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use_pre_norms=[False, False, False, False], |
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use_conv_embed=True, |
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use_postln=True, |
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use_postln_in_modulation=False, |
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scaling_modulator=True, |
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use_layerscale=True, |
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use_checkpoint=False, |
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): |
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super().__init__() |
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self.num_layers = len(depths) |
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self.embed_dim = embed_dim |
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self.patch_norm = patch_norm |
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self.out_indices = out_indices |
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self.frozen_stages = frozen_stages |
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self.patch_embed = PatchEmbed( |
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patch_size=patch_size, |
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in_chans=in_chans, |
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embed_dim=embed_dim, |
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norm_layer=norm_layer if self.patch_norm else None, |
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use_conv_embed=use_conv_embed, |
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is_stem=True, |
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use_pre_norm=False) |
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self.pos_drop = nn.Dropout(p=drop_rate) |
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dpr = [ |
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x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) |
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] |
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self.layers = nn.ModuleList() |
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for i_layer in range(self.num_layers): |
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layer = BasicLayer( |
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dim=int(embed_dim * 2**i_layer), |
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depth=depths[i_layer], |
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mlp_ratio=mlp_ratio, |
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drop=drop_rate, |
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drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], |
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norm_layer=norm_layer, |
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downsample=PatchEmbed if |
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(i_layer < self.num_layers - 1) else None, |
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focal_window=focal_windows[i_layer], |
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focal_level=focal_levels[i_layer], |
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use_pre_norm=use_pre_norms[i_layer], |
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use_conv_embed=use_conv_embed, |
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use_postln=use_postln, |
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use_postln_in_modulation=use_postln_in_modulation, |
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scaling_modulator=scaling_modulator, |
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use_layerscale=use_layerscale, |
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use_checkpoint=use_checkpoint) |
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self.layers.append(layer) |
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num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] |
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self.num_features = num_features |
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for i_layer in self.out_indices: |
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layer = norm_layer(num_features[i_layer]) |
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layer_name = f'norm{i_layer}' |
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self.add_module(layer_name, layer) |
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def forward(self, x): |
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x = self.patch_embed(x) |
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Wh, Ww = x.size(2), x.size(3) |
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x = x.flatten(2).transpose(1, 2) |
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x = self.pos_drop(x) |
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outs = {} |
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for i in range(self.num_layers): |
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layer = self.layers[i] |
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x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) |
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if i in self.out_indices: |
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norm_layer = getattr(self, f'norm{i}') |
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x_out = norm_layer(x_out) |
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out = x_out.view(-1, H, W, |
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self.num_features[i]).permute(0, 3, 1, |
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2).contiguous() |
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outs['res{}'.format(i + 2)] = out |
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return outs |
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class Mlp(nn.Module): |
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"""Multilayer perceptron.""" |
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def __init__(self, |
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in_features, |
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hidden_features=None, |
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out_features=None, |
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act_layer=nn.GELU, |
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drop=0.): |
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super().__init__() |
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out_features = out_features or in_features |
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hidden_features = hidden_features or in_features |
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self.fc1 = nn.Linear(in_features, hidden_features) |
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self.act = act_layer() |
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self.fc2 = nn.Linear(hidden_features, out_features) |
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self.drop = nn.Dropout(drop) |
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def forward(self, x): |
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x = self.fc1(x) |
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x = self.act(x) |
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x = self.drop(x) |
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x = self.fc2(x) |
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x = self.drop(x) |
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return x |
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class FocalModulation(nn.Module): |
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"""Focal Modulation. |
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Args: |
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dim (int): Number of input channels. |
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0 |
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focal_level (int): Number of focal levels |
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focal_window (int): Focal window size at focal level 1 |
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focal_factor (int, default=2): Step to increase the focal window |
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""" |
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def __init__(self, |
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dim, |
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proj_drop=0., |
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focal_level=2, |
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focal_window=7, |
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focal_factor=2, |
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use_postln_in_modulation=False, |
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scaling_modulator=False): |
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super().__init__() |
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self.dim = dim |
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self.focal_level = focal_level |
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self.focal_window = focal_window |
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self.focal_factor = focal_factor |
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self.use_postln_in_modulation = use_postln_in_modulation |
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self.scaling_modulator = scaling_modulator |
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self.f = nn.Linear(dim, 2 * dim + (self.focal_level + 1), bias=True) |
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self.h = nn.Conv2d( |
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dim, dim, kernel_size=1, stride=1, padding=0, groups=1, bias=True) |
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self.act = nn.GELU() |
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self.proj = nn.Linear(dim, dim) |
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self.proj_drop = nn.Dropout(proj_drop) |
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self.focal_layers = nn.ModuleList() |
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if self.use_postln_in_modulation: |
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self.ln = nn.LayerNorm(dim) |
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for k in range(self.focal_level): |
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kernel_size = self.focal_factor * k + self.focal_window |
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self.focal_layers.append( |
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nn.Sequential( |
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nn.Conv2d( |
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dim, |
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dim, |
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kernel_size=kernel_size, |
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stride=1, |
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groups=dim, |
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padding=kernel_size // 2, |
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bias=False), |
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nn.GELU(), |
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)) |
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def forward(self, x): |
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"""Forward function. |
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Args: |
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x: input features with shape of (B, H, W, C) |
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""" |
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B, nH, nW, C = x.shape |
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x = self.f(x) |
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x = x.permute(0, 3, 1, 2).contiguous() |
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q, ctx, gates = torch.split(x, (C, C, self.focal_level + 1), 1) |
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ctx_all = 0 |
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for level in range(self.focal_level): |
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ctx = self.focal_layers[level](ctx) |
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ctx_all = ctx_all + ctx * gates[:, level:level + 1] |
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ctx_global = self.act(ctx.mean(2, keepdim=True).mean(3, keepdim=True)) |
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ctx_all = ctx_all + ctx_global * gates[:, self.focal_level:] |
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if self.scaling_modulator: |
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ctx_all = ctx_all / (self.focal_level + 1) |
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x_out = q * self.h(ctx_all) |
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x_out = x_out.permute(0, 2, 3, 1).contiguous() |
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if self.use_postln_in_modulation: |
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x_out = self.ln(x_out) |
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x_out = self.proj(x_out) |
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x_out = self.proj_drop(x_out) |
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return x_out |
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class FocalModulationBlock(nn.Module): |
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"""Focal Modulation Block. |
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Args: |
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dim (int): Number of input channels. |
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. |
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drop (float, optional): Dropout rate. Default: 0.0 |
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drop_path (float, optional): Stochastic depth rate. Default: 0.0 |
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU |
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norm_layer (nn.Module, optional): Normalization layer. |
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Default: nn.LayerNorm |
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focal_level (int): number of focal levels |
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focal_window (int): focal kernel size at level 1 |
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""" |
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def __init__(self, |
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dim, |
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mlp_ratio=4., |
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drop=0., |
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drop_path=0., |
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act_layer=nn.GELU, |
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norm_layer=nn.LayerNorm, |
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focal_level=2, |
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focal_window=9, |
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use_postln=False, |
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use_postln_in_modulation=False, |
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scaling_modulator=False, |
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use_layerscale=False, |
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layerscale_value=1e-4): |
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super().__init__() |
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self.dim = dim |
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self.mlp_ratio = mlp_ratio |
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self.focal_window = focal_window |
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self.focal_level = focal_level |
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self.use_postln = use_postln |
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self.use_layerscale = use_layerscale |
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self.dw1 = nn.Conv2d( |
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dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) |
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self.norm1 = norm_layer(dim) |
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self.modulation = FocalModulation( |
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dim, |
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focal_window=self.focal_window, |
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focal_level=self.focal_level, |
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proj_drop=drop, |
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use_postln_in_modulation=use_postln_in_modulation, |
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scaling_modulator=scaling_modulator) |
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self.dw2 = nn.Conv2d( |
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dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) |
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self.drop_path = DropPath( |
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drop_path) if drop_path > 0. else nn.Identity() |
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self.norm2 = norm_layer(dim) |
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mlp_hidden_dim = int(dim * mlp_ratio) |
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self.mlp = Mlp( |
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in_features=dim, |
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hidden_features=mlp_hidden_dim, |
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act_layer=act_layer, |
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drop=drop) |
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self.H = None |
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self.W = None |
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self.gamma_1 = 1.0 |
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self.gamma_2 = 1.0 |
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if self.use_layerscale: |
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self.gamma_1 = nn.Parameter( |
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layerscale_value * torch.ones(dim), requires_grad=True) |
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self.gamma_2 = nn.Parameter( |
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layerscale_value * torch.ones(dim), requires_grad=True) |
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def forward(self, x): |
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"""Forward function. |
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Args: |
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x: Input feature, tensor size (B, H*W, C). |
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H, W: Spatial resolution of the input feature. |
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""" |
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B, L, C = x.shape |
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H, W = self.H, self.W |
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assert L == H * W, 'input feature has wrong size' |
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x = x.view(B, H, W, C).permute(0, 3, 1, 2).contiguous() |
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x = x + self.dw1(x) |
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x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) |
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shortcut = x |
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if not self.use_postln: |
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x = self.norm1(x) |
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x = x.view(B, H, W, C) |
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x = self.modulation(x).view(B, H * W, C) |
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x = shortcut + self.drop_path(self.gamma_1 * x) |
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if self.use_postln: |
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x = self.norm1(x) |
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x = x.view(B, H, W, C).permute(0, 3, 1, 2).contiguous() |
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x = x + self.dw2(x) |
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x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) |
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if not self.use_postln: |
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) |
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else: |
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x = x + self.drop_path(self.gamma_2 * self.mlp(x)) |
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x = self.norm2(x) |
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return x |
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class BasicLayer(nn.Module): |
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"""A basic focal modulation layer for one stage. |
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Args: |
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dim (int): Number of feature channels |
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depth (int): Depths of this stage. |
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. |
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Default: 4. |
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drop (float, optional): Dropout rate. Default: 0.0 |
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drop_path (float | tuple[float], optional): Stochastic depth rate. |
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Default: 0.0 |
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norm_layer (nn.Module, optional): Normalization layer. |
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Default: nn.LayerNorm |
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downsample (nn.Module | None, optional): Downsample layer at the |
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end of the layer. Default: None |
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focal_level (int): Number of focal levels |
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focal_window (int): Focal window size at focal level 1 |
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use_conv_embed (bool): Use overlapped convolution for patch |
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embedding or now. Default: False |
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use_checkpoint (bool): Whether to use checkpointing to save memory. |
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Default: False |
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""" |
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def __init__( |
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self, |
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dim, |
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depth, |
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mlp_ratio=4., |
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drop=0., |
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drop_path=0., |
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norm_layer=nn.LayerNorm, |
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downsample=None, |
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focal_window=9, |
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focal_level=2, |
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use_conv_embed=False, |
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use_postln=False, |
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use_postln_in_modulation=False, |
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scaling_modulator=False, |
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use_layerscale=False, |
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use_checkpoint=False, |
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use_pre_norm=False, |
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): |
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super().__init__() |
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self.depth = depth |
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self.use_checkpoint = use_checkpoint |
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self.blocks = nn.ModuleList([ |
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FocalModulationBlock( |
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dim=dim, |
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mlp_ratio=mlp_ratio, |
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drop=drop, |
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drop_path=drop_path[i] |
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if isinstance(drop_path, list) else drop_path, |
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focal_window=focal_window, |
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focal_level=focal_level, |
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use_postln=use_postln, |
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use_postln_in_modulation=use_postln_in_modulation, |
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scaling_modulator=scaling_modulator, |
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use_layerscale=use_layerscale, |
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norm_layer=norm_layer) for i in range(depth) |
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]) |
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if downsample is not None: |
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self.downsample = downsample( |
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patch_size=2, |
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in_chans=dim, |
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embed_dim=2 * dim, |
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use_conv_embed=use_conv_embed, |
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norm_layer=norm_layer, |
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is_stem=False, |
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use_pre_norm=use_pre_norm) |
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|
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else: |
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self.downsample = None |
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def forward(self, x, H, W): |
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"""Forward function. |
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|
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|
Args: |
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x: Input feature, tensor size (B, H*W, C). |
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H, W: Spatial resolution of the input feature. |
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""" |
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for blk in self.blocks: |
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blk.H, blk.W = H, W |
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if self.use_checkpoint: |
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x = checkpoint.checkpoint(blk, x) |
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|
else: |
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x = blk(x) |
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|
if self.downsample is not None: |
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x_reshaped = x.transpose(1, 2).view(x.shape[0], x.shape[-1], H, W) |
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|
x_down = self.downsample(x_reshaped) |
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|
x_down = x_down.flatten(2).transpose(1, 2) |
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Wh, Ww = (H + 1) // 2, (W + 1) // 2 |
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return x, H, W, x_down, Wh, Ww |
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else: |
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return x, H, W, x, H, W |
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class PatchEmbed(nn.Module): |
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"""Image to Patch Embedding. |
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|
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Args: |
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patch_size (int): Patch token size. Default: 4. |
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|
in_chans (int): Number of input image channels. Default: 3. |
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|
embed_dim (int): Number of linear projection output channels. |
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|
Default: 96. |
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norm_layer (nn.Module, optional): Normalization layer. |
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|
Default: None |
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|
use_conv_embed (bool): Whether use overlapped convolution for |
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|
patch embedding. Default: False |
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|
is_stem (bool): Is the stem block or not. |
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|
""" |
|
|
|
|
|
def __init__(self, |
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|
patch_size=4, |
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|
in_chans=3, |
|
|
embed_dim=96, |
|
|
norm_layer=None, |
|
|
use_conv_embed=False, |
|
|
is_stem=False, |
|
|
use_pre_norm=False): |
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|
super().__init__() |
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|
patch_size = (patch_size, patch_size) |
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|
self.patch_size = patch_size |
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|
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self.in_chans = in_chans |
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|
self.embed_dim = embed_dim |
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|
self.use_pre_norm = use_pre_norm |
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|
|
|
if use_conv_embed: |
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|
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|
if is_stem: |
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|
kernel_size = 7 |
|
|
padding = 3 |
|
|
stride = 4 |
|
|
else: |
|
|
kernel_size = 3 |
|
|
padding = 1 |
|
|
stride = 2 |
|
|
self.proj = nn.Conv2d( |
|
|
in_chans, |
|
|
embed_dim, |
|
|
kernel_size=kernel_size, |
|
|
stride=stride, |
|
|
padding=padding) |
|
|
else: |
|
|
self.proj = nn.Conv2d( |
|
|
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) |
|
|
|
|
|
if self.use_pre_norm: |
|
|
if norm_layer is not None: |
|
|
self.norm = norm_layer(in_chans) |
|
|
else: |
|
|
self.norm = None |
|
|
else: |
|
|
if norm_layer is not None: |
|
|
self.norm = norm_layer(embed_dim) |
|
|
else: |
|
|
self.norm = None |
|
|
|
|
|
def forward(self, x): |
|
|
"""Forward function.""" |
|
|
B, C, H, W = x.size() |
|
|
if W % self.patch_size[1] != 0: |
|
|
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) |
|
|
if H % self.patch_size[0] != 0: |
|
|
x = F.pad(x, |
|
|
(0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) |
|
|
|
|
|
if self.use_pre_norm: |
|
|
if self.norm is not None: |
|
|
x = x.flatten(2).transpose(1, 2) |
|
|
x = self.norm(x).transpose(1, 2).view(B, C, H, W) |
|
|
x = self.proj(x) |
|
|
else: |
|
|
x = self.proj(x) |
|
|
if self.norm is not None: |
|
|
Wh, Ww = x.size(2), x.size(3) |
|
|
x = x.flatten(2).transpose(1, 2) |
|
|
x = self.norm(x) |
|
|
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) |
|
|
|
|
|
return x |
|
|
|
