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