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| # -------------------------------------------------------- | |
| # FocalNet for Semantic Segmentation | |
| # Copyright (c) 2022 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # Written by Jianwei Yang | |
| # -------------------------------------------------------- | |
| import math | |
| import time | |
| import numpy as np | |
| import logging | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.checkpoint as checkpoint | |
| from timm.models.layers import DropPath, to_2tuple, trunc_normal_ | |
| from detectron2.utils.file_io import PathManager | |
| from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec | |
| from .registry import register_backbone | |
| logger = logging.getLogger(__name__) | |
| 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 | |
| use_postln (bool, default=False): Whether use post-modulation layernorm | |
| """ | |
| def __init__(self, dim, proj_drop=0., focal_level=2, focal_window=7, focal_factor=2, use_postln=False, use_postln_in_modulation=False, scaling_modulator=False): | |
| super().__init__() | |
| self.dim = dim | |
| # specific args for focalv3 | |
| 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 l in range(self.focal_level): | |
| ctx = self.focal_layers[l](ctx) | |
| ctx_all = ctx_all + ctx*gates[:, l:l+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.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.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" | |
| shortcut = x | |
| if not self.use_postln: | |
| x = self.norm1(x) | |
| x = x.view(B, H, W, C) | |
| # FM | |
| x = self.modulation(x).view(B, H * W, C) | |
| if self.use_postln: | |
| x = self.norm1(x) | |
| # FFN | |
| x = shortcut + self.drop_path(self.gamma_1 * x) | |
| if self.use_postln: | |
| x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x))) | |
| else: | |
| x = x + self.drop_path(self.gamma_2 * self.mlp(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 | |
| ): | |
| super().__init__() | |
| self.depth = depth | |
| self.use_checkpoint = use_checkpoint | |
| # build blocks | |
| 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)]) | |
| # patch merging layer | |
| 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 | |
| ) | |
| 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): | |
| super().__init__() | |
| patch_size = to_2tuple(patch_size) | |
| self.patch_size = patch_size | |
| self.in_chans = in_chans | |
| self.embed_dim = embed_dim | |
| if use_conv_embed: | |
| # if we choose to use conv embedding, then we treat the stem and non-stem differently | |
| if is_stem: | |
| kernel_size = 7; padding = 2; 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 norm_layer is not None: | |
| self.norm = norm_layer(embed_dim) | |
| else: | |
| self.norm = None | |
| def forward(self, x): | |
| """Forward function.""" | |
| _, _, 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])) | |
| x = self.proj(x) # B C Wh Ww | |
| 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 | |
| class FocalNet(nn.Module): | |
| """ FocalNet backbone. | |
| Args: | |
| pretrain_img_size (int): Input image size for training the pretrained model, | |
| used in absolute postion embedding. Default 224. | |
| patch_size (int | tuple(int)): Patch size. Default: 4. | |
| in_chans (int): Number of input image channels. Default: 3. | |
| embed_dim (int): Number of linear projection output channels. Default: 96. | |
| depths (tuple[int]): Depths of each Swin Transformer stage. | |
| mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. | |
| drop_rate (float): Dropout rate. | |
| drop_path_rate (float): Stochastic depth rate. Default: 0.2. | |
| norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. | |
| patch_norm (bool): If True, add normalization after patch embedding. Default: True. | |
| out_indices (Sequence[int]): Output from which stages. | |
| frozen_stages (int): Stages to be frozen (stop grad and set eval mode). | |
| -1 means not freezing any parameters. | |
| focal_levels (Sequence[int]): Number of focal levels at four stages | |
| focal_windows (Sequence[int]): Focal window sizes at first focal level at four stages | |
| use_conv_embed (bool): Whether use overlapped convolution for patch embedding | |
| use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. | |
| """ | |
| def __init__(self, | |
| pretrain_img_size=1600, | |
| patch_size=4, | |
| in_chans=3, | |
| embed_dim=96, | |
| depths=[2, 2, 6, 2], | |
| mlp_ratio=4., | |
| drop_rate=0., | |
| drop_path_rate=0.2, | |
| norm_layer=nn.LayerNorm, | |
| patch_norm=True, | |
| out_indices=[0, 1, 2, 3], | |
| frozen_stages=-1, | |
| focal_levels=[2,2,2,2], | |
| focal_windows=[9,9,9,9], | |
| use_conv_embed=False, | |
| use_postln=False, | |
| use_postln_in_modulation=False, | |
| scaling_modulator=False, | |
| use_layerscale=False, | |
| use_checkpoint=False, | |
| ): | |
| super().__init__() | |
| self.pretrain_img_size = pretrain_img_size | |
| 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 | |
| # split image into non-overlapping patches | |
| 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) | |
| self.pos_drop = nn.Dropout(p=drop_rate) | |
| # stochastic depth | |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule | |
| # build layers | |
| 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_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 | |
| # add a norm layer for each output | |
| for i_layer in out_indices: | |
| layer = norm_layer(num_features[i_layer]) | |
| layer_name = f'norm{i_layer}' | |
| self.add_module(layer_name, layer) | |
| self._freeze_stages() | |
| def _freeze_stages(self): | |
| if self.frozen_stages >= 0: | |
| self.patch_embed.eval() | |
| for param in self.patch_embed.parameters(): | |
| param.requires_grad = False | |
| if self.frozen_stages >= 2: | |
| self.pos_drop.eval() | |
| for i in range(0, self.frozen_stages - 1): | |
| m = self.layers[i] | |
| m.eval() | |
| for param in m.parameters(): | |
| param.requires_grad = False | |
| def init_weights(self, pretrained=None): | |
| """Initialize the weights in backbone. | |
| Args: | |
| pretrained (str, optional): Path to pre-trained weights. | |
| Defaults to None. | |
| """ | |
| def _init_weights(m): | |
| if isinstance(m, nn.Linear): | |
| trunc_normal_(m.weight, std=.02) | |
| if isinstance(m, nn.Linear) and m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.LayerNorm): | |
| nn.init.constant_(m.bias, 0) | |
| nn.init.constant_(m.weight, 1.0) | |
| if isinstance(pretrained, str): | |
| self.apply(_init_weights) | |
| logger = get_root_logger() | |
| load_checkpoint(self, pretrained, strict=False, logger=logger) | |
| elif pretrained is None: | |
| self.apply(_init_weights) | |
| else: | |
| raise TypeError('pretrained must be a str or None') | |
| def load_weights(self, pretrained_dict=None, pretrained_layers=[], verbose=True): | |
| model_dict = self.state_dict() | |
| missed_dict = [k for k in model_dict.keys() if k not in pretrained_dict] | |
| logger.info(f'=> Missed keys {missed_dict}') | |
| unexpected_dict = [k for k in pretrained_dict.keys() if k not in model_dict] | |
| logger.info(f'=> Unexpected keys {unexpected_dict}') | |
| pretrained_dict = { | |
| k: v for k, v in pretrained_dict.items() | |
| if k in model_dict.keys() | |
| } | |
| need_init_state_dict = {} | |
| for k, v in pretrained_dict.items(): | |
| need_init = ( | |
| ( | |
| k.split('.')[0] in pretrained_layers | |
| or pretrained_layers[0] == '*' | |
| ) | |
| and 'relative_position_index' not in k | |
| and 'attn_mask' not in k | |
| ) | |
| if need_init: | |
| # if verbose: | |
| # logger.info(f'=> init {k} from {pretrained}') | |
| if ('pool_layers' in k) or ('focal_layers' in k) and v.size() != model_dict[k].size(): | |
| table_pretrained = v | |
| table_current = model_dict[k] | |
| fsize1 = table_pretrained.shape[2] | |
| fsize2 = table_current.shape[2] | |
| # NOTE: different from interpolation used in self-attention, we use padding or clipping for focal conv | |
| if fsize1 < fsize2: | |
| table_pretrained_resized = torch.zeros(table_current.shape) | |
| table_pretrained_resized[:, :, (fsize2-fsize1)//2:-(fsize2-fsize1)//2, (fsize2-fsize1)//2:-(fsize2-fsize1)//2] = table_pretrained | |
| v = table_pretrained_resized | |
| elif fsize1 > fsize2: | |
| table_pretrained_resized = table_pretrained[:, :, (fsize1-fsize2)//2:-(fsize1-fsize2)//2, (fsize1-fsize2)//2:-(fsize1-fsize2)//2] | |
| v = table_pretrained_resized | |
| if ("modulation.f" in k or "pre_conv" in k): | |
| table_pretrained = v | |
| table_current = model_dict[k] | |
| if table_pretrained.shape != table_current.shape: | |
| if len(table_pretrained.shape) == 2: | |
| dim = table_pretrained.shape[1] | |
| assert table_current.shape[1] == dim | |
| L1 = table_pretrained.shape[0] | |
| L2 = table_current.shape[0] | |
| if L1 < L2: | |
| table_pretrained_resized = torch.zeros(table_current.shape) | |
| # copy for linear project | |
| table_pretrained_resized[:2*dim] = table_pretrained[:2*dim] | |
| # copy for global token gating | |
| table_pretrained_resized[-1] = table_pretrained[-1] | |
| # copy for first multiple focal levels | |
| table_pretrained_resized[2*dim:2*dim+(L1-2*dim-1)] = table_pretrained[2*dim:-1] | |
| # reassign pretrained weights | |
| v = table_pretrained_resized | |
| elif L1 > L2: | |
| raise NotImplementedError | |
| elif len(table_pretrained.shape) == 1: | |
| dim = table_pretrained.shape[0] | |
| L1 = table_pretrained.shape[0] | |
| L2 = table_current.shape[0] | |
| if L1 < L2: | |
| table_pretrained_resized = torch.zeros(table_current.shape) | |
| # copy for linear project | |
| table_pretrained_resized[:dim] = table_pretrained[:dim] | |
| # copy for global token gating | |
| table_pretrained_resized[-1] = table_pretrained[-1] | |
| # copy for first multiple focal levels | |
| # table_pretrained_resized[dim:2*dim+(L1-2*dim-1)] = table_pretrained[2*dim:-1] | |
| # reassign pretrained weights | |
| v = table_pretrained_resized | |
| elif L1 > L2: | |
| raise NotImplementedError | |
| need_init_state_dict[k] = v | |
| self.load_state_dict(need_init_state_dict, strict=False) | |
| def forward(self, x): | |
| """Forward function.""" | |
| tic = time.time() | |
| 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 | |
| if len(self.out_indices) == 0: | |
| outs["res5"] = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() | |
| toc = time.time() | |
| return outs | |
| def train(self, mode=True): | |
| """Convert the model into training mode while keep layers freezed.""" | |
| super(FocalNet, self).train(mode) | |
| self._freeze_stages() | |
| class D2FocalNet(FocalNet, Backbone): | |
| def __init__(self, cfg, input_shape): | |
| pretrain_img_size = cfg['BACKBONE']['FOCAL']['PRETRAIN_IMG_SIZE'] | |
| patch_size = cfg['BACKBONE']['FOCAL']['PATCH_SIZE'] | |
| in_chans = 3 | |
| embed_dim = cfg['BACKBONE']['FOCAL']['EMBED_DIM'] | |
| depths = cfg['BACKBONE']['FOCAL']['DEPTHS'] | |
| mlp_ratio = cfg['BACKBONE']['FOCAL']['MLP_RATIO'] | |
| drop_rate = cfg['BACKBONE']['FOCAL']['DROP_RATE'] | |
| drop_path_rate = cfg['BACKBONE']['FOCAL']['DROP_PATH_RATE'] | |
| norm_layer = nn.LayerNorm | |
| patch_norm = cfg['BACKBONE']['FOCAL']['PATCH_NORM'] | |
| use_checkpoint = cfg['BACKBONE']['FOCAL']['USE_CHECKPOINT'] | |
| out_indices = cfg['BACKBONE']['FOCAL']['OUT_INDICES'] | |
| scaling_modulator = cfg['BACKBONE']['FOCAL'].get('SCALING_MODULATOR', False) | |
| super().__init__( | |
| pretrain_img_size, | |
| patch_size, | |
| in_chans, | |
| embed_dim, | |
| depths, | |
| mlp_ratio, | |
| drop_rate, | |
| drop_path_rate, | |
| norm_layer, | |
| patch_norm, | |
| out_indices, | |
| focal_levels=cfg['BACKBONE']['FOCAL']['FOCAL_LEVELS'], | |
| focal_windows=cfg['BACKBONE']['FOCAL']['FOCAL_WINDOWS'], | |
| use_conv_embed=cfg['BACKBONE']['FOCAL']['USE_CONV_EMBED'], | |
| use_postln=cfg['BACKBONE']['FOCAL']['USE_POSTLN'], | |
| use_postln_in_modulation=cfg['BACKBONE']['FOCAL']['USE_POSTLN_IN_MODULATION'], | |
| scaling_modulator=scaling_modulator, | |
| use_layerscale=cfg['BACKBONE']['FOCAL']['USE_LAYERSCALE'], | |
| use_checkpoint=use_checkpoint, | |
| ) | |
| self._out_features = cfg['BACKBONE']['FOCAL']['OUT_FEATURES'] | |
| self._out_feature_strides = { | |
| "res2": 4, | |
| "res3": 8, | |
| "res4": 16, | |
| "res5": 32, | |
| } | |
| self._out_feature_channels = { | |
| "res2": self.num_features[0], | |
| "res3": self.num_features[1], | |
| "res4": self.num_features[2], | |
| "res5": self.num_features[3], | |
| } | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. | |
| Returns: | |
| dict[str->Tensor]: names and the corresponding features | |
| """ | |
| assert ( | |
| x.dim() == 4 | |
| ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!" | |
| outputs = {} | |
| y = super().forward(x) | |
| for k in y.keys(): | |
| if k in self._out_features: | |
| outputs[k] = y[k] | |
| return outputs | |
| def output_shape(self): | |
| return { | |
| name: ShapeSpec( | |
| channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] | |
| ) | |
| for name in self._out_features | |
| } | |
| def size_divisibility(self): | |
| return 32 | |
| def get_focal_backbone(cfg): | |
| focal = D2FocalNet(cfg['MODEL'], 224) | |
| if cfg['MODEL']['BACKBONE']['LOAD_PRETRAINED'] is True: | |
| filename = cfg['MODEL']['BACKBONE']['PRETRAINED'] | |
| logger.info(f'=> init from {filename}') | |
| with PathManager.open(filename, "rb") as f: | |
| ckpt = torch.load(f)['model'] | |
| focal.load_weights(ckpt, cfg['MODEL']['BACKBONE']['FOCAL'].get('PRETRAINED_LAYERS', ['*']), cfg['VERBOSE']) | |
| return focal |