692 lines
27 KiB
Python
692 lines
27 KiB
Python
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# --------------------------------------------------------
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# FocalNet for Semantic Segmentation
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# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Jianwei Yang
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# --------------------------------------------------------
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import math
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import time
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import numpy as np
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import logging
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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 timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from detectron2.utils.file_io import PathManager
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from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
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from .registry import register_backbone
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logger = logging.getLogger(__name__)
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class Mlp(nn.Module):
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""" Multilayer perceptron."""
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, 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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use_postln (bool, default=False): Whether use post-modulation layernorm
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"""
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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):
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super().__init__()
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self.dim = dim
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# specific args for focalv3
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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(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(dim, dim, kernel_size=kernel_size, stride=1, groups=dim,
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padding=kernel_size//2, bias=False),
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nn.GELU(),
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)
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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 l in range(self.focal_level):
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ctx = self.focal_layers[l](ctx)
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ctx_all = ctx_all + ctx*gates[:, l:l+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. 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, dim, mlp_ratio=4., drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm,
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focal_level=2, focal_window=9,
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use_postln=False, 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.norm1 = norm_layer(dim)
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self.modulation = FocalModulation(
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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
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)
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self.drop_path = DropPath(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(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, 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(layerscale_value * torch.ones((dim)), requires_grad=True)
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self.gamma_2 = nn.Parameter(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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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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# FM
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x = self.modulation(x).view(B, H * W, C)
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if self.use_postln:
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x = self.norm1(x)
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# FFN
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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 = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
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else:
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x = x + self.drop_path(self.gamma_2 * self.mlp(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. 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. Default: 0.0
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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downsample (nn.Module | None, optional): Downsample layer at the 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 embedding or now. Default: False
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
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"""
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def __init__(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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):
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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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# build blocks
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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] 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)
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for i in range(depth)])
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# patch merging layer
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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, 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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)
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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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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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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. Default: 96.
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norm_layer (nn.Module, optional): Normalization layer. Default: None
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use_conv_embed (bool): Whether use overlapped convolution for patch embedding. Default: False
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is_stem (bool): Is the stem block or not.
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"""
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def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, use_conv_embed=False, is_stem=False):
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super().__init__()
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patch_size = to_2tuple(patch_size)
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self.patch_size = patch_size
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self.in_chans = in_chans
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self.embed_dim = embed_dim
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if use_conv_embed:
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# if we choose to use conv embedding, then we treat the stem and non-stem differently
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if is_stem:
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kernel_size = 7; padding = 2; stride = 4
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else:
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kernel_size = 3; padding = 1; stride = 2
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
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else:
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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if norm_layer is not None:
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self.norm = norm_layer(embed_dim)
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else:
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self.norm = None
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def forward(self, x):
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"""Forward function."""
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_, _, H, W = x.size()
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if W % self.patch_size[1] != 0:
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x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
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if H % self.patch_size[0] != 0:
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x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
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x = self.proj(x) # B C Wh Ww
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if self.norm is not None:
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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.norm(x)
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x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
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return x
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class FocalNet(nn.Module):
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""" FocalNet backbone.
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Args:
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pretrain_img_size (int): Input image size for training the pretrained model,
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used in absolute postion embedding. Default 224.
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patch_size (int | tuple(int)): Patch 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. Default: 96.
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depths (tuple[int]): Depths of each Swin Transformer stage.
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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
|
||
|
|
}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def size_divisibility(self):
|
||
|
|
return 32
|
||
|
|
|
||
|
|
@register_backbone
|
||
|
|
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
|