Ross Wightman
2 years ago
parent
01aea8c1bf
commit
01fdf44438
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# --------------------------------------------------------
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# FocalNets -- Focal Modulation Networks
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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 (jianwyan@microsoft.com)
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# --------------------------------------------------------
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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.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert
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from ._builder import build_model_with_cfg
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from ._features_fx import register_notrace_function
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from ._registry import register_model
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__all__ = ['FocalNet']
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class FocalModulation(nn.Module):
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def __init__(
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self,
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dim,
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focal_window,
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focal_level,
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focal_factor=2,
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bias=True,
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proj_drop=0.,
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use_postln_in_modulation=False,
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normalize_modulator=False,
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):
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super().__init__()
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self.dim = dim
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self.focal_window = focal_window
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self.focal_level = focal_level
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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.normalize_modulator = normalize_modulator
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self.f = nn.Linear(dim, 2 * dim + (self.focal_level + 1), bias=bias)
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self.h = nn.Conv2d(dim, dim, kernel_size=1, stride=1, bias=bias)
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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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self.kernel_sizes = []
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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, dim, kernel_size=kernel_size, stride=1,
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groups=dim, padding=kernel_size // 2, bias=False),
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nn.GELU(),
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)
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)
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self.kernel_sizes.append(kernel_size)
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if self.use_postln_in_modulation:
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self.ln = nn.LayerNorm(dim)
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def forward(self, x):
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"""
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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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C = x.shape[-1]
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# pre linear projection
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x = self.f(x).permute(0, 3, 1, 2).contiguous()
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q, ctx, self.gates = torch.split(x, (C, C, self.focal_level + 1), 1)
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# context aggreation
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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 * self.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 * self.gates[:, self.focal_level:]
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# normalize context
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if self.normalize_modulator:
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ctx_all = ctx_all / (self.focal_level + 1)
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# focal modulation
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self.modulator = self.h(ctx_all)
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x_out = q * self.modulator
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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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# post linear porjection
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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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def extra_repr(self) -> str:
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return f'dim={self.dim}'
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class FocalNetBlock(nn.Module):
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r""" Focal Modulation Network Block.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resulotion.
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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 window size at first focal level
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layerscale_value (float): Initial layerscale value
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use_postln (bool): Whether to use layernorm after modulation
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"""
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def __init__(
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self,
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dim,
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input_resolution,
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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=1,
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focal_window=3,
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layerscale_value=1e-4,
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use_postln=False,
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use_postln_in_modulation=False,
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normalize_modulator=False,
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):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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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.norm1 = norm_layer(dim)
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self.modulation = FocalModulation(
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dim,
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proj_drop=drop,
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focal_window=focal_window,
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focal_level=self.focal_level,
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use_postln_in_modulation=use_postln_in_modulation,
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normalize_modulator=normalize_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(
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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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)
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self.gamma_1 = 1.0
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self.gamma_2 = 1.0
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if layerscale_value is not None:
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self.gamma_1 = nn.Parameter(layerscale_value * torch.ones(dim))
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self.gamma_2 = nn.Parameter(layerscale_value * torch.ones(dim))
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self.H = None
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self.W = None
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def forward(self, x):
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H, W = self.H, self.W
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B, L, C = x.shape
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shortcut = x
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# Focal Modulation
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x = x if self.use_postln else 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 = x if not self.use_postln else 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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x = x + self.drop_path(self.gamma_2 * (self.norm2(self.mlp(x)) if self.use_postln else self.mlp(self.norm2(x))))
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return x
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def extra_repr(self) -> str:
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return f"dim={self.dim}, input_resolution={self.input_resolution}, " \
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f"mlp_ratio={self.mlp_ratio}"
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class BasicLayer(nn.Module):
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""" A basic Focal Transformer layer for one stage.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resolution.
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depth (int): Number of blocks.
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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 | 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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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
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focal_level (int): Number of focal levels
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focal_window (int): Focal window size at first focal level
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layerscale_value (float): Initial layerscale value
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use_postln (bool): Whether to use layer norm after modulation
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"""
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def __init__(
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self,
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dim,
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out_dim,
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input_resolution,
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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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use_checkpoint=False,
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focal_level=1,
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focal_window=1,
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use_conv_embed=False,
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layerscale_value=1e-4,
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use_postln=False,
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use_postln_in_modulation=False,
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normalize_modulator=False
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):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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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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FocalNetBlock(
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dim=dim,
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input_resolution=input_resolution,
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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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norm_layer=norm_layer,
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focal_level=focal_level,
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focal_window=focal_window,
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layerscale_value=layerscale_value,
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use_postln=use_postln,
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use_postln_in_modulation=use_postln_in_modulation,
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normalize_modulator=normalize_modulator,
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)
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for i in range(depth)])
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if downsample is not None:
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self.downsample = downsample(
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img_size=input_resolution,
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patch_size=2,
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in_chans=dim,
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embed_dim=out_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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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 = x.transpose(1, 2).reshape(x.shape[0], -1, H, W)
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x, Ho, Wo = self.downsample(x)
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else:
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Ho, Wo = H, W
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return x, Ho, Wo
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def extra_repr(self) -> str:
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return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
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class PatchEmbed(nn.Module):
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r""" Image to Patch Embedding
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Args:
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img_size (int): Image size. Default: 224.
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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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"""
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def __init__(
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self,
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img_size=(224, 224),
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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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use_conv_embed=False,
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norm_layer=None,
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is_stem=False,
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):
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super().__init__()
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patch_size = to_2tuple(patch_size)
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patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
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self.img_size = img_size
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self.patch_size = patch_size
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self.patches_resolution = patches_resolution
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self.num_patches = patches_resolution[0] * patches_resolution[1]
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self.in_chans = in_chans
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self.embed_dim = embed_dim
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padding = 0
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kernel_size = patch_size
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stride = patch_size
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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
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padding = 2
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stride = 4
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else:
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kernel_size = 3
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padding = 1
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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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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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B, C, H, W = x.shape
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x = self.proj(x)
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H, W = x.shape[2:]
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x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
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if self.norm is not None:
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x = self.norm(x)
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return x, H, W
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class FocalNet(nn.Module):
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r""" Focal Modulation Networks (FocalNets)
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Args:
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img_size (int | tuple(int)): Input image size. 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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num_classes (int): Number of classes for classification head. Default: 1000
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embed_dim (int): Patch embedding dimension. Default: 96
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depths (tuple(int)): Depth of each Focal Transformer layer.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
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drop_rate (float): Dropout rate. Default: 0
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drop_path_rate (float): Stochastic depth rate. Default: 0.1
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norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
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patch_norm (bool): If True, add normalization after patch embedding. Default: True
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
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focal_levels (list): How many focal levels at all stages. Note that this excludes the finest-grain level.
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Default: [1, 1, 1, 1]
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focal_windows (list): The focal window size at all stages. Default: [7, 5, 3, 1]
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use_conv_embed (bool): Whether to use convolutional embedding.
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layerscale_value (float): Value for layer scale. Default: 1e-4
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use_postln (bool): Whether to use layernorm after modulation (it helps stablize training of large models)
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"""
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def __init__(
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self,
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img_size=224,
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patch_size=4,
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in_chans=3,
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num_classes=1000,
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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=0.1,
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norm_layer=nn.LayerNorm,
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patch_norm=True,
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use_checkpoint=False,
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focal_levels=[2, 2, 2, 2],
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focal_windows=[3, 3, 3, 3],
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use_conv_embed=False,
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layerscale_value=None,
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use_postln=False,
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use_postln_in_modulation=False,
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normalize_modulator=False,
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**kwargs,
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):
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super().__init__()
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self.num_layers = len(depths)
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embed_dim = [embed_dim * (2 ** i) for i in range(self.num_layers)]
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self.num_classes = num_classes
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self.embed_dim = embed_dim
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self.patch_norm = patch_norm
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self.num_features = embed_dim[-1]
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self.mlp_ratio = mlp_ratio
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# split image into patches using either non-overlapped embedding or overlapped embedding
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self.patch_embed = PatchEmbed(
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img_size=to_2tuple(img_size),
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim[0],
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use_conv_embed=use_conv_embed,
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norm_layer=norm_layer if self.patch_norm else None,
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is_stem=True
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)
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num_patches = self.patch_embed.num_patches
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patches_resolution = self.patch_embed.patches_resolution
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self.patches_resolution = patches_resolution
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self.pos_drop = nn.Dropout(p=drop_rate)
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# stochastic depth
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
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# build layers
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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=embed_dim[i_layer],
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out_dim=embed_dim[i_layer + 1] if (i_layer < self.num_layers - 1) else None,
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input_resolution=(
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patches_resolution[0] // (2 ** i_layer), patches_resolution[1] // (2 ** i_layer)),
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depth=depths[i_layer],
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mlp_ratio=self.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 (i_layer < self.num_layers - 1) else None,
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focal_level=focal_levels[i_layer],
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focal_window=focal_windows[i_layer],
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use_conv_embed=use_conv_embed,
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use_checkpoint=use_checkpoint,
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layerscale_value=layerscale_value,
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use_postln=use_postln,
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use_postln_in_modulation=use_postln_in_modulation,
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normalize_modulator=normalize_modulator
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)
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self.layers.append(layer)
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self.norm = norm_layer(self.num_features)
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self.avgpool = nn.AdaptiveAvgPool1d(1)
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self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, 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)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {''}
|
||||
|
||||
def forward_features(self, x):
|
||||
x, H, W = self.patch_embed(x)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for layer in self.layers:
|
||||
x, H, W = layer(x, H, W)
|
||||
x = self.norm(x) # B L C
|
||||
x = self.avgpool(x.transpose(1, 2)) # B C 1
|
||||
x = torch.flatten(x, 1)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
|
||||
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
|
||||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'patch_embed.proj', 'classifier': 'head',
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = {
|
||||
"focalnet_tiny_srf": _cfg(),
|
||||
"focalnet_small_srf": _cfg(url="https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth"),
|
||||
"focalnet_base_srf": _cfg(),
|
||||
"focalnet_tiny_lrf": _cfg(),
|
||||
"focalnet_small_lrf": _cfg(),
|
||||
"focalnet_base_lrf": _cfg(url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth'),
|
||||
"focalnet_large_fl3": _cfg(url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth', input_size=(3, 384, 384), num_classes=21842),
|
||||
"focalnet_large_fl4": _cfg(url="https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth", input_size=(3, 384, 384), num_classes=21842),
|
||||
}
|
||||
|
||||
|
||||
def checkpoint_filter_fn(state_dict, model):
|
||||
out_dict = {}
|
||||
if 'model' in state_dict:
|
||||
# For deit models
|
||||
state_dict = state_dict['model']
|
||||
for k, v in state_dict.items():
|
||||
if any([n in k for n in ('relative_position_index', 'relative_coords_table')]):
|
||||
continue # skip buffers that should not be persistent
|
||||
out_dict[k] = v
|
||||
return out_dict
|
||||
|
||||
|
||||
def _create_focalnet(variant, pretrained=False, **kwargs):
|
||||
model = build_model_with_cfg(
|
||||
FocalNet, variant, pretrained,
|
||||
pretrained_filter_fn=checkpoint_filter_fn,
|
||||
**kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_tiny_srf(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 6, 2], embed_dim=96, **kwargs)
|
||||
return _create_focalnet('focalnet_tiny_srf', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_small_srf(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=96, **kwargs)
|
||||
return _create_focalnet('focalnet_small_srf', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_base_srf(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=128, **kwargs)
|
||||
return _create_focalnet('focalnet_base_srf', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_tiny_lrf(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
|
||||
return _create_focalnet('focalnet_tiny_lrf', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_small_lrf(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
|
||||
return _create_focalnet('focalnet_small_lrf', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_base_lrf(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=128, focal_levels=[3, 3, 3, 3], **kwargs)
|
||||
return _create_focalnet('focalnet_base_lrf', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
# FocalNet large+ models
|
||||
@register_model
|
||||
def focalnet_large_fl3(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[3, 3, 3, 3], **kwargs)
|
||||
return _create_focalnet('focalnet_large_fl3', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_large_fl4(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[4, 4, 4, 4],
|
||||
use_conv_embed=True, layerscale_value=1e-4, **kwargs)
|
||||
return _create_focalnet('focalnet_large_fl4', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
#
|
||||
# @register_model
|
||||
# def focalnet_large_fl4(pretrained=False, **kwargs):
|
||||
# model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[4, 4, 4, 4], **kwargs)
|
||||
# return _create_focalnet('focalnet_large_fl4', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_xlarge_fl3(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=256, focal_levels=[3, 3, 3, 3], **kwargs)
|
||||
return _create_focalnet('focalnet_xlarge_fl3', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_xlarge_fl4(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=256, focal_levels=[4, 4, 4, 4], **kwargs)
|
||||
return _create_focalnet('focalnet_xlarge_fl4', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_huge_fl3(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=352, focal_levels=[3, 3, 3, 3], **kwargs)
|
||||
return _create_focalnet('focalnet_huge_fl3', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def focalnet_huge_fl4(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=352, focal_levels=[4, 4, 4, 4], **kwargs)
|
||||
return _create_focalnet('focalnet_huge_fl4', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
Loading…
Reference in new issue