Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Initial focalnet import, more refactoring needed for timm.

Browse Source
pull/1628/head
Ross Wightman 2 years ago
parent 01aea8c1bf
commit 01fdf44438
2 changed files with 617 additions and 0 deletions
Show Stats Download Patch File Download Diff File

1
timm/models/__init__.py
Unescape View File

@ -15,6 +15,7 @@ from .dpn import *
from .edgenext import * from .edgenext import *
from .efficientformer import * from .efficientformer import *
from .efficientnet import * from .efficientnet import *
from .focalnet import *
from .gcvit import * from .gcvit import *
from .ghostnet import * from .ghostnet import *
from .gluon_resnet import * from .gluon_resnet import *

616
timm/models/focalnet.py
Unescape View File

@ -0,0 +1,616 @@
# --------------------------------------------------------
# FocalNets -- Focal Modulation Networks
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Jianwei Yang (jianwyan@microsoft.com)
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._registry import register_model
__all__ = ['FocalNet']
class FocalModulation(nn.Module):
def __init__(
self,
dim,
focal_window,
focal_level,
focal_factor=2,
bias=True,
proj_drop=0.,
use_postln_in_modulation=False,
normalize_modulator=False,
):
super().__init__()
self.dim = dim
self.focal_window = focal_window
self.focal_level = focal_level
self.focal_factor = focal_factor
self.use_postln_in_modulation = use_postln_in_modulation
self.normalize_modulator = normalize_modulator
self.f = nn.Linear(dim, 2 * dim + (self.focal_level + 1), bias=bias)
self.h = nn.Conv2d(dim, dim, kernel_size=1, stride=1, bias=bias)
self.act = nn.GELU()
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.focal_layers = nn.ModuleList()
self.kernel_sizes = []
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(),
)
)
self.kernel_sizes.append(kernel_size)
if self.use_postln_in_modulation:
self.ln = nn.LayerNorm(dim)
def forward(self, x):
"""
Args:
x: input features with shape of (B, H, W, C)
"""
C = x.shape[-1]
# pre linear projection
x = self.f(x).permute(0, 3, 1, 2).contiguous()
q, ctx, self.gates = torch.split(x, (C, C, self.focal_level + 1), 1)
# context aggreation
ctx_all = 0
for l in range(self.focal_level):
ctx = self.focal_layers[l](ctx)
ctx_all = ctx_all + ctx * self.gates[:, l:l + 1]
ctx_global = self.act(ctx.mean(2, keepdim=True).mean(3, keepdim=True))
ctx_all = ctx_all + ctx_global * self.gates[:, self.focal_level:]
# normalize context
if self.normalize_modulator:
ctx_all = ctx_all / (self.focal_level + 1)
# focal modulation
self.modulator = self.h(ctx_all)
x_out = q * self.modulator
x_out = x_out.permute(0, 2, 3, 1).contiguous()
if self.use_postln_in_modulation:
x_out = self.ln(x_out)
# post linear porjection
x_out = self.proj(x_out)
x_out = self.proj_drop(x_out)
return x_out
def extra_repr(self) -> str:
return f'dim={self.dim}'
class FocalNetBlock(nn.Module):
r""" Focal Modulation Network Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
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 window size at first focal level
layerscale_value (float): Initial layerscale value
use_postln (bool): Whether to use layernorm after modulation
"""
def __init__(
self,
dim,
input_resolution,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
focal_level=1,
focal_window=3,
layerscale_value=1e-4,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.mlp_ratio = mlp_ratio
self.focal_window = focal_window
self.focal_level = focal_level
self.use_postln = use_postln
self.norm1 = norm_layer(dim)
self.modulation = FocalModulation(
dim,
proj_drop=drop,
focal_window=focal_window,
focal_level=self.focal_level,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_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.gamma_1 = 1.0
self.gamma_2 = 1.0
if layerscale_value is not None:
self.gamma_1 = nn.Parameter(layerscale_value * torch.ones(dim))
self.gamma_2 = nn.Parameter(layerscale_value * torch.ones(dim))
self.H = None
self.W = None
def forward(self, x):
H, W = self.H, self.W
B, L, C = x.shape
shortcut = x
# Focal Modulation
x = x if self.use_postln else self.norm1(x)
x = x.view(B, H, W, C)
x = self.modulation(x).view(B, H * W, C)
x = x if not self.use_postln else self.norm1(x)
# FFN
x = shortcut + self.drop_path(self.gamma_1 * x)
x = x + self.drop_path(self.gamma_2 * (self.norm2(self.mlp(x)) if self.use_postln else self.mlp(self.norm2(x))))
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, " \
f"mlp_ratio={self.mlp_ratio}"
class BasicLayer(nn.Module):
""" A basic Focal Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
focal_level (int): Number of focal levels
focal_window (int): Focal window size at first focal level
layerscale_value (float): Initial layerscale value
use_postln (bool): Whether to use layer norm after modulation
"""
def __init__(
self,
dim,
out_dim,
input_resolution,
depth,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False,
focal_level=1,
focal_window=1,
use_conv_embed=False,
layerscale_value=1e-4,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
FocalNetBlock(
dim=dim,
input_resolution=input_resolution,
mlp_ratio=mlp_ratio,
drop=drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
focal_level=focal_level,
focal_window=focal_window,
layerscale_value=layerscale_value,
use_postln=use_postln,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator,
)
for i in range(depth)])
if downsample is not None:
self.downsample = downsample(
img_size=input_resolution,
patch_size=2,
in_chans=dim,
embed_dim=out_dim,
use_conv_embed=use_conv_embed,
norm_layer=norm_layer,
is_stem=False
)
else:
self.downsample = None
def forward(self, x, H, W):
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 = x.transpose(1, 2).reshape(x.shape[0], -1, H, W)
x, Ho, Wo = self.downsample(x)
else:
Ho, Wo = H, W
return x, Ho, Wo
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
class PatchEmbed(nn.Module):
r""" Image to Patch Embedding
Args:
img_size (int): Image size. Default: 224.
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
"""
def __init__(
self,
img_size=(224, 224),
patch_size=4,
in_chans=3,
embed_dim=96,
use_conv_embed=False,
norm_layer=None,
is_stem=False,
):
super().__init__()
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
padding = 0
kernel_size = patch_size
stride = patch_size
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)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x)
H, W = x.shape[2:]
x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x, H, W
class FocalNet(nn.Module):
r""" Focal Modulation Networks (FocalNets)
Args:
img_size (int | tuple(int)): Input image size. Default 224
patch_size (int | tuple(int)): Patch size. Default: 4
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Focal Transformer layer.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
drop_rate (float): Dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
focal_levels (list): How many focal levels at all stages. Note that this excludes the finest-grain level.
Default: [1, 1, 1, 1]
focal_windows (list): The focal window size at all stages. Default: [7, 5, 3, 1]
use_conv_embed (bool): Whether to use convolutional embedding.
layerscale_value (float): Value for layer scale. Default: 1e-4
use_postln (bool): Whether to use layernorm after modulation (it helps stablize training of large models)
"""
def __init__(
self,
img_size=224,
patch_size=4,
in_chans=3,
num_classes=1000,
embed_dim=96,
depths=[2, 2, 6, 2],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
patch_norm=True,
use_checkpoint=False,
focal_levels=[2, 2, 2, 2],
focal_windows=[3, 3, 3, 3],
use_conv_embed=False,
layerscale_value=None,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
**kwargs,
):
super().__init__()
self.num_layers = len(depths)
embed_dim = [embed_dim * (2 ** i) for i in range(self.num_layers)]
self.num_classes = num_classes
self.embed_dim = embed_dim
self.patch_norm = patch_norm
self.num_features = embed_dim[-1]
self.mlp_ratio = mlp_ratio
# split image into patches using either non-overlapped embedding or overlapped embedding
self.patch_embed = PatchEmbed(
img_size=to_2tuple(img_size),
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim[0],
use_conv_embed=use_conv_embed,
norm_layer=norm_layer if self.patch_norm else None,
is_stem=True
)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
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=embed_dim[i_layer],
out_dim=embed_dim[i_layer + 1] if (i_layer < self.num_layers - 1) else None,
input_resolution=(
patches_resolution[0] // (2 ** i_layer), patches_resolution[1] // (2 ** i_layer)),
depth=depths[i_layer],
mlp_ratio=self.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_level=focal_levels[i_layer],
focal_window=focal_windows[i_layer],
use_conv_embed=use_conv_embed,
use_checkpoint=use_checkpoint,
layerscale_value=layerscale_value,
use_postln=use_postln,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator
)
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1d(1)
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)
Write Preview
Loading…
Cancel
Save