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@ -88,6 +88,7 @@ class IterableImageDataset(data.IterableDataset):
root,
reader=None,
split='train',
class_map=None,
is_training=False,
batch_size=None,
seed=42,
@ -102,6 +103,7 @@ class IterableImageDataset(data.IterableDataset):
reader,
root=root,
split=split,
class_map=class_map,
is_training=is_training,
batch_size=batch_size,
seed=seed,

@ -157,6 +157,7 @@ def create_dataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
download=download,
batch_size=batch_size,
@ -169,6 +170,7 @@ def create_dataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
batch_size=batch_size,
repeats=repeats,

@ -34,6 +34,7 @@ except ImportError as e:
print("Please install tensorflow_datasets package `pip install tensorflow-datasets`.")
exit(1)
from .class_map import load_class_map
from .reader import Reader
from .shared_count import SharedCount
@ -94,6 +95,7 @@ class ReaderTfds(Reader):
root,
name,
split='train',
class_map=None,
is_training=False,
batch_size=None,
download=False,
@ -151,6 +153,11 @@ class ReaderTfds(Reader):
# NOTE: the tfds command line app can be used download & prepare datasets if you don't enable download flag
if download:
self.builder.download_and_prepare()
self.remap_class = False
if class_map:
self.class_to_idx = load_class_map(class_map)
self.remap_class = True
else:
self.class_to_idx = get_class_labels(self.builder.info) if self.target_name == 'label' else {}
self.split_info = self.builder.info.splits[split]
self.num_samples = self.split_info.num_examples
@ -299,6 +306,8 @@ class ReaderTfds(Reader):
target_data = sample[self.target_name]
if self.target_img_mode:
target_data = Image.fromarray(target_data, mode=self.target_img_mode)
elif self.remap_class:
target_data = self.class_to_idx[target_data]
yield input_data, target_data
sample_count += 1
if self.is_training and sample_count >= target_sample_count:

@ -29,6 +29,7 @@ except ImportError:
wds = None
expand_urls = None
from .class_map import load_class_map
from .reader import Reader
from .shared_count import SharedCount
@ -42,13 +43,13 @@ def _load_info(root, basename='info'):
info_yaml = os.path.join(root, basename + '.yaml')
err_str = ''
try:
with wds.gopen.gopen(info_json) as f:
with wds.gopen(info_json) as f:
info_dict = json.load(f)
return info_dict
except Exception as e:
err_str = str(e)
try:
with wds.gopen.gopen(info_yaml) as f:
with wds.gopen(info_yaml) as f:
info_dict = yaml.safe_load(f)
return info_dict
except Exception:
@ -110,8 +111,8 @@ def _parse_split_info(split: str, info: Dict):
filenames=split_filenames,
)
else:
if split not in info['splits']:
raise RuntimeError(f"split {split} not found in info ({info['splits'].keys()})")
if 'splits' not in info or split not in info['splits']:
raise RuntimeError(f"split {split} not found in info ({info.get('splits', {}).keys()})")
split = split
split_info = info['splits'][split]
split_info = _info_convert(split_info)
@ -290,6 +291,7 @@ class ReaderWds(Reader):
batch_size=None,
repeats=0,
seed=42,
class_map=None,
input_name='jpg',
input_image='RGB',
target_name='cls',
@ -320,6 +322,12 @@ class ReaderWds(Reader):
self.num_samples = self.split_info.num_samples
if not self.num_samples:
raise RuntimeError(f'Invalid split definition, no samples found.')
self.remap_class = False
if class_map:
self.class_to_idx = load_class_map(class_map)
self.remap_class = True
else:
self.class_to_idx = {}
# Distributed world state
self.dist_rank = 0
@ -431,7 +439,10 @@ class ReaderWds(Reader):
i = 0
# _logger.info(f'start {i}, {self.worker_id}') # FIXME temporary debug
for sample in ds:
yield sample[self.image_key], sample[self.target_key]
target = sample[self.target_key]
if self.remap_class:
target = self.class_to_idx[target]
yield sample[self.image_key], target
i += 1
# _logger.info(f'end {i}, {self.worker_id}') # FIXME temporary debug

@ -17,6 +17,7 @@ from .edgenext import *
from .efficientformer import *
from .efficientformer_v2 import *
from .efficientnet import *
from .focalnet import *
from .gcvit import *
from .ghostnet import *
from .gluon_resnet import *

@ -0,0 +1,637 @@
""" FocalNet
As described in `Focal Modulation Networks` - https://arxiv.org/abs/2203.11926
Significant modifications and refactoring from the original impl at https://github.com/microsoft/FocalNet
This impl is/has:
* fully convolutional, NCHW tensor layout throughout, seemed to have minimal performance impact but more flexible
* re-ordered downsample / layer so that striding always at beginning of layer (stage)
* no input size constraints or input resolution/H/W tracking through the model
* torchscript fixed and a number of quirks cleaned up
* feature extraction support via `features_only=True`
"""
# --------------------------------------------------------
# FocalNets -- Focal Modulation Networks
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Jianwei Yang (jianwyan@microsoft.com)
# --------------------------------------------------------
from functools import partial
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import Mlp, DropPath, LayerNorm2d, trunc_normal_, ClassifierHead, NormMlpClassifierHead
from ._builder import build_model_with_cfg
from ._manipulate import named_apply
from ._registry import register_model
__all__ = ['FocalNet']
class FocalModulation(nn.Module):
def __init__(
self,
dim,
focal_window,
focal_level,
focal_factor=2,
bias=True,
use_post_norm=False,
normalize_modulator=False,
proj_drop=0.,
norm_layer=LayerNorm2d,
):
super().__init__()
self.dim = dim
self.focal_window = focal_window
self.focal_level = focal_level
self.focal_factor = focal_factor
self.use_post_norm = use_post_norm
self.normalize_modulator = normalize_modulator
self.input_split = [dim, dim, self.focal_level + 1]
self.f = nn.Conv2d(dim, 2 * dim + (self.focal_level + 1), kernel_size=1, bias=bias)
self.h = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
self.act = nn.GELU()
self.proj = nn.Conv2d(dim, dim, kernel_size=1)
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, groups=dim, padding=kernel_size // 2, bias=False),
nn.GELU(),
))
self.kernel_sizes.append(kernel_size)
self.norm = norm_layer(dim) if self.use_post_norm else nn.Identity()
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)
q, ctx, gates = torch.split(x, self.input_split, 1)
# context aggreation
ctx_all = 0
for l, focal_layer in enumerate(self.focal_layers):
ctx = focal_layer(ctx)
ctx_all = ctx_all + ctx * gates[:, l:l + 1]
ctx_global = self.act(ctx.mean((2, 3), keepdim=True))
ctx_all = ctx_all + ctx_global * gates[:, self.focal_level:]
# normalize context
if self.normalize_modulator:
ctx_all = ctx_all / (self.focal_level + 1)
# focal modulation
x_out = q * self.h(ctx_all)
x_out = self.norm(x_out)
# post linear projection
x_out = self.proj(x_out)
x_out = self.proj_drop(x_out)
return x_out
class LayerScale2d(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
gamma = self.gamma.view(1, -1, 1, 1)
return x.mul_(gamma) if self.inplace else x * gamma
class FocalNetBlock(nn.Module):
r""" Focal Modulation Network Block.
Args:
dim (int): Number of input channels.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
proj_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_post_norm (bool): Whether to use layernorm after modulation
"""
def __init__(
self,
dim,
mlp_ratio=4.,
focal_level=1,
focal_window=3,
use_post_norm=False,
use_post_norm_in_modulation=False,
normalize_modulator=False,
layerscale_value=1e-4,
proj_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm2d,
):
super().__init__()
self.dim = dim
self.mlp_ratio = mlp_ratio
self.focal_window = focal_window
self.focal_level = focal_level
self.use_post_norm = use_post_norm
self.norm1 = norm_layer(dim) if not use_post_norm else nn.Identity()
self.modulation = FocalModulation(
dim,
focal_window=focal_window,
focal_level=self.focal_level,
use_post_norm=use_post_norm_in_modulation,
normalize_modulator=normalize_modulator,
proj_drop=proj_drop,
norm_layer=norm_layer,
)
self.norm1_post = norm_layer(dim) if use_post_norm else nn.Identity()
self.ls1 = LayerScale2d(dim, layerscale_value) if layerscale_value is not None else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim) if not use_post_norm else nn.Identity()
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
use_conv=True,
)
self.norm2_post = norm_layer(dim) if use_post_norm else nn.Identity()
self.ls2 = LayerScale2d(dim, layerscale_value) if layerscale_value is not None else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
# Focal Modulation
x = self.norm1(x)
x = self.modulation(x)
x = self.norm1_post(x)
x = shortcut + self.drop_path1(self.ls1(x))
# FFN
x = x + self.drop_path2(self.ls2(self.norm2_post(self.mlp(self.norm2(x)))))
return x
class BasicLayer(nn.Module):
""" A basic Focal Transformer layer for one stage.
Args:
dim (int): Number of input channels.
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 (bool): Downsample layer at start of the layer. Default: True
focal_level (int): Number of focal levels
focal_window (int): Focal window size at first focal level
layerscale_value (float): Initial layerscale value
use_post_norm (bool): Whether to use layer norm after modulation
"""
def __init__(
self,
dim,
out_dim,
depth,
mlp_ratio=4.,
downsample=True,
focal_level=1,
focal_window=1,
use_overlap_down=False,
use_post_norm=False,
use_post_norm_in_modulation=False,
normalize_modulator=False,
layerscale_value=1e-4,
proj_drop=0.,
drop_path=0.,
norm_layer=LayerNorm2d,
):
super().__init__()
self.dim = dim
self.depth = depth
self.grad_checkpointing = False
if downsample:
self.downsample = Downsample(
in_chs=dim,
out_chs=out_dim,
stride=2,
overlap=use_overlap_down,
norm_layer=norm_layer,
)
else:
self.downsample = nn.Identity()
# build blocks
self.blocks = nn.ModuleList([
FocalNetBlock(
dim=out_dim,
mlp_ratio=mlp_ratio,
focal_level=focal_level,
focal_window=focal_window,
use_post_norm=use_post_norm,
use_post_norm_in_modulation=use_post_norm_in_modulation,
normalize_modulator=normalize_modulator,
layerscale_value=layerscale_value,
proj_drop=proj_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
)
for i in range(depth)])
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
def forward(self, x):
x = self.downsample(x)
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
return x
class Downsample(nn.Module):
r"""
Args:
in_chs (int): Number of input image channels
out_chs (int): Number of linear projection output channels
stride (int): Downsample stride. Default: 4.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(
self,
in_chs,
out_chs,
stride=4,
overlap=False,
norm_layer=None,
):
super().__init__()
self.stride = stride
padding = 0
kernel_size = stride
if overlap:
assert stride in (2, 4)
if stride == 4:
kernel_size, padding = 7, 2
elif stride == 2:
kernel_size, padding = 3, 1
self.proj = nn.Conv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride, padding=padding)
self.norm = norm_layer(out_chs) if norm_layer is not None else nn.Identity()
def forward(self, x):
x = self.proj(x)
x = self.norm(x)
return x
class FocalNet(nn.Module):
r""" Focal Modulation Networks (FocalNets)
Args:
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
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_overlap_down (bool): Whether to use convolutional embedding.
use_post_norm (bool): Whether to use layernorm after modulation (it helps stablize training of large models)
layerscale_value (float): Value for layer scale. Default: 1e-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.
"""
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
embed_dim=96,
depths=(2, 2, 6, 2),
mlp_ratio=4.,
focal_levels=(2, 2, 2, 2),
focal_windows=(3, 3, 3, 3),
use_overlap_down=False,
use_post_norm=False,
use_post_norm_in_modulation=False,
normalize_modulator=False,
head_hidden_size=None,
head_init_scale=1.0,
layerscale_value=None,
drop_rate=0.,
proj_drop_rate=0.,
drop_path_rate=0.1,
norm_layer=partial(LayerNorm2d, eps=1e-5),
**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.num_features = embed_dim[-1]
self.feature_info = []
self.stem = Downsample(
in_chs=in_chans,
out_chs=embed_dim[0],
overlap=use_overlap_down,
norm_layer=norm_layer,
)
in_dim = embed_dim[0]
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
layers = []
for i_layer in range(self.num_layers):
out_dim = embed_dim[i_layer]
layer = BasicLayer(
dim=in_dim,
out_dim=out_dim,
depth=depths[i_layer],
mlp_ratio=mlp_ratio,
downsample=i_layer > 0,
focal_level=focal_levels[i_layer],
focal_window=focal_windows[i_layer],
use_overlap_down=use_overlap_down,
use_post_norm=use_post_norm,
use_post_norm_in_modulation=use_post_norm_in_modulation,
normalize_modulator=normalize_modulator,
layerscale_value=layerscale_value,
proj_drop=proj_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
)
in_dim = out_dim
layers += [layer]
self.feature_info += [dict(num_chs=out_dim, reduction=4 * 2 ** i_layer, module=f'layers.{i_layer}')]
self.layers = nn.Sequential(*layers)
if head_hidden_size:
self.norm = nn.Identity()
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
hidden_size=head_hidden_size,
pool_type=global_pool,
drop_rate=drop_rate,
norm_layer=norm_layer,
)
else:
self.norm = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate
)
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
@torch.jit.ignore
def no_weight_decay(self):
return {''}
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
for l in self.layers:
l.set_grad_checkpointing(enable=enable)
@torch.jit.ignore
def get_classifier(self):
return self.classifier.fc
def reset_classifier(self, num_classes, global_pool=None):
self.classifier.reset(num_classes, global_pool=global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.layers(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
if name and 'head.fc' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.proj', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = {
"focalnet_tiny_srf": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth'),
"focalnet_small_srf": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth'),
"focalnet_base_srf": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth'),
"focalnet_tiny_lrf": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth'),
"focalnet_small_lrf": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth'),
"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), crop_pct=1.0, 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), crop_pct=1.0, num_classes=21842),
"focalnet_xlarge_fl3": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
input_size=(3, 384, 384), crop_pct=1.0, num_classes=21842),
"focalnet_xlarge_fl4": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
input_size=(3, 384, 384), crop_pct=1.0, num_classes=21842),
"focalnet_huge_fl3": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_huge_lrf_224.pth',
num_classes=0),
"focalnet_huge_fl4": _cfg(
url='https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_huge_lrf_224_fl4.pth',
num_classes=0),
}
def checkpoint_filter_fn(state_dict, model: FocalNet):
if 'stem.proj.weight' in state_dict:
return
import re
out_dict = {}
if 'model' in state_dict:
state_dict = state_dict['model']
dest_dict = model.state_dict()
for k, v in state_dict.items():
k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
k = k.replace('patch_embed', 'stem')
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
if 'norm' in k and k not in dest_dict:
k = re.sub(r'norm([0-9])', r'norm\1_post', k)
k = k.replace('ln.', 'norm.')
k = k.replace('head', 'head.fc')
if dest_dict[k].shape != v.shape:
v = v.reshape(dest_dict[k].shape)
out_dict[k] = v
return out_dict
def _create_focalnet(variant, pretrained=False, **kwargs):
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
FocalNet, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**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], focal_windows=[5] * 4,
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **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_post_norm=True, use_overlap_down=True, layerscale_value=1e-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], focal_windows=[5] * 4,
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **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],
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-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], focal_windows=[3] * 4,
use_post_norm=True, use_post_norm_in_modulation=True, use_overlap_down=True, layerscale_value=1e-4, **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],
use_post_norm=True, use_post_norm_in_modulation=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_huge_fl4', pretrained=pretrained, **model_kwargs)
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